A Thesis
entitled
Sensory Input and Mental Imagery in Second Language Acquisition
by
Sultana Mahbuba Nargis
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Master of Arts Degree in English with a Concentration in ESL
______Dr. Douglas W. Coleman, Committee Chair
______Dr. Stephen Christman, Committee Member
______Dr. Gaby Semaan, Committee Member
______Dr. Patricia R. Komuniecki, Dean College of Graduate Studies
The University of Toledo
December 2014
Copyright 2014, Sultana Mahbuba Nargis
This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of
Sensory Input and Mental Imagery in Second Language Acquisition
by
Sultana Mahbuba Nargis
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Arts Degree in English with a Concentration in ESL
The University of Toledo December 2014
In the field of second language acquisition, there is a dominant theory of language learning, specifically, the claims that the input for language learning consists of language by famous linguist Noam Chomsky (1964) and his followers. However, Douglas W
Coleman (Thesis adviser), Samuel Johnston, Yifei Xin, and the author hypothesized in
ENGL 6150, a master’s course in Fall 2011, that a person cannot learn a target language from the speech input alone. The findings of that study were published in Xin’s (2012) study which showed that the relevant input for language learning consists of the sound of speech in parallel with other sensory experiences, and what people learn is the ability to communicate. An even earlier study by Postica and Coleman (2006) indicated that mental imagery, the capacity of human brain to recreate sensory experience without external stimuli, could be a substitute to the required ‘parallel sensory input’ in Second Language
Acquisition.
Implementing a variation of Postica (2006) and Xin (2012) experiments, this study aimed to further investigate the role of mental imagery in generating sensory input for communication learning. Thus, the study set out to explore if mental imagery could be a substitute to the sensory input required for learning to communicate in a target
iii language. The findings of the study differ from the findings of Postica’s (2006). The variation in instrument design of this study from Postica’s (2006) design might have contributed to the different outcome of this study.
iv
To my husband without whose support, sacrifice, and inspiration, I could not complete this thesis and the master’s study at the University of Toledo.
Acknowledgements
My greatest gratitude goes to my thesis and graduate advisor, Dr. Coleman, without whose guidance, advice, support, and concern I would not be able to overcome the mental challenges I went through, and finally complete this study. He opened my eyes to see people interacting from a scientific point of view and better understand how humans communicate.
I specially need to thank my thesis committee members, Dr. Coleman, Dr.
Christman, and Dr. Semaan for their valuable time and support by providing their constructive feedback throughout the thesis process and report writing.
I am grateful to my parents who inspire me in my academic endeavor, support me unconditionally in my good times and difficult times.
I would also like to acknowledge my following classmates of Applied Linguistics class, Department of English, University of Toledo, who took part in conducting the experiments: Yifei Xin, Samuel Johnston, Rosemary Song, Timothy Escondo, Jeremy
Holloway, and Yifan Zhao.
Above all, I express my deepest gratitude to Almighty God for everything I achieved in my life, including the knowledge I gained from this study.
v Table of Contents
Abstract iii
Acknowledgements v
Table of Contents vi
List of Tables viii
List of Figures ix
I. Input in Second Language Learning: from a traditional view to a scientific view 1
A. What is language? 1
a. Traditional view of language learning 1
B. A device for language learning – how realistic is it? 6
C. Input is still the focus 8
D. Problems surrounding the assumed “input” in language learning 9
E. The true nature of input: viewing input from a real world perspective 12
F. An alternate view of language learning: shifting focus from language learning
to learning to communicate 13
G. Building a case for mental imagery – historical background 16
H. Mental imagery: from the perspectives of behaviorists to neuroscientists 16
I. Recent studies on mental imagery and its usefulness 23
J. Research questions 25
II. Methodology 26
A. Design 26
a. Study Selection of a target language 26
b. Participants 27
vi B. Instrument 27
C. Procedure 28
a. Study sheet 29
b. The assessment test 29
D. Hypotheses 32
III. Data Analysis 33
IV. Discussion 38
A. How the results of this study differ from previous studies 38
B. Limitations and Suggestions for Future Research 39
C. Concluding Remarks 42
References 43
Appendices
A. Participant’s Consent Form 53
B. Instructions to Participants 55
C. Mini-dialogs 58
D. Study Sheet 64
E. Comprehension Test 65
F. Answer Sheet and Questionnaire 66
G. Recoding Sheet 68
vii List of Tables
Table 1 Recoding (scoring) guide ...... 31
Table 2 Example recoding of test answers...... 32
Table 3 Possible predictors of learning ...... 37
viii List of Figures
Figure 1 Chomsky’s (1964, p. 26) assumed devices for (a) language processing, and
(b) language acquisition...... 4
Figure 2 Functional areas of the human brain (Curtesy: Mayfield Clinic)...... 20
Figure 3 Sample mini-dialog used in the experiment...... 28
ix Chapter One
Input in Second Language Learning: from a traditional view to a scientific view
What is language?
The definition of language changed from time to time to serve the intellectual need of the time. Plato introduced a debate on the origin of language through his dialogue
Cratylus which is considered to be the first linguistic text in Western linguistics (Seuren,
1998, p. 5). The center of the Cratylus debate is the difference between the two competing perspectives of language. According to one of the perspectives, language is
“inherently ‘true to life’, since words are given by nature, and not by convention”
(Seuren, 1998, p. 6); and according to the alternative perspective, “word forms are arbitrary and conventional” (Seuren, 1998, p. 7). At times, language is seen as something abstract, something social or cultural, something behavioral, something mental (Botha,
1992, p. xii), and at other times language is seen something material, a natural phenomenon, the object of a science, a type of a system, as something used, as something processed, as something organic, as something structural, as something produced and comprehended, and as data (Yngve, 1996, p. 10). However, this study is based on the premises of scientific study of learning to communicate.
Traditional view of language learning. Since early ages, humans developed communicative behavior. Humans communicate in numerous ways. According to a traditional view, the most common and distinct way humans communicate is called the use of language; in other words, humans can exchange messages and convey meaning through language. Nowadays, the term “language” applies to a wide variety of perspectives and conceptions (Botha, 1992, p. xii; Yngve, 1996, pp. 10-11) not only in
1 linguistics but also in other fields of study that are concerned with the human ability to communicate (psycholinguistics: Miller, 1967, Macphail, 1998; neuroscience: Bloom
2001, Lieberman, 2002; sociolinguistics: Mesthrie, 2000; second language acquisition:
Cairns, 1996, Rosenthal, 2000; second language teaching: Johnson, 1997, Danesi, 2003,
Mackey, 2005; multidisciplinary studies: Banich, 2003).
The ability to communicate beyond one’s own community gained importance as the early human communities came in contact with each other and engaged in trade, commerce, and exchange of knowledge. The emergence in the ancient world of grammar as part of an explanation of speaking and writing made it possible to encode human communicative behaviors on a large scale (Postica, 2006, p. 5), and eventually helped natives of different cultures learn how to communicate with each other, and to establish relationships.
Merchants quickly understood the advantages of being able to manifest culturally appropriate communicative behaviors to their customers and trade partners: “even a smattering of your client’s mother tongue works wonders in business. It also helps to safeguard against sharp practice” (Howatt, 1984, p. 6). The invention of the printing machine helped develop a medium which could dispatch information to a wider mass.
William Caxton printed the first ever text book for teaching English to non-English natives. The objective of the book was to satisfy merchants’ communicative needs: “Who this booke shall wylle lerne may well enterprise or take on honed merchandises from one land to another” (Howatt, 1984, p. 7).
During the nineteenth century, the study and research in philosophy and sciences grew tremendously. Language teaching drew its approaches and methods from the
2 research and findings of those fields, and experienced a similar growth which continued through the twentieth century. Additionally, attempts to apply scientific approaches to study of language started in the late nineteenth century and emerged in the twentieth century (Kelly, 1969; Howatt, 1984).
A shift in analysis of language from diachronic to synchronic analysis took place in the late nineteenth to early twentieth century when Saussure first viewed language as a
“semantic code” (Clarke, 1990, p. 143). In linguistics, “diachronic” refers to the development of a language over time by paying attention to affinity between languages and historical transmutations of sounds and by striving for the reconstruction of principal languages. Again, “synchronic” refers to the state of a language at a particular time by focusing on its structural features and characteristics and by using phonological, morphological and syntactic explanations including semantic and pragmatic aspects of it.
Codification which emerged as part of shift from diachronic to synchronic analysis is an agreement among the users of a sign as they recognize the relation between the signifier and the signified, and respect the agreement in practice. Roman Jakobson, a prominent linguist, significantly contributed to Saussure’s project for developing a general theory of signs. Jakobson calls this theory “semiotics” by analogy to the disciplines of linguistics and semantics (ibid).
Over the centuries, the study of language became diversified, and led to the emergence of what is known today as the fields of Second Language Acquisition and
Second Language Teaching. One of the most influential of all the linguistics theories of recent times is Chomsky’s “Innateness Hypothesis”. Chomsky (1964) suggests the existence of a ‘mental device’ that receives the morphemes and other abstract units or
3 well-formed sentences (‘primary linguistic data’) of language as input and produces a generative grammar as output.
structural (a) A utterance description
primary generative B (b) linguistic data grammar
Figure 1. Chomsky’s (1964, p. 26) assumed devices for (a) language processing, and (b) language acquisition
According to Chomsky, the child's mind is like a black box, a Language
Acquisition Device (LAD), the internal workings of which cannot be inspected. Into it goes the language data, samples of performance, and from it comes out grammatical competence. The child’s LAD takes in input and produces output. If something is found in the output that cannot be derived from the input, it must have come from the LAD itself. Chomsky (1968) postulated LAD as the following:
Having some knowledge of the characteristics of the acquired grammars and the
limitations on the available data, we can formulate quite reasonable and fairly
strong empirical hypothesis regarding the internal structure of the language
acquisition device that constructs the postulated grammars from the given data.
(p. 113)
Another one of the second language acquisition theories that shape the recent trend in
SLA, and FLA is Krashen’s Input Hypothesis. Krashen (1985) introduced the term
4 “comprehensible input”, and made it central to second language acquisition theory. Five hypotheses are part of this theory, the most important one being the Input Hypothesis:
[W]e acquire language in an amazingly simple way–when we understand
messages. We have tried everything else–learning grammar rules, memorizing
vocabulary, using expensive machinery, forms of group therapy, etc. What has
escaped us all these years, however, is the one essential ingredient:
comprehensible input. (p. vii)
But, only input of a very specific kind (i + 1) will be useful in altering a learner’s grammar. In Krashen’s view, the Input Hypothesis is central to all of acquisition, and it also has implications for the way Krashen defined “comprehensible input”. According to
Krashen, “comprehensible input” is a bit of language (i + 1) that is heard or read and that is slightly ahead of a learner’s current state of grammatical knowledge (i). It is given that, without understanding the language, no learning can take place. Second languages are acquired “by understanding messages, or by receiving ‘comprehensible input’ ”
(Krashen, 1985, p. 2).
According to Krashen, language containing structures a learner already knows essentially serves no purpose in acquisition. Similarly, he claims that language containing structures way ahead of a learner’s current knowledge is not useful, and a learner does not have the ability to “do” anything with those structures. Krashen defined a learner’s current state of knowledge as i and the next stage as i + 1. Thus, the input a learner is exposed to must be at the i + 1 level in order for it to be of use in terms of acquisition. “We move from i, our current level to i + 1, the next level along the natural order, by understanding input containing i + 1” (1985, p. 2).
5 A device for language learning – how realistic is it?
Cook (1991, 1993) summarized the following four steps of the argument in support of Chomsky’s Universal Grammar (UG).
(1) A native speaker of language knows something (feature) about language.
(2) The language feature cannot be learnt from primary linguistic data.
(3) The language feature is not learned from experience.
(4) The language feature must be built-in to human mind.
In the first step of the above argument, the native speaker’s knowledge is defined in terms of competence which is theoretically invalid. In order to determine the true nature of competence, we must know which aspects of performance are irrelevant data. The problem is that the only way to filter out the irrelevant data is to know ahead of time what constitutes competence (Reich, 1973).
The conclusion drawn in the step three of the above argument, that a given language feature is not learned from experience, depends upon accepting the following two assumptions: (a) we can ascribe to people an idealized, flawless competence, and (b) the only available information available to the learner is “primary linguistic data”, not any of the other sensory information. However, the evidence shows (a) to be false, and Klein's
(1986) Chinese Room analogy shows (b) to be false as well. From the perspective of human linguistics which is the premise of this study, step four implicitly creates a brain/mind — physical/mental “domain confusion” (Yngve, 1996) promoted by
Chomsky in many of his writings. This domain confusion is very prominent in Chomsky
(1986): “Mind and matter, mind and brain, have converged” (p. xxii); “suppose we proceed further to regard talk of mind as talk about the brain undertaken at a certain level
6 of abstraction” (p. 22); “statements about I-language... are... statements about something real and definite, about actual states of the mind/brain and their components” (pp. 26-27).
Similarly, there are numerous difficulties with Krashen’s hypothesis as well. First, the hypothesis comes short in specifying how to define and measure the level of knowledge.
To validate the hypothesis, a particular level (say, level 101) must be defined so that input contains of next linguistic level (say, level 102) can be assessed. Defining the level of competence is also required for the testing the learner for the next higher (target) linguistic level after the learner receives inputs for the target level. But, the definition of different levels of linguistic competence is missing from the hypothesis. Krashen (1982) only stated that “We acquire by understanding language that contains structure a bit beyond our current level of competence (i + 1). This is done with the help of context or extra-linguistic information” (p. 21).
Second, Krashen states that there has to be a sufficient quantity of the appropriate input, but he fails to explain what defines sufficient. Here, Krashen invalidly assumes a threshold at which the learning occurs; that there is some point where the learner has not yet learned something and then suddenly he has learned it (the point at which hearing the same input supposedly “makes no difference”). However, that is not how the brain works: each experience adds something new; what it adds seems to be less and less each time because each new experience is smaller and smaller part of all of the person's experiences. Furthermore, learning, at a neurological level, involves changes in synaptic strength; these changes are biochemical (Lawson 2003). Unlike the neuron's internal
“digital” on/off (an electrical signal), the change in synaptic strength (a chemical signal)
7 is analog. So, there is no threshold at which a learner didn't know (at all) and now he/she does (completely).
Additionally, Krashen did not explain how extra-linguistic information aids in actual acquisition, or internalization of a linguistic rule. If by understanding Krashen meant understanding at the level of meaning, we may be able to understand something that is beyond our grammatical knowledge. But, understanding does not automatically translate into grammatical acquisition. Thus, Krashen is right that a successful communication requires “comprehensible input” (extra linguistic context), however he is wrong when he assumes that language exists in the physical world, and it can be a part of input (Postica & Coleman 2006, p. 475).
Input is still the focus
Even though there have been controversies surrounding the treatment of language as input for language acquisition, input is still the focus in theories of language acquisition. The following are a few excerpts from different theories surrounding input and language acquisition. Input is a body of second language data, and UG is at the core of the learning process (Gass and Selinker, 2008, pp. 304-310). We first deal with the nature of the input to second language learners. We then focus on the interrelationship of second language use (especially conversation) and language learning. Corder (1967) made an important distinction between what he called input and intake. Input refers to what is available to the learner, whereas intake refers to what is actually internalized (or, in Corder’s terms, “taken in”) by the learner.
In response to Chomsky's (1964) assumption that the primary language data (PLD) consists of well-formed sentences in the target language, Morgan (1986) introduces an
8 alternative learning theory called the Bracketed Input Hypothesis. According to Morgan
(1986), “input” for language learning not only consists of “primary linguistic data”, it also contains bracketing information about the hierarchical structures present in it. He further insists that bracketing information is necessary for language acquisition to proceed in the face of strict limits on data, for children to learn complex grammars from simple input (Morgan, 1986, pp. 108-109).
Thus, although we see a number of variations of input for language learning, all of the variations of input contain ‘language’ in them (Saleemi, 1992). Saleemi (1992) shows that Chomsky and Miller, Fodor, Pinker, and Wexler and Culicover, all share the above assumption although input is referred to in different terms, such as ‘data’ (p. 8), ‘the available evidence’ (p. 3), and ‘environmental input’ (p. 10).
Problems surrounding the assumed input in language learning
Generative grammar in Chomsky’s innateness hypothesis is ‘unlearnable’ (Gold,
1967), because of the assumption that the input consists of “primary linguistic data”.
Additionally, in order for Chomsky’s process to work learners need to have access to both positive evidence — a set of grammatical sentences, and negative evidence — what is not grammatical (ibid). Chomsky (1975) responded to the above argument laid out by
Gold (1967), but he did not change his assumptions so that his theories could meet Gold’s challenge. Instead, he added another theory; the Universal Grammar, an important part of
Language Acquisition Device (LAD) that cannot be tested scientifically by comparison with observation.
Krashen consistently deviates from Chomsky’s assumption, and proposes that in SLA mere linguistic input is not enough, it must be comprehensible; however, as demonstrated
9 in the preceding section, his arguments are self-contradictory (Carroll, 2001). Krashen
(1985, p. 2), for example,has always explicitly recognized that additional input is necessary for SLA, when he has said, “We are able to understand language containing acquired grammar with the help of context, which includes extra linguistic information, our knowledge of the world, and previously acquired linguistic competence.” Even
Pinker (1994, p. 278), who championed the idea of Chomsky’s theory of Universal
Grammar, agrees with Krashen, and says, “Though speech input is necessary for speech development, a mere soundtrack is not sufficient”. Thus Krashen and Pinker indirectly support Klein’s conclusion that “input consists of a full range of sensory experience”
(Postica & Coleman 2006, p. 475).
Klein (1986) with his simple thought experiment shows that Chomsky’s “primary linguistic data” (1964) cannot be a part of “comprehensible input”. He explained that if we put a person in an empty room, and is exposed him/her to nothing but the sound of recorded Chinese, the person would not learn Chinese, no matter how long he/she stayed in the room. Apparently, Klein seems to support Krashen’s “comprehensible input” however, he distinguishes himself from Krashen’s proposition, when he says “what makes learning possible is the information received in parallel to the linguistic input in the narrower sense” (Postica & Coleman 2006, p. 475). Linguistic input in the narrower sense is “the sound waves” which are physically real, not “language” (ibid). In fact if language were contained in the “comprehensible input”, the Chinese room would work, and the learner would acquire some grammatical knowledge of Chinese.
Contrary to this view, a question may arise in our mind, if language is not a part of “comprehensible input”, how do learners in traditional classroom environments
10 apparently lacking in “comprehensible input” acquire communicative ability? We will get the answer to the preceding question if we understand how comprehension and input affect a person learning to communicate, and explain them in the real world terms of
Yngve (1996), and how mental imagery works in absence of sensory input from the perspective of neuroscience. In human linguistics perspectives (HLS), “comprehension in a real world sense is a physical change in state in one of the participants as a result of linkage events” (Postica & Coleman 2006, p. 475). The following example given by
Klein (1986) will illustrate how comprehension takes place as a result of a linkage event.
Suppose you are a Japanese visitor and you happen to be in Germany without
knowing a single word of German. You are having breakfast in your hotel with a
couple of Germans. One of the Germans turns to you and produces a sequence of
speech sounds like this:
(1) [axkoenənzi:mi:rma:ldaszaltsraIçənbItəšoe:n] (p. 59).
Klein points out that the concurrent events may (or may not) lead to language learning. For example, suppose the speaker looks at you, raises his eyebrows, glances down at the table toward the salt and pepper shakers, gestures toward them and then speaks, holding out an open hand afterwards? He/she might be perplexed as to which thing the German wanted, but would certainly be aware that the speaker was making a request. He/she might offer both, and let the German choose. If you know English, however, you would be likely to guess that one part of the sequence, [szalt], refers to salt, and would simply pass the salt. In “pass the salt” event learning and comprehension may occur due to the linkage event, whereas in absence of linkage in the “Chinese room” neither learning nor comprehension takes place.
11 The true nature of input: viewing input from a real world perspective
Krashen (1985) proposes that mere linguistic input is not enough; input must be understood. However, Klein (1986), Yngve (1996), and Coleman (2005, 2007) show that input must be a “full range of sensory experience parallel to the linguistic input”. Now the question arises “what is the nature of input?” The most recent view of input for language learning suggests shifting focus from language learning input to input for learning to communicate (Coleman, 2005), which requires investigating of the observable properties and actions of people in the real world.
In addition to conveying information about the phonological, grammatical, and lexical nature of the English language input to second language learners in an English- speaking social milieu includes cultural information within which the emergent meaning of the code must be situated and interpreted. Communicative competence can be defined simply as “what a speaker needs to know to communicate appropriately within a particular language community” (Saville-Troike, 2006). It involves knowing not only aspects of linguistic structure (although that is critical component of knowledge) but also when to speak (or not), what to say to whom, and how to say it appropriately in any given situation (ibid).
The learner must know who is speaking to whom, when and where, he must be able to watch the accompanying ‘body language’ (gesture, facial expression, etc.), and he must note the reactions of the listener. Eventually, he should be able to establish a relationship between identifiable segments of the sound stream and particular pieces of parallel information (Klein, 1986, p. 44).
12 An alternate view of language learning: shifting focus from language learning to learning to communicate
Knowledge of acquisition is not merely a matter of direct recording of sensory impressions, nor is the mere passage of time sufficient for innate structure become functional. Rather, acquiring new knowledge appears to involve a complex
“construction” process in which undifferentiated sensory impressions, properties of the developing organism’s brain and the organism’s unsuccessful (i.e. contradicted) behaviors interact in a dynamic and changing environment (Lawson, 2003, pp. 4-5).
Yngve (1996) points out problems with contemporary linguistics studies. Yngve argues that in conventional linguistics, the objects of language are studied more like philosophy which exists in mental domain than like science which exists in physical domain. He calls these problems “domain confusions”. Yngve (1996, pp. 4, 21-22) outlines the criteria for scientific study of language, and points out that input should be the real world objects — “who is speaking to whom”, “when and where”, “the reactions of the listener”, all of sensory experiences involved in a communicative channel. In fact, if language were contained in the “comprehensible input”, the Chinese Room would work to the extent that the learner would exit the room with grammatical knowledge of
Chinese, even if he/she still could not apply that knowledge to real-life interactions
(Postica & Coleman, 2006, p. 475). But, that is not the case, as Klein (1986) explains and shows that the relevant input consists of the full range of sensory experience available to the learner at a given time. This apparently supports Krashen’s (1985) “comprehensible input” theory. Klein, however, makes it clear that linguistic input does not make language
13 learning possible. Instead, “what makes learning possible is the information received in parallel to the linguistic input in the narrower sense (the sound waves)” (p. 44).
Xin’s study (2012) to which the author contributed too, showed that the subjects who were exposed solely to speech and text (considered to be primary linguistic data) did not show any results of learning. On the other hand, parallel sensory input seems to contribute significantly to language learning, even when the exposure was only less than
10 minutes in total. Second, the subjects who received parallel sensory input not only performed better in terms of meaning comprehension, but also in terms of grammatical pattern recognition (what I have referred to loosely as “linguistic structure, ” above).
Thus, looking for input in language learning does not lead us anywhere but to further controversies. Instead, we should focus on how people learn to communicate.
Meaning can be approached in physical-domain terms by considering communicating individuals who are participants in a linkage, in the Hard-Science Linguistics framework of Yngve (1996), as he explains it “in a human linguistics, context would no longer be assumed to reside in texts and utterances. It would be accommodated where it belongs, in the heads of the speakers and hearers themselves, where it would be understood in terms of postulated properties of the people and the structures and dynamic changes of these properties associated with the production and reception of the sound waves of speech and other forms of energy flow” (Yngve, 1996, p. 80). Yngve steers us towards real world objects as he further suggests “the part of the real world we are interested in studying includes people interacting by means of sound waves, light waves, and other physical means” (Yngve, 1996, p. 97). The analysis of comprehension and input in the physical domain leads to the following conclusion: “Comprehension” in a real-world sense is a
14 physical change in state in one of the participants as a result of linkage events. Its causes can include information in any of several channels in the linkage (Coleman 2005, p. 208).
Input is “the full range of sensory experience available to the learner at a given time”
(Coleman, 2005, p. 207). Thus, in human linguistics, input is the full range of sensory experience available to learners. In order to avoid further domain confusions (Yngve,
1996), Postica & Coleman (2006) refer to the full range of sensory experience available to learners that triggers a change in their physical state as input for learning to communicate. Postica & Coleman (2006, p. 2) point out that in traditional second or foreign language class room settings, the most common teaching materials used are textbooks containing translated dialogues, vocabulary lists, and the occasional illustration provided for window-dressing, accompanied by audiotapes and perhaps videotapes (but the latter all too often with speakers appearing as mere “talking heads” against a backdrop) . These classrooms apparently lack necessary information “received in parallel” (Klein 1986, p. 44) to the speech or text, “the linguistic input in the narrower sense” (ibid). There is not enough parallel sensory experience for the students to associate linguistic input to anything else in their experiences. According to Krashen’s
Input/Comprehension Hypothesis, learning (what Krashen refers as language acquisition) should not be possible in these circumstances. Thus, an apparent Klein's (1986) Chinese
Room like environment occurs in the traditional classroom setting of SLA or FLA due to lack of input for learning to communicate. However, the students acquire communicative ability from these learning environments.
For exploring the aforementioned apparent anomaly in the traditional second or foreign language classrooms, let us start with an understanding of how learning occurs
15 from a neuroscience perspective. Neuro-scientific research has long established that internal changes occurring in the human brain lead to the learned communicative behaviors (Bloom et al., 2001, pp. 316-58). Some learners manifest the same internal changes and their related behaviors in the absence of external input. This observation allows Postica and Coleman (2006) to hypothesize that using mental imagery students in the traditional classroom settings may substitute input for learning to communicate, in other words, wider contextual input, such as investigating observable properties and actions of people in the real world, and get out of the apparent Klein’s (1986) Chinese
Room like environment. Following Postica and Coleman’s footstep, the author would like to further explore the role of mental imagery as an alternate to parallel sensory input in the SLA or FLA classroom settings.
Building a case for mental imagery – historical background
Plato brought imagery into limelight (center stage) with his wax tablet metaphor.
Plato compared imagery to patterns engraved in wax as individual differences could be understood in terms of properties of wax, like its temperature, purity, etc.
Imagery has been playing a significant role in the field of memory (Yates, 1966) and motivation (McMahon, 1973). It is also believed to play an active role in visuo- spatial reasoning and creative thought. According to a dominant philosophical school of thought, imagery involves in all thought process, and provides the semantic foundation for language (Stanford University, 1997).
Mental imagery: from the perspectives of behaviorists to neuroscientists
Although mental imagery, a result of brain activity, has been recognized since the time of Plato, it remained as a puzzle for researchers until 1970s when a significant
16 amount of research took place. It is now an accepted fact that a change in the properties of a human organism, external or internal, voluntary or involuntary, is the result of brain activities taking place in specific, definable locations within the brain (Bloom et al.,
2001, p. 3). Due to the complex nature of the brain and the limited knowledge about internal function of the brain, all the events that occur in the brain have not yet been explained in terms of particular parts of the brain involved and the precise roles played by those parts of the brain (Bloom et al., 2001, pp. 3-4). However, recent advancement in neuroimaging technologies, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) opens the door for theories of imagery to be tested objectively in humans. The study of the brain currently interests scientists from a wide range of disciplines, and neuroscience is the modern field of research on the brain. The general purpose of neuroscience is “to link the biological and chemical properties of the brain and its component cells to behavior” (Bloom et al., 2001, p. 10), and the fundamental premise on which it builds can be described as follows. All the normal functions of the healthy brain and the disorders of the diseased brain, no matter how complex, are ultimately explainable in terms of the basic structural components of the brain and their function (Bloom et al., 2001, p. 3). Thus, the advancement in neuroscience enables researchers to show that mental imagery draws on much of the same neural machinery as a perception does in the same modality, and can engage in mechanisms used in memory, emotion, and motor control (Kosslyn et al., 2006, pp. 195-
96).
Over time, imagery has been seen playing an important role in memory, problem solving, creativity, emotion, and communication (Kosslyn et al., 2006, p. 4). According
17 to Kosslyn (1983), “Imagery is a basic form of cognition and plays a central role in many human activities - ranging from navigation to memory to creative problem solving… It is likely to be one of the first higher cognitive functions that will be firmly rooted in the brain” (p. 1). A mental image is defined as the neurological event that occurs when a representation of the type created during the initial phases of perception is present, but the stimulus is not actually being perceived; such representations preserve the perceptible properties of the stimulus and ultimately give rise to the subjective experience of perception (Kosslyn et al., 2006, p. 4). Again, Paivio (1986) explains mental imagery through Dual Coding Theory. Dual coding theory begins with the coding of verbal and non-verbal mental representations into two separate cognitive subsystems. In this theory, coding refers to capturing the external world and converting it to internal forms (Sadoski
& Paivio, 2001). Kosslyn et al. (2006) portrays mental imagery as embedded within and depended upon a mental representational system called “mentalese” from which imagery derives much or all of its semantic content. Researchers posit that the brain recreates visual, auditory, tactile experiences, etc. when perceptual memories are retrieved
(Kosslyn et al., 2006, p. 4). Mental imagery is thus viewed as a main constituent of an integrated and unified composite of diverse sensory images: visual, auditory, tactile, olfactory, and others (Damasio, 1999, p. 115).
The Dual Coding Theory (DCT) of memory research focused initially on memory and soon expanded to other cognitive phenomena. Memory remains crucial because it is a common basis of knowledge and thought. The emphasis on memory is further justified here because learning and memory are at the heart of educational goals. Especially important for DCT and its applications are the beneficial effects of concreteness and
18 imagery on memory (Paivio, 2006, Chapter 4). In regard to concreteness, memory performance generally increases from abstract words (e.g., truth, justice), to concrete words (e.g., chair, lobster), to objects (or their pictures). In the case of studies of language, results show that the concreteness effect occurs with materials ranging in length from words, to sentences, to long passages, with concrete memory exceeding abstract memory performance by a 2:1 ratio on average. The concreteness advantage is even more striking in associative memory tasks in which recalling of response items is cued by concrete stimulus words or by pictures.
Broca-Wernicke model, based on anatomical location of areas of the brain that have distinct functions, is considered the basis for human linguistic ability. Broca’s study published in 1861 ascribed the expressive language deficits (word-finding difficulties and impediments in speech production) of a patient who had suffered a series of strokes to damage to “Broca’s area,” a frontal region of the neo-cortex. In 1874, “receptive” deficits in the comprehension of language were ascribed to damage to a posterior area of the cortex, “Wernicke’s area”.
19
Figure 2. Functional areas of the human brain (Courtesy: Mayfield Clinic)
Subsequent research has shown that patients diagnosed with Broca’s aphasia often produced sentences having simplified syntax and had difficulties comprehending distinctions in meaning conveyed by syntax (Zurif, Carramazza & Meyerson, 1972).
Lichtheim claimed in 1885, which Geschwind restated in1970, that the neurological basis of human language was a system linking Wernicke’s area with Broca’s area. According to Lichtheim-Geschwind theory, incoming speech signals are first processed in Wernicke’s area; information is then transmitted via a hypothetical cortical pathway to Broca’s area which is served as the “expressive” language output device.
The Lichtheim-Geschwind theory was picked up by linguists, such as Chomsky
(1986) and Pinker (1994) to be a valid model of the neural architecture underlying human linguistic ability.
20 Chomsky (1980a, 1980b) retained Broca’s claim, and stated that the human brain contains a unique localized “language organ,” which regulates language independently of the neural mechanisms that are implicated in other aspects of human behavior or the behavior of other animals. Indeed, in this respect Chomsky owes much to Descartes, who in his letter of 1646 to the Marquis of Newcastle stated that “language belongs to man alone.” Chomsky focused on language “competence” or “knowledge of language” rather than the processes by which people make use of their knowledge of language
(Lieberman, 2000, p. 7). Similarly, Pinker (1994) states that “Genuine language . . . is seated in the cerebral cortex, primarily the left perisylvian region” (p. 334). He specifically identifies the “the human language areas . . . Wernicke’s and Broca’s areas and a band of fibers connecting the two” (p. 350). Deacon (1997) differs from Pinker as he views those areas of the brain as non-language-specific computational centers, and parts in a larger symbolic computational chain. According to Deacon (1997), a symbolic learning algorithm drives language acquisition, and learning occurs in a particular context, particular senses, types of motor actions, and ways of organizing the information
(p. 48).
However, Barsalou (1999) through his perceptual symbol systems shows that higher order cognitive functions including categorization, concepts, attention, working memory, long term memory, language, problem solving, decision making, skill, reasoning, and formal symbol manipulation are grounded in lower-level sensory-motor processing areas of the brain. Similarly, while explaining what the calls the “functional language system” (FLS), Lieberman (2000) argues that neural mechanisms that enable human language and cognition evolved from mechanisms adapted for motor control
21 similar to Darwinian process of evolution. Language has a long evolutionary history, and via neural system it is integrated with nonlinguistic and motor capacities.
Thus, Lieberman rejected Chomsky’s nativism, Fodorian modularity and algorithmic (symbolic/sequential) accounts of language summary. Lieberman argues that language is not an instinct but a learned skill enabled by a distributed parallel network involving many brain structures — the functional language system. The FLS, though uniquely human, derives from neural structures that regulate motor control.
Thus, the focus has been shifted from the neocortex to deep subcortical structures of which the basal ganglia, structures with reptilian origins, are particularly implicated
(Coleman, 2013). According to Lieberman, the key feature of language is speech, not syntax; lexical and syntactic abilities have simpler parallels in apes, but speech reflects species-specific facets of the human brain; understanding speech’s origins is the key to the evolution of language.
Lieberman (2000) explains language as a learned skill, based on a functional language system (FLS) that is distributed over many parts of the human brain; he thus shows that we cannot logically explain language as an instinct based on genetically transmitted knowledge coded in a discrete cortical “language organ.” He further argues that FLS regulates the comprehension and production of spoken language, which alone exists in no other living species. Moreover, the FLS is based on sensorimotor systems that originally evolved to do other things and continue to do them now. Although the neural bases of language include the neo-cortex, some of the key structures of the FLS are subcortical basal ganglia (p. 1).
22 The above finding that the neural functions are involved for human communicative process through speech lays the ground for exploring the role of mental imagery, the result of brain functions, in language learning.
Recent studies on mental imagery and its usefulness
Bunzeck et al. (2005) show auditory imagery and the perception of complex sounds share the same neural pathways. They demonstrate that the imagery and perception of complex sounds which do not contain speech or music rely on overlapping neural correlates of the secondary auditory cortex, but not the primary auditory cortex.
Another study by Mar (2011) shows a correlation between mental imagery and fiction reading suggesting that a shared network exists for Theory of Mind (ToM) and narrative comprehension (fiction reading). Since the brain structures involved in mental imagery and sensory perception overlap, it might so happen that the needed information received in parallel might be supplied within the brain from perceptual memories (as mental images) in the absence of external perceptual stimuli of the same type.
Additionally, Helene and Xavier (2006) demonstrate that training through imagery (without performing the actual motor task) can lead to the acquisition of implicit knowledge associated with the task performance. Furthermore, a study by Taktek et al.
(2004) shows that through mental imagery, young children can improve performance of physical exercise (discrete motor tasks). In another recent study, Berger and Ehrsson
(2013), through multiple experiments with three classic multisensory illusions (cross- bounce, ventriloquism, and McGurk illusions), demonstrate that neural signals generated by mental imagery (senses produced without actual stimuli) are capable of integrating
23 with the neural signals generated by real stimuli of a different type (modality), and can create multisensory percepts (p. 1367).
In the domain of motor skills and sports psychology, mental imagery has become an important component of a strategically organized learning experience. Several studies have demonstrated that the use of mental imagery, combined with physical practice, contributes to the optimization of motor performance (Grouios, 1992; Lesley & Gretchen,
1997; Martin & Hall, 1995; Overby, Hall, & Haslam, 1998; Screws & Surburg, 1997;
Taktek, 2000; Wrisberg & Ansel, 1989 as cited in Taktek, 2004, p. 80).
Mental imagery capacity refers to person’s aptitude to imagine scenes, objects, or movements. Vividness refers to the degree of activation applied to representational units in order to generate an image. We can classify people in three categories based on their capacity of mental imagery creation: imager, occasional imager, and non-imager. Non- imagers generally do not benefit from imagery experiences. However, imagers and occasional imagers can effectively use mental imagery to better adjust their movements to the learning activities in which they are engaged (Chevaier, 1995; Denis, 1985, 1989;
Fishburne & Hall, 1987; Marks, 1977; Roure et al., 1999; Ryan & Simons, 1982 as cited in Taktek, 2004, p. 84).
Mental imagery works better for the tasks in which the cognitive component is dominant (Feltz & Landers, 1983; Paivio, 1985). For example, throwing toward a target, a discrete motor task, requires cognitive ability of fine visuo-motor adjustments. Thus, those tasks prove to be sensitive to mental imagery (Denis, 1985, 1989; Denis, Chevalier,
& Eloi, 1989). However, mental imagery is ineffective when the task involves a muscular
24 endurance activity (Denis, 1989; Denis, Chevalier, & Eloi, 1989; Hinshaw, 1991 as cited in Taktek, 2004, pp. 87-88).
Ahsen (2001), Hinshaw (1991) and Murphy (1994) have suggested that the emotional and motivational states (level of anxiety, personality traits, cognitive style and confidence level) of the participant are important factors in analyzing the effects of mental imagery on motor skills acquisition and performance. Learner may perform better in acquiring communicative ability while learning with mental imagery (Postica &
Coleman, 2006, Postica, 2006).
The above discussion and findings that mental imagery triggers sensory perception lead the author to formulate the following research question pertinent to the study.
Research questions
Can parallel sensory experience generated by mental imagery help acquire the appropriate form (grammar) and appropriate order of coherent and relevant speech articulation motor tasks in a target language? In other words, can mental imagery be a substitute for the sensory input required for learning to communicate in a target language?
25 Chapter 2
Methodology
Design
Based on the above discussion, we assume that input in the traditional classroom setting where there is a lack of sensory input, a learner’s brain receives any amount of input available to the brain, and it produces mental images as substitutes to sensory input.
Brain generated mental images cause changes to the internal properties of the learner, and they enable him/her learning to communicate in English in the case of SLA/FLA classroom setting. For investigating the research question (presented at the end of chapter
1), we set up an experiment in a target language.
Selection of a target language. The potential participants were expected to be composed of native English speakers, and they were likely to have some level of experience in communicating through a non-native language as majority of the U.S high school students undertake at least one foreign language class. One of the key features in designing the experiment was to minimize the confounding factor due to participants’ previous language experience. To achieve this goal, the research team (see below) looked for a target language to which the participants would have the least possible exposure.
The potential subjects were expected to having minimal, if any, exposure to a simulated
(artificial) language than to a natural language. For this reason, the team adopted an artificial language called Térus as the target language. Térus was designed by Professor
Douglas Coleman of The University of Toledo, and it was previously used in other similar studies (Postica, 2006; Xin, 2012). Inspired by the methodology used in
Saussure’s analogy of the chess game, Dr. Coleman created Térus by systematically
26 rotating the place of articulation of Polish consonants while preserving a simplified syntactical structure. For instance, [t] became [k] and [k] became [p]; thus, Polish [tak] became Térus [kap] with [a] remaining unchanged (Postica, 2006, p. 34). The dialogs used in the experiment were originally drafted in English, and later translated into Térus by Dr. Coleman.
Participants. Participants of the study were university students selected from
Composition I and II classes during Fall 2013 and Spring 2014 semesters of the
University of Toledo. The instructors of the targeted Composition I and II classes were contacted via email, and their assistance was requested in conducting the experiment with their students for 15-20 minutes during one of the regular class sessions. After obtaining the permission from the instructor of the class, the members of the research team visited the class at the prescheduled time, briefed the students about the experiment, and asked them if they would like to participate in the experiment. All the participants were adult
(18+ years old) unpaid volunteers, both male and female. There was no academic credit awarded for participating in the experiment. Participants were divided into two groups:
Experimental group and Control group. The grouping of the participants was done following random stratified method (group matched by size and level). The entire class was selected either as an experimental group or as a control group.
Instrument
The participants of the study read and heard a series of mini-dialogues
(conversations) among three characters (students) in a classroom scene. Timothy
Escondo, Jeremy Holloway, Sultana Nargis, Rosemary Song, and Yifan Zhao, students in
Applied Linguistics I class of the University of Toledo, worked with Professor Douglas
27 Coleman and produced the mini-dialogues used in this study. The texts of the mini dialogs in Térus (target language) along with English translations were shown on the
Power Point slides. The participants in the experimental group saw (for generating mental imagery) the following instructions on a separate (additional) slide: “Try to see the action in your mind as you are reading and listening.” The experimental group further saw parenthetical materials (stage directions) next to each dialog text. The participants in the control group did not see the additional slides and the parenthetical “stage directions”.
The control group saw the exact same texts of the mini dialogs in Térus along with
English translations. A sample of the dialogs with parenthetical stage direction used in the experimental group is shown in Figure 3.
Figure 3. Sample mini-dialog with parenthetical stage direction used in the experimental group
Procedure
Each session of the experiment was administered by the members of the research team. The experimenters started the session by starting the Power Point slides on the
28 overhead projector. The experimenters distributed consent forms, explained the consent process, and collected the forms after the participants read and signed the forms. A sample of the consent form is presented in Appendix A. Following the consent procedure, the instructions to participants were shown on the Power Point slides. The text of the instructions is presented in Appendix B. Each dialog was played three times automatically with a three second interval. There were three mini dialogs. The mini- dialogs are presented in Appendix C. The participants were allowed to read along the dialogs. Following the dialogs, the participants were given a five minute study period for comprehending the target language.
Study sheet. The participants used a study sheet consisting of i) the texts of the mini dialogs as seen in the Power Point slides in target language along with English translations, and ii) a vocabulary list (not shown on the Power Point slides) of the words used in the mini dialogues. The study sheet is presented in Appendix D.
The assessment test. Following the study period, the participants were given an assessment test. The test consisted of ten questions to measure knowledge of accuracy
(what is usually considered to be syntactically and grammatically accurate in the target language) and knowledge of meaning – sensible (coherent and relevant) in the target language from a pragmatic (HSL), not a linguistic-semantics point of view. Each question had the following four types of responses.
1) Both syntactically accurate and pragmatically meaningful in the target
language (accurate and sensible)
2) Syntactically accurate but not pragmatically meaningful in the target language
(accurate but not sensible)
29 3) Pragmatically meaningful but not syntactically accurate in the target language
(not accurate but sensible)
4) Neither syntactically accurate nor pragmatically meaningful in the target
language (neither accurate nor sensible)
Order of the responses was randomized from question to question. In each of the test items, all four responses are constructed to measure if the participants of the study learned something generalized from the mini dialogues instead of simply having the part of dialogues memorized. None of the four responses actually matched a segment of the mini dialogues.
In addition to the assessment test, the participants were asked several other survey questions to identify the learning strategies used by the subjects during their study period.
The test is presented in Appendix E. The participants got five minutes to complete the assessment test and the learning strategy questionnaire. The answer sheet and the strategy questionnaire are presented in Appendix F. The participants were not asked to answer demographic information (age, gender, etc.).
In a total of eight sessions (sections of the English Composition I and II class), a total of 175 students participated in the experiment, and 173 effective tests were used for the data analysis, 115 in the experimental group, and 58 in the control group. Due to incomplete responses, assessment tests of 2 participants were excluded from the data analysis. Initially, an equal number of participants (58 each) were selected for each of the groups. However, there were not enough self-reported imagery participants in the group available for a secondary test where assessment test results among self-reported imagery users and self-reported non-imagery users were compared. As a result, additional
30 participants were recruited for the experimental group to create a balance of self-reported imagery and self-reported non-imagery users in the group.
The tests were then scored twice according to the coding guide (see Fig. 4). Each test was scored for two factors: (1) accuracy, meaning what is usually considered to be acceptability of grammatical structures and (2) sense, which referred to comprehension of the meaning from a pragmatic (HSL), not a linguistic-semantics point of view. When a subject chose either of the answers that have the accurate form, he/she would accumulate
1 point for the accuracy score, and similarly, for either of the answers that have the correct meaning he/she would receive 1 point for the sense score. Since there were ten question items in total, each test received two scores each on the scale of 0-10. See
Appendix G for the complete recoding (scoring) sheet.
Table 1 Recoding (scoring) guide
Recoded Variables and Assigned Answer Values it_correct = 1 Accurate form and Sensible it_accurate = 1 it_sense = 1 it_correct = 0 Accurate form and Nonsensical it_accurate = 1 it_sense = 0 it_correct = 0 Inaccurate form and it_accurate = 0 Sensible it_sense = 1
it_correct = 0 Inaccurate form and Nonsensical it_accurate = 0 it_sense = 0
31 Table 2 Example of recoding of test answers
Item # 1. Tsost. Recoded Variables and Assigned Answer Values it_correct1 = 0 A ku tsost. it_accurate1 = 0 it_sense1 = 1 it_correct1 = 0 Kap, ku na? it_accurate1 = 0 it_sense1 = 0 it_correct1 = 1 Tsost. Wap so na? it_accurate1 = 1 it_sense1 = 1 it_correct1 = 0 Eshkákmwo. it_accurate1 = 1 it_sense1 = 0
Hypotheses
HA1 = Subjects instructed to create mental imagery by visualizing of the event
(the experimental group) will score higher than the subjects without instruction
for visualization (the control group).
HA2 = Subjects reported using mental imagery (self-reported imagers) as a
learning strategy will score higher than the subjects reported not using mental
imagery (self-reported non-imagers) as a learning strategy.
H0 = Subjects without instruction for visualization (the control group), will score
equal to or higher than the subjects instructed to create mental imagery by
visualizing of the event (the experimental group).
32 Chapter 3
Data Analysis
The collected data were first entered into a spreadsheet. The data from the spreadsheet were then loaded into SPSS (Statistical Package for the Social Sciences) for further analysis. Using the recoding guide mentioned in chapter 2, the data were recoded into different variables. The data contained one grouping variable in which mental imagery group was coded as Experimental, and the control group was coded as Control.
Since each subject received two scores, the scores were coded into two variables - one on accuracy, and one on sense. A response on the test was labelled “accurate” if it followed a pattern that was seen somewhere in the stimulus dialogues. For example, a speaker's references to their own states and actions typically end with –an, e.g., nan '(I) have', tlotlasan '(I) am sorry', nisan 'I have to', etc. Similarly, a response was categorized as
“sensible/meaningful” if there was evidence somewhere in the stimulus dialogues that it fits the context created by the test item. For example, if someone hands you a pencil and says, tlesan ('here you go') and you answer gzampewan ('thanks' / 'I thank'), that's sensible/meaningful because it fits the context according to available evidence. But if you answer tsost ('hi'), the available evidence shows that the response doesn't fit the context.
Two more variables, each having possible value of 0 to 10, were created to represent the sum of accuracy (total_accur) and a sum of sense (total_sense) scores of all the ten items for each subject.
Since the data were not certain to be interval level, the data were analyzed through a frequency test to determine if the skewness and kurtosis of the two groups were within the acceptable range of -2 and +2. The results from the descriptive statistical
33 analysis revealed that the skewness of -.152 for the sense scores, and -.376 for the accuracy scores. Similarly, the results from the analysis showed kurtosis of -.611 and -
.239 for the sense and accuracy scores respectively. Thus, both skewness and kurtosis of the data were within the acceptable range.
In order to examine the normality of distribution, a one-sample Kolmogorov-
Smirnov (K-S) test was performed. If the p-value was greater than .05, the test distribution would be considered normal, meaning the data could be treated as being at the interval level. The p-value for the K-S test performed on the sense scores was less than .05, indicating that the data were not normally distributed, and it could not be treated as being at the interval level. Likewise, the p-value for the K-S test performed on the accuracy scores was also less than .05, suggesting that the data were not normally distributed, and it could not be treated as being at the interval level.
Based on the ordinal nature of the data, and the number of groups, the median scores were compared by using the Mann-Whitney test to see if there was a significant difference between the two groups: the experimental and control group. The test results showed that the p-values of both sense and accuracy scores were greater than .05, indicating that there was not a significant difference in terms of the central tendencies of the test scores from the two groups. The mean rank of the mental imagery experimental group’s test scores on sense was 93.73 (N= 58), whereas the mean rank of the control groups’ test scores on sense was 83.60 (N=115). Similarly, on the accuracy test, the mean rank for the mental imagery experimental group was 83.91 (N= 58), and for the control group was 88.56 (N=115). The above analysis shows that the mental imagery experimental group got higher sense score than the control group. However, the
34 difference in sense scores between the two groups is not statistically significant (p-
Value=.101). Similarly, there was not a significant difference in accuracy scores of the two groups (p-Value=.278). Thus, I was not able to reject the null hypothesis as the subjects without instruction for visualization (the control group) scored statistically equal to the subjects instructed to create mental imagery by visualizing of the event (the experimental group).
Again, I performed another set of Mann-Whitney tests of the entire sample for finding out if there is a significant difference of sense or accuracy scores between the following two subgroups of the subjects: (i) imagery user, who reported using imagery
(responded “yes” to the second question of the strategy questionnaire), and (ii) non- imagery user, who reported not using imagery (responded “no” to the second question of the strategy questionnaire). The results from the tests show that the mean rank of the sense scores of the imagery users is 90.17 (N= 59), and the mean rank of the sense scores of the non-imagery users is 85.36 (N= 114), p-Value = .271. The mean rank of the accuracy scores of the imagery users is 90.82 (N= 59), and the mean rank of the accuracy scores of the non-imagery users is 85.02 (N= 114), p-Value = .231. Subjects who reported using imagery did not perform better either in sense scores or accuracy scores than the subjects who reported not using imagery since the difference of the mean rank of the scores between the two subgroups is not statistically significant.
Similar tests were performed among the self-reported imagery users and non- imagery users of the experimental group and the control group separately. The results of the control group show that there is no difference in either sense or accuracy scores between the two subgroups. The results of the experimental group show that there is no
35 difference in sense scores between the two subgroups. But, among the control group, the self-reported imagery subgroup scored higher in accuracy score than the non-imagery subgroup did. However, the difference of accuracy scores between the two subgroups of the control group is not statistically significant.
Since I was also interested in finding out whether or not there was any learning occurred for either of the groups of the subjects, I decided to run a second set of analysis to compare the assessment test results to chance. As mentioned previously in chapter two, each of the test items was designed in a way that there was a fifty-fifty chance to get a correct answer for both sense and accuracy scores. Thus, if learning occurred during the experiment, the majority of the subjects from a group would receive a score higher than
5, since there were10 test items in total. Similarly, if no learning occurred, the majority of the subjects would receive a score around 5. For this, I created two new variables, learning_accur derived from total accuracy (total_accur) , and learning_sense derived from total sense (total_sense) each of them having two possible values: 0 for total score ranging from 0 to 5, and 1 for total score ranging from 6 to 10. I compared the learning accuracy (learning_accur) and learning sense (learning_sense) scores of the two groups against a fifty percent probability using the Binomial test. In the results for accuracy learning scores, the mental imagery experimental group performed similar to chance
(48%, which is close to chance; p-Value= .448), meaning they performed no differently from chance; the control group did demonstrate learning (61%, which is greater than chance; p-Value=.0125). That is, subjects who received parenthetical stage direction
(mental imagery) did not learn syntactical (grammar) accuracy, but subjects who did not receive parenthetical direction (control group) did learn. The mental imagery
36 experimental group performed above chance on the learning sense (comprehension) score
(74%, which is greater than chance, p-Value= .00); the control group did the similar learning on sense score (66%, which is greater than chance, p-Value= .0005).
I had a further interest to find out if any of the six self-reported learning strategies was a significant predictor of either accuracy (grammar) or sense (comprehension) learning. Seven factors were considered as possible predictors of learning (Table 3): the six variables from learning strategy questionnaire, plus the type of treatment the subjects received. In the statistical analysis, the type of treatment was assigned a variable called treatment_type, with the values of 1 for the mental imagery experimental group and 2 for the control group.
Table 3
Possible predictors of learning
treatment_type Silent Imagine Vocabulary Covered Remember Dialogue
I performed a discriminant analysis (stepwise) to identify predictors of learning scores. From the discriminant analysis, none of the learning strategies or the treatment type was a significant predictor for accuracy (grammar) or sense (comprehension) learning. Only somewhat significant predictor of accuracy learning was the silent reading strategy (Silent): Wilk’s U = .98, Exact F = 3.470, df1 = 1, df2 = 171, p-Value =
.064.
37 Chapter 4
Discussion
How the results of this study differ from previous studies
As mentioned earlier (in chapter one), this study is a follow-up of several previous studies conducted by faculty and students of English Department at the University of
Toledo, more specifically, the studies of Postica (2006), and Xin (2012). This study is similar to the study of Postica (2006) in the sense that like Postica’s study, this study is designed to measure the effect of mental imagery on second language learning. However, the instruction provided to the experimental group for generating mental imagery of this study differs from that of Postica’s study. Furthermore, this study has a design feature similar to Xin’s (2012) study as the experiments of both studies aim to measure subjects’ learning of both syntactical accuracy (form or grammar) and sense (pragmatic comprehension) accuracy in a target language. Again, this study differs from both the studies of Postica (2006) and Xin (2012). Unlike Postica’s study, this study did measure syntactical and sense accuracy separately, and unlike Xin’s study this study did aim to measure the role of mental imagery in learning to communicate in a second language.
Like the study of Posica, this study also found that there was no difference in language learning due to subjects' assumed use of mental imagery during the treatment phase. However, Postica (2006) found a relationship between subjects’ self-reported imagery usage and language learning which is different from the findings of this study.
The results of Postica’s study indicate that subjects who reported using mental imagery did better in language learning assessment test than the subjects who reported not using mental imagery.
38 Limitations and Suggestions for Future Research
Based on Mar (2011) findings that show a positive correlation between mental imagery and narrative comprehension (fiction reading), there was an assumption that narratives (parenthetical stage directions) would trigger mental imagery in the experiment of this study. According to the results of this study, a question occurs whether parenthetical stage direction is sufficient to create mental imagery. It is rather difficult to create, control, and measure mental imagery. In this study there was no validity check on whether the instruction and parenthetical stage direction provided to the subjects of the experimental group truly attributed to mental imagery during the treatment (language learning) phase.
Again, the following variation of the design of this study might be a contributing factor to producing a different outcome of this study from Postica’s.
i) Postica (2006) used a memory test to keep the channel open, whereas this study did not have a memory test.
ii) Postica (2006) used audiovisual prompts (video clips). On the other hand, this study used text-only prompts. This may have biased against imagers.
iii) The presence of the parenthetical material ("stage directions") may actually have been a distraction because their presence gave the participants less time to focus on the target language (Terus) and its English translations.
iv) The parenthetical material may have had no effect in stimulating non-imagers to use imagery. If (iii) and (iv) both hold true, we might even expect the imagers
(experimental group) to come out worse off on the comprehension test rather than better.
39 I would like to discuss the following features and properties of mental imagery which might shed further lights on instrument design of future studies on the role of mental imagery in language learning.
As mentioned in Chapter One (p. 24), people can be classified into three categories based on their capacity of generating imagery: imager, occasional imager, and non-imager (Taktek, 2004, p. 84). Non-imagers generally do not benefit from imagery experiences, but the other two categories do. Considering the imagery capability of the subjects, the following would be a better design strategy – the subjects imagery capability would be assessed first, and then they would be divided into the following categories: imager, occasional-imager, and non-imager. After the subjects are grouped by imagery capability, they could be further sub grouped into experimental and control groups. Such a design could help determine if mental imagery capability of the participants has any effect on the outcome of the experiments.
The design of the experiment can be further improved by tailoring the instruction.
The instruction of the current study is more likely to generate external imagery which is less effective than the internal imagery. Mahoney & Avener (1977) defined internal and external perspectives of mental imagery as the following:
In external imagery, a person views himself from the perspective of an external
observer (much like in home movies). Internal imagery, on the other hand,
requires an approximation of real-life phenomenology such that the person
actually imagines being inside his/her body and experiencing those sensations
which might be expected in the actual situation. (p. 137).
40 Also, a different and irrelevant parenthetical stage direction could be given to the control group. This would eliminate confounding factor, if any, due to distraction caused by parenthetical stage direction.
It would also be worthy to explore if gender and handedness had any effect on mental imagery capacity. According to Paivio and Clark (1990), the results of the different studies demonstrated that boys possess superior capacities when compared with girls in terms of dynamic imagery. However, girls have higher capacities for static imagery. Static imagery is the form of imagery expresses the evocation of stationary and fixed objects while the dynamic form of imagery expresses the evocation of action scenes in which the objects are in movement or in the process of transformation (Piaget, &
Inhelder, 1966) or rotation (Kosslyn et al., 1988).
The effect of handedness may be considered in future studies as recent studies indicate that memory performance is related to handedness (Christman, Propper, &
Brown, 2006; Christman, Propper, & Dion, 2004; Propper et al., 2005), and inconsistent handedness is linked to more successful foreign language vocabulary learning (Kempe et al., 2009).
Furthermore, emotional and motivational states (level of anxiety, personality traits, cognitive style, and confidence level) of the participants are important factors which may affect performance of mental imagery on acquisition of motor skills (Ahsen,
2001; Hinshaw, 1991; & Murphy, 1994). Studies like this one should consider participants’ emotional and mental sates, along with the other factors mentioned above.
41 Concluding Remarks
A study of this nature demands a much larger support than what is available in a master’s study. Imagery is difficult to measure, and it is further difficult to generate imagery in a controlled manner such as the experimental group of this study. Future studies aiming to explore the role of mental imagery in classroom learning will have a better foundation to begin with once it is understood how effectively imagery can be evoked, and how it can be measured.
Although the results of this study did not produce expected outcome, it has provided important implications for designing similar studies, such as effectiveness of parenthetical material in stimulating mental imagery. Moreover, this study was a significant learning experience for me as it encompassed the role of mental imagery which is a comparatively new and emerging research area of psychology, second language learning, and human communicative behaviors. I hope the findings of this study will guide future studies that aim to explore the evocation and the role of mental imagery in second language acquisition by overcoming the limitations encountered in this study.
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52 Appendix A
Participant’s Consent Form
53
54 Appendix B
Instructions to Participants – Control Group
Control Group Slide 1
Control Group Slide 2
55
Control Group Slide 3
Instructions to Participants – Experimental Group
Experimental Group Slide 1
56
Experimental Group Slide 2
Experimental Group Slide 3
57 Appendix C
Mini-dialogs – Control Group
Control Group Mini-dialog 1
58
Control Group Mini-dialog 2
59
Control Group Mini-dialog 3
60
Mini-dialogs – Experimental Group
Experimental Group Mini-dialog 1
61
Experimental Group Mini-dialog 2
62
Experimental Group Mini-dialog 3
63 Appendix D
Study Sheet
Control Group – Study Sheet
Experimental Group – Study Sheet
64 Appendix E
Comprehension Test
65 Appendix F
Answer Sheet and Questionnaire
66
67 Appendix G
Recoding Sheet
Recoded Variables Recoded Variables Recoded Variables Recoded Variables and Assigned and Assigned and Assigned
Item and Assigned Values
Values Values Values
Answer Answer Answer Answer
it_correct1 = 0 it_correct1 = 0 it_correct1 = 1 it_correct1 = 0 1 A it_accurate1 = 0 B it_accurate1 = 0 C it_accurate1 = 1 D it_accurate1 = 1 it_sense1 = 1 it_sense1 = 0 it_sense1 = 1 it_sense1 = 0 it_correct2 = 0 it_correct2 = 1 it_correct2 = 1 it_correct2 = 0 2 A it_accurate2 = 0 B it_accurate2 = 0 C it_accurate2 = 1 D it_accurate2 = 0 it_sense2 = 1 it_sense2 = 0 it_sense2 = 1 it_sense2 = 0 it_correct3 = 1 it_correct3 = 0 it_correct3 = 0 it_correct3 = 0 3 A it_accurate3 = 1 B it_accurate3 = 0 C it_accurate3 = 1 D it_accurate3 = 0 it_sense3 = 1 it_sense3 = 1 it_sense3 = 0 it_sense3 = 0 it_correct4 = 0 it_correct4 = 0 it_correct4 = 0 it_correct4 = 1 4 A it_accurate4 = 0 B it_accurate4 = 0 C it_accurate4 = 1 D it_accurate4 = 1 it_sense4 = 1 it_sense4 = 0 it_sense4 = 0 it_sense4 = 1 it_correct5 = 0 it_correct5 = 0 it_correct5 = 0 it_correct5 = 1 5 A it_accurate5 = 1 B it_accurate5 = 0 C it_accurate5 = 0 D it_accurate5 = 1 it_sense5 = 0 it_sense5 = 0 it_sense5 = 1 it_sense5 = 1 it_correct6 = 0 it_correct6 = 0 it_correct6 = 1 it_correct6 = 0 6 A it_accurate6 = 1 B it_accurate6 = 0 C it_accurate6 = 1 D it_accurate6 = 0 it_sense6 = 0 it_sense6 = 1 it_sense6 = 1 it_sense6 = 0 it_correct7 = 0 it_correct7 = 1 it_correct7 = 0 it_correct7 = 0 7 A it_accurate7 = 0 B it_accurate7 = 1 C it_accurate7 = 0 D it_accurate7 = 1 it_sense7 = 1 it_sense7 = 1 it_sense7 = 0 it_sense7 = 0 it_correct8 = 0 it_correct8 = 0 it_correct8 = 1 it_correct8 = 0 8 A it_accurate8 = 0 B it_accurate8 = 0 C it_accurate8 = 1 D it_accurate8 = 1 it_sense8 = 1 it_sense8 = 0 it_sense8 = 1 it_sense8 = 0 it_correct9 = 0 it_correct9 = 0 it_correct9 = 0 it_correct9 = 1 9 A it_accurate9 = 1 B it_accurate9 = 0 C it_accurate9 = 0 D it_accurate9 = 1 it_sense9 = 0 it_sense9 = 1 it_sense9 = 0 it_sense9 = 1 it_correct10 = 0 it_correct10 = 1 it_correct10 = 0 it_correct10 = 0 10 A it_accurate10 = 1 B it_accurate10 = 1 C it_accurate10 = 0 D it_accurate10 = 0 it_sense10 = 0 it_sense10 = 1 it_sense10 = 0 it_sense10 = 1
68