Reaction Times to Predictable Visual Patterns Reflect Neural Responses in Early Visual Cortex

Natural scenes contain a variety of visual cues that facilitate boundary perception (e.g., luminance, contrast, and texture). Here we explore whether single neurons in early visual cortex can process both contrast and texture cues. We recorded neural responses in cat A18 to both illusory contours formed by abutting gratings (ICs, texture-defined) and contrast-modulated gratings (CMs, contrast-defined). We found that if a neuron responded to one of the two stimuli, it also responded to the other. These neurons signaled similar contour orientation, spatial frequency, and movement direction of the two stimuli. A given neuron also exhibited similar selectivity for spatial frequency of the fine, stationary grating components (carriers) of the stimuli. These results suggest that the cue-invariance of early cortical neurons extends to different kinds of texture or contrast cues, and might arise from a common nonlinear mechanism.

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A Varying Role for Abstraction in Models of Category Learning Constructed from Neural Representations in Early Visual Cortex

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01339 ◽

2019 ◽

Vol 31 (1) ◽

pp. 155-173 ◽

Cited By ~ 5

Author(s):

J. Brendan Ritchie ◽

Hans Op de Beeck

Keyword(s):

Visual Cortex ◽

Category Learning ◽

Neural Representation ◽

Neural Responses ◽

Object Categories ◽

Neural Representations ◽

Learning Tasks ◽

Multiple Dimensions ◽

Wide Range ◽

Early Visual Cortex

The human capacity for visual categorization is core to how we make sense of the visible world. Although a substantive body of research in cognitive neuroscience has localized this capacity to regions of human visual cortex, relatively few studies have investigated the role of abstraction in how representations for novel object categories are constructed from the neural representation of stimulus dimensions. Using human fMRI coupled with formal modeling of observer behavior, we assess a wide range of categorization models that vary in their level of abstraction from collections of subprototypes to representations of individual exemplars. The category learning tasks range from simple linear and unidimensional category rules to complex crisscross rules that require a nonlinear combination of multiple dimensions. We show that models based on neural responses in primary visual cortex favor a variable, but often limited, extent of abstraction in the construction of representations for novel categories, which differ in degree across tasks and individuals.

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Linking V1 Activity to Behavior

Annual Review of Vision Science ◽

10.1146/annurev-vision-102016-061324 ◽

2018 ◽

Vol 4 (1) ◽

pp. 287-310

Author(s):

Eyal Seidemann ◽

Wilson S. Geisler

Keyword(s):

Visual Cortex ◽

Human Performance ◽

Neural Responses ◽

Conceptual Approach ◽

Visual Neuroscience ◽

V1 Neurons ◽

Visual Tasks ◽

Early Visual Cortex ◽

The Moment ◽

And Behavior

A long-term goal of visual neuroscience is to develop and test quantitative models that account for the moment-by-moment relationship between neural responses in early visual cortex and human performance in natural visual tasks. This review focuses on efforts to address this goal by measuring and perturbing the activity of primary visual cortex (V1) neurons while nonhuman primates perform demanding, well-controlled visual tasks. We start by describing a conceptual approach—the decoder linking model (DLM) framework—in which candidate decoding models take neural responses as input and generate predicted behavior as output. The ultimate goal in this framework is to find the actual decoder—the model that best predicts behavior from neural responses. We discuss key relevant properties of primate V1 and review current literature from the DLM perspective. We conclude by discussing major technological and theoretical advances that are likely to accelerate our understanding of the link between V1 activity and behavior.

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Visual Awareness Is Limited by the Representational Architecture of the Visual System

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00855 ◽

2015 ◽

Vol 27 (11) ◽

pp. 2240-2252 ◽

Cited By ~ 22

Author(s):

Michael A. Cohen ◽

Ken Nakayama ◽

Talia Konkle ◽

Mirta Stantić ◽

George A. Alvarez

Keyword(s):

Visual Cortex ◽

Visual System ◽

Visual Masking ◽

Visual Awareness ◽

Neural Responses ◽

Dorsal Pathway ◽

Visual Hierarchy ◽

Early Visual Cortex ◽

Ventral Pathway ◽

Brain Behavior

Visual perception and awareness have strict limitations. We suggest that one source of these limitations is the representational architecture of the visual system. Under this view, the extent to which items activate the same neural channels constrains the amount of information that can be processed by the visual system and ultimately reach awareness. Here, we measured how well stimuli from different categories (e.g., faces and cars) blocked one another from reaching awareness using two distinct paradigms that render stimuli invisible: visual masking and continuous flash suppression. Next, we used fMRI to measure the similarity of the neural responses elicited by these categories across the entire visual hierarchy. Overall, we found strong brain–behavior correlations within the ventral pathway, weaker correlations in the dorsal pathway, and no correlations in early visual cortex (V1–V3). These results suggest that the organization of higher level visual cortex constrains visual awareness and the overall processing capacity of visual cognition.

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Faculty Opinions recommendation of Neural activity in early visual cortex reflects behavioral experience and higher-order perceptual saliency.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1007232.90607 ◽

2002 ◽

Author(s):

Shimon Ullman

Keyword(s):

Visual Cortex ◽

Neural Activity ◽

Higher Order ◽

Early Visual Cortex

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Faculty Opinions recommendation of Bottom-up dependent gating of frontal signals in early visual cortex.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1161778.623380 ◽

2009 ◽

Author(s):

Jitendra Sharma ◽

Hiroki Sugihara

Keyword(s):

Visual Cortex ◽

Bottom Up ◽

Early Visual Cortex

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Faculty Opinions recommendation of Confuse your illusion: feedback to early visual cortex contributes to perceptual completion.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717976773.793470558 ◽

2013 ◽

Author(s):

Chris Baker

Keyword(s):

Visual Cortex ◽

Perceptual Completion ◽

Early Visual Cortex

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Decoding the future from past experience: learning shapes predictions in early visual cortex

Journal of Neurophysiology ◽

10.1152/jn.00753.2014 ◽

2015 ◽

Vol 113 (9) ◽

pp. 3159-3171 ◽

Cited By ~ 7

Author(s):

Caroline D. B. Luft ◽

Alan Meeson ◽

Andrew E. Welchman ◽

Zoe Kourtzi

Keyword(s):

Visual Cortex ◽

Large Scale ◽

Sequence Structure ◽

Neural Populations ◽

Early Visual Cortex ◽

Temporal Structures ◽

Future Events ◽

Sensory Predictions ◽

Actual Stimulus ◽

The Brain

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.

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