Combining Computational Modeling and Neuroimaging to Examine Multiple Category Learning Systems in the Brain

Abstract

Considerable evidence has argued in favor of multiple neural systems supporting human category learning, one based on conscious rule inference and one based on implicit information integration. However, there have been few attempts to study potential system interactions during category learning. The PINNACLE (Parallel Interactive Neural Networks Active in Category Learning) model incorporates multiple categorization systems that compete to provide categorization judgments about visual stimuli. Incorporating competing systems requires inclusion of cognitive mechanisms associated with resolving this competition and creates a potential credit assignment problem in handling feedback. The hypothesized mechanisms make predictions about internal mental states that are not always reflected in choice behavior, but may be reflected in neural activity. Two prior functional magnetic resonance imaging (fMRI) studies of category learning were re-analyzed using PINNACLE to identify neural correlates of internal cognitive states on each trial. These analyses identified additional brain regions supporting the two types of category learning, regions particularly active when the systems are hypothesized to be in maximal competition, and found evidence of covert learning activity in the “off system” (the category learning system not currently driving behavior). These results suggest that PINNACLE provides a plausible framework for how competing multiple category learning systems are organized in the brain and shows how computational modeling approaches and fMRI can be used synergistically to gain access to cognitive processes that support complex decision-making machinery.

Continuous executive function disruption interferes with application of an information integration categorization strategy

“…Strictly speaking, this could be a single-system account but has many similarities with a multiple-systems framework because different cognitive processes may be important for implementing each type of strategy. Although there is convincing evidence (e.g., Maddox & Filoteo, 2001;Nomura et al, 2007;Nomura & Reber, 2012) that these strategies are subserved by distinct neurobiological systems, this is not the focus of the present article, nor is it an especially important point for the present findings. Instead, the important point is that there are multiple approaches for learning new categories and executive functions may be important for mediating the transition between these approaches, regardless of whether they are construed as separate systems, strategies, or otherwise.…”

Compensatory processing during rule-based category learning in older adults

“…Categories come in many different types, from simple featural categories (e.g., objects that are red) to much more complicated relational concepts (e.g., chases or conduit; see Ashby & Maddox, 2011; Kéri, 2003; Rips et al, 2012). Neuropsychological (e.g., Koenig, Smith, Moore, Glosser, & Grossman, 2007; Reber, Knowlton, & Squire, 1996; Smith et al, 2013; Smith & Grossman, 2008; Ullman et al, 1997), electrophysiological (e.g., Folstein & Van Petten, 2004; Morrison, Reber, Bharani, & Paller, 2015), and neuroimaging studies (e.g., Foerde, Knowlton, & Poldrack, 2006; Nomura, Maddox, & Reber, 2007; Nomura et al, 2007; Nomura & Reber, 2012; Reber, Martinez, & Weintraub, 2003) have suggested that there is an explicit, rule-based mechanism to learn categories which is distinct from an implicit, featural similarity-based mechanism.…”

“…The sensitivity of neuroimaging has already established structural differences between two types of category learning. In particular, rule-based category learning has been found to be dependent on the prefrontal cortex and the medial temporal lobes (MTLs), areas associated with age-related structural and functional decline (for review see Dennis & Cabeza, 2008) and is distinguished from implicit, featural, category learning which is dependent on striatal-frontal circuitry (e.g., Foerde et al, 2006; Nomura, Maddox, & Reber, 2007; Nomura et al, 2007; Nomura & Reber, 2012; Reber et al, 2003). Additionally, although both younger and older adults activated the MTL during an explicit category-learning task, functional magnetic resonance imaging (fMRI) results showed that younger adults’ MTL activity was significantly greater than older adults’ (Dennis & Cabeza, 2011).…”

The contribution of temporary storage and executive processes to category learning

“…Category learning refers to the course of acquiring new categories by extracting their characteristic features from a collection of stimuli as category members (Ashby & Ell, 2001;Nomura & Reber, 2012). A typical category learning task presents individuals with a set of stimuli belonging to unfamiliar categories and requires them to infer and retain the rules that are used to assign the stimuli to separate categories (Ashby & Maddox, 2005).…”