As easy to memorize as they are to classify: The 5–4 categories and the category advantage

Blair, Mark R.; Homa, Donald

doi:10.3758/bf03195812

Cited by 51 publications

(69 citation statements)

References 38 publications

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“…The finding that a similarity-based model cannot account for the typicality gradient in goal-derived categories supports the hypothesis that the human conceptual system is flexible and can accommodate different types of representations and different systems of categorization (e.g., Ashby & Valentin, 2005;Blair & Homa, 2003;Minda & Smith, 2001;Poldrack & Foerde, 2008;Voorspoels, Storms, & Vanpaemel, 2011). Next to similarity-based representations such as exemplar and prototype representations, idealness, too, seems to be an organizing principle of category representations.…”

Section: Ideals As Representations Of Conceptssupporting

confidence: 53%

Idealness and similarity in goal-derived categories: A computational examination

2012

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The finding that the typicality gradient in goalderived categories is mainly driven by ideals rather than by exemplar similarity has stood uncontested for nearly three decades. Due to the rather rigid earlier implementations of similarity, a key question has remained-that is, whether a more flexible approach to similarity would alter the conclusions. In the present study, we evaluated whether a similarity-based approach that allows for dimensional weighting could account for findings in goal-derived categories. To this end, we compared a computational model of exemplar similarity (the generalized context model; Nosofsky, Journal of Experimental Psychology. General 115: [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57] 1986) and a computational model of ideal representation (the ideal-dimension model; Voorspoels, Vanpaemel, & Storms, Psychonomic Bulletin & Review 18:1006-114, 2011 in their accounts of exemplar typicality in ten goal-derived categories. In terms of both goodnessof-fit and generalizability, we found strong evidence for an ideal approach in nearly all categories. We conclude that focusing on a limited set of features is necessary but not sufficient to account for the observed typicality gradient. A second aspect of ideal representations-that is, that extreme rather than common, central-tendency values drive typicality-seems to be crucial.

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Section: Ideals As Representations Of Conceptssupporting

confidence: 53%

Idealness and similarity in goal-derived categories: A computational examination

2012

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“…As discussed by Blair and Homa (2003) and as we found in our examination of novel items, the incidence of rule use might be high on transfer trials even when a memory-based strategy is dominant on training items. Therefore, it is possible that any measure of strategy use based only on generalization to transfer items may give a distorted picture of the relative dominance of competing strategies.…”

Section: Discussionmentioning

confidence: 86%

Strategy shifts in classification skill acquisition: Does memory retrieval dominate rule use?

et al. 2006

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Strategies adopted by participants during skill acquisition do not necessarily remain stable over training or practice trials, but rather often shift in the direction of speeding up responses and easing the cognitive load (Schunn & Reder, 2001). A number of formal models, notably by Logan (1988) and Rickard (1997), have been proposed to account for strategy shifts. These models have focused on a shift from performance based on a rule (algorithm, calculation) to performance based on the retrieval of previously experienced items from memory. In these models, retrieval from long-term memory is viewed as a singlestep, possibly automatic solution procedure for problems that might otherwise require multiple rule-based computations. In tasks to which these models have been applied, participants are first instructed in the rule or algorithm that governs responses. Thus, although there are certain fundamental differences between them, both the Logan and Rickard models assume that performance is under control of an algorithm-based strategy at the outset and that, at some point in practice, there is a transition in control from a strategy based on the algorithm to a strategy based on instance-memory retrieval.The Logan (1988) and Rickard (1997) models may be limited by the tasks to which they have been applied (e.g., alphabet arithmetic). In these tasks, only two strategies are possible. Participants are given the rule (or algorithm) in advance and told to use the rule-based strategy at the outset. Under these conditions, guessing is unlikely and a transition from memory retrieval to rule use is virtually impossible. In a binary classification task, using a simple self-report procedure to identify strategies, Bourne, Healy, Parker, and Rickard (1999) found that strategy shifts are not always in the rule-to-memory direction. When participants were not preinstructed in the rule or algorithm for a task, most of them reported guessing on early trials. After some significant number of repetitions of individual stimuli, most participants adopted a preferred strategy, which might or might not be memory based. Specifically, most participants preferred a rule-based strategy in what was called a natural task condition. This task was called natural because it involved a linguistic rule for pronunciation of English words (i.e., participants had to choose one of two pronunciations, "thee" or "thuh," for the definite article the, depending on a given noun or adjective; see Raymond, Fisher, & Healy, 2002, for evidence that college students are not fully knowledgeable about this rule despite its presence in all English dictionaries). In contrast, most participants eventually preferred a memory-based strategy in an artificial task condition (in which participants had to base their response on whether meaningless letter strings conformed to an alphabetical sequence). Overall measures (accuracy and response time [RT]) showed that performance was better under the control of the preferred strategy-either rule or memory-than under the nonprefer...

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“…Or is there a point at which, if the taught classification is too complex, the unsupervised component is simply suppressed? It is worth pointing out here that even though there have been several demonstrations of naïve observers learning complex classifications (e.g., McKinley & Nosofsky, 1995;Minda & Smith, 2000;Nosofsky, 1988) some researchers have questioned whether performance in such tasks reflects categorization behavior as such, as opposed, for example, to memorization of which category labels go with which stimuli (Blair & Homa, 2003).…”

Section: Discussionmentioning

confidence: 99%

Supervised versus Unsupervised Categorization: Two Sides of the Same Coin?

Pothos

Edwards

Perlman

2011

Quarterly Journal of Experimental Psychology

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As easy to memorize as they are to classify: The 5–4 categories and the category advantage

Cited by 51 publications

References 38 publications

Idealness and similarity in goal-derived categories: A computational examination

Idealness and similarity in goal-derived categories: A computational examination

Strategy shifts in classification skill acquisition: Does memory retrieval dominate rule use?

Supervised versus Unsupervised Categorization: Two Sides of the Same Coin?

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