Limits...
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.


Model architecture.The core component of the model is the motor plan formation field that dynamically integrates information from disparate sources. It receives excitatory inputs (green lines) from: i) the spatial sensory input field that encodes the angular representation of the alternative goals, ii) the goods-value field that encodes the expected benefits for moving towards a particular direction and iii) the context cue field that represents information related to the contextual requirements of the task. The motor plan formation field also receives inhibitory inputs (red line) from the action cost field that encodes the action cost (e.g., effort) required to move in a particular direction. All this information is integrated by the motor plan formation field into an evolving assessment of the “desirability” of the alternative options. Each neuron in the motor plan formation field is linked with a motor control schema that generates a direction-specific policy πj to move in the preferred direction of that neuron. The output activity of the motor plan formation field weights the influence of each individual policy on the final action-plan (see “Model architecture” in the results section for more details).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4372613&req=5

pcbi.1004104.g001: Model architecture.The core component of the model is the motor plan formation field that dynamically integrates information from disparate sources. It receives excitatory inputs (green lines) from: i) the spatial sensory input field that encodes the angular representation of the alternative goals, ii) the goods-value field that encodes the expected benefits for moving towards a particular direction and iii) the context cue field that represents information related to the contextual requirements of the task. The motor plan formation field also receives inhibitory inputs (red line) from the action cost field that encodes the action cost (e.g., effort) required to move in a particular direction. All this information is integrated by the motor plan formation field into an evolving assessment of the “desirability” of the alternative options. Each neuron in the motor plan formation field is linked with a motor control schema that generates a direction-specific policy πj to move in the preferred direction of that neuron. The output activity of the motor plan formation field weights the influence of each individual policy on the final action-plan (see “Model architecture” in the results section for more details).

Mentions: The basic architecture of the framework is a set of dynamic neural fields (DNFs) that capture the neural processes underlying cue perception, motor plan formation, valuation of goods (e.g., expected reward/punishment, social reward, selection bias, cognitive bias) and valuation of actions (e.g., effort cost, precision required), Fig. 1. Each DNF simulates the dynamic evolution of firing rate activity within a neural population. It is based on the concept of population coding, in which each neuron has a response tuning curve over some set of inputs, such as the location of a good or the end-point of a planned movement, and the responses of a neuronal ensemble represent the values of the inputs. The functional properties of each DNF are determined by the lateral interactions within the field and the connections with the other fields in the architecture. Some of these connections are static and predefined, whereas others are dynamic and change during the task. The projections between the fields are topologically organized, that is, each neuron in one field drives activation of the corresponding neuron (coding for the same direction) in the fields to which it projects.


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)

Model architecture.The core component of the model is the motor plan formation field that dynamically integrates information from disparate sources. It receives excitatory inputs (green lines) from: i) the spatial sensory input field that encodes the angular representation of the alternative goals, ii) the goods-value field that encodes the expected benefits for moving towards a particular direction and iii) the context cue field that represents information related to the contextual requirements of the task. The motor plan formation field also receives inhibitory inputs (red line) from the action cost field that encodes the action cost (e.g., effort) required to move in a particular direction. All this information is integrated by the motor plan formation field into an evolving assessment of the “desirability” of the alternative options. Each neuron in the motor plan formation field is linked with a motor control schema that generates a direction-specific policy πj to move in the preferred direction of that neuron. The output activity of the motor plan formation field weights the influence of each individual policy on the final action-plan (see “Model architecture” in the results section for more details).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004104.g001: Model architecture.The core component of the model is the motor plan formation field that dynamically integrates information from disparate sources. It receives excitatory inputs (green lines) from: i) the spatial sensory input field that encodes the angular representation of the alternative goals, ii) the goods-value field that encodes the expected benefits for moving towards a particular direction and iii) the context cue field that represents information related to the contextual requirements of the task. The motor plan formation field also receives inhibitory inputs (red line) from the action cost field that encodes the action cost (e.g., effort) required to move in a particular direction. All this information is integrated by the motor plan formation field into an evolving assessment of the “desirability” of the alternative options. Each neuron in the motor plan formation field is linked with a motor control schema that generates a direction-specific policy πj to move in the preferred direction of that neuron. The output activity of the motor plan formation field weights the influence of each individual policy on the final action-plan (see “Model architecture” in the results section for more details).
Mentions: The basic architecture of the framework is a set of dynamic neural fields (DNFs) that capture the neural processes underlying cue perception, motor plan formation, valuation of goods (e.g., expected reward/punishment, social reward, selection bias, cognitive bias) and valuation of actions (e.g., effort cost, precision required), Fig. 1. Each DNF simulates the dynamic evolution of firing rate activity within a neural population. It is based on the concept of population coding, in which each neuron has a response tuning curve over some set of inputs, such as the location of a good or the end-point of a planned movement, and the responses of a neuronal ensemble represent the values of the inputs. The functional properties of each DNF are determined by the lateral interactions within the field and the connections with the other fields in the architecture. Some of these connections are static and predefined, whereas others are dynamic and change during the task. The projections between the fields are topologically organized, that is, each neuron in one field drives activation of the corresponding neuron (coding for the same direction) in the fields to which it projects.

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.