PulseDecisionMaking#

class braintools.cogtask.PulseDecisionMaking(t_fixation=Quantity(500., 'ms'), t_cue=Quantity(100., 'ms'), t_delay=Quantity(240., 'ms'), num_pulses=7, t_response=Quantity(150., 'ms'), num_choices=2, pulse_values=(-0.08, -0.04, -0.02, -0.01, 0.01, 0.02, 0.04, 0.08), **kwargs)[source]#

Pulse-Based Decision Making task.

Agent accumulates evidence from discrete pulses. Each pulse provides a small amount of evidence for one direction.

Structure: Fixation >> (Cue >> Delay) * N >> Response

Parameters:
  • t_fixation (Quantity) – Fixation duration (default: 500ms).

  • t_cue (Quantity) – Duration of each cue pulse (default: 100ms).

  • t_delay (Quantity) – Delay between pulses (default: 240ms).

  • num_pulses (int) – Number of evidence pulses (default: 7).

  • t_response (Quantity) – Response duration (default: 150ms).

  • num_choices (int) – Number of choices (default: 2).

  • pulse_values (Sequence[float]) – Possible evidence values (positive = choice 1, negative = choice 0) (default: (-0.08, -0.04, -0.02, -0.01, 0.01, 0.02, 0.04, 0.08)).

  • seed (int, optional) – Random seed.

Examples

>>> task = PulseDecisionMaking()
>>> X, Y, info = task.sample_trial(0)
define_features()[source]#

Define input and output features.

Override in subclass for class-based task definition.

Return type:

Tuple

Returns:

  • input_features (Feature or FeatureSet) – Input feature definitions.

  • output_features (Feature or FeatureSet) – Output feature definitions.

define_phases()[source]#

Define the phase structure.

Override in subclass for class-based task definition.

Returns:

The task phase structure (single phase or composition).

Return type:

Phase

trial_init(ctx)[source]#

Initialize trial-level state.

Override in subclass to set up trial parameters like ground_truth, stimulus indices, etc.

Parameters:

ctx (Context) – Trial context to populate with state.

Return type:

None