Modeling learned categorical perception in human vision

Integrating unsupervised and reinforcement learning in human categorical perception: A computational model

Categorical perception identifies a tuning of human perceptual systems that can occur during the execution of a categorisation task. Despite the fact that experimental studies and computational models suggest that this tuning is influenced by task-independent effects (e.g., based on Hebbian and unsupervised learning, UL) and task-dependent effects (e.g., based on reward signals and reinforcement learning, RL), no model studies the UL/RL interaction during the emergence of categorical perception. Here we have investigated the effects of this interaction, proposing a system-level neuro-inspired computational architecture in which a perceptual component integrates UL and RL processes. The model has been tested with a categorisation task and the results show that a balanced mix of unsupervised and reinforcement learning leads to the emergence of a suitable categorical perception and the best performance in the task. Indeed, an excessive unsupervised learning contribution tends to not identify task-relevant features while an excessive reinforcement learning contribution tends to initially learn slowly and then to reach sub-optimal performance. These results are consistent with the experimental evidence regarding categorical activations of extrastriate cortices in healthy conditions. Finally, the results produced by the two extreme cases of our model can explain the existence of several factors that may lead to sensory alterations in autistic people.

Categorical face perception in fish: How a fish brain warps reality to dissociate "same" from "different"

Categorical perception (CP) is the phenomenon by which a smoothly varying stimulus property undergoes a nonlinear transformation during processing in the brain. Consequently, the stimuli are perceived as belonging to distinct categories separated by a sharp boundary. Originally thought to be largely innate, the discovery of CP in tasks such as novel image discrimination has piqued the interest of cognitive scientists because it provides compelling evidence that learning can shape a category's perceptual boundaries. CP has been particularly closely studied in human face perception. In nonprimates, there is evidence for CP for sound and color discrimination, but not for image or face discrimination. Here, we investigate the potential for learned CP in a lower vertebrate, the damselfish Pomacentrus amboinensis. Specifically, we tested whether the ability of these fish to discriminate complex facial patterns tracked categorical rather than metric differences in the stimuli. We first trained the fish to discriminate sets of two facial patterns. Next, we morphed between these patterns and determined the just noticeable difference (JND) between a morph and original image. Finally, we tested for CP by analyzing the discrimination ability of the fish for pairs of JND stimuli along the spectrum of morphs between two original images. Discrimination performance was significant for the image pair straddling the boundary between categories, and chance for equivalent stimulus pairs on either side, thus producing the classic "category boundary" effect. Our results reveal how perception can be influenced in a top-down manner even in the absence of a visual cortex.

Visual Perceptual Learning and Models

Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision. Models, especially computational models, play a key role in behavioral and physiological investigations of the mechanisms of perceptual learning and for understanding, predicting, and optimizing human perceptual processes, learning, and performance. Performance improvements resulting from reweighting or readout of sensory inputs to decision provide a strong theoretical framework for interpreting perceptual learning and transfer that may prove useful in optimizing learning in real-world applications.

A connectionist approach to mapping the human connectome permits simulations of neural activity within an artificial brain

Data-driven models drawn from statistical correlations between brain activity and behavior are used to inform theory-driven models, such as those described by computational models, which provide a mechanistic account of these correlations. This article introduces a novel multivariate approach for bootstrapping neurologically-plausible computational models that accurately encodes cortical effective connectivity from resting state functional neuroimaging data (rs-fMRI). We show that a network modularity algorithm finds comparable resting state networks within connectivity matrices produced by our approach and by the benchmark method. Unlike existing methods, however, ours permits simulation of brain activation that is a direct reflection of this cortical connectivity. Cross-validation of our model suggests that neural activity in some regions may be more consistent between individuals, providing novel insight into brain function. We suggest this method to make an important contribution toward modeling macro-scale human brain activity, and it has the potential to advance our understanding of complex neurological disorders and the development of neural connectivity.