Multiphasic Value Biases: Raw Behaviour files and task code

Raw Behavioural data and task code from Corbett, Martinez-Rodriguez, Judd, O'Connell &amp; Kelly (2022), Multiphasic Value Biases in Fast-Paced Decisions. <br> Perceptual decisions are biased toward higher-value options when overall gains can be improved. When stimuli demand immediate reactions, the neurophysiological decision process dynamically evolves through distinct phases of growing anticipation, detection and discrimination, but how value biases are exerted through these phases remains unknown. Here, 17 participants performed a motion direction discrimination task under high speed pressure, in which one alternative was more valuable than the other. By parsing motor preparation dynamics in human electrophysiology, we uncovered a multiphasic pattern of countervailing biases operating in speeded decisions. Anticipatory preparation of higher-value actions began earlier, conferring a “starting point”-advantage at stimulus onset, but the delayed preparation of lower-value actions was steeper, conferring a value-opposed buildup rate bias. This, in turn, was countered by a transient deflection toward the higher value action evoked by stimulus detection. A neurally-constrained process model featuring anticipatory urgency, biased detection, and accumulation of growing stimulus-discriminating evidence, successfully captured both behavior and motor preparation dynamics. Thus, an intricate interplay of distinct biasing mechanisms serves to prioritise time-constrained perceptual decisions. <br> Task code in RVBDots_final.m Raw behaviour files generated from task code. <br> Naming conventions: ParticipantRegimeSessionNoBlockNo.mat or Participant_RegimeBlockNo.mat <br> e.g. P1_H1.mat %P1, high coherence blocked, block 1 <br> or P3I31.mat %P3, interleaved, session 3, block 1 <br> Regimes: H: High coherence blocked. L: Low coherence blocked. I (or HL for P1): Interleaved. <br> Files with N in the filename before the block number are 'neutral' practice blocks with no value bias (not used in the analyses). <br> <br> <br>