A Comparison of the neural correlates that underlie rule‐based and information‐integration category learning

Abstract

The influential competition between verbal and implicit systems (COVIS) model proposes that category learning is driven by two competing neural systems-an explicit, verbal, system, and a procedural-based, implicit, system. In the current fMRI study, participants learned either a conjunctive, rule-based (RB), category structure that is believed to engage the explicit system, or an information-integration category structure that is thought to preferentially recruit the implicit system. The RB and information-integration category structures were matched for participant error rate, the number of relevant stimulus dimensions, and category separation. Under these conditions, considerable overlap in brain activation, including the prefrontal cortex, basal ganglia, and the hippocampus, was found between the RB and information-integration category structures. Contrary to the predictions of COVIS, the medial temporal lobes and in particular the hippocampus, key regions for explicit memory, were found to be more active in the information-integration condition than in the RB condition. No regions were more activated in RB than information-integration category learning. The implications of these results for theories of category learning are discussed. Hum Brain Mapp 37:3557-3574, 2016. © 2016 Wiley Periodicals, Inc.

A dimensional summation account of polymorphous category learning

“…Ashby et al, 1998;Kemler Nelson, 1984;Smith & Shapiro, 1989;Ward, 1983), it is fully consistent with a substantial body of more recent work, across several different procedures. For example, it is consistent with results from the match-to-standards procedure Milton & Wills, 2004;Milton et al, 2009;Wills et al, 2013), the triad procedure (Wills et al, 2015), the criterial-attribute procedure (Wills et al, 2015), and information-integration category learning procedure (Carpenter et al, 2016;Edmunds et al, 2015Edmunds et al, , 2018Edmunds et al, , 2019Newell et al, 2013). It finds support from not only human behavioral data, but also from comparative work with rats and pigeons (Lea et al, 2018(Lea et al, , 2006Wills et al, 2009) and from functional imaging data in humans (Carpenter et al, 2016;Milton et al, 2009).…”

“…For example, the conclusions of the Kemler Nelson, Smith, and Ward procedures cited above can be shown to be artefacts of their analysis technique (Wills, Inkster, & Milton, 2015). A range of other results appearing to support overall similarity classification as a low effort classification mechanism (Filoteo, Lauritzen, & Maddox, 2010;Nomura et al, 2007;Smith et al, 2014;Spiering & Ashby, 2008;Waldron & Ashby, 2001;Zeithamova & Maddox, 2006), also turn out to be flawed (Carpenter, Wills, Benattayallah, & Milton, 2016;Le Pelley, Newell, & Nosofsky, 2019;Milton & Pothos, 2011;Newell, Dunn, & Kalish, 2010;Newell, Moore, Wills, & Milton, 2013;Tharp & Pickering, 2009;Wills et al, 2019). In summary, the existing evidence is largely compatible with the idea that overall similarity classification is more effortful than single dimension classification.…”

A Meta-Analysis Suggests Different Neural Correlates for Implicit and Explicit Learning

“…Category learning, and more specifically the dot-category learning employed here, has been found to rely on either implicit or explicit learning systems, depending on the task structure and instructions (Ashby and O’Brien, 2005; Ashby and Maddox, 2011; Carpenter et al, 2016; Reber et al 1998; Milton et al, 2011; Seger and Miller, 2010). If the dot learning was accompanied by motor instructions (such as point to center of dot pattern), or the task was an A-/not-A category distinction, implicit memory was used (Squire and Knowlton, 1995; Zeithmova et al, 2008).…”

Due Process in Dual Process: Model‐Recovery Simulations of Decision‐Bound Strategy Analysis in Category Learning

“…However, in the COVIS literature, arguably the most common category structures are the two-category unidimensional and informationintegration category structures shown in Fig. These category structures are often considered within the COVIS literature to be good choices for comparing explicit and procedural category learning (although see Carpenter, Wills, Benattayallah, & Milton, 2016;Edmunds et al, 2015;Nosofsky et al, 2005; for arguments to the contrary) because they differ markedly in verbalizability while being matched on several key attributes such as within-category similarity, between-category distance and the optimal accuracy a participant could achieve (usually 95% or above; Smith et al, 2014Smith et al, , 2015. These category structures are often considered within the COVIS literature to be good choices for comparing explicit and procedural category learning (although see Carpenter, Wills, Benattayallah, & Milton, 2016;Edmunds et al, 2015;Nosofsky et al, 2005; for arguments to the contrary) because they differ markedly in verbalizability while being matched on several key attributes such as within-category similarity, between-category distance and the optimal accuracy a participant could achieve (usually 95% or above; Smith et al, 2014Smith et al, , 2015.…”

“…1. These category structures are often considered within the COVIS literature to be good choices for comparing explicit and procedural category learning (although see Carpenter, Wills, Benattayallah, & Milton, 2016;Edmunds et al, 2015;Nosofsky et al, 2005; for arguments to the contrary) because they differ markedly in verbalizability while being matched on several key attributes such as within-category similarity, between-category distance and the optimal accuracy a participant could achieve (usually 95% or above; Smith et al, 2014Smith et al, , 2015.…”