The Role of Corticostriatal Systems in Speech Category Learning Corticostriatal Speech Category Learning

One of the most difficult category learning problems for humans is learning non-native speech categories. While feedback-based category training can benefit speech learning, the mechanisms underlying these benefits are unclear. In this fMRI study, we investigated neural and computational mechanisms underlying feedback-dependent speech category learning in adults. Positive feedback activated a large corticostriatal network including the dorsolateral prefrontal cortex, inferior parietal lobule, middle temporal gyrus, caudate, putamen, and the ventral striatum. Successful learning of nonnative speech categories was contingent upon the activity of domain-general category learning systems: the fast-learning reflective system, involving dorsolateral prefrontal cortex that develops and tests explicit rules based on the feedback content, and the slowlearning reflexive system, involving the putamen in which the stimuli are implicitly associated with category responses based on the reward value in feedback. Computational modeling of response strategies revealed significant use of reflective strategies early in training and greater use of reflexive strategies later in learning. Reflexive strategy use was associated with increased activation in the putamen. Our results demonstrate a critical role for the reflexive corticostriatal learning system as a function of response strategy and proficiency during speech category learning. Introduction What neural mechanisms underlie language acquisition in adulthood? Learning speech sounds of a new language is argued to be a difficult category learning problem in adulthood. For instance, native Japanese speakers find it difficult to learn to categorize English /r/ vs. /l/ sounds (Iverson et al. 2003). This difficulty is likely due to the high variability and multidimensional nature of speech categories (Hillenbrand et al. 1995; Jongman et al. 2000; Vallabha et al. 2007; Holt and Lotto 2008, 2010). Adequate feedback can significantly enhance speech category learning in adults (McCandliss et al. 2002; McClelland and Patterson 2002; Norris et al. 2003; Goudbeek et al. 2008). Trialby-trial feedback is therefore ubiquitously used in speech training paradigms. However, little is known about the neural mechanisms underlying feedback-based error reduction in speech learning (Holt and Lotto 2008, 2010). Understanding the neural mechanisms underlying feedback-based learning is critical because subtle variations in feedback characteristics can significantly modulate speech learning rates (Chandrasekaran et al. 2013). Furthermore, it would contribute to our general knowledge of the neural mechanisms involved in learning a second language. Outside the speech domain, previous research examining visual category learning has identified at least two partially dissociable neural systems that process feedback: a reflective system, wherein processing is under conscious control, and a reflexive system that is not under conscious control (Ashby and Alfonso-Reese 1998; Poldrack and Packard 2003; Ashby and Ennis 2006; Nomura et al. 2007; Seger and Miller 2010). The reflective system, as also referred to as the rule-based learning system in the literature, uses working memory and executive attention to develop and test verbalizable rules based on feedback (Maddox and Ashby 2004). It relies on an executive corticostriatal loop that primarily involves the dorsolateral prefrontal cortex (DLPFC), head of the caudate nucleus, the anterior cingulate cortex and the hippocampus. These brain regions contribute to the generation, selection, and maintenance of verbalizable rules. In contrast, the reflexive learning system, which has also been referred to as the procedural-based learning system, is not consciously penetrable, non-verbalizable, and operates by associating perception with actions that lead to immediate reward (Maddox and Chandrasekaran, 2014). During reflexive learning, a single medium-spiny neuron in the striatum implicitly associates an abstract motoric response with a group of sensory cells. Learning occurs within cortical–striatal synapses, wherein plasticity is facilitated by a reinforcement signal from the ventral striatum (Ashby and Ennis 2006; Seger 2008). A recent study examining visual category learning showed that the putamen is critical to reflexive learning (Waldschmidt and Ashby 2011). Animal research has shown that both the reflective and reflexive circuitries receive direct input from several auditory regions (Reale and Imig 1983; Yeterian and Pandya 1998). While the role of the reflective auditory loop has been extensively studied (Romanski et al. 1999; Rauschecker and Scott 2009), much less is known about the role of the reflexive learning system in speech processing. In the current study, we examined the hypothesis that optimal speech category learning is mediated by the neural circuitry underlying the reflexive learning system. We hypothesized that explicit, reflective learning of speech categories is difficult due to the multidimensional nature and high variability of speech categories. In addition, dimensions underlying speech categories are integral and often difficult to verbalize (Lisker 1986; Hillenbrand et al. 1995; Jongman et al. 2000; Vallabha et al. 2007; Holt and Lotto 2008, 2010). Integral dimensions stimuli, by definition, are difficult to attend selectively (Shepard 1964; Garners 1974; Ashby 1992). Indeed, when the mode of stimulus presentation and the nature of the trial-by-trial feedback were manipulated in a recent behavioral study examining speech learning (Chandrasekaran et al. 2013), learning was enhanced under conditions that were previously shown to augment reflexive learning in the visual domain (Maddox et al. 2003; Maddox et al. 2008). Computational modeling of behavioral data collected in a similar learning paradigm revealed that optimal speech category learning is associated with initial use of reflective strategies followed by a transition to the use of reflexive computational strategies (Maddox and Chandrasekaran 2014). Despite this growing body of evidence that suggest that speech category learning is reflexive, there currently is no neural evidence of the relative role of the two learning systems in speech categorization. To this end, we employ a combination of behavioral, neural, and computational modeling methods to evaluate the mechanisms underlying feedback-dependent speech category learning. Specifically, we predict that optimal speech category learning will be associated with increased processing in the putamen, which is hypothesized to be involved in a ‘motor loop’ that implicitly associates stimuli with category responses within the motor cortex. We used an individual differences approach as well as computational modeling to assess the mechanistic link between learning and computations within the domain-general learning systems. Adult native speakers of English (N = 23) learned novel speech categories (Mandarin tone categories, Figure 1) while blood oxygenation level dependent (BOLD) responses were collected. Participants made a category response to each stimulus, which resulted in positive or negative feedback. Neural activation during stimulus presentation and feedback processing were separately estimated using an optimized rapid event-related design. Behavioral accuracies were calculated and decision-bound models were applied at the individual participant level to provide a window onto cognitive processing and the computational strategies employed at different stages of category learning. Materials and Methods