EvidenceAccumulation

class braintools.cogtask.EvidenceAccumulation(t_fixation=Quantity(500., 'ms'), t_evidence=Quantity(2000., 'ms'), t_response=Quantity(500., 'ms'), num_choices=2, coherences=(0, 6.4, 12.8, 25.6, 51.2), noise_sigma=Quantity(1., 'ms^0.5'), pop_per_choice=10, **kwargs)

Evidence Accumulation task.

Similar to PDM but designed for spiking networks. Agent accumulates noisy evidence over time and makes a decision.

Structure: Fixation >> Evidence >> Response

Parameters:

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

• t_evidence (Quantity) – Evidence accumulation duration (default: 2000ms).

• t_response (Quantity) – Response duration (default: 500ms).

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

• coherences (Sequence[float]) – Evidence coherence levels (default: (0, 6.4, 12.8, 25.6, 51.2)).

• noise_sigma (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Evidence noise (default: 1.0 * u.ms**0.5).

• seed (int, optional) – Random seed.

Examples

>>> task = EvidenceAccumulation()
>>> task = EvidenceAccumulation(num_choices=4, t_evidence=3000*u.ms)
>>> X, Y, info = task.sample_trial(0)

define_features()

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()

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)

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