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Joumal of Experimental Psychology: General1991 , Vol. 120, No. 4, 339-357

Copyriab! 1991 by the American Psychological Associatio'1l.lnc.0096-3445/91/~3.

A Computational Model of Semantic Memory Impairment: ModalitySpecificity and Emergent Category Specificity

Martha J. Farah and James L. McClellandCarnegie Mellon University

It is demonstrated how a modality-specific semantic memory system can account for category-specific impairments after brain damage. In Experiment I, the hypothesis that visual andfunctional knowledge play different roles in the representation of living things and nonlivingthings is tested and confirmed. A parallel distributed processing model of semantic memory inwhich knowledge is subdivided by modality into visual and functional components is described.In Experiment 2, the model is lesioned, and it is confirmed that damage to visual semanticsprimarily impairs knowledge of living things, and damage to functional semantics primarilyimpairs knowledge of nonliving things. In Experiment 3, it is demonstrated that the modelaccounts naturally for a finding that had appeared problematic for a modality-specific architec-ture, namely, impaired retrieval of functional knowledge about living things. Finally, in Experi-ment 4, it is shown how the model can account for a recent observation of impaired knowledgeof living things only when knowledge is probed verbally.

How is semantic memory organized? Two general answersto this question have been proposed. One is that semanticmemory is organized by taxonomic category, such that differ-ent parts of the system represent knowledge about objectsfrom different categories. Alternatively, semantic memorycould be subdivided by modality of knowledge, such that onecomponent is responsible for visual information about ob-jects, another for auditory information, and so on.

Patients with selective losses of knowledge after brain dam-age appear to provide a direct source of evidence on theorganization of semantic memory. Unfortunately, this evi-dence yields conflicting answers. In most cases, the lossesappear to be tied to specific modalities, resulting in impairedrecognition of objects in just one modality (e. , visual orauditory agnosia) or in impaired manipulation of objects withspecific uses, despite intact recognition of them (apraxia; e.

g.,

a key might be pulled, rather than turned). These observationsare consistent with recent neurophysiological data showingthat most cortical neurons are modality-specific, even inregions that were traditionally viewed as supramodal associ-ation areas (e. , Sereno & Allman, 1991). In some cases,however, brain damage seems to cause category-specific lossesof knowledge, which cut across different modalities. Specifi-cally, there are patients who seem to have lost their knowledgeof living things, and others who seem to have lost theirknowledge of nonliving things. These observations suggestthat the architecture of semantic memory incorporates at least

This research was supported by Office of Naval Research GrantNSOOI4-89-J3016, National Institute of Mental Health (NIMH)Grant ROI MH48274, National Institutes of Health Career Devel-opment Award K04-NSOI405, NIMH Career Development AwardMHOO385, and National Science Foundation Grant BNS 88- 12048.

We thank Robin Rochlin for assistance in data collection.Correspondence concerning this article should be addressed to

Martha J. Farah or James L. McClelland, Department of Psychology,Carnegie Mellon University, Pittsburgh, Pennsylvania 15213"3890.

two general, taxonomically defined subsystems, for represent-ing knowledge of living and nonliving things.

In this article, we attempt to resolve the apparent conflictbetween these two types of neuropsychological evidence. Afterreviewing the neuropsychological evidence for category spec-ificity in semantic memory, we present a parallel distributedprocessing (POP) model in which the architecture distin-guishes only between modalities of knowledge, but whendamaged, displays category specificity similar to that of thepatients described in the neuropsychological literature.

Impairments in Knowledge of Living andNonliving Things

The most commonly observed semantic memory dissocia-tion is between impaired knowledge of living things withrelatively preserved knowledge of nonliving things. In the firstreport of this phenomenon, Warrington and Shallice (1984)described 4 patients who were much worse at identifyingliving things (animals, plants) than nonliving things (inani-mate objects). All 4 of these patients had recovered fromherpes encephalitis, and all had sustained bilateral temporallobe damage. Two of the patients were studied in detail andshowed a selective impairment for living things across a rangeof tasks, both visual and verbal. Table 1 shows examples oftheir performance in a visual identification task, in whichthey were to identify by name or description the item shownin a colored picture, and in a verbal definition task, in whichthey were to provide definitions when the names of thesesame items were presented auditorily. Examples of their def-initions are also shown in Table 1.

Farah, McMullen , and Meyer (1991) studied 2 head-injuredpatients whose knowledge of living things appeared to beselectively disrupted. We examined their picture recognitionperformance as a function of the living-nonliving distinctionas well as many other possibly confounded factors that mightinfluence performance, including complexity, familiarity,

339

340 MARTHA J. FARAH AND JAMES L. McCLELLAND

Table 1

Performance of Two Patients With Impaired Knowledge of Living Things on VariousSemantic Memory TasksCase Living thing Nonliving thing

JBRsay

Picture identification90%75%

JBRSBY

Spoken word definition79%52%

JBR Parrot: don t knowDaffodil: plantSnail: an insect animalEel: not wellOstrich: unusual

Examples of definitionsTent: temporary outhouse, living homeBriefcase: small case used by students to carry papersCompass: tools for telling direction you are goingTorch: hand-held lightDustbin: bin for putting rubbish in

say Duck: an animal

Wasp: bird that fliesCrocus: rubbish material

Holly: what you drinkSpider: a person looking for things,

he was a spider for his nation orcountry

name frequency, name specificity (i. , basic object level orsubordinate level), and similarity to other objects. A regressionanalysis showed that even with all of these factors accountedfor, the living-nonliving distinction was an important predic-tor of recognition performance.

Other cases of selective impairment in knowledge of livingthings include additional postencephalitic patients described

by Pietrini et al. (1988), Sartori and Job (1988), and Silveriand Gainotti (1988); a patient with encephalitis and strokesdescribed by Newcombe, Mehta, and de Haan (in press); anda patient with a focal degenerative disease described by Basso,Capitani, and Laiacona (1988). In all of these cases, there wasdamage to the temporal regions, known to be bilateral exceptin Case 2 of Farah et al. (1991), Case 1 of Pietrini et al., andthe case of Basso et al. , in which there was evidence only left temporal damage.

The opposite dissociation-namely, impaired knowledge

of nonliving things with relatively preserved knowledge ofliving things-has also been observed. Warrington andMcCarthy (1983, 1987) described 2 cases of global dysphasiaafter large left-hemisphere strokes, in which semantic knowl-edge was tested in a series of matching tasks. Table 2 showsthe results of a matching task in which the subjects were askedto point to the picture, in an array, that corresponded to aspoken word. Their performance with animals and flowerswas reliably better than with nonliving things. One of thesesubjects was also tested with a completely nonverbal matchingtask, in which different-looking depictions of objects or ani-mals were to be matched to one another in an array, andshowed the same selective preservation of knowledge of ani-mals relative to inanimate objects.

Wheelbarrow: object used by people to take materialabout

Towel: material used to dry peoplePram: used to carry people, with wheels and a thing to

sit onSubmarine: ship that goes underneath the seaUmbrella: object used to protect you from water that

comes

Implications of the Living-Nonliving Dissociationsfor Models of Normal Semantic Memory

The most straightforward interpretation of the double dis-sociation between knowledge of living and nonliving things isthat these two bodies of knowledge are represented by twoseparate category-specific components of semantic memory.This interpretation is consistent with the view that semanticmemory is organized along taxonomic lines, at least as far asthe distinction between living and nonliving things is con-cerned. However, Warrington and colleagues (e.g., Warring-ton & McCarthy, 1983, Warrington & Shallice, 1984) sug-gested an alternative interpretation, according to which se-mantic memory is fundamentally modality-specific. Theyargued that selective deficits in knowledge of living and non-living things may reflect the differential weighting of infor-mation from different sensorimotor channels in representing

Table 2

Performance of Two Patients With Impaired Knowledge ofNonliving Things on Various Semantic Memory Tasks

Category

Case Animal Flower Object

Spoken word-picture matchingVER 86% 96% 63%YOT 86% 86% 67%

Picture-picture matchingYOT 100% 69%

SEMANTIC MEMORY IMPAIRMENT 341

knowledge about these two categories. More specifically, theypointed out that living things are distinguished primarily bytheir sensory attributes, whereas nonliving things are distin-guished primarily by their functional attributes. For example,knowledge of an animal, such as a leopard, by which it isdistinguished from other similar creatures, is predominantlyvisual. In contrast, knowledge of a desk, by which it isdistinguished from other furniture, is predominantly func-tional (i.e. , what it is used for). Thus, the distinctions betweenimpaired and preserved knowledge in the cases reviewedearlier may not be living-nonliving distinctions per se, butrather sensory-functional distinctions.

The sensory-functional hypothesis seems preferable to astrict living-nonliving hypothesis for two reasons. First, it ismore consistent with what is already known about brainorganization. As mentioned earlier, it is well known thatdifferent brain areas are dedicated to representing informationfrom specific sensory and motor channels. Functional knowl-edge could conceivably be tied to the motor system. In anycase, there is prior evidence for the selective vulnerability ofknowledge offunctional attributes after left-hemisphere dam-age: Goodglass and Baker (1976) found that left hemisphere-damaged aphasic patients had particular difficulty relating anamed object to a word describing its use compared withwords describing its sensory qualities or words denoting otherobjects in the same category. A second reason for preferringthe sensory-functional hypothesis to the living-nonliving hy-pothesis is that exceptions to the living-nonliving distinctionhave been observed in certain cases. For example, Warringtonand Shallice (1984) reported that their patients, who weredeficient in their knowledge ofliving things, also had impairedknowledge of gemstones and fabrics. Warrington and Mc-Carthy s (1987) patient, whose knowledge of most nonlivingthings was impaired, seemed to have retained good knowledgeof very large outdoor objects, such as bridges or windmills. Itis at least possible that knowledge of these aberrant categoriesof nonliving things is primarily visual.

Unfortunately, there is a problem with the hypothesis thatliving things impairments are just impairments in sensoryknowledge and nonliving things impairments are just impair-ments in functional knowledge. This hypothesis seems topredict that cases of living things impairment should showgood knowledge of the functional attributes of living thingsand cases of nonliving things impairment should show goodknowledge of the visual attributes of nonliving things. Theevidence available in cases of nonliving things impairment islimited to performance in matching-to-sample tasks, whichdoes not allow one to distinguish knowledge of visual orsensory attributes from knowledge of functional attributes.However, there does appear to be adequate evidence availablein cases of living things impairment, and in at least somecases, it disconfirms these predictions.

Knowledge of Nonvisual Attributes of Living Thingsin Cases of Living Things Impairment

Consider the definitions ofliving and nonliving things givenby Warrington and Shallice s (1984) 2 cases (Table 1). Al-though the definitions of nonliving things may be somewhat

skimpy on visual detail, in keeping with the sensory-func-tional hypothesis, the definitions of living things do not showpreserved functional knowledge. If these subjects have lostjust their visual semantic memory, they should be able toretrieve the functional attributes of living things; for example,parrots are kept as pets and can talk, daffodils are a springflower, and so on.

In the other cases of living things impairment, visual andfunptional knowledge have been compared directly, and func-tional knowledge of living things ranges from mildly to se-~rely impaired. Newcombe et al. (in press) presented their

subject with triads of words, with the instruction to grouptogether two of the words according to either the visualsimilarity of the words' referents or some factual commonality(e. , normally found in the United Kingdom). When thewords named nonliving things, their subject performed withinnormal limits. However, when the words named living things,their subject performed significantly worse than control sub-jects, even when the grouping was based on factual, ratherthan visual, properties. Silveri and Gainotti (1988) assessedthe ability of their patient to identify animals on the basis oftwo kinds of spoken definition: visual descriptions of theanimal's appearance, such as " an insect with broad, coloredornate wings" for butterfly, and nonvisual descriptions, ofeither metaphorical verbal associations to the animal, such asking of the jungle" for lion, or functions of the animal, such

as "the farm animal that bellows and supplies us with milk"for cow. Although the subject was worse at identifying animalsfrom visual discriptions than from nonvisual descriptions, he

performed poorly with both and identified only 58% of theanimals on the basis of nonvisual descriptions (which controlsubjects had rated easy). A patient of Basso et al. (1988) alsoappeared to be better at retrieving nonvisual information, butnonvisual information was not intact. These different typesof knowledge were tested by naming a word and then askinga multiple-choice question about it. The question tappedcategorical information, such as "is it a bird, mammal, fishor reptile?"; functional information, such as "does it live inItaly or the desert?"; or visual information, such as "does ithave a smooth back or is it hump-backed?" The patientperformed at chance on the categorical as well as on the visualquestions and performed less than perfectly with the func-tional questions (35 out of 42). (It should be noted that notall of the words denoted living things. Basso et al. tested thepatient with words he had failed to match with pictures; mostof these were living things. The results for living and nonlivingthings were not separately reported.

Similarly, Sartori and Job (1988) found better performancein their case in tests tapping nonvisual, rather than visualknowledge of animals, but their subject nevertheless appearedmildly impaired in nonvisual tasks. For example, in definingliving and nonliving things, the subject made numerous fac-tual errors about nonvisual characteristics of animals andvegetables, twice as many as were made about nonlivingthings. The subject also made occasional errors in identifyinganimals with their characteristic sounds or environments,although in the absence of normative data it is difficult tointerpret these results. Farah, Hammond, Mehta, and Ratcliff(1989) tested the ability of I of the head-injured patients

342 MARTHA J. FARAH AND JAMES L. McCLELLAND

described earlier (Farah et al., 1991 , Case 1) to retrieve visualand nonvisual knowledge about living and nonliving thingsand compared the subject's performance to age- and educa-tion-matched normal subjects. The patient's performance felloutside of normal limits only for visual knowledge of livingthings. However, whereas he performed at an average level inretrieving nonvisual information about nonliving things, heperformed below-average in retrieving nonvisual informationabout living things, and the discrepancy between the twolevels of performance with these two kinds of question waslarger than for any of the 12 control subjects. Unpublishedobservations of Case 2 of Farah et al. (1991) are that thesubject was impaired at retrieving functional informationabout animals, such as knowing which animal provides woolas well as at recognizing animal sounds. When given the testdesigned by Farah et al. (1989), she performed at chance onthe questions concerning visual as well as nonvisual propertiesof living things, whereas she performed far-above-change withnonvisual properties of nonliving things.In sum, the sensory-functional hypothesis seems more

attractive then the living-nonliving hypothesis because it ismore in keeping with what is already known about brainorganization. However, it does not seem able to account forall of the data. In particular, it does not seem able to accountfor the impaired ability of these patients to retrieve nonvisualinformation about living things.

The goal of our model is to demonstrate that the sensory-functional hypothesis is sufficient to account for these seman-tic memory impairments when it is taken together with acertain conception of mental representation; specifically, theidea of active, distributed representations, in which the acti-vation of the representation depends on mutual supportamong different parts of the representation. This idea iscommon to a wide range of recurrent POP models (e.Anderson, Silverstein, Ritz, & Jones, 1977; McClelland &Rumelhart, 1985). We show that a model of semantic mem-ory with active distributed representations consisting of justtwo types of semantic information, visual and functional, canbe lesioned to produce selective impairments in knowledge ofliving things and nonliving things. More important, we showhow such a model can account naturally for the impairmentof both visual and functional knowledge of living things afterdamage confined to visual semantics. Finally, we also showhow this model can account for a recently described case inwhich knowledge of living things was impaired only whenprobed verbally, which had initially been interpreted as evi-dence that semantic memory is subdivided not only by cate-gory of knowledge but also by modality of access.

Before presenting the simulation model and the results oflesioning the model , we describe an experiment that tests thebasic assumption of the sensory-functional hypothesis,

namely, that living things are known primarily by their sen-sory features and that nonliving things are known primarilyby their functional features.

Experiment 1

In this experiment, normal subjects read dictionary defini-tions of living and nonliving things and underlined all occur-

rences of visual and functional descriptors. This testedwhether there is a difference in the importance of sensory(specifically, visual) and functional properties for the meaningof living and nonliving things and provided us with a quan-titative estimate of the ratio of visual to functional featuresfor the representations of living and nonliving things in themodel.

Method

Materials. The lists of living and nonliving things were takenfrom Warrington and Shallice s (1984) Experiment 2. Definitionswere copied from the American Heritage Dictionary (1969) andprinted in a random order.

Procedure. Subjects read for either visual descriptors or functionaldescriptors. If they read for visual descriptors, they were told tounderline all occurrences of words describing any aspect of the visualappearance of an item. If they read for functional descriptors, theywere told to underline all occurrences of words describing what theitem does or what it is for.

Subjects. Forty-two undergraduate students from Carnegie Mel-lon University participated in exchange for course credit. Half readfor visual descriptors, and half for functional descriptors.

Results and Discussion

Subjects who read for visual descriptors underlined anaverage of 2.68 visual descriptors for each living thing and

57 for each nonliving thing. Subjects who read for functionaldescriptors underlined an average of 0.35 functional descrip-tors for each living thing and 1. 11 for each nonliving thing.The resultant ratios of visual to functional features are 7.7 : 1for living things and 1.4: 1 for nonliving things. Thus, thesedata confirm the hypothesis that visual attributes are moreimportant than functional attributes for defining living things,but do not support the converse hypothesis that functionalattributes are more important than visual attributes for defin-ing nonliving things: Subjects found more visual descriptorsthan functional descriptors in the definitions of nonliving

things, but did not find more functional than visual descrip-tors for nonliving things. One of the interesting conclusionsof the simulation to be described is that a large difference inthe number of visual and functional attributes for livingthings, with a much smaller difference in the same directionfor nonliving things, is sufficient to account for both the livingthings impairments and the nonliving things impairments.The overall ratio of visual to functional features, combiningliving and nonliving things, is 2.9 : 1.

Model

In POP systems, a representation consists of a pattern ofactivation across a network of highly interconnected neuron-like units (Anderson et al., 1977; Hinton, McClelland, &Rumelhart, 1986; McClelland & Rumelhart, 1985). The unitscan be thought of as each representing some aspect of theentity being represented by the pattern (although these aspectsneed not be nameable features or correspond in any simpleway to our intuitions about the featural decomposition ofthese concepts). For example, in the case of living and nonliv-

SEMANTIC MEMORY IMPAIRMENT 343

ing things, some . of the units would represent aspects of thevisual qualities of the item, and other units would representaspects of the item s functional roles. The extent to whichactivation in one unit causes activation in the other units towhich it is connected depends on the connection strengths,or weights, between the units. Presenting a stimulus to thenetwork results in an initial pattern of activation across theunits, with some units being activated and others not. Thispattern then begins to change as each unit receives activationfrom the other units to which it is connected within thenetwork. Eventually, a stable pattern results, with each unitholding a particular activation value as a result of the inputsit is receiving from the other units to which it is connected.

Figure 1 shows the architecture of the model. There arethree main pools of units, corresponding to verbal inputs oroutputs (name units), visual inputs or outputs (picture units),and semantic memory representations. The semantic memoryunits are divided into visual units and functional units. Thereare bidirectional connections between units both within andbetween pools, with the exception that there are no directconnections between the name and picture units. There are24 name units; 24 picture units; and 80 semantic memoryunits, divided into 60 visual semantic and 20 functionalsemantic units, according to the roughly 3 : 1 ratio obtainedin Experiment l.

The specific processing assumptions of this model are thesame as for the distributed memory model of McClelland andRumelhart (1985). In brief, units can take on continuousactivation values between - 1 and + I. The weights on theconnections between units can take on any real values (posi-tive, negative, or zero). There are no thresholds in the modeland the influence of each unit on the input to each other unit

is just the activation of the influencing unit multiplied by thestrength of the relevant connection. Processing is synchro-

nous; that is, on each cycle the total input to each unit iscalculated on the basis of the activation levels of the units towhich it is connected and the weights on those connections,and the activation levels of all units are then updated simul-taneously. Activation levels are updated according to a non-linear activation function, which keeps activations boundedbetween -1 and + 1. Inputs are presented for 10 cycles.

FUNCTIONAL VISUAL

SEMANTICSYSTEMS

PERIPHERALINPUT

SYSTEMSVERBAL VISUAL

Figure 1. Schematic diagJ'am of the parallel distributed processing

model of semantic memory.

Ten living and 10 nonliving things were represented asrandomly generated patterns of -1 and + lover all three poolsof units. The representation of each item included the full 24name units and picture units, but only subsets of the semanticmemory units to capture the different ratios of visual andfunctional information in living and nonliving things, Livingthings were represented with an average of 16. 1 visual and

1 functional units, and nonliving things were representedwith an average of 9.4 visual and 6.7 functional units. Allpatterns contained both types of semantic memory unit.

A simple error-correcting learning procedure was used totrain the network to produce the correct semantic and namepattern when presented with each picture pattern, and thecorrect semantic and picture pattern when presented witheach name pattern. On each training trial, the name of thepicture corresponding to one of the living or nonliving thingswas presented to the name or picture input units, and thenetwork was allowed to settle for 10 cycles. The weightsamong the units were then adjusted using the delta rule(Rumelhart, Hinton, & McClelland, 1986) to minimize thedifference between the resultant activation of each unit andits correct activation.2 To distribute the work of producing

the desired outputs over as much of the network as possible,the weights were all multiplicatively reduced by 2% of theirvalue at the end of each training epoch (i.e. , each pass throughthe full set of 40 training trials). This procedure, known asweight decay, tends to keep individual weights from growinglarge, thereby forcing the network to distribute the associa-tions across a larger number of connections. This results innetworks that are more resistant to partial damage. Trainingwas continued for 100 epochs. From the point of view of thetraining procedure, there are no hidden units, so back-prop-

agation is not necessary.

To assess the generality of results obtained with this model4 variants of the model were also tested. The first 2 variantsconsisted of the exact same architecture and training proce-dure for setting weights, but with training terminated after 50and 200 epochs. Although both of these variants were trainedsufficiently well that they performed perfectly before damagethe different final patterns of weights might be expected torespond differently to damage. The third variant consisted ofthe same architecture with a different training procedure. Inthis case, there was no weight decay, and the network wastherefore expected to show less resistance to damage. A fourthvariant consisted of the original architecture and training

I Many recent connectionist models, such as the models of spelling-

to-sound translation of Rosenberg and Sejnowski (1986) or Seiden-berg and McClelland (1989), have used only unidirectional connec-tions from input by way of internal units to output and have com-puted activations in a single, feed-forward pass. At least in the lattercase, the use of a feed-forward architecture was a simplificationadopted for the sake of tractability and did not represent a change ofprinciple in favor of feed-forward information processing.

2 Note that the training procedure is not meant to simulate the

process by which people acquire semantic memory knowledge. It ismerely a tool for creating a pattern of connection strengths thatembodies the assumed associations between patterns in the differentpools of units.

344 MARTHA J. FARAH AND JAMES L. McCLELLAND

procedure, but with a different proportion of visual andfunctional semantic units in the model. Because one group ofsubjects in Experiment 1 identified visual attributes used indefining living and nonliving things, and the other groupidentified functional attributes used in defining living andnonliving things, the ratio of visual to functional semanticunits obtained in Experiment 1 was computed from differentsubjects' data. Instead of using the results of Experiment 1 toset this ratio in the model, in the third variant, we arbitrarilyset the numbers of visual and functional semantic units to beequal (i.e., 40 semantic units of each type). We used the data of Experiment 1 only to set the ratios of visual units in therepresentations of living and nonliving things and of func-tional units in the representations of living and nonlivingthings, which were ratios obtained within subjects. In thisversion of the model, living things were represented with anaverage of 10.6 visual and 4.0 functional units, and nonlivingthings were represented with an average of 6.2 visual and 12.functional units. The effects of lesions on the performance ofthe basic model and its variants were then explored.

Experiment 2

The goal of this experiment is to test the hypothesis thatselective impairments in knowledge of living and nonlivingthings can be explained by selective damage to visual andfunctional semantic memory representations, respectively.We test this hypothesis by lesioning the model and observingits performance at associating pictures and names of bothliving and nonliving things. Picture-naming is a kind ofpicture-name association task, in which the picture is givenand the name must be produced. In this model, picture-naming consists of presenting the picture portion of a patternin the picture units, letting the network settle, and thenreading the resultant pattern in the name units. Matching-to-sample, as used by Warrington and McCarthy (1983, 1987),is another kind of picture-name association task, in whichthe name is given and the correct picture must be selectednom among a choice set. In this model, it consists of present-ing the name portion of a pattern in the name units, lettingthe network settle, and then reading the resultant pattern inthe picture units. In each case, the model's performance oneach pattern was scored as correct if the resulting patternmatched the correct pattern more closely than any of theother 19 possible patterns.

Method

Twelve types of simulation were run, corresponding to 0% , 20%40% , 60%, 80%, and 99% damage to the visual and the functionalsemantic memory units. The different degrees of damage werebrought about by subjecting each unit of the relevant pool of semanticmemory units to a 0

, .

2, .

, .

6, .8, or .99 chance of being damaged.Each of the 12 simulations was damaged five times each, with thedamage being reapplied to an intact network each time. For each ofthese simulations, 40 picture-name association trials were run: 20picture-naming trials, in which each of the picture patterns waspresented to the network and the resultant name patterns were scoredand 20 matching-to-sample trials, in which each of the name patternswas presented to the network and the resultant picture patterns scored.

This procedure was applied to the original model and to the fourvariants described earlier.

Results and Discussion

Table 3 shows the results nom the simulations of visualand functional semantic memory damage to the basic model.When visual semantic memory units are damaged, the effectis greater on the naming ofliving things than nonliving things.

i As can be seen in Figure 2, the greater the damage, the greaterthe dissociation between performance with living and nonliv-ing things. When functional semantic memory units are dam-aged, the only effect is on nonliving things, and this effectalso increases with increasing damage.

The pattern of results obtained with the four variants of themodel was similar, as shown in Figures 3-6. Figure 3 showsthe results of visual and functional semantic memory damagewhen learning was terminated after half as many trials as inthe basic model. Figure 4 shows the results of lesioning visualand functional semantic memory when learning continuedfor twice as long as in the basic model. In both variants, visualsemantic memory damage affects performance with livingthings more than with nonliving things, and functional se-mantic memory damage affects performance with nonlivingthings more than with living things. Figure 5 shows the effectsof semantic memory damage on the model trained wi~tweight decay. Damage has a much larger effect overall on theperformance of this model, consistent with the tendency ofweight decay to produce more distributed and thus morerobust representations. However, as in the previous modelsdamage to visual semantics impairs performance on livingthings more than on nonliving things, and damage to func-

Table 3

Performance of the Basic Model. as Measured by Probabilityof Correctly Associating Names and Pictures, for Living andNonliving Things, After Different Amounts of Damage toVisual and Functional Semantics Units

Probabilitycorrect for

nonlivingthingsAmount of

damage

Probabilitycorrect for

living things

Damage to visual semantic memory

1.00 1.0097 .02 0.91 .04 0.88 .05 0.80 .06 0.73 .06 0.Damage to functional semantic memory

1.00 1.00 20 1.00 1.00 40 0.93 .04 1.00 60 0.88 .05 1.00 80 0.87 .05 1.00 99 0.73 .06 1.00 Amount of damage refers to the percentage of visual or functional

semantics units destroyed.

SEMANTIC MEMORY IMPAIRMENT 345

Basic model

..

Nonliving

I.Mng

..

Nonliving

I.Mng

100

'" damage to vlaual aemantlc memory

Basic model

'" damage to functional ..mantlc memory

Figure 2. Perfonnance of the basic model, as measured by proba-bility of correctly associating names and pictures, for living andnonliving things, after different amounts of damage to visual andfunctional semantics units.

tional semantics has the opposite effect. Figure 6 shows theresults of lesioning a model in which the overall numbers

visual and functional semantic memory units were arbitrarilyset to be equal, with the ratios of each type of semantic

memory attribute in the representations of items being set bythe within-subjects data from Experiment 1 , as before. As inthe previous models, lesioning visual semantics causes dispro-portionate impairment of performance with living things, andlesioning functional semantics causes disproportionate im-pairment of performance with nonliving things.

Another way of assessing the effects of damage to eithervisual or functional semantics on the network' s knowledge

living and nonliving things is to compare the pattern

activation obtained in the semantic units after damage whena picture or name is presented with that obtained beforedamage. One way to quantify this comparison is using thedot product of the pattern obtained and the target pattern.The bigger the dot product, the better the match. Table 4 andFigure 7 show the average dot products, normalized to 1 forthe undamaged network, for the semantic memory patternsafter different degrees of damage to visual and functionalsemantics for the basic model. Figures 8- 11 show the sameinformation graphically for the four variants of the basic

model. The dot products indicate that damage to visual

Training stopped after 50 cycles

... ..

Nonlivingliving

100

'" damage to vlaual aemantlc memory

100

Training stopped after 50 cycles

... ..

NonlIving

I.Mng

100

'" damage to functional aamantlc memory

Figure 3. Performance of one variant of the basic model, as mea-sured by probability of correctly associating names and pictures, forliving and nonliving things, after damage to visual and functionalsemantics units, with training stopped after 50 epochs.

346 MARTHA J. FARAH AND JAMES L. McCLELLAND

Training continued for 200 cycles

Nonliving... living

% d8m8gelo Vl8U818em8ntle m8mory

Training continued for 200 cycles

Nonliving... living

100

100

% d8mege 10 functlon81 eementle memory

Figure 4. Performance of one variant of the basic model , as mea-sured by probability of correctly associating names and pictures, forliving and nonliving things, after damage to visual and functionalsemantics units, with training continued for 200 epochs.

semantics impairs the semantic representation ofliving thingsmore than nonliving things and damage to functional seman-tics has the opposite effect.

In summary, the basic prediction of the sensory-functionalhypothesis was borne out: Damage to visual semantic memoryimpaired knowledge of living things to a greater extent thannonliving things, and damage to functional semantic memoryimpaired knowledge of nonliving things to a greater extentthan living things. This result was general across five differentimplementations of the model and across two different waysof measuring model performance.

Experiment 3

Earlier it was noted that at least some cases of living thingsimpairment are impaired at accessing functional as well asvisual information about living things. On the face of things,this phenomenon seems to disconfirm the sensory-functionalhypothesis and requires that the model incorporate into itsarchitecture an explicit distinction between knowledge ofliving and nonliving things. The goal of this experiment is tofind out whether the model can account for impaired accessto functional information about living things after damage tovisual semantic memory units.

Trained without weight decay

NonlIving

... Uving

100

% d8mege 10 vI8u81 88m8nlle memory

Trained without weight decay

NonlIving... Uving

100

% d8m.ge 10 functlon81 8em8nlle memory

Figure 5. Performance of one variant of the basic model, as mea-sured by probability of correctly associating names and pictures, forliving and nonliving things, after damage to visual and functionalsemantics units, trained without weight decay.

SEMANTIC MEMORY IMPAIRMENT 347

Equal numbers of visual and functional semantic units

..

Nonliving... LMng

100

% d8mage to vl8Ueleemantlc memory

Equal numbers of visual and functional semantic units

..

Nonliving... living

100

% demage to functlon.l88mentlc memory

Figure 6. Performance of one variant of the basic model, as mea-sured by probability of correctly associating names and pictures, forliving and nonliving things, after damage to visual and functionalsemantics units, with equal numbers of visual and functional seman-tics units.

If it were the case that representations need a certaincritical mass" to become activated-so that even if a portion

of the representation were spared by brain damage, it couldnot be accessed in the absence of other parts of the represen-tation-then the sensory-functional hypothesis could explainthe apparent across-the-board impairments in knowledge ofliving things as follows: Given that most of the semanticmemory features in the representations of living things arevisual features, and they have been destroyed, then those fewfunctional features associated with the representation mightlack the critical mass to become activated. In fact, most POP

models display just this critical mass effect. The effect arisesbecause the ability of any given unit to attain and hold itsproper activation value depends on collateral connectionswith other units in the network. Although POP systems arerobust to small amounts of damage, if a large proportion ofthe units participating in a given representation are destroyed,the remaining units will not receive the necessary collateralinputs to achieve their proper activation values.

Method

Rather than elaborate the model with additional pools of inputand output units to represent questions and answers, for the purposeof sim wating question-answering tasks, we have assessed the availabil-ity offunctional semantic memory information in the model directly:Input patterns (names or pictures) were presented, the network wasallowed to settle, and the resultant patterns of activation in thefunctional semantic memory units were recorded. As in the previousexperiment, the quality of the semantic memory representation wasmeasured by a normalized dot product, in this case, in just thefunctional semantic memory units. The procedures for training anddamaging the model were the same as for Experiment 2.

Results and Discussion

Table 5 and Figure 12 show the average scaled dot productsof the obtained and correct functional semantic memorypatterns for living and nonliving things, at each degree ofdamage to the visual semantic memory units. As predicted,damage to visual semantic memory impairs access to func-tional semantic memory disproportionately for living things.As can be seen in Figure 13, essentially the same results were

Table 4Performance of the Basic Model, as Measured by the DotProduct of the Correct and Obtained Semantic Patterns, forLiving and Nonliving Things, After Different Amounts ofDamage to Visual and Functional Semantics Units

Scaled dotproduct of

semantic unitsfor nonliving

things

Scaled dotproduct of

semantic unitsfor living things

Amount ofdamage

Damage to visual semantic memory1.00 1.0087 .02 0.72 .67 .02 0.5950 .01 0.42 .01 0.

Damage to functional semantic memory1.00 1.0020 0.77 .02 0.40 0.60 .02 0.60 0.55 .02 0.80 0.49 .02 0.99 0.36 .01 0.

Amount of damage refers to the percentage of visual or functionalsemantics units destroyed.

348 MARTHA J. FARAH AND JAMES L. McCLELLAND

Basic model

.II

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Nonliving... living

100

% damage to vfaual eemantlc memory

Basic model

ocr

..

NonIM"II... living

% damage to functional 88mantlc memory

Figure 7. Performance of the basic model, as measured by the dotproduct of the correct and obtained semantic patterns, for living andnonliving things, after different amounts of damage to visual andfunctional semantics units.

obtained for the four variants of the basic model describedearlier. The different variants display the effect to differentdegrees, but all show the same qualitative pattern, namely,impaired activation of functional semantic memory, more sofor living than nonliving things, after visual semantic memorydamage.

Although damage to visual semantic memory impairs re-trieval of functional knowledge of living things, it affectsfunctional knowledge of living things less than visual knowl-edge. This can be seen by comparing Figures 7- 11, whichshow the dot products of the obtained and correct patternover all of semantics after visual semantic damage, to Figures

12 and 13, which show the dots products for functionalsemantics in particular. This pattern is consistent with thebehavior of the patients reviewed earlier, whose impairmentsin knowledge of living things tend to be more obvious in thevisual than in the functional domain.

Experiment 4

A third type of dissociation involving living and nonlivingthings was recently described by McCarthy and Warrington(1988). They described a patient with progressive aphasia and

100

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Training stopped after 50 cycles

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Nonliving... Uving

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% damaga to functlonalaamentlc memory

Figure 8. Performance of one variant of the basic model, as mea-sured by the dot product of the correct and obtained semanticpatterns, for living and nonliving things, after damage to visual andfunctional semantics units, with training stopped after 50 epochs.

SEMANTIC MEMORY IMPAIRMENT 349

Trained without weight decay

100

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Training continued for 200 cycles

...

ocr

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Nonliving... Uving

% damage to vlaual88mantlc memory

Training continued for 200 cycles

ocr

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% damage to functional 88mantlc mampry

Figure 9. Performance of one variant of the basic model, as meas-ured by the dot product of the correct and obtained semantic patterns,for living and nonliving things, after damage to visual and functionalsemantics units, with training continued for 200 epochs.

left temporal hypometabolism of unstated etiology. This sub-ject' s knowledge ofliving things appeared to be impaired onlywhen tested verbally. As shown in Table 6, he was able toidentify pictures of both living and nonliving things and todefine nonliving things that were named aloud to him. How-ever, he was impaired at defining living things that werenamed aloud. Table 6 also shows examples of his responsesto visually and verbally probed animals.

In their discussion of this patient, McCarthy and Warring-ton (1988) suggested that the pattern of impaired and pre-served performance implies that semantic memory may

subdivided by both category and modality of access. Accord-ing to this interpretation, there is one store of knowledgeabout living things for access by verbal systems, another storeof knowledge about living things for access by visual systemsa store of knowledge about nonliving things for verbal accessand so on. The goal of this experiment was to simulate thebehavior of McCarthy and Warrington s case with the presentmodel, which does not have separate knowledge stores eitherfor living and nonliving things or for different input modali-ties. This was accomplished by damaging the connectionsbetween the name units and the visual semantics units.

% damaga to visual 88manllc memory

Trained without weight decay

1.0

..

Nonliving... Uving

100

% damage to functional 88manllc memory

Figure 10. Performance of one variant of the basic model, as

measured by the dot product of the correct and obtained semanticpatterns, for living and nonliving things, after damage to visual andfunctional semantics units, trained without weight decay.

350 MARTHA 1. FARAH AND JAMES L. McCLELLAND

Equal numbers of visual and functional semantic units

..

Nonliving... Uving

100

% damage to vlaual eemantlc memory

Equal numbers of visual and functional semantic units

..

Nonliving... living

100

% damage to functional ..mantlc memory

Figure 11. Performance of one variant of the basic model, as

measured by the dot product of the correct and obtained semanticpatterns, for living and nonliving things, after damage to visual andfunctional semantics units, with equal numbers of visual and func-tional semantics units.

Method

The model was damaged by destroying the connections that gofrom the name units to the visual semantics memory units. Sixdifferent simulations were run, corresponding to different degrees ofdamage to these connections: destruction of 0%, 20%, 40%, 60%80%, and 100% of the connections between name and visual seman-tics units, randomly chosen. As in Experiment 2, the performance ofthe network after damage was tested in two ways. First, we scored thepercentage of trials on which, given a picture, the correct name couldbe selected or, given a name, the correct picture could be selected.Second, we calculated the normalized dot product between the ob-

Table 5

Performance of the Basic Modelfor Functional Knowledge ofLiving and Nonliving Things, as Measured by the DotProduct of the Co"ect and Obtained Functional SemanticPatterns, After Different Am.ounts of Damage to VisualSemantics Units

Scaled dot Scaled dotproduct of product offunctional functional

semantic units semantic unitsfor nonliving for living

Amount of things things

damage SE 1.00 1.00 20 0.95 .02 0.92 .40 0.91 .02 0.84 .60 0.89 .02 0.80 .80 0.84 .02 0.70 .99 0.81 .02 0.65 .

Amount of damage refers to the percentage of visual semantics unitsdestroyed.

tained and target semantic memory patterns when either a picture ora name was presented.

Results and Discussion

Table 7 and Figure 14 show the percentage correct forname-picture association after different degrees of damage toconnections from name units to visual semantics units in thebasic model. Like the case of McCarthy and Warrington(1988), the impairment of the model has both category spec-ificity and modality specificity. The model is by far the most

Basic model

1.0

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Nonliving... LIving

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'" damagato vlaual..mantlc memory

Figure 12. Performance of the basic model for functional knowledgeof living and nonliving things, as measured by the dot product of thecorrect and obtained functional semantic patterns, after differentamounts of damage to visual semantics units.

SEMANTIC MEMORY IMPAIRMENT 351

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Training stopped after 50 cycles

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Trained without weight decay

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... Uving

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% damage to vlaual eemantlc memory

Training continued for 200 cycles

100

% damage to vlaual aemantlc memory

Equal numbers of visual and functional semantic units

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ocr 0& Nonliving... Living

100

% damega to vlaual ..mantlc memory

Figure 13. Performance of the four variants of the basic model for functional knowledge of living andnonliving things, as measured by the dot product of the correct and obtained functional semantic

patterns, after different amounts of damage to visual semantics units. ((top left) Training stopped after50 epochs. (top right) Training continued for 200 epochs: (bottom left) Trained without weight decay.(bottom right) Equal numbers of visual and functional semantics units.

impaired with living things presented verbally, next mostimpaired with nonliving things presented verbally, and leastimpaired with pictures of either living or nonliving things.One curious aspect of the model's performance is the bettercomprehension of the names of nonliving things when theconnections between names and visual semantics are entirelydestroyed than when they are 80% destroyed. The poor per-formance at 80% disconnection is interpretable as a kind ofinterference caused by the extremely noisy patterns of acti-vation entering the semantics units from the name units. The20% remaining connections evidently produce inappropriatepatterns of activation in the visual semantics units, thereby

interfering with the ability of collateral connections fromfunctional semantics to activate the correct patterns in visualsemantics.

Figure 15 shows the performance of the four variants of themodel when damaged and then tested as just described. Thesame qualitative pattern of results is found in each case, withthe worst performance by far found for named living things.

Table 8 and Figure 16 show the average normalized dotproducts of the obtained and correct semantic memory pat-terns for living and nonliving things, presented as names andpictures, for the basic model. Figure 17 shows the samemeasures for the four other versions of the model. The dot

352 MARTHA 1. FARAH AND JAMES L. McCLELLAND

Basic modelTable 6Performance of a Patient Whose Semantic MemoryImpairment Was Confined to Knowledge of Living ThingsWhen Probed Verbally

% of living % of nonlivingProbe things identified things identified

Spoken word 33% 89%Picture 94% 98%

Note: Examples ofidentifications of living things include rhinoceros(spoken word: "animal, can t give you any functions ; picture: "enor-mous, weighs over I ton, lives in Africa ) and dolphin (spoken word:a fish or a bird"; picture: "dolphin lives in water. . . they are trained

to jump up and come out. . . In America during the war they startedto get this particular animal to go through to look into ships.

products reveal essentially the same qualitative pattern performance as the percentage correct measure. The activa-tion of semantic memory by pictures is relatively unimpairedat all levels of damage in this model, whereas the activationof semantic memory by names is impaired, particularly forthe names of living things.

In summary, we have shown that the behavior of McCarthyand Warrington s (1988) patient can be accounted for in arelatively parsimonious way, by postulating damaged connec-tions between name units and visual semantics units. Onepossible objection to this account is based on McCarthy andWarrington s observation that the patient' s performance wasconsistent, in terms of specific items failed, from testingsession to testing session. It has been proposed (Sha1lice, 1987)that consistency implies damage to representations, whereas

Table 7

Performance of the Basic Model, as Measured by Probabilityoj Correctly Associating Names and Pictures, for Living andNonliving Things, Probed Verbally and Pictorially, AfterDifferent Degrees of Damage to the Connections LinkingName Units to Visual Units

Probabilitycorrect for

nonliving thingsAmount of

damage

Probabilitycorrect for

living things

Picture1.00 1.001.00 1.001.00 1.001.00 1.001.00 1.001.00 1.00

Name1.00 1.00 20 0.98 .02 0.92 .40 0.92 .04 0.90 .60 0.76 .06 0.56 .80 0.66 .07 0.30 .99 0.80 .06 0.00

Amount of damage refers to the percentage of name-visual semanticconnections destroyed.

.. Noniving name

... UvIng-

.. Non'Uv pictu18

100

damage to connectlona from nam.. to vlaual.amentlc8

Figure 14. Performance of the basic model, as measured by proba-bility of correctly associating names and pictures, for living andnonliving things, probed verbally and pictorially, after different de-grees of damage to the connections linking name units to visual units.(Non/ Liv indicates nonliving or living.

impaired access to representations should lead to variable

performance. It is certainly true that some types of accessdisorders would lead to variable performance (e. , noise in atelephone line). However, in the context of the present modelit can be seen that there is no necessary relation betweendisorders of representation versus access, on the one handand damage to units versus connections, on the other. Dam-age to connections in this model leads to high consistency initems failed. This is because certain connections are moreimportant for activating some representations than others,and so whenever a given subset of connections is destroyedthe subset of representations that is most dependent on thoseconnections will always suffer.

General Discussion

The existence of selective impairments for knowledge ofliving and nonliving things would seem to imply that thearchitecture of semantic memory consists of at least sometaxonomically defined components. However, we have shownthat a simple model of semantic memory with only modality-specific components can account for all three types of cate-gory-specific semantic memory impairment that have beenobserved with patients. Let us examine some of the generalimplications of these findings for cognitive psychology andneuropsychology as well as some cautions that should

borne in mind while interpreting the results of our model.

Limitations of the Present Model

The model we have presented here is a simple one, designedto test some very general principles concerning the relationsbetween modality-specific and category-specific knowledge.

SEMANTIC MEMORY IMPAIRMENT 353

Training stopped after 50 cycles Training continued for 200 cycles

..

Nonliving name

..

Nonliving name

... living name... living

.. NonILN pic"'".. NonIUv pic"'"

100 100

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Trained without weight decay Equal numbers of visual and functional semantic units

..

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..

Nonliving name

... living name... living name

..

NorJ\..N pic"'".. NorVllv pic"'r.

100100

damaga to connections from namas to visual semanticsdamage to connections from namas to visual semantics

Figure 15. Performance of the four variants of the basic model, as measured by probability of correctly

associating names and pictures, for living and nonliving things, probed verbally and pictorially, afterdifferent degrees of damage to the connections linking name units to visual units. ((top left) Trainingstopped after 50 epochs. (top right) Training continued for 200 epochs. (bottom left) Trained withoutweight decay. (bottom right) Equal numbers of visual and functional semantics units.

Non/ Liv indicates

nonliving or living.

Our goal was to determine whether these principles could

account for certain general findings that have emerged acrossa number of different studies of patients with different im-pairments in semantic memory. We have not attempted toprovide a detailed account of the ways that semantic memoryis used in naming pictures, defining words, and so on, or ofthe precise nature of the damage in cases of semantic memoryimpairment.

For example, the model has only two kinds of semantic

memory representations: visual and functional. We couldhave added semantics derived from other perceptual modali-ties (e. , auditory, tactile), and we could have subdivided the

fairly general concept of functional semantic memory intomore specific components. Whereas such elaborations of themodel might change the sizes of the dissociations found herethey would probably not change the basic qualitative patterns(unless the proportions of added semantic units were nega-tively correlated with the visual and functional units in termsof the numbers participating in the representations of livingand nonliving things).

Another way in which the model is simplified and unreal-istic is that there is no difference between name and picturerepresentations in the kinds of relations they have with se-mantic memory. For example, it might be expected that the

i .

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354 MARTHA J. FARAH AND JAMES L. McCLELLAND

Table 8

Performance of the Basic Model, as Measured by the DotProduct of the Correct and Obtained Semantic Patterns forLiving and Nonliving Things, Probed Verbally andPictorially, After Different Degrees of Damage to theConnections Linking Name Units to Visual Units

Scaled dotproduct in

semantic unitsfor nonliving

thingsAmount of

damage

Scaled dotproduct in

semantic unitsfor living things

Picture1.00 1.00

Name1.00 1.00

0.46

Amount of damage refers to the percentage of name-visual semanticconnections destroyed.

perceptual representations of pictures would have a closer(more systematic, more robust, or both) set of connectionswith the visual semantic representations than the name rep-resentations have. If we had included this difference in themodel, differences between the size of the dissociation foundin picture-naming compared with purely verbal tasks, such asdefinitions, might have been found. Specifically, one mightexpect the effects of damage to visual semantics to be morepronounced in tasks involving picture processing. There is ahint of such a difference in the patients' data shown in Table

For simplicity's sake, we have also assumed that the effectsof brain damage can be simulated by destroying the neuron-like units or the connections between such units. Howeverthe effects of herpes encephalitis, head injury, and stroke onneural functioning may be more fully captured by the com-bined effects of destroying units and connections as well asby other changes to the network, such as adding noise to theconnection strengths or to the activation levels of the units,changing the maximal activation values of the units, or chang-ing the rate at which activation decays. These different waysof damaging the network would be expected to have slightlydifferent effects on its performance after damage. For exam-ple, adding noise to a certain pool of units would lead to lowconsistency in the particular test items failed from one test toanother, whereas destroying units or connections would leadto high consistency. Nevertheless, these differences would notchange the basic patterns concerning the category specificityand modality specificity of the deficits reported here.

A final word of caution in relating our simple model topatient behavior is that the measures of performance that we

have used with the model are not the same as those that havebeen used with patients. The 20-alternative, forced-choicepicture-name association task is somewhat similar to thepicture-naming and matching-to-sample tasks that have beenused with patients, but reading the dot product of the actualand expected semantic memory patterns is quite an abstrac-tion from the question-answering tasks used with patients.This problem is, however, not unique to comparisons betweencomputer simulations and patients. Different patients havebeen studied with different tasks, which makes precise inter-patient comparisons impossible as well. However, the diffi-culties with neither precise interpatient nor simulation-patientcomparisons prevent us from generalizing about commonqualitative patterns of impairment and their possible under-lying causes.

We also wish to note that the present model is not intendedto account for category-specific impairments in cognitivesystems other than semantic memory. Selective dissociationshave been documented within the visual recognition system,affecting just face recognition or just printed-word recognition(e.g., Farah, 1991) and, within the lexical system , affectingname retrieval for categories as specific as colors, letters, orbody parts (e. , GoodglasS, Wingfield, Hyde, & Theurkauf,1986). From the point of view of the present model, theseimpairments would be located in the "visual" and "verbal"input systems, which we have not attempted to model withany verisimilitude. Our results are relevant to these othercategory-specific phenomena only in a very general way: Theyalert us to the fact that every neuropsychological dissociationneed not have a corresponding distinction in the cognitivearchitecture.

Basic model

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2 + Nonliving name.. Uving nanw

NorVUv pic""

100

d8meg8 to connectlona from names to vlIU81 88II'Ilntlcl

Figure 16. Performance of the basic model, as measured by the dotproduct of the correct and obtained semantic patterns for living andnonliving things, probed verbally and pictorially, after different de-grees of damage to the connections linking name units to visual units.(Non/Liv indicates nonliving or living.)

SEMANTIC MEMORY IMPAIRMENT 355

Training stopped after 50 cycles Training continued for 200 cycles

.!! .!!

Nonliving Nonliving name

.. Uving II81II8 .. Uvlng nanw

NoNUv picture Non/UV picture

100 100

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Trained without weight decay Equal numbers of visual and functional semantic units

.!! .!!

Nonliving name Nonliving name

.. Uvlng nanw .. Uvlng nanw

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100 100

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Figure 17. Performance of the four variants of the basic model, as measured by the dot product of thecorrect and obtained semantic patterns for living and nonliving things, probed verbally and pictorially,after different degrees of damage to the connections linking name units to visual units. ((top left)Training stopped after 50 epochs. (top right) Training continued for 200 epochs. (bottom left) Trainedwithout weight decay. (bottom right) Equal numbers of visual and functional semantics units. Non/Livindicates nonliving or living.

General Implications

Having enumerated some of the ways in which the presentmodel may be incomplete or inaccurate in detail and someneuropsychological phenomena that it is not intended toexplain, we now review the general principles that the modelhas been successful in demonstrating. First, the model hasshown how category-specific impairments can arise after dam-age to a system that has no category-specific components.Specifically, it has shown how impairments in knowledge ofliving things and nonliving things-and even impairments

knowledge of living things when just probed verbally-can beaccounted for without postulating a semantic memory systemwith any inherently category-specific components. Instead,these impairments can . all be accounted for by a relativelysimple semantic memory architecture, in which there are justtwo components of semantic memory, which differ from oneanother by modality and not by category.

The ability of a modality-specific semantic memory archi-tecture to account for category-specific semantic memoryimpairments depends, of course, on there being a correlationbetween modality of knowledge and category of knowledge.

356 MARTHA J. FARAH AND JAMES L. McCLELLAND

In this case, this ability depends on the fact that living thingsare known primarily through their visual attributes, whichwas suggested years ago by Warrington and her colleaguesand which we verified in Experiment 1. One way of describingthe relation between the living-nonliving distinction and thevisual-functional distinction is that they are confounded, inthe same way that we might speak of confounded factors inan experiment. However, such a description does not fullycapture the degree to which the impairments are category-specific. In patients with impaired knowledge of living things,knowledge about functional properties of living things is alsoimpaired. This is true of the model as well and can beexplained in terms of a very general property of distributedrepresentations, in which the different parts of the represen-tation provide mutual support for one another. Althoughsuch representations are robust to small amounts of damagelarger amounts will deprive the intact parts of the represen-tation of needed support. As a result ' even those intact partswill be unable. to attain their proper activation levels. Thuscategory specificity is an emergent property of the networkunder certain kinds of damage.

Figure 1 is a box-and-arrow outline of our model, showingthe different types of representations involved in semanticmemory and their relations to one another. This is the levelof description at which most models are cast in cognitiveneuropsychology. In many cases, this level of detail has beensufficient, and many cognitive impairments have been suc-cessfully interpreted as the simple deletion of a box or anarrow. However, the semantic memory impairments dis-cussed here provide an example of the limitations of thisapproach and of the need to understand what goes on withinthe boxes. As discussed earlier, it is not apparent why damageto the visual semantic memory component of the modelwould result in impaired access to functional semantic mem-ory knowledge about living things. To explain this, in thecontext of the model shown in Figure 1 at any rate, one mustdescribe the system at a more detailed level of analysis, whichincludes the internal workings of the boxes. The effect ofvisual semantic memory damage on functional knowledge ofliving things can be explained in terms of the kinds of repre-sentations and computations taking place inside the outlinedcomponents in Figure 1. In more general terms, the macro-structure of the system s behavior-what categories or mo-dalities of knowledge are spared or impaired-does not justdepend on the macrostructure of the system: for example,what different categories or modalities of knowledge there areand which has access to which other. It also depends on themicrostructure of the system: how items are representedwithin each box and how representations in one box activaterepresentations in other boxes.

The question of whether POP models accurately reflect themicrostructure of human cognition is a controversial one,which cannot be settled on the basis of any single result.Nevertheless, the present results suggest that two very generalproperties of POP models are explanatory of some otherwisepuzzling phenomena and hence provide some degree of con-firmation for the psychological reality of at least these prop-erties of POP. The first property is the involvement of allparts of a network, directly or indirectly, in the computations

that intervene between an input in one part of the system andan output in another part. This property accounts naturally

for the effects of damage localized to one part of semanticmemory on the ability to associate names and pictures ofitems that are represented in still-intact parts of semanticmemory. At the macroscopic level of analysis, it is not clearwhy eliminating one of two or more possible routes frompictures to names (such as pictures to functional semantics tonames) should result in impaired ability to associate pictureswith their names, so long as another possible route (such aspictures to visual semantics to names) is still intact. Thesecond of these properties is the need for collateral support inactivating one portion of a representation from other parts ofthe same representation. This property accounts naturally forthe effects of damage to visual semantics on the retrieval offunctional information about living things. Again, at the

macroscopic level of analysis, it is not clear why loss ofknowledge of the appearance of something would affect theability to access knowledge of its functions. Thus, the explan-atory power of the model presented here depends on it havingthese properties of POP models. The POP mechanisms arenot an incidental aspect of the model's implementation;rather, they playa crucial explanatory role.

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Received October 17, 1990Revision received April 15, 1991

Accepted April 23, 1991 .

Call for Nominations for JEP: Human Perception and Performance

The Publications and Communications (P&C) Board has opened nominations for theeditorship of the Journal of Experimental Psychology: Human Perception and Performancefor a 6-year term starting January 1994. James E. Cutting is the incumbent editor.

Candidates must be members of APA and should be available to start receiving manuscriptsearly in 1993 to prepare for issues published in 1994. Please note that the P&C Boardencourages more participation by members of underrepresented groups in the publicationprocess and would particularly welcome such nominees. To nominate candidates, prepare astatement of one page or less in support of each candidate. Submit nominations to

Howard E. Egeth , Chair, Search Committee, JEP: HPPDepartment of PsychologyJohns Hopkins UniversityCharles & 34th StreetsBaltimore, Maryland 21218

Other members of the search committee are Lynn A. Cooper, Robert G. Crowder, andDavid E. Meyer. First review of nominations will begin January 15, 1992.