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Modeling learned categorical perception in human vision

Overview of attention for article published in Neural Networks, May 2012
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Article details
Title
Modeling learned categorical perception in human vision
Published in
Neural Networks, May 2012
DOI 10.1016/j.neunet.2012.05.001
Pubmed ID
Authors
Abstract

A long standing debate in cognitive neuroscience has been the extent to which perceptual processing is influenced by prior knowledge and experience with a task. A converging body of evidence now supports the view that a task does influence perceptual processing, leaving us with the challenge of understanding the locus of, and mechanisms underpinning, these influences. An exemplar of this influence is learned categorical perception (CP), in which there is superior perceptual discrimination of stimuli that are placed in different categories. Psychophysical experiments on humans have attempted to determine whether early cortical stages of visual analysis change as a result of learning a categorization task. However, while some results indicate that changes in visual analysis occur, the extent to which earlier stages of processing are changed is still unclear. To explore this issue, we develop a biologically motivated neural model of hierarchical vision processes consisting of a number of interconnected modules representing key stages of visual analysis, with each module learning to exhibit desired local properties through competition. With this system level model, we evaluate whether a CP effect can be generated with task influence to only the later stages of visual analysis. Our model demonstrates that task learning in just the later stages is sufficient for the model to exhibit the CP effect, demonstrating the existence of a mechanism that requires only a high-level of task influence. However, the effect generalizes more widely than is found with human participants, suggesting that changes to earlier stages of analysis may also be involved in the human CP effect, even if these are not fundamental to the development of CP. The model prompts a hybrid account of task-based influences on perception that involves both modifications to the use of the outputs from early perceptual analysis along with the possibility of changes to the nature of that early analysis itself.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 4 7%
Serbia 1 2%
Spain 1 2%
Unknown 53 90%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 17 29%
Researcher 10 17%
Student > Master 8 14%
Student > Bachelor 5 8%
Professor > Associate Professor 4 7%
Other 8 14%
Unknown 7 12%
Readers by discipline
Readers by discipline Count As %
Psychology 25 42%
Computer Science 6 10%
Engineering 4 7%
Medicine and Dentistry 3 5%
Mathematics 2 3%
Other 7 12%
Unknown 12 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 December 2012.
All research outputs
#8,535,684
of 25,374,917 outputs
Outputs from Neural Networks
#491
of 1,790 outputs
Outputs of similar age
#59,750
of 176,570 outputs
Outputs of similar age from Neural Networks
#4
of 12 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,790 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 176,570 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.