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Can responses to basic non-numerical visual features explain neural numerosity responses?
by
Harvey, Ben M.
, Dumoulin, Serge O.
in
Acuity
/ Adult
/ Age
/ Animal behavior
/ Animal models
/ Attention
/ Brain Mapping - methods
/ Cognition
/ Cognition - physiology
/ Cognitive ability
/ Computer programs
/ Cortex (parietal)
/ Debates
/ Experiments
/ Female
/ Functional magnetic resonance imaging
/ Handedness
/ Humans
/ Image processing
/ Macaca
/ Magnetic Resonance Imaging - methods
/ Male
/ Mathematical Concepts
/ Mathematical models
/ Neural coding
/ Neuroimaging
/ Neurons
/ Parietal Lobe - physiology
/ Prefrontal cortex
/ Research methodology
/ Scaling
/ Segmentation
/ Substantia grisea
/ Visual cortex
/ Visual discrimination
/ Visual perception
/ Visual Perception - physiology
/ Young Adult
2017
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Can responses to basic non-numerical visual features explain neural numerosity responses?
by
Harvey, Ben M.
, Dumoulin, Serge O.
in
Acuity
/ Adult
/ Age
/ Animal behavior
/ Animal models
/ Attention
/ Brain Mapping - methods
/ Cognition
/ Cognition - physiology
/ Cognitive ability
/ Computer programs
/ Cortex (parietal)
/ Debates
/ Experiments
/ Female
/ Functional magnetic resonance imaging
/ Handedness
/ Humans
/ Image processing
/ Macaca
/ Magnetic Resonance Imaging - methods
/ Male
/ Mathematical Concepts
/ Mathematical models
/ Neural coding
/ Neuroimaging
/ Neurons
/ Parietal Lobe - physiology
/ Prefrontal cortex
/ Research methodology
/ Scaling
/ Segmentation
/ Substantia grisea
/ Visual cortex
/ Visual discrimination
/ Visual perception
/ Visual Perception - physiology
/ Young Adult
2017
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Can responses to basic non-numerical visual features explain neural numerosity responses?
by
Harvey, Ben M.
, Dumoulin, Serge O.
in
Acuity
/ Adult
/ Age
/ Animal behavior
/ Animal models
/ Attention
/ Brain Mapping - methods
/ Cognition
/ Cognition - physiology
/ Cognitive ability
/ Computer programs
/ Cortex (parietal)
/ Debates
/ Experiments
/ Female
/ Functional magnetic resonance imaging
/ Handedness
/ Humans
/ Image processing
/ Macaca
/ Magnetic Resonance Imaging - methods
/ Male
/ Mathematical Concepts
/ Mathematical models
/ Neural coding
/ Neuroimaging
/ Neurons
/ Parietal Lobe - physiology
/ Prefrontal cortex
/ Research methodology
/ Scaling
/ Segmentation
/ Substantia grisea
/ Visual cortex
/ Visual discrimination
/ Visual perception
/ Visual Perception - physiology
/ Young Adult
2017
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Can responses to basic non-numerical visual features explain neural numerosity responses?
Journal Article
Can responses to basic non-numerical visual features explain neural numerosity responses?
2017
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Overview
Humans and many animals can distinguish between stimuli that differ in numerosity, the number of objects in a set. Human and macaque parietal lobes contain neurons that respond to changes in stimulus numerosity. However, basic non-numerical visual features can affect neural responses to and perception of numerosity, and visual features often co-vary with numerosity. Therefore, it is debated whether numerosity or co-varying low-level visual features underlie neural and behavioral responses to numerosity. To test the hypothesis that non-numerical visual features underlie neural numerosity responses in a human parietal numerosity map, we analyze responses to a group of numerosity stimulus configurations that have the same numerosity progression but vary considerably in their non-numerical visual features. Using ultra-high-field (7T) fMRI, we measure responses to these stimulus configurations in an area of posterior parietal cortex whose responses are believed to reflect numerosity-selective activity. We describe an fMRI analysis method to distinguish between alternative models of neural response functions, following a population receptive field (pRF) modeling approach. For each stimulus configuration, we first quantify the relationships between numerosity and several non-numerical visual features that have been proposed to underlie performance in numerosity discrimination tasks. We then determine how well responses to these non-numerical visual features predict the observed fMRI responses, and compare this to the predictions of responses to numerosity. We demonstrate that a numerosity response model predicts observed responses more accurately than models of responses to simple non-numerical visual features. As such, neural responses in cognitive processing need not reflect simpler properties of early sensory inputs.
•We quantitatively test whether numerosity-tuned neural response reflect other visual features.•We quantify how non-numerical features change in common numerosity stimuli.•Parietal fMRI voxels respond to changes in stimulus numerosity.•Non-numerical stimulus feature changes predict these responses less well than numerosity.•Cognitive neural responses need not reflect low-level stimulus properties.
Publisher
Elsevier Inc,Elsevier Limited
Subject
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