Evolution of conceptual structure.

Homa, Donald; Rhoads, Deborah; Chambliss, Daniel F.

doi:10.1037/0278-7393.5.1.11

Cited by 92 publications

(105 citation statements)

References 19 publications

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“…Evidence consistent with the hypothesis that category learning can increase the salience of attributes shared by members of the same category has been obtained by Homa, Rhoads, and Chambliss (1979). They found, as a result of category learning, that pairs of stimuli belonging to the same category increased in judged similarity, whereas pairs belonging to contrasting categories decreased in judged similarity.…”

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confidence: 71%

Perceptual learning in visual category acquisition

1987

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The attribute structure of a set of dot patterns was studied by having subjects segment (parse) the dots of each pattern into parts or subunits by drawing circles around groups of dots from each pattern. These parsing data were obtained for subjects who had no prior experience with the patterns and for subjects who had previously learned to identify the patterns as members of one of four categories. Analyses of the parsing data indicated that category learning increased the salience of large subunits that were similar in orientation for patterns that were members of the same category. This evidence for perceptual learning was obtained even when the category training procedure required learning to identify the patterns individually, suggesting that attribute abstraction and item learning are not incompatible. It was also obtained without an increase in overall intersubject agreement. The latter result led us to question the usefulness of intersubject agreement as an index of category knowledge.One of the strategies available to subjects learning the category membership of a set of stimuli is to determine the attributes that are likely to be shared by members of the same category but that are unlikely to be shared by members of contrasting categories (i.e., diagnostic attributes). Brooks (1978) refers to this strategy as analytic. The relative difficulty of analytic category learning depends on how readily shared/discriminative attributes can be abstracted (i.e., tested for diagnosticity independent of other attributes).We would expect most subjects to begin category training analytically by testing attributes that are precategorically salient (a similar assumption is made in Fried and Holyoak's, 1984, category density model). These are the attributes that subjects would be most likely to notice and remember in examining the stimuli individually. If the precategorically salient attributes are not diagnostic of category membership, the subject could persist in an analytic strategy, continuing the search for attributes that are likely to be shared by members of the same category, but not by members of contrasting categories. This would lead the subject to consider, as potentially diagnostic of category membership, attributes that were not the most salient prior to category training. Successful category learning based on searching for shared/discriminative attributes could therefore be accompaniedby increases, relative to precategorical values, in the salience of attributes shared by members of the same category. tive strategy could result in category learning without changes in attribute salience; Brooks, 1978, andMedin, Dewey, andMurphy, 1983, demonstrated that under certain conditions, subjects forgo testing for diagnostic attributes and simply learn to identify stimuli as individuals.)An analytic strategy was also implicit in Gibson's (1969) assertion that perceptual learning involves an active search aimed at discovering attributes that discriminate among stimuli that are to receive different responses. As for c...

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confidence: 71%

Perceptual learning in visual category acquisition

1987

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“…The patterns that changed categories are now less similar to the other patterns in their category (lower within-category similarity), and more similar to the patterns in the other category (higher between-categories similarity). Category structure, commonly defined as the ratio of within-category similarity to between-categories similarity (e.g., Homa, Rhoads, & Chambliss, 1979;Smith, Murray, & Minda, 1997), is therefore dramatically reduced. Category structure is easily confounded with linear separability.…”

Section: Linear Separabilitymentioning

confidence: 99%

Expanding the search for a linear separability constraint on category learning

Blair

Homa

2001

Memory & Cognition

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“…In The relationships between category overlap and the correlation between the dimensions of a category to its label are intrinsically bound together. Our previous measure of structural ratio of a category (Homa et al, 1979)-the ratio of within-to between-category distances or dissimilarities-is similar but not identical to the correlation of a category's dimensions to its label. If similarity is analytically defined by the number of feature matches between any two patterns, then the structural ratios for correlated low overlap, correlatead high overlap, uncorrelated low overlap, and uncorrelated high overlap were .447, .773, .475, and .868, respectively.…”

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confidence: 87%

“…Therefore, to produce the high-overlap categories, each dimension of one of the categories in the low-overlap condition was incremented by one value, thereby reducing its separation from the other category. The end result was that within-category similarity remained the same but between-category similarity was modified, thereby reducing the ratio of within-category to between-category similarity, a ratio that we have previously used to define category structure (Homa, Rhodes, & Chambliss, 1979). Importantly, the effect of reducing category overlap had the effect of increasing the correlation between the category label and its dimensions.…”

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Category inference as a function of correlational structure, category discriminability, and number of available cues

2012

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Two experiments investigated category inference when categories were composed of correlated or uncorrelated dimensions and the categories overlapped minimally or moderately. When the categories minimally overlapped, the dimensions were strongly correlated with the category label. Following a classification learning phase, subsequent transfer required the selection of either a category label or a feature when one, two, or three features were missing. Experiments 1 and 2 differed primarily in the number of learning blocks prior to transfer. In each experiment, the inference of the category label or category feature was influenced by both dimensional and category correlations, as well as their interaction. The number of cues available at test impacted performance more when the dimensional correlations were zero and category overlap was high. However, a minimal number of cues were sufficient to produce high levels of inference when the dimensions were highly correlated; additional cues had a positive but reduced impact, even when overlap was high. Subjects were generally more accurate in inferring the category label than a category feature regardless of dimensional correlation, category overlap, or number of cues available at test. Whether the category label functioned as a special feature or not was critically dependent upon these embedded correlations, with feature inference driven more strongly by dimensional correlations.Keywords Categorization . Concepts . Inductive reasoning . Expertise . MemoryThe experimental study of human categories has an extensive history, beginning with the seminal study of Hull (1920) and continuing today into the identification of variables critical to the shaping of concepts and the development of formal, quantitative models of classification.Hull introduced the classification paradigm that dominates most current research today. In this paradigm, the subject initially assigns a number of patterns into designated categories, followed by a transfer test containing old and new instances. By manipulation of variables in the learning phase and then evaluating transfer performance, Hull was able to draw a number of conclusions about the learning and representation of concepts; for example, concepts were learned more rapidly in the order from simple to complex rather than the reverse, transfer was better following learning of many patterns shown infrequently rather than a few patterns presented numerous times, and so forth.However, categories provide functions above and beyond classification. Bruner, Goodnow, and Austin (1966) summarized a number of additional utilities of categories: Once learned, they permit generalization to novel instances, thereby reducing the need for new learning; they simplify the incredible complexity of the environment into a manageable set of units, thereby facilitating a host of cognitive functions, including logical reasoning and communication; they are adaptive so that harmful or threatening stimuli can be responded to appropriately; and they permit i...

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Evolution of conceptual structure.

Cited by 92 publications

References 19 publications

Perceptual learning in visual category acquisition

Perceptual learning in visual category acquisition

Expanding the search for a linear separability constraint on category learning

Category inference as a function of correlational structure, category discriminability, and number of available cues

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