Reinstating the Frontal Lobes? Having More Time to Think Improves Implicit Perceptual Categorization

Newell, Ben R.; Moore, Christopher P.; Wills, Andy J.; Milton, Fraser

doi:10.1177/0956797612457387

Cited by 26 publications

(26 citation 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: 56%

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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: 56%

“…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: 76%

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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“…However, some researchers question this assumption (Newell, Dunn, & Kalish, 2011; Newell, Moore, Wills, & Milton, 2013; Nosofsky, Stanton, & Zaki, 2005; Stanton & Nosofsky, 2007; for a counter, see Ashby, 2014). For example, Nosofsky et al (2005) proposed that the RB versus II button-switch dissociation was due to cognitive complexity differences between the tasks, Stanton and Nosofsky (2007) proposed that the RB versus II feedback processing dissociation reported by Maddox, Ashby, Ing, and Pickering (2004) was due to perceptual discriminability differences between the tasks, and Newell et al (2013) argued that the RB versus II dissociation reported by Filoteo, Lauritzen, and Maddox (2010) was because of feedback processing time differences between the tasks. Obviously, no single study can test all these different hypotheses.…”

Section: Introductionmentioning

confidence: 99%

Declarative strategies persist under increased cognitive load

et al. 2015

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When humans simultaneously execute multiple tasks, performance on individual tasks suffers. Complementing existing theories, this article poses a novel question to investigate interactions between memory systems supporting multi-tasking performance: When a primary and dual task both recruit declarative learning and memory systems, does simultaneous performance of both tasks impair primary-task performance because learning in the declarative system is reduced, or because control of the primary task is passed to slower procedural systems? To address this question, participants were trained on either a perceptual categorization task believed to rely on procedural learning or one of three different categorization tasks believed to rely on declarative learning. Task performance was examined with and without a simultaneous dual task thought to recruit working memory and executive attention. To test whether the categories were learned procedurally or declaratively, the response keys were switched after a learning criterion had been reached. Large impairments in performance after switching the response keys are taken to indicate procedural learning, and small impairments are taken to indicate declarative learning. Our results suggest that the declarative memory categorization tasks (regardless of task difficulty) were learned by declarative systems, regardless of whether they were learned under dual-task conditions.

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“…Although some studies have suggested that implicit sequence learning can proceed with little or no use of attentional resources (Cleeremans & Jiménez, 1998;Frensch, Lin, & Buchner, 1998), other studies have shown impaired implicit learning when attention is diverted away from the primary implicit-learning task (Shanks & Channon, 2002;Shanks, Rowland, & Ranger, 2005). Still other results support the view that inattention may actually improve implicit learning (Filoteo, Lauritzen, & Maddox, 2010;Nemeth, Janacsek, Polner, & Kovacs, 2013; though see Newell, Moore, Willis, & Milton, 2013, for a reevaluation of Filoteo et al, 2010).…”

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confidence: 98%

Unaware yet reliant on attention: Experience sampling reveals that mind-wandering impedes implicit learning

et al. 2015

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Although implicit learning has been widely studied, controversy remains regarding its reliance on attentional resources. A central issue in this controversy is the question of how best to manipulate attention. The usual approach of comparing implicit learning in a serial reaction time (SRT) task under single-versus dual-task conditions is known to be problematic, because the secondary task may not only divert attention away from the primary task, but also interfere with the implicit-learning process itself. To address this confound, in the present study we used an experience-sampling instead of a dual-task approach. We assessed lapses of attention (mindwandering) with experience-sampling thought probes during a standard implicit-learning SRT task. The results revealed a significant negative correlation between mind-wandering and implicit learning. Thus, greater task focus was associated with improved implicit sequence learning. This result suggests that, at least in the context of this SRT task, optimal implicit learning relies on attention.

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Reinstating the Frontal Lobes? Having More Time to Think Improves Implicit Perceptual Categorization

Cited by 26 publications

References 3 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

Declarative strategies persist under increased cognitive load

Unaware yet reliant on attention: Experience sampling reveals that mind-wandering impedes implicit learning

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