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A probabilistic, distributed, recursive mechanism for decision-making in the brain

Fig 8

Effect of variations of free parameters on the time course of the model LIP in rMSPRT.

Each solid and dashed set of lines is the mean of correct trials in a single Monte Carlo experiment, with 800 total trials, 25.6% coherence and N = 2; simulation as in Fig 6c–6e. Computations aligned at decision initiation. Solid: inRF. Dashed: outRF. Blue: with parameters as tuned for this study. Green: increasing parameter value by 50%, keeping other parameters as tuned. Red: decreasing it by 50%, keeping others as tuned. Black: removing the effect of the tested parameter (l = 0, wyu = 0, n = 1), keeping others as tuned. (a) Varying the baseline, l. (b) Varying the cortico-thalamic weight, wyu. (c) Varying the data scaling factor, n.

Fig 8

doi: https://doi.org/10.1371/journal.pcbi.1006033.g008