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52
UMI
THE EFFECTS OF PRIMING ON RECOGNITION LATENCIES TO FAMILIAR AND UNFAMILIAR ORTHOGRAPHIC FORMS OF JAPANESE WORDS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree of Doctor of Philosophy in the Graduate
School of the Ohio State University
by
Kim Ainsworth-Damell, M.A., BA.
*****
The Ohio State University 1998
Dissertation Committee: Approved by Professor Julie E. Boland, Adviser
Professor Keith Johnson Adviser Professor J. Marshall Unger Linguistics Graduate Program UMI Number: 9833940
Copyright 1998 by Ainsworth-Damell, Kim
Ail rights reserved.
UMI Microform 9833940 Copyright 1998, by UMI Company. All rights reserved.
This microform edition is protected against unauthorized copying under Title 17, United States Code.
UMI 300 North Zeeb Road Ann Arbor, MI 48103 ABSTRACT
One of the longest standing debates in the visual word recognition literature concerns whether or not the transparency of the symbol-sound relationship for an orthographic system influences how words in that system are processed. The current study investigates this issue with Japanese, a language with multiple orthographies, including the phonologically transparent hiragana and the phono logically opaque kanji.
Utilizing a visual lexical decision task, I presented native speakers of Japanese with the kanji and hiragana forms of nouns that were either familiar in both orthographies
(orthographically neutral) or only familiar in kanji (kanji dominant).
In Experiment 1, targets were preceded by one of three visual primes: a semantically related real word, a semantically unrelated real word, or a string of three asterisks. In all three priming conditions, orthographically familiar targets were recognized more quickly and accurately than unfamiliar ones. Importantly, the expected priming effect was found, but it did not interact with the orthographic depth of the target word.
In Experiment 2, targets were preceded by one of three auditory primes: the spoken form of the target, a phonologically legal nonword, or a 100 ms tone. The pattern of the results was the same as in Experiment I, with the additional finding of a larger priming effect for unfamiliar orthographic forms compared to their familiar counterparts.
In Experiment 3, targets were preceded one o f five visual primes: the target word in the same orthography, the target word in the opposite orthography, a nonword in the same orthography, a nonword in the opposite orthography, or a string of asterisks. The patterns of response times and error rates were similar to the previous two experiments, and the larger priming effect for unfamiliar orthographic forms compared to familiar orthographic forms was again observed.
Collectively, these findings are consistent with previous work demonstrating that orthographic familiarity has a greater influence over the processing of written words in
Japanese than orthographic depth (e.g., Besner & Hildebrandt, 1987; Hirose, 1984, 1985,
1992; Sasanuma, Sakuma, & Tatsumi, 1988). They extend the literature by demonstrating that orthographic familiarity effects are reliable within-word and that phonological and orthographic identity priming affect the recognition speed for Japanese words according to orthographic familiarity, not orthographic depth. These results support a weighted network model in which the strength of the associations between semantic, phonological, and orthographic information reflects the reader’s experience.
lU For Jim, Jazz, and Lacy
IV ACKNOWLEDGMENTS
No project like this can be completed alone, and it is with the deepest gratitude
that I acknowledge the help and support of the following people: Jim Ainsworth-Damell,
for his love, dedication, and unwavering faith in my ability to succeed; Julie E. Boland, for
her intellectual and emotional support throughout my graduate career, as well as her
patience and enthusiasm during my development as a psycholinguist; Tadahisa Kondo,
for providing me with access to the NTT Lexical Familiarity Database and sharing with
me his expertise in computer programming, the Japanese language, and experimental
design; Keith Johnson, for his sense of humor, his willingness to be my sounding board at a moment’s notice, and his advice on how to interpret and present my data; Mineharu
Nakayama and J. Marshall Unger for their patience and valuable advice on how to
interpret my results and improve earlier drafts of this manuscript; Rob Fox and Mary
Beckman, for their assistance with the statistical analysis; and Arnold M. Zwicky for being my mentor and my finend, and encouraging me to investigate the theoretical questions that I foimd compelling, regardless of “market trends.”
I also thank Tatsuya Hirahara, Taeko Wydell, and Kiyoko Yoneyama for providing me with their professional expertise in language processing research, as well as their native speaker judgments, and Taeko Wydell and Ram Frost for their comments on earlier versions of this manuscript.
Lastly, I thank my parents, who raised me with a love and respect for learning, and who taught me that the approval of others is never so important as the approval of oneself.
This research was made possible by an International Graduate Research
Fellowship from the National Security Education Program and a Visiting Research
Fellowship from NTT Basic Research Laboratories.
VI VITA
August 23, 1968 ...... Bom - Boulder, Colorado
1992 ...... B.A. Japanese, University of Hawai’i
1996 ...... M.A. Linguistics, The Ohio State University
1992 - present ...... Graduate Teaching and Research Associate, The Ohio State University
PUBLICATIONS
Ainsworth-Damell, K., Shulman, H.G., and Boland, J.E. (1998). Dissociating brain responses to syntactic and semantic anomalies: Evidence from event-related potentials. The Joumal of Memory and Language. Volume 38(1), 112-130.
Ainsworth-Damell, K. (1997). A Review of Studies in Written Language and Literacy, Volume 3: Writing and Literacy in Chinese, Korean, and Japanese, by Insup Taylor and M. Martin Taylor, 1995. Joumal of the Association of Teachers of Japanese.
Darnell, K., Shulman, H.G., and Boland, J.E. (1996). Exploring the independence of brain response to syntactic and semantic anomalies. The Ohio State University Working Papers in Linguistics: Papers from the Linguistics Laboratory, Volume 47.
vu Damell, K., Boland, J., and Nakayama, M. (1994). The influence of orthography and sentence constraint on the processing of nouns in Japanese. The Ohio State University Working Papers in Linguistics: Papers from the Linguistics Laboratory, Volume 44.
FIELDS OF STUDY
Major Field: Linguistics
vm TABLE OF CONTENTS
Dedication ...... v
Acknowledgments ...... vi
Vita ...... viii
List of Tables ...... xii
List of Figures ...... xiii
Chapters:
1. Background and overview ...... I
I. I Orthographic depth and visual word recognition ...... 2 1.11 Empirical evidence ...... 3 1.12 Theoretical implications ...... 6 1.2 The current study ...... 7 1.21 Experimental design ...... 10 1.22 The weighted network model ...... 11 1.23 Predictions ...... 25 1.3 Methodological and theoretical issues ...... 26 1.31 Visual complexity and stimulus length ...... 27 1.32 Selecting a response task ...... 30 1.33 Choosing a theoretical framework ...... 34 1.4 Summary ...... 38
2. Experiment 1: Semantic priming ...... 41
2.1 Method ...... 43 2.11 Participants ...... 43 2.12 Materials ...... 44 2.13 Procedure ...... 46 ix 2.2 Results and discussion ...... 47 2.21 Response time analysis ...... 48 2.22 Error analysis ...... 54
3. Experiment 2: Phonological identity priming ...... 60
3.1 Method ...... 62 3.11 Participants ...... 62 3.12 Materials ...... 62 3.13 Procedure ...... 62 3.2 Results and discussion ...... 63 3.21 Response time analysis ...... 64 3.22 Error analysis ...... 69 3.3 Comparison of data from Experiments 1 and 2 ...... 73 3.31 Response time analysis ...... 75 3.32 Error analysis ...... 80
4. Experiment 3: Orthographic priming ...... 82
4.1 Method ...... 84 4.11 Participants ...... 84 4.12 Materials ...... 84 4.13 Procedure ...... 85 4.2 Results and discussion ...... 85 4.21 Response time analysis ...... 86 4.22 Error analysis ...... 94 4.3 Summary ...... 102
5. Conclusions ...... 104
5.1 Orthographic depth ...... 104 5.2 Making a case for the weighted network model ...... 106 5.21 How children learn to read ...... 107 5.22 Symbol-sound consistency effects...... 110 5.23 Neighborhood effects ...... I ll 5.24 Rejecting pseudohomophones ...... 113 5.25 The future of the weighted network model 114 5.3 Directions for future research ...... 115 5.4 Conclusion ...... 117
X Bibliography...... 118
XI LIST OF TABLES
Table Page
1.1 Examples of kanji dominant and orthographically neutral nouns ...... 9
1.2 Mean values for kanji dominant stimuli by mora length and prime type . 29
1.3 Mean values for orthographically neutral stimuli by mora length and prime type ...... 30
2.1 Mean response times in ms from Experiment 1 ...... 48
2.2 Analysis of variance for mean response times from Experiment 1 ...... 49
2.3 Mean error rates for Experiment 1 ...... 55
3.1 Mean response times in ms from Experiment 2 ...... 63
3.2 Analysis of variance for mean response times from Experiment 2 ...... 66
3.3 Mean error rates for Experiment 2 ...... 69
3.4 Analysis of variance for mean response times from Experiments 1 and 2 75
4.1 Mean response times in ms for Experiment 3 ...... 86
4.2 Analysis of variance for mean response times from Experiment 3 ...... 87
4.3 Mean error rates for Experiment 3 ...... 95
4.4 Analysis of variance tor mean error rates from Experiment 3 ...... 97
XU LIST OF FIGURES
Figure Page
LI The PDF model from Seidenberg and McClelland (1989) ...... 12
1.2 The printed words SPOON, COOP, and COON as represented by multiple layers of o-nodes in the weighted network model ...... 13
1.3 The nodes and weighted connections involved in hypothetical orthographic and phonological representations of the word SPOON ..... 17
1.4 A basic dual-route model of word recognition ...... 35
3.1 Mean RTs by prime type for Experiments 1 and 2 ...... 77
3.2 Mean RTs for each target orthography condition by prime type for Experiments 1 and 2 ...... 79
xm CHAPTER I
BACKGROUND AND OVERVIEW
Linguists and psychologists alike have sought for decades to understand the
architecture of the language processing system and the way in which stored
representations for the different facets of linguistic information interact within the human
mind. The word, as the smallest independent unit of meaning, has been a popular focus
of study, in part because the complete mental representation for a word must somehow
encode information from every tier of language knowledge: pronunciation (phonetics and phonology), lexical structure and linking properties (morphology), grammatical function
(syntax), and meaning and thematic properties (semantics). Thus, by understanding how we recognize words and access the stored information about them, we can gain a great deal of insight into how language comprehension works.
The current study addresses two pressing questions from the visual word recognition literature. First, does the transparency of the phonological assignment for the characters of a given orthographic system affect how quickly words in that orthography are recognized? Second, does the familiarity of an orthographic form have a greater impact than orthographic depth on the recognition latencies for words following semantic. phonological, or orthographic primes? This chapter provides the background for each of
these questions, interwoven with discussion of how the current study addresses the
concerns presented by the existing literature. I will present three experiments that
manipulate phonological transparency and orthographic familiarity in Japanese text.
These experiments were designed to test a weighted network model based on the parallel
distributed processing (PDP) model proposed by Seidenberg and McClelland (1989).
This weighted network model will be contrasted with traditional models of visual word
recognition that presume different routes to the lexicon based on orthographic depth and the principle of phonological delay.
1.1 Orthographic Depth and Visual Word Recognition
The writing systems of the world represent spoken language in a great variety of ways. On one end of the continuum are deep orthographies, such as Chinese hanzi or
Japanese kanji, in which each character shares a pronunciation with numerous other characters, and may have multiple pronunciations of its own. On the other end of the continuum are shallow orthographies, such as Serbo Croatian or Italian, with a given character usually having a single pronunciation that is shared with no other characters.
The orthographic depth hypothesis proposes a theoretical connection between the degree to which a language’s orthography reflects its sound system and how printed words are recognized (Katz & Feldman, 1981; Turvey, Feldman, & Katz, 1984). According to this hypothesis, words written in shallow orthographies are easy for the reader to recode into a phonological representation by means of grapheme-phoneme correspondence rules. The phonological code derived from this process is then used to access semantic information,
just as the phonological code for a spoken word would be. Words in deep orthographies,
conversely, are recognized without recourse to phonology, because their symbol-sound
assignments are so variant. Instead, the reader must learn to map the visual form of a
word directly onto the appropriate lexical entry in order to access the meaning and
pronunciation.’ As a corollary to the orthographic depth hypothesis, the fact that words
in shallow orthographies must undergo an additional stage of processing before lexical
access can be achieved is assumed to result in slower recognition latencies for words in
these items compared to words in deep orthographies (Norris & Brown, 1985; Paap,
Noel, & Johansen, 1992; Seidenberg, 1985). This corollary is referred to in the literature
as phonological delay.
1.11 Empirical evidence
Indeed, several cross-linguistic studies have supported the orthographic depth
hypothesis. In a typical report. Frost, Katz, and Bentin (1987) examined the role of
phonological recoding in three languages that vary in their degree of orthographic depth:
unpointed Hebrew-, English, and Serbo Croatian. Targets were manipulated in terms of their frequency (high or low), and preceded by a semantic associate or an unrelated lexical
^ It is necessary to note here that proponents of the orthographic depth hypothesis generally treat the distinction between deep and shallow orthographies as if it were binary, but this interpretation is oversharp. Rather, orthographic depth is a continuum, with the orthographies of the world scattered between the “deep” and “shallow” endpoints.
2 Unpointed Hebrew, the form used by skilled readers, does not contain the diacritics that indicate vowels, and therefore “does not convey the ftill phonemic structure of the printed word, (presenting) the reader...with phonological ambiguity” (Frost, 1994). For this reason, it is deemed a deep orthography. Pointed Hebrew, which does contain all of the vowel diacritics, is considered shallow. 3 item. As expected. Frost et al. found that naming latencies for Hebrew, the deepest orthography, were faster for high frequency items and items that had been semantically primed. Few effects of frequency or priming were found in latencies for words in Serbo
Croatian, which has the shallowest orthography. For English, which has an orthography in the middle of the depth continuum, naming speeds were less affected by frequency and semantic facilitation that those to Hebrew words, but more than those in Serbo Croatian.
Frost and his colleagues claimed that pattern of results obtained because the participants were pronouncing words in deep orthographies by accessing stored phonological representations in the lexicon. The speed with which these phonological representations could be accessed was influenced by semantic priming and lexical frequency, because information about a word’s meaning and its lexical frequency are also stored in its lexical entry. In the case of words in shallow orthographies, participants were generating pronunciations for each item pre-lexically, using grapheme-phoneme correspondence rules. Because this procedure did not require accessing the target’s lexical entry, semantic priming and lexical frequency had little influence on the speed with which the task was performed.
Frost et al. (1987) offered further support for their claim that words in deep and shallow orthographies were pronounced using different routines with data showing that words in deep orthographies that followed nonwords in the stimulus list were pronounced more slowly than those that were preceded by another real word. No effect of the lexicality of the stimulus item before the target was found for words in shallow orthographies. Because nonwords are presumed not to have lexical entries, and thus must
be pronounced by means of grapheme-phoneme correspondence rules, Frost and his
colleagues interpreted this result as evidence that readers of words in deep orthographies
were having to change their processing strategy to pronounce real words and nonwords,
and this strategy shift resulted in slower pronunciation latencies. Readers of words in shallow orthographies did not display a slow down, because they used the same processing strategy to pronounce both real words and nonwords.
The importance o f phonological recoding for the processing of words in shallow orthographies has been reiterated in numerous otlier reports (e.g. Frost, 1994; Katz &
Feldman, 1981; Lukatela & Turvey, 1990; Lukatela, Popadic, Ognjenovic, & Turvey,
1980; Lukatela, Savic, Urosevic, & Turvey, 1997; Turvey, Feldman, & Lukatela, 1984), reports that have led some researchers to suggest that readers of shallow orthographies are obligated to phonologically recode items before accessing the lexicon. Still others have claimed that the development or identification of a prelexical phonological representation is mandatory for the recognition of words in any orthography, even very deep ones like
Chinese hanzi or Japanese kanji (e.g.. Frost, 1998; Lukatela et al., 1997; Perfetti & Bell,
1991; Perfetti, Bell, & Delaney, 1988; Perfetti & Zhang, 1995; Tan, Hoosain, & Peng,
1995; Tan & Perfetti, 1997; Van Orden & Goldinger, 1994; Van Orden, 1987; Van Orden,
Johnston, and Hale, 1988; Van Orden, Pennington, & Stone, 1990; Zhang & Perfetti,
1993). This is not to suggest, however, that everyone is convinced of the primacy of phonological recoding to the visual word recognition process, even for words in shallow orthographies. In fact, numerous studies have suggested that orthographically words in
shallow orthographies can access the lexicon without recourse to prelexical phonology,
provided that they have high lexical frequency, high orthographic familiarity, or an
unpredictable stress pattern (e.g., Bajo, Burton, Burton, & Canas, 1994; Baluch & Besner,
1991; Besner & Hildebrandt, 1987; Hirose, 1984, 1985; Milota, Widau, McMickell,
Juola, & Simpson, 1997; Sasanuma, Sakuma, & Tatsumi, 1988; Tabossi & Laghi, 1992)
1.12 Theoretical implications
The conflicted nature of the existing data regarding the role of phonology in visual word recognition makes it very difficult to determine whether orthographic depth plays any role whatsoever in this process. By one interpretation of the literature, words in both deep and shallow orthographies may be recognized lexically. By another, one which does not presume that the phonological delay hypothesis is true, shallow orthographic forms are always processed nonlexically; it is simply that phonologically recoding high frequency or orthographically familiar words in shallow orthographies is faster than doing so for other words in shallow orthographies, because the relevant symbol-sound assignments are more familiar to the reader. A third possibility is that deep and shallow orthographies are processed according to the same routine, with prelexical phonological representations being available for both deep and shallow orthographic representations, and that factors other than orthographic depth determine how quickly meaning will be accessed. Discriminating among these options is difficult, because so much depends on what
one accepts as “theoretical fact.” The phonological delay hypothesis? The existence of
independent lexical and nonlexical processing routines? Indeed, both of these premises
would be strongly challenged if, all other factors being equal, it was found that words in
deep orthographies and words in shallow orthographies could be recognized at
comparable speed and were similarly sensitive to various types of linguistic priming (the
logic of the relevant predictions will be discussed in the introductions of Chapters 1, 2,
and 3). The challenge in this task lies in the fact that “all other factors” is an extremely
broad category, and one that makes the development of appropriate stimuli very difficult.
1.2 The Current Study
The current study was designed to answer the question of whether words in deep
and shallow orthographies can be recognized with comparable speed and accuracy
following different kinds of linguistic primes. I chose Japanese as the focus language for
my experiments, because the Japanese writing system has both deep and shallow
orthographies: phonomorphograms^, called kanji, and two syllabaries, hiragana and
katakana. Within the Japanese writing system, each of these orthographies has come to
have a specific range of uses. In theory, any word can be written in hiragana, because
^ Terms such as “logogram” and “ideogram” are more commonly used, but these labels are inaccurate and misleading, because they suggest that phonology is uninvolved in the processing of such orthographies. In fact, there is evidence that phonological representations are identified during the recognition of hanzi and kanji, and that they always play some role in how words in these orthographies are processed (e.g., Liu, Zhu, & Wu, 1992; Horodeck, 1987; Matsimaga, 1994; Tan & Perfetti, 1997). As an alternative, the term “phonomorphogram” has been proposed for hanzi (Wu et al., 1992), and 1 extend the application of this term to kanji (as well as the related terms “phonomorphography”, and “phonomorphographic”). each hiragana character represents a mora, the sound unit of Japanese/ For onomatopoeic
expressions, emphasized words, and recent foreign loan words, katakana are used instead
of hiragana. Kanji are substituted for hiragana in words of Sino-Japanese origin, words
coined on the model of classical Chinese, and many native Japanese words; the kanji in
these cases generally represent content morphemes, such as nouns and roots of verbs,
adjectives and some adverbs. Thus, a single word may be composed of both kanji (root)
and hiragana (inflectional suffix), and a single sentence may be composed of a mixture of
all three orthographies.
Throughout this manuscript words that normally appear in kanji, but can be
written in hiragana, are referred to as “kanji dominant.” This reflects that the kanji form is
more familiar to readers than the hiragana form, although both are entirely legible. In
complement to these words, there are some highly frequent Japanese nouns that are
written in hiragana about as often as they are written in kanji, and are thus visually
familiar to readers in both orthographies. These words are called “orthographically
neutral”. It is important to note that the categories “kanji dominant” and
“orthographically neutral” are not in binary opposition; rather, they reflect regions on a
continuum of relative orthographic familiarity for the kanji and hiragana forms of words.
Thus, the difference between the familiarity of the kanji form and the familiarity of the hiragana form varies in each category, but this difference is smaller for orthographically
^ There are a few anachronistic spelling conventions in Japanese that require writing certain words differently than they actually sound (e.g., the long vowel lod is written as if it were pronounced /ou/; the syntactic particle pronounced /wa/ is written with the hiragana character pronounced /ha/). 8 neutral words. Examples of kanji dominant and orthographically neutral words are
provided in Table 1.1.
Dominance BCanji Hiragana Romanization Gloss
BCD sekiyu ‘oil, petroleum’ hokubei ‘North America’
ON futon ‘Japanese mattress’
A# iZ /u O /v ninjin ‘carrot’ Note. BCD = kanji dominant, ON = orthographically neutral.
Table 1.1: Examples of kanji dominant and orthographically neutral nouns.
The issue of orthographic familiarity is important in Japanese, because several studies have shown that Japanese readers are faster to recognize a word in a hiragana or katakana form if the word often appears in that orthography, compared to a word that normally appears in kanji, but is transcribed into hiragana or katakana (Besner &
Hildebrandt, 1987; Hirose, 1984, 1985; Sasanuma et al., 1988). This orthographic familiarity effect is so robust that recognition latencies for words that are familiar in katakana are faster than those for the same words transcribed into hiragana, even though both orthographies map character-for-character with Japanese phonology (Besner &
Smith, 1992). Assuming that phonological delay is responsible for difference in processing speed for familiar and unfamiliar shallow orthographic forms, these findings suggest that familiar shallow forms are recognized lexically, like words in deep
orthographies (i.e., kanji forms), while unfamiliar shallow forms are recognized
nonlexically (Besner & Hildebrandt, 1987; Besner & Smith, 1992; Hirose, 1984, 1985;
Sasanuma et al., 1988).
By taking advantage of the dissociation between orthographic depth and
orthographic familiarity created by kanji dominant and orthographically neutral words, 1
was able to do much more than compare the responses to familiar and unfamiliar shallow
forms as previous studies had done. Rather, I was able to examine responses to deep and
shallow forms of the same word, because each of my stimuli could be written in kanji or
hiragana. Thus, 1 was able to determine if words in deep and shallow orthographies could
be processed in a similar way, while avoiding potential confoimds that could arise from
comparing data from different languages and different readers. In addition, I eliminated
differences of lexical frequency, lexical category, syllable length, and concreteness between
my deep and shallow items that may have been problematic for previous studies of
orthographic depth.
1.21 Experimental design
The manipulation of orthographic familiarity was performed in combination with three types of priming tasks: semantic (Experiment 1), phonological (Experiment 2), and orthographic (Experiment 3). In Experiment 1, targets were preceded with one of three visual primes: a word strongly related to the meaning of the target (Related), a word unrelated to the meaning of the target (Unrelated), or a string of three asterisks (Asterisk).
10 In Experiment 2, a phonological priming element was added to the paradigm by preceding targets with three types of auditory primes: the target word (Identity), a nonword with no mora overlapping with the target (Nonword), and a 100 ms tone prime (Tone). This permitted an analysis of how prior activation of the phonological and semantic representations of the target influenced recognition latencies for hiragana and kanji forms for orthographically neutral and kanji dominant words. In Experiment 3, the priming series was completed with the addition of an orthographic component. Targets were preceded by visual primes consisting of the same word as the target in the same orthography (ID-Same), the same word in the opposite orthography (ID-Different), a nonword in the same orthography as the target (NW-Same), a nonword in tiie opposite orthography (NW-Different), or a string of three asterisks (Asterisk). This allowed me to prime the orthographic, phonological, and semantic representations of the targets simultaneously and determine if the hiragana and kanji representations of words afford the same degree of priming for same-script and cross-script targets.
1.22 The weighted network model
Before describing the predictions of the current study, it is useful to first provide an overview of the theoretical fiamework within which the results will be interpreted.
The model I am developing, which I refer to as the “weighted network model”, is based on the parallel-distributed processing (PDF) model developed by Seidenberg and McClelland
(1989). The PDF model is a computational one, designed to simulate patterns in behavioral visual word recognition data that have been found using the naming task.
11 Within models of this type, lexical processing is described in terms of the activation of
semantic, orthographic, and phonemic nodes (s-nodes, o-nodes, and p-nodes,
respectively) that are linked in a complex associative network. Each word in an
individual’s vocabulary has a specific pattern of s-node, o-node, and p-node activations
associated with it, and is discriminated from other items based on this pattern.^ A
diagram of the PDF model with examples of orthographic inputs for GAVE, HAVE, and
the nonword MAVE and their corresponding phonological outputs (i.e., pronunciations)
is given in Figure 1.1.
GAVE /gelv/ HAVE /hav/ MAVE / m e l v /
Figure 1.1. The PDF model from Seidenberg and McClelland (1989).
^ The original PDP fiamework described by Seidenberg and McClelland (1989) has no mechanism fix- storing discrete lexical entries, which makes it imique from other models of word recognition. Semantic, phonological, and orthographic codes for words are computed on the spot, expressed in terms of activation patterns across the appropriate nodes.
12 Within the PDP and weighted network models, connections between nodes are established when two nodes are simultaneously activated by a given stimulus. In the
PDP model, nodes of different types do not have direct connections with one another
(e.g., o-nodes do not connect directly with p-nodes). Rather, mediating layers of “hidden nodes” translate activation from nodes of one type to nodes of another type. These hidden nodes also permit multiple nodes of a given type that are simultaneously activated to stimulate one or more nodes of another node type. In the weighted network model, no hidden layers of nodes are assumed. Thus, nodes of different types that are co-activated by a given stimulus can establish a connection, just as nodes of the same type do.
Without hidden nodes, however, it difficult to conceptualize how activation is spread between nodes of different types unless nodes in general are organized into levels of complexity. For example, if we assume a language with an alphabetic writing system, then each o-node represents a unique letter, each p-node represents a phoneme, and each s-node represents a semantic feature. Collectively, these individual nodes make up the base level of the weighted network. When two or more nodes of a particular type are co activated, they establish a connection and form a simple “node pattern”. For example, the o-nodes for #S and _P could form a connection to create the simple o-node pattern #SP, representing the letter cluster onset for words like SPOON, SPEECH, and SPAN. All such simple node patterns, including those for o-node, p-node, and s-node patterns, compose the second layer of network. Beyond this is the complex node pattern layer, where simple node patterns and individual nodes combine to represent the word form in a
13 particular linguistic dimension (i.e., spelling, sound, or meaning). As an example, the complex o-node pattern for the letter string SPOON would be made up of the node pattern for #SP, the node pattern for 0 0 , and the individual node for N#. Figure 1.2 illustrates the various levels of nodes and node patterns involved in the weighted network’s orthographic representation of the words SPOON, COOP, and COON.
Complex o-node pattern ^ SPOON COOP COON
Simple o-node pattern #SP OO
Individual o-node ( #c N # U P#
Figure 1.2. The printed words SPOON, COOP, and COON as represented by multiple layers of o-nodes in the weighted network model. The # symbol indicates a word boundary.
For both the PDP and weighted network models, the amount of activation that can spread along a given connection is determined by the frequency of co-activation o f the nodes on either end. Furthermore, co-activation frequency is conceptualized as a numerical weight on the connection between two nodes. Larger numbers reflect heavier
14 weights, and the heavier a connection is, the more activation it can accommodate at a given time. The sum of all weights on connections extending from a given node equals one. For connectionist models where weights are specified, it is conunon to characterize connection weights as the embodiment of a model’s “knowledge” about the world (Plaut &
McClelland, 1993; Seidenberg & McClelland, 1989). Thus, as the model’s experience changes with each processing event, so do the weights change to reflect what the model has “learned”. Weights within the weighted network model change as the network’s experience changes. This means that processing a word automatically alters the weights on connections between all of the nodes in the representation for that word, including the orthographic, phonological, and semantic nodes. The amount that a weight will change depends on the extent to which processing a word one time contributes to the overall knowledge the model has for that word. The more familiar the network is with the word, the less weights related to the processing of that word will change as the result of processing it a single time.
Figure 1.3 diagrams the connections between the o-nodes and the p-nodes in a weighted network representation for the word SPOON. Also indicated are likely weights on the relevant connections; these indicate what percentage of the activation available in an individual node is distributed to the node pattern at the other end of the connection.
The reader will note that in several cases, the sum of the weights on the connections extending from a given node in the diagram does not equal one; this is because the node has connections with other nodes that are not shown. For example, the weight on the
15 connection from the 0 0 o-node to the /u/ p-node is only .5, because OO can also be pronounced /U/, as in “book”, and loi, as in “door”. It should also be mentioned why some of the connection weights are so small, such as the .0001 weight on the connection between the N# o-node and the SPOON o-node. This is because there are many, many words in English that end with the letter N, and the activation in the N# o-node must be distributed to all of them in proportion to the frequency with which the reader has encountered each of them in text. Thus, the o-node pattern for the word SPOON receives a minimal amount of activation specifically from the N# o-node.
Another important feature of the weighted network model and distributed processing models in general is the inhibitory connection. As with facilitory connections, inhibitory connections are established between nodes that are frequently co-activated, and the amount of inhibition that spreads along the connection is dependent on the frequency of this co-activation. However, inhibitory connections differ from facilitory connections in that inhibitory connections can only be established between nodes or node patterns of the same type and complexity level (e.g., individual o-nodes can only inhibit other individual o-nodes; word level p-node patterns can only inhibit other word level p-node patterns). The purpose of these inhibitory connections is to restrict the spread of activation as much as possible to the nodes in the representation for the stimulus, thereby increasing the chance that the complex node patterns encoding the stimulus will achieve the recognition threshold before those for another word.
16 #s .0001 N# #SP .0001 .001 SPOON
.05 OO .0001
Ai/ .0001 .0001 /spu:n/ .0001 /n / .001
/sp/ .001
Figure 1.3. The nodes and weighted connections involved in hypothetical orthographic and phonological representations of the word SPOON.
17 As a result o f the assumptions described thus far, the weighted network
architecture makes specific predictions with respect to the issue of orthographic depth.
Assume first that there is an o-node that represents a particular orthographic character.®
This o-node (the “source node”) must be able to activate the p-node (or “goal node”) for
each of its possible pronunciations, with the amount of activation being distributed to
each goal node according to the weight on the relevant connection. The node for the most
frequently used pronunciation will receive the most activation, because the connection
between the source node and this goal node is heavier. The more exclusive the connection
from a particular o-node and p-node (i.e., the more shallow the orthography), the more activation the p-node will receive. Similarly, the more exclusive the relationship between the p-node and the o-node, the more activation the o-node will receive from the p-node.
Thus, the more uniform the symbol-sound correspondence in an orthography, the more activation the appropriate pronunciation for each character in a string will receive, and the
faster that pronunciation will be identified as the appropriate one to use.
Because the PDP model was designed to explain visual word recognition in
English, some modifications to the architecture were necessary for the weighted network version to accommodate Japanese. First, 1 assume that each hiragana character is represented by an individual o-node, while each kanji character is represented by a simple o-node pattern (but see Footnote 6). This assumption is based purely on the numbers:
^ The term “node” is used here for the sake of simpicity, although technically a single character could be so visually complex that it is represented by a node pattern (i.e. several connected individual nodes representing its components). 18 there are approximately 50 unique hiragana characters that can be modified with rule- governed diacritics to generate over 100 possible hiragana forms, while there are approximately 2000 kanji characters used in everyday reading and writing. At the individual o-node level, I assume kanji forms are characterized as “stroke bimdles” — subsets of stroke combinations that are familiar to readers and can be stored and referenced as whole units, simplifying the encoding process (Paradis, Hagiwara, &
Hildebrandt, 1985).^ At the next level of orthographic representation (i.e., the simple o- node pattern level), stroke bimdles combined to create individual kanji characters..*
Second, I presume that multiple o-node patterns can be cormected to the same p-node and s-nodes patterns. This assumption is especially important for processing Japanese, because it permits two distinct o-node patterns for each lexical item — one for the kanji representation and one for the hiragana representation. Conceivably, such an arrangement would be necessary for processing items such as COLOR and COLOUR in English, although Seidenberg and McClelland (1989) make no specific claims to this effect.^ Third,
^ How many of these stroke bimdles there are is not clear, but a reasonable number (around 100) can be assumed from the number of radicals, or highly familiar stroke bundles, that are used to organize kanji character dictionaries. Positing 100 basic stroke bundles for the lowest level of kanji representation is also desirable, because this permits the model to have similar numbers of individual o-nodes for the hiragana and kanji orthographies.
^ There are two exceptions to this generalization. First, most radicals are, in themselves, unique kanji characters, in addition to being a component of more complex kanji characters. These “radical characters” would be represented by a single o-node within the weighted network model, just like individual hiragana characters. Second, some of the more complex radicals (those with many strokes) can be decomposed into simpler radicals, and thus are represented in the model as a small node pattern, just like other composite kanji characters.
^ In order to accommodate homophones and homographs, the PDF model must permit certain node patterns of one type to link with multiple sets of other nodes. For example, the p-node pattern for the English sequence [be:r] must be connected to the o-nodes for BEAR and BARE, as well as the s-nodes representing the meanings denoted by each spelling. Similarly, the o-node pattern for TEAR must have 19 I envision the individual p-nodes in the weighted network as representing morae, not phonemes, because Japanese has a mora-based sound system. Moreover, because each hiragana character has a single pronunciation, each individual p-node is associated with the o-node for a particular hiragana character. Because kanji characters have multiple pronunciations and these pronunciations generally consist o f multiple morae, the simple o-node patterns for kanji characters will be connected with multiple p-nodes and p-node patterns. Fourth, because the weight of a given connection within the weighted network model is determined by the frequency with which the relevant association is made, the o- nodes in the representation for the familiar orthographic form of a word should have heavier connections to the relevant p-nodes and s-nodes than the o-nodes representing an unfamiliar orthographic form of the same word. This should result in more activation being disseminated to and from nodes in the representation for familiar orthographic form, facilitating the recognition of the word via this form.
A final set of assumptions are required to explain how the lexical decision task and the priming manipulation will work within the weighted network model. To begin, recall that the PDP model was designed primarily to simulate differences in naming latencies and does not presume the existence of a lexicon per se. As a result, the mechanism for performing lexical decision within this framework is not well defined. Rather, Seidenberg and McClelland (1989) say simply, “For lexical decision, in which the subject’s task is to judge whether a stimulus is a familiar word, we assume that a measure like the connections to the p-node patterns for [ti:r] and [te:r], as well as the s-node patterns for the noun and verb 20 orthographie error score is actually used in making this judgment,” (p. 529). The PDP
model calculates this orthographic error score using the difiFerences between the target
activation value for each orthographic node and the actual activation computed by the
network (see Besner, Twilley, McCann, & Seergobin (1990) for a critique of this
characterization of lexical decision, and Seidenberg & McClelland (1990) fora response to
this critique).
Because the weighted network model is not a computational model, but a
theoretical one, it is not sensible to describe the lexical decision process within the
weighted network model in terms of error scores. Rather, I assume that lexical decisions
within the weighted network are based on the sum of activation values in the complex o-
node, s-node, and p-node patterns that define a word, in addition to a time-out criterion
(cf. Coltheart, Davelaar, Jonasson, & Besner, 1977). Thus, when the target word is
visually presented, this stimulates the o-nodes that represent the individual characters
(for hiragana) or stroke bundles (for kanji) that make up the word. Then, activation spreads up to the next level of o-node patterns (e.g., word level patterns for hiragana, character level patterns for kanji), and spreads out to the p-nodes and s-nodes with which the individual o-nodes are linked. This spreading of activation continues until the complex o-node pattern, s-node pattern, and p-node pattern that represent all facets of the word-level information about a lexical item collectively receive enough activation from
meanings of each of these pronunciations. In the case o f COLOR and COLOUR, two sets of o-nodes must connect with the same sets of p-nodes and s-nodes. 21 the stimulus to reach the recognition threshold and signal the language processor that the target has been identified.
Because the activation level for a word is defined as the sum of the activation values across the complex o-node, s-node, and p-node patterns that compose its representation, it is possible for different combinations of activation values across these three patterns to result in identification of the target. For example, if the recognition threshold is set at 5 activation units, a particular word would be identified as the stimulus if its complex o-node pattern had an activation level of 3, its complex p-node pattern had an activation level of 1.5, and its complex s-node pattern had an activation level of .5.
Similarly, the o-node pattern could have an activation level of .75, the p-node pattern could have an activation level of 4, and the s-node pattern could be at .25. What combination of activation values emerges firom the spreading activation process will depend on the nature of the input (i.e., orthographic, phonological, or semantic), the number of nodes contained in the overall representation for a word, the number of connections each of these nodes possess, and the weights on each of these connections.
In most cases, a unique combination of o-nodes, s-nodes, and p-nodes emerges very quickly from the weighed network with sufficient activation to indicate that the target has been identified. The precise speed with which this occurs depends how frequently the nodes in a word’s representation are co-activated: the higher this frequency, the more activation is distributed along the relevant connections, and the faster that the complex node patterns can achieve sufficient activation to attain the recognition
22 threshold. Thus, high frequency words should be identified more quickly than low frequency words, because more activation spreads among the nodes in representations that are frequently used. The speed with which a highly activated combination of node- pattems emerges from the network is important, because if the build-up of activation at the complex node pattern level is too slow, the model will reject the stimulus as an unknown character string. The time-out criterion by which this rejection occurs is set according to recent performance demands, with the reader’s goal being to recognize words as efficiently as possible, given the rate of presentation of the stimuli and the ease with which the reader can “recover” imidentified words through contextual or pragmatic cues.
With respect to the current study, this time-out criterion should come into play in two specific circumstances: when targets are nonwords, and thus few (if any) connections exist between the o-nodes, s-nodes, and p-nodes representing the linguistic features of these items; and when targets are orthographically unfamiliar, such that the weights on the connections between the nodes in their orthographic representation are generally light and therefore unable to carry large amounts of activation. In both cases, targets should be rejected as words more frequently than orthographically familiar real word targets.
Priming within the weighted network model is based on two assumptions. First, activation of the network can be initiated from any node type. Second, the activation that builds up during the processing of the prime does not disappear immediately after it peaks. Rather, it degenerates gradually, leaving an ever decreasing amount of residual activation in any nodes (or node patterns) that were stimulated during the processing of
23 the prime (cf. Morton, 1979). The more residual activation left in the target’s nodes as a result of processing the prime, the less activation that must be generated by the target itself for the recognition threshold to be reached. The amount of residual activation available during the processing of the target depends on the delay between the processing of the prime and the processing of the target, and the number of nodes or node patterns in the target that receive residual activation from the prime. This is referred to as the
“residual activation hypothesis”.
Although the temporal aspect of the residual activation hypothesis will not be tested in the current study, the “overlap” factor will. The weighted network model predicts that response times to targets will be shorter following identity primes than following semantically related primes, because only a small portion of the nodes in the representation for the prime are connected to those for the target; thus, the prime would only be be able to leave residual activation in a few of the nodes or node patterns in the target’s representation, and would only decrease the need for new activation (i.e., from the target) a small amount. However, if a prime were to activate all of the nodes in a target’s representation (e.g., the spoken form of a target would activate all of the nodes in the p-node representation for that word, followed by those in the o-node and s-node representations), this would leave an abundance of activation throughout the target’s nodes. As a result, when the target was actually presented, only a minimal amount of processing would be required to activate the complex o-node, s-node, and p-node patterns sufficiently for recognition to occur.
24 1.23 Predictions
The weighted network model makes three major predictions with respect to the current study. First, I expected the traditional priming effect, such that primes that stimulated one or more of the s-node, p-node, or o-node patterns for the target would facilitate recognition more than unrelated real word, nonword, or neutral primes. Related to this, residual activation hypothesis predicted that the more nodes in the representation for the target that were activated by the prime, the larger the priming effect should be.
Second, I anticipated that the orthographic dominance of the target (i.e. whether it is a kanji dominant or orthographically neutral word) and the orthography in which the target is presented (e.g., kanji or hiragana) would interact under all priming conditions, with the orthographically familiar forms of words being recognized more quickly than their unfamiliar counterparts. Third, familiar orthographic forms were expected to respond similarly to all types of priming, regardless of the orthographic depth of the familiar form.
This is because the o-node representations for familiar orthographic forms of a word should have well-established connections with the p-node and s-node representations for that word. The caveat to this final prediction is that the weighted network model assumes that o-nodes representing hiragana forms have more exclusive connections with particular p-nodes representing morae than do the o-nodes or o-node patterns representing kanji forms. As a result, the connections on weights between hiragana o- nodes and mora p-nodes should always be heavier than those on connections between kanji o-nodes and mora p-nodes; this should make more activation available to o-nodes
25 and o-node patterns in hiragana representations than to those in kanji representations.
Thus, the hiragana forms of orthographically neutral words could be recognized more quickly than their kanji counterparts under some circumstances, even though both forms are visually familiar.
As a corollary to these primary hypotheses, it was also expected that error rates for unfamiliar orthographic forms would be higher than those for familiar forms. The reasoning behind this relates to the major assumption underlying the weighted network model: less activation spreads between less frequently used connections in the network.
As a result, it should take longer for activation to build up in the node patterns for unfamiliar orthographic forms to a level that recognition can occur, thereby inducing a higher level of risk that the lexical processor will “time out” when processing these items and determine that they are not actual words. Furthermore, it was anticipated that error rates to orthographically unfamiliar items would be smallest in the related and identity prime conditions, because the residual activation created by the prime would provide a
“jump start” for these o-node patterns, thereby requiring less activation from the target pattern itself for the recognition threshold to be attained and reducing the chance of rejection.
1.3 Methodological and Theoretical Issues
In designing the current study, there were a number of issues that it was necessary to address with regard to the physical features of the stimuli, the type of experimental
26 task, and the theoretical framework within which the results would be interpreted. Each of these topics will be discussed in turn.
1.31 Visual complexity and stimulus length
A major concern when comparing responses to words in kanji and hiragana is that these two orthographies often differ in their visual complexity. This is because hiragana characters are all constructed from 6 or fewer strokes, while a kanji can have over 25 strokes. Experiments done on hanzi, which are closely related to kanji, have shown that readers’ judgments of visual complexity and response times to familiar phonomorphograms increase linearly with the number of strokes (Qian, Reinking, &
Yang, 1994). This suggests that recognition or naming latencies to kanji forms could be longer than those for their corresponding hiragana forms simply because it is more difficult to visually encode characters with greater numbers of strokes. Fortunately, this problem is tempered by the fact that certain subsets of stroke combinations in visually dense kanji characters are familiar to readers and may be stored and referenced as whole units, simplifying the encoding process (Paradis, et al., 1985).
In addition, Paradis et al. (1985) point out that the character-mora ratio for kanji and hiragana generally differ. A single kanji character usually has a two mora pronunciation, while each hiragana character represents a single mora. Thus, for every kanji character a reader sees, he or she will have to see an average of two hiragana characters to retrieve the same phonological information.
27 In order to minimize potential differences in visual complexity and the character- mora ratio for the stimuli in the current study, only high frequency lexical items were used. High frequency kanji characters tend to have fewer strokes than low frequency characters, and it is more likely that they will possess familiar stroke combinations that readers can process as whole units (Paradis et al., 1985). Moreover, stimuli were matched across dominance conditions for auditory, visual, and combined auditory and visual familiarity according to scores in the NTT Lexical Familiarity Database (Kondo, Amano,
& Mazuka, 1996). Familiarity scores for kanji forms of both kanji dominant and orthographically neutral words were also matched, as were the scores for kanji and hiragana forms of orthographically neutral words. In addition, stimuli were restricted in length to those with two-character kanji forms and three- to four-character hiragana forms in order to reduce differences in the character-to-mora ratio for the two orthographies
(Paradis et al, 1985). The lexical and domain specific familiarity scores for kanji dominant and orthographically neutral words are provided by number of mora and experimental list in Tables 1.2 and 1.3.
28 Lexical Familiarity Script Familiarity
List Aud/Vis Auditory Visual Hiragana BCanji Three Mora 1 5.59 (.35) 5.43 (.42) 5.57 (.36) 2.39 (.06) 4.90 (.02) 2 5.53 (.38) 5.34 (.42) 5.55 (.35) 2.38 (.07) 4.91 (.06) M 5.56 (.37) 5.38 (.42) 5.56 (.36) 2.38 (.07) 4.91 (.04) Four Mora 1 5.60 (.38) 5.41 (.47) 5.67 (.34) 2.39 (.07) 4.92 (.06) 2 5.61 (.31) 5.45 (.37) 5.65 (.33) 2.38 (.08) 4.92 (.05) M 5.60 (.34) 5.43 (.42) 5.66 (.33) 2.39 (.07) 4.92 (.06) Grand M 5.58 (.36) 5.41 (.42) 5.61 (.35) 2.38 (.07) 4.91 (.05) Note. Lexical familiarity based on a 7-point scale. Script familiarity based on a 5- point scale. Standard deviations are given in parentheses. All data taken from NTT Lexical Familiarity Database (Kondo et al., 1996).
Table 1.2: Mean values for kanji dominant stimuli by mora length and prime type.
29 Lexical Familiarity Script Familiarity
List Aud/Vis Auditory Visual Hiragana Kanji Three Mora I 5.66 (.49) 5.73 (.45) 5.67 (.53) 3.33 (.30) 4.83 (.17) 2 5.75 (.47) 5.82 (.45) 5.67 (.57) 3.32 (.30) 4.84 (.22)
M 5.71 (.48) 5.78 (.45) 5.67 (.55) 3.32 (.30) 4.84 (.20) Four Mora I 5.47 (.46) 5.62 (.37) 5.44 (.46) 3.24 (.24) 4.84 (.11) 2 5.54 (.50) 5.66 (.45) 5.53 (.45) 3.25 (.26) 4.84 (.16)
M 5.51 (.48) 5.64 (.41) 5.48 (.46) 3.25 (.25) 4.84 (.14)
Grand M 5.61 (.49) 5.71 (.44) 5.58 (.51) 3.28 (.28) 4.84 (.17)
Note. Lexical familiarity based on a 7-point scale. Script familiarity based on a 5- point scale. Standard deviations are given in parentheses. All data taken from NTT Lexical Familiarity Database (Kondo et al., 1996).
Table 1.3: Mean values for orthographically neutral stimuli by mora length and prime type.
1.32 Selecting a response task
In studies of visual word recognition using isolated stimuli, the two most popular experimental tasks are naming and lexical decision. Lexical decision requires that the participant determine whether the target represents a word in his or her vocabulary.
Naming calls for the participant to pronounce the target. Both tasks are assumed to tap
30 the lexical access process, but choosing which is best suited for a particular study can be a challenge because of questions concerning how effectively each task does its job.
The lexical decision task is probably the most widely used in the visual word recognition literature. When developing stimuli for use with this task, it is important that the words and nonwords be closely matched on variables like bigram frequency, spelling regularity, and visual complexity so that participants cannot make their lexicality judgments based on orthographic or phonological criteria (Hino & Lupker, 1996; Parkin,
1982; Parkin & Underwood, 1983; Seidenberg et al., 1985; Waters & Seidenberg, 1985).
Moreover, the order of the stimuli must be carefully chosen, so that orthographic or phonological patterns in items presented early on do not influence the way in which later items are judged (Glanzer & Ehrenreich, 1979; Gordon, 1983). One of the major critiques of the lexical decision task is that the experimenter cannot be sure if the participant is making their lexical judgment on the desired criteria (i.e., whether or not she was successful in using the target to access a lexical entry) or something else (e.g., whether or not the target subjectively “looks” or “ sounds” like a word to the participant). To encourage participants to use the lexical access criteria, experimenters usually try to make their nonwords as word-like as possible, so that subjective judgments of lexicality are ineffective.
Naming has the advantage of being more natural than lexical decision for the participant to do: people pronounce words out loud all the time, but adults are rarely asked to judge whether a given character string represents a real word or not. The main
31 problem with using naming as an experimental task in the current study is that the
participant is obligated to generate a phonological representation in order to perform the
response task. This is troublesome if one is trying to detect or influence the degree of
phonological activation at a given point in time with the experimental manipulation,
because the task itself could affect what one finds. A related concern with the naming
task, and one that is particularly relevant for the current study, is that naming doesn’t
have to reflect lexical access. This is because words in shallow orthographies can be
pronounced according to grapheme-phoneme correspondence rules, without word recognition or lexical access ever taking place (e.g., Forster, 1981). Because this strategy
is not available for words in deep orthographies, naming latencies to deep and words in shallow orthographies could differ as the result of the task, not naturally occurring processing phenomena.
In sum, neither naming nor lexical decision is the perfect response task. However,
1 chose to use lexical decision. The stimuli for the current study were tightly controlled with respect to frequency, length, visual complexity, and concreteness, which was expected to eliminate concerns of inconsistency regarding manipulations of these factors.
Also, the nonwords used as primes and fillers were developed from actual Japanese words of extremely low frequency to insure that they were very “wordy”. The lexical decision task was also preferable to me because it would not emphasize the phonological properties of the target words as the naming task would. This was important, because a primary goal of this project was to investigate how different types of priming influenced
32 the speed with which familiar and unfamiliar orthographic forms were recognized, and to determine if representations of deep and shallow orthographic forms v/ere differently sensitive to pretarget activation of their semantic, phonological, or orthographic representations. Inducing phonological activation with a response task like naming could introduce a confounding factor that would make the results more difficult to interpret.
Another reason for choosing the lexical decision task was that Baluch and Besner
(1991) made a specific claim about this task with regard to the processing of words in shallow orthographies — a claim that stands in direct opposition to the predictions of the weighted network model. Namely, these researchers argued that the presence of nonwords in the stimulus set can lead participants to phonologically recode orthographically shallow target items prior to lexical access, even if it is possible to recognize them directly from their orthographic representation. As a result, the processing delay caused by phonological recoding causes words in shallow orthographies that should be recognized quickly (like words in deep orthographies) to instead be recognized more slowly, producing an artificial difference between words in the two types o f orthographies. In fact, some of Besner’s own work has shown orthographically familiar shallow forms of Japanese words are recognized more quickly than unfamiliar forms (Besner & Hildebrandt, 1987; Besner & Smith, 1992). Baluch and Besner’s finding suggests that the lexical decision task will cause orthographically familiar shallow forms to be recognized nonlexically, just like unfamiliar shallow forms, assuming that
33 orthographically familiar shallow forms are normally recognized lexically (Besner &
Hildebrandt, 1987; Besner & Smith, 1992; Hirose, 1984, 1985; Sasanuma et al., 1988).
Separating this prediction from the dual-route architecture that Baluch and Besner
(1991) assume, the simple implication is that lexical decision should force the participants in my study to process the hiragana forms of words in the same way, irrespective of whether these forms are familiar or unfamiliar. Were this to happen, the differences in recognition latencies for familiar and unfamiliar hiragana forms that are predicted by the weighted network model should disappear, as should the anticipated differences in semantic priming effects for familiar and unfamiliar hiragana forms. Thus, by using the lexical decision task, 1 was able to test the validity of Baluch and Besner’s claims in addition to the hypotheses of the weighted network model.
1.33 Choosing a theoretical framework
Over the years, a number of models of visual word recognition have been proposed. These models differ with regard to the amount o f control the reader has in applying phonological knowledge during text processing, as well as the number of processing routines a reader may use to access the lexicon. The most broadly popular framework for these models is “dual-route” in that it involves both lexical and nonlexical recognition routines (e.g., Coltheart, 1978; Katz & Feldman, 1981; Turvey et al., 1984).
According to this dual-route framework, printed words are recognized either by a direct mapping of the orthographic representation onto their appropriate lexical entries (the lexical routine), or via the application of symbol-sound correspondence rules to the
34 orthographie representation, which generates a phonological representation that then
facilitates lexical access (the nonlexical routine). Some researchers have argued that the
reader chooses which routine to use, based on a visual analysis of the text and his or her
knowledge of grapheme-phoneme regularity (e.g., Coltheart, 1978). Others have said that
every stimulus activates both routines, and whichever routine produces an output first is
responsible for accessing the lexicon (e.g., Seidenberg et al., 1984). A diagram of a basic
dual-route model is provided in Figure 1.4.
DIRECT VISUAL ROUTE orthographic input { LEXICON
phonological recoding
INDIRECT PHONOLOGICAL ROUTE
Figure 1.4. A basic dual-route model of word recognition.
Assuming that phonology is strategically controlled is problematic for several
reasons (Seidenberg et al., 1984; Waters & Seidenberg, 1985). First, the vast majority of
the world’s writing systems do not fully encode all phonological information. English, the very language for which the Coltheart’s (1978) dual-route model was developed to explain, is particularly vague in this regard, with some words being very regular in their
35 symbol-sound correspondences, while others are completely irregular and can only be
pronounced by memory. Add to this the issue of consistency, or how many words with
a given spelling are pronounced in the same way, and the problem becomes even more
overwhelming (Glushko, 1979). Thus, it is unclear how a reader could visually assess the
phonological transparency of an item without actually trying to recode it into a
phonological representation and using that representation to access the lexicon. Second, it
is not obvious what orthographic and phonological units are the best upon which to base
symbol-sound mapping rules. In English, one might guess that letters and phonemes were
the best choice (cf. Coltheart, 1978), but effects of spelling regularity and consistency
suggest that the organization of the coda (i.e., the unit of sound extending from the first
vowel to the end of the word) is more important, at least for monosyllabic words
(Glushko, 1979; Seidenberg et al., 1984; Waters & Seidenberg, 1985). Moreover, for
orthographies in which a character represents a syllable or a morpheme, there is evidence that readers organize phonological representations according to these units, not individual phonemes (e.g., Wydell, Patterson, & Humphreys, 1993; Wydell, Butterworth, &
Patterson, 1995; Tan & Perfetti, 1997). Thus, if strategic phonological control is used, it must be based on language-specific symbol-sound units.
If phonological representations are developed automatically, not strategically, and these representations facilitate access to semantic information in parallel with orthographic representations, then either a dual-route model or a cormectionist model is suitable to describe the visual word recognition process. Indeed, Seidenberg et al. (1984)
36 conceived of their “horse-race” dual-route explanation of regularity and consistency
effects as subsumable within the interactive activation framework of Rumelhart and
McClelland (1981), which is a connectionist frameworkThis point is relevant to the
current study, because 1 have chosen to use a modified, non-computational version of the
PDP model as the basis for my experiments. This stands in contrast to the traditional
choice for investigations of orthographic depth, which have presumed a dual-route
architecture, and generally one in which strategic phonology was applied (e.g. Bajo et al.,
1994; Baluch & Besner, 1991; Besner & Hildebrandt, 1987; Besner & Smith, 1992; Frost,
1994; Frost et al., 1987; Hirose, 1984, 1985; Katz & Feldman, 1981; Lukatela et al., 1980;
Lukatela & Turvey, 1990; Turvey et al., 1984; Sasanuma et al., 1988; Simpson & Kang,
1994; Tabossi & Laghi, 1992).
In sum, most dual-route models presume three things; involving phonology in visual word recognition automatically slows the process down (e.g., Norris & Brown,
1985; Paap et al., 1992; Seidenberg, 1985); in order to be efficient, the lexical processor eliminates or postpones the involvement of phonology if possible (e.g., Coltheart, 1978;
Katz & Feldman, 1981); and semantic representations are only available after lexical access has occurred (e.g., Coltheart, 1978; Katz & Feldman, 1981; Seidenberg, 1985).
Conversely, the weighted network model assumes that more activation spreads between nodes that are frequently co-activated. The simplicity of this latter hypothesis permits
As this example suggests, the theoretical difierences between dual-route and connectionist models ate narrowing as dual-route models are modified to accommodate the data provided in support of connectionist fi-ameworks. As a result, the claim that a model is “dual-route” or “cormectionist” depends more on the philosophical history of the researcher using it than on the structural aspects of the model itself. 37 phenomena as diverse as phonological transparency, lexical frequency, spelling-sound
consistency, and priming effects to be explained in terms of the same mechanism, rather
than several unrelated processes. Intuitively, this makes the weighted network
framework more parsimonious than a dual-route architecture, and thus preferable as a
base for scientific study.
1.4 Summary
The orthographic depth hypothesis (Katz & Feldman, 1981; Turvey, Feldman, &
Katz, 1984) presumes that the phonological transparency of an orthographic system directly influences what processing strategy readers use to access semantic information.
Words written in shallow orthographies are recoded into a phonological representation by means of grapheme-phoneme correspondence rules, and then this phonological code is used to access meaning. Words in deep orthographies, conversely, are recognized without recourse to phonology, because their symbol-sound assignments are so variant. Instead, the reader must learn to map the visual form of a word directly onto the appropriate lexical entry in order to access the meaning and pronunciation. The fact that words in shallow orthographies must undergo an additional stage of processing before lexical access can be achieved is assumed to result in slower recognition latencies for words in these orthographies compared to words in deep orthographies (Norris & Brown, 1985; Paap,
Noel, & Johansen, 1992; Seidenberg, 1985).
The empirical evidence regarding the orthographic depth hypothesis is mixed. In its favor, some researchers have found that lexical decision and naming latencies to printed
38 words increase with orthographic transparency, while effects of lexical level factors like
word frequency and semantic priming decrease (e.g.. Frost, 1994; Frost et al., 1987;
Lukatela et al., 1980; Lukatela & Turvey, 1990; Morton & Sasanuma, 1984; Turvey et al.,
1984). Others, however, have found evidence that sublexical phonology may be
bypassed by words in shallow orthographies o f high lexical frequency (Bajo et al., 1994;
Seidenberg, 1985) and high orthographic familiarity (Besner & Hildebrandt, 1987; Besner
& Smith, 1992; Hirose, 1984, 1985; Sasanuma et al., 1985). In addition, prelexical activation of phonology can depend on the experimental task (Baluch & Besner, 1991) and how efficient phonological recoding is for processing the majority of the stimuli being presented (Milota et al., 1997; Simpson & Kang, 1994; Tabossi & Laghi, 1992).
The current study challenges the hypothesis that words in deep and shallow orthographies are processed differently by means of a within-word comparison of familiar and unfamiliar orthographic forms of Japanese words. Stimuli consisted of nouns taken from the NTT Lexical Familiarity Database (Kondo et al., 1996), half of which were kanji dominant, half of which were orthographically neutral. Items were controlled for lexical frequency, character-to-mora ratio of the hiragana and kanji forms, as well as auditory, visual, and combined auditory and visual familiarity. Visual lexical decision was the experimental task. Three experiments were performed, with each utilizing a different priming task. The results were interpreted in terms of a weighted network model, based on the Seidenberg and McClelland (1989) PDP model. The benefits of this architecture
39 over the traditional dual-route framework used for investigations of orthographic depth are discussed.
40 CHAPTER 2
EXPERIMENT 1: SEMANTIC PRIMING
It has long been suggested that semantic information will be accessed differently by characters like kanji or hanzi than syllabic or phonemic characters. Underlying this assumption is the belief that deep characters possess a degree of meaning that the letters of an alphabet or the characters of a syllabary do not (Aoki, 1990; Elman, Takahashi, and
Tohsaku, 1981; Hatta, 1978). With regard to Japanese, it is true that there are a few kanji characters for which the relationship between the character and its meaning is almost iconic (e.g., 'mouth' □ , ‘tree’ 7^ ), and most kanji compounds are made up of component characters with individual meanings that collectively suggest the compound’s meaning (e.g., ‘automobile’ is composed of the kanji meaning ‘self È , ‘move’ W] and
‘cart’ ^ ). However, some kanji compounds are made up of characters that were chosen not for the meanings they represent, but for their common pronunciations. The meanings of such compounds must be memorized, because they cannot be inferred from the meanings that are associated with the individual component characters (e.g.,
‘congratulations’ is composed of the kanji for ‘eye’ @ , ‘exit’ Üü , and ‘save’ ).
41 The weighted network model does not presume that the kanji forms of words inherently have stronger connections with their semantic representations than the hiragana forms. Rather, it predicts that the weight of the connection between a particular orthographic representation and a semantic representation will depend on the frequency with which a concept is associated with that orthographic form. Thus, the kanji forms of kanji dominant words will have heavier connections to their semantic representations than their corresponding hiragana forms because they are more familiar. By the same token, the kanji and hiragana forms of orthographically neutral words will both have heavy connections with their semantic representations, because each form is familiar. From the weighted network perspective, it is easy to see how the misperception that kanji are more closely associated with meaning could have developed, given that the vast majority of content morphemes in Japanese are kanji dominant. People have simply mistaken an effect of orthographic dominance for an effect of orthographic depth, because the two phenomena are confounded in the case of kanji dominant words.
This experiment tested the hypothesis o f the weighted network model that overall recognition latency and the size of the priming effect for a given target should be determined by orthographic familiarity of the target, not its orthographic depth. The primary prediction was that an interaction of orthographic dominance and target orthography would obtain, such that RTs to familiar orthographic forms would not differ in any prime condition, while RTs to unfamiliar orthographic forms would always be longer than those to their familiar counterparts. Also, the difference in RTs between the
42 semantically related and asterisk prime conditions (i.e., the priming effect) was not expected to differ for familiar orthographic forms, regardless of their orthographic depth, while the priming effect for unfamiliar orthographic forms was expected to differ from those for familiar orthographic forms. This pattern of results would rule out any model that assumes phonological delay, in that phonological delay calls for orthographically shallow forms to be recognized more slowly than orthographically deep forms. Thus, such a model would predict that kanji forms would always be recognized faster than hiragana forms.
With regard to error rates, the weighted network model predicted that unfamiliar orthographic forms should elicit more false-negative responses than familiar orthographic forms. This is because less activation spreads along less frequently used connections, thereby inducing a higher level of risk that the lexical processor will “time out” and falsely identify unfamiliar items as nonwords. Furthermore, error rates should be smallest in the related prime condition, because the prelexical activation provided to the target word’s node pattern should give it a better chance o f attaining threshold within the time window for word recognition.
2.1 Method
2.11 Participants
Twenty students from colleges and universities in the Tokyo area participated in this experiment for a nominal fee. All of the students were native speakers of Japanese who had scored above average on the 100 RAKAN test for reading ability for kanji words
43 developed by NTT (Kondo, Wydell, & Amano, 1998). They reported no reading
disabilities, had normal or corrected-to-normal vision, and were between the ages of 18-
28.
2.12 Materials
Stimuli for this experiment consisted of 360 Japanese nouns from the NTT Lexical
Familiarity Database. All were of high lexical frequency, and had two character kanji forms and three or four character hiragana forms. Half of the items were kanji dominant and half were orthographically neutral. Within each dominance condition, three and four mora items were balanced separately on five scales from the NTT Lexical Familiarity
Database: auditory familiarity, visual familiarity, simultaneous auditory and visual familiarity, and the respective familiarity of the hiragana and kanji forms.
The nonword filler items were based upon three- and four-mora Japanese nouns of extremely low frequency. Kanji forms for these items were created by using characters from the set of real word stimuli, to insure that the kanji forms of our real word and nonword items were comparably in visual complexity. These forms were then checked by three native Japanese speakers to insure that the intended pronunciations for the new kanji pairs were acceptable and probable, and that no real words were created. Because the one-to-one symbol-sound relationship between hiragana and Japanese phonology did not permit any manipulation of the hiragana forms of these “nonwords”, a secondary test was performed on these and the newly created kanji forms to insure their unrecognizability as real words. After completing the experiment, participants were
44 presented with a computerized list of the nonwords, with each item appearing in the orthography that the person saw it in during the experiment. Participants rated each item on a scale of 1 to 7 for the likelihood that it was a real word, with 1 indicating that the item was clearly not a word and 7 indicating that it was definitely a word. Less than 4% of the hiragana forms of nonwords received a score either a 6 or 7, suggesting that these items were truly novel to the participants
Three types of primes were prepared: semantically related (related), semantically unrelated (unrelated), and neutral (asterisk). Related and unrelated primes were real words that were normed for their relatedness by a second group of participants from the same demographic group as those that were involved in this experiment. Unrelated primes did not share mora or characters with their corresponding targets. Related and unrelated primes were presented in a dominant orthography, such that most primes were in kanji, but some were in hiragana or katakana. The asterisk prime was a string of three asterisks (***).
Items from the two target groups in each dominance condition were divided among the priming conditions and balanced according to the lexical and orthographically familiarity factors described above. This resulted in 120 targets for the identity, unrelated, and asterisk priming conditions, 60 of which were kanji dominant and 60 of which were orthographically neutral. Two stimuli lists were prepared. On each list, half of the targets in each dominance and priming condition were presented in their hir%ana form and half were presented in their kanji form. The target orthography for the target
45 was counterbalanced, such that words that appeared in their kanji form on List 1 would appear in hiragana on List 2 and vice versa. The same procedure was followed for nonword stimuli. Including real word and nonword stimuli, there were 360 items on each list that appeared in kanji and 360 that appeared in hiragana.
2.13 Procedure
The stimuli were presented in a randomized order to each participant. Ten participants saw each list. The experiment was conducted in a darkened, soundproof room, with the participant seated approximately one meter away from the monitor on which the stimuli would appear. Individual primes and targets were presented at the center of the black monitor screen in a white 24 point font. The participant was instructed to read both the primes and the targets and determine as quickly as possible if the character strings she saw second were real words in Japanese. Responses were made by pressing one of two keys on an ergonomically designed response box with the index finger and middle finger of the participant’s right hand. The index key was marked with the symbol O marii ‘good’ (for real words); the middle key was marked X batsu ‘bad’
(for nonwords).
At the onset of a trial, a fixation cross appeared in the center of the screen for 500 ms. The prime appeared immediately at the fixation cross offset, and remained on the screen for 500 ms, followed by 500 ms of blank screen. The target then appeared at the center of the monitor screen and remained there until the participant made her lexical judgment. When this was done, the screen went blank for 1500 ms, after which the
46 fixation cross would reappear and the next trial would begin. Trials were presented in blocks containing 30 targets. After five blocks were presented, the participant was escorted from the experiment room and allowed to take a 20 minute break. Half of this break time was spent providing normative data for the target words in the blocks that had just been seen. After the break, the participant returned to the experiment room. This procedure was repeated four times, until all 720 stimulus items had been judged. Each participant’s session lasted approximately three hours.
2.2 Results and Discussion
The response time and accuracy for each target item was stored for each participant. Only correct responses were used in the RT analysis. Due to an error in the computer program responsible for storing participants’ data, a small number of response times were deleted or superimposed over the response times for nearby items. Less than
.06% of the overall data were lost. Approximately 4% of the data points were removed because they fell outside of the response window of +Z-2.5 times the standard deviation from the mean response time for a given participant in a given experimental condition; no boundary value substitution was used. The remaining data are summarized in Table 2.1.
47 Priming Type Dominance Orthography Related Unrelated Asterisk Mean
ON Hiragana 605(64) 664 (62) 658(109) 642 (84) Kanji 613 (67) 677 (90) 651(65) 647 (78) fCD Hiragana 695(89) 803 (107) 780(121) 759(115) Kanji 598(67) 674 (67) 655(60) 642 (71)
Mean 628 (81) 704(100) 686 (106) Note. All values are rounded to the nearest millisecond. Standard deviations are given in parentheses. Dominance = orthographic dominance. Orthography = target orthography. ON = orthographically neutral, KD = kanji dominant.
Table 2.1 : Mean response times in ms from Experiment 1.
2.21 Response time analysis
Condition means were computed by subjects and by items for use in analyses of variance (ANOVA) with two levels of list and repeated measures on two levels of target orthography (kanji and hiragana), two levels of orthographic dominance (kanji dominant and orthographically neutral), and three levels of prime type (related, unrelated, and asterisk). List was a between-subjects variable, while all other variables were within- subjects. A summary of these analyses is given in Table 2.2.
48 Subjects Items Source df F d f F Between subjects List 1 .18 1 5.71* 18 (661148.75) 58 (5114.99)
Within subjects Prime Type (FT) 2 61.58** 2 65.83** 36 (2082.74) 116 (4682.58)
Orthographic Dominance (GO) 1 139.46** 1 106.65** 18 (1355.30) 58 (5717.39)
Target Orthography (TO) 1 45.80** 1 92.59** 18 (4137.92) 58 (6149.35)
PTxOD 2 3.46"^ 2 5.32** 36 (1525.74) 116 (6159.38)
PTxTO 2 1.38 2 1.752 36 (1772.11) 116 (6786.83)
OD X TO 1 107.26** 1 118.50** 18 (2051.24) 58 (5940.11)
PT x OD X TO 2 1.94 2 1.20 36 (946.53) 116 (4674.05) Note. **£ < .01. *£ < .05. < .10. All £- values for within-subject variables are adjusted according to the Greenhouse-Geisser correction. Values given in parentheses indicate mean square error.
Table 2.2: Analysis of variance for mean response times from Experiment 1.
49 AU main effects were reliable by subjects and items, and were in the expected direction. For the effect o f prime type, items in the related prime condition were 58 ms faster on average than those in the asterisk condition and 76 ms faster than those in the unrelated condition. Although this finding does not distinguish among competing models, it is consistent with the weighted network’s prediction that residual activation from the prime in some portion o f the nodes in the target word’s representation would make it easier for that representation to achieve the threshold for recognition.. As for effect of orthographic dominance, orthographically neutral forms were responded to 56 ms more quickly overaU than kanji dominant forms. This is attributable to the fact that both the kanji and hiragana forms of orthographicaUy neutral words were familiar to participants, and therefore were recognized quickly. The effect of target orthography reflected that the average RT to kanji forms were 56 ms faster overaU than the average RT to hiragana forms.
Crucially, the interaction of orthographic dominance and target orthography predicted by the weighted network model was fully reliable and in the expected direction.
Namely, the mean RTs to the unfamiliar hiragana forms of kanji dominant words were slower overall than those to their familiar kanji counterparts, while RTs to the familiar kanji and hiragana forms of orthographically neutral words did not differ. This
50 interpretation was confirmed by paired t-tests on the overall means for kanji and hiragana forms in each dominance condition (a = .05)/
The interaction of prime type and orthographic dominance was marginal by subjects, but reliable by items. To the extent that this effect is real, the kanji dominant forms were primed more than the orthographically neutral forms, when preceded by a semantically related prime. Specifically, the familiar hiragana and kanji forms of orthographically neutral words respectively received 53 ms and 38 ms of priming, when compared to the asterisk baseline, while the unfamiliar hiragana forms of kanji dominant words received 85 ms of priming, and their familiar kanji counterparts received 57 ms of priming. Thus, while the mean RTs to familiar orthographic forms were all faster than those to unfamiliar orthographic forms in the related prime condition, just as the weighted network model predicted, the unfamiliar forms received 29-47 ms more priming than any of the familiar orthographic forms. However, this finding must be interpreted cautiously, because the interaction of prime type, orthographic dominance, and target orthography did not approach significance.
In fact, the weighted network model predicts that unfamiliar orthographic forms should be more sensitive to priming than familiar orthographic forms, because the weights on connections going to and from nodes in familiar orthographic representations are less flexible than those in unfamiliar orthographic representations. In other words, changing the weight on a light connection is easier to do than on a heavy connection. Weights in
‘ AH t-tests performed in this study were paired t-tests by subjects and items on the least square means of 51 priming bias for orthographically unfamiliar forms. A weight shift on a single connection
significant enough to create the observed priming bias would have vast ramifications for
the weights on all the connections in its immediate neighborhood (i.e., the connections going fi’om nodes that that the weight shifted node connects to), because the sum of all
weights on connections extending from a given node equals one. Tiny shifts, on the other hand, would only require minor changes to the weights on connections leading away from the nodes involved in the shifts, thereby causing minimal “damage” to the recognition network as a whole. Furthermore, the size of the weight shift effect should be directly related to the amount of overlap between the nodes in the prime’s representation and those in the target’s representation. The more nodes that the two items have in common, the more connections in the o-node representation for the target that will experience weight shifts during the processing of the prime, and the more activation the unfamiliar orthographic representation as a whole will be able to channel.
The weighted network model makes specific predictions about the nature of the interaction of orthographic dominance and target orthography, so two additional
ANOVAs were conducted to test the model further. If recognition latency is influenced more by orthographic familiarity than by orthographic depth, as the weighted network model predicts, then it should be the case that RTs for the familiar hiragana and kanji forms of orthographically neutral words do not differ, while the familiar kanji forms of kanji dominant words are recognized more quickly than their unfamiliar hiragana counterparts. To test this hypothesis, separate ANOVAs were performed on the mean
53 response times for orthographically neutral and kanji dominant items, with repeated
measures on three levels of prime type and two levels of target orthography. For
orthographically neutral words, this analysis revealed a main effect of prime type
[Fi(2,36) = 26.65, £ < .01; ^(2,116) = 17.41, £ < .01], but participants showed no
differences in their response times to the hiragana and kanji forms of orthographically
neutral words [Fs < 1.0]. In contrast, the analysis of kanji dominant items revealed a
main effect of target orthography [F,(l,18) = 83.45, £ < .01; ^(1,58) = 171.22, £ < .01],
and a main effect of prime type |Ti(2,36) = 44.65, £ < .01; F\(2,l 16) = 39.93, £ < .01], in
addition to a marginal interaction of the two factors |Ti(2,36) = 2.80, £ < . 10; £ 2(2 , 116)
= 2.40, £ < .10]. This confirmed that the mean RTs to the unfamiliar hiragana forms of
kanji dominant words were slower than those to their familiar kanji counterparts, and was
consistent with the predictions of the weight shift effect described in the primary
analysis, whereby unfamiliar orthographic forms are more to semantic priming than
familiar orthographic forms.
2.22 Error analysis
The mean error data is provided for each condition in Table 2.3. An ANOVA was
performed on the number of errors in each condition by subjects and items, with two
levels of list and repeated measures on target orthography (kanji and hiragana),
orthographic dominance (kanji dominant and orthographically neutral), and prime type
(related, unrelated, and asterisk). This analysis revealed a pattern of errors very similar to the real word response time data. Namely, there was a main effect of prime type
54 [Fi(2,36) = 45.72, £ < -01; £2(2 , 116) = 18.69, £ < .01], with fewer errors made in the related prime condition than in the unrelated or asterisk conditions. Also, there was a main effect of orthographic dominance [£,(1,18) = 59.85 £ < .01; £ 2( 1,58) = 47.28, £ <
.01], such that fewer errors were made to orthographically neutral items than kanji dominant items. In addition, there was a main effect of target orthography [£,(1,18) =
28.41, £ < .01; £2( 1,58) = 31.34, £ < .01], with kanji forms eliciting fewer errors overall than hiragana forms.
Priming Condition
Dom Ortho Related Unrelated Asterisk Mean
ON Hiragana .80(1.01) 1.85(1.27) 1.20(1.06) 1.28(1.18) Kanji 1.25 (1.55) 2.25 (2.17) 2.25 (2.81) 1.92(2.25) KD Hiragana 2.35(2.16) 7.85 (3.03) 6.50 (2.59) 5.57 (3.49) Kanji .80(1.11) 2.25 (2.69) 1.75(1.71) 1.60(2.01)
Mean 1.30(1.63) 3.55 (3.43) 2.93 (2.99)
Note. Standard deviations are given in parentheses. Dom = orthographic dominance. Ortho = target orthography. ON = orthographically neutral, KD = kanji dominant.
Table 2.3: Mean error rates from Experiment 1.
Tliese findings are entirely consistent with the predictions o f the weighted network model. Namely, the residual activation left by the related prime reduced the number of errors, because it reduced the amount of new activation needed for the target
55 representations to reach the recognition threshold before the lexical processor “timed-out”
and rejected them as nonwords. Also, more errors were made to kanji dominant words as
a whole and to hiragana forms as a whole, because 50% of the items in each of these
conditions were orthographically unfamiliar. This is understandable, assuming that the
activation levels for node patterns representing unfamiliar orthographic forms were slower
to increase, because of lighter weights on the relevant connections. Thus, these
representations were more likely to fall short in terms of the activation needed to attain
recognition threshold before the processor rejected them as possible exemplars of real
words.
The pattern of interactions in the error data also supported the hypothesis that activation levels increase more slowly for orthographically unfamiliar forms of words, in that conditions containing unfamiliar orthographic forms always had higher error rates.
First, there was an interaction of prime type and orthographic dominance [Fi(2,36) =
18.36, £ < .01; £2(2 , 116) = 7.49, £ < .01], whereby fewer errors were made to orthographically neutral words than to kanji dominant words across the three prime conditions. Second, an effect of prime type and target orthography was found [£,(1,18)
= 107.29, £ < .01; £2( 1,58) = 3.05, £ < .10], with fewer errors made to kanji forms compared to hiragana forms in each prime condition. Third, there was an interaction of orthographic dominance and target orthography [£,(1,18) = 107.29, £ < .01; £ 2( 1,58) =
68.23, £ < .01], whereby participants were more likely to mistake unfamiliar orthographic forms for nonwords than familiar orthographic forms.
56 To test my interpretations of these interactions, separate ANOVAs were performed on items for each dominance condition, with two levels of list and repeated measures on two levels of target orthography and three levels of prime type. For orthographically neutral words, this revealed a main effect of prime type that was significant by subjects but marginal by items [F,(2,36) = 12.25, £ < .01; £2(2 , 116) = 2.58,
£ < .10], as well as an effect of target orthography that was marginal by subjects but significant by items [£,(1,18) = 3.26, £ < .10; £,(1,58) =4.16, £ < .05]. The main effect of prime type reflected that fewer errors were made overall to orthographically neutral words in the related prime condition, with the most made in the unrelated prime condition. Thus, while seeing a related word prior to the target reduced error rates, seeing a semantically unrelated word actually increased error rates, relative to the asterisk baseline. As for the effect of target orthography, fewer errors were made to hiragana forms of orthographically neutral words than to their corresponding kanji forms, even though both forms were orthographically familiar. This suggests that hiragana forms were easier to recognize, which relates to the weighted network model’s assumption that hiragana forms receive more activation overall during lexical processing as a result of the exclusive connections between the o-nodes and p-nodes that represent them and their pronunciations. Furthermore, the fact that no interaction o f prime type and target orthography was observed for orthographically neutral items supports the claim that hiragana and kanji forms of these items were similarly sensitive overall to the priming manipulation, regardless of the fact that the two forms differ in their orthographic depth.
57 When an identical ANOVA was performed on the error rates for kanji dominant
items, robust main effects of prime type [Fi(2,36) = 39.98, £ < .01; £2(2 , I I 6) = 17.22, 2
< .01] and target orthography were found [£,(1,18) = 92.02, £ < .01; £ 2( 1,58) = 70.22, £
< .01], in addition to an interaction of prime type and target orthography [£i(2,36) =
20.98, £ < .01; £2(2 , 116) = 13.25,£< .01]. The effect of prime type was similar to the
one for orthographically neutral items, in that the fewest errors were made to items in the
related prime condition and the most were made in the unrelated prime condition.
However, the effect of target orthography on kanji dominant words was just the opposite
to the one for orthographically neutral items, with hiragana forms inducing many more
errors overall than kanji forms. According to the weighted network model, this
phenomena is attributable to the fact that the hiragana forms of kanji dominant words are
unfamiliar; thus, it takes longer for the complex node patterns representing these forms to
achieve the threshold value of activation, compared to those for their familiar kanji
counterparts.
Lastly, the error data revealed a three-way interaction between prime type,
orthographic dominance, and target orthography [£i(2,36) = 12.86, £ < .01; £,(2,116) =
5.84, £ < .05]. A review of the mean number of errors for items in each prime and
dominance condition showed that participants correctly identified targets as valid
exemplars of real lexical items more often when these forms were preceded by semantically related primes. Moreover, the number of errors made to unfamiliar items was reduced much more by the related prime than were the number of errors made to
58 familiar items. This latter finding is related to trend in the response time analysis,
whereby unfamiliar forms received slightly more priming than familiar forms. The
additional activation provided by the priming allowed the lexical patterns containing
unfamiliar orthographic representations to reach the threshold for recognition more often
(as the result of residual activation and the weight shift effect), and thus they were less
frequently rejected as nonwords in the related prime condition.
Collectively, the findings of this experiment are consistent with previous findings
in Japanese visual word recognition literature regarding the general importance of
orthographic familiarity in the processing of words in hiragana and katakana (Besner &
Hildebrandt, 1987; Besner & Smith, 1992; Hirose, 1984, 1985; Sasanuma et al, 1985).
Furthermore, they extend this literature with two important findings. First, orthographic
familiarity effects occur within-word. Second, when orthographic familiarity and
orthographic depth are not confounded, as in the case of orthographically neutral words,
hiragana and kanji representations are similarly sensitive to semantic priming, and elicit comparable recognition latencies and numbers of errors. Not only do these findings strongly suggest that previous claims concerning the semantic “superiority” of kanji and hanzi characters over syllabic characters and letters (Aoki, 1990; Elman et al., 1981;
Hatta, 1978) were misguided, but they support the hypothesis that orthographic familiarity is a better predictor than orthographic depth of the way that a printed word will be processed.
59 CHAPTERS
EXPERIMENT 2: PHONOLOGICAL IDENTITY PRIMING
At the very heart of the distinction between deep and shallow orthographies is the belief that the orthographic representations of shallow words have a more clearly specified relationship with their phonological representations than the orthographic representations of deep words, and therefore are processed in a different way. From a dual-route perspective, one might assume, then, that orthographically shallow words would be more sensitive to phonological priming than orthographically deep words, because residual activation of the phonological representation would increase the speed with which a shallow word could map onto the appropriate phonological code, and that phonological code could then access the lexicon.
However, the weighted network model predicts that orthographic familiarity, not depth, will determine how quickly words in deep and shallow orthographies will be recognized following activation of the phonological representation. The model presumes that the kanji and hiragana forms of an orthographically neutral word will be identified as acceptable exemplars of the word at similar speeds after phonological identity priming, because the o-node pattern associated with each of these forms is well-connected with
60 other representations for the word and can distribute a large amount of activation among
its component nodes and node patterns. For kanji dominant words, on the other hand,
the kanji form should always be recognized more quickly than the hiragana form, because
the o-node representation for the kanji form has heavier coimections with the p-node
representation for the word than does the o-node representation for the hiragana form.
As a result, more activation should spread to the kanji o-node pattern during the processing of the phonological prime, and more residual activation should be left in the kanji o-node pattern.
Another hypothesis of the weighted network model being tested is the “weight shift” hypothesis proposed in Chapter 2. As the reader will recall, 1 found a larger difference in response times between the related and asterisk prime conditions for unfamiliar orthographic forms than for familiar orthographic forms. This resulted, I claimed, because processing the related prime caused weight shifts on some of the connections in the o-node representations of orthographically unfamiliar targets.
However, the weight shift effect in Experiment 1 was not fully reliable; 1 attributed this to the fact that the representations for semantically related primes overlap minimally with those for their targets. Were the prime’s representation to have more in common with the one for the target, the weight shift effect should be statistically significant These very circumstances should arise in the identity prime condition from in the current experiment, thereby providing an ideal environment to test for the proposed weight shift effect.
Furthermore, the weight shift hypothesis predicts that error rates to orthographically
61 unfamiliar targets should also decrease as node overlap between the prime and target
increases, because the faster the node patterns for the target item can build-up activation to achieve the recognition threshold, the less likely it is that the time-out criterion will come into play and reject the target as unknown character string.
3.1 Method
3.11 Participants
Twenty students from the same demographic group as in Experiment 1 participated in exchange for a nominal fee.
3.12 Materials
The same target stimuli were used as in Experiment 1. Three types of primes were prepared: identity, nonword, and tone. Identity primes consisted of digitized recordings of our critical target items as spoken by a female representative of the participant group. Nonword primes were similarly prepared exemplars of the filler items.
The tone prime was a single, computer-generated, 100 ms tone. Two stimulus lists were prepared according to the guidelines outlined for Experiment 1.
3.13 Procedure
The procedure was similar to that for Experiment 1, except that primes were presented over headphones at a comfortable listening level. At the onset of a trial, a fixation cross appeared in the center of the screen for 500 ms. When the cross disappeared, the prime was given. Inunediately after the offset o f the prime, the target appeared at the center of the monitor screen and remained there until the participant
62 pressed a key on the response box. Participants were told that they would hear some
word or noise before a new character string appeared, but that they should simply listen
to this sound string and make no special judgment about whether or not the string
represented a word. Ten participants saw each list.
3.2 Results and Discussion
Priming Condition
Dominance Orthography Identity Nonword Tone Mean
ON Bfiragana 500(81) 656(96) 629 (103) 595(115) Kanji 514(83) 644(92) 630(113) 596(112) BCD Hiragana 539(107) 774(154) 767 (152) 693 (176) Kanji 516(71) 642(87) 627 (92) 595 (100)
Mean 517(86) 679(122) 663 (130) Note. All values are rounded to the nearest millisecond. Standard deviations are given in parentheses. ON = orthographically neutral, BCD = kanji dominant.
Table 3.1: Mean response times in ms from Experiment 2
Response time and error data were collected as in Experiment 1. Due to the error described in Experiment 1, .05% of the overall data was lost. Response time cut-offs
were set for each participant in each experimental condition at +/- 2 .S times the standard deviation from his or her mean response time. Approximately 3% of the data points were
63 outliers according to this criterion and were removed from the data prior to statistical analysis. The remaining data are summarized in Table 3.1.
3.21 Response time analysis
ANOVAs were performed by subjects and by items on the mean response times for each condition with two levels of list and repeated measures on two levels of orthographic dominance (orthographically neutral and kanji dominant), two levels of target orthography (hiragana and kanji), and three levels of prime type (identity, nonword, and tone). A summary of these analyses is given in Table 3.2.
Fully significant main effects of prime type, orthographic dominance, and target orthography were found. All effects were in the expected direction. For the effect of prime type, items in the phonological identity prime condition were 146 ms faster on average than those in the tone condition and 162 ms faster than those in the nonword condition. While this finding does not discriminate among models, it is consistent with the weighted network’s prediction that residual activation in some portion of the nodes in the target word's representation would make it easier for the relevant complex node patterns to achieve the threshold for recognition. For effect of orthographic dominance, orthographically neutral forms were responded to 49 ms more quickly overall than kanji dominant forms. A main effect of target orthography was also found, whereby kanji forms were recognized 48 ms faster overall than hiragana forms.
All possible interactions were found and were fully reliable. The three-way interaction of prime type, orthographic dominance, and target orthography suggests that
64 recognition latencies for unfamiliar hiragana forms o f kanji dominant words were reduced
more by the phonological identity prime than were those for any of the familiar forms. A
trend in this direction was found in the previous experiment, and was explained in terms
of a “weight shift effecfAccording to this explanation, processing ± e prime directly
affected the relative experience the model/reader had with the target word, causing small
weight shifts on some of the cormections within the target’s o-node representation.
Together, these weight shifts permitted an increase the amount of activation flowing
through the o-node representation for the unfamiliar orthographic form, thereby
facilitating its recognition.
One problem with interpreting the interaction of prime type, orthographic
dominance, and target orthography as a result o f the weight shift effect is that the
interaction also could have obtained because the RTs to unfamiliar orthographic forms in the nonword prime condition were slower than those in the tone prime condition, while the reverse pattern was present in the same conditions for all other critical target forms.
To determine whether or not the nonword-tone pattern in the data was causing the
interaction, a post-hoc ANOVA was performed on the data with the identity prime condition removed. In this analysis, no three-way interaction was found [Fi(l,18) = 2.00,
2 - .18; £2( 1,58) = .44, 2 = .51]. However, when a second post hoc ANOVA was performed with nonword prime condition removed, the prime type by orthographic dominance by target orthography interaction was again found |£i(l,18) = 52.41, 2 < 01;
65 ^(1,58) = 20.75, 2. 01]. Thus, the weight shift interpretation of the interaction was supported, and the weighted network model’s predictions were upheld.
Subjects Items Source df F df F Between subjects List I .38 1 19.29** 18 (110489.03) 58 (6338.45) Within subjects Prime Type (FT) 2 196.42** 2 409.31** 36 (3231.59) 116 (4902.54) Orthographic Dominance (CD) 1 109.29** 1 125.98** 18 (1282.63) 58 (3801.45) Target Orthography (TO) 1 19.08** 1 101.23** 18 (7450.75) 58 (4832.75) FT X OD 2 19.43** 2 12.31** 36 (633.43) 116 (3670.90) FT X TO 2 29.23** 2 19.09** 36 (995.86) 116 (5138.79)
OD X TO 1 38.02** 1 76.61** 18 (3890.38) 58 (7163.54) FT X OD X TO 2 23.82** 2 10.80** 36 (626.58) 116 (4942.13) Note. **2 < .01. All £-values for within-subject variables are adjusted according to the Greenhouse-Geisser correction. Values given in parentheses indicate mean square error.
Table 3.2: Analysis of variance for mean response times from Experiment 2.
66 Given the three-way interaction, it is unsurprising that the interaction of prime
type and orthographic dominance was also significant. According to the weighted
network model and the weight shift hypothesis, this interaction should reflect that RTs to
kanji dominant items were reduced more between the phonological identity and tone
prime conditions than those to orthographically neutral items. Indeed, the direction of the
effect was as predicted: kanji dominant items received 169 ms o f priming, while
orthographically neutral items received 122 ms. In order to determine if the difference in
priming effects was significant, however, I compared the differences in the mean RTs for
the identity and asterisk prime conditions by dominance condition using a paired t-test.
As predicted, this test showed that the 47 ms overall difference in priming effects between orthographically neutral and kanji dominant items was significant.
Similarly, an interaction of prime type and target orthography was also found in the primary analysis, suggesting that recognition latencies to hiragana forms were reduced more overall between the phonological identity and tone prime conditions than those to kanji forms. To test this interpretation of the interaction, I performed another paired t- test on the difference scores produced by subtracting the mean RT for items in the phonological identity condition from the mean RT for items in the tone condition for kanji and hiragana targets. This test revealed that the 65 ms difference in priming between kanji and hiragana forms was significant. In fact, this effect and its direction were specifically predicted by the weighted network model, in that o-node representations for hiragana forms should receive more activation overall than those for kanji forms, because they have
67 the benefit of exclusive connections between their character-level o-nodes and their mora-
level p-nodes.
Most importantly for the weighted network model, an interaction of orthographic dominance and target orthography was also found. Specifically, it was expected that the
RTs to the familiar kanji and hiragana forms of orthographically neutral words would not differ, while the RTs to the unfamiliar hiragana forms of kanji dominant words would be slower overall than those to their familiar kanji counterparts. This hypothesis was confirmed by paired t-tests performed on the mean RTs for the kanji and hiragana forms in each dominance condition.
To test the related hypothesis of the weighted network model that mean RTs to the kanji and hiragana forms of orthographically neutral words would should not differ, while the RTs to familiar kanji forms of kanji dominant words should be faster than those to their unfamiliar hiragana counterparts, two separate ANOVAs were performed on the mean response times for orthographically neutral and kanji dominant items, with repeated measures on three levels of prime type and two levels of target orthography. For orthographically neutral words, this analysis revealed a main effect of prime type
(Ti(2,36) = 119.93, £ < .01; £2(2 , 116) = 173.49, £ < .01]. The only other effect that approached significance was the interaction of prime type and target orthography, but it was only marginal in the subject analysis and insignificant in the item analysis |£i(2,36) =
3.39, £ < .10; £2(2 , 116) = 1.20, £ = .30]. These results contrast clearly with those to a parallel ANOVA performed on the mean RTs to kanji dominant items. This showed
68 main effects of prime type [Pi(2,36) = 217.77, £ < .01; £ 2(2 , 116) = 303.47, £ < .01] and target orthography [£i(l,18) = 33.46, £ < .01; £ 2( 1,68) = 143.88, £ < .01], as well as an interaction of prime type and target orthography |£i(l,18) = 38.09, £ < .01; £ 2(1,68) =
23.08, £ < .01]. In other words, target orthography was only a factor for kanji dominant items, half of which were orthographically unfamiliar. This supports the claim the weighted network model that orthographic familiarity is a better predictor of recognition latency than orthographic depth.
3.22 Error analysis
Priming Condition
Dom Ortho Identity Nonword Tone Mean
ON Hiragana .46 (.69) 1.36 (1.67) .80(1.11) .87(1.21) Kanji .46 (.69) 1.66 (1.39) 1.30(1.22) 1.13 (1.23)
KD Hir^ana 1.30(1.26) 4.80 (2.71) 6.00 (2 .86) 3.70 (2.90) Kanji .76(1.02) 1.46 (1.64) 1.66(1.67) 1.26(1.42)
Mean .74 (.99) 2.31 (2.34) 2.16(2.46) Note. Standard deviations are given in parentheses. Dom = orthographic dominance. Ortho = target orthography. ON = orthographically neutral, KD = kanji dominant.
Table 3.3: Mean error rates from Experiment 2.
A summary of the error data is provided in Table 3.3. An ANOVA performed on the mean error rates by subjects and items revealed a pattern of results very similar to the
69 real word response time data, as well as the findings firom Experiment 1. For example,
there were fully reliable main effects of prime type [Fi(2,36) = 32.16, £ < .01; £2(2 ,116)
= 20.80, £ < .01], orthographic dominance [Fi(l,18) = 48.27, £ < .01; £ 2( 1,58) = 42.09, £
< .01], and target orthography [£i(l,18) = 23.80, £ < .01; £ 2(1,58) = 30.17, £ < .01], all
of which were in the expected direction. In addition, all possible interactions were foimd;
these will each be described in turn.
The three-way interaction of prime type, orthographic dominance, and target
orthography was fully reliable [£i(2,36) = 17.13, £ < .01; £2(2 ,116) = 4.53, £ < .05]. The
weight shift hypothesis predicts that this effect should reflect that the error rate to
unfamiliar hiragana forms of kanji dominant words was reduced more in the phonological
identity prime condition (compared to the tone prime condition baseline) than the error rate to any of the familiar orthographic forms. However, the certainty of this interpretation was in question, given that the error rates to kanji dominant and orthographically neutral words patterned differently in the nonword and tone prime conditions. Because this latter discrepancy could be contributing to the interaction, a post-hoc ANOVA like the one in the RT analysis was performed on the error data. With the error data from the nonword prime condition removed, the three-way interaction was still found [£i(l,18) = 25.88, £ < .01; £2( 1,58) = 11.52, £ < .01]. Thus, the interpretation of the effect that is consistent with the predictions of the weighted network model seems to be appropriate.
70 The interaction of prime type and orthographic dominance was significant
[Fi(2,36) = 10.97, £ < .01; £2(2 ,116) = 5.02, £ < .01]. The weight shift hypothesis
predicts that error rates to kanji dominant words should be reduced more by the phonological identity prime than should error rates to orthographically neutral words, because one half of the kanji dominant words were presented in an orthographically unfamiliar form. In fact, this was true: the number of errors made to kanji dominant items in the phonological identity prime condition was an average of 2.25 less than to these same items in the tone prime condition; between the same two condition, errors to orthographically neutral items were reduced on average only .60. A paired t-test performed on these average differences was also significant. Thus, the weight shift hypothesis was supported, as was residual activation hypothesis: pretarget activation of the phonological representation for an item made it more likely that the highest level o- node, s-node, and p-node patterns for the item would achieve the activation threshold prior to the temporal cut-off established for the task.
The interaction of prime type and target orthography was significant in the subject analysis, but marginal in the item analysis [£i(2,36) = 67.45, £ < .01; £2(2 ,116) =
2.80, £ < .10]. According to the weight shift hypothesis and the weighted network model, this effect should reflect that the phonological identity prime condition reduced the number of errors made to hiragana targets more than it reduced the number of errors made to kanji targets. Indeed, the error rate to hiragana targets was reduced by an averse of over two errors in the phonological identity prime condition compared to the tone
71 prime condition, while the one for kanji targets was only reduced an average .8 errors.
The statistical reality of this difference was evaluated with a paired t-test and found to be significant.
Most importantly, the interaction of orthographic dominance and target orthography was found [Fi(l,18) = 67.45, £ < .01; Fi(l,58) = 36.01, £ < .01]. A paired t-test performed on the mean error rates for kanji and hiragana forms of orthographically neutral words across the three prime conditions revealed no statistical difference, while a similar test on error rates for kanji dominant items revealed that significantly more errors were made overall to hiragana forms compared to kanji forms.
The weighted network model predicted that the error rates for the hiragana and kanji forms of orthographically neutral words should be statistically similar in each prime condition, while those for the hiragana forms of kanji dominant words should be higher than for their corresponding kanji forms. To confirm this, separate ANOVAs were performed on items from each dominance condition, with two levels of list repeated measures on two levels of target orthography and three levels of prime type. For orthographically neutral words, this revealed a only main effect of prime type that was significant |Ti(2,36) = 15.18, £ < .01; £2(2 , 116) = 6.66, £ < .01]. For kanji dominant words, conversely, there were nbust main effects of prime type [£i(2,36) = 26.15, £ <
.01; £2(2 ,116) = 16.77, £ < .01] and target orthography were found [£((1,18) = 49.70, £ <
.01; £2(1,58) = 52.84, £ < .01], in addition to an interaction of prime type and target orthography [£((2,36) = 15.07, £ < .01; £2(2 , 116) = 5.07, £ < .01]. Thus, error rates to
72 the hiragana and kanji forms of orthographically neutral words were similar across priming
conditions, while the unfamiliar hiragana forms of kanji dominant words elicited markedly
more errors than their familiar kanji counterparts.
In sum, both the response time and error data supported the claims of the
weighted network model. These results are consistent with previous research regarding
the importance of orthographic familiarity in recognizing words (e.g. Besner &
Hildebrandt, 1987; Besner & Smith, 1992; Hirose, 1984,1985; Sasanuma et al, 1985), and
replicate the pattern found to semantic primes in Experiment 1 with a new set of
phonological identity primes. In addition, because the deep and shallow forms of
orthographically neutral words responded similarly to phonological identity priming, this
data provides further support for the hypothesis that readers are able to process the two
types of orthographies in the same way. Moreover, this similarity in processing strategy
is mediated by orthographic familiarity. Were this not the case, the hiragana and kanji
forms of kanji dominant words would have patterned like their orthographically neutral counterparts, which they did not.
3.3 Comparison of Data from Experiments 1 and 2
When preparing the stimuli for Experiments 1 and 2, care was taken to use the same subset of lexical items in the neutral prime conditions (i.e., asterisk and tone), and to minimally exchange items from the strong linguistic (i.e., semantically related and phonological identity) and weak linguistic (i.e., semantically unrelated and nonword) prime conditions in order to facilitate a comparison of the two groups of data. This
73 comparison was intended to provide additional insight into the way semantic and
phonological priming differ in their impact on the word recognition process.
In statistical terms, the current analysis was expected to yield an interaction of experiment and prime type, whereby phonological identity primes would reduce recognition latencies and error rates more than semantically related primes. Although this analysis cannot discriminate among competing models, 1 provide it for the sake of completeness. According to the weighted network model, this interaction would occur because phonological identity primes leave more residual activation in the node patterns of the targets that they precede compared to semantically related primes. Specifically, the complex p-node pattern of the target would be directly activated in the phonological identity prime condition, leading to activaton o f the associated s-node and o-node patterns. In contrast, only a portion of the target’s complex s-node pattern would be activated by the semantically related prime from Experiment 1, and little or no activation of the target’s p-node and o-node patterns would be expected. This imbalance would result in the complex node patterns for the target being much more activated following phonological identity priming; in turn, the threshold for recognition would be reached more quickly and more often than in the semantic priming case. Furthermore, interactions of experiment with prime type and target orthography, as well as prime type, orthographic dominance, and target orthography were expected, because these effects were reliable in Experiment 2, but not in Experiment 1.
74 Subjects Items Source df F df F Between subjects Experiment (EXP) I 3.92“ 1 146.97** 38 (85092.19) 118 (6912.89) Within subjects Prime Type (PT) 2 225.08** 2 380.68** PT X EXP 2 33.63** 2 73.37** 76 (2949.83) 236 (5098.38) Orthographic Dominance (OD) I 250.13** 1 230.77** OD X EXP 1 1.39 1 .84 38 (1308.74) 118 (4701.24) Target Orthography (TO) 1 54.68** 1 179.12** TO X EXP 1 .28 1 .26 38 (6035.90) 118 5901.27 PT X OD 2 15.63** 2 14.98**
PT X OD X EXP 2 1.12 2 .91 76 (1049.69) 236 (4843.64) PT X TO 2 15.58** 2 14.83** PT X TO X EXP 2 5.23** 2 3.47* 76 (1516.05) 236 (6009.51) OD X TO 1 118.59** 1 185.80** OD X TO X EXP 1 1.16 1 .72 38 (3072.29) 118 (6716.01) PT X OD X TO 2 11.92** 2 8.54** PT X OD X TO X EXP 2 5.12** 2 3.36* 76 (983.49) 236 (4959.66) Note. **£ < .01, .05, ^ < .10. All £-values for within-subject variables are adjusted according to the Greenhouse-Geisser correction. Values given in parentheses indicate mean square error.
Table 3.4: Analysis of variance for mean response times from Experiments 1 and 2.
75 3.31 Response time analysis
An ANOVA was performed on the mean response time data for each condition on two levels of experiment and repeated measures on three levels of prime type
(semantically related/ phonological identity, semantically unrelated/nonword, and asterisk/tone), two levels of orthographic dominance (orthographically neutral and kanji dominant), and two levels of target orthography (hiragana and kanji). The results of this analysis are summarized above in Table 3.4.‘
Mean RTs to items in Experiment 2 were 53 ms faster overall than those in
Experiment 1, but this effect was significant only in the item analysis. An interaction of prime type and experiment also obtained in the direction predicted by the weighted network model: items in the phonological identity prime condition were recognized an average of 110 ms faster than items in the semantically related prime condition. Much smaller differences in the same direction were found between the mean RTs in the semantically unrelated and nonword prime conditions (25 ms) and the asterisk and tone prime conditions (23 ms). The mean RTs for each experiment in each prime condition are plotted in Figure 3.1.
' All main effects and interactions of variables not including experiment were fully reliable (gs < .01). These will not be discussed further, because they are uninformative with respect to the ways that semantic and phonological priming differ in their influence on recognition latency. They will also not be reported for the analysis of errors across the two experiments. 76 750 Semantic « 700 (Exp. 1) S Phonological •S 650 (Exp. 2) a a 600 es
500 4----- Related/ Unrelated/ Asterisk/ Identity Nonword Tone Prime Type
Figure 3.1 : Mean RTs by prime type for Experiments 1 and 2.
Given that both the tone and asterisk primes are nonlinguistic and neutral with respect to the targets they preceded, the difference between the two baseline conditions implies that participants in Experiment 2 were slightly faster overall in making their responses than those in Experiment 1, and that at least 23 ms of the difference between similar conditions across Experiments 1 and 2 is due entirely to the disparity in the baselines produced by the two participant groups. If this is so, then the 25 ms difference between the semantically unrelated and nonword prime conditions probably does not represent any meaningful difference in processing time. The 110 ms difference in the
77 semantically related and phonological identity conditions, however, is still large enough to
be informative.
Experiment also interacted with the combination of prime type and target
orthography. The prediction of the weighted network model is that RTs to hiragana
forms should be faster overall following phonclogica’ identity primes than following
semantically related primes. This should occur because phonological identity primes
leave more residual activation in the node patterns of their targets than do semantically
related primes (as supported by the interaction of experiment and prime type), and
because hiragana forms are able to take greater advantage of residual activation than kanji
forms. This latter claim has two sources. First, the orthographic representations for
hiragana forms have more exclusive connections at the individual node level between o-
nodes and p-nodes than do their kanji counterparts; this is simply how the weighted network model encodes orthographic depth. Second, half of the hiragana targets in the current study are orthographically unfamiliar, while all of the kanji targets are orthographically familiar. If the weight shift hypothesis holds true, then the orthographically unfamiliar hiragana forms should be reduced more between the strong linguistic and baseline conditions than their familiar counterparts.
78 750 — • — Exp. I -o Hiragana — ■— Exp. 1 >« Kanji -a — o - - Exp. 2 «8 600 Hiragana ------Exp. 2 Kanji
Related/ Unrelated/ Asterisk/ Identity Nonword Tone Prime Type
Figure 3.2: Mean RTs for each target orthography condition by prime type for Experiments 1 and 2.
The mean RTs for each experiment by target orthography are plotted in Figure
3.2. As predicted, responses to hiragana forms in the phonological identity prime
condition were 130 ms faster on average than those in the semantically related prime
condition, while responses to kanji forms in the phonological identity prime condition
were an average of 91 ms faster than those in the semantically related prime condition. In
the other cross-experiment prime condition contrasts, differences between response times to hiragana and kanji forms were between 19-32 ms, most of which could be attributed to baseline differences between the two experiments.
The predicted four-way interaction of experiment, prime type, orthographic dominance, and target orthography was also found. The weighted network model
79 predicts that this effect should reflect that RTs to orthographically unfamiliar hiragana forms of kanji dominant words were reduced more by phonological identity primes than semantically related primes, compared to the appropriate baseline conditions (the weight shift effect). Indeed, if the priming effects for each dominance condition and target orthography are calculated, it (urns out that the hiragana and kanji forms of orthographically neutral words were recognized more quickly (by 105 ms and 99 ms, respectively) after phonological identity primes than after semantically related primes.
The same pattern was observed for hiragana and kanji forms of kanji dominant words, which were respectively recognized 156 ms and 82 ms more quickly in the phonological identity prime condition compared to the semantically related prime condition. All other cross-experiment contrasts resulted in differences ranging fi’om 8-33 ms, most of which could be attributed to the baseline discrepancy between the two experiments.
3.32 Error analysis
The mean error rates were calculated for each condition and submitted to an
ANOVA with the same parameters as the one described for response times. There was main effect of experiment, whereby more errors were made overall in Experiment 1 than in
Experiment 2 [F[(l,38) = 87.55, £ < .05; ^(1,118) = 24.37, £ < .01]. This is consistent with the expectation that higher levels of residual activation and the weight shift effect will reduce the amount of new activation required for a target to be recognized, thereby decreasing the number of times real words will be rejected as nonwords because they were unable to attain threshold before the time-out criterion.
80 The only fully reliable interaction was that of experiment, orthographic
dominance, and target orthography QFi(l,38) = 28.59, £ < .01; £ 2(1,118) = 6.11, £ < .05].
This resulted from there being 50% more errors produced in response to the hiragana
forms of kanji dominant words in Experiment 1 than to these same items in Experiment 2 .
Assuming the residual activation and weight shift hypotheses, it could be that this
difference is the result of substantially fewer errors being made to orthographically
unfamiliar targets in the phonological identity condition compared to the semantically
related condition. However, because phonological identity primes were present in only
one-third of the critical trials, a more likely explanation is that participants in Experiment
2 simply made fewer errors in every condition than those in Experiment 1. This suggests
that phonological identity priming may lead participants to set a longer time-out criterion
than semantic priming, thereby allowing the lexical patterns for more items to reach the
recognition threshold.
In conclusion, the comparison of the response time and error data for Experiments
1 and 2 supports the assertion that phonological identity priming provides more residual activation and causes a broader weight shift effect than semantic priming, and that, overall, the additional activation made available to the higher level node patterns representing the target shortens recognition latencies and reduces false-negative responses.
81 CHAPTER 4
EXPERIMENT 3: ORTHOGRAPHIC IDENTITY PRIMING
Recall that the weighted network model being assumed for the current study is composed of interconnected patterns of semantic, phonological, and orthographic nodes.
Experiment I focused on activating the s-node patterns representing the target items and observing how this activation influenced the speed with which different orthographic forms of targets were recognized. Experiment 2 investigated how prior activation of both the phonological and semantic representations of targets influenced recognition latencies as a function of their orthographic familiarity and orthographic depth. In the current and final experiment, the priming series was completed by focusing on the direct and separate activation of the hiragana and kanji representations of the targets. This was done by manipulating prime orthography in a visual identity priming paradigm. The identity and nonword prime conditions were split, with half of the primes preceding targets in the same orthography and half preceding targets in a different orthography. This manipulation permitted me to explore an aspect of visual word recognition that has yet to be addressed in the literature: How do response latencies for hiragana and kanji targets differ when they are preceded by identity primes in the same orthography compared to
82 identity primes in a different orthography? Furthermore, does orthographic depth or orthographic familiarity have any influence on the pattern o f results?
In Experiments 1 and 2, 1 found that unfamiliar orthographic forms were always recognized more slowly and less accurately than familiar orthographic forms, and that
RTs to familiar hiragana and kanji forms were not differently facilitated by semantically related or phonologically identical primes. These findings are consistent with the predictions of the weighted network model. In addition, I observed the largest priming effects with orthographically unfamiliar target forms preceded by strong linguistic primes
(i.e., a semantically related word or the phonological identity of the target); 1 explained this in terms of the weight shift hypothesis, which focuses on the difference in the degree of flexibility of connection weights anchored to familiar and unfamiliar orthographic representations. The same pattern of results for familiar and unfamiliar orthographic forms was anticipated in the current experiment.
However, the novel manipulation of prime orthography allows for some additional predictions. In particular, 1 expected that same orthography identity primes should facilitate the recognition of targets more than different orthography identity primes. This was based on the residual activation hypothesis, whereby the activation generated by the prime while it is being processed does not immediately disappear once it peaks, but rather degrades over time toward the default base level. Fiuthermore, the amount of residual activation that is available to the target should depend on the degree of overlap between the nodes representing the prime and those representing the target. Thus, same
83 orthography identity primes should produce the most facilitation, because the s-, o-, and
p-nodes that compose their representations are identical to those of their targets’. By the same token, different orthography identity primes should produce less facilitation than same orthography identity primes, because only the p-node and s-node patterns for the hiragana and kanji form of a lexical item are the same.
4.1 Method
4.11 Participants
Twenty students from the same demographic group as the participants in the previous two experiments completed this experiment.
4.12 Materials
Target items for this experiment were the same as in Experiments 1 and 2. Five types of primes were prepared. ID-same primes were the same as their target words.
ID-different primes were the same words as their targets, but in the opposite orthography. NW-same primes were nonwords of the same length as their targets that were presented in the same orthography; they did not overlap with their targets in characters or morae. NW-different primes were similar to NW-same primes, except that they were in the opposite orthography from their targets. Asterisk primes were strings of three asterisks, just as in Experiment 1.
Items were balanced across two experimental lists according to orthographic dominance, target orthography, and prime type. Because there were more prime conditions in this experiment than in the previous two, fewer items were available for
84 each condition. Thus, the ID-same, ID-different, and asterisk conditions each contained
80 items, while the NW-same and NW-different conditions each contained 60 items. The
same 360 nonwords were used as before and distributed across the conditions so that
there were an equal number of real word and nonword targets, as well as real word and
nonword primes. In total, each participant saw 720 stimuli, half of which were in
hiragana and half of which were in kanji.
As in previous experiments, filler trials consisted of nonword targets preceded by
different types of primes that paralleled the priming manipulation for real word targets.
In this case, nonword targets were preceded by same and different orthography identity
primes (ID-same and ID-different, respectively), real word primes in the same
orthography (RW-same), real word primes in the opposite orthography (RW-different),
or a string of three asterisks (Asterisk).
4.13 Procedure
The procedure was the same as in Experiment 1. Ten participants saw each list.
4.2 Results and Discussion
Response time and error data for each target item were collected as in the previous
two experiments. The data from one subject was unusable due to a computer problem
during stimulus presentation. Computer error also prevented .25% of the remaining
overall data from being used. Cut-offs on response time were set for each participant in each experimental condition at +/- 2 .S times the standard deviation from his or her mean response time. Approximately 2% of the data points were outliers according to this
85 criterion and were removed from the data prior to statistical analysis. The remaining data are summarized in Table 4.1.
Prime Type
Identity Nonword
Dom Ortho ID-Same ID-Diff NW-Same NW-Diff Asterisk Mean ON Hiragana 508 (77) 519(90) 675(93) 706(138) 719(112) 625 (138) Kanji 480 (79) 540 (97) 691(89) 724(125) 703(132) 628(144) KD Hiragana 525 (79) 522 (93) 778(144) 879 (154) 833 (144) 707(198) Kanji 458(80) 544 (96) 683 (114) 717(118) 709 (124) 622(148)
Mean 493 (81) 531 (93) 707(117) 756(150) 741 (137)
Note. All values are rounded to the nearest millisecond, Standard deviations are given in parentheses. Dom = orthographic dominance. Ortho = target orthography. ON = orthographically neutral, KD = kanji dominant.
Table 4.1 : Mean response times in ms for Experiment 3.
4.21 Response time analysis
ANOVAs were performed on the condition means for subjects and items on two levels of list and repeated measures on two levels of prime type (identity and nonword), two levels of prime orthography (same and different), two levels of orthographic dominance (orthographically neutral and kanji dominant), and two levels of target
8 6 orthography (hiragana and kanji)/ A summary of these analyses, minus interactions with
the list variable, is given in Table 4.2.
Subjects Items Source df F df F
Between subjects
List 1 .35 1 25.19** 17 (137492.94) 28 (4159.82)
Within subjects
Prime Type (FT) 1 339.43** 1 1387.60** 17 (10705.47) 28 (4253.17)
Prime Orthography (PC) 1 26.05** 1 39.88** 17 (5497.39) 28 (7738.05)
Orthographic Dominance (OD) 1 31.25** 1 47.74** 17 (2603.54) 28 (3128.84)
Target Orthography (TO) 1 23.68** 1 33.03** 17 (3935.52) 28 (3973.40)
Table 4.2: Analysis of variance for mean response times from Experiment 3.
Table 4.2 continued
' Initially, ANOVAs were performed on the condition means for subjects and by items on two levels of list and repeated measures on two levels of target orthography (kanji and hiragana), two levels of orthographic dominance (kanji dominant and orthographically neutral), and five levels of prime type (ID- same, ID-different, NW-same, NW-different, and asterisk). T-tests performed on the NW-different and asterisk conditions revealed that they were not statistically different by subjects and were only marginally different by items [L = 1.16, p > .20; 1% = 1.71, p < .10], so the asterisk prime data was removed from the subsequent analysis. This permitted the reorganization of the prime type variable into two levels of prime type (identity and nonword) and two levels of prime orthography (Same and Different). 87 Table 4.2 (coat.)
PTxPO 1 .72 1 .70 17 (2473.53) 28 (7027.14)
PTxOD 1 34.94** I 25.27** 17 (2293.74) 28 (5760.57)
PTxTO 1 13.26** 1 13.77** 17 (2613.32) 28 (5283.69)
POxOD 1 2.27 1 5.50* 17 (3359.50) 28 (3368.70)
PC xTO 1 3.19^ 1 1.52 17 (1928.19) 28 (5996.01)
ODxTO 1 68.85** 1 72.95** 17 (1835.48) 28 (3400.59)
PT X PO X OD 1 2.36 1 6.24* 17 (1599.86) 28 (2491.38)
PT X PC X TO 1 13.19** 1 18.46** 17 (3841.56) 28 (4689.77)
PT X OD X TO 1 32.51** 1 29.02** 17 (2255.91) 28 (5171.02)
PO X OD X TO 1 .60 1 1.97 17 (1612.96) 28 (4858.57)
PT X PO X OD X TO 1 6.38* 1 5.37* 17 (2039.03) 28 (6490.46) Note. '"*2 ^ .05, ^ < .10. Ali £- values for within-subject variables are adjusted according to the Greenhouse-Geisser correction. Values given in parentheses indicate mean square error.
8 8 All main effects were fully reliable and in the expected direction. Specifically,
there was a main effect of prime type, such that targets preceded by identity primes were
recognized an average of 220 ms faster than targets preceded by a nonword prime. Also,
there was a main effect of prime orthography, whereby targets preceded by a prime in the
same orthography were recognized 44 ms faster overall than those preceded by a prime in
the opposite orthography. As predicted by the weighted network model, conditions
containing unfamiliar hiragana forms of kanji dominant words appeared to be slower
overall than others. This would explain, at least in part, the main effect of orthographic
dominance, where orthographically neutral targets were responded to 33 ms faster overall
than kanji dominant targets, as well as the main efiect of target orthography, where kanji
targets were responded to an average of 34 ms more quickly overall than hiragana targets.
Most importantly for the weighted network model, an interaction of orthographic
dominance and target orthography was found. With respect to the direction of this
interaction, the model predicts that the mean RTs to hiragana and kanji forms of
orthographically neutral words should not differ from each other, while the mean RTs to
kanji forms of kanji dominant words should be faster than those to their corresponding
hiragana forms. Indeed, paired t-tests (a = .05) between the mean RTs for the hiragana and kanji forms of words in each dominance condition supported these predictions.
As for the other fully reliable interactions, prime type interacted with orthographic dominance, such that RTs to orthographically neutral items were faster in the nonword baseline condition than those to kanji dominant words, but kanji dominant
89 words were primed more by identity primes than orthographically neutral words. Given
that half of the kanji dominant items are orthographically unfamiliar, this pattern is precisely what the weighted network model would predict. Kanji dominant items should be responded to more slowly overall, because it takes longer for node patterns representing the hiragana forms of these items to achieve the recognition threshold compared to those for any other target forms. Furthermore, the weight shift hypothesis predicts that the hiragana forms of kanji dominant words should display larger priming effects than other target forms, thereby increasing the size of the priming effect for kanji dominant items as a whole.
Prime type also interacted with target orthography, such that the mean RTs to hiragana (519 ms) and kanji (506 ms) targets did not differ statistically in the identity prime condition, but hiragana targets were responded to more slowly on average (759 ms) than kanji targets (704 ms) in the nonword prime condition. This pattern of results resembles the one observed in the prime type by orthographic dominance interaction, and has a similar interpretation in the weighted network model. Namely, hiragana items should be responded to more slowly overall, because half of these items are orthographically unfamiliar, and it takes longer for the nodes in an unfamiliar orthographic representation to achieve the recognition threshold compared to those in a familiar orthographic representation. Moreover, the weight shift hypothesis predicts that the hiragana forms of kanji dominant words should display larger priming effects than other target forms, thereby increasing the size of the priming effect for hiragana targets overall.
90 The residual activation hypothesis makes specific predictions concerning the three-way interaction of prime type, prime orthography, and target orthography.
Namely, targets in the identity prime condition should be recognized more quickly when preceded by a same orthography prime than a different orthography prime. This was true for kanji targets, which were recognized (74 ms) more qitickly when preceded by a kanji identity prime than a hiragana identity prime — a significant difference, according to paired t-tests on the mean RTs for kanji targets preceded by ID-same and ID-different primes. However, t-tests performed on the mean RTs for hiragana targets in the same two priming conditions did not reveal an> difference. While this latter result was clearly unanticipated, it is difficult to interpret, given that one half of the hiragana targets were orthographically unfamiliar. What would be more informative is to analyze the RTs to familiar and unfamiliar hiragana forms separately. In this case, the residual activation hypothesis would clearly predict that the familiar hiragana forms would pattern like familiar kanji forms in the ID-same and ID-different conditions, while the unfamiliar hiragana forms would pattern differently from all of these. In fact, a t-test performed on the mean RTs to hiragana forms of orthographically neutral words in the ID-same and ID- different conditions showed no significant difference, directly in opposition to the prediction of the residual activation hypothesis. Similarly, the same test performed on the mean RTs to hiragana forms of kanji dominant words showed no difference between the ID-same and ID-different conditions. Thus, orthographic familiarity had no influence on how RTs to hiragana forms were affected by ID-same and ID-different primes.
91 One way to reconcile these findings in the identity prime condition with the residual activation hypothesis within the parameters already established by the weighted network model is to appeal to the only difference in the structures of the o-node representations for the kanji and hiragana forms of words: the exclusive nature of the connections between the individual o-nodes in the hiragana representations and the p- nodes in the phonological representations for the target word. Because the o-nodes in the hiragana representation have a one-to-one relationship with mora level p-nodes, individual hiragana o-nodes receive almost all of the activation that passes through the p-nodes with which they are connected (minus whatever small amount is allotted to individual s-nodes or the rare kanji o-node pattern that has a single mora pronunciation). When the kanji identity prime is processed, this should spread activation throughout the representation for the word, including the p-node representation. This p-node representation, in turn, would distribute a large portion of this activation to a particular hiragana o-node representation. In this way, hiragana o-nodes could receive quite a bit of activation during the processing of the kanji form of the same word — much more than the kanji o-node representation would receive if the hiragana form of the word were the prime, given the large number of homophones written in kanji.
With respect to the three-way interaction o f prime type, orthographic dominance, and target orthography, the weighted network model predicts that mean RTs to the hiragana and kanji forms of orthographically neutral words should not differ within a single prime condition (i.e., identity or nonword), although they will display normal
92 effects of priming between the identity and nonword prime conditions. Furthermore,
mean RTs to orthographically unfamiliar items should be slower than those to
orthographically familiar items in the same prime condition. In fact, paired t-tests
performed on the mean RTs to the hiragana and kanji forms of orthographically neutral words in each of the prime conditions showed that they did not differ within prime condition, but mean RTs to both target forms were faster in the identity prime condition than in the nonword prime condition. A similar set of tests on the mean RTs to the hiragana and kanji forms of kanji dominant words revealed that the unfamiliar hiragana forms were recognized more slowly than their familiar kanji counterparts in the nonword prime condition in both the subject and item analysis; in the identity prime condition, however, the unfamiliar hiragana forms of kanji dominant words were only recognized more slowly than the familiar kanji forms in the subject analysis.
This three-way interaction also interacted with prime orthography, leading to a reliable four-way interaction of prime type, prime orthography, orthographic dominance, and target orthography. The effect of prime orthography (i.e., same or different) appears to be strongest for the kanji forms of orthographically neutral and kanji dominant words in the identity prime condition, and for the hiragana forms of kanji dominant words in the nonword prime condition. The effect seems to be weakest for the hiragana forms of orthographically neutral and kanji dominant words in the identity prime condition. This should not be surprising, given the pattern of results in the three-way interaction of prime type, prime orthography, and target orthography, whereby kanji forms were recognized
93 faster following ID-same primes over ID-different primes, but RTs hiragana forms did not differ across the two prime conditions. Thus, the current result is likely attributable to the same phenomena that caused the prime type, prime orthography, and target orthography interaction.
Overall, the predictions of the weighted network model have been well supported by the analysis of response times to real word targets. Familiar orthographic forms were recognized more quickly than unfamiliar orthographic forms, and identity primes facilitated the recognition of targets more than nonword primes. Furthermore, the residual activation hypothesis was supported through comparison of the data in the identity and nonword prime conditions.
4.22 Error analysis
The final portion of the analysis for the current experiment involved the error data for real word targets, which is summarized in Table 4.3. As in the response time analysis,
ANOVAs were performed by subjects and items on the number of errors in each condition with two levels of list and repeated measures on two levels of prime type
(identity and nonword), two levels of prime orthography (same and different), two levels of orthographic dominance (orthographically neutral and kanji dominant), and two levels of target orthography (hiragana and kanji). The results of this analysis are summarized in
Table 4.4.
94 Prime Type
Identity Nonword
Dom Ortho ID-Diff NW-Same NW-Diff AsteriskID-Same Mean
ON Hiragana .32 .37 .37 .74 1.00 .56
(.58) (.60) (.68) (1J3) (.94) (.90)
BCanji 1.12 .16 .58 1.00 1.37 .84
(1.45) (.38) (.61) ( 1.11) (.96) (1.05) KD Hiragana 1.42 .32 1.84 3.32 4.00 2.18 (1.39) .48 (1.26) (2.47) (3.02) (2.33) Kanji .53 .32 .42 1.26 .74 .65 (.70) (.58) (.96) (1.28) .93 (.97) Mean .84 .29 .80 1.58 1.78 (1.17) (.51) (1.08) (1.91) (2.13) Note. Standard deviations are given in parentheses. Dom = orthographic dominance. Ortho = target orthography. ON = orthographically neutral, KD = kanji dominant.
Table 4.3: Mean error rates for Experiment 3.
The main effects in the error analysis were generally similar in size and direction to those in the response time analysis. The weighted network model predicts that targets preceded by identity primes should be misjudged as nonwords less frequently than those preceded by nonword primes, and this pattern was observed. The model also predicts that fewer errors should be made to orthographically neutral targets as compared to kanji dominant targets, because one half of the kanji dominant targets are unfamiliar; indeed, a
95 main effect of orthographic dominance in the expected direction was observed.
Furthermore, more errors were made to hiragana targets than kanji targets; this is consistent with the weighted network model, in that one half of the hiragana targets are orthographically unfamiliar, and it should therefore take longer for their lexical patterns to achieve the activation threshold for recognition. The exception between the RT and error analyses was that in the latter, prime orthography was not significant in the subject analysis and was only marginally significant in the item analysis. Thus, whether a target was preceded by a prime in the same orthography or not did not influence the likelihood that that target would be correctly recognized as a real word, although there was a slight trend towards fewer errors when the prime and target orthography were the same.
The weighted network model also predicts an interaction of orthographic dominance and target orthography, such that there should be no difference in the error rates for the hiragana and kanji forms of orthographically neutral words, because both of these forms are orthographically familiar. At the same time, there should be more errors made to the unfamiliar hiragana forms of kanji dominant words than their familiar kanji counterparts. Paired t-tests on the current data confirmed that there was no difference in the error rates for the hiragana and kanji forms of orthographically neutral words, although marginally more errors were made to the kanji forms in the subject analysis. In addition, markedly more errors were made to the hiragana forms of kanji dominant words than their corresponding kanji forms.
96 Subjects Items
Source df F df F
Between subjects
List I .83 1 9.17**
17 ( 6.02 ) 28 (.52) Within subjects
Prime Type (PT) I 15.17** 1 36.99**
17 (1.82) 28 (. 68)
Prime Orthography (PO) 1 .64 1 2.91^ 17 (1.16) 28 (.41)
Orthographic Dominance (OD) I 29.62** 1 31.19** 17 (.91) 28 (.52)
Target Orthography (TO) 1 10.55** 1 11.56** 17 (1.18) 28 (.65)
PTx PO 1 23.29** 1 30.17** 17 (1.41) 28 (.61)
PT X OD 1 28.64** 1 17.55** 17 (.52) 28 (.58)
PT x TO 1 17.01** 1 6.63* 17 (.51) 28 (.92)
POxOD 1 3.69"^ 1 1.75 17 (.33) 28 (.69)
Table 4.4: Analysis of variance for mean error rates from Experiment 3.
Table 4.4 continued
97 Table 4.4 (cont.)
PO xTO 1 .72 1 .21 17 (.80) 28 (.64)
OD xTO I 41.01** 1 24.70** 17 (.85) 28 (.78)
PT X PO X OD 1 9.30** 1 3.81"^ 17 (.44) 28 (.63)
PT X PO X TO 1 .42 1 .50 17 (.43) 28 (.82)
PT X OD X TO 1 10.24** 1 5.46*
17 (.66) 28 ( 1.11)
PO X OD X TO 1 2.21 1 1.18 17 (.83) 28 (.45)
PT X PO X OD X TO 1 13.22** 1 7.42* 17 (.58) 28 (.50)
Note. **2 < .01, *2 < -05, ^ < .10. AU 2" values for within-subject variables are adjusted according to the Greenhouse-Geisser correction. Values given in parentheses indicate mean square error.
Several other interactions were also fully reliable. The prime type by prime orthography interaction suggested that there was no difference in the mean number of errors produced in response to targets in the ID-same and NW-same prime conditions, which was confirmed by a paired t-test. A similar test on the mean error rates for targets in the ID-different and NW-different conditions revealed that participants were markedly
98 more accurate in recognizing targets preceded by ID-different primes than those preceded
by NW-different primes. Thus, participants’ accuracy was influenced both by a prime
being the same word as the target and the prime being in the same orthography as the
target, with the latter being given priority.
The prime type by orthographic dominance interaction reflected that there was no
difference in the error rates to orthographically neutral targets preceded by identity or
nonword primes, while more errors were made to kanji dominant targets following
nonword primes than those following identity primes. This interpretation was confirmed
by paired t-tests on the mean error rates to the hiragana and kanji forms of targets in each
dominance condition preceded by identity and nonword primes. Similarly, a prime
orthography by orthographic dominance interaction was also found, whereby there was
no difference in the number of errors made to orthographically neutral targets preceded by
same orthography or different orthography primes, but there were more errors made to
kanji dominant targets preceded by different orthography primes than same orthography primes. This was confirmed by paired t-tests on the mean error rates to the hiragana and kanji forms of targets in each dominance condition preceded by same orthography and different orthography primes. Both of these interactions are quite reasonable, given that one half of the primes preceding orthographically neutral targets represented familiar orthographic forms of the target, while in the case of kanji dominant targets, only one- fourth of the primes were orthographically familiar.
99 The only fully reliable three-way interaction was that o f prime type, orthographic dominance and target orthography. As predicted by the weighted network model, error rates to the unfamiliar hiragana forms of kanji dom in an t words were reduced more between the nonword and identity prime conditions than for any other target condition.
The model also predicts that there should be no difference between the error rates to hiragana and kanji forms of orthographically neutral words in either the identity or nonword prime conditions, and this was confirmed by paired t-tests. In contrast, paired t-tests performed on the mean error rates for the hiragana and kanji forms of kanji dominant words revealed that significantly more errors were made to the unfamiliar hiragana forms in both prime conditions.
The four-way interaction of prime type, prime orthography, orthographic dominance, and target orthography was fiilly reliable. Recall that in the RT analysis, kanji forms in both dominance conditions were recognized faster following kanji identity primes compared to hiragana identity primes, while there was no difference in the speed with which hiragana forms in either dominance condition were recognized following kanji or hiragana identity primes. Extrapolating firom these results to the error analysis, it should be that error rates to kanji forms of both orthographically neutral and kanji dominant are smaller in the same orthography identity prime condition. At the same time, error rates for hiragana forms should not differ in either identity prime condition. In fact, paired t-tests showed a very different pattern of results. The error rate for kanji forms of orthographically neutral words was significantly smaller following kanji identity
100 primes than following hiragana identity primes, while there was no difference in the error rates for the kanji forms of kanji dominant words following either type of identity prime.
As for hiragana forms, there was no difference in error rates between the two identity prime conditions for the hiragana forms of orthographically neutral words. For the hiragana forms of kanji dominant words, conversely, significantly more errors were made following hiragana identity primes than in kanji identity primes.
The pattern of errors in the identity prime condition is difficult to interpret, particularly in relation to the RT analysis. Error rates to all targets were affected by kanji identity primes, regardless of the target’s orthographic depth. Hiragana identity primes reduced the number of errors for two of the three familiar orthographic forms, with the exception being the kanji forms of orthographically neutral words. Kanji identity primes also reduced the number of errors to unfamiliar orthographic targets more so than hiragana identity primes. Thus, the general pattern is that errors for familiar orthographic forms were reduced comparably by both kanji and hiragana identity primes, while errors for unfamiliar orthographic forms were reduced more by kanji identity primes. While the pattern is not perfect, it consistent with the prediction of the weighted network model that identity primes will influence the processing of familiar orthographic targets in a different way than they will influence the processing of unfamiliar orthographic targets.
Although the inconsistency of the identity prime RT and error data calls into question how effectively the pattern of RTs for a set of stimuli will predict the pattern of errors, I anticipated that error rates would generally be higher for targets that were
101 preceded by different orthography nonword primes than those preceded by same
orthography nonword primes, because of the residual activation effect on the component
characters of the nonword compounds. In fact, paired t-tests showed that for
orthographically neutral words, hiragana and kanji targets elicited comparable numbers of
errors when they were preceded by nonword primes in either orthography [hiragana
targets. For kanji dominant targets, however, there were fewer errors to targets that were
preceded by a same orthography nonword prime. Thus, the error rates for targets
preceded by nonword primes were mediated by orthographic dominance: targets that
were orthographically familiar were always more accurately recognized when preceded by
a prime in an orthography that was consistent with the familiar orthographic form(s) for
the lexical item being recognized.
4.3 Summary
Overall, the response time and error data from Experiment 3 supported the
predictions of the weighted network model. Specifically, identity primes facilitated the
recognition of targets more than nonword primes, and unfamiliar orthographic forms were
recognized more slowly and less accurately overall than familiar orthographic forms. In
addition to these results, a detailed analysis of the RT data by prime condition revealed
that hiragana forms of both orthographically neutral and kanji dominant words were similarly sensitive to priming by identities in either orthography, while kanji forms were more sensitive to kanji identities.
102 As in Experiments 1 and 2, the data from this experiment are also consistent with findings in previous work concerning the importance of orthographic familiarity when recognizing words in kana (e.g. Besner & Hildebrandt, 1987; Besner & Smith, 1992;
Hirose, 1984, 1985; Sasanuma et al., 1985). Furthermore, the present findings reiterate that the orthographic familiarity effect is observable within-word, and demonstrate that orthographic identity primes produce a similar pattern of results compared to semantic and phonological identity primes. The most important contribution of this experiment, however, is likely to be the way that it ties together the literature concerning orthographic depth and orthographic familiarity. Specifically, it suggests that orthographic depth may affect the processing of words at sublexical levels of representation (e.g., the consistency of symbol-sound correspondences), but orthographic familiarity is the primary influence on the speed and accuracy with which words are recognized overall.
103 CHAPTERS
CONCLUSIONS
5.1 Orthographic Depth
The goal of the current study was to address two of the primary questions in the visual word recognition literature: First, does the transparency of the phonological assignment for the characters of a given orthographic system affect how quickly words in that orthography are recognized as real words? Second, does the familiarity of an orthographic form have a greater impact than orthographic depth on the recognition latencies for words following semantic, phonological, or orthographic primes? With reference to the first query, the answer seems to clearly be, “No.” The previous literature has presumed that words in shallow orthographies have a more clearly defined relationship with their phonological representations than those in deep orthographies, while words in deep orthographies are more closely associated to their semantic representations than those in shallow orthographies. If so, hiragana should be more sensitive to phonological priming than kanji, while kanji are more sensitive to semantic priming than hiragana. The data fi-om Japanese presented here failed to confirm either of these predictions: recognition latencies to hiragana forms of words were no more
104 sensitive to phonological priming than their corresponding kanji forms, providing that the hiragana forms were familiar to readers. Similarly, recognition latencies to familiar kanji and familiar hiragana forms were comparably influenced by semantic primes. In fact, the largest effect of semantic priming was found to unfamiliar hiragana forms, directly refuting the claim that kanji forms are more strongly or directly associated to semantic representations than hiragana forms.
The only exception to the finding that familiar orthographic forms pattern similarly in response to priming, regardless of their orthographic depth, was foimd when orthographic identity primes were used. In this case, familiar hiragana forms words were similarly primed by both their kanji and hiragana identities, while familiar kanji words were primed more by their kanji identity than by their hiragana identity. I propose to reconcile these results with the rest of the current findings by positing that the exclusive symbol-sound connections between hiragana characters and mora, compounded with the large number of kanji homophones, provides more residual activation to the o-node representation for a hiragana target preceded by a kanji prime than it does to the o-node representation for a kanji target preceded by a hiragana prime. In other words, orthographic depth does have some impact on how the kanji and hiragana forms are influenced by primes (at least those that represent an orthographic identity of the target), but orthographic familiarity has a much greater impact overall on the recognition latencies of printed words.
105 5.2 Making a Case for the WeightedNetwork Model
The ability of the weighted network model to predict the pattern of results found
in the current study demonstrates that several of the premises that underlie parallel dual-
route models of visual word recognition are not necessary. For example, the weighted
network model can explain differences in recognition latency for familiar and unfamiliar
orthographic forms in terms of a single mechanism; connections between linguistic nodes
that are weighted according to co-activation frequency. The dual-route model specifies
two routines, and requires that words in deep orthographies always be processed along a pathway that circumvents the pre-lexical identification of a phonological representation.
Words in shallow orthographies are generally processed along a phonological recoding pathway: if one accepts the phonological delay hypothesis, then highly frequent or orthographically familiar shallow words may be processed along the nonphonological pathway, like orthographically deep forms; if one does not presume phonological delay, then the phonological recoding process is simply faster for highly frequent or orthographically familiar shallow items.
Given the complicated nature of dual-route explanations of the visual word recognition process, we might forego the dual-route approach in favor of the weighted network model simply on the basis of Occam’s Razor. However, the weighted network model also has a broad explanatory capacity with respect to the visual word recognition literature that provides grounds for preferring this model over a dual-route architecture.
1 0 6 5.21 How children leam to read
The weighted network model predicts that children leam to read by establishing connections between orthographic representations for words and their corresponding phonological representations, no matter what the depth of the writing system they are learning! The depth of the orthography being learned is reflected in the exclusivity of the connections between individual character representations and their corresponding phonological representations. Otherwise, representations for words in all types of orthographies are organized and accessed in the same way.
Explaining how children leam to read using a dual-route model is a much more complicated endeavor. Perhaps the simplest strategy is to assume that the way in which children become literate depends on what kind of orthography they are trying to master.
In this scenario, children learning a shallow orthography should recognize words by phonologically recoding each character in the word, then using the newly generated phonological representation to access the meaning; children learning a deep orthography should recognize words by associating the visual form of a word with its meaning, after which they should retrieve the pronunciation. However, this prediction is inconsistent with reports from the developmental literature in which learners o f deep orthographies are shown to experience phonological interference while recognizing words. For example, Hu and Catts (1993) demonstrated that first and third grade readers of Mandarin Chinese
^ I assume that the connection between the orthographic representation for a word and the semantic representation will be established immediately following the development o f the coimection between the orthographic and phonological representations. This is because the activation of the phonological
107 consistently had a more difficult time discriminating phonologically similar Hanzi characters. Also, the more similar two hanzi in a pair were in phonological assignment, the greater the degree of discrimination difficulty the children displayed for each character.
Such data poses problems for dual-route model, because it predicts that children learning to read hanzi should not experience phonological interference during word recognition, because they retrieve a word’s pronunciation only after they have accessed its particular lexical entry.
Of course, one way aroimd this difficulty is to assume that inexperienced readers of any type of orthography utilize an indirect routine (i.e., phonological recoding) to recognize written words, then develop and use a direct routine when their reading skill attains a certain level of sophistication. This would explain why experienced readers of deep orthographies appear to recognize words differently under some circumstances than experienced readers who know a shallow orthography. However, this account suggests that readers of deep orthographies should be able to rely on a phonological recoding strategy to recognize low frequency, unfamiliar, or novel words. Such a prediction stands in direct contrast to the basic tenet of dual-route models that words in deep orthographies are always recognized by means of mapping their visual form directly onto the appropriate lexical entry.
The weighted network model, on the other hand, can explain the appearance of phonological interference during the recognition of words in deep orthographies quite
representation (by the orthographic representation) will spread to the semantic representation, causing it to be co-activated with the orthographic representation. 108 simply. Namely, the difficulty in identifying one hanzi character as unique from another
should be increased when the characters share a phonological representation, because
activation of the phonological representation should be distributed to all of the
orthographic representations a child knows to have that pronunciation. Moreover, the
inexperienced child reader will not be overly familiar with any particular characters, so the
weights on the connections to the different orthographic representations would be
similarly weighted. This would result in activation from the phonological representation
reaching homophonous orthographic representations at a similar speed, and thus making it
more difficult to tell which character was actually being perceived — particularly when the
orthographic representations are not well developed, as in the case o f children just
learning to read.
Data from English can even reveal shortcomings of the traditional dual-route approach to explaining visual word recognition. For example, Hulme, Snowling, and
Quinlan (1991) showed that the children who leamto read the fastest are those that are conscious of symbol-sound relationships at the letter, rime, and whole word levels. While a dual-route model that relies soley on grapheme-phoneme correspondence rules to generate phonological representations has no way to explain this result, tiie weighted network model can accommodate it easily by correlating letters to individual o-nodes, rimes to smaller o-node patterns, and the written form of a word to the full o-node pattern. Of course, the existence of these different levels of mental representation themselves do not mandate “phonological consciousness,” but it does seem reasonable
109 that children who have successfully established connections between all of these levels of representation and the appropriate p-node representations should recognize words faster than children who do not, because more nodes and connections are involved in the spreading of activation; this should allow the complex o-node, p-node, and s-node patterns for the word to attain threshold more quickly. Furthermore, children who have multi-layered connections between orthography and phonology should be able to extract more commonalties, such as regular spelling patterns, from text, because they are sensitive to more kinds of relevant associations.
5.22 Symbol-sound consistency effects
It has been shown in a number of different studies that words with highly consistent symbol-sound correspondences are named and recognized more quickly than those that do not (e.g., Glushko, 1979; Seidenberg, Waters, Barnes, & Tanenhaus, 1984;
Waters & Seidenberg, 1985). Thus, a word like MUST is easier to pronounce than a word like HAVE, because the rime -UST is always pronounced /a.sX/ in English, while -
AVE can be pronounced /æv/ (as in HAVE) or /ev/ (as in GAVE). Assuming that an orthographic rime is represented by a simple o-node pattern and a phonological rime is represented by a simple p-node pattern, the weighted network model explains consistency effects in terms of connection weight: a node or node pattern will distribute more activation along heavier, more frequently used connections. For words like MUST, the o-node pattern for UST has a solitary cormection to the p-node pattern /ASt/, and so a
110 large amount- of the activation in the UST pattern will spread to the /ASt/, facilitating the
rapid pronunciation of the word. Alternately, words like HAVE stimulate the AVE o- node pattern, which has connections to both the p-node pattern for /æv/ and the one for
/ev/. This means that the activation from AVE must be distributed to more connections, allowing a smaller portion for each goal pattern. Less activation going to the goal pattern results in the complex node patterns for the target taking longer to achieve threshold, and thus taking longer to pronounce.
There is also evidence that as the frequency of a word increases, the size of consistency effects diminishes (e.g., Seidenberg et al., 1984; Waters & Seidenberg, 1985).
This finding also fits well within the weighted network model, in that higher frequency items will develop heavier connections between their rime-level node patterns and complex node patterns than lower frequency items. These heavier connections will spread more activation, thereby allowing the overall activation level of the complex patterns to increase at a faster rate, and thus reach the recognition threshold sooner.
5.23 Neighborhood effects
A related phenomenon from the literature is the “neighborhood effect.” The neighborhood for a word contains all of the words in an individual’s vocabulary that differ from the word by a single character (for an orthographic neighborhood) or a single sound unit (for a phonological neighborhood). For example, the word LOOK is an orthographic
- Some activation must spread to the o-nodes that compose the UST pattern, and some must spread to those for words like MUST, DUST, LUST, etc. Ill and phonological neighbor of the word BOOK, and an orthographic neighbor of the word
LOOT.
The relevant finding in the literature is that recognition latencies for words that
have orthographic neighbors with higher lexical frequencies are slower than those to words
that have no higher frequency neighbors (e.g, Pugh, Rexer, Peter, & Katz, 1994). From
the perspective of the weighted network model, this effect is easy to explain. If a word is
low frequency, then the weights on the connections between the nodes in the orthographic, phonological, and semantic representations for the word will be light, and unable to carry very much activation at a given time. This will result in it taking more time for activation to build up in the complex node patterns for the word, slowing down the speed of recognition. If, in addition to being low frequency, the target has has a high
frequency neighbor, then it should take even longer to for the target item to be recognized, because the heavy connections between the nodes in the representation for the high frequency neighbor should “siphon off’ a good portion activation in the orthographic and phonological nodes the neighbor and the target have in common. The reason that the low frequency target is ever recognized at all is that inhibitory connections from the target’s representation eventually eliminate the nodes representing the neighbor from the loop of spreading activation, trapping activation in the target’s representation and allowing its complex node patterns to achieve threshold.
More challenging for the weighted network model is a finding related to the basic neighborhood effect, whereby a low frequency word with many high frequency neighbors
112 is recognized more quickly than one that has a single high frequency neighbor or no high frequency neighbors at all (Grainger & Jacobs, 1996; Sears, Hino, & Lupker, 1995). At first glance, it would appear that the more high frequency neighbors a word has, the less activation it would receive from the nodes that it shared with its neighbors, and the slower it would be recognized. However, this prediction overlooks several points. First, the connections that the many high frequency neighbors and the low frequency target have in common will be very heavy and, therefore, will bear large amounts of activation to higher level node patterns. Second, inhibitory connections from the nodes representing the target to the nodes representing the elements of the neighbors that are inconsistent with the target should trap activation in the target’s nodes, further contributing to the amount of activation needed to reach the recognition threshold. Lastly, the high frequency neighbors themselves will inhibit each other, preventing one another from extracting more activation from the target’s representation. In this way, much of the activation that was originally in the connections of the high frequency neighbors becomes available to the target, making it easier for its complex node patterns to achieve the recognition threshold.
5.24 Rejecting pseudohomophones
Another well-documented effect in the word recognition literature is that participants are slower and less accurate to reject a nonword that sounds like a real word
(e.g., BRANE) as a legitimate lexical item than they are to reject a nonword that does not sound a real word (e.g., PRANE) (e.g.. Van Orden et al., 1988). From the perspective of the weighted network model, this occurs because pseudohomophones activate the
113 phonological representation for a real word, which then spreads activation to the semantic
and orthographic representations for the word. It the sum of activation across the
complex o-node, s-node, and p-node patterns for the word achieves the threshold for
recognition, the nonword will be mistaken for the real word it resembles. The more
features that the pseudohomophone and the real word have in common (e.g., phonemes
and/or letters in the same position in the string), the more likely a case of “mistaken
identity” is to occur.
5.25 The future of the weighted network model
In sum, my weighted network model is able to explain a wide variety of
psycho linguistic phenomena by means of three different types of linguistic nodes (i.e., s- nodes, o-nodes, and p-nodes) that share activation by means of connections that are weighted according to co-activation frequency. It should be noted however, that the weighted network model is an exemplar of a larger class of models, some of which were developed specifically for visual or auditory word recognition, and most of which assume an alphabetic, syllable-based language. These models include Seidenberg and
McClelland’s (1989) PDF model, Grainger and Jacob’s (1996) multiple-read-out model, and Rumelhart and McClelland’s (1982) interactive activation model. The PDF model was chosen as a starting point for the weighted network model because it is a well- established model with considerable empirical support, and could easily be adapted to accommodate Japanese. Of course, it is possible that another style of model will ultimately prove more explanatory. However, given the wide range of effects that the
114 weighted network model is able to accommodate, one would need substantial empirical
evidence in order to reject it as a viable theoretical framework of the word recognition
process.
5.3 Directions for Future Research
The results of the current study and the success of the weighted network model
suggest several interesting lines of future research. First, it is important that these same
experiments be performed using the naming task, to make sure that the results of the
lexical decision task are generalizable to other tasks and to allow the comparison of these
results with other literature in the area. Other tasks that might be tried include a semantic
categorization task, where participants are asked to determine as quickly as possible if
familiar and unfamiliar forms of words are exemplars of a particular semantic category (cf.
Van Orden, 1987; Wydell, Patterson, and Humphreys, 1993), and different types of
masked priming (cf., Perfetti & Bell, 1991; Perfetti & Zhang, 1995), to find out if
semantic and phonological information are available at the same time course for familiar
and unfamiliar orthographic forms. Similarly, I could manipulate the length of the
presentation of the prime and the target, to see if different patterns emerge for kanji and
hiragana forms of words based on their orthographic depth or orthographic familiarity.
Another interesting track this research could take would be to study how priming
by a sentential context influenced the recognition of kanji and hiragana forms of words. In
fact, 1 have explored this issue previously (see Darnell, Boland, and Nakayama, 1994) using a less refined set of stimuli and participants that had lived in the U.S. for some
115 period of time, and found that familiar hiragana forms were recognized more quickly than
unfamiliar kanji forms of the same words and unfamiliar hiragana forms of other words
when incorporated into a semantically biased sentential context, but not when they appeared in a semantically neutral context. Familiar kanji forms were read at comparable speeds in predictive and nonpredictive contexts, and were not responded to more quickly than either unfamiliar hiragana forms of the same words or unfamiliar kanji forms of other words.
Given that earlier studies (e.g. Besner & Hildebrandt, 1987; Hirose, 1984, 1985;
Sasanuma et al., 1988) reported that familiar kana forms presented in isolation were read more quickly than unfamiliar kana forms of similar length, it was expected that familiar hiragana forms would be recognized more quickly than unfamiliar hiragana forms when embedded in neutral sentence contexts. The fact that this result did not obtain in the
Darnell et al., (1994) study suggests that word recognition interacts with sentence integration, such that residual activation effects generated at the lexical level are not large enough to measure unless the targets items are in a highly predictable context. Also, it is puzzling that familiar kanji forms were not recognized more quickly than unfamiliar kanji forms, regardless of how predictable the word was in its carrier phrase. This finding was particularly striking in light of the strong intuitive preference Japanese speakers have for seeing the kanji forms of kanji dominant words. How could a native speaker consciously prefer a particular orthography for a word and not have that expectation manifest in response time?
1 1 6 5.4 Conclusion
In three experiments, I have demonstrated that familiar orthographic forms of
Japanese words are similarly sensitive to semantic, phonological, and orthographic
priming, regardless of the orthographic depth of the form. This poses a direct challenge to the orthographic depth hypothesis, and suggests that readers’ experience may play a more fundamental role in word recognition than previous theories have described. In addition, 1 have presented a weighted network model of visual word recognition that simply and effectively explains the pattern of results in terms of readers’ experience.
The idea that deep and shallow orthographies can be processed the same way has important implications for empirical studies of languageprocessing. Most obviously, if orthographic depth does not play a major role in how word recognition and lexical access take place, researchers can then feel more ftee to conduct cross-orthographic (i.e., cross- linguistic) studies that investigate the processing of words and make broader generalizations from their findings about the mental representations involved in reading.
It is my hope that the evidence presented here will advance the case for less orthography- specific models of visual word recognition, and encourage others to investigate the ways that an individual’s experience can affect his or her language processing strategies.
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125 IMAGE EVALUATION TEST TARGET (Q A -3 )
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