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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.A single target, which can be acquired with either a hand or an eye movement, is presented at about 50 time-steps after the trial onset. The activity of the neurons tuned to this target increases in both DNFs that plan hand and eye movements, since the framework does not know whether it is a “reach” or a “saccade” trial. Once the “green” cue is presented about 20 time-steps after the target onset (the hypothetical (x,y) location of the green cue is represented by the green dot in the Cartesian plot to the right panel), the neuronal activity in the reaching DNF (bottom right field) becomes sufficiently strong, due to the excitatory inputs from the context cue neurons, to inhibit the saccade DNF (upper right field). The competition is resolved shortly and the framework generates a direct reaching movement (green trace) to the target.
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pcbi.1004104.g004: Characteristic example of the simulated model activity during an effector choice task.A single target, which can be acquired with either a hand or an eye movement, is presented at about 50 time-steps after the trial onset. The activity of the neurons tuned to this target increases in both DNFs that plan hand and eye movements, since the framework does not know whether it is a “reach” or a “saccade” trial. Once the “green” cue is presented about 20 time-steps after the target onset (the hypothetical (x,y) location of the green cue is represented by the green dot in the Cartesian plot to the right panel), the neuronal activity in the reaching DNF (bottom right field) becomes sufficiently strong, due to the excitatory inputs from the context cue neurons, to inhibit the saccade DNF (upper right field). The competition is resolved shortly and the framework generates a direct reaching movement (green trace) to the target.

Mentions: Let’s assume for now that the weights of the context cue neurons are already learned, such that the framework knows that “red” and “green” cues indicate saccades and reaches, respectively. Fig. 4 depicts a trial from an “effector-decision” task with a single target similar to the neurophysiological study described above [29]. A target is presented 50 time-steps after the trial onset, followed by a “green” cue signal 20 time-steps later. Consistent with the experimental findings, once the target is presented, the activity of the populations of neurons tuned to the direction of the target increases in the motor plan formation DNFs for both reaching and saccadic movements, since the model does not yet know whether it is a “reach” or “saccade” trial. Notice that during that time, the neuronal activity in the reach DNF is lower than the activity in this field for the single target trial with no effector competition (see Fig. 3 upper panel), due to the inhibitory interactions with the motor plan formation DNF for saccadic movements. Once the green cue is presented, the neuronal activity in the reach DNF becomes strong enough to inhibit the saccade DNF and conclusively wins the competition, and the model generates a direct reaching movement to the target.


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.A single target, which can be acquired with either a hand or an eye movement, is presented at about 50 time-steps after the trial onset. The activity of the neurons tuned to this target increases in both DNFs that plan hand and eye movements, since the framework does not know whether it is a “reach” or a “saccade” trial. Once the “green” cue is presented about 20 time-steps after the target onset (the hypothetical (x,y) location of the green cue is represented by the green dot in the Cartesian plot to the right panel), the neuronal activity in the reaching DNF (bottom right field) becomes sufficiently strong, due to the excitatory inputs from the context cue neurons, to inhibit the saccade DNF (upper right field). The competition is resolved shortly and the framework generates a direct reaching movement (green trace) to the target.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004104.g004: Characteristic example of the simulated model activity during an effector choice task.A single target, which can be acquired with either a hand or an eye movement, is presented at about 50 time-steps after the trial onset. The activity of the neurons tuned to this target increases in both DNFs that plan hand and eye movements, since the framework does not know whether it is a “reach” or a “saccade” trial. Once the “green” cue is presented about 20 time-steps after the target onset (the hypothetical (x,y) location of the green cue is represented by the green dot in the Cartesian plot to the right panel), the neuronal activity in the reaching DNF (bottom right field) becomes sufficiently strong, due to the excitatory inputs from the context cue neurons, to inhibit the saccade DNF (upper right field). The competition is resolved shortly and the framework generates a direct reaching movement (green trace) to the target.
Mentions: Let’s assume for now that the weights of the context cue neurons are already learned, such that the framework knows that “red” and “green” cues indicate saccades and reaches, respectively. Fig. 4 depicts a trial from an “effector-decision” task with a single target similar to the neurophysiological study described above [29]. A target is presented 50 time-steps after the trial onset, followed by a “green” cue signal 20 time-steps later. Consistent with the experimental findings, once the target is presented, the activity of the populations of neurons tuned to the direction of the target increases in the motor plan formation DNFs for both reaching and saccadic movements, since the model does not yet know whether it is a “reach” or “saccade” trial. Notice that during that time, the neuronal activity in the reach DNF is lower than the activity in this field for the single target trial with no effector competition (see Fig. 3 upper panel), due to the inhibitory interactions with the motor plan formation DNF for saccadic movements. Once the green cue is presented, the neuronal activity in the reach DNF becomes strong enough to inhibit the saccade DNF and conclusively wins the competition, and the model generates a direct reaching movement to the target.

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.