The effects of category overlap on information-integration and rule-based category learning

Ell, Shawn W.; Ashby, F. Gregory

doi:10.3758/bf03193362

Cited by 45 publications

(70 citation statements)

References 41 publications

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“…A widely held view is that making category assignments probabilistic increases the likelihood of procedural-learning strategies (Knowlton, Squire, & Gluck, 1994;Knowlton, Mangels, & Squire, 1996). In contrast to this prediction, Ell and Ashby (2006) reported that the use of probabilistic category assignments in a rule-based task had no effect on the tendency of participants to use strategies (e.g., onedimensional) that appeared to be explicit. Even so, Ell and Ashby's results could not rule out the hypothesis that in the probabilistic conditions, the explicit-appearing strategies had actually been learned procedurally.…”

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confidence: 44%

Procedural learning of unstructured categories

2012

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Unstructured categories are those in which the stimuli are assigned to each contrasting category randomly, and thus there is no rule-or similarity-based strategy for determining category membership. Intuition suggests that unstructured categories are likely to be learned via explicit memorization that is under the control of declarative memory. In contrast to this prediction, neuroimaging studies of unstructured-category learning have reported task-related activation in the striatum, but typically not in the hippocampusresults that seem more consistent with procedural learning than with a declarative-memory strategy. This article reports the first known behavioral test of whether unstructured-category learning is mediated by explicit strategies or by procedural learning. Our results suggest that the feedback-based learning of unstructured categories is mediated by procedural memory.

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confidence: 44%

Procedural learning of unstructured categories

2012

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“…Factor 3, dopamine, serves as a reward-mediated signal and will, therefore, be released only when positive feedback is given. Although there is large empirical support for COVIS (for recent reviews, see Ashby & Maddox, 2005;Ashby & Valentin, 2005;Ell & Ashby, 2006;Maddox & Filoteo, 2005), the semisupervised conditions seem incompatible. According to COVIS, the feedback trials in which a correct classification was made would strengthen the synapses and boost learning.…”

Section: Discussionmentioning

confidence: 99%

“…This category task is frequently used in research (e.g., Ashby & Ell, 2001;Ashby & Gott, 1988;Ashby & Maddox, 1990, 1992Ashby & O'Brien, 2007;Ashby & Waldron, 1999;Ell & Ashby, 2006). The defining characteristic of the information-integration task is that participants have to combine the perceptual information of both underlying stimulus dimensions simultaneously to obtain good performance.…”

Section: Methodsmentioning

confidence: 99%

Semisupervised category learning: The impact of feedback in learning the information-integration task

Vandist

Schryver

Rosseel

2009

Attention, Perception, & Psychophysics

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In a standard supervised classification paradigm, stimuli are presented sequentially, participants make a classification, and feedback follows immediately. In this article, we use a semisupervised classification paradigm, in which feedback is given after a prespecified percentage of trials only. In Experiment 1, feedback was given in 100%, 0%, 25%, and 50% of the trials. Previous research reported by Ashby, Queller, and Berretty (1999) indicated that in an information-integration task, perfect accuracy was obtained supervised (100%) but not unsupervised (0%). Our results show that in both the 100% and 50% conditions, participants were able to achieve maximum accuracy. However, in the 0% and the 25% conditions, participants failed to learn. To discover the influence of the no-feedback trials on the learning process, the 50% condition was replicated in Experiment 2, substituting unrelated filler trials for the no-feedback trials. The results indicated that accuracy rates were similar, suggesting no impact of the no-feedback trials on the learning process. The possibility of ever learning in a 25% setting was also researched in Experiment 2. Using twice as many trials, the results showed that all but 2 participants succeeded, suggesting that only the total number of feedback trials is important. The impact of the semisupervised learning results for ALCOVE, COVIS, and SPEED models is discussed.

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“…Initially, the hypothesis-testing system is in control, but control will generally shift in favor of the procedural-based system in II tasks (e.g., Ell & Ashby, 2006). Because of this competition, manipulations designed to interfere with the hypothesis-testing system can actually facilitate learning in II tasks (De Caro, Thomas, & Beilock, 2008;Maddox, Love, Glass, & Filoteo, 2008;Markman, Maddox, & Worthy, 2006;Worthy, Markman, & Maddox 2009).…”

Section: Categorization As a Model Taskmentioning

confidence: 99%

When bad stress goes good: increased threat reactivity predicts improved category learning performance

2010

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The way in which we respond to everyday stressors can have a profound impact on cognitive functioning. Maladaptive stress responses in particular are generally associated with impaired cognitive performance. We argue, however, that the cognitive system mediating task performance is also a critical determinant of the stresscognition relationship. Consistent with this prediction, we observed that stress reactivity consistent with a maladaptive, threat response differentially predicted performance on two categorization tasks. Increased threat reactivity predicted enhanced performance on an information-integration task (i.e., learning is thought to depend upon a proceduralbased memory system), and a (nonsignificant) trend for impaired performance on a rule-based task (i.e., learning is thought to depend upon a hypothesis-testing system). These data suggest that it is critical to consider both variability in the stress response and variability in the cognitive system mediating task performance in order to fully understand the stress-cognition relationship.

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The effects of category overlap on information-integration and rule-based category learning

Cited by 45 publications

References 41 publications

Procedural learning of unstructured categories

Procedural learning of unstructured categories

Semisupervised category learning: The impact of feedback in learning the information-integration task

When bad stress goes good: increased threat reactivity predicts improved category learning performance

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