Mutual Information and Categorical Perception

Towards the most prägnant Gestalt: Leveling and sharpening as contextually dependent adaptive strategies

Gestalt psychologists posited that we always organize our visual input in the best way possible under the given conditions. Both weakening or removing unnecessary details (i.e., leveling) and exaggerating distinctive features (i.e., sharpening) can contribute to achieving a better organization. When is a feature leveled or sharpened, however? We investigated whether the importance of a feature for discrimination among alternatives influences which organizational tendency occurs. Participants were simultaneously presented with four figures composed of simple geometrical shapes, and asked to reconstruct one of these figures in such a way that another participant would be able to recognize it among the alternatives. The four figures differed either qualitatively or only quantitatively (i.e., far or close context). Regarding quantitative differences, two feature dimensions were varied, with one manifesting a wider range of variability across the alternatives than the other. In case of a smaller variability range, the target figure was either at the extreme of the range or had an in-between value. As expected, the results indicated that sharpening occurred more often for the feature with an extreme value, for the feature exhibiting more variability, and for the features of figures presented in the close context, than for the feature with a non-extreme value, exhibiting less variability, or in the far context. In line with Metzger's (1941) definition of prägnant Gestalts, the essence of a Gestalt is context-dependent, and this will influence whether leveling or sharpening of a feature will lead to the best organization in the specific context.

Bringing Bayes and Shannon to the Study of Behavioural and Neurobiological Timing and Associative Learning

Bayesian parameter estimation and Shannon’s theory of information provide tools for analysing and understanding data from behavioural and neurobiological experiments on interval timing—and from experiments on Pavlovian and operant conditioning, because timing plays a fundamental role in associative learning. In this tutorial, we explain basic concepts behind these tools and show how to apply them to estimating, on a trial-by-trial, reinforcement-by-reinforcement and response-by-response basis, important parameters of timing behaviour and of the neurobiological manifestations of timing in the brain. These tools enable quantification of relevant variables in the trade-off between acting as an ideal observer should act and acting as an ideal agent should act, which is also known as the trade-off between exploration (information gathering) and exploitation (information utilization) in reinforcement learning. They enable comparing the strength of the evidence for a measurable association to the strength of the behavioural evidence that the association has been perceived. A GitHub site and an OSF site give public access to well-documented Matlab and Python code and to raw data to which these tools have been applied.

The myth of categorical perception

Categorical perception (CP) is likely the single finding from speech perception with the biggest impact on cognitive science. However, within speech perception, it is widely known to be an artifact of task demands. CP is empirically defined as a relationship between phoneme identification and discrimination. As discrimination tasks do not appear to require categorization, this was thought to support the claim that listeners perceive speech solely in terms of linguistic categories. However, 50 years of work using discrimination tasks, priming, the visual world paradigm, and event related potentials has rejected the strongest forms of CP and provided little strong evidence for any form of it. This paper reviews the origins and impact of this scientific meme and the work challenging it. It discusses work showing that the encoding of auditory input is largely continuous, not categorical, and describes the modern theoretical synthesis in which listeners preserve fine-grained detail to enable more flexible processing. This synthesis is fundamentally inconsistent with CP. This leads to a different understanding of how to use and interpret the most basic paradigms in speech perception-phoneme identification along a continuum-and has implications for understanding language and hearing disorders, development, and multilingualism.

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.

When unsupervised training benefits category learning

Humans continuously categorise inputs, but only rarely receive explicit feedback as to whether or not they are correct. This implies that they may be integrating unsupervised information together with their sparse supervised data - a form of semi-supervised learning. However, experiments testing semi-supervised learning are rare, and are bedevilled with conflicting results about whether the unsupervised information affords any benefit. Here, we suggest that one important factor that has been paid insufficient attention is the alignment between subjects' internal representations of the stimulus material and the experimenter-defined representations that determine success in the tasks. Subjects' representations are shaped by prior biases and experience, and unsupervised learning can only be successful if the alignment suffices. Otherwise, unsupervised learning might harmfully strengthen incorrect assumptions. To test this hypothesis, we conducted an experiment in which subjects initially categorise items along a salient, but task-irrelevant, dimension, and only recover the correct categories when sufficient feedback draws their attention to the subtle, task-relevant, stimulus dimensions. By withdrawing feedback at different stages along this learning curve, we tested whether unsupervised learning improves or worsens performance when internal stimulus representations and task are sufficiently or insufficiently aligned, respectively. Our results demonstrate that unsupervised learning can indeed have opposing effects on subjects' learning. We also discuss factors limiting the degree to which such effects can be predicted from momentary performance. Our work implies that predicting and understanding human category learning in particular tasks requires assessment and consideration of the representational spaces that subjects entertain for the materials involved in those tasks. These considerations not only apply to studies in the lab, but could also help improve the design of tutoring systems and instruction.

Strategy Development and Feedback Processing During Complex Category Learning

In this study, 38 young adults participated in a probabilistic A/B prototype category learning task under observational and feedback-based conditions. The study compared learning success (testing accuracy) and strategy use (multi-cue vs. single feature vs. random pattern) between training conditions. The feedback-related negativity (FRN) and P3a event related potentials were measured to explore the relationships between feedback processing and strategy use under a probabilistic paradigm. A greater number of participants were found to utilize an optimal, multi-cue strategy following feedback-based training than observational training, adding to the body of research suggesting that feedback can influence learning approach. There was a significant interaction between training phase and strategy on FRN amplitude. Specifically, participants who used a strategy in which category membership was determined by a single feature (single feature strategy) exhibited a significant decrease in FRN amplitude from early training to late training, perhaps due to reduced utilization of feedback or reduced prediction error. There were no significant main or interaction effects between valence, training phase, or strategy on P3a amplitude. Findings are consistent with prior research suggesting that learners vary in their approach to learning and that training method influences learning. Findings also suggest that measures of feedback processing during probabilistic category learning may reflect changes in feedback utilization and may further illuminate differences among individual learners.