Dual-learning systems during speech category learning

Abstract

Dual-systems models of visual category learning posit the existence of an explicit, hypothesistesting '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. Experiment 1 and 2 examined the effect of manipulating the timing (immediate vs. delayed) and information content (full vs. minimal) of feedback. Dual-systems 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 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.

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.…”

“…In the auditory domain, the presence of feedback has been shown to enhance speech category learning in two investigations, but full and minimal feedback conditions could not be compared, as there were only two possible categories (Goudbeek et al, 2008;McCandliss et al, 2002). Full and minimal feedback conditions have been compared in speech category learning with four categories, but generalizability was limited, as the relevant dimensions had not been constrained by the experimenters (Chandrasekaran et al, 2014b). The current study is the first to suggest that non-speech auditory categories with separable optimal decision boundaries can be learned faster with full, relative to minimal, feedback.…”

“…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). However, naturalistic categories, such as phonetic categories, can have multiple relevant perceptual dimensions (Lisker, 1986).…”

Non-native phonetic learning is destabilized by exposure to phonological variability before and after training

“…For example, the presentation of the various tokens during the task may have allowed listeners to track the statistical distributions of the two categories (e.g., Maye and Gerken, 2000), and participants in the present study may have been able to utilize this information to improve their performance on both tasks. However, some studies have shown that learners typically rely on explicit strategies in early stages of the learning process (e.g., Chandrasekaran et al, 2014); therefore, implicit learning may not be helpful to participants at this point. If it is the case that participants are learning from the discrimination task, we argue that the Two Vowel group had more opportunities to learn because they received twice as many tokens in the discrimination assessments and should therefore show more improvement.…”

The invariance problem in the acquisition of non-native phonetic contrasts: From instances to categories

“…On the other hand, effective processing of speech is likely to be rather automatic and not require extensive effortful processing. In fact, Chandrasekaran and colleagues have proposed that successful learning of non-native speech sound contrasts is most evident in individuals who shift to implicit, procedural processing systems early in learning (Chandrasekaran, Yi & Maddox, 2014). In the next section, we will consider how declarative and procedural systems are affected by sleep -a factor which is known to affect learning in other domains, but is relatively unexplored in non-native speech sound learning.…”