The Origin of Exemplar Effects in Rule-Driven Categorization.

Lacroix, Guy; Giguère, Gyslain; Larochelle, Serge

doi:10.1037/0278-7393.31.2.272

Cited by 17 publications

(22 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Going beyond previous studies relying on the same paradigm (Hahn et al, 2010;Lacroix et al 2005: Regehr & Brooks, 1993Thibaut & Gelaes, 2006), our study shows that the balance between exemplar and rule use also depends on category structure (eight or 20 training stimuli). In this respect, it is interesting to compare category types 8-30 and 20-12, which had the same number of learning trials.…”

Section: Exemplar and Rule Influence Combinedsupporting

confidence: 51%

“…Overall, there is strong evidence for exemplar effects with strongly individuated stimuli or features in the case of additive rules (Regehr & Brooks, 1993;Lacroix et al, 2005;Thibaut & Gelaes, 2006), and there is consistent, but much smaller, evidence from single-feature rules with stimuli that appeared as a collection of features rather than as an integrated whole (Hahn et al, 2010) or that were composed of interchangeable features (Lacroix et al, 2005). A generalization of these results is that when memory for learning items is made stronger and more distinctive, the exemplar effects are larger.…”

mentioning

confidence: 89%

“…In the low similarity condition, the difference between the GoodT and BadT test items was smaller but significant. What is notable about this effect is that it operates at retrieval rather 1 One might argue that participants are using simpler rules that do "well enough" at training but lead to errors on the BadT items (see Lacroix et al 2005;Wills et al 2015, for discussion). This seems possible when participants must discover the rule but is less likely when they're told the rule, which is constantly in view.…”

Section: Exemplar and Rule Influence Combinedmentioning

confidence: 99%

See 2 more Smart Citations

Does practice in category learning increase rule use or exemplar use—or both?

Thibaut¹,

Gelaes²,

Murphy

2018

Mem Cogn

View full text Add to dashboard Cite

Categorization research has demonstrated the use of both rules and remembered exemplars in classification, although there is disagreement over whether learners shift from one to the other or use both strategies simultaneously. Theoretical arguments can motivate predictions for both rule use and exemplar use increasing with more practice. We describe a single large experiment (n = 190) that manipulated the number of training items (category size), the number of presentations of each training item, and the similarity between the training and the transfer stimuli in order to discover when rules and exemplars are most likely to be used. Results showed that rules and exemplars both influenced classification and that exemplars were used more often with smaller categories, with more training on items, and when test items were similar to training items. There was no consistent evidence of a shift from rule-based to exemplar-based categorization with more learning. Importantly, we found a number of conditions in which rules and exemplars were both used, even within individual participants. We discuss our results in terms of hybrid models of classification.

show abstract

Section: Exemplar and Rule Influence Combinedsupporting

confidence: 51%

mentioning

confidence: 89%

Section: Exemplar and Rule Influence Combinedmentioning

confidence: 99%

See 1 more Smart Citation

Does practice in category learning increase rule use or exemplar use—or both?

Thibaut¹,

Gelaes²,

Murphy

2018

Mem Cogn

View full text Add to dashboard Cite

show abstract

“…Half of the items had high similarity to the training items, and half had low similarity to the training items. In contrast to previous work on similarity and explicit rule use (e.g., Allen and Brooks, 1991; Regehr and Brooks, 1995; Lacroix et al, 2005; Folstein and van Petten, 2010; but see also Folstein et al, 2008), the similarity manipulation depended entirely on features that were not mentioned in the rule and which were consequently entirely irrelevant to correct classification. Furthermore, given the design of the test set, responding based on similarity alone would lead to chance performance (i.e., accuracy levels of 50%).…”

Section: Introductionmentioning

confidence: 84%

Ignore Similarity If You Can: A Computational Exploration of Exemplar Similarity Effects on Rule Application

Brumby

Hahn

2017

Front. Psychol.

View full text Add to dashboard Cite

It is generally assumed that when making categorization judgments the cognitive system learns to focus on stimuli features that are relevant for making an accurate judgment. This is a key feature of hybrid categorization systems, which selectively weight the use of exemplar-and rule-based processes. In contrast, Hahn et al. (2010) have shown that people cannot help but pay attention to exemplar similarity, even when doing so leads to classification errors. This paper tests, through a series of computer simulations, whether a hybrid categorization model developed in the ACT- showed that while the presence of an exemplar-similarity effect on response times was associated with classification errors it was possible to fit both measures to the observed data for an unsupervised version of the task (i.e., in which no feedback on accuracy was given). Difficulties arose when the model was applied to a supervised version of the task in which explicit feedback on accuracy was given. Modeling results show that the exemplar-similarity effect is diminished by feedback as the model learns to avoid the error-prone exemplar-route, taking instead the accurate rule-route. In contrast to the model, Hahn et al. found that people continue to exhibit robust exemplar-similarity effects even when given feedback. This work highlights a challenge for understanding how and why people combine rules and exemplars when making categorization decisions.

show abstract

“…These theories judge an object to be a category member if it shares some proportion of similar features of other category members. In contrast, other approaches stress the importance of rule‐based categorization (e.g., Lacroix, Giguere, & Larochelle, 2005; Medin & Ortony, 1989). These theories focus on the role of specific rules to elicit an explanation of why an object is a member of a given category.…”

mentioning

confidence: 99%

The role of explanation in categorization decisions

Stibel

2006

Int J Psychol

View full text Add to dashboard Cite

Research into the role of explanation on the categorization process has yielded conflicting conclusions. Some theorists stress the importance of explanation, arguing that explanations provide a causal structure necessary to the categorization process. Others discount its significance, arguing that explanation is neither necessary nor sufficient for categorization. Experimentally, explanation has shown modest success in accounting for some categories but not others. Across three experiments, we test whether the central features of a category are the ones that capture the most explanatory structure. The objectives of the current study are threefold: to determine the importance of explanation in natural kind and artifact categorization; to understand the implications of feature correlations as they relate to explanation; and to further delineate the benefit of explanation outside of a functional role. Experiment 1 demonstrates that explanation plays a more dominant role in artifact versus natural kind categorization. Experiment 2 provides evidence that correlated features are neither necessary nor sufficient for categorization. Moreover, it demonstrates that, in the absence of correlation, people still rely on explanation for artifacts but not for natural kinds. Experiment 3 tests to what extent functional information underlies explanation in artifact categorization. We demonstrate that functional relations are not necessary for explanatory‐based categorization. Our results, coupled with previous evidence, suggest a dichotomous role for explanation‐based categorization. On the one hand, explanation is virtually ignored when categorizing biological kinds; on the other, explanation fosters artifact categorization. Implications for categorization and category‐specific disorder are considered.

show abstract

The Origin of Exemplar Effects in Rule-Driven Categorization.

Cited by 17 publications

References 35 publications

Does practice in category learning increase rule use or exemplar use—or both?

Does practice in category learning increase rule use or exemplar use—or both?

Ignore Similarity If You Can: A Computational Exploration of Exemplar Similarity Effects on Rule Application

The role of explanation in categorization decisions

Contact Info

Product

Resources

About