Tasks¶
Classes representing each task
-
class
tasks.
SyntheticData
¶ Object representing synthetic data
- cumreward_param_plot :
- Plots the cumulative reward against model parameters. Useful to determine the relationship between reward acquisition and model parameters for a given task.
- plot_cumreward :
- Plots the cumulative reward over time for each subject
-
plot_cumreward
()¶ Plots cumulative reward over time for each subject
-
class
tasks.
bandit
(narms=2, rewards=[1, 0], rprob='stochastic', rprob_sd=0.025, rprob_bounds=[0.2, 0.8])¶ Simple one-step bandit task.
- narms : int
- Number of arms
- rewards : ndarray(shape=(2))
- First entry is the reward, if gained, and the second entry is the magnitude of the loss
- rprob : {ndarray(shape=(narms)), ‘stochastic’}
- Probabilty of reward for each arm of the task. One can either specify the probabilities for each arm or enter ‘stochastic,’ which will vary the reward probability by a gaussian random walk
- simulate(nsubjects,ntrials)
- Runs the task on simulated subjects
-
class
tasks.
ortho_gng
(rewards=[1, 0, -1])¶ Model of the orthogonalized go-nogo task from Guitart-Masip et al. (2012)
rewards : list
[1] Guitart-Masip, M. et al. (2012) Go and no-go learning in reward and punishment: Interactions between affect and effect. Neuroimage 62, 154–166
-
class
tasks.
twostep
(ptrans=0.7, rewards=[1, 0])¶ Model of the two-step task (Daw et al. 2011).
- ptrans : ndarray
- Probability of transitioning from state 0 to either state 1 or 2 depending on the choice made at the first step of the task.
- simulate
- Generates synthetic data from the task.
[1] Daw, N.D. et al. (2011) Model-based influences on humans’ choices and striatal prediction errors. Neuron 69, 1204–1215