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Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

Overview of attention for article published in PLoS Computational Biology, November 2012
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Title
Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making
Published in
PLoS Computational Biology, November 2012
DOI 10.1371/journal.pcbi.1002774
Pubmed ID
Authors

Christian Klaes, Sebastian Schneegans, Gregor Schöner, Alexander Gail

Abstract

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Japan 2 2%
Switzerland 1 <1%
France 1 <1%
United Kingdom 1 <1%
Portugal 1 <1%
Brazil 1 <1%
Belgium 1 <1%
Unknown 105 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 23%
Student > Ph. D. Student 24 21%
Student > Master 12 10%
Professor 9 8%
Student > Doctoral Student 9 8%
Other 23 20%
Unknown 12 10%
Readers by discipline Count As %
Neuroscience 26 23%
Agricultural and Biological Sciences 18 16%
Psychology 16 14%
Computer Science 13 11%
Medicine and Dentistry 5 4%
Other 21 18%
Unknown 16 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 July 2014.
All research outputs
#15,184,741
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#6,529
of 8,964 outputs
Outputs of similar age
#110,014
of 192,286 outputs
Outputs of similar age from PLoS Computational Biology
#73
of 129 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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We're also able to compare this research output to 129 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.