Residual Information of Previous Decision Affects Evidence Accumulation in Current Decision

Abstract

Bias in perceptual decisions can be generally defined as an effect which is controlled by factors other than the decision-relevant information (e.g., perceptual information in a perceptual task, when trials are independent). The literature on decision-making suggests two main hypotheses to account for this kind of bias: internal bias signals are derived from (a) the residual of motor signals generated to report a decision in the past, and (b) the residual of sensory information extracted from the stimulus in the past. Beside these hypotheses, this study suggests that making a decision in the past per se may bias the next decision. We demonstrate the validity of this assumption, first, by performing behavioral experiments based on the two-alternative forced-choice (TAFC) discrimination of motion direction paradigms and, then, we modified the pure drift-diffusion model (DDM) based on the accumulation-to-bound mechanism to account for the sequential effect. In both cases, the trace of the previous trial influences the current decision. Results indicate that the probability of being correct in the current decision increases if it is in line with the previously made decision even in the presence of feedback. Moreover, a modified model that keeps the previous decision information in the starting point of evidence accumulation provides a better fit to the behavioral data. Our findings suggest that the accumulated evidence in the decision-making process after crossing the bound in the previous decision can affect the parameters of information accumulation for the current decision in consecutive trials.

The interaction between elapsed time and decision accuracy differs between humans and rats

“…We therefore recruited a second cohort of 55 new subjects (Phase 2). In an effort to reduce inter-subject variability, the enrollment criteria specified a narrower age range (18)(19)(20)(21)(22)(23)(24)(25) and required that the subject was not currently taking any psychiatric medication, in addition to normal or corrected vision and hearing (by self-report). To improve motivation, compensation was linked to performance (number of correct responses, or "points" earned).…”

“…The accuracy and timing of sensory discriminations have been used to study mechanisms of decision-making for over a century (1). In particular, a visual random dot coherent motion task (2,3 ) has been extensively used to study decision-making in human and monkeys (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22). In each trial, many small high-contrast dots are plotted at random locations in part of the visual field of a subject.…”

Optimizing sequential decisions in the drift-diffusion model

“…Adjusting the initial belief increases reward rate by decreasing response time, but not the probability of a correct response. Changes in initial bias have a larger effect than adjustments in threshold, and there is evidence that human subjects do adjust their initial beliefs across trials (Fründ et al, 2014; Olianezhad et al, 2016; Braun et al, 2018). On the other hand, the effect of a dynamic threshold across trials diminishes as the number of trials increases: is nearly the same for constant thresholds θ as for when allowing dynamic thresholds θ 1: n .…”

Optimal models of decision-making in dynamic environments

“…1b) is used to generate correct choices, human observers adapt to these trial-to-trial correlations and their response times are accurately modeled by drift diffusion 11 or ballistic models 16 with biased initial conditions. Feedback or decisions across correlated trials impact different aspects of normative models 31 including accumulation speed (drift) [32][33][34] , decision bounds 11 , or the initial belief on subsequent trials 12,35,36 . Given a sequence of dependent but statistically identical trials, optimal observers should adjust their initial belief and decision threshold 16,28 , but not their accumulation speed in cases where difficulty is fixed across trials 18 .…”

“…Thus, optimal models predict that observers should, on average, respond more quickly, but not more accurately 28 . Empirically, humans 12,35,36 and other animals 29 do indeed often respond faster on repeat trials, which can often be modeled by per trial adjustments in initial belief. Furthermore, this bias can result from explicit feedback or subjective estimates, as demonstrated in studies where no feedback is provided (Fig.…”