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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 two targets.Left: Neuronal activity of the DNFs that plan saccade (upper row) and reaching (bottom row) movements in a free-choice task with two competing targets. Two targets located in the left and the right hemifield at equal distance from the “hand” and “eye” origin are presented at 50 time-steps followed by a “free-choice” cue signal (red and green cues are presented simultaneously) 50 time-steps later, which indicates that the framework is free to choose any effector to acquire any of the two targets. Since there is no effector preference to bias the effector competition, it takes longer for the model to decide whether to use the “hand’ or the “eye” to acquire the target. As a result, the competition between the targets is usually resolved before the movement onset, resulting frequently in direct movements to the selected target (green trace is a characteristic example of reaching movement in a free-choice trial). Right: Similar to the left panel but for a “cued-reaching” trial (green cue). The effector competition is resolved shortly after the cue is presented and the movement starts sooner than the free-choice trial due to the excitatory inputs from the context cue neurons. Thus, the competition between the targets is usually not resolved before the movement onset resulting in curved trajectories (green trace is a characteristic example of reaching movement in a “cued-reaching” trial).
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pcbi.1004104.g005: Characteristic example of the simulated model activity during an effector choice task with two targets.Left: Neuronal activity of the DNFs that plan saccade (upper row) and reaching (bottom row) movements in a free-choice task with two competing targets. Two targets located in the left and the right hemifield at equal distance from the “hand” and “eye” origin are presented at 50 time-steps followed by a “free-choice” cue signal (red and green cues are presented simultaneously) 50 time-steps later, which indicates that the framework is free to choose any effector to acquire any of the two targets. Since there is no effector preference to bias the effector competition, it takes longer for the model to decide whether to use the “hand’ or the “eye” to acquire the target. As a result, the competition between the targets is usually resolved before the movement onset, resulting frequently in direct movements to the selected target (green trace is a characteristic example of reaching movement in a free-choice trial). Right: Similar to the left panel but for a “cued-reaching” trial (green cue). The effector competition is resolved shortly after the cue is presented and the movement starts sooner than the free-choice trial due to the excitatory inputs from the context cue neurons. Thus, the competition between the targets is usually not resolved before the movement onset resulting in curved trajectories (green trace is a characteristic example of reaching movement in a “cued-reaching” trial).

Mentions: These results suggest that effector selection utilizes a similar mechanism to action selection with multiple target goals. This raises the question of how the brain may solve a competition that involves both multiple goals and effectors. To address this question, we designed and simulated a novel visuomotor decision task that involves multiple competing targets which can be acquired by either saccadic or reaching movements. To the best of our knowledge, this task has not been studied yet with experiments in humans or animals. The left and right panels in Fig. 5 illustrate the simulated neural activity in motor plan formation DNFs for eye and hand movements for a characteristic trial in “free-choice” and ‘cued-reaching” sessions, respectively. In the free-choice condition, two equally rewarded targets are presented in both hemifields 50 time-steps after the beginning of the trial followed by a free-choice cue (i.e., red and green cues simultaneously presented on the center of the screen) 50 time steps later. In this condition, the framework is free to choose either of the two effectors to acquire any of the targets. In the cued condition, a green or a red cue is presented 50 time steps after the onset of the targets, indicating which effector should be used to acquire either of the targets. There are several interesting features in the simulated neuronal activities and behavior:


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 two targets.Left: Neuronal activity of the DNFs that plan saccade (upper row) and reaching (bottom row) movements in a free-choice task with two competing targets. Two targets located in the left and the right hemifield at equal distance from the “hand” and “eye” origin are presented at 50 time-steps followed by a “free-choice” cue signal (red and green cues are presented simultaneously) 50 time-steps later, which indicates that the framework is free to choose any effector to acquire any of the two targets. Since there is no effector preference to bias the effector competition, it takes longer for the model to decide whether to use the “hand’ or the “eye” to acquire the target. As a result, the competition between the targets is usually resolved before the movement onset, resulting frequently in direct movements to the selected target (green trace is a characteristic example of reaching movement in a free-choice trial). Right: Similar to the left panel but for a “cued-reaching” trial (green cue). The effector competition is resolved shortly after the cue is presented and the movement starts sooner than the free-choice trial due to the excitatory inputs from the context cue neurons. Thus, the competition between the targets is usually not resolved before the movement onset resulting in curved trajectories (green trace is a characteristic example of reaching movement in a “cued-reaching” trial).
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Related In: Results  -  Collection

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pcbi.1004104.g005: Characteristic example of the simulated model activity during an effector choice task with two targets.Left: Neuronal activity of the DNFs that plan saccade (upper row) and reaching (bottom row) movements in a free-choice task with two competing targets. Two targets located in the left and the right hemifield at equal distance from the “hand” and “eye” origin are presented at 50 time-steps followed by a “free-choice” cue signal (red and green cues are presented simultaneously) 50 time-steps later, which indicates that the framework is free to choose any effector to acquire any of the two targets. Since there is no effector preference to bias the effector competition, it takes longer for the model to decide whether to use the “hand’ or the “eye” to acquire the target. As a result, the competition between the targets is usually resolved before the movement onset, resulting frequently in direct movements to the selected target (green trace is a characteristic example of reaching movement in a free-choice trial). Right: Similar to the left panel but for a “cued-reaching” trial (green cue). The effector competition is resolved shortly after the cue is presented and the movement starts sooner than the free-choice trial due to the excitatory inputs from the context cue neurons. Thus, the competition between the targets is usually not resolved before the movement onset resulting in curved trajectories (green trace is a characteristic example of reaching movement in a “cued-reaching” trial).
Mentions: These results suggest that effector selection utilizes a similar mechanism to action selection with multiple target goals. This raises the question of how the brain may solve a competition that involves both multiple goals and effectors. To address this question, we designed and simulated a novel visuomotor decision task that involves multiple competing targets which can be acquired by either saccadic or reaching movements. To the best of our knowledge, this task has not been studied yet with experiments in humans or animals. The left and right panels in Fig. 5 illustrate the simulated neural activity in motor plan formation DNFs for eye and hand movements for a characteristic trial in “free-choice” and ‘cued-reaching” sessions, respectively. In the free-choice condition, two equally rewarded targets are presented in both hemifields 50 time-steps after the beginning of the trial followed by a free-choice cue (i.e., red and green cues simultaneously presented on the center of the screen) 50 time steps later. In this condition, the framework is free to choose either of the two effectors to acquire any of the targets. In the cued condition, a green or a red cue is presented 50 time steps after the onset of the targets, indicating which effector should be used to acquire either of the targets. There are several interesting features in the simulated neuronal activities and behavior:

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.