Category Number Impacts Rule-Based but Not Information-Integration Category Learning: Further Evidence for Dissociable Category-Learning Systems.

Maddox, W. Todd; Filoteo, J. Vincent; Hejl, Kelli D.; Ing, A. David

doi:10.1037/0278-7393.30.1.227

Cited by 76 publications

(161 citation statements)

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“…Mean overall accuracy for participants learning structure 1 was .74 (SD 0 .10), and for structure 2 was .70 (SD 0 .16), and there was no significant difference between conditions overall, t(51) 0 1.01, p 0 .32, or in the last block of 24 trials, t(51) 0 0.49, p 0 .63. Participants' performance is comparable to that in a similar study by Maddox et al (2004). Due to the lack of differences between these groups in the initial analyses, we collapsed across the two conditions.…”

Section: Resultsmentioning

confidence: 89%

“…Participants in Experiment 2 learned one of two category structures that approximately corresponded to the fourcategory information-integration and rule-based structures used by Maddox, Filoteo, Hejl, and Ing (2004). These structures are difficult to learn perfectly and may encourage a variety of learning strategies but equate the number of category dimensions that are required for classification.…”

Section: Methodsmentioning

confidence: 99%

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Temporal characteristics of overt attentional behavior during category learning

Chen

Meier

Blair

et al. 2012

Atten Percept Psychophys

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Many theories of category learning incorporate mechanisms for selective attention, typically implemented as attention weights that change on a trial-by-trial basis. This is because there is relatively little data on within-trial changes in attention. We used eye tracking and mouse tracking as finegrained measures of attention in three complex visual categorization tasks to investigate temporal patterns in overt attentional behavior within individual categorization decisions. In Experiments 1 and 2, we recorded participants' eye movements while they performed three different categorization tasks. We extended previous research by demonstrating that not only are participants less likely to fixate irrelevant features, but also, when they do, these fixations are shorter than fixations to relevant features. We also found that participants' fixation patterns show increasingly consistent temporal patterns. Participants were faster, although no more accurate, when their fixation sequences followed a consistent temporal structure. In Experiment 3, we replicated these findings in a task where participants used mouse movements to uncover features. Overall, we showed that there are important temporal regularities in information sampling during category learning that cannot be accounted for by existing models. These can be used to supplement extant models for richer predictions of how information is attended to during the buildup to a categorization decision.Keywords Attention . Eye tracking . Categorization . Eye movements . Optimization . Learning . Error . Modeling . Temporal regularity . Visual cognition To interact with the world, people must learn how to sort through the vast amount of information it presents. One of the difficulties in doing so is that much of this information is irrelevant to the task at hand. Consequently, it is essential to allocate limited cognitive resources to important aspects of the environment through selective attention. The notion of selective attention is recognized within a broad range of research areas, from working memory (Awh, Anllo-Vento, & Hillyard, 2000) and priming (Tipper & Cranston, 1985) to cognitive development over the human life span (Plude, Enns, & Brodeur, 1994). Category learning, a paradigm that elicits voluntary control of attention, provides a unique opportunity for observing and formally analyzing how attention shifts over time in response to the relevance of available information. The present study uses eye tracking and mouse tracking to document temporal characteristics of overt top-down attentional behavior during categorization tasks and how this changes over the course of learning.One of the most influential models in the categorylearning literature, the generalized context model (GCM;Nosofsky, 1986), models the role of attention in learning as a weighting mechanism for incoming stimulus information. This type of model hypothesizes that relevant information gains more attention weight as a consequence of learning. For example, after learning the task of di...

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Section: Resultsmentioning

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Temporal characteristics of overt attentional behavior during category learning

Chen

Meier

Blair

et al. 2012

Atten Percept Psychophys

View full text Add to dashboard Cite

show abstract

“…Maddox, Bohil, & Ing, 2004;Maddox, Filoteo, Hejl, & Ing, 2004;Maddox, Filoteo, & Lauritzen, 2007;Maddox & Ing, 2005;Maddox, Love, Glass, & Filoteo, 2008;Zeithamova & Maddox, 2006) and dissociations, predicted by COVIS, still emerge. However, this evidence is not, perhaps, as compelling as it first appears and in recent years it has attracted substantive critiques on a variety of bases from separate labs (e.g Dunn et al, 2012;Newell et al, , 2013Stanton & Nosofsky, 2013;Zaki & Kleinschmidt, 2013).…”

Section: Dimensionalitymentioning

confidence: 99%

Feedback can be superior to observational training for both rule-based and information-integration category structures

Edmunds

Milton

Wills

2015

Quarterly Journal of Experimental Psychology

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Our thanks go to Angus B. Inkster for his help coding the verbal reports.TRAINING TYPE AND CATEGORIZATION. AbstractThe effects of two different types of training on rule-based and information-integration category learning were investigated in two experiments. In observational training, a category label is presented, followed by an example of that category and the participant's response. In feedback training, the stimulus is presented, the participant assigns it to a category and then receives feedback about the accuracy of that decision. Ashby, Maddox, and Bohil (2002) reported that feedback training was superior to observational training when learning information-integration category structures, but that training type had little effect on the acquisition of rule-based category structures. These results were argued to support the COVIS dual-process account of category learning. However, a number of non-essential differences between their rule-based and information-integration conditions complicate interpretation of these findings. Experiment 1 controlled, between category structures, for participant error rates, category separation, and the number of stimulus dimensions relevant to the categorization. Under these more controlled conditions, rule-based and information-integration category structures both benefitted from feedback training to a similar degree. Experiment 2 maintained this difference in training type when learning a rule-based category that had otherwise been matched, in terms of category overlap and overall performance, with the rule-based categories used in Ashby et al.These results indicate that differences in dimensionality between the category structures in Ashby et al. is a more likely explanation for the interaction between training type and category structure than the dual-system explanation they offered.KEYWORDS: COVIS, categorization, implicit, explicit, feedback.TRAINING TYPE AND CATEGORIZATION. 3Ashby and Maddox (2011) stated that many researchers now assume multiple systems are involved in category learning. To the extent that this claim is accurate, it is down in no small part to the behavioral dissociations reported by Ashby, Maddox and colleagues. These studies tend to find a differential effect of a manipulation on the learning of two types of category structure: rule-based and information-integration. argue that these dissociations are predicted by one particular dual-system model of category learning, COVIS (COmpetition between Verbal and Implicit Systems; Ashby, Alfonso-Reese, Turken, & Waldron, 1998;Ashby, Paul, & Maddox, 2011), which assumes the existence of two competing systems of category learning. The strength of the case for COVIS is, of course, not a function of the number of dissociations that have been reported, but rather of the number that prove to be reliable and valid. Indeed, there is a growing body of work that casts doubt on the validity or interpretation of a high proportion of these dissociations (e.g. Dunn, Newell, & Kalish, 2012;Newell, Dunn, & Kalish, 2010;...

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“…Flexibly trying rules leads to better performance because the participant may try and abandon incorrect rules prior to discovering the correct rule. Rule-based learning is effortful, and factors that influence working memory and explicit processing also interfere with it (Maddox, Filoteo, Hejl, & Ing, 2004;Waldron & Ashby, 2001;Zeithamova & Maddox, 2006;Zeithamova & Maddox, in press). …”

Section: Regulatory Fit and Flexibilitymentioning

confidence: 99%

Differential effects of regulatory fit on category learning

Grimm¹,

Markman²,

Maddox³

et al. 2008

Journal of Experimental Social Psychology

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Motivation affects the degree to which people engage in tasks as well as the processes that they bring to bear. We explore the proposal that a fit between a person's situationally-induced self-regulatory focus and the reward structure of the task that they are pursuing supports greater flexibility in processing than does a mismatch between regulatory focus and reward structure. In two experiments, we prime regulatory focus and manipulate task reward structure. Our participants perform a rulebased learning task whose solution requires flexible strategy testing as well as an informationintegration task for which flexible strategy use hinders learning. Across two experiments, we predict and obtain a three-way interaction between regulatory focus, reward structure, and task. Relative to a mismatch, a match leads to better rule-based task performance, but worse performance on the information-integration task. We relate these findings to other work on motivation and choking under pressure.

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Category Number Impacts Rule-Based but Not Information-Integration Category Learning: Further Evidence for Dissociable Category-Learning Systems.

Cited by 76 publications

References 64 publications

Temporal characteristics of overt attentional behavior during category learning

Temporal characteristics of overt attentional behavior during category learning

Feedback can be superior to observational training for both rule-based and information-integration category structures

Differential effects of regulatory fit on category learning

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