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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.


Related in: MedlinePlus

A simplified version of the model architecture for reaching decision tasks in the presence of two competing targets.The motor plan formation field encodes the direction of the intended arm movement in a special reference frame centered on the hand. The goods-based decision values and the spatial sensory inputs are therefore transformed from allocentric to egocentric representations centered on the hand before being input to the motor plan formation field. The motor plan formation field integrates information about the spatial location of the targets, the expected reward attached to each target and the action cost required to pursue the targets into a single variable named “relative desirability”. The relative desirability encodes the “attractiveness” of the individual M reach policies at a given time and state and is used to weigh the influence of these policies on the final policy. Note that M is the number of neurons with activation level above a threshold γ. Once the final policy is determined, the framework implements that policy at the given time and state resulting in an action-plan (i.e., sequences of actions) that drives the hand closer to the target (see Results and Methods sections for more details).
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pcbi.1004104.g002: A simplified version of the model architecture for reaching decision tasks in the presence of two competing targets.The motor plan formation field encodes the direction of the intended arm movement in a special reference frame centered on the hand. The goods-based decision values and the spatial sensory inputs are therefore transformed from allocentric to egocentric representations centered on the hand before being input to the motor plan formation field. The motor plan formation field integrates information about the spatial location of the targets, the expected reward attached to each target and the action cost required to pursue the targets into a single variable named “relative desirability”. The relative desirability encodes the “attractiveness” of the individual M reach policies at a given time and state and is used to weigh the influence of these policies on the final policy. Note that M is the number of neurons with activation level above a threshold γ. Once the final policy is determined, the framework implements that policy at the given time and state resulting in an action-plan (i.e., sequences of actions) that drives the hand closer to the target (see Results and Methods sections for more details).

Mentions: In the current section, we present a simplified version of the framework that involves only reaching, Fig. 2. The reach motor plan formation DNF encodes the direction of intended arm movements in a spatial reference frame centered on the hand. Various decision values are therefore transformed from allocentric to egocentric representations centered on the hand before being input to the reach motor plan formation DNF. The DNF receives input encoding the location of the stimulus, the expected reward associated with moving in each direction and the action cost. The location of each stimulus is encoded in the spatial sensory input field as a Gaussian population code centered on the direction of the stimulus with respect to the hand. The expected reward for moving to given locations is effector-independent and encoded as a multivariate Gaussian population in allocentric coordinates. This representation is transformed into a one dimensional vector in the goods value field representing the expected reward for moving in each direction, centered on the hand position. These values, as well as the action cost field inputs, are summed and applied to the reach motor plan formation DNF. The DNF is run until it reaches a peak activity level above γ, after which its values are used to weigh the policy of each motor schema, resulting in a reach movement.


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)

A simplified version of the model architecture for reaching decision tasks in the presence of two competing targets.The motor plan formation field encodes the direction of the intended arm movement in a special reference frame centered on the hand. The goods-based decision values and the spatial sensory inputs are therefore transformed from allocentric to egocentric representations centered on the hand before being input to the motor plan formation field. The motor plan formation field integrates information about the spatial location of the targets, the expected reward attached to each target and the action cost required to pursue the targets into a single variable named “relative desirability”. The relative desirability encodes the “attractiveness” of the individual M reach policies at a given time and state and is used to weigh the influence of these policies on the final policy. Note that M is the number of neurons with activation level above a threshold γ. Once the final policy is determined, the framework implements that policy at the given time and state resulting in an action-plan (i.e., sequences of actions) that drives the hand closer to the target (see Results and Methods sections for more details).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004104.g002: A simplified version of the model architecture for reaching decision tasks in the presence of two competing targets.The motor plan formation field encodes the direction of the intended arm movement in a special reference frame centered on the hand. The goods-based decision values and the spatial sensory inputs are therefore transformed from allocentric to egocentric representations centered on the hand before being input to the motor plan formation field. The motor plan formation field integrates information about the spatial location of the targets, the expected reward attached to each target and the action cost required to pursue the targets into a single variable named “relative desirability”. The relative desirability encodes the “attractiveness” of the individual M reach policies at a given time and state and is used to weigh the influence of these policies on the final policy. Note that M is the number of neurons with activation level above a threshold γ. Once the final policy is determined, the framework implements that policy at the given time and state resulting in an action-plan (i.e., sequences of actions) that drives the hand closer to the target (see Results and Methods sections for more details).
Mentions: In the current section, we present a simplified version of the framework that involves only reaching, Fig. 2. The reach motor plan formation DNF encodes the direction of intended arm movements in a spatial reference frame centered on the hand. Various decision values are therefore transformed from allocentric to egocentric representations centered on the hand before being input to the reach motor plan formation DNF. The DNF receives input encoding the location of the stimulus, the expected reward associated with moving in each direction and the action cost. The location of each stimulus is encoded in the spatial sensory input field as a Gaussian population code centered on the direction of the stimulus with respect to the hand. The expected reward for moving to given locations is effector-independent and encoded as a multivariate Gaussian population in allocentric coordinates. This representation is transformed into a one dimensional vector in the goods value field representing the expected reward for moving in each direction, centered on the hand position. These values, as well as the action cost field inputs, are summed and applied to the reach motor plan formation DNF. The DNF is run until it reaches a peak activity level above γ, after which its values are used to weigh the policy of each motor schema, resulting in a reach movement.

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.


Related in: MedlinePlus