Seeing What You Believe: Cognitive Mechanisms of Flexible Integration of Priors in Visual Decisions

Beliefs and expectations, or priors, shape our perception of the environment (Gold & Stocker, 2017). In an ever-changing world, priors must be flexibly and continuously integrated into sensory decision processes to guide adaptive behavior. Nonetheless, its underlying cognitive mechanisms are not well understood. The Drift Diffusion Model (DDM) is a widely used model for studying visual decision-making (Gold & Shadlen, 2007; Ratcliff & Rouder, 1998). Previous studies have shown that priors can increase the starting point of evidence accumulation and the drift rate (Dunovan & Wheeler, 2018; Dunovan, Tremel, & Wheeler, 2014; Thakur, Basso, Ditterich, & Knowlton, 2021). However, these studies often overlook the potential effects of priors on decision threshold and non-decision time parameters. The goal of this study was to dissociate the effects of priors on multiple cognitive mechanisms in visual decisions. Specifically, I tested how the strength of prior beliefs affects: (a) the integration of momentary sensory evidence; (b) the amount of evidence required to decide; (c) pre-stimulus presentation processes; and (d) non-evidence accumulation effects. For the present study, eight participants completed a behavioral task that required tracking the cue validity across trials and using the cue information flexibly. The task combined a reversal learning and a random dot motion discrimination task and involved three main decisions per trial: cue choice, confidence, and motion direction. After choosing one of the two possible cues (orange vs. blue), participants judged how confident (low vs. high) they were that the chosen cue will turn out to be invalid. Then, participants received a direction of motion, and subsequently, participants judged the motion direction of the random dots. The cue direction was displayed with a predetermined but unknown validity. Each participant completed a maximum of 320 trials, which were divided into informative and non-informative blocks. The interval of an informative block varied from 15-30 trials. The validity of the cue in informative blocks was set at 80% or 30%, while the validity of the cues in non-informative blocks was set at 30% for both cues. At the end of each trial, participants received rewards for their motion judgment and cue choice. The reward for the cue choice depended on the confidence reported earlier in the trial. To evaluate the validity of the estimated belief in the prior, we tested whether belief strength is associated with confidence and the true contingency. Belief strength was higher when participants reported high confidence in their cue choice (t(7) = 5.31, p = .001). Furthermore, when belief strength was higher, participants chose the best cue for the block more often than when belief strength was low (t(7) = 24.52, p < .001). Altogether, these findings provide evidence of validity for trial-wise measures of belief strength. Regarding the effect of belief strength (or prior strength), the posterior estimates of the cognitive models show that the strength of belief affects various aspects of visual decision-making. When the cue was valid, stronger beliefs increased the drift rate (rate of evidence accumulation, 95% HDI = [1.09, 2.2]), increased the response bias towards the direction indicated by the cue (95% HDI = [.056, .228]), increased the threshold (amount of evidence needed to reach a decision, 95% HDI = [.019, .33]), and reduced non-decision time (secondary processes involved in the decision execution, 95% HDI = [.06, 11]). In contrast, when the cue was invalid, stronger beliefs had the opposite effects on these parameters. Overall, belief strength modulates the DDM parameters depending on the accuracy of the belief for a given trial. The main goal of this study was to behaviorally dissociate the effect of belief on visual decision-making using trial-wise estimates of belief strength. The effects on drift rate reflect the ramping of activity in parietal regions that scale with the strength of evidence (Hanks et al., 2015). In the present study, the effect of belief strength on the drift rate is congruent with biased evidence sampling driven by post-decisional confidence (Rollwage et al., 2020). The effects on the starting point are usually interpreted as a choice response bias (Dunovan et al., 2014; Dunovan & Wheeler, 2018). The origin of such biases in the starting point can be a result of a tendency to accept belief-congruent evidence, motor preparation (de Lange, Rahnev, Donner, & Lau, 2013), or even an increase in the sensitivity of low-level sensory representations before stimulus presentation (Kok, Failing, & de Lange, 2014). Although DDM does not dissociate between these subcomponents, it is possible to constrain them neurophysiologically (Harris & Hutcherson, 2022). Effects on the evidence accumulation threshold are associated with speed-accuracy trade-offs (Bogacz, Wagenmakers, Forstmann, & Nieuwenhuis, 2010). In the present study, we observed an effect of belief on decision threshold, suggesting that belief strength increases the amount of evidence that needs to be accumulated when the belief is congruent with visual input. This effect might be caused by a compensation mechanism to maintain high accuracy when the belief is invalid for a particular trial. The non-decision time parameter has often been neglected in the literature. Despite its marginalization, it might reflect important processes. For example, the latency of N200 potentials, which is associated with the encoding of visual stimuli, seems to track non-decision times (Nunez, Gosai, Vandekerckhove, & Srinivasan, 2019). The effect of non-decision time found in this study could emerge from the evidence-encoding onset, evidence accumulation onset, or post-decision motor execution time (Kelly, Corbett, & O’Connell, 2021). In the future, we will leverage the temporal dynamics of decision-making using neurophysiological recordings to constrain and dissociate these parameters (Harris & Hutcherson, 2022).