Perceptual dimensions influence auditory category learning

Individual differences in working memory impact the trajectory of non-native speech category learning

What is the role of working memory over the course of non-native speech category learning? Prior work has predominantly focused on how working memory might influence learning assessed at a single timepoint. Here, we substantially extend this prior work by examining the role of working memory on speech learning performance over time (i.e., over several months) and leverage a multifaceted approach that provides key insights into how working memory influences learning accuracy, maintenance of knowledge over time, generalization ability, and decision processes. We found that the role of working memory in non-native speech learning depends on the timepoint of learning and whether individuals learned the categories at all. Among learners, across all stages of learning, working memory was associated with higher accuracy as well as faster and slightly more cautious decision making. Further, while learners and non-learners did not have substantially different working memory performance, learners had faster evidence accumulation and more cautious decision thresholds throughout all sessions. Working memory may enhance learning by facilitating rapid category acquisition in initial stages and enabling faster and slightly more careful decision-making strategies that may reduce the overall effort needed to learn. Our results have important implications for developing interventions to improve learning in naturalistic language contexts.

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.

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.

Auditory category learning is robust across training regimes

Multiple lines of research have developed training approaches that foster category learning, with important translational implications for education. Increasing exemplar variability, blocking or interleaving by category-relevant dimension, and providing explicit instructions about diagnostic dimensions each have been shown to facilitate category learning and/or generalization. However, laboratory research often must distill the character of natural input regularities that define real-world categories. As a result, much of what we know about category learning has come from studies with simplifying assumptions. We challenge the implicit expectation that these studies reflect the process of category learning of real-world input by creating an auditory category learning paradigm that intentionally violates some common simplifying assumptions of category learning tasks. Across five experiments and nearly 300 adult participants, we used training regimes previously shown to facilitate category learning, but here drew from a more complex and multidimensional category space with tens of thousands of unique exemplars. Learning was equivalently robust across training regimes that changed exemplar variability, altered the blocking of category exemplars, or provided explicit instructions of the category-diagnostic dimension. Each drove essentially equivalent accuracy measures of learning generalization following 40 min of training. These findings suggest that auditory category learning across complex input is not as susceptible to training regime manipulation as previously thought.

The nature of non-native speech sound representations

Most current theories and models of second language speech perception are grounded in the notion that learners acquire speech sound categories in their target language. In this paper, this classic idea in speech perception is revisited, given that clear evidence for formation of such categories is lacking in previous research. To understand the debate on the nature of speech sound representations in a second language, an operational definition of "category" is presented, and the issues of categorical perception and current theories of second language learning are reviewed. Following this, behavioral and neuroimaging evidence for and against acquisition of categorical representations is described. Finally, recommendations for future work are discussed. The paper concludes with a recommendation for integration of behavioral and neuroimaging work and theory in this area.

Long-term priors constrain category learning in the context of short-term statistical regularities

Cognitive systems face a constant tension of maintaining existing representations that have been fine-tuned to long-term input regularities and adapting representations to meet the needs of short-term input that may deviate from long-term norms. Systems must balance the stability of long-term representations with plasticity to accommodate novel contexts. We investigated the interaction between perceptual biases or priors acquired across the long-term and sensitivity to statistical regularities introduced in the short-term. Participants were first passively exposed to short-term acoustic regularities and then learned categories in a supervised training task that either conflicted or aligned with long-term perceptual priors. We found that the long-term priors had robust and pervasive impact on categorization behavior. In contrast, behavior was not influenced by the nature of the short-term passive exposure. These results demonstrate that perceptual priors place strong constraints on the course of learning and that short-term passive exposure to acoustic regularities has limited impact on directing subsequent category learning.

A neural network model of the effect of prior experience with regularities on subsequent category learning

Categories are often structured by the similarities of instances within the category defined across dimensions or features. Researchers typically assume that there is a direct, linear relationship between the physical input dimensions across which category exemplars are defined and the psychological representation of these dimensions. However, this assumption is not always warranted. Through a set of simulations, we demonstrate that the psychological representations of input dimensions developed through long-term prior experience can place very strong constraints on category learning. We compare the model's behavior to auditory, visual, and cross-modal human category learning and make conclusions regarding the nature of the psychological representations of the dimensions in those studies. These simulations support the conclusion that the nature of psychological representations of input dimensions is a critical aspect to understanding the mechanisms underlying category learning.

Do Infants Really Learn Phonetic Categories?

Early changes in infants’ ability to perceive native and nonnative speech sound contrasts are typically attributed to their developing knowledge of phonetic categories. We critically examine this hypothesis and argue that there is little direct evidence of category knowledge in infancy. We then propose an alternative account in which infants’ perception changes because they are learning a perceptual space that is appropriate to represent speech, without yet carving up that space into phonetic categories. If correct, this new account has substantial implications for understanding early language development.

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.

Comparing perceptual category learning across modalities in the same individuals

Category learning is a fundamental process in human cognition that spans the senses. However, much still remains unknown about the mechanisms supporting learning in different modalities. In the current study, we directly compared auditory and visual category learning in the same individuals. Thirty participants (22 F; 18-32 years old) completed two unidimensional rule-based category learning tasks in a single day - one with auditory stimuli and another with visual stimuli. We replicated the results in a second experiment with a larger online sample (N = 99, 45 F, 18-35 years old). The categories were identically structured in the two modalities to facilitate comparison. We compared categorization accuracy, decision processes as assessed through drift-diffusion models, and the generalizability of resulting category representation through a generalization test. We found that individuals learned auditory and visual categories to similar extents and that accuracies were highly correlated across the two tasks. Participants had similar evidence accumulation rates in later learning, but early on had slower rates for visual than auditory learning. Participants also demonstrated differences in the decision thresholds across modalities. Participants had more categorical generalizable representations for visual than auditory categories. These results suggest that some modality-general cognitive processes support category learning but also suggest that the modality of the stimuli may also affect category learning behavior and outcomes.

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.