Semantic Fields in Low-Functioning Autism - Springer Link

Journal of Autism and Developmental Disorders, Vol. 32, No. 6, December 2002 (© 2002)

Semantic Fields in Low-Functioning Autism Katharina Boser,1,5 Susannah Higgins,2 Anne Fetherston, Melissa Allen Preissler,3 and Barry Gordon4

Restricted semantic fields and resultant stimulus overselectivity are often thought to be typical of low-functioning autism, as is a strong visual processing preference. However, these conclusions may in part be an artifact of testing methodology. A 12-year-old, low-functioning and nonverbal autistic boy was tested on an auditory word-to-picture selection task. The picture foils were chosen to have visual features, semantic features, both, or neither in common with the correct answer. Errors were made more often to semantically than to visually related items, and he showed generalization to items that had not been explicitly trained. This is taken as evidence that his semantic fields are broader than otherwise apparent, and that he was capable of expanding his semantic representations independently of specific training. KEY WORDS: Low-functioning autism; single word learning; semantic knowledge; assessment; categorization; overselectivity.

a label applied to any excessively narrow or idiosyncratic assignment of features or categories within a given perceptual modality (Anderson & Rincover, 1982; Koegel & Wilhelm, 1973; Reynolds, Newsom, & Lovaas, 1974). This form of overselectivity has been thought to be a problem responding to “complexity,” such that children with autism select only single components within a complex stimulus. Stimulus overselectivity in this sense has been claimed to result in the poor learning and language deficits found in autism (Burke, 1991). There are a multitude of possible interpretations of overselectivity, including: lack of generalization in learning, “literal” interpretation of meaning, deficit of selective attention, and failure to adequately test hypotheses about environmental stimuli (Matthews, 1994). Such problems similarly would be expected to affect learning and language use. It is certainly true that many of these individuals seem to have only limited ability to comprehend and express language. Yet standard tests of conceptual and linguistic understanding may give a false impression of what the child with autism actually knows, particularly given their known impairments in attention and motivation and limited perceptive/expressive skills. In this

INTRODUCTION It is widely believed that low-functioning individuals with autism have a very restricted and concrete understanding of information, manifested in stimulus overselectivity, lack of generalization, and little apparent organization by meaning. Stimulus overselectivity has been defined several different ways in the literature on autism. In its earliest uses, it referred to reliance on only one sensory modality of several that were relevant to a task (Lovaas, Schreibman, Koegel, & Rehm, 1971). Stimulus overselectivity has also been

1

Department of Neurology, Division of Cognitive Neurology, Johns Hopkins School of Medicine. 2 Department of Neurology, Division of Cognitive Neurology, Johns Hopkins School of Medicine. 3 Department of Psychology, New York University. 4 Department of Neurology, Division of Cognitive Neurology, and Department of Cognitive Science, Johns Hopkins School of Medicine. 5 Correspondence should be addressed to: Johns Hopkins School of Medicine, Department of Neurology, Division of Cognitive Neurology, 600 North Wolfe Street, Meyer 222, Baltimore, MD 212877222; Phone: (410) 955-2655; Fax: 410-955-0188; e-mail: [email protected]

563 0162-3257/02/1200-0563/0 © 2002 Plenum Publishing Corporation

564 study, we used a method new to the autism literature to examine the conceptual space in one low-functioning child with autism to determine whether a richer conceptual structure was available than might otherwise have been apparent. The method was to examine the nature of the errors that were made to appropriately chosen foils. This method has been widely used in the aphasia literature (Kay, Lesser, & Colthart, 1992), as well as in assessing lexical acquisition processes in children (Gentner & Imai, 1995; Imai, Gentner, & Uchida, 1994). A single-case study design was chosen for this initial exploration, as is also widely accepted in the aphasia literature. The single-case design was motivated both by the theoretical need to take idiosyncratic abilities into account and because of the volume of data required. Although there is considerable debate about the content and structure of the semantic representational system (or systems), there is no doubt that in normal adults concepts can be flexibly adapted to new contexts or problems as well as communicative uses. Flexibility of reference is a powerful central feature of our semantic system. For example, the same items can be represented as members of different categories depending upon context and task demands. Moreover, multiple terms can also refer to the same item (e.g., a car can be a Toyota Tercel or, more generally, a car that is foreign-made) (Miller, 1999). Ignoring irrelevant features, considering context, and selecting/ attending to the appropriate information are some of the important behavioral mechanisms associated with flexibility of semantic knowledge. The ability to categorize is thought to reflect both the nature of the underlying representations and the flexibility necessary to focus on the features relevant for the task at hand. Therefore, although conceptual knowledge is broader than categorization (Solomon, Medin, & Lynch, 1999), categorization has long been used as a probe of conceptual abilities. Object categorization can be accomplished by considering perceptual or conceptual features. Perceptual features are those inherent in the representation of the item. Conceptual features are those that are not directly derived from the object representation; they are more abstract. These abstract features may be spatial proximity, temporal grouping, or existence of a common function, goal, or association (such as “things I take to school”). Clearly, different kinds of categories can be formed that may require different and perhaps opposing forms of learning (Wisniewski & Bassok, 1999). Some researchers have attempted to capture the distinction often claimed between natural categories and

Boser, Higgins, Fetherston, Preissler, and Gordon nonnatural categories in terms of perceptual compared with functional features (Farah & McClelland, 1991; Warrington & McCarthy, 1997). Arguably, the distinction between perceptual and conceptual features in real stimuli is not always clear, perhaps because of the normal confounding between perceptual and conceptual features in the real world (Gonnerman, Andersen, Devlin, Kempler, & Seidenberg, 1997; Small, Hart, Nguyen, & Gordon, 1995). Both perceptual and conceptual categorization require flexible selectivity. For example, there are times when a perceptual feature such as color will be important and other times when it will not, or when a different feature (such as roundness) is important. Feature selectivity itself seems to be a complex process. Many animals and young children are unable to select feature dimensions in categorization and object recognition tasks, relying instead on what seem to be holistic strategies (Kemler, 1983; Tighe & Tighe, 1978). Abstract differentiation of perceptual dimensions has been observed in explicit selection tasks most often in older children and adults (Tighe, Tighe, & Schechter, 1975) and in some highly trained apes (Holyoak & Thagard, 1997). Similarly, discrimination and categorization based on nonperceptual, functional properties of objects are also more difficult to demonstrate in animals (Holyoak & Thagard, 1997) and young children (Landau, Smith, & Jones, 1998). A large developmental literature exists struggling with the issue of exactly when in development the child moves from being a more “concrete,” stimulus-bound, and holistic learner to having notions of the operations that transform concrete into abstract mental representations (Carey & Gelman, 1991). In addition to the complex theoretical issues involved in studying conceptual and categorical abilities, there are also practical issues occasioned by the needs for appropriate stimuli and responses. Even younger children with typical development have limited receptive and response abilities, necessitating special methods for investigation. For example, researchers have found it necessary to use implicit measures such as preferential looking to study conceptual knowledge in infants (Baillergeon, 1994; Spelke, 1994). In tasks conducted primarily with mentally retarded individuals with some limited verbal abilities, McIlvane and colleagues have demonstrated the existence of “emerging” equivalence relations among stimuli. This refers to the ability to respond spontaneously to relationships that are not directly trained but inferred from the existing trained relations (Carr, Wilkinson, Blackman, & McIlvane, 2000; Dube, McIlvane, Maguire, Mackay, & Stoddard, 1989). An example of

Semantic Fields in Low-Functioning Autism such relations is transitivity, whereby elements do not merely exert a “stimulus-response” influence, but appear to substitute for one another. After the experimenter has trained the subject with a simple set of stimulus relations such as A → B and B → C, transitivity is shown when C is chosen for A. Transitivity as well as symmetry (e.g., B → A) is believed to underlie the basis for flexible semantic relations and generative behavior as is needed for representational language (Sidman & Tailby, 1982). However, the relationships trained in these studies are mostly arbitrary, sharing neither perceptual nor semantic relations. This makes it impossible to test for generalization to new items or the emergence of abstract rule formation necessary for semantic concepts. Furthermore, such relations have only rarely been established in nonverbal individuals with autism (But see Carr et al., 2000). Standard tests of receptive language such as the Peabody Picture Vocabulary Test (PPVT-III) (Dunn & Dunn, 1997) may not be able to uncover underlying semantic relationships because they do not control for the similarity of features between the target and distracter items. Furthermore, they have not specifically been designed for low-functioning children with autism. Only a few studies have directly compared perceptual with conceptual categorization by methods that control for the severe attentional and processing capacity impairments in low-functioning children with autism or other severe developmental disorders. Reports examining the availability of categorization skills provide mostly contradictory findings. Ungerer and Sigman (1987) demonstrated categorization skills of both functional and perceptual relationships in low-functioning children with autism using a successive touching technique originally designed for testing infant categorization. On the other hand, others have claimed that perceptual categorization according to two feature dimensions is available, but functional associative categorization is not (Fay & Schuler, 1980; Schuler & Borman, 1982; Schulman, Yirmiya, & Greenbaum, 1995). Tager-Flusberg (1985a, 1985b) found that the children with autism in her study “did not demonstrate idiosyncratic or narrow conceptual relationships.” But this was attributed to the verbal ability of the subjects, implying that idiosyncratic or narrow conceptual relationships might be expected to be present in nonverbal individuals, often of lower mental age. Theories of novel concept development in children have claimed that language acts as the mediator between early similarity-driven and later abstract rulebased categorization (Gentner & Medina, 1998; Vygotsky, 1962). Gentner & Medina (1998) specifically claim

565 that when two objects are given the same name, this invites comparison, leading to representations that are more abstract. Evidence for this claim stems from a task based on Markman’s (1989) word /no-word choice task. In the word condition, a “standard” is given a novel name (“dax”) and the child is asked to choose another “dax.” In the no-word condition the subject is asked to choose the alternative that “goes with” the standard. When only one “standard” was provided, 4 yearolds were more likely to choose the visually related alternative; however, when two standards from the same category were given the same novel name, 4-yearold subjects were more likely to choose a taxonomically related object than a visually related alternative (Gentner & Namy, 1999; Markman, 1989). Furthermore, Imai et al. (1994) demonstrated evidence for a “shape-to-taxonomic” shift. Although both age-groups chose more alternatives related by shape in the word condition than the no-word, 5-year-olds made significantly more taxonomic choices than 4-year-olds. To differentially examine the roles of early perceptually driven and “surface” similarity-based processes compared to taxonomic, semantic information, we presented our subjects with choices much as in the Gentner et al. studies noted above. We designed materials for an auditory word-to-picture matching task well known to the subject that controlled for the visual and/ or semantic relation between distracter and target items. An equal number of natural and nonnatural target items was included to examine any different response to these category types. We chose to present auditory words as the input stimuli, with color photographs as the responses, for several reasons. Visual/perceptual abilities typically appear to be normal if not supernormal in individuals with autism (e.g., hyperlexia) (Happé & Frith, 1996). Therefore, besides being easier as responses, visual targets might also be expected to be more transparent in terms of the effects of perceptual versus semantic relationships. Furthermore, by presenting in the auditory route but testing in the visual one, we would also give maximum opportunity for our subject to demonstrate “overselectivity” in its original sense of difficulty using information from different modalities. We hypothesized that if the subject’s responses were overly narrow, we would find more errors to unrelated or visually related distracters rather than semantically related ones. We also wanted to maximize the subject’s need for flexibility in the visual response route. Accordingly, the subject was trained to associate spoken words to line drawings of objects and then tested for his ability to associate the same spoken words with unfamiliar color photographs of the objects on

566 which he had been trained. Thus, if the subject relied solely on particular visual characteristics of the trained line drawings, we should not expect to see generalization to unfamiliar color photographs. Finally, we used training data to assess the subject’s ability to generalize both to new depictions of the same trained item and new word labels.

SUBJECT AI (not his real initials) was 12 years old at the time of the study. Fully informed consent was obtained for all testing in compliance with the clinical research protocols of our institution. He was the product of a normal pregnancy and delivery. By parental report, initial motor milestones appeared to be normal, with sitting up at 6 months and walking at 13 months. He had some words as a toddler, but his language abilities were probably less than expected, and word production stopped around age 2. At about that time he also began to demonstrate hand flapping, lack of attention, eye gaze, and lack of pointing, and he ceased to produce or respond to speech. Medical and psychological evaluations (including MRI and EEG) established a diagnosis of autism and were deemed sufficient to rule out other conditions. No dysmorphia was ever apparent, either in early childhood or at the current time. Hearing tests as an infant were probably within normal limits; he may have a borderline mild hearing loss on one side. Before testing, he had a behavioral soundfield audiogram that revealed hearing sensitivity within normal limits in at least one ear. Functional hearing also appeared to be intact in everyday situations, as judged by his responses to sounds associated with preferred items (e.g., M&Ms dropping on a tabletop). AI had been enrolled in special schools since early childhood. At the time of the testing reported here, he had been in a private school for special needs children, where he received 2:1 or 3:1 instruction. Just as the evaluations reported here were ending, he was placed in a full-time home educational program, with 1:1 instruction. Approximately 2 years before the study reported here, at age 10, AI had a formal evaluation by a specialized autism center. On the auditory comprehension subtest of the PLS-III (Zimmerman, Steiner, & Pond, 1992), he scored 18 items. On the PLS-III he obtained a standard score of 40%, with a rank of less than 1. He appeared to understand some verbs in context, (e.g., “come here”), but had no understanding of quantity, pronouns, use of objects, descriptive concepts, or

Boser, Higgins, Fetherston, Preissler, and Gordon part /whole relations. Although he requested some food items using PECS, he made no spontaneous use of the system and had minimal communicative intentions. The play skills subtest of the ADOS-G (Lord, Rutter, & DiLavore, 1997) was administered at the same time to investigate symbolic abilities. Based on the evaluation it was reported that he had no spontaneous instrumental gestures and one use of point with no eye contact. He also had a lack of consistent or flexible use of eye contact but did look when his name was called. He had little interest in toys and other objects (e.g., balloons, bubbles, toy dog) used in the assessment and no spontaneous initiation of joint attention. He used orientation of the experimenter’s eyes to look at an object but engaged in no pretend play or turn taking and was highly distractible, with unusual and persistent sensory responses (e.g., continually throwing a scarf and catching it on his face). The Peabody Picture Vocabulary Test (PPVT-III) was administered again approximately 1 year before the start of this experimental testing and again just before the experimental testing. At both times, AI’s standard score was 38 and his raw score was 18. AI did not achieve a basal score, and testing was continued until he responded incorrectly six consecutive times. AI often responded to the lower left picture when he appeared unsure of the target. At the time of the experimental study reported here, AI’s vocalizations were limited. A range of sounds were produced, however, including one sound that appeared to be connected with his correct choice of items in the task we will describe. He was learning to use a picture exchange system for communication, training on this having been started 3 years before. He had approximately 250 symbols in his communication book and used approximately 35 on a regular basis. AI’s basic motor skills include the ability to roller blade and ski. He is inconsistent in his ability to imitate hand clapping and tongue protrusion. He is unable to dress and clean himself independently. Through training he can successfully complete basic wooden tongue-in-groove puzzles and fit pegs into peg-boards and string beads together on a string. AI is relatively uninterested in objects that require self-manipulation. On the other hand, he is very motivated by interaction with colorful stimuli, in particular when these are presented on the computer, whether static or moving. Prior Vocabulary Training Starting at age 8, AI’s education included learning communication skills using the PECS (Bondy &

Semantic Fields in Low-Functioning Autism Frost, 1994) system, basic imitation skills, gross motor imitation, following directions, and functional life skills. Initial vocabulary training was with physical pictures and symbols, done in the typical tabletop fashion. Beginning at age 10, much of the vocabulary training AI received was conducted with the use of a specialized Foundations™ series computer system designed specifically for ABA training (Infostructure, Yardley, PA). All pictures were presented on an 800 ⫻ 600 LCD screen with built-in touchscreen capability (initially, an LG Electronics and later a Microtouch ™ ). AI generally responded positively to the use of the computer; he was often quite eager to use it and would become upset at times if the computer was not made immediately available. We present an analysis of the training data spanning 2 years prior to our test to relate performance on our assessment to the number of training trials received for categories we tested. We included as trained only those items that matched our target stimulus words exactly. For example, four items trained as plural words were not considered the same as the item tested in the singular form (e.g., “socks” were trained but “sock” was tested). Two items were trained using a different label than used in the test, and were similarly not counted as trained (e.g., the item “stomach” was the tested word whereas “belly” was the label used to refer to the same item in training). Experimental Assessment Purpose The auditory word-to-picture-matching task was designed to determine the nature of AI’s representation of concrete, highly imageable nouns presented in the auditory modality. In particular, the task allowed us to evaluate whether the choice of a particular picture icon was influenced by the visual and /or the semantic similarity of the distracter icons with the target. In particular, we predicted that if the subject was learning on an overselective basis, errors should be more to items visually related or unrelated (sharing some irrelevant feature) with the target. Natural items and nonnatural items were separately tested to evaluate whether the fact that visual/perceptual features are more generally associated across natural relative to non-natural items would result in different learning of the items. Task Stimuli and Design Stimuli. Picture stimuli were 3D color pictures from the Hemera™ Premium Photo Image Collection

567 (Hull, Quebec, Canada) and Silverlining ™ software (Poughkeepsie, NY) and several scanned photographs. All pictures were presented on a white background and were at least 3 ⫻ 4 in size. All background or adjoining visual information was removed from the pictures by using Adobe Photoshop ™ 5.0. Color, and perspective and size adjustments were made as necessary. The choice of target stimulus items was based on the most frequent concrete objects that were part of AI’s training both at school and in the home. We also chose appropriate items from a list based upon norms for training early vocabulary (Rescorla, 1990). We eliminated any abstract nouns or words that were ambiguous with respect to grammatical class, words indicating collections of things, activities, locations, and imperatives (e.g., “lunch,” “music,” “drink,” “roller blades,” “door,” “playground,” “go”). Word labels were primarily “basic-level” terms rather than superordinate terms for a particular object (Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1978). For certain wellknown food items we chose to use the specific “subordinate” term used by family and therapists in the environment (e.g., Fritos, Doritos, M&Ms, croissant). Some items in a category were tested twice. In this case, each instance was a different exemplar. For example, car was tested in one case as a red VW and in another as a green Corvette. Distracter items were not phonologically related to the target. We avoided creating similarity between two objects along a feature dimension that was not a necessary feature for that item (e.g., grapes were matched on shape rather than color because they can be either purple or green). In some cases, perspective or size was manipulated to highlight a particular shared visual feature. For example, a French fry and a hotdog from the category “fast food” were paired as semantically similar items that share visual features. In this task, the items were made to be roughly the same size and were angled in the same direction (color and roundness were not changed) to enhance their similarity on the page. For some items, certain salient aspects of the object rather than a global attribute (shape, color) were used in matching visual similarity. For example, we used the two round wheels of a car with the two round elements of a set of binoculars or glasses. Design. The task consisted of 300 unique trials in which the subject was asked to chose among three visually presented stimuli the item that best matched a word spoken by the experimenter (i.e., auditory wordto-picture matching). The stimulus vocabulary was organized into 30 categories (15 natural categories and

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Boser, Higgins, Fetherston, Preissler, and Gordon Table I. List of Semantic Categories

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Natural categories

Non-Natural categories

barnyard animals basic food body parts breakfast desserts drinks fast food fruit insects people pets plant/tree snacks vegetables zoo animals

appliances bath building/street clothes containers furniture instruments jewelry kitchen utensils school sewing items sports items tools toys vehicles

15 non-natural) (see Table I). Each category contained 10 unique target vocabulary items. A list of the 300 vocabulary items is included in Appendix A. The stimulus items for each category were administered in separate blocks. The visual pictures corresponding to the target words were presented in one of two distracter conditions (Table II). In condition 1, the target picture was presented with one distracter picture that was both visually and semantically related to the target and a second distracter picture neither visually nor semantically related to the target. In condition 2, the target picture was presented together with one distracter picture that was a member of the same semantic category but was not visually similar to the target. The second distracter picture was visually but not semantically related to the target item. The number of trials for each condition was balanced across trials within each category (half were condition 1 and half were condition 2 trials) and across both natural and non-natural target items. Condition presentation was randomized within each block /category. Position of the target picture and position of distracter pictures were pseudo-randomized so that the same position was not correct more than two times in a row. Across the 300 unique trials, we ensured a “trialunique” presentation of stimuli whereby target pictures never appeared as distracter items in other trials. Furthermore, no distracter item was presented sequentially across trials. This design was followed to avoid the subject’s use of a strategy, particularly one in which he might be able to use the principle of “contrast” to eliminate a known distracter item as a choice (many of the target items were trained). Clark (1987) and others have

proposed that the contrast principle is among several mechanisms for word learning exploited in normal lexical acquisition. In our task, however, we did not want the subject to choose the target correctly merely because he recognized that a distracter did not match the auditory stimulus. On the other hand, we also did not want the subject to choose a distracter item incorrectly merely because the item was one that had been heavily trained (i.e., response bias). This task design helped ensure that neither response strategy was adopted. The items were presented in alphabetical order by category (as they appear in Appendix A). This order was repeated five times (total trials, n ⫽ 1500) to ensure consistency and reliability of a response. Procedures The individuals who tested AI were experienced in working with him, but they were informed only minimally about the study design during the testing. Before beginning any trial, AI was presented with a choice of edible or tangible rewards (e.g., M&Ms, pretzels, beads, etc.). He had been previously trained to a “ready” response for which he would have to look at the experimenter and put his hands flat on his legs. After AI responded appropriately to the “ready” command, the experimenter gave behavior-specific praise, such as “good looking” or “good hands down,” and gave him a token to put on his token board. After accumulating five tokens, AI received his reward. AI was reinforced for a correct “ready” response (i.e., proper sitting and attending) regardless of his performance in the task. Once AI was attentive, the experimenter presented a laminated photographic display depicting the three test icons. After 1 to 2 seconds the experimenter said, “Touch ___.” AI usually responded immediately, but occasionally changed his response. Only his initial response was scored. AI often vocalized a /di/ as he pointed to the icon. These vocalizations corresponded to the correct target choice about 90% of the time. The experimenter was instructed not to interact or respond to the subject in any way during the response. All sessions were videotaped, and these videos were subsequently analyzed by a separate researcher to ensure that no prompting occurred. Testing sessions were conducted several times a week and lasted about an hour. At each session, AI was able to complete about three to six categories (up to 60 trials). He was allowed to take breaks in-between each category, that is, after 10 items. The categories were presented in alphabetical order from appliances to zoo animals so natural and non-natural categories were

Semantic Fields in Low-Functioning Autism

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Table II. Examples of Trial Conditions Distracter conditions for natural targets Target

Distracter 1

Distracter 2

flower

leaf (⫹semantic, ⫹visual)

lock (⫺semantic, ⫺visual)

potato

celery (⫹semantic, ⫺visual)

wood ball (⫺semantic, ⫹visual)

Condition 1

Condition 2

Distracter conditions for non-natural targets Target

Distracter 1

Distracter 2

van

car (⫹semantic, ⫹visual)

stereo (⫺semantic, ⫺visual)

drums

clarinet (⫹semantic, ⫺visual)

binoculars (⫺semantic, ⫹visual)

Condition 1

Condition 2

interspersed. This testing order was maintained at each presentation so that the maximum number of items intervened between presentations of the same target stimuli. The testing was started at age 12:1 and continued until age 12:7. RESULTS Overall Analysis of Performance Across Trials Across all 1500 trials, AI was correct on 980 (65%) and incorrect on only 520. There was no significant difference in his overall errors in the distracter trial conditions (condition 1 ⫽ 270 errors, condition 2 ⫽ 250 errors) or between conditions pertaining to

the characteristics of the target (such as natural versus non-natural). Distractor Category Effects AI’s errors were clearly influenced by the distractor categories, as shown in Fig. 1, depicting his choice of distracter items for each of the two conditions. In condition 1, AI was significantly more likely to choose distracter pictures that shared both visual and semantic features (87%, 234/270) than those that shared no features with the target (13%, 36/270; z ⫽ 17, p ⬍ .0001). In condition 2, AI was significantly more likely to choose distracter items that shared only semantic features with the target (57%, 149/250) than

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Boser, Higgins, Fetherston, Preissler, and Gordon

Fig. 1. AI chose semantically related distracter items significantly more than visually related ones.

those sharing only visual features (40%, 101/250; z ⫽ 4.2, p ⬍ .001).

Performance by Category for Items: Analysis of Consistency of Response Across Presentations Table III shows AI’s responses for each of the 10 items within each target category by the consistency of his response across five target presentations. Table III separates items by whether the item was (1) always correct (five of five times); (2) correct at least 3 times of the five (n ⫽ 101); (3) correct only one or two times of the five (n ⫽ 81); or (4) never correct on the five trials (n ⫽ 18). AI correctly identified 100 targets at each of five presentations of the 300 target items (34%). AI was more likely to be consistently correct for items presented in condition 2 than condition 1 (58 versus 42, z ⫽ 2.12, p ⬍.03). Only 18 items were incorrect for all of five presentations. For 10 of these, AI consistently chose the same semantically related item. (e.g., cake for rice cake, pig for goose, spatula for fork). For only one item did AI consistently choose a distracter that was not from the same semantic category as the target, that is, “radio” for “van.” Thus, even for these consistently incorrect items, AI chose a visually related distracter less frequently than a semantically related one. AI gave the most consistently correct responses (3– 5 times correct) to natural categories (11/15), most of which were food categories (basic food, breakfast, desserts, fast food, plant/tree, snacks, barnyard animals, drinks, fruit, vegetables, zoo animals). However, a number of consistently correct responses were also found with the non-natural categories (6/15) (clothes, school and sports items, building/street, kitchen utensils and vehicles).

Table III. Analysis of Correct Items by Categorya Never correct Natural Categories barnyard animals basic food body parts breakfast desserts drinks fast food fruit insects people pets plant/tree snacks vegetables zoo animals TOTALS

Correct 1–2 times

Correct 3–4 times

Always correct

1 0 0 0 1 0 0 0 1 0 0 2 0 0 0 5

1 3 5 3 2 1 0 2 2 3 3 0 0 3 1 29

5 2 2 1 1 5 1 5 3 3 3 3 2 5 5 46

3 5 3 6 6 4 9 3 4 4 4 5 8 2 4 70

Non-Natural Categories appliances 1 bath 2 building/street 2 clothes 0 containers 1 furniture 0 instruments 2 jewelry 1 kitchen utensils 1 school 0 sewing items 2 sports items 0 tools 0 toys 0 vehicles 1 TOTALS 13

4 3 1 1 5 4 4 5 3 1 6 3 6 4 2 52

5 4 6 3 4 3 3 3 5 3 2 2 4 4 4 55

0 1 1 6 0 3 1 1 1 6 0 5 0 2 3 30

Combined Total

81

101

100

a

18

Bold items indicate good performance on a category.

Semantic Fields in Low-Functioning Autism Figure 2 illustrates AI’s consistency for natural and non-natural items. AI’s performance on natural categories was significantly greater than with non-natural for items that were always correct (70% compared with 30%) (z ⫽ 5.52, p ⬍.001). See Appendix B for examples of trials from AI’s best natural and non-natural categories. AI was correct on non-natural items 3 or 4 times (n ⫽ 55) for approximately the same number of items as he was correct only 1 or 2 times (n ⫽ 52), but there were significantly fewer natural items that were correct only 1 or 2 times (29 compared with 46 that were 3–5 times correct, z ⫽ 2.53, p ⬍.01). Error Analysis by Item: Evidence for a Preferred Choice of Semantic Distracters Although he may have responded more consistently correct with natural items, AI’s errors were more likely to be toward semantic than visual distracters for both non-natural and natural target items. For non-natural categories, this was particularly true for those categories on which he performed best, that is, clothes, school, sports items, and vehicles. Correspondingly, AI showed greater variability of responding and more perceptual distracter choices for natural categories on which he performed poorly. Note that “variable” responding was coded for an item if one distracter was chosen for some presentations and the other on different presentations of the same target item. Table IV illustrates that the consistency of AI’s choice of a distracter item that shared only semantic features compared with the choice of a visually related

571 distracter in condition 2 was significant for natural items across all incorrect items. For example, AI only chose 9 visually related but 22 semantically related items in this category type. For non-natural items, there appeared to be little difference between the choice of distracter type when all incorrect items were included (19 ⫹ Semantic ⫺Visual compared with 15 ⫺Semantic ⫹ Visual). However, for 24 of the incorrect responses for non-natural items, AI responded variably. Within his variable responses, most errors were semantically related to the target. In fact, the lower half of Table IV indicates that of 19 variable responses, 14 were mostly to semantically related distracters. This analysis provides support for the fact that AI consistently chose semantically but not visually related foils instead of visually but not semantically related ones in condition 2 across all types of target items. Thus the apparent difference between performance on natural compared with non-natural items appeared to depend more upon AI’s familiarity and perhaps intrinsic motivation or interest in the items within the individual category. A confound between trained items and natural items will be discussed in a later section. Some examples of AI’s choice of semantically related foils across category types can be found in Appendix C. Error Analysis: Preference for Items Sharing Superordinate Category Value The item analysis demonstrated that AI respected the superordinate category of the target in his choice of distracter item. For items correct 3 or 4 times (n ⫽

Fig. 2. AI was more likely to always choose the correct natural than non-natural target item (z ⫽ 5.52, p⬍.0001).

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Boser, Higgins, Fetherston, Preissler, and Gordon Table IV. Distracter Response by Item Total Incorrect

Distracter

Natural

Condition 1 ⫹Semantic ⫹Visual ⫺ Semantic ⫺Visual Variable⫻ SubTotal Condition 2 ⫹Semantic ⫺Visual ⫺ Semantic ⫹Visual Variable⫻ SubTotal TOTAL

Non-Natural

Total

40 2 3 45

49 6 7 62

89 8 10 107

22 9 4 35 80

19 15 24 58 120

41 24 28 93 200

For items consistently incorrect (0, 1, 2 times) Distracter Condition 1 ⫹Semantic ⫹Visual ⫺Semantic ⫺Visual Variable⫻ SubTotal Condition 2 ⫹Semantic ⫺Visual ⫺ Semantic ⫹Visual Variable⫻ SubTotal TOTAL

Natural

Non-Natural

Total

16 1 2 19

24 3 4 31

40 4 6 50

8 5 2 15 34

9 6 19* 34 65

17 11 21 49 99

⫻

A variable response was coded when AI chose one distracter item at one presentation(s) and another at a different presentation of the same trial. * Note: For non-natural items in Condition 2, he chose semantically but not visually related distracters more often than visually but not semantically related ones when the response was variable (14/19; 74%).

101), only 10 of AI’s responses did not match the target with respect to natural/non-natural category. For items correct 1 or 2 times (n ⫽ 81), 12 of AI’s responses did not match the target category. Finally, only 2 of 18 items that were always incorrect did not share the superordinate category value of the target. Thus, overall, AI only chose distracters that did not share the superordinate category value of the target for 24/166 (14%) of all possible items on which such a response was possible. Training Data: Evidence for Generalization We analyzed AI’s computer-training data for the 2-year period before our testing and found evidence for generalization and the effect of the training on very similar but not identical items used in our test. First,

we found evidence for knowledge of items on which AI had not been explicitly trained. Thirty-two of the 100 consistently correct items were “untrained” items (12 non-natural/20 natural items). An additional 26 items were untrained items that were correct four of the five times presented. Of 157 items that were not trained, AI correctly chose 61 items (39%) four or five times of the five presentations. These can be found in Appendix A. Fig. 3 shows the number of different categories for which AI demonstrated correct or near correct performance for untrained items. Second, we found an effect of training on performance and generalization to our stimuli. AI was consistently more likely to correctly choose trained than untrained items, although the specific photographs we used were unfamiliar. Figure 4 indicates that the percent of items that were always correct were more likely to be trained. Of the 300 target items on the assessment, AI had been trained on 143 during this period in a picture selection task using Mayer-Johnson color line drawings (see Appendix A for these items). AI correctly chose 93 of the 143 trained items (65%) four or five times out of five presentations. More than two-thirds of these (66/97) were natural items. For non-natural categories, there were several categories in which more training trials corresponded with better performance (see clothes, furniture, school). Third, although the amount of training on individual items was generally correlated with AI’s performance, the relationship between amount of training and performance did not hold for all categories tested. The categories with the most amount of training are italicized in Table V. For example, although AI received the greatest number of trials, as well as different exemplars for body parts (2854 trials, 33 exemplars), this was one of his worst categories within natural items (only five were consistently correct). A similar finding can be shown for the category bath items. On the other hand, he was trained only average or below average amounts for many categories on which he performed very well (e.g., fast food, people, drinks, plant/tree, sports items, and snacks). This finding may be indicative of generalization to items that were not trained or AI may already have learned these categories/items before the point at which we began our analysis. We considered whether AI’s better performance on natural items may have been due to the fact that more of these items were trained (Fig. 5). More trained items were natural items (60 versus 40% untrained), and the number of different items trained within each non-natural category was much less than in natural categories. Yet, the amount of training on natural kind

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Fig. 3. AI demonstrated good performance (i.e., 4–5 x correct) on a number of untrained items from different categories.

items only exceeded non-natural items by only about 50 trials (see Table V). Furthermore, an analysis of the type of errors in trained compared with untrained items demonstrates that AI made a greater number of semantic errors in condition 2 for trained as well as untrained items (Fig. 6). It appears that, although important, training itself cannot account for the difference in performance between natural and non-natural categories. Instead, familiarity and motivation also play a role in AI’s different performance across category types.

DISCUSSION The present study demonstrates the availability of a broader representation for semantic information in a low-functioning child with autism than might otherwise have been predicted. The finding that the majority of his errors in condition 2 were conceptual and not perceptual and the number of unrelated errors illustrates this point. Although AI chose distracter items sharing only visual features primarily for those items that were less familiar or untrained, even for untrained items

Fig. 4. AI was more likely to always correctly choose trained items than untrained items.

574

Boser, Higgins, Fetherston, Preissler, and Gordon Table V. Trials and items trained for target itemsa

Natural categories

Average # of trials

Total trials

Items

Barnyard animals Basic food Body parts Breakfast Desserts Drinks Fast food Fruit Insects People Pets Plant/tree Snacks Vegetables Zoo animals

78 46 133 50 57 29 44 85 45 205 88 44 25 62 63

549 276 1194 348 229 169 266 592 181 410 615 87 127 432 378

7 6 9 7 4 6 6 7 4 2 7 2 5 7 6

Totals Category Average

70

5853 395

85

Average # of trials

Total trials

Items

Appliances Bath Building/Street Clothes Containers Furniture Instruments Jewelry Kitchen utensils School Sewing items Sports items Toys Vehicles

40 59 63 73 39 69 10 9 53 44 66 17 41 94

281 470 189 364 78 345 10 37 158 265 66 52 41 844

7 8 3 5 2 5 1 4 3 6 1 3 1 9

Totals Category Average

48

3200 229

58 143

Non-Natural categories

a

Bold and italicized items received the most training

more errors were semantically rather than visually related to the target. Although natural-kind items were more consistently correct, semantic errors were more common for both natural and non-natural items. AI also demonstrated generalization, correctly identifying at all five presentations many items which had not been overtly trained in his educational program. Although we clearly cannot account for items that AI may have learned in other, less programmed environments, he undoubtedly generalized from the line drawings used in training to unfamiliar photographs.

In the normal individual, representations are claimed to arise from either perceptual features (Dixon, Bub, & Arguin, 1997; Gonnerman et al., 1997) or more abstract, semantic characteristics (Shelton & Caramazza, 1999). The availability of abstract, semantic category information in autism has been challenged on the basis that representations are overly concrete and selective (Burke, 1991). Although AI’s performance on this task cannot be categorized as normal, our results provide evidence against the notion that his representations of objects are solely based upon concrete, perceptually available features. Stimulus Overselectivity and Mental Representations in Autism Although widely cited, the term “stimulus overselectivity” has been used in operationally different ways (Burke, 1991; Klinger & Dawson, 1995; Lovaas, Schreibman, Koegel, & Rehm, 1971). Lovaas et al. (1971) initially trained three groups of children (normal controls mentally retarded subjects, and subjects with autism) to press a lever after three simultaneous cues (i.e., a blast of air, a light, and a sound). In a subsequent test condition the control subjects responded to all three stimuli, mentally retarded individuals responded to only two, and children with autism responded to only one of the cues. In later studies of overselectivity, a range of different cognitive functions was measured (including object or picture identification, recognition, rule-based learning, and categorization) and the definition of stimulus complexity has varied. In some studies the complex stimuli contained multiple features within one item or modality, either auditory (Reynolds et al., 1974), or visual (Anderson & Rincover, 1982; Koegel & Wilhelm, 1973), whereas others presented single features across multiple objects (Stromer, McIlvane, Dube, & Mackay, 1993). Complexity has also been defined in terms of the automaticity of information processing rather than features of the stimulus (Minshew, Goldstein, & Siegel, 1997; Renner, Klinger, & Klinger, 2000). Overselective and concrete responding has been reported in research on categorization in autism, although the results are conflicting. Some studies have claimed that low-functioning children with autism cannot associate objects according to functional features, but use only concrete perceptual features (Fay & Schuler, 1980; Schulman et al., 1995). Some of these studies are difficult to interpret because a large number of distracter items were presented and task requirements were switched within the same session. Moreover, the dif-

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Fig. 5. AI was trained on more natural items than non-natural items.

ference in complexity between the functional/conceptual objects (e.g., things that fly) compared with simpler geometric shapes (e.g., circles and squares) was not controlled. Other research on categorization skills in autism has demonstrated positive results (Ungerer & Sigman, 1987; Tager-Flusberg, 1985a, 1985b). Unlike identification, categorization does not require precise discrimination of items or of their features/characteristics. Nevertheless, it is likely that they both tap into the same underlying representation. Indeed, it is possible that items in the same category activate a set of relevant

features (a global familiarity), which would allow for inclusion of other either visually or semantically similar items. This hypothesis, supported by our present findings, would explain the positive results for individuals with autism in categorization tasks relative to their impairment in labeling and identification of specific objects. Our results are in accord with studies demonstrating that overselectivity in autism may depend upon the explicit nature of tasks and stimuli. Clearly some overselectivity on AI’s part occurred given the percentage of errors to visually related items. Taken to-

Fig. 6. AI was more likely to choose semantic than visual distracters for both trained and untrained target items.

576 gether, these findings suggest that overselectivity may be an impairment of task processing rather than of the internal representation of concepts. For example, Zeaman and House (1979) demonstrated that retarded individuals took longer to master a discrimination task, but were just as accurate as control subjects after training. This finding is supported by other studies with mentally retarded individuals showing that allowing a period of pre-exposure to individual dimensions of the stimulus compound may help to counteract overselectivity (Schneider & Salzberg, 1982). Similarly, training that has been beneficial in reducing stimulus overselectivity in autism starts with easier discriminations before moving on to harder ones (Hedbring & Newsom, 1985). Indeed, some forms of overselective responding may not be unique to autism, because some normal control subjects as well as mentally retarded subjects show overselectivity, whereas not all subjects with autism do so (see Gersten, 1980, for a review). Based on the lack of differences between normal, mental-age-matched controls, and subjects with autism in a visual overselectivity task, Schover & Newsom (1976) argued that overselectivity is a feature of developmental delay. Anderson & Rincover (1982) also showed that subjects with autism were no more overselective than normal control subjects in selecting a picture of individual dot elements instead of a gestalt circle form after a circular formation of dots was trained. Moreover, certain forms of training have enabled some children with autism to remediate their overselective responding (e.g., using within-stimulus prompts [Schreibman, Charlop, & Koegel, 1982], faded prompts [Schreibman, 1975], multiple cue training [Koegel & Schreibman, 1977], overexaggeration of features, and repeated exposure to the same stimulus [Schreibman, Koegel, & Craig, 1977]). Although improvement in overselective responding was not demonstrated in one study of mentally retarded individuals, even with extensive training with the same stimuli set, the task may have been too difficult for training to have been effective (Stromer et al., 1993). Naming and Semantic Representations Several studies have questioned whether overselective responding is due to a language deficit (Gersten, 1980; Kovattana & Kraemer, 1974), an issue also debated in the literature on the formation of equivalence classes (McIlvane & Dube, 1996; Stromer & Mackay, 1996). McIlvane and Dube (1996) have argued that the naming ability of mentally retarded indi-

Boser, Higgins, Fetherston, Preissler, and Gordon viduals is correlated with the ability to form abstract equivalence classes that can “emerge” beyond overtly trained stimulus-response relationships. However, the availability of equivalence classes has only recently been demonstrated in a child with autism having little to no language abilities (Carr et al., 2000). We demonstrate in this study that abstract, semantic representations are available in a nonverbal child with autism. Although their result is in agreement with our findings, McIlvane and colleagues have not studied the same kind of representational issues. We are continuing to test more children with autism with a range of verbal abilities in our task to examine to extent to which verbal ability plays a role in the correct identification and categorization of these concrete objects. An experiment showing a deficit in the normal use of prototypes for concept formation provides an example of the level at which we must investigate the ability to form abstract representations (Klinger & Dawson, 1995, 2001). It is possible that the ability to use prototypes effectively may determine verbal and nonverbal abilities in autism. Summary and Future Directions Our results suggest that low-functioning individuals with autism may be less concrete and overselective in their demonstration of semantic knowledge when given an appropriate task and stimuli. In our assessment, AI displayed behavior implicating a representation for categorical information that was at times overly broad. Although AI’s results cannot be considered normal, and he may still exhibit overselectivity, previous studies have not elicited the type of errors demonstrated here. Furthermore, our results highlight the importance of the surrounding context and task conditions in the child with autism’s ability to discriminate and identify representational objects. Notably, because the distracters in most standardized tasks are not controlled relative to the target item, they may not provide an accurate measure of the receptive knowledge of a child with autism. Many standardized tests assume that language processing in children with autism differs only in terms of degree and thus they are compared along a continuum with normal children. Results from our study indicate that other tests may be needed for proper evaluation of this special population. The errors we found in AI’s performance may be a product of either a normal system operating with degraded processing (either slower speed/efficiency or less capacity) or from impaired representations (i.e., abnor-

Semantic Fields in Low-Functioning Autism mality of the semantic system itself). More work is needed to determine the actual locus of the deficit. We may find that errors are generated at an activation level, in which case there may be attentional mechanisms regulating the proper engagement of stimulus dimensions. The deficit could occur at the point of selection, where the threshold among competing individual features may be too low to select the proper item. Finally, connections between and among elements may be improperly established so that items are either too broadly associated or

577 overly narrow. Sperber and McCauley (1984) have found that word-to-picture priming in children with mental retardation occurs for semantically related pairs and may be a “more direct index of stored category knowledge than are data from traditional category usage tasks” (p. 147). We are currently planning to investigate the semantic and visual factors studied here in a preferential looking task with AI and other subjects. Examining responses to pictures in a priming task may help determine the locus of impairment and refine our conclusions.

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ACKNOWLEDGMENTS This work was supported in part by gifts from anonymous donors, by the Developmental Cognitive Neuroscience Gift Fund, the Therapeutic Cognitive Neuroscience Professorship, and by the Benjamin A. Miller Family Endowment for Aging, Alzheimer’s, and Autism. We thank Linda Edwards, Brielle Boedart, and Dr. Stephen Krappes of The Forum School for their help collecting the training data. Drs. Dana Boatman and Argye Hillis provided useful comments on the research. REFERENCES Anderson, N. B., & Rincover, A. (1982). The generality of overselectivity in developmentally disabled children. Journal of Experimental Child Psychology, 34, 217–230. Baillergeon, R. (1994). Physical reasoning in young infants: Seeking explanations for impossible events. British Journal of Developmental Psychology, 12, 9 –33. Bondy, A., & Frost, L. (1994). The picture-exchange communication system. Focus on Autistic Behavior, 9, 1–19. Burke, J. C. (1991). Some developmental implications of a distur-

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