Decisions are made based on the integration of available evidence. The noise in evidence accumulation leads to a particular speed-accuracy tradeoff in decision-making, which can be modulated and optimized by adaptive decision threshold setting. Given the effect of pre-SMA activity on striatal excitability, we hypothesized that the inhibition of pre-SMA would lead to higher decision thresholds and an increased accuracy bias. We used offline continuous theta burst stimulation to assess the effect of transient inhibition of the right pre-SMA on the decision processes in a free-response two-alternative forced-choice task within the drift diffusion model framework. Participants became more cautious and set higher decision thresholds following right pre-SMA inhibition compared with inhibition of the control site (vertex). Increased decision thresholds were accompanied by an accuracy bias with no effects on post-error choice behavior. Participants also exhibited higher drift rates as a result of pre-SMA inhibition compared with the vertex inhibition. These results, in line with the striatal theory of speed-accuracy tradeoff, provide evidence for the functional role of pre-SMA activity in decision threshold modulation. Our results also suggest that pre-SMA might be a part of the brain network associated with the sensory evidence integration.

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Previous studies showed that both human and non-human animals can discriminate between different quantities (i.e., time intervals, numerosities) with a limited level of precision due to their endogenous/representational uncertainty. In addition, other studies have shown that subjects can modulate their temporal categorization responses adaptively by incorporating information gathered regarding probabilistic contingencies into their time-based decisions. Despite the psychophysical similarities between the interval timing and nonverbal counting functions, the sensitivity of count-based decisions to probabilistic information remains an unanswered question. In the current study, we investigated whether exogenous probabilistic information can be integrated into numerosity-based judgments by mice. In the task employed in this study, reward was presented either after few (i.e., 10) or many (i.e., 20) lever presses, the last of which had to be emitted on the lever associated with the corresponding trial type. In order to investigate the effect of probabilistic information on performance in this task, we manipulated the relative frequency of different trial types across different experimental conditions. We evaluated the behavioral performance of the animals under models that differed in terms of their assumptions regarding the cost of responding (e.g., logarithmically increasing vs. no response cost). Our results showed for the first time that mice could adaptively modulate their count-based decisions based on the experienced probabilistic contingencies in directions predicted by optimality.

Neuroimaging research has identified a network of brain regions that is consistently more engaged when people think about the minds of other people than when they engage in nonsocial tasks. Activations in this “mentalizing network” are sometimes interpreted as evidence for the domain-specificity of cognitive processes supporting social thought. Here, we examine the alternative possibility that at least some activations in the mentalizing network may be explained by uncertainty. A reconsideration of findings from existing functional MRI studies in light of new data from independent raters suggests that (a) social tasks used in past studies have higher levels of uncertainty than their nonsocial comparison tasks and (b) activation in a key brain region associated with social cognition, the dorsomedial prefrontal cortex (DMPFC), may track with the degree of uncertainty surrounding both social and nonsocial inferences. These observations suggest that the preferential DMPFC response observed consistently in social scenarios may reflect the engagement of domain-general processes of uncertainty reduction, which points to avenues for future research into the core cognitive mechanisms supporting typical and atypical social thought.

Endogenous timing uncertainty results in variability in time-based judgments. In many timing tasks, animals need to incorporate their level of endogenous timing uncertainty into their decisions in order to maximize the reward rate. Although animals have been shown to adopt such optimal behavioral strategies in time-based decisions, whether they can optimize their behavior under exogenous noise is an open question. In this study, we tested mice and rats in a task that required them to space their responses for a minimum duration (DRL task) in different task conditions. In one condition, the minimum wait time was fixed, whereas in other conditions minimum wait time was a Gaussian random variable. Although reward maximization entailed waiting longer with added exogenous timing variability, results indicated that both mice and rats became more impulsive and deviated from optimality with increasing levels of exogenous noise. We introduce a reward-rate-dependent sampling function to SET to account for optimal performance in noiseless and suboptimal performance in noisy environments.