Human Perceptual Learning and Categorization

Carvalho, Paulo F.; Goldstone, Robert L.

doi:10.1002/9781118650813.ch10

Cited by 4 publications

(5 citation statements)

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“…On the other hand, ‘perceptual learning’ refers to the ‘experience-dependent enhancement of our ability to make sense of what we see, hear, feel, taste or smell’ [ 131 ]. Interestingly, the work [ 2 ] suggests that category learning and perceptual learning could share specific learning mechanisms, as in the case of the emergence of categorical perception. Despite these commonalities, controversial evidence highlights some differences between these processes.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the fact that many brain structures innately code specific physical regularities of the world, there are learning mechanisms that allow the adaptation of perceptual processes to the environment demands. For example, during the solution of a categorisation task sensory processes increase the between-category differences and decrease the within-category difference, a phenomenon called ‘categorical perception’ (CP; [ 1 , 2 ]). In [ 3 ] we corroborated the idea that, during category learning, a CP-like effect can be supported by a top-down selection of perceptual representations.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…“Is there a hip fracture present?”) about a single stimulus and are then informed if they were correct. Although similar techniques are used in the categorisation literature (category learning and perceptual learning likely result from overlapping mechanisms; Carvalho & Goldstone, 2016 ), we refer to this as standard perceptual training as our review focuses on the perceptual training literature. However, recent successes with alternative training techniques—for example, training participants with comparison images (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the effectiveness of different perceptual training methods in a difficult visual discrimination task with ultrasound images

et al. 2023

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Recent work has shown that perceptual training can be used to improve the performance of novices in real-world visual classification tasks with medical images, but it is unclear which perceptual training methods are the most effective, especially for difficult medical image discrimination tasks. We investigated several different perceptual training methods with medically naïve participants in a difficult radiology task: identifying the degree of hepatic steatosis (fatty infiltration of the liver) in liver ultrasound images. In Experiment 1a (N = 90), participants completed four sessions of standard perceptual training, and participants in Experiment 1b (N = 71) completed four sessions of comparison training. There was a significant post-training improvement for both types of training, although performance was better when the trained task aligned with the task participants were tested on. In both experiments, performance initially improves rapidly, with learning becoming more gradual after the first training session. In Experiment 2 (N = 200), we explored the hypothesis that performance could be improved by combining perceptual training with explicit annotated feedback presented in a stepwise fashion. Although participants improved in all training conditions, performance was similar regardless of whether participants were given annotations, or underwent training in a stepwise fashion, both, or neither. Overall, we found that perceptual training can rapidly improve performance on a difficult radiology task, albeit not to a comparable level as expert performance, and that similar levels of performance were achieved across the perceptual training paradigms we compared.

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“…There is evidence that much learning, of numerous concepts, and how they relate and constrain one another, and ultimately a sense of what is probable or common in everyday experience in humans and animals, takes place without explicit teaching, achieved largely through (active) observing (Sheridan, 1973;Gopnik and Meltzoff, 1997;Rakison and Oakes, 2003;Law et al, 2011). Inspired by considerations of early human learning, and in particular perceptual and category learning, that likely continue throughout life (Gibson, 1963;Gopnik and Meltzoff, 1997;Kellman and Garrigan, 2009;Carvalho and Goldstone, 2016), here we propose and explore an approach to efficient unsupervised learning of discrete and discernible (interpretable/structured) concepts, in a sparse manner, in the text domain. The learning is achieved in a cumulative bottom-up fashion.…”

Section: Introductionmentioning

confidence: 99%

An information theoretic score for learning hierarchical concepts

Madani

2023

Front. Comput. Neurosci.

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How do humans learn the regularities of their complex noisy world in a robust manner? There is ample evidence that much of this learning and development occurs in an unsupervised fashion via interactions with the environment. Both the structure of the world as well as the brain appear hierarchical in a number of ways, and structured hierarchical representations offer potential benefits for efficient learning and organization of knowledge, such as concepts (patterns) sharing parts (subpatterns), and for providing a foundation for symbolic computation and language. A major question arises: what drives the processes behind acquiring such hierarchical spatiotemporal concepts? We posit that the goal of advancing one's predictions is a major driver for learning such hierarchies and introduce an information-theoretic score that shows promise in guiding the processes, and, in particular, motivating the learner to build larger concepts. We have been exploring the challenges of building an integrated learning and developing system within the framework of prediction games, wherein concepts serve as (1) predictors, (2) targets of prediction, and (3) building blocks for future higher-level concepts. Our current implementation works on raw text: it begins at a low level, such as characters, which are the hardwired or primitive concepts, and grows its vocabulary of networked hierarchical concepts over time. Concepts are strings or n-grams in our current realization, but we hope to relax this limitation, e.g., to a larger subclass of finite automata. After an overview of the current system, we focus on the score, named CORE. CORE is based on comparing the prediction performance of the system with a simple baseline system that is limited to predicting with the primitives. CORE incorporates a tradeoff between how strongly a concept is predicted (or how well it fits its context, i.e., nearby predicted concepts) vs. how well it matches the (ground) “reality,” i.e., the lowest level observations (the characters in the input episode). CORE is applicable to generative models such as probabilistic finite state machines (beyond strings). We highlight a few properties of CORE with examples. The learning is scalable and open-ended. For instance, thousands of concepts are learned after hundreds of thousands of episodes. We give examples of what is learned, and we also empirically compare with transformer neural networks and n-gram language models to situate the current implementation with respect to state-of-the-art and to further illustrate the similarities and differences with existing techniques. We touch on a variety of challenges and promising future directions in advancing the approach, in particular, the challenge of learning concepts with a more sophisticated structure.

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Human Perceptual Learning and Categorization

Cited by 4 publications

References 142 publications

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

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

Evaluating the effectiveness of different perceptual training methods in a difficult visual discrimination task with ultrasound images

An information theoretic score for learning hierarchical concepts

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