The effect of feedback delay and feedback type on perceptual category learning: The limits of multiple systems.

Dunn, John C.; Newell, Ben R.; Kalish, Michael L.

doi:10.1037/a0027867

Cited by 46 publications

(80 citation statements)

References 58 publications

(134 reference statements)

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“…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). Our explanation is, therefore, entirely compatible with the existing evidence.…”

Section: Dimensionalitysupporting

confidence: 80%

“…This paper, therefore, adds to the growing literature (e.g. Dunn et al, 2012;Newell et al, , 2013Stanton & Nosofsky, 2007 Table 7. Comparison of the models assigned to each participant in the model-based analysis with those they reported using.…”

Section: Resultsmentioning

confidence: 89%

“…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;Newell, Moore, Wills, & Milton, 2013;Stanton & Nosofsky, 2007. In light of this accumulation of critiques, it becomes particularly important to assess the remaining dissociations.…”

mentioning

confidence: 99%

“…As the key conceptual claim of COVIS is that there are two mechanisms of learning, it could be argued that these analyses have failed to identify multiple systems only because the difference in learning between training types just happened to be the same for rule-based and information-integration learning by the end of training. To examine the validity of this claim, we used state-trace analysis (Bamber, 1979;Loftus, Oberg, & Dillon, 2004), which has previously been used with great success on this type of category learning data Dunn et al, 2012).…”

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confidence: 99%

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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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Section: Dimensionalitysupporting

confidence: 80%

Section: Resultsmentioning

confidence: 89%

mentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…Drawing inferences about monotonicity in state-trace analysis is a difficult statistical problem. There has been recent progress using parametric bootstrapping (Dunn, Newell, & Kalish, 2012;Newell & Dunn, 2008;Newell, Dunn, & Kalish, 2010) and Bayesian approaches with order constrained hypotheses (Prince, Brown, & Heathcote, 2012). Although these approaches are promising and each have merit, we do not implement them here due to our combination of within-and between-subjects measures, and the high dimensionality of the stimuli used in Experiment 2.…”

Section: Analytic Approachmentioning

confidence: 99%

The best of times and the worst of times are interchangeable.

Hawkins¹,

Marley²,

Heathcote³

et al. 2014

Decision

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We commonly determine the most preferred (best) and least preferred (worst) of a set of options, yet it is unclear whether the two choices are based on the same or different information. We examined best and worst choices using discrete choice tasks, where participants selected either the best option from a set, the worst option, or selected both the best and the worst option. One experiment used perceptual judgments of area, and another used consumer preferences for various attributes of mobile phones. In both domains, we found that the task (best, worst, or best and worst) does not alter the preferences expressed for the best (respectively, the worst) option. We also observed that the choice probabilities were consistent with a single latent dimension -options that were frequently selected as best were infrequently selected as worst, and vice versa -both within and between respondents. A quantitative model of choice and response time provided convergent evidence on those relations, with model variants that assumed an inverse relationship between the estimated parameters for best and worst choices accounting well for the data. We conclude that the diverse types of best and worst choices that we studied can be conceived as opposing ends of a single continuous dimension rather than distinct latent entities. We discuss these results in the light of rather different results for accepting (e.g., purchasing) and rejecting (e.g., not purchasing) options from a set.

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