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A biologically plausible computational theory for value integration and action selection in decisions with competing alternatives.

Christopoulos V, Bonaiuto J, Andersen RA - PLoS Comput. Biol. (2015)

Bottom Line: This information is diverse, relating to both the dynamic value of the goal and the cost of acting, creating a challenging problem in integrating information across these diverse variables in real time.We introduce a computational framework for dynamically integrating value information from disparate sources in decision tasks with competing actions.We evaluated the framework in a series of oculomotor and reaching decision tasks and found that it captures many features of choice/motor behavior, as well as its neural underpinnings that previously have eluded a common explanation.

View Article: PubMed Central - PubMed

Affiliation: Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.

ABSTRACT
Decision making is a vital component of human and animal behavior that involves selecting between alternative options and generating actions to implement the choices. Although decisions can be as simple as choosing a goal and then pursuing it, humans and animals usually have to make decisions in dynamic environments where the value and the availability of an option change unpredictably with time and previous actions. A predator chasing multiple prey exemplifies how goals can dynamically change and compete during ongoing actions. Classical psychological theories posit that decision making takes place within frontal areas and is a separate process from perception and action. However, recent findings argue for additional mechanisms and suggest the decisions between actions often emerge through a continuous competition within the same brain regions that plan and guide action execution. According to these findings, the sensorimotor system generates concurrent action-plans for competing goals and uses online information to bias the competition until a single goal is pursued. This information is diverse, relating to both the dynamic value of the goal and the cost of acting, creating a challenging problem in integrating information across these diverse variables in real time. We introduce a computational framework for dynamically integrating value information from disparate sources in decision tasks with competing actions. We evaluated the framework in a series of oculomotor and reaching decision tasks and found that it captures many features of choice/motor behavior, as well as its neural underpinnings that previously have eluded a common explanation.

No MeSH data available.


Characteristic example of the simulated model activity during an effector choice task with three targets.Neuronal activity of the DNFs that plan saccade (upper row) and reaching (bottom row) movements during a “cued-saccade” trial (note the red cue), in which the context cue is presented prior to target onset. The competition between the effectors is resolved shortly after the context cue is presented. Once the locations of the targets are shown, the framework has already selected the effector (i.e., eye in this trial) and the competition between the targets is resolved quite fast resulting in direct saccadic movements to the selected target (right panel).
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pcbi.1004104.g007: Characteristic example of the simulated model activity during an effector choice task with three targets.Neuronal activity of the DNFs that plan saccade (upper row) and reaching (bottom row) movements during a “cued-saccade” trial (note the red cue), in which the context cue is presented prior to target onset. The competition between the effectors is resolved shortly after the context cue is presented. Once the locations of the targets are shown, the framework has already selected the effector (i.e., eye in this trial) and the competition between the targets is resolved quite fast resulting in direct saccadic movements to the selected target (right panel).

Mentions: Finally, we designed and simulated another novel experiment with competing goals and effectors, but this time the effector cue was presented before the targets. In such a scenario, the motor plan formation DNF for the effector associated with the given cue received enough input to almost completely inhibit the other DNF. Once the targets appeared, the decision activity proceeded exclusively in the DNF for the cued effector and the only thing that remained to be resolved was the competition between the targets. Fig. 7 depicts such a scenario with 3 targets, in which the cue is presented 50 time steps after the trial starts, followed by the target onset 50 time steps later. Notice that the effector competition is resolved almost immediately after cue onset. All of these findings are novel and have not been validated in human or animal experimental studies, suggesting new avenues to understand the neural and behavioral mechanisms of action-selection in decisions with competing alternatives.


A biologically plausible computational theory for value integration and action selection in decisions with competing alternatives.

Christopoulos V, Bonaiuto J, Andersen RA - PLoS Comput. Biol. (2015)

Characteristic example of the simulated model activity during an effector choice task with three targets.Neuronal activity of the DNFs that plan saccade (upper row) and reaching (bottom row) movements during a “cued-saccade” trial (note the red cue), in which the context cue is presented prior to target onset. The competition between the effectors is resolved shortly after the context cue is presented. Once the locations of the targets are shown, the framework has already selected the effector (i.e., eye in this trial) and the competition between the targets is resolved quite fast resulting in direct saccadic movements to the selected target (right panel).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4372613&req=5

pcbi.1004104.g007: Characteristic example of the simulated model activity during an effector choice task with three targets.Neuronal activity of the DNFs that plan saccade (upper row) and reaching (bottom row) movements during a “cued-saccade” trial (note the red cue), in which the context cue is presented prior to target onset. The competition between the effectors is resolved shortly after the context cue is presented. Once the locations of the targets are shown, the framework has already selected the effector (i.e., eye in this trial) and the competition between the targets is resolved quite fast resulting in direct saccadic movements to the selected target (right panel).
Mentions: Finally, we designed and simulated another novel experiment with competing goals and effectors, but this time the effector cue was presented before the targets. In such a scenario, the motor plan formation DNF for the effector associated with the given cue received enough input to almost completely inhibit the other DNF. Once the targets appeared, the decision activity proceeded exclusively in the DNF for the cued effector and the only thing that remained to be resolved was the competition between the targets. Fig. 7 depicts such a scenario with 3 targets, in which the cue is presented 50 time steps after the trial starts, followed by the target onset 50 time steps later. Notice that the effector competition is resolved almost immediately after cue onset. All of these findings are novel and have not been validated in human or animal experimental studies, suggesting new avenues to understand the neural and behavioral mechanisms of action-selection in decisions with competing alternatives.

Bottom Line: This information is diverse, relating to both the dynamic value of the goal and the cost of acting, creating a challenging problem in integrating information across these diverse variables in real time.We introduce a computational framework for dynamically integrating value information from disparate sources in decision tasks with competing actions.We evaluated the framework in a series of oculomotor and reaching decision tasks and found that it captures many features of choice/motor behavior, as well as its neural underpinnings that previously have eluded a common explanation.

View Article: PubMed Central - PubMed

Affiliation: Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.

ABSTRACT
Decision making is a vital component of human and animal behavior that involves selecting between alternative options and generating actions to implement the choices. Although decisions can be as simple as choosing a goal and then pursuing it, humans and animals usually have to make decisions in dynamic environments where the value and the availability of an option change unpredictably with time and previous actions. A predator chasing multiple prey exemplifies how goals can dynamically change and compete during ongoing actions. Classical psychological theories posit that decision making takes place within frontal areas and is a separate process from perception and action. However, recent findings argue for additional mechanisms and suggest the decisions between actions often emerge through a continuous competition within the same brain regions that plan and guide action execution. According to these findings, the sensorimotor system generates concurrent action-plans for competing goals and uses online information to bias the competition until a single goal is pursued. This information is diverse, relating to both the dynamic value of the goal and the cost of acting, creating a challenging problem in integrating information across these diverse variables in real time. We introduce a computational framework for dynamically integrating value information from disparate sources in decision tasks with competing actions. We evaluated the framework in a series of oculomotor and reaching decision tasks and found that it captures many features of choice/motor behavior, as well as its neural underpinnings that previously have eluded a common explanation.

No MeSH data available.