Academic journal article Psychonomic Bulletin & Review

Dual-Learning Systems during Speech Category Learning

Academic journal article Psychonomic Bulletin & Review

Dual-Learning Systems during Speech Category Learning

Article excerpt

Published online: 4 September 2013

© Psychonomic Society, Inc. 2013

Abstract Dual-system models of visual category learning posit the existence of an explicit, hypothesis-testing reflective system, as well as an implicit, procedural-based reflexive system. The reflective and reflexive learning systems are competitive and neurally dissociable. Relatively little is known about the role of these domain-general learning systems in speech category learning. Given the multidimensional, redundant, and variable nature of acoustic cues in speech categories, our working hypothesis is that speech categories are learned reflexively. To this end, we examined the relative contribution of these learning systems to speech learning in adults. Native English speakers learned to categorize Mandarin tone categories over 480 trials. The training protocol involved trial-by-trial feedback and multiple talkers. Experiments 1 and 2 examined the effect of manipulating the timing (immediate vs. delayed) and information content (full vs. minimal) of feedback. Dual-system models of visual category learning predict that delayed feedback and providing rich, informational feedback enhance reflective learning, while immediate and minimally informative feedback enhance reflexive learning. Across the two experiments, our results show that feedback manipulations that targeted reflexive learning enhanced category learning success. In Experiment 3, we examined the role of trial-to-trial talker information (mixed vs. blocked presentation) on speech category learning success. We hypothesized that the mixed condition would enhance reflexive learning by not allowing an association between talker-related acoustic cues and speech categories. Our results show that the mixed talker condition led to relatively greater accuracies. Our experiments demonstrate that speech categories are optimally learned by training methods that target the reflexive learning system.

Keywords Speech perception . Category learning . Human memory and learning . Perceptual learning

Introduction

A large body of behavioral and neuroscience research suggests that visual category learning is mediated by at least two separate, albeit partially overlapping, learning systems (Ashby &Maddox,2005; Knowlton, 1999; Nomura & Reber, 2008; Poldrack & Packard, 2003). The explicit, reflective learning system depends on working memory and executive attention to develop and test hypotheses and rules for explicit classifi- cation. Processing in this system is available to conscious awareness and is mediated by a circuit primarily involving the dorsolateral prefrontal cortex, anterior cingulate, and an- terior caudate nucleus (Ashby & Ell, 2001; Seger & Miller, 2010). The implicit, procedural-based, reflexive learning sys- tem is not consciously penetrable and operates by associating perception with actions that lead to reinforcement via feed- back. Dual-system models predict that the two systems are complementary in learning various category structures, some of which are reflective-optimal, and others reflexive-optimal. Although more than 20 years of research has motivated the dual-system framework, this model has not been systemati- cally applied to examine speech category learning.

Previous speech-learning studies have examined category learning as an emergent property of unsupervised and/or supervised learning processes (Goudbeek, Cutler, & Smits, 2008; McClelland, Fiez, & McCandliss, 2002;Norris, McQueen, & Cutler, 2003; Toscano & McMurray, 2010; Vallabha, McClelland, Pons, Werker, & Amano, 2007). In unsupervised learning, statistical regularities in the input lead to category representations in sensory regions through a process of implicit Hebbian learning (Goudbeek et al., 2008; Goudbeek, Swingley, & Smits, 2009; McClelland et al., 2002). More recent, computationally based, unsupervised- learning models incorporate competition in addition to statis- tical learning (McMurray, Aslin, & Toscano, 2009; Toscano & McMurray, 2010). …

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