Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area

Law, Chi-Tat; Gold, Joshua I.

doi:10.1038/nn2070

Cited by 454 publications

(606 citation statements)

References 48 publications

(11 reference statements)

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“…The neural mechanisms of perceptual learning have been explained as changes in the tuning properties of neurons that provide sensory information and/or changes in how these sensory responses are routed to and weighted by decision-making areas (10,21,32). Our results suggest that both of these processes work in concert to improve task performance.…”

Section: Discussionmentioning

confidence: 75%

“…Electrophysiological and brain imaging studies have shown that visual perceptual learning alters neural response properties in primary visual cortex (3,4) and extrastriate areas including V4 (5) and MT+ (middle temporal/medial superior temporal cortex) (6), as well as object selective areas in the inferior temporal cortex (7,8). An alternative hypothesis proposes that perceptual learning is mediated by downstream cortical areas that are responsible for attentional allocation and/or decision-making, such as the intraparietal sulcus (IPS) and anterior cingulate cortex (9,10).…”

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confidence: 99%

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Perceptual learning modifies the functional specializations of visual cortical areas

Chen

Cai

Zhou

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Training can improve performance of perceptual tasks. This phenomenon, known as perceptual learning, is strongest for the trained task and stimulus, leading to a widely accepted assumption that the associated neuronal plasticity is restricted to brain circuits that mediate performance of the trained task. Nevertheless, learning does transfer to other tasks and stimuli, implying the presence of more widespread plasticity. Here, we trained human subjects to discriminate the direction of coherent motion stimuli. The behavioral learning effect substantially transferred to noisy motion stimuli. We used transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms underlying the transfer of learning. The TMS experiment revealed dissociable, causal contributions of V3A (one of the visual areas in the extrastriate visual cortex) and MT+ (middle temporal/medial superior temporal cortex) to coherent and noisy motion processing. Surprisingly, the contribution of MT+ to noisy motion processing was replaced by V3A after perceptual training. The fMRI experiment complemented and corroborated the TMS finding. Multivariate pattern analysis showed that, before training, among visual cortical areas, coherent and noisy motion was decoded most accurately in V3A and MT+, respectively. After training, both kinds of motion were decoded most accurately in V3A. Our findings demonstrate that the effects of perceptual learning extend far beyond the retuning of specific neural populations for the trained stimuli. Learning could dramatically modify the inherent functional specializations of visual cortical areas and dynamically reweight their contributions to perceptual decisions based on their representational qualities. These neural changes might serve as the neural substrate for the transfer of perceptual learning.perceptual learning | motion | psychophysics | transcranial magnetic stimulation | functional magnetic resonance imaging P erceptual learning, an enduring improvement in the performance of a sensory task resulting from practice, has been widely used as a model to study experience-dependent cortical plasticity in adults (1). However, at present, there is no consensus on the nature of the neural mechanisms underlying this type of learning. Perceptual learning is often specific to the physical properties of the trained stimulus, leading to the hypothesis that the underlying neural changes occur in sensory coding areas (2). Electrophysiological and brain imaging studies have shown that visual perceptual learning alters neural response properties in primary visual cortex (3, 4) and extrastriate areas including V4 (5) and MT+ (middle temporal/medial superior temporal cortex) (6), as well as object selective areas in the inferior temporal cortex (7,8). An alternative hypothesis proposes that perceptual learning is mediated by downstream cortical areas that are responsible for attentional allocation and/or decision-making, such as the intraparietal sulcus (IPS) and anterior...

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Section: Discussionmentioning

confidence: 75%

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confidence: 99%

Perceptual learning modifies the functional specializations of visual cortical areas

Chen

Cai

Zhou

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…The role of learning in modifying early sensory processing remains highly debated (Adini et al, 2002;Teich and Qian, 2003). Recent studies suggest that learning alters later decision-related processes thought to reweight the contributions of early sensory representations (Dosher and Lu, 1999;Li et al, 2004;Law and Gold, 2008;Jacobs, 2009). Our findings suggest that learning modifies early recurrent processing (Roelfsema and van Ooyen, 2005;Roelfsema, 2006) within higher visual and parietal areas engaged in the integration (Ostwald et al, 2008) and categorization (Freedman and Assad, 2006) of global visual forms.…”

Section: Discussionmentioning

confidence: 99%

“…Some studies argue that learning alters early sensory processing (Adini et al, 2002;Teich and Qian, 2003), while others propose that learning alters later decision-related processes (Dosher and Lu, 1999;Li et al, 2004;Law and Gold, 2008;Jacobs, 2009). Previous fMRI studies have implicated both occipitotemporal and frontoparietal circuits in shape learning (Dolan et al, 1997;Gauthier et al, 1999;Grill-Spector et al, 2000;Chao et al, 2002;Kourtzi et al, 2005;Op de Beeck et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Learning Acts on Distinct Processes for Visual Form Perception in the Human Brain

Mayhew

Li²,

Kourtzi³

2012

J. Neurosci.

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Learning is known to facilitate our ability to detect targets in clutter and optimize brain processes for successful visual recognition. Previous brain-imaging studies have focused on identifying spatial patterns (i.e., brain areas) that change with learning, implicating occipitotemporal and frontoparietal areas. However, little is known about the interactions within this network that mediate learningdependent improvement in complex perceptual tasks (i.e., discrimination of visual forms in clutter). Here we take advantage of the complementary high spatial and temporal resolution of simultaneous EEG-fMRI to identify the learning-dependent changes in spatiotemporal brain patterns that mediate enhanced behavioral sensitivity in the discrimination of global forms after training. We measured the observers' choices when discriminating between concentric and radial patterns presented in noise before and after training. Similarly, we measured the choices of a pattern classifier when predicting each stimulus from EEG-fMRI signals. By comparing the performance of human observers and classifiers, we demonstrated that learning alters sensitivity to visual forms and EEG-fMRI activation patterns related to distinct visual recognition processes. In particular, behavioral improvement after training was associated with changes in (1) early processes involved in the integration of global forms in higher occipitotemporal and parietal areas, and (2) later processes related to categorical judgments in frontal circuits. Thus, our findings provide evidence that learning acts on distinct visual recognition processes and shapes feedforward interactions across brain areas to support performance in complex perceptual tasks.

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“…Over the last several decades, random-dot kinematograms (RDKs) have emerged as an effective psychophysical stimulus to evaluate low-level motion processing [1][2][3][4][5]. While different versions of this type of stimulus have been created across several software platforms (e.g., MATLAB [6] and PsychoPy [7,8]), currently, no implementation of this stimulus exists for public use in web-based experiments where the subjects complete the experimental task using web browsers.…”

Section: Introductionmentioning

confidence: 99%

A Random-Dot Kinematogram for Web-Based Vision Research

Rajananda¹,

Lau²,

Odegaard

2018

JORS

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Web-based experiments using visual stimuli have become increasingly common in recent years, but many frequently-used stimuli in vision research have yet to be developed for online platforms. Here, we introduce the first open access random-dot kinematogram (RDK) for use in web browsers. This fully customizable RDK offers options to implement several different types of noise (random position, random walk, random direction) and parameters to control aperture shape, coherence level, the number of dots, and other features. We include links to commented JavaScript code for easy implementation in web-based experiments, as well as an example of how this stimulus can be integrated as a plugin with a JavaScript library for online studies (jsPsych).

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Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area

Cited by 454 publications

References 48 publications

Perceptual learning modifies the functional specializations of visual cortical areas

Perceptual learning modifies the functional specializations of visual cortical areas

Learning Acts on Distinct Processes for Visual Form Perception in the Human Brain

A Random-Dot Kinematogram for Web-Based Vision Research

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