Two‐stage model in perceptual learning: toward a unified theory

Shibata, Kazuhisa; Sagi, Dov; Watanabe, Takeo

doi:10.1111/nyas.12419

Cited by 72 publications

(59 citation statements)

References 145 publications

(450 reference statements)

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“…Our work suggests that category learning causes direct modifications to visual cortex, a matter of some controversy in the broader perceptual learning literature (Shibata, Sagi, & Watanabe, 2014). While attention-gated plasticity is a prime candidate mechanism (Sasaki, Nanez, & Watanabe, 2010), other mechanisms, such as influence from top-down links from more anterior regions of temporal cortex (Peterson, Cacciamani, Barense, & Scalf, 2012) and semantic influences (Collins & Olson, 2014; Gauthier, James, Curby, & Tarr, 2003) are also possible.…”

Section: Neural Correlates Of Stable Dimensional Modulationmentioning

confidence: 87%

Category Learning Stretches Neural Representations in Visual Cortex

Folstein

Palmeri

Gulick

et al. 2015

Curr Dir Psychol Sci

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We review recent work that shows how learning to categorize objects changes how those objects are represented in the mind and the brain. After category learning, visual perception of objects is enhanced along perceptual dimensions that were relevant to the learned categories, an effect we call dimensional modulation (DM). DM stretches object representations along category-relevant dimensions and shrinks them along category-irrelevant dimensions. The perceptual advantage for category-relevant dimensions extends beyond categorization and can be observed during visual discrimination and other tasks that do not depend on the learned categories. fMRI shows that category learning causes ventral stream neural populations in visual cortex representing objects along a category-relevant dimension to become more distinct. These results are consistent with a view that specific aspects of cognitive tasks associated with objects can account for how our visual system responds to objects.

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Section: Neural Correlates Of Stable Dimensional Modulationmentioning

confidence: 87%

Category Learning Stretches Neural Representations in Visual Cortex

Folstein

Palmeri

Gulick

et al. 2015

Curr Dir Psychol Sci

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show abstract

“…Recently, Shibata, Sagi and Watanabe (2014) proposed a model with two distinct stages of learning: a feature-based and a task-based plasticity. They propose that the former reflects changes in early perceptual areas related to the representation of basic features whereas the later reflects changes in higher-level cognitive areas.…”

Section: Discussionmentioning

confidence: 99%

Rapid and long-lasting learning of feature binding

Yashar

Carrasco

2016

Cognition

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How are features integrated (bound) into objects and how can this process be facilitated? Here we investigated the role of rapid perceptual learning in feature binding and its long-lasting effects. By isolating the contributions of individual features from their conjunctions between training and test displays, we demonstrate for the first time that training can rapidly and substantially improve feature binding. Observers trained on a conjunction search task consisting of a rapid display with one target-conjunction, then tested with a new target-conjunction. Features were the same between training and test displays. Learning transferred to the new target when its conjunction was presented as a distractor, but not when only its component features were presented in different conjunction distractors during training. Training improvement lasted for up to 16 months, but, in all conditions, it was specific to the trained target. Our findings suggest that with short training observers’ ability to bind two specific features into an object is improved, and that this learning effect can last for over a year. Moreover, our findings show that while the short-term learning effect reflects activation of presented items and their binding, long-term consolidation is task specific.

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“…Despite several decades of investigations, neuronal mechanisms of VPL remain debated (Sasaki et al, 2010) (Shibata et al, 2014).…”

Section: Introductionmentioning

confidence: 99%