Task and distribution sampling affect auditory category learning

Left-handed voices? Examining the perceptual learning of novel person characteristics from the voice

We regularly form impressions of who a person is from their voice, such that we can readily categorise people as being female or male, child or adult, trustworthy or not, and can furthermore recognise who specifically is speaking. How we establish mental representations for such categories of person characteristics has, however, only been explored in detail for voice identity learning. In a series of experiments, we therefore set out to examine whether and how listeners can learn to recognise a novel person characteristic. We specifically asked how diagnostic acoustic properties underpinning category distinctions inform perceptual judgements. We manipulated recordings of voices to create acoustic signatures for a person's handedness (left-handed vs. right-handed) in their voice. After training, we found that listeners were able to successfully learn to recognise handedness from voices with above-chance accuracy, although no significant differences in accuracy between the different types of manipulation emerged. Listeners were, furthermore, sensitive to the specific distributions of acoustic properties that underpinned the category distinctions. We, however, also find evidence for perceptual biases that may reflect long-term prior exposure to how voices vary in naturalistic settings. These biases shape how listeners use acoustic information in the voices when forming representations for distinguishing handedness from voices. This study is thus a first step to examine how representations for novel person characteristics are established, outside of voice identity perception. We discuss our findings in light of theoretical accounts of voice perception and speculate about potential mechanisms that may underpin our results.

Distribution-dependent representations in auditory category learning and generalization

A fundamental objective in Auditory Sciences is to understand how people learn to generalize auditory category knowledge in new situations. How we generalize to novel scenarios speaks to the nature of acquired category representations and generalization mechanisms in handling perceptual variabilities and novelty. The dual learning system (DLS) framework proposes that auditory category learning involves an explicit, hypothesis-testing learning system, which is optimal for learning rule-based (RB) categories, and an implicit, procedural-based learning system, which is optimal for learning categories requiring pre-decisional information integration (II) across acoustic dimensions. Although DLS describes distinct mechanisms of two types of category learning, it is yet clear the nature of acquired representations and how we transfer them to new contexts. Here, we conducted three experiments to examine differences between II and RB category representations by examining what acoustic and perceptual novelties and variabilities affect learners' generalization success. Learners can successfully categorize different sets of untrained sounds after only eight blocks of training for both II and RB categories. The category structures and novel contexts differentially modulated the generalization success. The II learners significantly decreased generalization performances when categorizing new items derived from an untrained perceptual area and in a context with more distributed samples. In contrast, RB learners' generalizations are resistant to changes in perceptual regions but are sensitive to changes in sound dispersity. Representational similarity modeling revealed that the generalization in the more dispersed sampling context was accomplished differently by II and RB learners. II learners increased representations of perceptual similarity and decision distance to compensate for the decreased transfer of category representations, whereas the RB learners used a more computational cost strategy by default, computing the decision-bound distance to guide generalization decisions. These results suggest that distinct representations emerged after learning the two types of category structures and using different computations and flexible mechanisms in resolving generalization challenges when facing novel perceptual variability in new contexts. These findings provide new evidence for dissociated representations of auditory categories and reveal novel generalization mechanisms in resolving variabilities to maintain perceptual constancy.

Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning

The ability to organize variable sensory signals into discrete categories is a fundamental process in human cognition thought to underlie many real-world learning problems. Decades of research suggests that two learning systems may support category learning and that categories with different distributional structures (rule-based, information-integration) optimally rely on different learning systems. However, it remains unclear how the same individual learns these different categories and whether the behaviors that support learning success are common or distinct across different categories. In two experiments, we investigate learning and develop a taxonomy of learning behaviors to investigate which behaviors are stable or flexible as the same individual learns rule-based and information-integration categories and which behaviors are common or distinct to learning success for these different types of categories. We found that some learning behaviors are stable in an individual across category learning tasks (learning success, strategy consistency), while others are flexibly task-modulated (learning speed, strategy, stability). Further, success in rule-based and information-integration category learning was supported by both common (faster learning speeds, higher working memory ability) and distinct factors (learning strategies, strategy consistency). Overall, these results demonstrate that even with highly similar categories and identical training tasks, individuals dynamically adjust some behaviors to fit the task and success in learning different kinds of categories is supported by both common and distinct factors. These results illustrate a need for theoretical perspectives of category learning to include nuances of behavior at the level of an individual learner.

Impaired and Spared Auditory Category Learning in Developmental Dyslexia

Categorization has a deep impact on behavior, but whether category learning is served by a single system or multiple systems remains debated. Here, we designed two well-equated nonspeech auditory category learning challenges to draw on putative procedural (information-integration) versus declarative (rule-based) learning systems among adult Hebrew-speaking control participants and individuals with dyslexia, a language disorder that has been linked to a selective disruption in the procedural memory system and in which phonological deficits are ubiquitous. We observed impaired information-integration category learning and spared rule-based category learning in the dyslexia group compared with the neurotypical group. Quantitative model-based analyses revealed reduced use of, and slower shifting to, optimal procedural-based strategies in dyslexia with hypothesis-testing strategy use on par with control participants. The dissociation is consistent with multiple category learning systems and points to the possibility that procedural learning inefficiencies across categories defined by complex, multidimensional exemplars may result in difficulty in phonetic category acquisition in dyslexia.

The acquisition of speech categories: Beyond perceptual narrowing, beyond unsupervised learning and beyond infancy

An early achievement in language is carving a variable acoustic space into categories. The canonical story is that infants accomplish this by the second year, when only unsupervised learning is plausible. I challenge this view, synthesizing five lines of developmental, phonetic and computational work. First, unsupervised learning may be insufficient given the statistics of speech (including infant-directed). Second, evidence that infants "have" speech categories rests on tenuous methodological assumptions. Third, the fact that the ecology of the learning environment is unsupervised does not rule out more powerful error driven learning mechanisms. Fourth, several implicit supervisory signals are available to older infants. Finally, development is protracted through adolescence, enabling richer avenues for development. Infancy may be a time of organizing the auditory space, but true categorization only arises via complex developmental cascades later in life. This has implications for critical periods, second language acquisition, and our basic framing of speech perception.

Working memory relates to individual differences in speech category learning: Insights from computational modeling and pupillometry

Across two experiments, we examine the relationship between individual differences in working memory (WM) and the acquisition of non-native speech categories in adulthood. While WM is associated with individual differences in a variety of learning tasks, successful acquisition of speech categories is argued to be contingent on WM-independent procedural-learning mechanisms. Thus, the role of WM in speech category learning is unclear. In Experiment 1, we show that individuals with higher WM acquire non-native speech categories faster and to a greater extent than those with lower WM. In Experiment 2, we replicate these results and show that individuals with higher WM use more optimal, procedural-based learning strategies and demonstrate more distinct speech-evoked pupillary responses for correct relative to incorrect trials. We propose that higher WM may allow for greater stimulus-related attention, resulting in more robust representations and optimal learning strategies. We discuss implications for neurobiological models of speech category learning.

Brain-behavior relationships in incidental learning of non-native phonetic categories

Research has implicated the left inferior frontal gyrus (LIFG) in mapping acoustic-phonetic input to sound category representations, both in native speech perception and non-native phonetic category learning. At issue is whether this sensitivity reflects access to phonetic category information per se or to explicit category labels, the latter often being required by experimental procedures. The current study employed an incidental learning paradigm designed to increase sensitivity to a difficult non-native phonetic contrast without inducing explicit awareness of the categorical nature of the stimuli. Functional MRI scans revealed frontal sensitivity to phonetic category structure both before and after learning. Additionally, individuals who succeeded most on the learning task showed the largest increases in frontal recruitment after learning. Overall, results suggest that processing novel phonetic category information entails a reliance on frontal brain regions, even in the absence of explicit category labels.

Perceptual dimensions influence auditory category learning

Human category learning appears to be supported by dual learning systems. Previous research indicates the engagement of distinct neural systems in learning categories that require selective attention to dimensions versus those that require integration across dimensions. This evidence has largely come from studies of learning across perceptually separable visual dimensions, but recent research has applied dual system models to understanding auditory and speech categorization. Since differential engagement of the dual learning systems is closely related to selective attention to input dimensions, it may be important that acoustic dimensions are quite often perceptually integral and difficult to attend to selectively. We investigated this issue across artificial auditory categories defined by center frequency and modulation frequency acoustic dimensions. Learners demonstrated a bias to integrate across the dimensions, rather than to selectively attend, and the bias specifically reflected a positive correlation between the dimensions. Further, we found that the acoustic dimensions did not equivalently contribute to categorization decisions. These results demonstrate the need to reconsider the assumption that the orthogonal input dimensions used in designing an experiment are indeed orthogonal in perceptual space as there are important implications for category learning.

Auditory information-integration category learning in young children and adults

Adults outperform children on category learning that requires selective attention to individual dimensions (rule-based categories) due to their more highly developed working memory abilities, but much less is known about developmental differences in learning categories that require integration across multiple dimensions (information-integration categories). The current study investigated auditory information-integration category learning in 5- to 7-year-old children (n = 34) and 18- to 25-year-old adults (n = 35). Adults generally outperformed children during learning. However, some children learned the categories well and used strategies similar to those of adults, as assessed through decision-bound computational models. The results demonstrate that information-integration learning ability continues to develop throughout at least middle childhood. These results have implications for the development of mechanisms that contribute to speech category learning.