Toward a dual-learning systems model of speech category learning

Abstract

More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article, we describe a neurobiologically constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, unidimensional rules to more complex, reflexive, multi-dimensional rules. In a second application, we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

Auditory categories with separable decision boundaries are learned faster with full feedback than with minimal feedback

“…In the visual domain, it has been suggested that full feedback discourages learners from switching to using inseparable boundaries, rather than separable boundaries, to approximate the category structure (Maddox et al, 2008). The disruptive effect of full feedback has been replicated in speech category learning, where the optimal decision boundaries are posited to be inseparable (Chandrasekaran et al, 2014a;Chandrasekaran et al, 2014b;Maddox and Chandrasekaran, 2014). One possible explanation for the equivalent performance between full and minimal feedback conditions in the auditory domain is that the learners were less likely to be initially biased towards using separable boundaries, when learning less easily verbalizable non-speech auditory categories than with speech auditory categories.…”

“…When participants learn Mandarin Chinese lexical tone categories, where the optimal decision boundaries are posited to be inseparable (Chandrasekaran et al, 2010;Maddox and Chandrasekaran, 2014;Chandrasekaran et al, 2014a), adult learners initially attempt to identify the tone categories using separable boundaries but gradually transition to using inseparable boundaries (Maddox and Chandrasekaran, 2014;Chandrasekaran et al, 2014a). Minimal feedback, relative to full feedback, leads to faster learning (Chandrasekaran et al, 2014b).…”

Effect of explicit dimensional instruction on speech category learning

“…We predict that explicit instruction to the pitch direction dimension will speed the transition from simple unidimensional reflective, rule-based strategies (e.g. rules related to pitch height) to a more optimal reflexive strategy that weights both dimensions (equally) during decision (Chandrasekaran, et al, 2014a; Maddox & Chandrasekaran, 2014a; Maddox, Chandrasekaran, Smayda, & Yi, 2013; Maddox et al, 2014). …”

“…DLS assumes that speech category learning involves a competition between a reflective system that is rule-based and relies on executive function processes in the prefrontal cortex, and a reflexive system that is procedural and relies upon dopamine-mediated reward signals in the striatum (Chandrasekaran, et al, 2014a; Maddox & Chandrasekaran, 2014a; Yi, Maddox, Mumford, & Chandrasekaran, 2014) 1 . The DLS approach derives from Ashby and Townsend's (1986) General Recognition Theory that is a multidimensional extension of signal detection theory (Green & Swets, 1967).…”

“…Much of what we know about the learning systems underlying category learning comes from the visual domain. An extensive body of behavioral, neuropsychological and neuroimaging work (reviewed in Chandrasekaran, et al, 2014a) suggests that visual category learning is mediated by at least two neural systems: a reflective (rule-based) learning system that actively generates verbal rules, is pre-frontally-mediated and relies heavily on executive function and working memory, and a reflexive (procedural-based) learning system that involves pre-decisional integration of information across dimensions, is striatally-mediated and does not rely on executive function and working memory. When the optimal classification rule is easily verbalizable, the reflective learning system dominates.…”