Once the weights of these connections have been learned (see “Sensori-motor association learning in effector choice tasks” section), the field excites all neurons in the motor plan formation field corresponding to the cued effector.

The model architecture designed to simulate effector choice tasks with single or multiple targets.

We extended the present computational theory to model effector choice tasks by duplicating the architecture of the framework and designating one network for sac-cades and one for reaches.

The operation of the framework can be easily understood in the context of particular reaching choice tasks that involve action selection in the presence of competing targets.

in supporting information shows analytically the architecture of the framework for effector choice tasks ).

Sensorimotor association learning in effector choice tasks .

Numerous choice tasks have been used to study decision processes.

Some of these choice tasks , specifically n-armed bandit, information sampling and foraging tasks, pose choices that tradeoff immediate and future reward.

We have applied markov decision process models (MDPs/POMDPs) to choice tasks that have been used to study the eXplore-eXploit tradeoff, information sampling and foraging.

We used markov decision processes, either partially observed (POMDPs) or fully observed (MDPs) to model several choice tasks .

Perceptual inference tasks, as well as many other choice tasks , are often modeled using a drift-diffusion framework, and it is assumed that when an evidence bearing particle crosses a threshold a decision is made.

We first clarify the differences between these three types of decision making model in the remainder of the introduction, before giving results on how these models compare on a simple perceptual choice task involving action on synthetic data, and then give empirical support for embodied choice models from motion tracking experiments during decision making.

Here we incorporate these assumptions in four computational models and test them in a simulation of a simple perceptual choice task involving action.

All simulations represent a two-alternative forced choice task (2AFC) in which an action must be made to one of two targets to indicate the decision (Fig.

A The observed distribution of consumption at the group level by participants for whom anticipation-discounting functions derived one-off choice tasks were available (N = 23).

In the one-off choice task , the frequency of choosing sooner pain indicates the extent of negative time preference, and is a correlate of dread.

Data summarizing behavior on the previously published binary intertemporal choice task ; the frequency With Which each participant chose the sooner of the two options for delayed painful shocks in binary choice experiment (previously published, [33], 81 Table), and the maximum likelihood parameter estimates resulting from fitting an exponential dread-discounting model (previously published, [33], jag—framing model) to the observed choices.