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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.


Simulated neural activity, movement time and approach direction of reaching trajectories, in free-choice and cued-reaching trials in an effector choice task.A: Time course of the average activity (20 trials) of the two populations of neurons tuned to the selected (solid black line) and the non-selected (discontinuous black line) targets prior to movement onset, from the DNFs that plan reaching (green color) and saccade (red color) movements in the “free-choice” sessions. Data shown only when reaches were selected. Notice that the framework selects first which effector to use to perform the task and then it chooses the target. The average activity from the saccade DNF for the selected and non-selected targets overlaps. B: Similar to panel A, but for the “cued-reaching” sessions. The competition between the effectors is resolved almost immediately after the cue onset. C: Mean movement (i.e., response) time from 20 reaching trajectories in a free-choice task (i.e., model is free to choose to perform hand or eye movement to acquire each of the targets) and a cued task, in which the model was instructed to perform reaches. The error bars are ± standard error. The movement time in the free-choice trials was significantly lower than the movement time in the cued-reaching trials (two-sample t-test, p < 10−7). D: Mean approach direction of 20 reaching movements for the first 50 time-steps in a free-choice task and a cued-reaching task. The error bars are ± standard error. Approach direction at 0 deg. indicates that initial reaching movements were made towards the intermediate location between the two targets. Notice that free-choice trials are characterized with straight reaching movements to the selected target, whereas the cued-reaching trials are dominated mostly by curved reaching movements to the selected target (two-sample t-test, p < 10−4).
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pcbi.1004104.g006: Simulated neural activity, movement time and approach direction of reaching trajectories, in free-choice and cued-reaching trials in an effector choice task.A: Time course of the average activity (20 trials) of the two populations of neurons tuned to the selected (solid black line) and the non-selected (discontinuous black line) targets prior to movement onset, from the DNFs that plan reaching (green color) and saccade (red color) movements in the “free-choice” sessions. Data shown only when reaches were selected. Notice that the framework selects first which effector to use to perform the task and then it chooses the target. The average activity from the saccade DNF for the selected and non-selected targets overlaps. B: Similar to panel A, but for the “cued-reaching” sessions. The competition between the effectors is resolved almost immediately after the cue onset. C: Mean movement (i.e., response) time from 20 reaching trajectories in a free-choice task (i.e., model is free to choose to perform hand or eye movement to acquire each of the targets) and a cued task, in which the model was instructed to perform reaches. The error bars are ± standard error. The movement time in the free-choice trials was significantly lower than the movement time in the cued-reaching trials (two-sample t-test, p < 10−7). D: Mean approach direction of 20 reaching movements for the first 50 time-steps in a free-choice task and a cued-reaching task. The error bars are ± standard error. Approach direction at 0 deg. indicates that initial reaching movements were made towards the intermediate location between the two targets. Notice that free-choice trials are characterized with straight reaching movements to the selected target, whereas the cued-reaching trials are dominated mostly by curved reaching movements to the selected target (two-sample t-test, p < 10−4).

Mentions: The competition between the effectors is solved earlier than the competition between the targets for both cases. This finding is shown better in Fig. 6A and B that depict the average activity of the two populations of neurons tuned to the selected (solid black lines) and the non-selected (discontinuous black lines) targets from both DNFs that plan reaching (green) and eye (red) movements in the free-choice and cued-reaching sessions, respectively. Notice that in both sessions, the framework chooses first which effector to use (i.e., hand or eye) and then it selects which target to acquire. This is explained by the massive inhibitory interconnections between the DNFs that plan reaches and saccades.


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)

Simulated neural activity, movement time and approach direction of reaching trajectories, in free-choice and cued-reaching trials in an effector choice task.A: Time course of the average activity (20 trials) of the two populations of neurons tuned to the selected (solid black line) and the non-selected (discontinuous black line) targets prior to movement onset, from the DNFs that plan reaching (green color) and saccade (red color) movements in the “free-choice” sessions. Data shown only when reaches were selected. Notice that the framework selects first which effector to use to perform the task and then it chooses the target. The average activity from the saccade DNF for the selected and non-selected targets overlaps. B: Similar to panel A, but for the “cued-reaching” sessions. The competition between the effectors is resolved almost immediately after the cue onset. C: Mean movement (i.e., response) time from 20 reaching trajectories in a free-choice task (i.e., model is free to choose to perform hand or eye movement to acquire each of the targets) and a cued task, in which the model was instructed to perform reaches. The error bars are ± standard error. The movement time in the free-choice trials was significantly lower than the movement time in the cued-reaching trials (two-sample t-test, p < 10−7). D: Mean approach direction of 20 reaching movements for the first 50 time-steps in a free-choice task and a cued-reaching task. The error bars are ± standard error. Approach direction at 0 deg. indicates that initial reaching movements were made towards the intermediate location between the two targets. Notice that free-choice trials are characterized with straight reaching movements to the selected target, whereas the cued-reaching trials are dominated mostly by curved reaching movements to the selected target (two-sample t-test, p < 10−4).
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Related In: Results  -  Collection

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

pcbi.1004104.g006: Simulated neural activity, movement time and approach direction of reaching trajectories, in free-choice and cued-reaching trials in an effector choice task.A: Time course of the average activity (20 trials) of the two populations of neurons tuned to the selected (solid black line) and the non-selected (discontinuous black line) targets prior to movement onset, from the DNFs that plan reaching (green color) and saccade (red color) movements in the “free-choice” sessions. Data shown only when reaches were selected. Notice that the framework selects first which effector to use to perform the task and then it chooses the target. The average activity from the saccade DNF for the selected and non-selected targets overlaps. B: Similar to panel A, but for the “cued-reaching” sessions. The competition between the effectors is resolved almost immediately after the cue onset. C: Mean movement (i.e., response) time from 20 reaching trajectories in a free-choice task (i.e., model is free to choose to perform hand or eye movement to acquire each of the targets) and a cued task, in which the model was instructed to perform reaches. The error bars are ± standard error. The movement time in the free-choice trials was significantly lower than the movement time in the cued-reaching trials (two-sample t-test, p < 10−7). D: Mean approach direction of 20 reaching movements for the first 50 time-steps in a free-choice task and a cued-reaching task. The error bars are ± standard error. Approach direction at 0 deg. indicates that initial reaching movements were made towards the intermediate location between the two targets. Notice that free-choice trials are characterized with straight reaching movements to the selected target, whereas the cued-reaching trials are dominated mostly by curved reaching movements to the selected target (two-sample t-test, p < 10−4).
Mentions: The competition between the effectors is solved earlier than the competition between the targets for both cases. This finding is shown better in Fig. 6A and B that depict the average activity of the two populations of neurons tuned to the selected (solid black lines) and the non-selected (discontinuous black lines) targets from both DNFs that plan reaching (green) and eye (red) movements in the free-choice and cued-reaching sessions, respectively. Notice that in both sessions, the framework chooses first which effector to use (i.e., hand or eye) and then it selects which target to acquire. This is explained by the massive inhibitory interconnections between the DNFs that plan reaches and saccades.

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.