superstats.simulation.cognitive.rdm#

Racing Diffusion Model simulator.

Functions

sample_rdm(v_base, v_diff, a_base, tau, ...)

Sample from the Racing Diffusion Model (RDM).
superstats.simulation.cognitive.rdm.sample_rdm(v_base, v_diff, a_base, tau, bias, sigma_diff, num_accumulators=2, correct_idx=None, sigma_base=1.0, dt=0.001, max_steps=10000)[source]#

Sample from the Racing Diffusion Model (RDM).

Simulates num_accumulators independent diffusion accumulators racing from a starting point of 0 toward their own threshold; the first to cross wins and determines the response and response time. On each trial, the accumulator at index correct_idx[i] is treated as the correct/target accumulator: it receives a drift advantage of v_diff, a bias-scaled threshold, and noise scaled by sigma_diff. All other accumulators on that trial share the disadvantaged drift, an unscaled threshold, and noise fixed at sigma_base.

Parameters:
v_basenp.ndarray of shape (num_trials,)

Base drift rate shared by all accumulators before the correct/incorrect adjustment.

v_diffnp.ndarray of shape (num_trials,)

Drift rate difference between the correct and incorrect accumulators. The correct accumulator gets v_base + v_diff / 2; all other accumulators get v_base - v_diff / 2.

a_basenp.ndarray of shape (num_trials,)

Base threshold distance from the origin for each trial.

taunp.ndarray of shape (num_trials,)

Non-decision times for each trial.

biasnp.ndarray of shape (num_trials,)

Threshold scaling factor in [0, 1] for the correct/target accumulator: its threshold is a_base * bias. All other accumulators use the unscaled threshold a_base.

sigma_diffnp.ndarray of shape (num_trials,)

Noise scaling factor in [0, +inf) for the correct/target accumulator: its noise SD is sigma_base * sigma_diff. All other accumulators always use sigma_base directly.

num_accumulatorsint

Number of racing accumulators per trial (fixed across trials).

correct_idxnp.ndarray of shape (num_trials,), optional

Index (into 0 .. num_accumulators - 1) of the correct/target accumulator for each trial. If left empty, accumulator 0 is treated as correct on every trial.

sigma_basefloat, optional, default: 1.0

Diffusion noise standard deviation of the non-correct accumulators. Fixed (not estimated per trial) for identifiability.

dtfloat, optional, default: 0.001

Time step size.

max_stepsint, optional, default: 10000

Maximum number of diffusion steps per trial before timing out.

Returns:
datadict of np.ndarray

Named decision data. “response_time” contains response times (or -1.0 on timeout) and “choice” contains the index of the winning accumulator (or -1.0 on timeout). Each array has shape (num_trials,).

Parameters:
Return type:

dict[str, ndarray]