When more is less: Feedback effects in perceptual category learning

Maddox, W. Todd; Love, Bradley C.; Glass, Brian D.; Filoteo, J. Vincent

doi:10.1016/j.cognition.2008.03.010

Cited by 75 publications

(96 citation statements)

References 36 publications

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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).…”

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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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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“…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). Thus, increased threat reactivity would be expected to facilitate the procedural-based system, resulting in enhanced accuracy on an II task.…”

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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“…Behavioral studies of category learning with cognitive feedback suggest that tasks, which force subjects to rely on gradually acquired stimulus-outcome contingencies, are sensitive to the nature and timing of feedback (Maddox et al, 2003(Maddox et al, , 2008. During trial and error learning in these tasks, striatal areas are activated (Poldrack et al, 2001;Cincotta and Seger, 2007) and patients with Parkinson's disease, which is characterized by the loss of dopaminergic input to the striatum, are impaired (Filoteo and Maddox, 2007).…”

Section: Introductionmentioning

confidence: 99%

Comparing the Neural Basis of Monetary Reward and Cognitive Feedback during Information-Integration Category Learning

Daniel

Pollmann²

2010

J. Neurosci.

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The dopaminergic system is known to play a central role in reward-based learning (Schultz, 2006), yet it was also observed to be involved when only cognitive feedback is given (Aron et al., 2004). Within the domain of information-integration category learning, in which information from several stimulus dimensions has to be integrated predecisionally (Ashby and Maddox, 2005), the importance of contingent feedback is well established (Maddox et al., 2003). We examined the common neural correlates of reward anticipation and prediction error in this task. Sixteen subjects performed two parallel information-integration tasks within a single event-related functional magnetic resonance imaging session but received a monetary reward only for one of them. Similar functional areas including basal ganglia structures were activated in both task versions. In contrast, a single structure, the nucleus accumbens, showed higher activation during monetary reward anticipation compared with the anticipation of cognitive feedback in information-integration learning. Additionally, this activation was predicted by measures of intrinsic motivation in the cognitive feedback task and by measures of extrinsic motivation in the rewarded task. Our results indicate that, although all other structures implicated in category learning are not significantly affected by altering the type of reward, the nucleus accumbens responds to the positive incentive properties of an expected reward depending on the specific type of the reward.

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When more is less: Feedback effects in perceptual category learning

Cited by 75 publications

References 36 publications

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

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

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

Comparing the Neural Basis of Monetary Reward and Cognitive Feedback during Information-Integration Category Learning

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