A Third Route for Reading? Implications from a Case ofPhonological Dyslexia
Denise H. Wu, Randi C. Martin and Markus F. Damian1
Rice University, Houston, Texas, USA and 1University of Bristol, Bristol, UK
Abstract
Models of reading in the neuropsychological literature sometimes only include two routes from print to sound, a lexicalsemantic route and a sublexical phonological route. Other researchers hypothesize an additional route that involves adirect connection between lexical orthographic representations and lexical phonological representations. This so-called‘third route’ has been invoked to account for the preserved oral reading of some patients who show severe semanticimpairments and a disruption of the sublexical phonological route. In their summation hypothesis, Hillis and Caramazzaproposed that reading in these cases could result from a combination of partial lexical semantic information and partialsublexical phonological information, thus obviating the need for the third route. The present study examined the caseof a phonological dyslexic patient (ML) who exhibited preserved word reading, even for items he could not name, alongwith a non-word reading impairment. The relationship between ML’s naming and reading, and the influence of semanticvariables on his reading were examined. The results of this examination are interpreted as supporting the existence ofthe third route.
Introduction
How many possible routes for reading a word aloud areavailable to a fluent English speaker? A general dual-routeapproach was initially proposed more than two decades ago(Coltheart, 1978). The fundamental property of dual-routemodels of reading is the idea that skilled readers have attheir disposal two different procedures for converting printto speech. These two routes are, roughly speaking, a diction-ary look-up procedure and a letter-to-sound conversion ruleprocedure. The former procedure is also called the lexicalsemantic route, as it goes through the lexicons and thesemantic system, while the latter procedure is also called thesublexical route because it produces the sound of a word bymapping sublexical letter units (e.g. graphemes, syllables)onto sounds without consulting the lexicon (see Fig. 1).Some people refer to the sublexical route as the grapheme-to-phoneme conversion route, although there is considerableevidence that units larger than single graphemes are involved(Treiman and Zukowski, 1988; Lesch and Martin, 1998).
According to the dual-route model, oral reading can beachieved through the lexical semantic route, the sublexicalroute, or cooperation between the two. Print is first analysedvisually and letter detectors are activated. These detectorsthen send activation to both the input orthographic lexiconand the sublexical system. After accessing the lexical entryin the input orthographic lexicon, the activation in the lexical
Correspondence to: R. C. Martin, Psychology Department MS 25, Rice University, Houston, TX 77251, USA. Tel: �1 713 348 3417; Fax: �1 713 3485221; e-mail: [email protected]
semantic route proceeds to the semantic system. The relevantsemantic attributes of the word would be activated and thenthe activation would be sent to the output phonologicallexicon. The intended lexical unit should receive the mostactivation and be highly activated. It in turn would sendactivation to the phoneme system. On the other hand, forthe sublexical route, only sublexical orthographic-to-soundconversion would be involved and there would be no activa-tion of semantic or lexical properties of the word throughthis route. This procedure would simply convert the visualinput into the corresponding phonemes based on correspond-ences in the language. Those representations in the phonemesystem that correspond to the input graphemes are thusretrieved. According to recent versions of the dual-routemodel, the lexical representations that share these phonemesin the output phonological lexicon would also receive back-ward activation from the phoneme system (e.g. Coltheartet al., 2001). Thus, both the phoneme system and the outputphonological lexicon would receive input from the lexicalsemantic route and the sublexical route. The selected responsecould be based on the most activated unit at either the outputphonological level or the phonemic level (see Fig. 1). Ineither case, both the sublexical and lexical semantic routeswould contribute to the resulting activation. An illustrativeexample is the computational dual-route cascaded model
proposed by Coltheart et al. (2001). In this model, everyconnection is bi-directional and, hence, each level wouldreceive feed-forward activation and feedback from con-nected systems.
Strong evidence for the existence of the lexical semanticroute comes from deep dyslexic patients who make semanticerrors in reading and show a large effect of concretenessin reading accuracy (Coltheart, 1980; Kremin, 1982). Theexistence of the sublexical route is also well accepted asnormal readers can easily produce the sound of a pseudoword(a pronounceable non-word) that does not have a representa-tion in the lexicon. Additional evidence for the sublexical(grapheme-to-phoneme conversion) route comes from surfacedyslexia. The characteristic of this impairment is that theability to read non-words and regular words aloud is select-ively preserved relative to that of reading irregular words(e.g. reading ‘one’ as ‘own’). Moreover, exception words areoften read as the grapheme-to-phoneme conversion rulesspecify. For example, patient KT (McCarthy and Warrington,1986) achieved an accuracy of 100% with non-words and81% with regular words, whereas his accuracy was only 41%with irregular words. Among his errors on irregular words,KT regularized at least 71% of them.
In addition to the two routes described above, a third directlexical route has been proposed to account for the behaviorof some brain-damaged patients (e.g. Funnell, 1983; Coltheart
Fig. 2. Three-route model of reading.
and Funnell, 1987; Ellis and Young, 1988; Coslett, 1991;Lambon Ralph et al., 1995). These patients showed fluentand accurate reading of at least some irregular words, despitehaving very impaired comprehension of the same words.Because sublexical rules cannot be applied to irregular words,word reading cannot be achieved by this route. The patients’poor comprehension also makes it unlikely that correct wordreading is achieved by the semantic route. Therefore, it hasbeen argued that a direct route is needed for accessing thepronunciation of words without accessing semantic informa-tion. Specifically, this third route directly connects the inputorthographic lexicon and the output phonological lexiconwithout going through the semantic system (see Fig. 2).
One piece of evidence supporting this view comes from apatient reported by Coltheart et al. (1983). Their patientmade errors in the comprehension of an irregular, printedword that corresponded to the meaning of its homophone(i.e. the word with the same pronunciation but differentspelling). For example, this patient read ‘steak’ aloud cor-rectly but defined it as ‘fencing post’. Because ‘steak’ isirregular, and the patient was evidently not accessing thecorrect semantic information, it appears that neither thesublexical nor the lexical semantic route was responsible forhis correct reading.
A patient, WB, reported by Funnell (1983), also providedstrong evidence for the third, direct route for word reading.
WB showed a striking disruption of the sublexical route ashe could not produce the sound of any single letters or non-words. He also could not pronounce pseudohomophones (e.g.‘brane’), whereas he was able to read correctly words sharingthe same phonology with these pseudohomophones (e.g.‘brain’). This latter finding indicates that his poor non-wordreading could not be attributed to difficulty in producingunfamiliar phonological forms. His word reading across awide range of frequencies was generally good (86–93%correct). However, his word reading was probably notachieved through the semantic route. For example, on a setof words for which WB showed an impaired ability to makesemantic judgments for both spoken and written forms, hisability to match spoken forms of the same words to theirwritten forms was perfect. Given WB’s disrupted sublexicaland semantic systems, it seems necessary to have a third,direct lexical route for accessing phonology without knowingthe meaning of a printed word to account for his good word-reading ability.
Coslett (1991) reported a patient, WT, who also supportedthe existence of the third route for reading. Similarly to WB,WT showed impairments on both the sublexical route andthe lexical semantic route. Her reading, writing, and repetitionof non-words were poor (0–67% correct). In writing, repeti-tion, and a semantic judgment task, WT performed poorlyon low-imageability words. However, her word-reading per-formance was excellent and unaffected by imageability.
Although the deficit observed in WB and WT stronglysuggested that there is a third, direct lexical route for wordreading, Hillis and Caramazza (1991, 1995) provided adual-route account of these findings, termed the summationhypothesis. They argued that these patients’ preserved word-reading abilities were the product of the summation of outputfrom a partially preserved semantic route and a partiallypreserved sublexical route. Although neither of these twomechanisms was well preserved enough in these patients tosupport the performance on semantic tasks (which solely relyon the semantic system) nor on non-word tasks (which solelyrely on the sublexical route), the cooperation of these tworoutes enabled relatively spared word reading, as word readingcould draw on both routes. According to the summationhypothesis, only two routes were needed to account for thepatients’ performance, and this was more parsimonious thanhypothesizing the third route.
How can the summation hypothesis account for thepatients’ behavior summarized above? For a patient withimpairments on both the semantic and the sublexical routes,the dual-route model assumes that the summation of theactivation from these two routes is sufficient to activate thecorrect response in the phonemic system and the outputphonological lexicon. Suppose that a patient has a semanticdeficit which results in difficulties in picture naming (e.g.Caramazza et al., 1990; Hillis et al., 1990). For example,due to this semantic deficit, the picture ‘tulip’ may activatethe semantic representations of ‘rose’ and ‘daisy’ as muchas ‘tulip’. Due to the activation from these semantic repres-
Fig. 3. The summation hypothesis.
entations, the corresponding representations in the outputphonological lexicon and the phoneme system for thesewords would all in turn receive a certain amount of activation(see left-hand side of Fig. 3). In the case of picture naming,there is no other information from the input that would helpto select the correct response from these candidates and thepatient would thus probably produce a semantic error. Sup-pose that the patient also has some problem with the sublexicalroute, as revealed through poor non-word reading. Eventhough the patient cannot produce the correct response for,say, the non-word ‘trelp’, the phoneme system may stillreceive subthreshold activation for some of the sounds (e.g.the phonemes /t/ and /p/) from the disrupted sublexical route.In turn, the representations in the output phonological lexiconsharing these phonemes would be weakly activated throughfeedback from the phonemic level (see right-hand side ofFig. 3). Now, suppose that the patient is given the writtenword ‘tulip’. Processing via the semantic route would resultin some activation of ‘tulip’, together with other semanticallyrelated words in the output phonological lexicon. At the sametime, the sublexical route would also feed some activationto the phonemes in ‘tulip’ in the phoneme system and then,through feedback, to those words which share these phonemesin the output phonological lexicon. In the output phonologicallexicon, the word ‘tulip’ would receive the most activationfrom both routes and, according to the summation hypothesis,the summation of these two sources of input could potentially
be enough for the patient to select and then produce thecorrect response.
Based on the summation hypothesis, correct word readingfor patients with disrupted lexical semantic and sublexicalroutes would be achieved only if semantic representationswere partially preserved and the sublexical mechanism waspartially functional. Therefore, these patients should not beable to read words for which they showed no semanticknowledge at all. Although patients WB and WT showedsevere deficits on those semantic tasks like picture naming,picture–word matching, and semantic judgment, they seemedto have some spared semantic knowledge of those words thatthey could correctly read but failed to make correct responsesto in semantic tasks. For example, WB’s performance onpicture–word matching was influenced by the semantic relat-edness between the target and the foil, which would not bepredicted if he had no semantic knowledge of the words.WT’s word reading was slightly influenced by the regularityof the stimuli (making eight errors out of 70 irregular wordsversus one error out of 70 regular words), which was alsonot predicted by complete disruption to the sublexical route.However, the regularity effect was not significant.
In support of their arguments, Hillis and Caramazza (1991,1995) reported several patients who fulfilled the predictionsof the summation hypothesis. One of their cases, JJ, demon-strated the strongest evidence for it. JJ never made a word-reading error on a word for which he showed some semanticknowledge. For example, he read the irregular word ‘sword’correctly and defined it as ‘a weapon...I can’t recall anymore’.JJ read correctly words for which he showed no comprehen-sion only if those words had a regular spelling. Therefore,Hillis and Caramazza proposed that JJ used a combinationof residual semantic knowledge and partially preserved sub-lexical processing to accomplish reading.
GLT was another patient providing evidence for the sum-mation hypothesis (Hillis and Caramazza, 1995). Like WBand WT, GLT showed relatively good word reading (88%correct) despite having disrupted sublexical and semanticroutes. Evidence for the interaction of the two was obtainedfrom pseudohomophone-reading and picture-naming tasks.His pseudohomophone reading was poor (20% correct) whenthese stimuli were presented alone. However, he showedimproved pseudohomophone reading if some semanticinformation was provided. When a pseudohomophone waswritten under the category label of the corresponding realword, he correctly read 73% of these pseudohomophones.When a pseudohomophone was presented with a picturecorresponding to the real word, he correctly read 88% ofthem, even though the items were chosen to be pictures thathe could not name alone. When the same pseudohomophoneswere tested again without any semantic information, hisperformance dropped to 21% correct again. Therefore, hisrelatively good performance was not caused by recoveryfrom his deficit; rather, he received aid from the relevantsemantic information. Similarly, picture naming improvedwhen some phonological information was provided. Among
those pictures that he could not name, GLT’s picture-namingability improved when a correct phonological cue was given.His accuracy of picture naming given a phonological cuewas 9/10 for cues coming from words he had read correctlyand only 2/10 for cues coming from words he had misread.For example, the word ‘trumpet’ had been read correctly anda pictured trumpet plus the cue /tr/ elicited ‘trumpet’, whereasthe word ‘harp’ had been read as ‘carp’ and a pictured harpplus the cue /h/ elicited ‘I don’t know’. Interestingly, incorrectbut semantically related phonemic cues elicited semanticerrors for 7/10 of the items he had produced correctly inresponse to the written word (e.g. a pictured trumpet � /fl/elicited ‘flute’) and for 9/10 items he had misread (e.g. apictured harp � /fl/ elicited ‘flute’ in a different session).
Other evidence consistent with the summation hypothesiswas that GLT’s word reading was better preserved for wordshe comprehended correctly. His word reading was 95%correct for the items for which he showed correct comprehen-sion in the picture verification task, but only 72% correct forthe words to which he responded incorrectly. The samepattern was found for the items used in a synonym-judgmenttask: his accuracy was 92% for the items for which heshowed correct comprehension, but only 44% correct for theitems on which he made errors.
While the results from JJ and GLT are consistent with thesummation hypothesis, they do not rule out a three-routeapproach; that is, if the third route is damaged, reading wouldbe expected to rely on a combination of semantic andsublexical information. Thus, to account for the data fromthese patients, one would have to assume that they havedamage to all three routes. Hillis and Caramazza’s point,however, is that two routes may be sufficient to account forpreviously reported patients who, despite an impairment tothe sublexical route, are able to read words for which theyhave poor comprehension. Their approach also provides amotivated account of why reading success should be relatedto the degree of disruption of semantic knowledge forcertain words.
There are problems with the summation approach, though.For example, to explain WT’s non-homogeneous performanceon the reading, writing, and repetition tasks (Coslett, 1991),Hillis and Caramazza suggest an additional impairment inthe parsing of auditorily presented words into sublexicalunits. Even though this account provides a means to explainpoor repetition and writing to dictation due to insufficientinput from the auditorily presented stimuli into the sublexicalmechanism, the summation hypothesis then loses its appealof simplicity by suggesting more loci of deficits.
Another problem with the summation hypothesis is that itseems very difficult to falsify. That is, even if a patient showsa very severe impairment in either the semantic system orthe sublexical mechanism yet has good reading, it could stillbe possible to argue that there is some spared informationwithin the damaged route, although there is no objectiveevidence of it from accuracy data (as for WB’s grapheme-to-phoneme conversion ability). For example, even though a
patient was completely unable to read aloud any non-wordsor sound out any individual letters, a critic may argue thatthe patient should show a subtle effect indicating processingof non-word phonology if properly tested, such as showinglonger reaction times to reject pseudohomophones than othernon-words in a lexical decision task [see Buchanan et al.(1994)].
Recently, some researchers have presented additional casesthat seem to provide a strong challenge to the summationhypothesis by providing more convincing evidence of veryimpaired comprehension of irregular words that are readcorrectly. Cipolotti and Warrington (1995) reported a patient,DRN, whose comprehension of both low-frequency regularand exception words showed a sharp contrast to word readingof the same items. Although DRN could only name five of20 pictures whose written forms consisted of irregularlyspelled picturable words, such as ‘yacht’ and ‘bouquet’, hecorrectly read aloud 17 of the 20 irregular words. Among 69low-frequency irregular words that he was tested on, DRNachieved an accuracy of 96% on word reading. On the otherhand, he only defined 29% of the written forms of thesewords correctly. A similar case, DC, was reported by LambonRalph et al. (1995). Among the 182 irregular words givento her, DC read aloud 94% of them, but only defined 26%correctly when judged by a lax criterion. Even when avery lax criterion to score the definition was applied, shecomprehended only 48%, which was still much lower thanher nearly perfect reading of these words.
Despite the strong evidence for the existence of a thirdroute for reading, proponents of the summation hypothesiscan still argue that it is difficult to establish convincingly acomplete lack of comprehension for those words that can bepronounced correctly [see Rapp et al. (2001) for a discussionalong these lines]. According to this reasoning, the summationhypothesis cannot be falsified even when there is a dramaticdiscrepancy between the accuracy of comprehension andreading on irregular words, as some other test may revealthe preservation of at least some semantic information. Onepossible means of testing the summation hypothesis, however,is to examine reaction time data for word reading and picturenaming from a patient, rather than just comparing the patient’saccuracy on different tasks. As mentioned earlier, picturenaming is assumed to draw on the same semantic representa-tions as employed in reading [see also Riddoch et al. (1988)].Thus, a disruption in naming certain pictures that is due toa semantic deficit or a deficit in accessing a phonologicalrepresentation from a semantic representation should lead toslower reading times for the names of those pictures forpatients with a disruption of the sublexical route.
The logic of this approach is as follows: whenever thelexical semantic route is damaged for some words (and thesublexical route is disrupted), summation should be involvedin the accurate reading of these words. Even though summa-tion may result in these words being read accurately, onewould expect them to be read more slowly than words forwhich semantics are preserved. That is, if only weak activation
Fig. 4. Computational dual-route model.
from semantics and the sublexical route is available for thesewords, the activation in the output phonological lexiconshould take longer to reach threshold than for words forwhich there is greater activation coming from the lexicalsemantic route. Consequently, for a patient with a disruptionof both the lexical semantic and the sublexical route, thereshould be a greater correlation between word-reading andpicture-naming latencies than for control subjects, and, inparticular, long reading times should be seen for wordscorresponding to pictures that he or she cannot name. Notethat these predictions should be fulfilled if the summationhypothesis is true, even when there is no complete lackof lexical semantic or sublexical processing. As we willdemonstrate in the next section via computational modeling,reaction time data are sensitive to any partial impairment onthe lexical semantic and the sublexical routes. Therefore,we exploit the reaction time data for word reading andcomprehension from a patient to examine the validity of thesummation hypothesis.
Computational modeling
These predictions seem to us to follow rather directly fromthe summation hypothesis. To validate our intuitions, weimplemented a connectionist dual-route model, albeit in asimplified form. The model, which is schematized in Fig. 4,consists of five layers of nodes: an orthographic (i.e. letter)layer, a phonological (i.e. phoneme) layer, two separate
Table 1. Network specifications(A) Letters and phonemes allowed in each position
Position Letters Phonemes
1 B C D G H M L N P R T b k d g h l m n p r t2 A E I O U a e i o u3 B D G N M P T b d g n m p t
(B) Words in each semantic category
Indoor objects Animals Body parts Foods Outdoor objects
BED BUG GUT BUN BOGCAN CAT HIP HAM LOGCOT COW LEG NUT MUDCUP DOG LIP POPMAT PIG RIB RUMMUG RAMPAN RAT
lexical layers for input orthography and output phonology,and one semantic layer.
The lexical semantic pathway consists of a route in whichinput orthographic units map onto input orthographic lexicalrepresentations which are themselves connected to semanticfeatures. These semantic features send activation to the outputphonological lexical layer. The sublexical pathway proposedin the summation hypothesis consists of a route in whichinput orthographic units directly map onto output phonolo-gical units that then feed activation to the output phonologicallexicon. Thus, the lexical units in the output phonologicallexicon receive independent input from both the lexicalsemantic and the sublexical pathway.
The stimulus specifications were taken from Plaut andShallice’s (1993) study on deep dyslexia. Their model imple-mented the reading of a set of 40 three- or four-letter wordsfrom five semantic categories. On the orthographic level,individual letters were coded in a position-specific manner.At the semantic level, each word was represented by theactivation of, on average, 15 out of a total of 68 semanticfeatures such that within-category similarity was greaterthan between-category similarity. For current purposes, thesestimuli were simplified in that only three-letter words wereemployed, and words were excluded that did not permit astraightforward grapheme-to-phoneme transformation. Thus,13 words were eliminated from the set. The remaining 27words and their corresponding orthographic and phonologicalspecifications are displayed in Table 1 [for a description ofthe semantic features and their assignment to the stimuluswords, see Plaut and Shallice (1993)].
The general architecture of the current model was closelyderived from the Interactive Activation framework describedin McClelland and Rumelhart (1981, 1986), although againa few details were simplified. Each node in the networkpossesses a real valued activation level ranging between 0and 1. Activation proceeds exclusively in a forward fashionthrough the network, and between-level connections arealways excitatory. Furthermore, the input orthographic and
output phonological lexical layers implement the principleof competition by having within-level inhibitory connections.
At the beginning of each trial simulation, all nodes in thetrained model were set to 0. Reading of a particular wordwas simulated by setting the corresponding orthographicinput units to a value of 1. For each simulated time step, thenet input for each node in the network (excluding theorthographic input layer) was calculated by summing theweighted incoming excitatory activation and subtractinginhibitory activation (in the case of the lexical layers).Activation of each node was updated by adding the net inputto the activation state, scaling it to a range of between 0 and1, and having it decay a certain amount [for details ofthe activation and updating functions, see McClelland andRumelhart (1981)]. Activation at the lexical output layer wastaken to constitute the dependent variable of the simulation.
Parameter settings for the following simulations werechosen such that additional assumptions entering the modelwere minimized. Inhibition within both lexical layers wasset to the same value. Furthermore, all connection weightsin the model were chosen so that the sum of activationpassing through them was equalized. For instance, the lexicalinput layer receives input from three orthographic units,whereas semantic features receive input from only one lexicalunit. As a result, the connection weights between the inputlexicon and semantics were chosen to be three times asstrong as those between orthography and the input lexicon.All other weights were chosen in a similar manner (seeTable 2). The only deviation from this principle is describedin the next section.
As a first step in attempting to model the reading processas laid out by the summation hypothesis, the parameters wereadjusted such that the lexical semantic and the sublexicalroutes both had an approximately equal influence on theactivation level and selection probability of the target lexicalnode. To achieve this end, each of the two routes wasselectively disabled, and the influence of the intact route wasassessed. In order to assure approximately equal weight toboth routes, the connections between the semantic layer andthe output lexicon had to be set to a slightly lower valuethan suggested by the principle described in the precedingsection (w � 0.0075 instead of w � 0.01) (see Table 2).Figure 5 shows the resulting activation levels for each routeseparately as well as both routes combined when the model
Fig. 5. Performance of the model presented with the input ‘leg’, receivinginput from both routes combined or each route separately.
Fig. 6. Activation of the target ‘leg’ as well as competing lexical items withnormal setting, reduced sublexical route, and additional semantic impairment.
was presented with the input ‘leg’. With the output lexiconreceiving input from both the lexical semantic and thesublexical pathways, activation levels were at a value ofapproximately 0.75 at cycle 50. Each route separately yieldeda maximum activation of merely approximately 0.60 (theinfluence of both routes was non-additive due to the non-linearity of the activation function). Other sampled wordsshowed equivalent results.
As a next step, we attempted to simulate phonologicaldyslexia in which, by definition, the sublexical conversionroute is impaired. The performance of the model undernormal settings was compared with a constellation in whichthe connection weights for the sublexical route were reducedfrom w � 0.05 to w � 0.005. Note that this manipulationdid not totally eliminate the sublexical route, but greatlyweakened its influence. Figure 6 shows the activation ofthe target word ‘leg’. Not surprisingly, the activation wassubstantially reduced and was maximal at 0.63.
Finally, we attempted to model the observed deficit in thelexical semantic route for some selected items (body parts,for example). For the following simulation, the weightsconnecting the semantic features corresponding to the cat-egory of body parts and their corresponding lexical units inthe output lexicon were reduced from the default setting of0.0075 to 0.00451. The category of body parts was selectedbecause a patient (ML—reported later) showed selectivenaming difficulty on these items. The results display a further
reduction in the level of target activation. Given that thesublexical route is damaged and the lexical semantic routeis selectively impaired for body part items (i.e. the word‘leg’), the model achieved a lower level of activation forthese items at 90 cycles and took more cycles to exceed thesame level of activation (see Fig. 6).
Let us assume that the threshold for correct word reading,for the current trained network, is only 0.4 in terms ofactivation level. That is to say, when a word is presented tothe model, the correct response would be produced wheneverthe activation level in the output phonological level reaches0.4, and the number of cycles needed is an index of thereaction time for such a response [for an example, seeGrainger and Jacobs (1996)]. If the summation hypothesis iscorrect, according to the performance of this computationalmodel we would expect to observe longer word-readinglatency for those words with a lexical semantic deficit inaddition to the sublexical impairment because it would takemore cycles to reach this threshold. Thus, a reaction timedifference should be observed even if reading accuracywas normal.
In order to ensure that the above patterns are not specificto the chosen input word but generalize to other words aswell, all items from the semantic category ‘body parts’ weretested. Figure 7 shows the averaged outcome from thesesimulations. The absolute activation level of the target wordis not the only way in which the outcome of a simulationcan be conceptualized. Alternatively, the selection probabilityof the target word that takes into account the activation ofcompeting units via Luce’s (1959) choice rule as well as aweighted average of the activation level at preceding timesteps can be computed [see McClelland and Rumelhart (1981)for details]. These values are shown in Fig. 7B. Finally, acommonly used measure is the deviation of the activationvector from the desired pattern, as measured by the summedsquared error of the least mean square error (LMS). Thismeasure is reported in Fig. 7C. For either means, assumingsome threshold for selecting and producing a particular word,longer reaction times are predicted for items with somelexical semantic impairment when the contribution of thesublexical pathway is diminished.
Case study
The predictions derived from the summation hypothesis wereexamined by studying a patient, ML, who fits the classificationof phonological dyslexic, as he reads words very well buthas great difficulty with sublexical phonological coding(Lesch and Martin, 1998). According to the summationhypothesis, ML should rely more heavily on the semanticroute to achieve word reading, given that his sublexical routeis severely damaged. If this prediction is true, then MLshould show a greater influence of semantic variables, likeimageability and frequency, on reaction time for word reading.However, this pattern was not found in ML’s word-readingresponses (Park and Martin, 2001). He only showed a much
Fig. 7. Performance averaged across all five targets from the semantic category‘body parts’ with normal setting, reduced sublexical route, and additionalsemantic impairment. (A) Activation, (B) selection probability, and (C) leastmean square error (LMS).
greater imageability effect than control subjects on low-frequency words but not on high-frequency words (seeTable 3 and more discussion below).
If ML achieves word reading mainly via the lexicalsemantic route, he should show a greater correlation betweenpicture-naming time and word-reading time than controlsubjects, as picture naming and word reading draw on thesame semantics-to-phonology connections (see Fig. 3). Thatis, ML should rely on the semantics-to-phonology connectionsin the lexical semantic route for both tasks, whereas controlsubjects will use that route for the picture-naming task butthat route plus the sublexical route for the word-reading task2.
A further prediction derives from ML’s selective difficultyin naming items from the category of body parts. We willdemonstrate that this naming deficit is due to a disruptionin the connections between semantic representations andphonological representations for these items. That is, hissemantic representations for these items appear to be intact,but he has difficulty accessing their names from semantics.
Table 3. ML’s and five control subjects’ reading accuracy (%) and readinglatency (ms) for high- and low-imageability words
High imageability Low imageability
MLHigh frequency
Accuracy 100 99Reaction time 744 752
Low frequencyAccuracy 95 91Reaction time 760 874
Five controlsHigh frequency
Accuracy 100 100Reaction time 594 625
Low frequencyAccuracy 99 98Reaction time 602 645
Table 4. Correct percentage of ML’s word reading across different word classes
PALPA, Psycholinguistic Assessments of Language Processing in Aphasia.
For those pictures that ML has difficulty naming, his wordreading should be slower (relative to control items that hecan name) because the weakened input from the semanticroute for these items should lead to longer times for theirphonological representations to reach threshold.
Patient description
ML is a 60-year-old male who suffered a left-hemispherecerebral vascular accident in May 1990. A computed tomo-graphy scan revealed an infarction involving the left frontaland parietal opercula. Atrophy in the left temporal operculumwas noted, as was mild diffuse cortical atrophy. ML hadcompleted 2 years of college study and prior to his injuryhad been employed as a draftsman. ML exhibits mild agram-matism and word-finding difficulties in his spontaneousspeech.
ML’s word reading is mostly intact. While he read mostword classes at a very high level, testing completed severalyears ago indicated that he appeared to have a slight impair-ment in reading function words (Lesch and Martin, 1998).When tested on word lists obtained from Roeltgen et al.(1983), ML obtained 100% (40/40) correct on nouns and75% (30/40) correct on function words (see Table 4). On theword lists from the Psycholinguistic Assessments of Lan-guage Processing in Aphasia (PALPA) (Kay et al., 1992),ML also obtained 100% (20/20) correct for nouns, adjectives,and verbs, and 85% (17/20) correct for function words (see
Roeltgen et al. (1983) non-words Simple Score Complex Score
Two phonemes ep 6/10 aub 5/10Three phonemes nud 4/10 shev 4/10Four phonemes scod 4/10 tharp 2/10Five to seven phonemes epzim 2/4 endurf 0/4
PALPA, Psycholinguistic Assessments of Language Processing in Aphasia.
Table 4). More recent testing indicates that his function wordreading is now highly accurate, but he still shows significantlylonger reaction times for reading function words than otherwords matched in frequency and imageability.
Other evidence confirms that ML’s deficit in reading functionwords cannot be due to their low imageability, as his readingof low-imageability words appears to be mildly impaired onlyfor low-frequency words, and function words are high-frequency words. Table 3 shows his reading reaction times andaccuracy on a set of content words from different word classes(nouns, verbs, and adjectives) where frequency and imageabil-ity were manipulated. ML did not show an imageability effecton high-frequency words. For low-frequency words, althoughhis imageability effect was within the normal range for accu-racy, his imageability effect with reaction times was muchgreater than that of any of the controls (see Table 3). The greaterinfluence of imageability on low-frequency words may beregarded as evidence for the reliance on the lexical semanticroute in reading. However, this pattern does not necessarilycontradict the existence of the third route, as the strength ofactivation deriving from the direct lexical route would beexpected to be sensitive to frequency. In other words, theactivation from the direct lexical route to the output phonolo-gical level would accumulate more slowly for low-frequencywords, hence there would be a greater opportunity for inputfrom the lexical semantic route to affect the reading of suchwords. The greater imageability effect on low-frequency wordsis actually consistent with the fact that ML’s sublexical routeis severely impaired.
In contrast to his near perfect accuracy on reading almostall classes of words, ML performed poorly on non-wordreading. When tested on non-word lists obtained fromRoeltgen et al. (1983) and a set of non-words obtained fromthe PALPA (Kay et al., 1992), ML only obtained 38% correctacross the two sets of non-words (see Table 5) (Lesch andMartin, 1998). His errors on the PALPA were mainly(11/16) lexicalizations (e.g. soaf → ‘soft’, dringe → ‘dirge’),which indicated that he utilized lexical information, ratherthan sublexical processing, to perform the task. Among allof the 169 non-words tested, ML correctly read only 69
(41%) of them. When provided with the written letter, MLproduced an appropriate sound for only 9/26 letters, eventhough he could name 23 out of 26 individual letters correctly(Lesch and Martin, 1998).
More recent testing on non-word reading indicates thatML’s deficit persists. His accuracy and error patterns on thenon-words from the PALPA were the same (8/26 correct withmainly lexicalization errors). Among the 26 letters, he onlycorrectly sounded out two in lowercase and one in uppercase,which is worse than previously reported (Park and Martin,2001). Clearly, ML’s sublexical route is impaired. Note thatwe are not claiming that ML’s sublexical route is completelydisrupted as he is able to read some non-words correctly.However, the output of this route is degraded and, accordingto the modeling results, a degraded sublexical route shouldlead to slowed word reading.
As discussed in Martin and Lesch (1996), ML generallyshowed good comprehension, as evidenced by his perform-ance on single-word processing tasks. On the Peabody PictureVocabulary task (Dunn and Dunn, 1981), in which the subjectmust select from four pictures the one matching a word, heobtained a standard score of 113 (control µ � 100, σ � 15).On a task in which he had to choose from two items the onemore related to a third, he scored 88% correct, where themean for young controls was 86%. ML’s picture-namingability was generally quite accurate, which is also an indica-tion of preserved semantic representations. He was 97%correct on the 175-item Philadelphia Naming Test (Roachet al., 1994), which was above the mean for controls.
Experiment 1. Correlations between reading andnaming
As discussed above, picture naming is thought to draw onthe same semantics-to-phonology connections that are usedin reading via the lexical semantic route (Riddoch et al.,1988; Caramazza et al., 1990; Hillis et al., 1990). Accordingto the summation hypothesis, ML’s word reading shouldinvolve mainly these semantics-to-phonology connectionsbecause of his disrupted sublexical route for reading. Fornormal readers, word reading would rely on both the semanticand the sublexical routes. Thus, it is expected that thecorrelation between picture-naming and word-reading timesshould be higher for ML than for control subjects. On theother hand, if the correlation for ML is not higher than forcontrol subjects, the result would indicate that ML relies ona third route for reading which is non-semantic.
Method
Participants. ML and eight age-matched controls particip-ated in this experiment. All participants were reimbursed atthe rate of $7/h.
Materials. Two hundred and fifty pictures from Snodgrassand Vanderwart (1980) were prepared for use in the naming
part of this experiment. Each picture was digitized to about9 � 7 cm and presented in the center of the computer screen.The names of these same pictures were prepared for use inthe reading part of this experiment. Each word was in12-point font and presented in the center of the computerscreen. The 250 stimuli were divided into two lists. In onesession, a subject saw 125 stimuli in a picture-naming taskand the other 125 in a word-naming task. In the secondsession, the assignment of the lists to the picture- or word-naming task was reversed. Thus, a particular item was seenonly once in a session in either a picture or a word format.The order of the stimuli within a list was randomized acrossdifferent subjects.
Procedure. ML and all control subjects completed two 1 hsessions approximately 1 week apart. ML and three controlsubjects were tested twice for the two sessions to assess theconsistency of performance over time. The second sessionof the test and the first session of the retest were 3 weeksapart. In the first session, the first part was either picturenaming or word reading, and the second part was the othertask. In the second session, the order of the two tasks wasthe same as that in the first session. The order of the twotasks in the first and second sessions of the retest wasreversed from the initial test.
In both the naming and the reading tasks, there were 10practice trials prior to the experimental trials. Each of thestimuli (pictures or words) was presented one at a time, inthe center of a Macintosh computer screen. On every trial, afixation point was presented for 500 ms accompanied by abeep. Three hundred milliseconds after the removal of thefixation point, either a picture or a word appeared. Thesubjects were instructed to name the picture or to read theword as quickly and accurately as possible. The reactiontime of the subject’s verbal response was recorded by avoice-activated key. The picture or the word disappeared assoon as the voice-activated key was triggered. The experimen-ter recorded online whether the trial was valid by pressing akey. An invalid trial may be due to the malfunction of thevoice-activated key, a subject’s incorrect response, or ahesitation. The next trial was initiated 1 s after the experimen-ter’s online judgment. The whole experiment was tape-recorded at the same time and the subject’s naming andreading responses were transcribed after the experiment.
Results and discussion
Across the test and retest sessions, ML’s picture naming andword reading were at a near normal level of accuracy, eventhough he had slightly more invalid trials for reading thanthe controls (see Table 6). All but two of these invalid trialswere caused by hesitations or stuttering, rather than otherreading difficulties. ML’s mean picture-naming time was1274 ms and his mean word-reading time was 658 ms. Thestandard deviation of the picture-naming times was verylarge because ML seemed to have a very long reaction time
when more than one name could possibly be applied to thepicture. The mean reaction times for the controls (includingthe retest for three controls) were 939 ms for naming and623 ms for reading (see Table 6). ML’s mean reaction timefor picture naming was slightly outside the normal rangewhereas his mean reaction time for word reading was closeto the normal subjects’ mean. Thus, across both tasks, hisaccuracy was not very different from the normal subjects’and he only showed slightly slower reaction times than theirsin picture naming.
The main concern of this experiment was whether MLshowed a greater correlation between the reaction times forpicture naming and word reading than the controls. Theuncorrected correlations are shown for ML and the controlsin the top half of Table 7, where it is clear that ML did notshow a greater correlation than the controls. However, giventhat a brain-damaged patient like ML may show greatervariability in reaction times, the correlations may be low dueto unreliability in the measures, and thus these correlationsshould be corrected for unreliability. The correlation betweenthe test and the retest for each task was calculated for MLand the three control subjects who were tested twice. ForML, the test–retest correlation was 0.20 for reading and 0.39for naming. For the three controls, the test–retest correlationwas 0.49 for reading and 0.47 for naming (see bottom halfof Table 7). The correlations between picture-naming timesand word-reading times were corrected for attenuation (whichadjusts for unreliability as reflected in the test–retest correla-tions; Pedhazur, 1982, pp. 112–114). After this correction,ML’s correlation between naming and reading latenciesincreased from 0.02 to 0.08. The correlation between namingand reading latencies for the controls before the correctionfor task reliability was 0.03, with a range from –0.12 to 0.15.After the correction, the correlation for the controls became0.27 with a range from 0.05 to 0.40. It is clear that ML didnot show a greater correlation between picture-naming timesand word-reading times. Thus, the summation hypothesiswas not supported.
Experiment 2. Body part naming and reading
Because ML has a deficit in the sublexical route, thesummation hypothesis predicts that ML should have particulardifficulty reading those words corresponding to the names ofthe pictures that he had difficulty naming. Although ML’snaming was highly accurate overall, the few errors that hedid make seemed to be concentrated in the category of bodyparts. A test sampling several items from several differentcategories verified that ML has a specific deficit in namingbody parts. According to the summation hypothesis, MLshould show at least some degree of difficulty reading thewords corresponding to these body parts. In experiment 2,body parts and words from other categories were used asstimuli for both naming and reading. Experiment 2 had fourparts. In part A, we first verified that ML showed significantlyworse naming of body part pictures than control pictures.
(A) Uncorrected correlations between naming and reading times for ML and 10 control subjects
Correlation of naming and reading times
ML r � 0.02 (P � 0.10)Controls r � 0.05 (range –0.12 to 0.20)
(B) Consistency of naming and reading, and correlations between naming and reading times after correction for attenuation for ML and three controls
Consistency of naming Consistency of reading Correlation of naming and readingtimes after correction
ML r � 0.39 (P � 0.001) r � 0.20 (P � 0.003) r � 0.08Controls r � 0.49 (range 0.32–0.59) r � 0.47 (range 0.36–0.53) r � 0.27 (range 0.05–0.40)
We then attempted to elucidate the locus of this body part-naming deficit. If this deficit was due to a visual or a visual-to-semantic stage of processing, then the results should notbe relevant to the summation hypothesis. To address thisissue, in part B we assessed naming to definition, where adeficit in body part naming would not be expected if thedifficulty was in a visual stage of processing specific topictures. In part C, we assessed comprehension of body partsversus control pictures using a picture–word matching task.Good performance on body parts on the comprehension testwould indicate that the naming deficit was not due to a visualprocessing or semantic deficit, but rather to a category-specific deficit in accessing output phonology from semantics.Finally, in part D, we assessed word reading for the bodypart and control picture names.
Part A. Body part versus control picture naming
Method
Participants. ML and eight controls participated in thisexperiment. The control participants were matched to ML interms of age and education. All participants were reimbursedat the rate of $7/h.
Materials. Fifty pictures of body parts were prepared foruse in the naming part of this experiment. Each picture wasdigitized to about 9 � 7 cm and presented in the center of
the computer screen. Due to the ambiguity of some picturesdepicting certain body parts, an arrow pointing to a specificbody part was added to 35 of the pictures. Another 50 controlpictures from several different categories were also prepared(see Appendix A). The control pictures matched the picturesof body parts in frequency (Francis and Kucera, 1982) on aone-to-one basis. An arrow pointing to a specific part of theintended object was also added to 35 control pictures andthe subjects were asked to name the specific part. This wasto equate the specificity of the intended response (whether itis the whole object or just a part of the object) between thetwo sets of pictures.
In order to verify that the body part pictures were not morevisually complex than the control pictures, we obtained ratingsof complexity. Ten college students were instructed to rate thevisual complexity of each of the pictures of the body parts andthe control objects on a five-point scale (1, very simple; 5, verycomplex). Complexity was defined as the amount of detail orintricacy on lines, including the arrow (if there was one) andeverything else, in the picture. They were told to rate thecomplexity of the drawing itself, rather than the complexity ofthe real-life object it represented. Each subject was testedindividually and shown 12 pictures from the whole set to allowthem to anchor the scale. Rather than the body parts beingmore complex, they were actually rated as significantly lesscomplex than the control pictures (t(9) � 6.03, P � 0.001; meanfor body parts � 2.28; mean for control pictures � 2.83).
ControlsAccuracy 73.3 (range 62–84) 75.5 (range 66–86) –2.2 (range –20 to 14)Reaction time 1229 (SD � 319; range 1037–1555) 1138 (SD � 324; range 912–1423) 91 (range 10–146)
SD, standard deviation.
Procedure. In this naming task, the body part pictureswere randomly intermixed with the control pictures. Theprocedure was the same as for picture naming in experiment1, except that there were 100 stimuli rather than 125.
Results
Accuracy. Both the control subjects and ML had highererror rates overall on these materials than for the materialsin experiment 1. The invalid trials in this task for the controlsubjects came mainly from hesitations, although they usuallyproduced the intended or an acceptable response eventually.This was not true for ML’s errors on body parts. In 25 invalidtrials with body parts, ML hesitated but gave the correctresponse in four trials. He did not produce any response infive trials, gave an inappropriate name of a body part in eighttrials, and produced a non-word in one trial. For the otherinvalid trials, ML produced acceptable but not the intendedresponses.
The controls performed at about the same level on thebody parts and the other stimuli (73.3 versus 75.5% correct).The difference in accuracy between body parts and controlstimuli was not significant for the control subjects at thegroup level (P � 0.82), nor for seven of eight individualsubjects (all but one, P � 0.11). The accuracy difference forthe control subjects ranged from –20 to 14%. One controlsubject showed a significantly higher error rate on body partsthan control objects (62 versus 82%, χ2
(n�100) � 4.96,P � 0.03). ML showed a significantly worse performanceon the body part pictures (50 versus 74% correct,χ2
(n�100) � 6.11, P � 0.01) and showed a larger differencein accuracy between the body part and control pictures thanany of the controls (see Table 8). His accuracy in namingbody part pictures was below the range of the controls,whereas his accuracy for the control pictures was close tothe controls’ mean.
Reaction times. ML’s naming latency for these pictures ofbody parts was 2365 ms and for the control pictures was1904 ms, although the difference was not significant due tohis extremely large standard deviations (P � 0.21). Thecontrol subjects also showed somewhat longer naming timesfor body parts than control pictures (see Table 8). Thedifference was significant at the group level (F(1,7) � 21.49,P � 0.002), and for two of the eight control subjects when
their data were analysed individually. However, ML showeda much larger difference in reaction times than the controls.It took him 461 ms longer to name a body part picture thana control picture, compared with 91 ms for the controlsubjects (range: 10–146 ms) (see Table 8). These dataconfirmed the error rate data in showing that ML had specificdifficulty in naming pictures of body parts.
Part B. Naming to definitionMethod
Participants. ML and four controls matched for age andeducation participated in this experiment. All participantswere reimbursed at the rate of $7/h.
Materials. Definitions for each body part and control objectused in experiment 2A were created (see Appendix B). Theonly item in part A not included in this experiment was‘shoulder blade’, as no appropriate definition could be givenwithout mentioning the word ‘shoulder’. In the definition ofa body part, we tried to avoid using body parts other thanvery common ones (e.g. ear, foot). From ML’s performancein the previous experiment, we found that he showed nonaming difficulty with pictures of very common body parts.However, it should be noted that if ML showed worseperformance on the definitions of body parts, the deficit couldarise either from his impaired understanding of the provideddefinition or from the connection between semantics and theoutput phonological lexicon, but not from the visual pro-cessing of pictures. Twenty fillers were also included inthe testing.
Procedure. In this task, the definitions of the body parts,the control objects, and the fillers were all intermixed andpresented in a random order. There were 10 practice trialsprior to the experimental trials. On every trial, a beepsounded, followed at 500 ms by a fixation point presentedin the center of a Macintosh computer screen. Five hundredmilliseconds later, a definition appeared below the fixationpoint. The subjects were instructed to name a word thatmatched the definition as quickly and as accurately aspossible. The fixation point disappeared immediately afterthe voice key was triggered, indicating that the subject hadinitiated a response. The definition stayed on the screen until
286 D. H. Wu, R. C. Martin and M. F. Damian
Table 9. Naming to definition accuracy (%) in experiment 2B for ML and the control subjects
Body parts Control objects Difference
ML 73.5 (36/49) 90 (45/50) –16.5Controls 87.8 (43/49; range 83.7–95.9) 93.5 (47/50; range 92–96) –5.7 (range –10.3 to 3.9)
the experimenter pressed a key on the response box toindicate whether the trial was valid or not and whether theresponse was correct. The next trial was initiated 1 s afterthe experimenter’s key press. The whole experiment wassimultaneously tape-recorded and the subject’s namingresponses were digitized and transcribed following the testing.
Results
Consistent with his performance on picture naming, MLperformed substantially worse on naming to definitions ofbody parts (73.5% correct) than control objects (90% correct)(χ2 � 4.5, P � 0.05, n � 99). Three of the four controlsalso showed a somewhat worse performance on the body partsthan control objects. However, neither the group difference forthe controls (87.8 versus 93.5%) nor the difference forindividual subjects reached significance. The differencebetween ML’s performance on body parts and control objectswas outside the normal range (see Table 9). ML made sevenmore errors on body parts than the control mean, whereashe made only two more errors on control objects than thecontrol mean.
A more fine-grained analysis of ML’s responses revealedthat he failed to provide any response to the definitions offour body parts (finger, heel, toes, tongue), while he failedto do so for only one control object (sleeves). For the fourbody parts that ML did not respond to at all, only ‘tongue’was named by one control subject as ‘buds’ and by anothercontrol subject as ‘mouth’, both of which were acceptableresponses given the definition (‘the thing that people use totaste’). Among the 36 body parts that he named correctly,ML took longer than 10 s to name 42% (15/36) of them. Onthe other hand, this degree of slowness was true only for24% (11/45) of the 45 control objects he named correctly.Although these long response latencies may have been duein part to ML’s difficulty in reading and understanding thedefinitions, such difficulties would not have been expectedto give rise to longer times for body parts than control items.
ML’s difficulty with naming to definitions of body partsmirrored his performance in experiment 2A, as the 16.5%disadvantage for body parts in definitions was similar tothe 24% disadvantage for pictures3. This correspondenceindicates that his naming deficit for body parts does notresult from difficulty processing their visual representationsor accessing semantics from their visual representations.Rather, the results suggest that his impairment with bodyparts arises from either the semantic system or the connectionsto the output phonological lexicon from semantics. In thefollowing experiment, we used a picture–word matching task
to assess ML’s semantic representations of body parts inorder to adjudicate between these two loci.
Part C. Comprehension of body parts and controlitems
In this experiment, we assessed ML’s comprehension of bodypart and control pictures using a timed picture–word matchingtask for the same stimuli used in the picture-naming task inpart A. If ML has an impairment in the semantic representa-tion of body parts, we should observe a similar inferiorperformance on body part pictures relative to pictures ofcontrol objects, as observed in the naming task. On the otherhand, if ML’s difficulty is limited to an impaired connectionbetween the semantic system and the output phonologicallexicon, then he should perform normally on picture–wordmatching for body part pictures. It should be noted that ifML performs well on this task, it will provide furtherconfirmation that his difficulty with body parts is not related tovisual processing or the visual representations of body parts.
Method
Participants. ML and five controls participated in thepicture–word matching task. The control participants werematched to ML in age and education. All participants werereimbursed at the rate of $7/h.
Materials. The same set of pictures of body parts andcontrol objects used in experiment 2A (the naming task) wasused in this experiment.
Each of the 100 pictures used was paired with the correctname of the picture, a semantically related distracter, or anunrelated distracter (see Appendix C). For the body partstimuli, the semantic distracter was always another body partused in experiment 2A. However, as the control objects wereselected from a wide range of categories, there were notenough semantically related distracters to choose from thisset. Thus, except for two items, words other than those usedin experiment 2A were selected as the semantically relateddistracters for the control objects. The frequencies (Francisand Kucera, 1982) of the correct name of the picture, thesemantically related distracter, and the unrelated distracterwere matched across the two sets of stimuli (body parts:35.8, 35.8, and 36.1; control objects: 33.6, 31, and 33,respectively).
Three lists of stimuli were prepared to be tested in threeseparate sessions. In every list there were 50 body partpictures, 50 control object pictures, and 36 filler pictures.
A third route for reading? 287
Among the 50 body part pictures and the 50 control objectpictures of every list, one third of them were paired with thecorrect picture names, one third with the semantically relateddistracters, and the other one third with the unrelated distrac-ters. The 36 filler pictures were always paired with the correctpicture names to balance the numbers of ‘yes’ and ‘no’ trialsin every list. The same picture was paired with its correctname in, say, list 1, with a semantically related distracter inlist 2, and with an unrelated distracter in list 3. Special carewas taken to ensure that for every list no picture and noword was presented more than once. In every list, the pictureswere presented in a random order. The order in which everysubject received the lists was counterbalanced.
Procedure. All subjects completed three 20 min sessions;each session was approximately 1 week apart. There were12 practice trials prior to 136 experimental trials in everysession. All the pictures (12 practice objects, 50 body parts,50 control objects, and 36 fillers) were presented one at atime, in the center of a Macintosh computer screen. On everytrial, a fixation point was presented for 800 ms accompaniedby a beep; 200 ms after the removal of the fixation point, apicture and a word written below the picture appearedsimultaneously. The written word was the correct name ofthe picture, a semantically related distracter, or an unrelateddistracter. The subjects were instructed to judge as quicklyand as accurately as possible whether the picture and theword matched by pressing the M or V key on the keyboard.Due to ML’s mild right-sided hemiparesis, he and the controlsubjects were instructed to make the ‘yes’ response bypressing the M key using the left index finger and the ‘no’response by pressing the V key using the left ring finger.The reaction time of the subject’s key-press response wasrecorded by the computer. The picture and the word disap-peared as soon as the key-press response was made, and,after 1200 ms, the next trial began.
Results and discussion
In contrast to the results for picture naming and naming todefinition, ML performed close to ceiling in terms of accuracyfor both body parts and control pictures (see Table 10). Evenwith semantically related distracters, ML’s performance onthe body part pictures was very high and within the range ofthe controls. With regard to reaction latencies, the controlsshowed longer reaction times for body parts than for controlpictures (see Table 10), consistent with the findings forpicture naming. The difference was not significant at thegroup level (F � 1, P � 0.54), nor for three of the fivecontrols when their data were analysed individually. For theother two controls, the reaction times on body part pictureswere significantly longer than those on control object pictures(F(1,87) � 6.32, P � 0.014, and F(1,81) � 5.62, P � 0.020,respectively). ML’s latencies were on average 3113 ms forpictures of body parts and 2974 ms for control pictures,but this difference was far from significant (F � 1,
P � 0.88). In contrast to his picture-naming performance,ML’s difference in reaction times to these two sets of pictureswas within the normal range. It took him 140 ms longer torespond to a body part picture than to a control picture, muchcloser to the mean of 94 ms for the control subjects (range:14–236 ms) (see Table 10).
To make sure that ML did not have more difficulty inmatching the body part picture with its name than the controlsubjects, a subset of 45 body parts and 45 control objects wasselected for which the controls’ performance was relativelysimilar, both in terms of accuracy and response latency (seeTable 11). On these pictures, ML, like the controls, showedalmost perfect accuracy. Although ML’s reaction times werelonger overall, he was no slower with body parts than withcontrol objects. In fact, his means went in the oppositedirection with faster times for the body parts (see Table 11).Among these results, one particularly important point to noteis that ML actually responded much faster to a body partpicture than a control object picture when they were pairedwith a semantically related distracter.
The results so far indicate that ML’s deficit in namingbody parts cannot be attributed to a disruption of visualprocessing or a disruption of visual or semantic representa-tions for body parts. First, the pictures of the body partswere rated as less complex. A deficit in the visual analysisof pictures should be manifested as more difficulty with morecomplex pictures rather than the pattern observed. Second,ML showed a similar discrepancy between body parts andcontrol items when naming from definitions as when namingpictures. Third, there was no evidence indicating that MLshowed any specific difficulty in perceiving and makingsemantic judgments on pictures of body parts. In other words,ML’s difficulty with body part pictures does not result fromprocesses prior to access of the semantic representation.Instead, his selective difficulty is most probably caused by adisruption of the connection between the semantic systemand the output phonological lexicon, which is shared by thelexical semantic route for word reading.
Part D. Reading of body part and control itemnamesMethod
Participants. ML and the eight controls who participatedin experiment 2A (picture naming) also participated in thisexperiment. The control participants were matched to ML inage and education. All participants were reimbursed at therate of $7/h.
Materials and procedures. The written names of both thebody part and control pictures used in experiment 2A (picturenaming) were prepared for use in this experiment. The namesof the body parts and the control objects were mixed togetherand presented in a random order. The procedure was thesame as for word reading in experiment 1, except that therewere 100 stimuli rather than 125.
288 D. H. Wu, R. C. Martin and M. F. Damian
Table 10. Picture–word matching accuracy (%) and latency (ms) in experiment 2C for ML and five control subjects
Table 11. A subset (45 body parts and 45 control objects) of digitized responses of picture–word matching accuracy (%) and latency (ms) for ML and fivecontrol subjects
Accuracy. In contrast to his difficulty naming body partpictures compared with control pictures, ML read the bodypart and control words nearly perfectly (98 versus 96%correct, respectively). The control subjects performed atceiling with a mean accuracy of 99% for both sets of pictures(see Table 12). The invalid trials in this task for both ML
and the control subjects came mainly from the malfunctionof the voice-activated key.
Reaction times. ML’s word reading was slower overallthan the mean for the controls (754 versus 594 ms). BothML’s and the control subjects’ mean reaction times werevery similar for the body part and control words: 7 ms faster
A third route for reading? 289
Table 12. Word-reading accuracy (%) and response time (ms) in experiment 2D for ML and the control subjects
ControlsAccuracy 99.1 (range 94–100) 98.9 (range 98–100) 0.2Reaction time 588 (SD � 63; range 459–715) 600 (SD � 70; range 469–717) –12 (range –2 to 29)
SD, standard deviation.
for body parts for ML and 12 ms faster for body parts forthe control subjects (see Table 12). The differences werenon-significant for ML and for seven of the eight controlsanalysed individually. When analysed as a group, the 12 msadvantage for body parts for the control subjects was signific-ant (F(1,7) � 9.35, P � 0.02). The 7 ms effect for ML wasclearly within the range of effects shown by the controls4.
Discussion
The contrasting results for ML on picture naming and wordreading are not consistent with the summation hypothesis.That is, he showed a very large disadvantage for namingbody parts in both error rates and reaction times, but nodifference between body parts and control words in oralreading. As discussed earlier, the summation hypothesiswould predict longer reaction times for items for which thereis some disruption in the lexical semantic route.
Two concerns need to be addressed, however, before strongconclusions can be drawn. First, a substantial number oftrials in the picture-naming task were excluded for ML, dueto the production of extraneous utterances like ‘uh’ or ‘um’,even though he eventually produced the correct response.We wanted to determine if including such trials for ML andthe controls would still confirm the previous results. Second,given that the controls showed longer reaction times for bodyparts than control objects in the picture-naming task, theresults raise the issue of whether there is something in theearly visual processing of some of the body part pictures thatmay make these trials difficult. That is, both the controls andML may have some difficulty in identifying body parts frompictures that is unrelated to complexity (e.g. within-categoryvisual similarity). Because ML’s picture-naming reactiontimes were overall much longer than the controls, he mayhave been simply showing an exaggerated effect of thisvisual processing difficulty. While ML’s performance onexperiment 2C argues against the hypothesis that he showsan exaggerated effect of any difficulty in the visual processingof pictures, we thought it wise to address this concern in thecurrent experiment as well. To do so, we analysed a subsetof pictures where naming times for body parts and controlpictures were closely matched for control subjects.
Further analyses
Reaction times for all correct responses. The responses ofML and five of the eight age- and education-matched controlshad been recorded on tape and digitized. Reaction times weredetermined from the digitized responses for all trials inwhich the subject eventually produced the correct response.Response latency was measured as the interval between theend of the fixation point and the beginning of the intendedresponse. The results are summarized in Table 13.
ML gave the intended responses on only 31 out of the50 pictures of body parts and his accuracy was outside thenormal range. On the other hand, he named 38 out of50 pictures of control objects correctly and performed withinthe normal range. Although his accuracy difference on bodyparts and control objects did not reach significance(χ2
(n�100) � 2.29, P � 0.13), ML also had slower responsesto body parts. His naming time on body parts (3543 ms) was848 ms longer than that on control objects (2695 ms), adifference which reached significance (F(1,67) � 4.062,P � 0.048). Again, although the controls also showed alonger naming time on body parts (1985 ms) than on controlobjects (1911 ms), the difference was relatively small (74 ms)and was not significant at the group level (F(1,4) � 3.87,P � 0.12), nor for four out of five control subjects whenanalysed individually. ML’s difference was even furtheroutside the range of the control subjects when these trialswith hesitations were included.
Matching body part and control pictures on naminglatencies for controls. A subset of stimuli was selected thatconsisted of 38 body parts and 38 control objects which hadapproximately the same naming times for the control subjects,1944 and 1949 ms, respectively (see Table 14). At least threeout of five control subjects produced the intended responsefor the pictures in this subset and the reaction time analysisshowed no difference between body parts and control objectsat group and individual levels (all Ps � 0.37). For thesestimuli, ML still showed a much longer naming time forbody parts than for control objects. The difference was 748ms and obviously outside the normal range, but this differencefailed to reach significance (P � 0.12) on this relativelysmall number of trials, given his large standard deviation forthe body part pictures. Although the accuracy difference
290 D. H. Wu, R. C. Martin and M. F. Damian
Table 13. Digitized responses of picture naming and voice-key recorded responses of word reading for ML and five control subjects
Body parts Control objects Difference
Picture-naming taskML
Accuracy 62 (31/50) 76 (38/50) –14Reaction time 3543 2695 848
ControlsAccuracy 77.2 (39/50; range 66–84) 81.2 (41/50; range 72–86) –4 (range –20 to 10)Reaction time 1985 (range 1820–2147) 1911 (range 1723–2107) 74 (range 1–140)
Word-reading taskML
Accuracy 98 (49/50) 96 (48/50) 2Reaction time 750 757 –7
ControlsAccuracy 98.5 (49/50; range 94–100) 99 (49.5/50; range 98–100) –0.5Reaction time 649 (range 537–715) 659 (range 546–717) –10 (range –2 to 25)
Table 14. A subset (38 body parts and 38 control objects) of digitized responses of picture naming and voice-key recorded responses of word reading for MLand five control subjects
ControlsAccuracy 89.5 (34/38; range 79–97) 87.4 (33/38; range 76–92) 2.1 (range –10.5 to 31.6)Reaction time 1944 (SD � 267; range 1805–2109) 1949 (SD � 328; range 1754–2149) –5 (range –50 to 51)
Word-reading taskML
Accuracy 100 (38/38) 94.7 (36/38) 5.3Reaction time 752 767 –15
ControlsAccuracy 98.7 (37/38; range 95–100) 98.7 (37/38; range 97–100) 0Reaction time 639 (range 530–694) 665 (range 545–730) –26 (range –49 to 3)
SD � standard deviation.
also failed to reach significance (P � 0.30), ML’s resultsconformed to the pattern for the entire set. He named only26 out of these 38 body parts correctly, a performance levelwhich fell outside the normal range, whereas his accuracyon control objects was within the normal range (see Table 14).
ML’s word reading for the names corresponding to thissubset of pictures again showed the pattern reported previ-ously for the entire set. He was slightly more accurate andhad somewhat faster reaction times for the body part namesthan for the control names. The reaction time advantage forthe body part names was within the range shown by thecontrols (see Table 14).
If the summation hypothesis is correct, ML should havehad at least some degree of difficulty reading those wordsfor which he had problems naming the corresponding pictures.This prediction was not fulfilled. ML read all of the selectedbody parts and most of the selected control objects correctly.Although his reading times were generally slower than thecontrol subjects’, ML showed no difference between readingthe words of body parts and of control objects. In fact, MLhad slightly faster reading times on body parts than on controlobjects, just like the control subjects (see Table 14). Unlike
his performance on picture naming, there was no evidenceto suggest that ML had any more reading difficulty withwords for body parts compared with those for control objects.
As discussed earlier, the results from experiments 2A–Cindicate that ML’s deficit in naming body parts is due to adisruption in the link between semantics and output phono-logy for body parts. This link is used in the lexical semanticpathway for reading. It is clear, however, that ML’s word-reading performance showed no correspondence to his pic-ture-naming performance. Given that his sublexical routewas severely damaged, as demonstrated in previous studies,a third lexical route is needed to account for his normalreading of words that caused him difficulty in picture naming5.
General discussion
Some researchers have argued that there is a third route forreading which connects the input orthographic/graphemiclexicon directly to the output phonological lexicon. This thirdroute has been postulated to account for preserved readingin patients with a disruption of both sublexical and lexicalsemantic routes (e.g. Funnell, 1983; Coslett, 1991). Hillis
and Caramazza (1991, 1995) have argued that the third routeis superfluous and have offered the summation hypothesis toaccount for these patients’ relatively preserved word-readingabilities. According to the summation hypothesis, wordreading can be achieved through the cooperation of thepartially impaired lexical semantic and sublexical routes. Ifthis summation hypothesis is correct, word reading shouldheavily rely on one route when the other route is damaged.In other words, the performance on word reading should bemore highly correlated with the performance of using thelexical semantic route if the sublexical route is disrupted(and more highly correlated with the performance relyingon sublexical grapheme-to-phoneme correspondences if thesemantic route is impaired). In this study, we examined abrain-damaged patient ML who showed a severe deficit inhis sublexical route. According to the summation hypothesis,ML’s word reading should rely more on the lexical semanticroute and should show a large influence of semantic factors.ML’s word reading should also be more highly related topicture naming than for the control subjects. Contrary tothese predictions, ML showed no larger correlation betweenpicture naming and word reading than the controls. Moreover,for a particular set of stimuli that he had difficulty naming(body parts), ML did not show longer times reading thosewords than control words. The evidence indicates that hisbody part-naming deficit is due to a disruption in theconnections between semantics and output phonology forbody parts—connections that should also be used in the lexicalsemantic route for reading. According to the summationhypothesis, ML should show at least some degree of difficultyreading body part names, given his severely impaired sublex-ical route. This pattern was not found.
The current discussion has been framed in terms oftraditional dual-route models in which the sublexical routecarries out grapheme-to-phoneme conversions to determinethe pronunciation of a written word. Plaut et al. (1996)proposed a dual-route computational model to account forboth normal and impaired word reading in which the non-semantic route does not carry out grapheme-to-phonemeconversion according to rules. Instead, letter sequences aremapped to sounds using a connectionist architecture in whichhidden units intervene between graphemic and phonologicalrepresentations. This architecture allows for mapping betweenletters and sounds for both regular and irregular spelling–sound patterns in the same set of nodes and connections. Theway this model accounted for the relatively preserved wordreading demonstrated by patients WB and WT was verysimilar to the summation hypothesis. Basically, Plaut et al.(1996) claimed that the cooperation between the phonologicalmechanism (i.e. orthographic–phonological correspondence)and the semantic mechanism may be sufficient for good wordreading (Plaut et al., 1996, pp. 102–103). As indicated bythe modeling work described earlier, such an account wouldpredict longer reading latencies for words for which there isa deficit in the lexical semantic route. This pattern was not
observed for ML, however. Thus, this dual-route connectionistmodel could not account for the present results either.
In summary, the predictions derived from the summationhypothesis were not supported by the patient data presentedhere. The results for patient ML instead indicate that a directroute from lexical orthographic representations to lexicalphonological representations is needed.
Acknowledgements
This research was supported by NIH grant no. DC-00218 toRice University. We would like to thank ML and the controlsubjects for their participation in this project. We would alsolike to thank Michael McClosky for the helpful discussion,and Jessica Mejia, Angela McHardy, and Laura Matzen fortheir assistance in testing.
Notes1Further reduction in the semantic influence causes theselection of an erroneous lexical item. This is due to the factthat under Plaut and Shallice’s semantic specification, thesimilarity between items (even across semantic categories)is quite high. Therefore, when the weights between thesemantic and the lexical output layer are reduced below acertain value, related words from semantically unimpairedcategories will receive more net input than target words fromsemantically impaired categories. In the above simulation,the impaired sublexical pathway was simulated by modifyingconnection weights such that the incoming net input was10% of the original level. In contrast, the semantic impairmentwas modeled by merely reducing semantic input to 60%of the original level. However, both modifications showcomparable reductions in target activation levels. Doubtless,this again demonstrates the non-linearity of the activationfunction: when one source of input has already been substan-tially reduced (as in the impairment of the sublexical path-way), the modification of the remaining pathway will showa magnified impact.2Even though there are other processes involved in picturenaming that are not shared by word reading (i.e. visualanalysis of pictures, access to stored structural knowledgeabout objects, access to semantic knowledge from the struc-tural representations), a greater correlation between word-reading and picture-naming latencies should still be expectedfor patients with a disruption to the sublexical route than forcontrol subjects, as the contribution from the sublexicalroute would mitigate the effect of the lexical route fornormal subjects.3One point to be noted is that both ML and the controlsperformed better in naming to definitions than to pictures(see Tables 8 and 9). Although this result is somewhatcounterintuitive, as one may expect that definitions are moreambiguous than pictures, it should be kept in mind thatthe invalid trials of picture naming included hesitations(especially so for the control subjects) even though correct
responses were eventually given. Such trials were regardedas accurate in the naming to definition task as long as theintended responses were produced. In addition, due to thenature of some pictures (e.g. the pictures of ‘cheek’ and‘bread’ could also be named as ‘face’ and ‘sandwich’,respectively), their definitions (‘the part of the face whererouge is put’ for ‘cheek’ and ‘the food that is cut into slicesand eaten with butter’ for ‘bread’) are actually less ambiguous,thus higher accuracy.4The one control subject who performed significantly worseon naming the body part pictures compared with controlitems did not show any evidence of difficulty reading thebody part names. For this subject, the mean accuracies onthe body part pictures versus the control pictures were 62and 82%, respectively, and the mean reaction times were1262 and 1245 ms, respectively.5Given that ML showed an imageability effect on wordreading with low-frequency words, his word reading of low-frequency body parts may also be expected to show someinfluence from the impaired lexical semantic route. Presum-ably this was not found because the frequency of the bodypart stimuli was substantially higher than that for the low-imageability words (mean frequencies 33.64 and 8.58,respectively).
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Received on 3 January, 2001; resubmitted on 15 February, 2002;accepted on 8 March, 2002
A third route for reading? 293
A third route for reading? Implicationsfrom a case of phonological dyslexia
D. H. Wu, R. C. Martin and M. F. DamianAbstractModels of reading in the neuropsychological literature sometimes only includetwo routes from print to sound, a lexical semantic route and a sublexicalphonological route. Other researchers hypothesize an additional route thatinvolves a direct connection between lexical orthographic representations andlexical phonological representations. This so-called ‘third route’ has beeninvoked to account for the preserved oral reading of some patients who showsevere semantic impairments and a disruption of the sublexical phonologicalroute. In their summation hypothesis, Hillis and Caramazza proposed thatreading in these cases could result from a combination of partial lexicalsemantic information and partial sublexical phonological information, thusobviating the need for the third route. The present study examined the caseof a phonological dyslexic patient (ML) who exhibited preserved word reading,even for items he could not name, along with a non-word reading impairment.The relationship between ML’s naming and reading, and the influence ofsemantic variables on his reading were examined. The results of thisexamination are interpreted as supporting the existence of the third route.
JournalNeurocase 2002; 8: 274–95
Neurocase Reference Number:O261
Primary diagnosis of interestPhonological dyslexia
Author’s designation of caseML
Key theoretical issued The necessity of a direct route from input orthographic lexicon to output
phonological lexicon for reading
Key words: word reading; summation hypothesis; phonological dyslexia
Scan, EEG and related measuresComputed tomography scan
Other assessmentPicture naming and word reading of pictures from Snodgrass and Vanderwart(1980). Picture naming, definition naming, picture–word matching and wordreading of body parts and control objects
Lesion locationd Infarction of left frontal and parietal opercula and mild atrophy of left
temporal operculum
Lesion typeInfarction and atrophy
LanguageEnglish
Appendix A. Pictures of body parts and controlobjects used in experiment 2A
aNumber of occurrences among 1 014 000 graphic words in the corpus.
294 D. H. Wu, R. C. Martin and M. F. Damian
Appendix B. Pictures of body parts and control objects used in experiment 2B
Body part Definition Control object Definition
Ankle The part of the leg which can be sprained Diamond The expensive stone on an engagement ringArch The curved part of the bottom of the foot Lens The glass part of a pair of glassesArm The part of the body used to throw things Key The thing for opening and closing locksBeard The hair on a man’s face Cap The kind of hat that a baseball player wearsBelly button The indentation on the surface of the abdomen Tusk The long front teeth of an elephantCalf The back part of the lower half of a leg Sleeve The piece of clothing that covers the armCheek The part of the face where rouge is put String The part of a violin that the bow touchesChin The lower part of the face below the mouth Shirt The item of clothing worn on the top half of the bodyEar The thing that people hear with Belt The item of clothing worn to hold pants upEarlobe The part of the ear that can be pierced Filament The part of a light bulb that burnsElbow The joint in the middle of the arm Button The common fastener used on shirtsEye The thing that people see with Window The opening on the wall that a curtain coversEyebrow The hair on the bony arch just above the eye Pedal The thing that a bicycle rider turns to power the bicycleEyelash The little hairs around the eye Doorknob The thing that a person turns to open a doorFinger The part of the hand on which a ring is worn Bread The food that is cut into slices and eaten with butterFingernail The hard substance that protects the end of the finger Parachute The thing that helps a skydiver to land safelyFist The ball that a hand is clenched into Cigarette The thing that people smokeFoot The body part that you wear a shoe on Box The thing that is used for packing or storing thingsForehead The top part of the face Arrow The sharp thing shot from a bowHair The thing that grows on people’s heads Paper The flat thing for writing or typing onHand The thing that you shake when you meet someone Church The building where people go to worship on SundayHeel The back part of the bottom of the foot Pants The clothing that covers the legsHip The joint at the top of the leg Trunk The nose of an elephantIris The colored part of the eye Shoelace The part of a shoe that gets tiedJaw The bone that moves up and down for chewing Onion The vegetable that can make people cryKnee The joint in the middle of the leg Fish The animal that has fins and lives underwaterKnuckle The joint in the middle of a finger Mane The hair on a horse’s neckLeg The body part used to walk or run Roof The part of a house covered with shinglesLips The body part that chap stick is put on Candle The thing made of wax that has a wick that burnsMouth The body part used for talking or eating Doctor The professional who prescribes drugs to sick peopleMustache The hair on a man’s upper lip Buckle The fastener on a beltNeck The part of the body that holds the head up Bottle The container that you buy wine inNipple The body part that an infant sucks on Giraffe The spotted animal with a very long neckNose The thing that people smell with Moon The large disk that shines in the sky at nightNostril The opening in the nose Hinge The part of a door that attaches to the framePalm The part of the hand that touches when people clap Envelope The thing used to send letters inPupil The black circle in the center of the eye Crown The thing that a king wears on his headRib The bone in a person’s chest Accordion The portable instrument with a keyboard and bellowsShin The front part of the lower half of the leg Ruler The measuring tool that can help to draw straight linesShoulder The joint at the top of the arm Wheel The part of a car that is round and rolls along the roadShoulder blade n/a Wrench The tool used for twisting boltsSkull The bone inside a person’s head Pouch The pocket where baby kangaroos are carriedTeeth The things inside the mouth used for chewing Bridge The road over waterThigh The upper part of the leg Drawer The part of a desk or dresser where things are storedThumb The appendage on a hand that is shorter than the fingers Hose The long rubber tube for carrying waterThumb nail The hard substance at the end of a person’s thumb Snowman The thing that children build that has a carrot noseToes The appendages on the front of a foot Veil The thing that a bride wears over her faceTongue The thing that people use to taste Corn The yellow vegetable that gets eaten on the cobWaist The part of the body where a belt is worn Leaf The green things that grow on treesWrist The joint between the arm and the hand Pillow The thing for people to rest their heads on in bed
A third route for reading? 295
Appendix C. Pictures of body parts and control objects used in experiment 2C
Body part Semantically related distracter Unrelated distracter Control object Semantically related distracter Unrelated distracter
Ankle Knee Choir Diamond Gold SackArch Shin Globe Lens Magnifying glass GrillArm Leg Sign Key Lock PoetryBeard Mustache Storm Cap Helmet HorizonBelly button Nipple Gaggle Tusk Ivory PancakeCalf Thigh Bee Sleeve Pocket LogCheek Forehead Sauce String Bow SpyChin Ear Ranch Shirt Pants MirrorEar Chin Movie Belt Rope LessonEarlobe Nostril Pebble Filament Battery RobotElbow Wrist Puzzle Button Zipper CigarEye Heel Radi Window Curtain GameEyebrow Pupil Dime Pedal Step TattooEyelash Iris Scorpion Doorknob Hinge Pin cushionFinger Thumb Highway Bread Butter TissueFingernail Thumb nail Pecan Parachute Airplane ScribbleFist Palm Treat Cigarette Lighter PondFoot Hand Rain Box Bowl DustForehead Cheek Jar Arrow Knife BushHair Skull Letter Paper Notebook FloorHand Foot Water Church Office FamilyHeel Eye Cork Pants Shirt ToastHip Waist Rag Trunk Hoof RecipeIris Eyelash Strawberry Shoelace Sock ClawJaw Teeth Rail Onion Carrot PotteryKnee Ankle Yard Fish Lobster TapeKnuckle Toes Chalk Mane Tail BakeryLeg Arm Railroad Roof Chimney TreeLips Tongue Brick Candle Torch LemonMouth Nose Afternoon Doctor Patient JazzMustache Beard Kitten Buckle Snap SwampNeck Shoulder File Bottle Glass TelephoneNipple Belly button Macaroni Giraffe Zebra ScarecrowNose Mouth Garden Moon Star ScaleNostril Earlobe Wagon Hinge Doorknob LoafPalm Fist Joke Envelope Stamp JailPupil Eyebrow Vacuum Crown Hat FloodRib Shoulder blade Kite Accordion Trumpet OasisShin Arch Magnet Ruler Inch CricketShoulder Neck Beach Wheel Spoke VisionShoulder blade Rib Clamp Wrench Hammer ManholeSkull Hair Crumb Pouch Bag EraserTeeth Jaw Ball Bridge Road GasThigh Calf Duck Drawer Lamp UmbrellaThumb Finger Hail Hose Pump PineappleThumb nail Fingernail Neighbor Snowman Sled BeakToes Knuckle Wheat Veil Cloak DockTongue Lips Bench Corn Potato PencilWaist Hip Shed Leaf Root BubbleWrist Elbow Luggage Pillow Cushion Glue
