NEUROBIOLOGY OF LANGUAGE
Edited by GREGORY HICKOK Department of Cognitive Sciences, University of California, Irvine, CA, USA
STEVEN L. SMALL Department of Neurology, University of California, Irvine, CA, USA
AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier SECTION J
SPEAKING CHAPTER 56 Word Production from the Perspective of Speech Errors in Aphasia Myrna F. Schwartz1 and Gary S. Dell2 1Moss Rehabilitation Research Institute, Elkins Park, PA, USA; 2University of Illinois, Urbana-Champaign, Beckman Institute, Urbana, IL, USA
A common strategy for understanding the inner 1995) and clinical profiles (Blumstein, 1973; Jefferies & workings of a complex system is to study the ways in Lambon Ralph, 2006; Romani & Galluzzi, 2005). which it breaks down. Research in naturally occurring Furthermore, computational models of word produc- speech errors (“slips of the tongue”) and research in tion have been used to simulate lesions in aphasia and aphasia both take advantage of this strategy. Language test competing theories of how errors arise. We exem- scientists study speech errors for insight into the work- plify this strategy with our own computational studies ings of the cognitive system that enables the skilled of naming and repetition. production of words and sentences. Neuroscientists study language deficits in aphasia to learn how the mechanisms of language processing are represented in 56.1 SPEECH ERRORS IN APHASIA: THE the brain. This chapter presents research that falls at NEUROLOGICAL TRADITION the intersection of these two historical traditions. The studies we highlight use computational models to sim- The long history of neurological research in aphasia ulate the cognitive mechanisms responsible for speech has been dominated by the classical syndromes or sub- errors in aphasia and large-scale lesion-symptom map- types. These include, but are not limited to, Broca’s ping to link those cognitive mechanisms with brain aphasia (BA), Wernicke’s aphasia (WA), Conduction regions. aphasia (CA), and Anomic aphasia (AA). Patients are Naturally occurring speech errors are the product of assigned to subtypes based primarily on how they per- momentary, functional disruption within a normal lan- form on clinical examination of expressive language guage system. Aphasia causes structural damage that (fluency, grammar, naming), receptive language (com- has persistent functional consequences. The most prehension of spoken words and sentences), and repe- important of these, for the present purposes, is exacer- tition (of words and sentence). Within this framework, bation of the normal tendency to occasionally select BA is considered primarily a disorder of expressive the wrong word or the wrong phonological segments speech, featuring dysfluency, altered and inconsistent when speaking (Dell, Schwartz, Martin, Saffran, & articulation (“apraxia of speech”), and simplified Gagnon, 1997; Ellis, 1985; Freud, 1953). Aphasia creates grammar. WA is primarily a disorder of speech com- vulnerability to speech errors in spontaneous speech prehension, CA is primarily a disorder of repetition, and in single word production tasks, such as spoken and AA is primarily a disorder of word retrieval. object naming and auditory word repetition. The high Speech error tendencies also enter into the diagnosis degree of experimental control afforded by naming of WA and CA. Patients with WA characteristically and word repetition tasks has made them the vehicle produce speech that is fluent but replete with errors of choice for studying how patients’ error types and (historically, “paraphasias”) that particularly distort error frequencies vary in relation to target properties the semantic and also phonological content, sometimes (Cuetos, Aguado, Izura, & Ellis, 2002; Kittredge, Dell, to a degree that obscures the intended meaning. CA Verkuilen, & Schwartz, 2008; Nickels & Howard, 1994, patients are less compromised in their functional
Neurobiology of Language. DOI: http://dx.doi.org/10.1016/B978-0-12-407794-2.00056-0 701 © 2016 Elsevier Inc. All rights reserved. 702 56. WORD PRODUCTION FROM THE PERSPECTIVE OF SPEECH ERRORS IN APHASIA communication. Their comprehension is quite good normal speech depends on the integrity of the and their speech is more semantically coherent. Their auditory-articulatory mapping served by the dorsal primary deficits are inability to repeat spoken speech, route (for discussion and evidence, see Buchsbaum reduced auditory-verbal short-term memory span, and et al., 2011). vulnerability to phonemic errors in all types of produc- Apart from the Dual Stream theory, the legacy of tion tasks. Whereas WA patients are generally the classical aphasia model is seen in modern empiri- unaware of their errors, errorful productions in CA are cal studies of the relationship between aphasia syn- often accompanied by successive repair attempts, dromes and speech error patterns. This issue was referred to as “conduit d’approache.” featured in Blumstein’s (1973) seminal monograph, “A The following speech samples exemplify paraphasic phonological investigation of aphasic speech.” The speech production in WA (Ex. 1) and CA (Ex. 2). The monograph’s linguistic analysis of phonemic parapha- patients were asked to describe the Cookie Theft picture, sias in the speech of those with Broca’s, Wernicke’s which shows a woman washing dishes at an overflow- and Conduction aphasia revealed surprising unifor- ing sink while a girl and boy, the latter perched on a mity in the patterns of phonological breakdown. For tipping stool, grab cookies from a cabinet (Goodglass example, in all three groups, phonemes were substi- & Kaplan, 1983). tuted more often than they were added or deleted, substitutions were strongly constrained by similarity (1) “So the two boys work together an one is sneakin’ in acoustic-articulatory features, and errors tended to around here, making his...work an’ his further funnas replace marked structures (i.e., more complex, less fre- his time he had.” quent, and later acquired) with less marked ones. (2) “Well this um...somebody’s...ah mather is takin Blumstein (1973) recognized that the mechanism for the...washin’ the dayshes [material deleted] and paraphasia might well be different in the three aphasic there’s a...then the girl...not the girl...the boy who’s groups, notwithstanding their linguistic similarities. In getting the cooking is on this ah...strool and’ startin’ to line with this, researchers who study phonemic errors fall off.” in WA often start from the assumption that these The aphasia syndrome classification is rooted in the patients suffer from a central lexical deficit that 19th century Broca Wernicke Lichtheim model of impacts the retrieval of phonological and semantic language and brain. The well-known claims of the information in language production, as it does in com- model are that motor and auditory speech engrams prehension. In contrast, researchers who study phone- localize to the left inferior frontal gyrus (IFG) and left mic errors in CA tend to emphasize that such errors posterior superior temporal gyrus (pSTG), respectively; occur even in tasks with an optional or obligatory these sensory and motor engrams are interconnected “direct” route that bypasses the lexicon (e.g., repeating through the arcuate fascisculus. IFG lesions give rise to and reading words). On this basis (and others), it has the expressive deficit in BA, pSTG lesions to the com- been argued that phonemic paraphasia in at least some prehension deficit in WA, and arcuate lesions to the CA patients arises subsequent to lexical processing repetition deficit in CA. The paraphasic speech pat- (i.e., at a postlexical phonological or phonetic stage of terns of WA and CA patients arise because the intact word production) (Caplan, Vanier, & Baker, 1986; motor speech engrams are deprived of governing Garrett, 1984; Goldrick & Rapp, 2007; Pate, Saffran, & input from the speech comprehension center due to Martin, 1987). direct damage (WA) or disconnection (CA) The notion that different mechanisms underlie the (Compston, 2006; Geschwind, 1965). phonemic paraphasias of CA, WA, and BA patients is The aphasia syndromes and classical model have an difficult to reconcile with the homogenizing impact of enduring legacy. The contemporary Dual Stream the- partial recovery. Experience suggests that some ory of auditory language processing (Hickok & patients who are diagnosed in the acute phase with Poeppel, 2004, 2007) bases its characterization of the WA or BA in time evolve either to the CA profile, with dorsal stream on neuroimaging data and evidence con- its characteristic pattern of phonemic paraphasia and cerning CA. The theory assigns to the dorsal stream conduit d’approache, or to AA. This reminds us that the function of mapping between auditory and articu- what looks to be a difference in kind (e.g., phonemic latory representations in speech. It identifies an area in errors, which resemble a plausible target, vs. neolo- the posterior Sylvian fissure at the temporoparietal gisms, which do not) may actually be one of degree. boundary (area Spt) that plays a central role in this Moreover, a particular diagnostic feature may become dorsal stream specialization and postulates that lesions salient only on resolution of another symptom that ini- here give rise to the CA symptom complex. Phonemic tially masked or distorted its expression, as when the paraphasia in the speech of CA patients features resolution of apraxia of speech reveals the presence of importantly in the theory, providing evidence that phonemic paraphasia in BA.
J. SPEAKING 56.2 TWO STAGES OF LEXICAL ACCESS IN PRODUCTION 703
In our own cognitive studies of phonological and Semantic Semantics other errors in aphasia, we classify and interpret errors activation without regard to the speaker’s aphasia subtype by applying a theoretical model that ascribes word-level substitutions to the lexical-semantic stage of lexical access and sublexical deviations to the lexical- L-Level L Level phonological stage. The following section explains this selection further.
Phoneme 56.2 TWO STAGES OF LEXICAL ACCESS Phonemes IN PRODUCTION selection
In the 1970s and 1980s, linguistic analysis of normal FIGURE 56.1 The generic two-stage account of spoken naming. Distributed semantic concepts map onto lexical nodes, which, in speech errors gave rise to a seminal psycholinguistic turn, map onto phonemes. (From Rapp and Goldrick (2000). Reprinted theory of sentence production (Dell, 1980; Fromkin, with permission from the publisher (American Psychological Association)). 1971; Garrett, 1975, 1980; MacKay, 1972; Shattuck- Hufnagel, 1979; Stemberger, 1985). The theory holds that planning a sentence involves the construction of with units or nodes representing known words1; and a multiple successive representations. A conceptual component that represents articulable phonemes. He semantic representation is constructed first, followed hypothesized that damage-induced reduction in the by two linguistic levels that use syntactic and phono- flow of activation between the lexical-semantic system logical information, respectively. Construction of the and the speech output lexicon would compromise the two linguistic levels involves building structure- competitive dynamics among nodes in the speech out- representing frames and inserting content that is put lexicon. This would impact the spread of activa- retrieved from the mental lexicon. The lexical units tion down to the phoneme level, resulting in that fill slots in the syntactic frame are semantically utterances comprising “correct phonemes intermingled and syntactically specified words. The units that with inappropriate ones” (p. 132). Addressing the fill slots in the phonological frame are phonological sources that contribute to the activation of inappropri- segments. It follows that entries in the mental lexical ate phonemes, Ellis discussed top-down, cascading have multilevel representations and are retrieved in activation from the lexical-semantic layer, feedback stages. Speech errors, according to the theory, arise loops between phonemes and output lexicon, and per- when the wrong word/segment is retrieved from the sistence of activation from prior utterances. lexicon, or the right word/segment is retrieved but Addressing why some neologisms have strong overlap inserted into the wrong slot. with the target while others are more remote, he spec- The application of this theory to aphasia followed ulated that the mix of inappropriate and appropriate quickly. Researchers used the sentence planning phonemes might be related to patient factors (i.e., framework to explain grammatical deficits (Saffran, severity of the lexical access deficit) as well as target 1982; Schwartz, 1987) and the multistage lexical factors (long, infrequent targets more likely to yield retrieval model to explain aphasics’ errors in word remote neologisms). production (Buckingham, 1980, 1987; Butterworth, Ellis’ informal model laid the groundwork for later 1979; Ellis, Miller, & Sin, 1983; Garrett, 1984; Miller & computational models that implemented many of its Ellis, 1987; Pate et al., 1987; Schwartz, Saffran, Bloch, & features. The basic architecture of several such models Dell, 1994). The incorporation of interactive activation is shown in Figure 56.1 (from Rapp & Goldrick, 2000). principles into multistage lexical models added to their This is a generic two-stage account of spoken naming, range and power for explaining aphasic production in which distributed semantic concepts map onto lexi- errors. An early, excellent example is Ellis’ (1985) pro- cal nodes, and these, in turn, map onto phonemes. The posal for how disruption of lexical dynamics could first stage of naming is about selecting the lexical node give rise to phonemic errors. that corresponds to the semantic concept; the second Ellis’s informal (i.e., nonimplemented) model of stage is about selecting the phonemes. The many-to- word retrieval featured three cognitive components: a one mapping from distributed semantics to lexical conceptual semantic system; a speech output lexicon units creates competition among semantic neighbors
1Ellis (1985) chose to remain agnostic on the question of whether the units in the “speech output” lexicon are the same lexical units that are accessed in comprehension. This was an important issue at the time (Allport, 1984) and it remains so today (Gow, 2012; Hickok, 2012).
J. SPEAKING 704 56. WORD PRODUCTION FROM THE PERSPECTIVE OF SPEECH ERRORS IN APHASIA and sets the stage for semantic and other lexical substi- between discrete-stage theories, which do not allow tution errors to arise during step 1. Competition at the any cross-stage influences (Levelt, Roelofs, & Meyer, phoneme level, induced by processes described by 1999) and highly interactive models in which the map- Ellis (1985), invites phoneme substitution errors, most ping between meaning and the output of lexical forms of which create nonwords. Models that use a noisy is achieved in a single settling step (Plaut & Shallice, activation function can produce errors even in the 1993). unlesioned (default) state. Models that simulate apha- In its current form, the interactive two-step model sia do so by altering parameters of the model to reduce assumes that aphasia affects naming by weakening the efficiency of activation spreading and thus increase connections between semantics and L-level (s-weight the impact of noise. lesion) and/or those between L-level and phonemes (p-weight lesion). S-weight lesions instantiate a lexical- semantic disorder; p-weight lesions instantiate a 56.2.1 The Interactive Two-Step Model of lexical-phonological disorder. Most patients have both, Lexical Access in Naming but the severity of each lesion can vary independently of the other, and the two together determine what The generic model glosses over important differ- types of errors are seen and in what proportions. ences in how alternative models formalize the seman- Our evaluations of the model have mostly used tic level and the lexical level, and how much they computational case series methods (Dell et al., 1997; integrate or separate the processing that goes on at Foygel & Dell, 2000; Schwartz, Dell, Martin, Gahl, & each level (for discussion, see Rapp & Goldrick, 2000). Sobel, 2006). This starts with systematic collection of Dell’s interactive two-step model of normal and apha- behavioral measures from a large sample of chronic sic naming (Dell & O’Seaghdha, 1991; Dell et al., 1997; left hemisphere stroke survivors representing all the Foygel & Dell, 2000) occupies a middle ground major subtypes. All have self-reported language defi- cits confirmed by the Western Aphasia Battery (WAB; TABLE 56.1 Taxonomy of Error Typesa,b Kertesz, 1982) or other language evaluation. Among the battery of tests that each participant performs is Semantic: Real word response that is a synonym, category the 175-item test Philadelphia Naming Test (PNT) coordinate, superordinate, subordinate, or strong associate of the target (e.g., bus for van; leash for dog) (Roach, Schwartz, Martin, Grewal, & Brecher, 1996), which assesses basic-level naming of line-drawn Mixed: Real word response that qualifies as a semantic error and objects. PNT responses are categorized into six catego- that meets the criterion for phonological similarity (e.g., snake for snail) ries: correct responses and the five error types shown in Table 56.1, which are expressed as proportions (rela- Formal: Real word response that meets the criterion for phonological tive to all responses). For each patient in the series, the similarity (e.g., shaft for fish) model is fit by finding the values of the s-weight and Unrelated: Real word response that is neither semantically nor p-weight parameters that give the best match to the phonologically similar to the target (e.g., camp for banana) patient’s actual response proportions (see Table 56.2 Nonword: String of phonemes that does not constitute a word in the for example). The model is evaluated for the goodness language. Most such errors pass the phonological similarity criterion, of the quantitative fits and whether there are patients and, depending on the study, this can be a requirement for inclusion with particular response patterns that the model can- (e.g., goath for goat (phonologically similar); tuss for cane (dissimilar) not fit well (called “deviating patterns”). a Roach et al. (1996). A study performed in 2006 fits the interactive bThe criterion for phonological similarity is that response and target share at least one phoneme in corresponding syllable or word position or two phonemes in any position, two-step model to the naming response proportions not counting unstressed vowels. of 94 patients with diverse aphasia presentations
TABLE 56.2 Naming Response Category Proportions from a Sample Patient from the Moss Databasea and the Model’s Simulated Proportions Generated When Best Fitting Parameters Are Chosen
Response categories Correct Semantic Formal Mixed Unrelated Nonwords
Examples “cat” “dog” “cabin,” “mat” “rat” “log” “cag,” “gat”
Patient naming 0.49 0.07 0.04 0.05 0.04 0.30
Model naming 0.52 0.07 0.08 0.02 0.04 0.26
aMirman et al. (2010). Best fitting parameter values: s 5 0.020; p 5 0.016 (root mean squared deviation 5 0.03). The example responses assume that the target is a picture of a cat.
J. SPEAKING 56.3 MODEL-INSPIRED LESION ANALYSIS OF SEMANTIC ERRORS 705
(Schwartz et al., 2006). The model explained 94.4% of D’Esposito, Aguirre, & Farah, 1997; Whitney, Kirk, the total variance in naming proportions. There were O’Sullivan, Lambon Ralph, & Jefferies, 2012). two deviating patterns. One was attributable to the Although most of the evidence for an executive- model’s failure to account for perseverations. The deficit account of semantic errors comes from experi- other deviant pattern featured low rates of correctness mental paradigms that exaggerate semantic competi- with errors largely restricted to the categories of tion and, hence, the need for executive control, semantic errors and omissions. This “pure semantics” Jefferies and Lambon Ralph (2006) argued that an pattern of deviation highlights limitations in the mod- executive deficit may be responsible for semantic el’s treatment of semantic errors (Dell et al., 1997; errors in conventional naming as well. They supported Foygel & Dell, 2000; Rapp & Goldrick, 2000). We con- this with evidence that the naming errors of stroke sider this further in the following section. aphasics included semantic errors like apple - “worm,” where target and error are related to one another thematically rather than taxonomically, as is 56.3 MODEL-INSPIRED LESION typical of most semantic errors (e.g., apple - pear). ANALYSIS OF SEMANTIC ERRORS Jeffries and Lambon Ralph hypothesized that thematic error production in conventional naming is symptom- The case series methods we used in the model- atic of failure to access semantic representations in fitting studies is well-suited to group-level voxel-based accordance with task goals—an executive function def- lesion-symptom mapping (VLSM: Bates et al., 2003; for icit they attributed to damage in left frontal and/or discussion of case series methods, see Schwartz & Dell, temporoparietal regions (Corbett et al., 2011; Noonan 2010). VLSM aims to identify brain voxels anywhere in et al., 2010). the brain that carry a correlation between lesion status We examined these alternative accounts in a study and symptom severity of sufficient strength to pass a that used VLSM to map the lesions that correlated statistical threshold that corrects for the thousands of with rates of taxonomic and thematic semantic errors voxels tested. VLSM was inspired by functional neuro- on the PNT (Schwartz et al., 2011). As noted, taxo- imaging, and the resulting brain maps resemble those nomic errors are the most common type of semantic produced in fMRI studies. Where fMRI maps tell us error in naming; such errors are categorically related which areas activate during a particular cognitive task to the target as coordinate, superordinate, or subordi- or process, VLSM maps tell us where lesions cause nate. Much rarer is the thematic semantic error in derailments in that task or function. We used this tech- which the target and error are from different categories nique to address controversies surrounding the cogni- but often play complementary roles in events and sen- tive and neural basis of semantic error production. tences. Most error-coding schemes combine taxonomic The interactive two-step model associates all seman- and thematic errors into a general semantic error cate- tic errors with the transmission of activation from gory; this is also true of the PNT coding scheme semantic to lexical representations (i.e., the error locus (Table 56.1). For the 2011 study, we subdivided the is postsemantic and production-specific). Many, ourselves semantic error corpus by means of expert and norma- included, have questioned the adequacy of this tive judgments. account. Studies have shown that some patients, Separate counts of taxonomic and thematic errors including some with the “pure semantics” naming pat- were derived for the 86 aphasic individuals who par- tern, exhibit semantic difficulties in comprehension as ticipated, and their shared variance was regressed out well as production (Cloutman et al., 2009; Gainotti, of each measure. For both measures, we also regressed Miceli, Caltagirone, Silveri, & Masullo, 1981; Hillis, out the shared variance attributable to performance on Rapp, Romani, & Caramazza, 1990; Rapp & Goldrick, a semantic comprehension test with a high require- 2000; Schwartz et al., 2006). This raises the possibility ment for semantic control (Camel and Cactus Test or that in stroke aphasia, as in some degenerative demen- CCT; Bozeat, Lambon Ralph, Patterson, Garrard, & tias, semantic naming errors might be due to compro- Hodges, 2000; Jefferies & Lambon Ralph, 2006). We mised semantic representations. A second possibility is then conducted separate VLSM analyses on each of the that aphasics’ semantic errors result from an executive dependent measures. function disorder that affects the regulation of com- As expected, taxonomic errors (N 5 645) predomi- petition in the semantic system or more generally nated over thematic errors (N 5 134). Many patients (Badre & Wagner, 2007; Corbett, Jefferies, & Lambon made all or mostly taxonomic errors, but none did the Ralph, 2011; Hamilton, Martin, & Burton, 2009; Jefferies & reverse. Anatomically, however, there was a clear Lambon Ralph, 2006; Noonan, Jefferies, Corbett, taxonomic-thematic double dissociation (Figure 56.2). & Lambon Ralph, 2010; Schnur, Schwartz, Brecher, & Taxonomic errors localized to the left anterior tempo- Hodgson, 2006; Schnur et al., 2009; Thompson-Schill, ral lobe, including clusters of supra-threshold voxels in
J. SPEAKING 706 56. WORD PRODUCTION FROM THE PERSPECTIVE OF SPEECH ERRORS IN APHASIA
5.43 (A) (B) (C)
3.58 5.65 (D) (E) (F)
3.66
FIGURE 56.2 (A C) The VLSM t-maps for taxonomic semantic errors, residualized for thematic semantic errors and for a measure of seman- tic comprehension. (D F) The map for thematic semantic errors, residualized for taxonomic errors and semantic comprehension. Both maps were thresholded at a false discovery rate correction, q 5 0.02; critical t-value for taxonomic errors was 3.58 and critical t-value for thematic errors was 3.66. Maps are rendered on the MNI-space Colin27 template, at x-coordinates X 5260 (A, D); X 5256 (B, E); X 5252 (C, F). (Redrawn from Schwartz et al. (2011)). temporal pole (BA 38) and anterior portions of the some supporting evidence for this position (Kale´nine middle and inferior temporal gyri (BA 20 and BA 21). et al., 2009; Mirman & Graziano, 2012), much more Thematic errors, in contrast, localized to left temporo- research is required to establish whether the ATL and parietal cortices (TPCs), with the largest concentration TPC process complementary semantic information, of supra-threshold voxels in the angular gyrus (BA 39). and whether these regions are best conceived as multi- The ATL and TPC regions identified in these analy- modality convergence zones or amodal hubs (Binder & ses are known from both neuropsychological and neu- Desai, 2011; Patterson et al., 2007). Schwartz et al.’s roimaging research to play important roles in (2011) finding that regions within the left ATL and left multimodality semantic processing. Both have been TPC are uniquely or disproportionately concerned cited as possible semantic “hubs” or high-level “con- with semantically driven production would seem to vergence zones” (Binder & Desai, 2011; Binder, Desai, argue for modality specificity, at least within left hemi- Graves, & Conant, 2009; Damasio, Tranel, Grabowski, sphere sectors of these bilateral networks (for related Adolphs, & Damasio, 2004; Patterson, Nestor, & evidence and discussion, Schwartz et al., 2009; Walker Rogers, 2007). However, because the analyses by et al., 2011). However, Ueno, Saito, Rogers, and Schwartz et al. (2011) statistically controlled for seman- Lambon Ralph (2011) have proposed an alternative tic comprehension, the implication for cognitive theory account of the ATL locus of semantic errors in naming would seem to be that taxonomic and thematic seman- that may be compatible with the amodal account of tic errors arise at a level of the production system that ATL semantics. does not share resources with semantic comprehen- Finally, we return to the suggestion that the TPC is sion. This goes along with the model’s claim that involved in executive control, and that the production semantic errors arise subsequent to semantic proces- of thematic errors in naming is symptomatic of an sing in the course of retrieving the correct word. inability to access semantic processing in accordance Schwartz et al. (2011) suggested that the anatomical with task demands. Two pieces of evidence in the double dissociation between taxonomic and thematic Schwartz et al. (2011) study argue against this interpre- errors might be evidence of complementary semantic tation. First, regressing out the shared variance with hubs in ATL and TPC, the former specialized for taxo- CCT scores controlled for deficits in task-appropriate nomic (similarity) relations and the latter specialized semantic selection affecting comprehension. Second, for thematic (contiguity) relations. Although there is the study also included a VLSM of semantic
J. SPEAKING 56.4 SUMMATION DUAL-ROUTE MODEL OF REPETITION 707 circumlocution errors (e.g., apple - “they’re crunchy p-weight, hereafter, p) should be predictive of his or her to eat”). From a semantic control perspective, circum- accuracy in repeating words. This claim has been locutions and thematic errors are both “off task” (cir- tested in multiple single case and case series model- cumlocutions describe rather than name the object; fitting studies (Dell et al., 1997; Hanley, Dell, Kay, & thematic errors express a frequent associate). Baron, 2004; Martin, Dell, Saffran, & Schwartz, 1994; Nevertheless, the VLSM findings indicate that circum- Nozari, Kittredge, Dell, & Schwartz, 2010), resulting in locutions are cognitively closer to taxonomic errors in successive refinements of the model and the theory of that they strongly localize to the ATL. These data repetition that frames it. oppose the view that the left TPC subserves semantic The current model instantiates a summation dual- control in a task general manner. Either it plays no route theory of repetition. Words are repeated via a lex- role in semantic control or its role is modulated by ical route and a nonlexical route. The lexical route cor- task (production rather than comprehension) and the responds to step 2 in the naming model; the nonlexical type of semantic information that is regulated. route is contained in the connections from auditory As we noted, the study by Schwartz et al. (2011) of input directly to the output phoneme units. Activation semantic errors confirmed the model’s claim that these generated over both routes combines in the phoneme errors arise at a postsemantic, production-specific stage units to generate the final response. of lexical access. It reached this conclusion by control- An example will make the summation dual-route ling for alternative sources of semantic errors. model’s mechanisms and parameters more concrete. However, insofar as all of these alternative sources con- Let us start with the patient whose error pattern in tribute to observed semantic error counts, we would naming is given in Table 56.2. This individual makes expect them to be absorbed into the fitted s-weight many nonword errors in naming (0.30). In the model, (hereafter, s) parameter and reflected in lesion sites that these errors can only arise during the retrieval of pho- correlate with s. We examined this as part of a recent nological units; therefore, a high rate of such errors investigation of the lesions that correlate with the suggests difficulty in lexical-phonological retrieval or model parameters (Dell, Schwartz, Nozari, Faseyitan, & in other processes involving sublexical units. The low Coslett, 2013). We found that the voxels that correlated value of the fitted p parameter (0.016, where more than with s were widely distributed in the left hemisphere; 0.040 is normal) captures this difficulty in the model. If in addition to ATL and TPC, there was a large concen- word repetition were performed using the lexical route tration of such voxels in the prefrontal cortex (middle alone, then we would expect that this individual and inferior frontal gyri). Whether these prefrontal would make a comparable number of nonword errors regions are part of a large distributed semantic-feature in that task as well, because lexical-route word repeti- network or play a specific role in semantic control is a tion is, in the model, nothing more than the second question for future research. What is clear, though, is step of naming. We can use the model to make this that s itself is affected by processing at the semantic prediction precise by running just that step of the level in addition to the production-specific mapping model using the s and p parameters determined from from semantics to words. the person’s naming performance: (3) Predicted word repetition from lexical route model for 56.4 SUMMATION DUAL-ROUTE MODEL sample patient: OF REPETITION Correct Semantic Formal Mixed Unrelated Nonwords
This section takes a detailed look at the model’s 0.65 0.00 0.07 0.01 0.00 0.27 account of phonological errors in naming and its close relative, repetition. As in the discussion of semantic Notice that the predicted proportion of nonwords errors, we start by describing the relevant components for this patient from this model of repetition is similar of the model and how the implemented model was to what it was for naming (Table 56.2). It turns out, used to fit data from patients, followed by examination though, that a pure lexical-route approach to repetition of the neural loci for the parameters fit by the model. does not work in this case. The individual’s actual In the development of the interactive two-step word repetition is considerably better than predicted: model, naming and repetition have been linked through the assumption that the model’s second step (4) Obtained word repetition: (lexical-phonological retrieval) is used in both; there- fore, for a given patient, the status of lexical- Correct Semantic Formal Mixed Unrelated Nonwords phonological retrieval in naming (as indexed by the 0.95 0.00 0.01 0.00 0.00 0.04 proportion of phonological errors, or by the fitted
J. SPEAKING 708 56. WORD PRODUCTION FROM THE PERSPECTIVE OF SPEECH ERRORS IN APHASIA
Thus, it is possible that a nonlexical route to repeti- The dual-route model of word repetition has been tion is contributing to word repetition. We can show tested in studies that have duplicated the steps that we this by first measuring the effectiveness of this per- just illustrated with actual patient samples (Abel, son’s nonlexical route, and then by using the model to Huber, & Dell, 2009; Dell, Martin, & Schwartz, 2007). see if that route can explain the good repetition In these studies, word repetition performance is often performance. better predicted by the dual-route than a pure lexical To measure the nonlexical route, we test the indivi- route, or pure nonlexical route model. Moreover, the dual’s ability to repeat nonword stimuli that are of assumption that the two routes sum to create the out- similar phonological complexity as the naming targets. put (Hillis & Caramazza, 1991) appears necessary to This yields: explain the findings of Nozari et al. (2010). They found that in a large group of patients, the contribution of (5) Obtained nonword repetition: the nonlexical route was strong enough to reduce the rate of nonword errors in repetition (in comparison to Correct Lexicalization Errors Nonword Errors naming), yet its contribution did not detract from the 0.37 0.33 0.30 strength of the lexical frequency effect. By this we mean that in repetition, just as in naming, low- frequency targets elicited more nonword errors than Notice that repetition of nonwords is considerably high-frequency targets; the difference for high- versus worse (0.37 correct) than words (0.95 correct), a consis- low-frequency targets was as large for repetition as for tent finding in aphasia that demonstrates at least some naming. lexical contribution to word repetition. Because of this fact, we know that the nonlexical route cannot alone explain word repetition ability. 56.4.1 Behavioral and Neural Predictors of Next, we set up the model so that it has a nonlexical Dual-Route Model Parameters route to repetition, that is, connections between the auditory representation of the stimulus and phoneme The essential claim of the dual-route model is that nodes that mediate its production (e.g., as in Hanley word repetition is achieved by the sum of activations et al., 2004). Specifically, we make the strength of these over the lexical route (largely determined by parame- connections (the model’s nl parameter) just as strong ter p) and the nonlexical route (parameter nl). Thus, as necessary to simulate the patient’s nonword repeti- aphasics’ success in repeating words should be pre- tion ability: dicted primarily by nl and p. Dell et al. (2013) tested this prediction with data collected from 103 patients. (6) Predicted nonword repetition (nl 5 0.026; s 5 0.020; They developed a regression model with word repeti- p 5 0.016): tion accuracy as the dependent variable, and para- meters, p, nl, and a variety of neuropsychological Correct Lexicalization Errors Nonword Errors measures (nonverbal semantic comprehension, verbal 0.36 0.22 0.42 semantic comprehension, short-term memory, apraxia of speech) as independent variables.2 The resulting model had adjusted R2 5 0.61, with strong and signifi- Now, we can finally simulate dual-route word repe- cant contributions from both model parameters, nl and p. tition by running the model so that it combines the nl independently contributed 14% of the variance activation generated by the lexical route and that com- explained; p independently contributed 5%, a smaller ing from the nonlexical route: but significant value. A composite measure of verbal (7) Predicted word repetition from summation dual-route semantic comprehension also contributed a small model (nl 5 0.026; s 5 0.020; p 5 0.016): amount to the model, demonstrating an indirect influ- ence of semantics on repetition, a result expected from Correct Semantic Formal Mixed Unrelated Nonwords the interactive property of the model. None of the other variables was significant. 0.91 0.00 0.02 0.00 0.00 0.07 When Dell et al. (2013) used the same dataset to explore the relationship between p and nl, they found In this case, the predicted repetition (0.91 correct) is an association between the parameters (r 5 0.46) that quite close to what was obtained (0.95), thus support- was not expected. The model assumes that each ing the dual-route approach to repetition. parameter indexes the strength of a different set of
2All of the behavioral and lesion-mapping analyses performed by Dell et al. (2013) involving fitted parameters used the square-root transform of the values, which made parameter variation more equal across the scale and also made the distributions more normal.
J. SPEAKING 56.4 SUMMATION DUAL-ROUTE MODEL OF REPETITION 709 weights, and that each set of weights can be damaged Parameters reflect the overall response distribution in independently of the others. This assumption holds for ways that can be quite complex, so it is of interest to s and p: these parameters were indeed uncorrelated in know how the two sets of results compare. The VLSM this data set. However, the scatterplot relating p and nl and VPLM analyses of phonological processes tell a revealed that patients with weak p-weights also tended similar story, and so we restrict the present discussion to have weak nl weights, and there was a notable lack to the VLPM results reported by Dell et al. (2013). of patients with low values of nl (reflecting poor non- Figure 56.3 shows the voxels whose damaged status word repetition) who had high values of p. predicted the values of the p and nl parameters in the To further explore this association between the study by Dell et al. (2013). The two lesion maps over- parameters, as well as the contributions of other psy- lap substantially, with most of the shared voxels cholinguistic and neuropsychological measures in our occupying the anterior, inferior parietal lobe (supra- test battery, Dell et al. (2013) conducted regressions marginal and postcentral gyri). Voxels unique to nl with each parameter as the dependent variable. They were found in superior temporal auditory regions, spe- found that the sole (positive) predictors of nl were cifically STG, the posterior third of the planum tem- parameter p and auditory discrimination. p was pre- porale, and the cortex at the juncture of the parietal dicted positively by nl and negatively by the presence and temporal lobes, including area Spt. Voxels unique of speech apraxia (i.e., presence of apraxia predicts to p occupied more superior regions of the parietal low values of p [weak p-weights] reflecting many non- lobe along with portions of the insula, which is consid- word errors). These results suggest that the nonlexical ered important for speech articulation (Dronkers, repetition route, whose strength is indexed by nl, 1996). Notice how these findings accord with the pre- reflects a production ability that is shared with param- viously presented behavioral data in which the values eter p, along with the ability to process auditory input. of nl and p were positively correlated, but nl was The association of low p with speech apraxia indicates uniquely predicted by auditory discrimination and p the involvement of some sort of articulatory motor was uniquely predicted by the presence/absence of process in the mapping from words to phonemes. apraxia. Dell et al. (2013) also identified the voxels These relationships have been further studied with associated with word repetition, and these were found VLSM (Schwartz, 2014; Schwartz, Faseyitan, Kim, & to be closely associated with the nl and p voxels, as Coslett, 2012) and voxel-based lesion-parameter map- expected from the dual-route approach to word repeti- ping (VLPM; Dell et al., 2013). “Symptom” in this con- tion. To be specific, the dual-route model predicts that text refers to one of the response proportions word repetition should be most closely related to the generated from naming or repetition. “Parameter” sum of the nl and p parameters, because the model’s refers to the values of s, p,ornl that the model assigns output sums nonlexical and lexical sources of activa- to individuals based on their response proportions. tion. Figure 56.4 illustrates the close similarity between
(A) (B)
2.69 4.73 2.97 5.59
FIGURE 56.3 VLPM analyses of p-weight (red-yellow scale) and nl-weight (light blue to dark blue), both thresholded at a false discovery rate correction (q 5 0.05), rendered on the MNI-space Colin27 template. The critical t-value for p-weight is 2.69, and for nl-weight it is 2.97. (A) A sagittal slice at MNI coordinate x 5254. (B) A coronal slice at MNI coordinate y 5228. (Based on data reported in Dell et al. (2013) with permission from the publisher (Elsevier)).
J. SPEAKING 710 56. WORD PRODUCTION FROM THE PERSPECTIVE OF SPEECH ERRORS IN APHASIA
(A) (B) (C) (D)
FIGURE 56.4 Lesion masks derived from the VLSM analyses of repetition accuracy (in red) and the VLSM analysis of sum nl 1 p (in blue; over- lap shown in green). Statistical maps used to create masks were thresholded at a false discovery rate correction (q 5 0.05) and rendered on the MNI-space Colin27 template. The critical t-value for repetition accuracy was 2.42 and for sum nl 1 p it was 2.69. Sagittal slices are at MNI coordinate x 5254, x 5246, x 5238; coronal slices are at MNI coordinate y 5228. (From Dell et al. (2013) with permission from the publisher (Elsevier)). repetition voxels and those that associated with the Goldstein, 1992) assumes that phonological forms con- nl 1 p sum. sist of temporally coordinated gestures rather than Parameter p is not just associated with word repeti- abstract discrete segments. This approach to phonol- tion, though. Recall that it is derived from naming, not ogy is increasingly being used to interpret both lin- repetition. Specifically, in the model, p indexes step 2 guistic and psycholinguistic data (Goldstein, Pouplier, of the lexical access process in naming, as determined Chen, Saltzman, & Byrd, 2007). largely on the basis of phonemic errors. It should come The distinct functions of nl and p also appear to be as no surprise, then, that a VLSM of phonemic errors grounded in sensory-motor processes: nl’s distinct in naming yielded a map very similar to the one function includes the auditory processing of speech shown here for p (i.e., centered on frontoparietal and its translation into phonological units, whereas p’s sensory-motor cortices) (Schwartz et al., 2012). What is distinct functions include the mapping from lexical puzzling, though, is that p was not found to be associ- forms to phonemes (i.e., the original p-weights) and ated with Wernicke’s area and surrounding posterior aspects of articulation. The partial sensory-motor char- temporal and TPCs that are the long-hypothesized acterization is supported by nl’s positive association locus for lexical-phonological forms (Wernicke, 1874/ with auditory discrimination ability and the associa- 1969; and more recently, Graves, Grabowski, Mehta, & tion of apraxia of speech with low values of p. Gupta, 2008; Indefrey & Levelt, 2004; Wilson, Isenberg, Dell et al. (2013) also discussed the necessity of & Hickok, 2009). We explore this again in the follow- revising the model’s treatment of the s parameter in ing section of this chapter. First, though, we consider light of the broadly dispersed temporal and frontal the implication of these findings for the model itself. areas comprising the lesion map for s (see their We learned from the regression and lesion mapping Figure 7). They suggested that s not only is the lexical- analyses that parameters nl and p are not as separate semantic connections but also includes semantic as the model originally claimed. On this basis, Dell representations and processes that control them. This et al. (2013) suggested a revision of the model that treatment of s retains the original model’s claim that s expands both p’s and nl’s functions such that they is a separate parameter from p, as supported by the have a common function and distinct ones. The com- finding of no correlation between the parameter values mon function includes phonological representation. In and little similarity in their brain maps. the implemented model, the units of phonological representations were phonemes. However, finding that the p nl overlap localizes to parietal cortices (super- 56.5 IMPLICATIONS FOR marginal and postcentral) generally associated with NEUROCOGNITIVE MODELS OF sensory-motor processes invites speculation that pho- LANGUAGE nological representations have a sensory motoric char- acter (Gow, 2012). This is not inconsistent with The model’s account of cognitive processes in nam- linguistic theory, where the role of audition and articu- ing and repetition and the lesions that compromise lation in shaping phonological generalizations is read- those processes have much in common with the Dual ily acknowledged (Cole & Hualde, 2003). Moreover, Stream theory (Hickok & Poeppel, 2004). In particular, the theory of articulatory phonology (Browman & the distribution of nl and p is consistent with the
J. SPEAKING 56.5 IMPLICATIONS FOR NEUROCOGNITIVE MODELS OF LANGUAGE 711 dorsal route’s central role in the repetition of non- sound and articulation, whereas the ventral path is words (here, represented by nl) and words (both nl specialized for the unsystematic mapping between and p). More specifically, we propose that p and nl word meaning and word form. together represent the action of the dorsal stream and Lichteim 2’s dorsal pathway specialization makes it its role in the repetition of verbal stimuli (Baldo, well-suited for explaining our finding that nl and p Katseff, & Dronkers, 2012; Buchsbaum et al., 2011; mapped to dorsal stream areas, and particularly the Fridriksson et al., 2010; Hickok & Poeppel, 2004). The SMG, which the Lichtheim 2 model associates with dorsal stream is largely distinct from the processing extracting and representing the statistical structure associated with parameter s, which indexes semantic shared between speech sounds and phonotactics. processes (in the ventral stream) and their use during Moreover, the role of p in naming is expected in that production. Given that parameter p is derived solely model from the fact that the dorsal path also makes a from performance in the naming task, this means that contribution to naming. The model also provides a the part of the dorsal stream associated with p plays good account of our finding that parameter s is an important role during language production from strongly associated with temporal and frontal cortex. meaning. Finally, the fact that verbal semantic ability has a posi- To flesh out this proposal, we now turn to two tive effect on word repetition is expected from the recent models that extend the Dual Stream framework interactive property of the model. in ways that make useful contact with the present Hickok’s (2012, 2014) HSFC model links psycholin- approach. The Lichtheim2 model of Ueno et al. (2011) guistic approaches to production theories of motor and Hickok’s (2012, 2014) hierarchical state feedback con- control in speech. Word forms are retrieved and spo- trol (HSFC) model are computational, neurally speci- ken through a control network involving phonological fied models that deal with both word retrieval from targets at both the syllable and phoneme level; the con- meaning and repetition of phonological forms. Each trol network involves corresponding motor programs has points of convergence with the models and data for these units and acoustic and somatosensory feed- presented in this chapter. back to the target representations (Guenther, 2006). The Lichtheim 2 model of Ueno et al. (2011) is a multi- The crucial part of the HSFC model for our purposes leveled parallel-distributed processing (PDP) model of is that it hypothesizes different brain circuits for pro- language that links model processes with dorsal and gramming syllable and phoneme-level units. The ventral pathways in the brain. The name, Lichtheim 2, retrieval of whole syllable units involves a mapping reflects the fact that this model, like its namesake, is between Wernicke’s area (pSTG), which contains audi- concerned with the major aphasia syndromes. Ueno tory syllable targets, and BA44, which is part of et al. trained the model to name, comprehend, and Broca’s area containing syllable motor programs. repeat a large set of words, after which they lesioned it Retrieval of phoneme units proceeds through the ante- at different points along its dorsal and ventral route to rior supramarginal gyrus, which contains somatosen- simulate the qualitative profiles of impaired and sory phoneme targets, to vBA6-M1 for phoneme motor spared task performance typical of BA, WA, and CA. programs. To appreciate how Lichtheim 2 relates to our model, The HSFC model’s phonemic control circuit corre- some further details are necessary. In Ueno et al.’s sponds well with our lesion map for p and with the implementation, the dorsal path includes auditory cor- overlap of p and nl. Parameters p and nl derive largely tex and surrounding areas, the inferior SMG, and from phonemic errors, that is, responses such as “cap” insular-motor cortex. The ventral path also links audi- or “cag” for CAT. So the fact that these parameters are tory and insular-motor cortex, but through the tempo- more in line with the phonemic control circuits than ral and frontal lobes. The model’s connections link the with the syllabic control circuits supports this funda- various layers of these pathways, and their strengths mental division in the HSFC model and associates its are learned through training. To repeat words, the sensorimotor representations of phoneme targets with model learned to map from the auditory input to phonological error production. This allows us to motor output layers; to produce them, it learned to explain why these parameters mapped to parietal map from the semantic layer (associated with the ven- regions rather than to Wernicke’s area. tral anterior temporal lobe) to motor output. Because of the model’s learning algorithm and its interactive architecture, which allows for activation to flow bidi- 56.6 CONCLUSION rectionally, both the dorsal and ventral paths contrib- ute to both repetition and naming. Nonetheless, there The central notion of this chapter is that data from is some specialization in the paths. The dorsal path is aphasia provide a key link between behavioral studies more important for the systematic mapping between of speech errors and neurocognitive models of
J. SPEAKING 712 56. WORD PRODUCTION FROM THE PERSPECTIVE OF SPEECH ERRORS IN APHASIA language production. We reviewed how patterns of in lexical-phonological retrieval (DeLeon et al., 2007; slips by unimpaired speakers led to models that identi- Lu¨ ders et al., 1991) but are generally spared in fied representations and processing mechanisms in aphasia-producing strokes. Thus, our methods may production. These models were then made concrete by underestimate the contribution to aphasic speech developing computational implementations, such as errors from these (and other) underpowered brain our interactive two-step model. Once these computa- regions. tional models had been set up to explain speech error From another perspective, our case series samples patterns, they could be altered (or “lesioned”) to may suffer from being overly inclusive. By grouping explain aphasic error patterns. Our model incorporates together evidence from patients of all different types, a two-step lexical access process and a nonlexical pro- our analyses may have obscured differences in the cess for accessing phonology directly from auditory mechanisms and corresponding lesion sites of phono- input. It attributes the variety of aphasic error patterns logical errors produced by patients with CA versus to variation in model parameters: the semantic (s), BA, for example (Romani & Galluzzi, 2005). This goes phonological (p), and nonlexical (nl) parameters. In a back to the long-standing debates about the relevance final step, we have mapped the lesions that correlate of the clinico-anatomical model, with which we began with error patterns and parameters in large numbers the chapter. However, there is good reason to be opti- of individual patients to link model processes with mistic that the resolution to these debates is within brain areas. reach. Case series methods can accommodate increases Results of these studies have ramifications for con- in both breadth and depth of analysis (i.e., broader temporary neurocognitive accounts of language and lesion coverage and examination of effects in different brain. For example, we identified regions of the left subgroups; Schwartz & Dell, 2010). The limiting factor anterior temporal and temporoparietal regions where is sample size, but the expansion of institutional lesions disrupt semantically driven word production recruitment infrastructures and multisite collaborations (step 1 retrieval) over and above their impact on makes this a surmountable problem. The most impor- semantic comprehension. Lesions in these regions ren- tant reason for optimism is the range of sophisticated der the system vulnerable to mis-selection of words tools that are available to aphasia researchers today. that share a categorical (anterior temporal region) or Our group’s work exemplifies the application of thematic (temporoparietal region) relation to the target. computational modeling and voxelwise mapping of These regions may be part of complementary semantic model-defined symptoms and processing parameters. hubs for language. They are also part of the ventral Others are correlating behavioral deficits with fMRI pathway for language, showing that, in addition to its and with a multimodal characterization of tissue dam- well-established role in comprehension, the ventral age (e.g., structural damage, hypoperfusion, and/or route makes an essential contribution to the retrieval white matter connectivity: Crinion, Warburton, of words based on their meaning. Lambon Ralph, Howard, & Wise, 2006; Fridriksson Lesions in the dorsal brain pathway also compro- et al., 2010; Han et al., 2013; Hillis et al., 2001; Turken mise the production of words from meaning (e.g., & Dronkers, 2011). These advances in the neurocogni- naming), but here the disruptive effect of lesions cen- tive characterization of impaired language complement ters on step 2 processes. We found that parameter p, neuroimaging studies of normal performance, making which indexes step 2 processes in naming and repeti- aphasia research as central to theory development tion, correlated with lesions in the left frontoparietal today as it has been historically. cortex and insula. The lesion map for nl, which indexes the nonlexical contribution to repetition, overlapped with p in postcentral and supramarginal gyri and Acknowledgments extended into posterior dorsal route sectors in Spt and STG. We propose that the area of overlap identifies the Preparation of this chapter was supported by grant RO1 DC000191- 32 from the National Institute on Deafness and Other brain’s representation of phonological units accessed Communication Disorders (NIDCD), National Institutes of Health in production and that its anterior parietal distribution (NIH). indicates that these representations might have a sensory-motor character. Like all methods, ours have their limitations. From References one perspective, the case series on which we based the analyses are too restrictive. Limiting the sample to Abel, S., Huber, W., & Dell, G. S. (2009). Connectionist diagnosis of lexical disorders in aphasia. Aphasiology, 23, 1353 1378. patients with stroke aphasia guaranteed poor lesion Allport, D. A. (1984). Speech production and comprehension: One coverage in areas such as the inferior temporal lobe lexicon or two? In W. Prinz, & A. F. Sanders (Eds.), Cognition and and fusiform gyrus. 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