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This article was downloaded by: [Ams/Girona*barri Lib] On: 17 October 2014, At: 01:02 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Journal of Intellectual and Developmental Disability Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/cjid20 Concept acquisition in children with mild intellectual disability: Factors affecting the abstraction of prototypical information Brett K Hayes a & Robert N Conway a a The University of Newcastle Published online: 22 Jan 2014. To cite this article: Brett K Hayes & Robert N Conway (2000) Concept acquisition in children with mild intellectual disability: Factors affecting the abstraction of prototypical information, Journal of Intellectual and Developmental Disability, 25:3, 217-234 To link to this article: http://dx.doi.org/10.1080/13269780050144280 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

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Page 1: Concept acquisition in children with mild intellectual disability: Factors affecting the abstraction of prototypical information

This article was downloaded by: [Ams/Girona*barri Lib]On: 17 October 2014, At: 01:02Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK

Journal of Intellectual andDevelopmental DisabilityPublication details, including instructions for authorsand subscription information:http://www.tandfonline.com/loi/cjid20

Concept acquisition inchildren with mild intellectualdisability: Factors affectingthe abstraction of prototypicalinformationBrett K Hayesa & Robert N Conwaya

a The University of NewcastlePublished online: 22 Jan 2014.

To cite this article: Brett K Hayes & Robert N Conway (2000) Concept acquisitionin children with mild intellectual disability: Factors affecting the abstraction ofprototypical information, Journal of Intellectual and Developmental Disability, 25:3,217-234

To link to this article: http://dx.doi.org/10.1080/13269780050144280

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all theinformation (the “Content”) contained in the publications on our platform.However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness,or suitability for any purpose of the Content. Any opinions and viewsexpressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of theContent should not be relied upon and should be independently verified withprimary sources of information. Taylor and Francis shall not be liable for anylosses, actions, claims, proceedings, demands, costs, expenses, damages,and other liabilities whatsoever or howsoever caused arising directly orindirectly in connection with, in relation to or arising out of the use of theContent.

Page 2: Concept acquisition in children with mild intellectual disability: Factors affecting the abstraction of prototypical information

This article may be used for research, teaching, and private study purposes.Any substantial or systematic reproduction, redistribution, reselling, loan,sub-licensing, systematic supply, or distribution in any form to anyone isexpressly forbidden. Terms & Conditions of access and use can be found athttp://www.tandfonline.com/page/terms-and-conditions

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Page 3: Concept acquisition in children with mild intellectual disability: Factors affecting the abstraction of prototypical information

Journal of Intellectual & Developmental Disability, Vol. 25, No. 3, pp. 217–234, 2000

1Address for Correspondence: Brett K. Hayes, School of Behavioural Sciences, University of Newcastle, Callaghan,N.S.W. , 2308, Australia, or via the Internet to [email protected] .

ISSN 1366-8250 print/ISSN 1469-9532 online/00/03217-18© 2000 Australian Society for the Study of Intellectual Disability Inc.DOI: 10.1080/1366825002000631 2

Concept acquisition in children with mildintellectual disability: Factors affecting the

abstraction of prototypical information1

BRETT K HAYES

ROBERT N CONWAY

The University of Newcastle

This study investigated the effects of variations in the number of instancescomprising a category on concept acquisition by 9- and 14-year-old children withmild intellectual disability and an intellectually average group matched forchronological and mental age. Children were exposed to either four or eightexemplars of a novel ill-defined visual category. They were then presented withold and new category exemplars and asked to identify items that had beenpresented previously. The pattern of children’s recognition responses indicatedthat intellectual disability had little effect on the ability to abstract a categoryprototype but did reduce use of exemplar-specific information for recognition.Prototype abstraction was enhanced in all groups by exposure to a larger numberof category exemplars. The implications for education and training are dis-cussed.

The ability to detect the similarities between discriminable objects and to treat suchobjects as a class or category is fundamental to human adaptive functioning. Thecentral role of such conceptual and categorical skills has been recognised in theories ofintelligence (e.g., Campione & Brown, 1984; Detterman & Sternberg, 1993; Salomon& Perkins, 1989) which suggest that variations in the ability to generalise learnedconcepts from familiar to novel environments is one of the key determinants ofindividual differences in intellectual functioning.

The generalisation of learned skills across environments has also often beenidentified as a particular problem in the instruction and training of people withintellectual disability (e.g., Conway & Gow, 1990; Das & Conway, 1992; Stokes &Baer, 1977). This failure to generalise may, in part, be attributed to the difficulties thatpeople with intellectual disability have in forming adequate conceptual representa-

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218 Hayes & Conway

tions of learned stimuli and skills (cf. Hayes, 1997; Hupp & Mervis, 1982). Theprocess of concept acquisition essentially involves learning the common propertiesthat characterise a class of discriminable stimuli (Butterfield, Slocum, & Nelson,1993; Medin, 1989). Once the concept is acquired it is assumed that an individual willbe able to recognise and respond appropriately not only to exemplars that wereencountered during learning but also to novel instances of the concept.

If people with an intellectual disability experience problems with concept acquisi-tion and storage then it follows that their generalisation of learned responses to novelconcept members will also be impaired. It would be very useful, therefore, to knowmore about the conceptual skills of children with intellectual disability and how thesecompare with those of children without disabilities. One way of approaching this issueis to find out what people with an intellectual disability have learned about commoneveryday concepts such as fruit and birds. Such studies of category knowledge haveoften found striking similarities between the way that people with intellectual disabil-ity and their peers without disabilities organise conceptual information in semanticmemory (e.g., Sperber & McCauley, 1984; Sperber, Ragain, & McCauley, 1976;Winters, 1985).

Such research is informative regarding the way in which the products of pastlearning are stored in the long term memories of people with intellectual disability.Unfortunately, however, these studies do not directly assess the factors that influencethe acquisition of categorical information. That is, they do not examine the cognitiveprocesses that underlie the way that people with an intellectual disability learn newconcepts and hence are of somewhat limited value in generating directions for newinstructional and training methods.

The second approach to investigating the conceptual skills of people with anintellectual disability involves a direct assessment of the concept learning process.Although such research has generated many useful strategies for teaching newconcepts (e.g., Carnine & Becker, 1982; Carnine, Gersten, Darch, & Eaves, 1985) it islimited in its application because of a reliance on training instances that have featureswhich are both necessary and sufficient for category membership, and which, there-fore, can be partitioned by means of a general rule. While the results of such researchmay be applicable to instructional design in highly structured learning domains thereare good reasons to believe that many of the concepts that are crucial for adaptivefunctioning do not conform to such a well-defined stimulus structure. A large body ofevidence now suggests that common object concepts such as fruit, animals, andfurniture have an ill-defined structure in that no set of features uniquely determinescategory membership (see Komatsu, 1992; Medin, 1989 for reviews). Instead featurestend to be related to the category in a probabilistic way, with certain features appearingmore frequently across exemplars than others. One important consequence of thisprobabilistic structure is that individual category exemplars vary in their typicality orrepresentativeness of the category as a whole. Hence, a “sparrow” is usually judged tobe a more typical instance of the bird category than a “penguin” because it containsmore of the features that are frequently associated with the category. Moreover, thisprobabilistic structure means that there is often disagreement about which category aparticular instance belongs to (Medin, 1989).

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Concept acquisition and generalisation 219

The recognition of the probabilistic nature of many concepts has shifted the focus ofresearch on concept acquisition away from the learning of explicit rules and towardsalternative learning processes. One such process is the abstraction of the exemplarfeatures that are common to many category members. A number of researchers (e.g.,Hayes-Roth & Hayes-Roth, 1977; Posnansky & Neumann, 1977) have suggested thatduring the course of exposure to category members, one abstracts a summary repre-sentation or prototype of the category that is composed of the most frequentlyoccurring exemplar features. The category assignment of novel instances is thendetermined by their similarity to stored prototypes. Such theories are supported by thefinding that when subjects are taught a new category using a set of instances that doesnot include the category prototype, the prototype is still classified more accuratelyduring transfer tests than are the previously seen examples that were used in training(Posner & Keele, 1968).

Specific-exemplar or “instance” models constitute an alternative approach to ex-plaining the learning of ill-defined or probabilistic categories. Such models assumethat the classification of a novel category exemplar is determined by its similarity toone or more previously encountered training instances rather than to a categoryprototype (Medin & Schaffer, 1978). According to this approach, “sparrow” is readilyassigned to the category bird because sparrows are similar to many other instances thathave previously been associated with the bird category.

Much concept learning research with intellectually average subjects has beenconcerned with determining the conditions under which children (e.g., Posnansky &Neumann, 1977) and adults (e.g., Medin, 1989) form prototype and exemplar-specificrepresentations of learned categories. Given the centrality of this issue to the categorylearning process, it is critical that we gain a better understanding of how children withintellectual disability represent probabilistic categories. In one study that addressedthis issue, Hayes and Taplin (1993a) exposed intellectually average children and thosewith a mild intellectual disability to the members of a novel ill-defined category andthen examined the extent to which categorical decisions about a transfer test set werecorrelated with the similarity of test items to specific study phase exemplars or to acategory prototype that was never presented at study. Intellectually average 9-year-olds, as well as 9- and 14-year-olds with an intellectual disability, were influenced byprototype similarity when determining the category membership of novel items. Onlythe intellectually average children, however, made additional use of similarity tospecific study exemplars in their categorical decisions.

These results suggest that an ability to abstract prototypical information from a setof exemplars is relatively unaffected by the presence of intellectual disability and isconsistent with an emerging view that prototype abstraction emerges earlier in thecourse of development than the learning of exemplar-specific details (e.g., Bomba &Siqueland, 1983; Hayes & Taplin, 1993b; Younger, 1990). If this is the case, then oneuseful strategy to promote the learning and transfer of new concepts by children withintellectual disability would be to establish training conditions that facilitate prototypeabstraction. Research with intellectually average subjects has identified a number oftask factors that might influence a learner’s sensitivity to prototypical information (seeHoma, 1984 for a review). One of the most widely studied of such factors is categorysize. Subjects trained with larger sets of category exemplars generally show more

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220 Hayes & Conway

accurate classification of novel items than those trained with fewer exemplars (Homa,Sterling, & Trepel, 1981), even when the total level of exposure to category items isequated across small and large category sizes (Homa, Dunbar, & Nohre, 1991). Thisfacilitation of transfer is thought to reflect an increased sensitivity to the frequentlyoccurring or prototypical features of the category. By expanding the training set, oneincreases the learner’s exposure to the number of features that are common to categoryinstances relative to characteristics that are idiosyncratic or shared by only a minorityof instances.

Such evidence suggests that prototype abstraction, and hence category learning andgeneralisation, by children with an intellectual disability might be enhanced throughthe manipulation of the number of training instances presented. There are clearparallels between this effect of category size on concept acquisition and the findingthat across a range of adaptive living tasks including the use of manual sign labels forobjects, street crossing, vending machine use and vocational skills, the acquisition andgeneralisation of appropriate behaviours is enhanced through the use of multipletraining exemplars. (Chadsey-Rusch, et al., 1993; Day & Horner, 1986; Horner &McDonald, 1982; Hupp & Mervis, 1982; Sprague & Horner, 1984).

This study aimed, therefore, to investigate the nature of the concept representationsformed by children with a mild level of intellectual disability and by intellectuallyaverage children, and how these representations are altered by variations in the size ofthe category to be learned. Many previous investigations of concept acquisition inchildren without disabilities (e.g., Hayes & Taplin, 1993b; Posnansky & Neumann,1977) have relied on methods that involve learning to discriminate between themembers of two or more artificial categories. Such paradigms have the limitation ofmaking relatively large demands on the attentional and memory capacities of thelearner. Given that children with an intellectual disability may find it more difficultthan their peers without disabilities to maintain their attention on such tasks forextended periods of time (Tomporowski & Tinsley, 1997) we employed a conceptacquisition task in which the demands for sustained attention during concept acquisi-tion are reduced. Following the recognition procedure developed by Franks andBransford (1971) a study phase was administered in which children were exposed toand asked to remember the members of just one novel category. The number ofdiscrete study items comprising the concept was manipulated between subjects.Children were then presented with a test set that contained the study phase items anda number of novel transfer items, including the category prototype, and asked to judgewhether each of these test items had been presented in the study phase. If prototypeabstraction has taken place, then the probability of making a “yes” recognitionresponse should be higher for the prototype than for any other novel item. Respondingbased on comparisons of test items to specific study exemplars will be examined bycomparing recognition responses to “old” study items and novel test items that have asimilar feature structure. Following the findings of researchers such as Homa et al.(1981) it was expected that the categorisation advantage for the prototype would bemagnified following exposure to a larger study set.

In both small and large category size conditions, the concept learning abilities of 9-and 14-year-old children with mild intellectual disability were compared with those ofintellectually average children who were matched on chronological age with the

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Concept acquisition and generalisation 221

younger group with intellectual disability and on mental age with the older children.The purpose of this double-matching was to determine whether intellectual disabilityaffects the type of information employed in making categorical decisions, and whetherany such differences are maintained once the effects of general developmental delayare controlled for (Baumeister, 1984; Hodapp & Zigler, 1997). This design alsopermits a cross-sectional examination of developmental changes in conceptualprocessing in children with an intellectual disability.

METHOD

Participants

Children with a mild level of intellectual disability were selected from special classesin public primary and secondary schools in the Hunter region of New South Wales,Australia. Intellectually average children were recruited from public primary schoolsin the same region. In general, the selection policies of the public education system inNew South Wales are such that only children who have been shown to have a full-scaleIQ on a comprehensive assessment instrument such as the WISC-III (Wechsler, 1991)or Stanford-Binet: Fourth Edition (Thorndike, Hagan, & Sattler, 1986) that is morethan two standard deviations below the average for a given age group would be placedin a special class. Nevertheless, in order to confirm the current intellectual functioningof participants, all children were administered the Kaufman Brief Intelligence Test (K-BIT; Kaufman & Kaufman, 1990). The K-BIT was originally devised as a brief,individually administered measure of verbal and nonverbal intelligence. The instru-ment has been shown to have good concurrent validity with both the WISC-III(Prewett, 1995) and the Stanford-Binet (Prewett & McCaffery, 1993). For thepurposes of this study, children from special classes were classified as having a mildintellectual disability if they obtained a K-BIT Composite score between 56 and 71,and were also judged by their classroom teacher to have significant deficits in adaptivebehaviour. Children from normal grade classes were classified as intellectuallyaverage if they obtained a K-BIT Composite score above 84. A total of 20 childrenfrom normal grade and 35 from special classes were assessed in this way. Fourchildren from the special classes and two children from normal grade classes wereexcluded because they did not satisfy these criteria.

The results of these assessments of intellectual functioning as well as age anddemographic details for the remaining 49 children are given in Table 1. The tableshows that the younger group of children with intellectual disability had a meanchronological age that was similar to the intellectually average group. An attempt wasmade to match the mental ages (MA) of the older children with intellectual disabilityand the intellectually average group, but as can be seen from Table 1 the mean MA forthe 14-year-olds with intellectual disability was somewhat lower than that of thechildren from regular grade classes. An attempt was also made to match the threegroups on socioeconomic status as indexed by the occupational status of the child’sparent or guardian (Daniel, 1983). After matching on these variables children wererandomly allocated to the two category size conditions.

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222 Hayes & Conway

Tab

le 1

Sum

mar

y of

par

t icip

ant c

har

acte

rist

ics

Gro

upN

Mea

n C

AA

ge R

ange

Mea

n K

-BIT

Mea

n K

-BIT

Mea

n K

-BIT

Mea

n M

AO

ccu-

(Yea

rs,

(Max

imum

-V

erba

l IQ

Vi s

ual I

Q (

and

Com

posi

te I

Qpa

tion

alM

onth

s)M

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um)

(and

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ndar

dst

a nda

rd(a

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ard

Stat

usa

devi

atio

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viat

ion)

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Chi

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nw

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tell

ectu

aldi

sabi

lity

199y

6m

8y 1

0m –

10y

0m

95.4

7 (1

5.00

)9 9

.26

(12.

55)

97.6

3 ( 1

2.49

)9.

25 (

1.12

)5.

20

Chi

ldre

n w

ith

inte

llect

ual

disa

bilit

y: 9

yea

rs15

9y 1

0m9y

0m

– 1

0y 8

m69

.07

(10.

80)

75.2

7 (9

.80)

68.9

(8.

68)

6.89

(0.

99)

4.91

Chi

ldre

n w

ithin

telle

ctua

ldi

sabi

lity

: 14

year

s16

14y

4m1 3

y 0m

– 1

4y 1

0m60

.94

(6.5

8)5 9

.31

(11.

04)

56.6

9 (7

.74)

8.10

(1.

00)

5.31

a Sc

ale

is c

onst

ruct

ed o

ver

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nge

of 1

-6 w

ith

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er s

core

s in

dica

ting

occu

pat i

ons

wit

h hi

gher

per

ceiv

ed s

t atu

s

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Concept acquisition and generalisation 223

Stimuli

The study phase stimuli took the form of computer graphics figures which varied onfour binary-valued dimensions. A full list of features appears in Table 2 and asummary of the structure of each study phase exemplar is given in Table 3. Examplesof the stimuli are given in Figure 1. In the small category size condition, fourexemplars comprising a single category were presented, while in the large categorysize condition eight exemplars were shown. Table 3 shows that the frequency distribu-tion of feature values was equivalent across the category size conditions with a featurevalue of “1” being the most frequently presented value for all dimensions. Thecategories were ill-defined in the sense that no single feature value was present in allexemplars. It can be seen from Table 3 that in order to generate a sufficient number oftraining and test items some stimuli were presented with four features while otherscontained only three. These three feature items only contained features that werefrequently associated with the category and the proportion of such items was identicalfor the small and large training sets.

Table 2

Description of feature values for each dimension of variation in the category

DimensionsFeature value Hands Body Arms Legs

1 Diamond Checkered Dot pattern Triangle0 Circular Diagonal lines Solid Oval

Table 3

Summary of feature structure of training exemplars for small (items 1-4) and large (items1-8) categories (using the feature value notation described in Table 2)

DimensionsItem a Hands Body Arms Legs

1 1 1 1 02 0 1 1 13 1 X 1 14 1 1 X 15 1 0 1 16 1 1 0 17 X 1 1 18 1 1 1 X

Note. X Denotes no feature present. In the case of the dimensions “Hands” and “Legs” the feature was simplyomitted from the item. In the case of “Body” and “Arms” the feature was presented as an outline but the filler patterndescribed in Table 2 was omitted.

a Half of the participants in the small category condition were presented with items 1-4 and the remainder werepresented with items 5-8. Participants in the large category condition saw all eight items.

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224 Hayes & Conway

Figure 1. Examples of experimental stimuli (Test items 1 1 1 1 and 0 1 0 0)

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Concept acquisition and generalisation 225

All participants were presented with 17 test phase stimuli containing four old (studyphase) items, eight “new” category members, the category prototype composed of thecombination of the most frequently occurring study phase features, and four “foil”items which had a structure which was highly dissimilar to the study set (i.e., only onefeature in common with the category prototype) (see Table 4). The latter items wereincluded to check whether children had abstracted the featural structure of the studyphase category. If they did then it was expected that they would respond “no” to thefoils during the test phase. None of the new, foil or prototype items had been presentedin the training phase. All stimuli were presented and responses recorded by a Toshiba3300SL laptop computer with a 19cm x 15.5cm grey-scale screen.

Table 4

Description of test phase stimuli (using the feature value notation described in Table 2)

DimensionsItem Type Hands Body Arms Legs

1 Old 1 1 1 02 Old 0 1 1 13 Old 1 X 1 14 Old 1 1 X 15 New 1 X 1 06 New 0 1 1 X7 New 0 0 1 18 New X X 1 19 New X 1 X 1

10 New 1 0 0 111 New X 1 1 X12 New 1 1 0 013 Prototype 1 1 1 114 Foil 1 0 0 015 Foil 0 1 0 016 Foil 0 0 1 017 Foil 0 0 0 1

Note: X denotes no feature present

Procedure

All children were tested individually at their school in two sessions. In the first session,after establishing rapport, the experimenter presented the category learning task. Inthe second session the K-BIT was administered. The category learning sessioncommenced with a study phase in which children were seated approximately 30 cmfrom the laptop computer screen and presented with the four or eight categoryexemplars. Each exemplar was presented individually for 6 seconds with a 2 second

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226 Hayes & Conway

inter-stimulus interval between presentations. In order to promote the encoding ofexemplar features the category set was shown twice with a different randomlydetermined order of presentation on each occasion. Children were told that theyshould look at the figures carefully because they would later be asked questions aboutthem and were instructed to pay particular attention to the four critical dimensionslisted in Table 3.

Upon completion of the study phase, all children proceeded to a test phase in whicheach item from the transfer set was presented in random order for 6 seconds. Childrenwere instructed to press one of two buttons located on the left and right of the computerkeyboard to indicate whether or not they had seen the item before. The buttons wereclearly marked with a “Y” (yes) and “N” (no) and the left-right position of theserelative to the child was counterbalanced across subjects.

RESULTS

In order to examine for possible differences in response bias between the three subjectgroups, the total proportion of “yes” recognition responses for all test items made bythe intellectually average children, and the 9- and 14-year-olds with intellectualdisability were entered into a one-way analysis of variance. The analysis failed to findany significant differences between the overall response probability for the threegroups. The findings of subsequent analyses of test phase performance, therefore,were unlikely to be contaminated by response bias differences between the groups.

In order to check that children in each condition had encoded at least some of thefeatural information of the study phase categories, the proportion of “yes” responses tofoil test items (see Table 5) for each participant group were tested against a chancevalue of 0.5. It was found that the probability of incorrectly saying “yes” to foil itemswas below chance for the intellectually average children (t(18) = -4.37, p < .001) andthe 14-year-olds with intellectual disability (t(14) = -2.18, p < .05) but was notsignificantly different from chance for the 9-year-old children with intellectual disa-bility (t(14) = -0.73, p = .48). The proportion of “yes” responses to foils were alsoentered into a 3 (group) x 2 (category size) analysis of variance. Three plannedcontrasts were used to investigate response differences between the various subjectgroups. The first contrast compared test phase responding for the intellectuallyaverage children and the nine-year-olds with intellectual disability. The secondcompared the performance of the intellectually average group with that of the 14-year-olds with intellectual disability. The third examined differences between the twogroups with intellectual disability. The analysis failed to reveal any significant differ-ences between the probability of responding “yes” to the foils across any of the subjector category size conditions. These results indicate that all of the groups tested didencode at least some of the featural information contained in the study exemplars.

The main analysis focussed on the proportion of children’s “yes” responses to testitems that belonged to the study category (see Table 5). These data were entered intoa 3 (group) x 2 (category size) x (3) (item: old, new, prototype) analysis of variance,with repeated measures on the last factor. The same group contrasts that were used inthe analysis of responses to foils were employed in this analysis. In addition, two

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Concept acquisition and generalisation 227

planned contrasts were used to investigate hypothesised differences between theproportion of “yes” responses to various types of test stimuli. Specifically, compari-sons were made between performance on the old and the new items, and on theprototype and new items. A Bonferoni adjustment to critical F values was made toaccount for the use of nonorthogonal comparisons and to maintain the family-wiseerror rate at a = .05 (Hall & Bird, 1986).

Table 5

Mean probability of saying “yes” (and standard deviation) for each type of test item

Participant group

Stimulus Type Nine-year-olds Nine-year-olds Fourteen-year-oldsfrom normal grade with an intellectual with an intellectualclasses disability disability

Small Category ConditionOld 0.83 (0.27) 0.79 (0.10) 0.79 (0.18)Prototype 0.90 (0.32) 0.83 (0.41) 0.67 (0.52)New 0.65 (0.18) 0.62 (0.19) 0.73 (0.17)Foils 0.35 (0.21) 0.37 (0.34) 0.33 (0.31)

Large Category ConditionOld 0.75 (0.22) 0.64 (0.31) 0.69 (0.27)Prototype 1.00 (0.00) 0.89 (0.33) 1.00 (0.00)New 0.31 (0.16) 0.42 (0.18) 0.47 (0.24)Foils 0.25 (0.18) 0.47 (0.40) 0.36 (0.26)

The analysis showed that all children tested were more likely to respond “yes” to thecategory prototype (M = .90) than to any other new test item (M = .49), F(1, 43) =58.51, MSE = 3.45, p < .001. As expected, this effect interacted with the size of thestudy phase category to which children were exposed, F(1, 43) = 13.16, MSE = 0.78,p < .01. The difference between the probabilities of saying “yes” to the prototype andnew items increased when children were exposed to a large (M

Proto – M

New = .56) as

opposed to a small study category (M Proto

– M New

= .18).In general, children were more likely to respond “yes” to old items (M = .75) than

to new items, F(1, 43) = 39.43, MSE = 1.47, p < .001. The specific nature of this effect,however, was found to depend upon both the size of the training category and thesubject group tested. A three-way interaction was found between the difference inresponding to old and new items, the size of the training category and the contrastcomparing the 9-year-old children with intellectual disability and the normal gradechildren, F(1, 43) = 6.89, MSE = .28, p < .05. Table 5 shows that for the intellectuallyaverage children a marked difference between recognition responding for “old” and“new” items was obtained when children were exposed to a large training category (M

Old – M

New = .44). The corresponding difference for the small category condition was

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228 Hayes & Conway

more modest (M Old

– M New

= .18). It is also notable that a two-way interaction betweenthe size of the difference in proportion of “yes” responses for old and new items andthe contrast comparing the intellectually average children with the 14-year-olds withintellectual disability approached significance, F(1, 43) = 3.07; MSE = 0.11; p = .13.The nature of this trend was such that differences in responding to old and new itemstended to be greater for the intellectually average children (M

Old – M

New = .31) than for

the 14-year-olds (M Old

– M New

= .14). All of these differences between responding toold and new items were examined using post-hoc Scheffe tests. The only differencethat was found to be statistically reliable in these post-hoc tests was the comparisonbetween old and new responding for the intellectually average children in the largecategory size group. No other main effects or interactions, including all of thosecomparing the responses of 9- and 14-year-old children with intellectual disability,reached significance.

The proportion of “yes” responses was significantly above chance for old items inthe intellectually average group t(18) = 5.27, p < .001, and for the 9-year-olds t(14) =3.06, p < .01, and the 14-year-olds with an intellectual disability t(14) = 3.76, p < .01.The probability of saying “yes” to prototype items was significantly above chance forthe intellectually average group t(18) = 8.50, p < .001, and for the 9-year-olds, t(14) =4.04, p < .01, and 14-year-olds with an intellectual disability t(14) = 4.04, p < .01. Theprobability of a “yes” response for new items was below chance for intellectuallyaverage children exposed to the large category t(9) = 4.04, p < .01.

In order to further test children’s sensitivity to the frequency structure of thecategory the new test items were subdivided into those which were more similar to thecategory prototype in that they shared at least two features with the prototype andcontained no more than one feature with a value of “0” (test items: 5,6, 8, 9 & 11), andthose which were less similar to the prototype in that they contained more than onecontrasting feature (test items: 7, 10 & 12). Across groups it was found that theproportion of “yes” responses was higher for the items that were similar to theprototype (M = .63) than for the less similar test items (M = 0.47), F(1, 43) = 5.13,MSE = .48, p< .05. This effect interacted with the category size factor such that thedifference in responding between the high and low similarity items was accentuatedfollowing exposure to the large category set, (high similarity: M = .64; low similarity:M = .38) relative to the smaller study set (high similarity: M = .61; low similarity: M= .59), F(1, 43) = 4.18, MSE = .39, p< .05.

Other researchers who have employed multidimensional stimuli like those used inthis study have reported that people with intellectual disability may attend to fewerstimulus dimensions than intellectually average subjects during a given stimulusexposure period (e.g., Huguenin, 1997; Zeaman & House, 1984). To check on thispossibility the probability of making a “yes” response to each of the four foil items wascalculated. Table 4 shows that each of these items had only one feature that wascharacteristic of the study phase category. If children were distributing their attentionequally across these dimensions then they should have been equally likely to respond“yes” to any of these items. An overall analysis of variance revealed that there was asignificant difference in response likelihood across the four foil items, F(3, 43) = 9.80,MSE = 1.54, p< .01. Post-hoc testing confirmed that this effect was largely due to an

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Concept acquisition and generalisation 229

increased probability of responding “yes” to test item 15 which had a prototypicalfeature on stimulus dimension 2 (“body pattern”) only, F(1, 43) = 19.33, MSE = 4.25,p< .001, indicating that children were giving more attentional weight to the bodypattern dimension. This effect, however, did not interact with either of the contrastscomparing the three subject groups, suggesting that there were no systematic groupdifferences in the way that attention was allocated to the stimulus dimensions.

DISCUSSION

The analysis of children’s responses to the test phase items shows clearly that each ofthe three groups tested was able to abstract the frequency structure of the study phasecategories and use this information as a basis for subsequent categorical decisions. Allchildren were more likely to accept the category prototype as a familiar item than anyother novel test stimulus, and correctly rejected the foil items. Furthermore, all threegroups were more likely to respond “yes” to novel test items when these closelyresembled the category prototype.

The high rate of “yes” responding to the prototype pattern relative to other novelstimuli is consistent with previous results obtained when similar testing procedureswere employed with intellectually average adults (Franks & Bransford, 1978) andchildren with an intellectual disability (Hayes & Taplin, 1993a). The current findings,therefore, support the view that when learning a novel, ill-defined category childrenwith an intellectual disability are just as likely to abstract the prototypical features ofobserved instances as intellectually average children of the same chronological ormental age. Moreover, the current findings suggest that such abstraction is relativelyindependent of individual differences in intellectual ability.

The difference between the proportion of “yes” responses for old and new test itemscan be taken as a measure of children’s use of the specific-exemplar informationcontained in the study phase stimuli for test phase decisions. Using this index it wasfound that only intellectually average children exposed to a large category showedreliable evidence of the use of exemplar-specific information. The fact that neither ofthe groups with an intellectual disability used such information to guide their testresponses is consistent with other findings that intellectual disability is associated witha decreased sensitivity to exemplar-specific information in concept learning (Hayes &Taplin, 1993a) as well as observations that people with a mild intellectual disabilityhave more difficulty than their intellectually average peers in retrieving and namingatypical or unusual members of common categories (Glidden & Mar, 1978).

The current study, however, extends our understanding of the concept learningabilities of children with an intellectual disability in an important way by showing thatprototype abstraction in intellectually average children and those with intellectualdisability is affected in similar ways by the number of category exemplars to whichthey were exposed. All groups of children tested showed evidence of increasedsensitivity to the common or prototypical features of the category following exposureto large as opposed to small category training sets. This finding suggests that theprocess of prototype abstraction in children is governed by similar task factorsregardless of the presence of an intellectual disability. The causal processes which

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230 Hayes & Conway

underlie this facilitation of prototype abstraction under conditions of increased cate-gory size have yet to be fully determined (Homa et al., 1981). Presumably, however,increments in category size assisted the children with an intellectual disability byincreasing their opportunity to encode the features of individual category membersand to compute the similarities and differences between these exemplar features.

Such findings add to our overall appreciation of the similarities and differencesbetween the conceptual information processing abilities of intellectually averagechildren and those with an intellectual disability. We have shown that prototypeabstraction is related to level of experience with or exposure to category members,regardless of the presence of an intellectual disability. Other work has shown that bothchildren with an intellectual disability and intellectually average children organisetheir concepts in a hierarchical fashion (e.g., Sperber et al., 1976), and that withincategories instances are ranked in terms of their shared features or typicality (Glidden& Mar, 1978; Weil, McCauley, & Sperber, 1978). The overall picture, at present, thenis one where the similarities between the conceptual processing abilities of the twopopulations outweigh the differences.

In terms of models of ill-defined category learning and representation, the respons-es of the intellectually average 9-year-olds are most consistent with a “mixed-model”of category representation in which both prototypical and exemplar information areemployed in categorisation decisions. In contrast, the test phase responses of childrenwith an intellectual disability were predominantly guided by the similarity of teststimuli to the category prototype with relatively little influence of specific-exemplarsimilarity. This trend persisted even when the effects of general developmental delaywere controlled for by matching for mental age. Moreover, there appeared to be littleevidence of a developmental change in the use of exemplar-specific information bychildren with an intellectual disability over the period from nine to fourteen years ofage.

These results have potentially important implications for approaches to educationand training. Bray, Fletcher, and Turner (1997) have argued that the education ofpeople with an intellectual disability should capitalise on their cognitive competenciesrather than focus only on the remediation of deficits. In this respect the results of thecurrent study imply that children with a mild level of intellectual disability would beexpected to be as good as their CA and MA peers in extracting prototypical informa-tion from a given set of training examples but could be expected to have moredifficulty in retaining the idiosyncratic characteristics of individual training stimuli.One potentially effective approach to maximising concept learning and generalisationin children with an intellectual disability, therefore, would be to focus on strategiesthat promote the learning of frequently occurring or prototypical information. The useof multiple training exemplars employed here and recommended by other theorists(e.g. Carnine & Becker, 1982) could be seen as an example on one such strategy. Itfollows that further useful training strategies may emerge from future examinations ofother variables identified in the categorisation literature as impacting on prototypeabstraction such as the variability of instances within a training set (e.g., Hupp &Mervis, 1982) and the type of the instructions given to trainees (e.g., Homa, Burruel,& Field, 1987).

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Concept acquisition and generalisation 231

It is also useful to examine the current findings in the light of recent discussionabout the nature of “transfer” of training (cf. Detterman & Sternberg, 1993; Salomon& Perkins, 1989). We have shown that children with a mild intellectual disability useinformation abstracted during experience with ill-defined category members as a basisfor responding to novel category exemplars. In this experiment the relationshipbetween the learning that took place during the study and responding to novel testitems is probably best interpreted as an instance of “near transfer” in that children weremaking responses to novel instances that, in most cases, resembled the training itemson two or more feature dimensions. While such generalisation of the learned conceptacross perceptual features might seem modest it should be noted that this kind oftransfer is close to that which some programs that teach vocational or living skills aimto achieve. Horner and McDonald (1982), for example, attempted to teach adolescentswith a severe disability generalisable skills in crimping and cutting electronic compo-nents using single or multiple exemplars of the components. Like the stimuli in thepresent study, the target set to which the trainees were supposed to generalise differedfrom the training set only in terms of the size, width and shape of the components.

The implications of our results for “far transfer” of skills and strategies across moreradical shifts in learning context is less clear. It is notable, however, that recentevidence suggests that the degree to which a person with an intellectual disability islikely to learn and transfer more complex strategies may be related to their level offunctioning on more basic cognitive tasks (e.g., Detterman et al., 1992). Hence, in thefuture it may be useful to explore the links between competence in “basic” processingtasks such as concept learning and the transfer of complex cognitive strategies.

Acknowledgments

This research was supported by a University of Newcastle Research ManagementCommittee Grant to the authors. The authors would like to thank the New SouthWales Department of School Education for allowing us access to the childrentested and would like to express our deep appreciation to the students and staff ofGateshead, Hamilton, Irrawang, Toronto, and Wallsend Public Schools, andBelmont, Broadmeadow, Raymond Terrace, Toronto and Wallsend High Schoolsfor their enthusiastic participation in the study. We would also like to thankChristine Selmes for her assistance with the data collection and Richard Dear forhis programming assistance.

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