Perceptual Category Learning Results in Modality-Specific Representations

Categorization is a fundamental cognitive skill that spans the senses. Even so, most research has focused on categorization and category learning in the visual modality. As a result, it is not yet clear how modality influences the perceptual and cognitive processes supporting category learning. In two experiments, we tested whether category learning results in amodal or modality-specific representations. We found strong evidence for modality-specific representations with independent learning across modalities. These results highlight the need to look beyond vision when constructing and testing models categorization and category learning. These findings also contribute to the longstanding debate on the amodal/modal nature of human knowledge, which is of broad interest to the cognitive science community

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

Abstract 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

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

Stimuli, data, and results from Roark, Paulon, Rebaudo, McHaney, Sarkar, & Chandrasekaran. Individual differences in working memory impact the trajectory of non-native speech 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 investigates auditory information-integration category learning in 5-7-year-old children (n = 34) and 18-25-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, 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

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 systems 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

Individual Variability in Strategies and Learning Outcomes in Auditory Category Learning

Learning the sounds of a new language depends on the ability to learn novel auditory categories. Multidimensional categories, whether speech or nonspeech, can be learned through feedback and different category structures are proposed to recruit separate cognitive and neural mechanisms. There is substantial individual variability in learning; however, it is rare to compare learning of different categories in the same individuals. Understanding the sources of variability has theoretical implications for category learning. In this study, we trained the same participants on three types of multidimensional auditory categories. Participants learned nonspeech rule-based, nonspeech information-integration, and Mandarin speech categories. Learning of all category types was related across individuals and differences in working memory similarly supported learning across tasks. There was substantial variability in learning outcomes and strategies used to learn the categories. There are multiple paths to successful learning and appreciation of individual variability is essential to understanding the mechanisms of learning

Within-trial variability improves rule-based, not information-integration, category learning

Categorization is a critical component of cognition and contributes to many complex processes, including speech perception. High variability within the environment is thought to initially slow learning while increasing the ability to generalize to novel exemplars. However, little is understood about the mechanisms driving this benefit of variability. The current study investigates the effect of pairing within-category variability with response and feedback within single category-learning trials. Participants who learned categories defined by boundaries orthogonal to the category dimensions—rule-based categories—had superior learning and were better able to generalize to novel exemplars when they were trained with within-trial variability compared to when only a single exemplar was presented on each trial. In contrast, participants who learned categories defined by boundaries involving reliance on both category input dimensions—information-integration categories—showed no enhancement of learning from within-category variability. This draws a distinction between overall variability in the acoustic environment and variability more tightly coupled with response and feedback. The influence of variability as experienced within a single trial differs substantially depending on the nature of the category learning challenge. The results have implications for learning speech categories and for further understanding the mechanisms that contribute to auditory category learning