Our results demonstrate poorer decision-making performance in pediatric OCD patients at the level of latent processes, specifically in terms of evidence accumulation.

One of the critical factors that guide choice behavior is the prior bias of the decision-maker with respect to different options, namely, the relative readiness by which the decision-maker opts for a specific choice. Although previous neuroimaging work has shown decision bias related activity in the orbitofrontal cortex, intraparietal sulcus (IPS) and dorsolateral prefrontal cortex, in a recent work by Javadi et al. (2015), primary motor cortex was also implicated. By applying transcranial direct current stimulation (tDCS), they have revealed a causal role of the primary motor cortex excitability in the induction of response time (RT) differences and decision bias in the form of choice probability. The current study aimed to replicate these recent findings with an experimental design that contained a sham group to increase experimental control and an additional testing phase to investigate the possible after-effects of tDCS. The conventional decision outputs such as choice proportion and RT were analyzed along with the theory-driven estimates of choice bias and non-decision related components of RTs (e.g., motor implementation speed of choices made). None of the statistical comparisons favored the alternative hypotheses over the null hypotheses. Consequently, previous findings regarding the effect of primary motor cortex excitability on choice bias and response times could not be replicated with a more controlled experimental design that is recommended for tDCS studies (Horvath et al., 2015). This empirical discrepancy between the two studies adds to the evidence demonstrating inconsistent effects of tDCS in establishing causal relationships between cortical excitability and motor behavior.

Two alternative forced choice (2AFC) paradigms, coupled with the unified analysis of accuracy and response times within specific decision theoretic frameworks, have provided a wealth of information regarding decision-making processes. One problem of associated experimental tasks is that they are typically not engaging and do not contain stimuli or task representations familiar to participants, resulting in contaminants in the data due to boredom and distraction. Furthermore, when investigating decision strategies, use of noisy stimulus attributes result in undesired variance in the perceptual process complicating the analysis and interpretation of results. To address these fundamental issues, we developed a 2AFC soccer game in which participants’ task is to score goals by making leftward or rightward shots after observing the trajectory of the goalkeeper within a trial. The goalkeeper’s location is repeatedly sampled from a normal distribution with a constant variance and a mean either to the left or right of the midpoint. We tested participants on three difficulty levels parameterized by the distance between the two means and expected the rate of evidence integration to decrease with increasing difficulty and after errors as characteristic of standard 2AFC tasks. Drift- diffusion model provided good fits to data, and their outputs confirmed our predictions outlined above. Furthermore, we found the evidence integration rates to be negatively correlated with individual differences in maladaptive perfectionism, but not in anxiety or obsessive-compulsive traits.

No abstract

People are often faced with decisions for which they need to sample noisy information from the environment. Sequential sampling models provide valuable insight into how people navigate such decisions, but the actual sampling process usually remains a black box. We propose a computationally light linear model that can elucidate what factors people use during this sampling process, and whether they are optimal in doing so. We simulated agents using our model on expanded judgement tasks with different error cost (Study 1) and sampling cost (Study 2) scenarios to determine the optimal strategies in each condition. We then tested human participants in these scenarios to see if they behave optimally and if our model could capture their sampling decisions. We found that our model fit human data well and that people could shift their sampling strategy in an optimal direction when the cost of making an error changed. When sampling cost was manipulated, however, we observed a non-optimal shift in sampling strategy. This study contributes novel insights into the effects of symmetrically manipulated cost, as well as the optimality and use of dynamic decision boundaries.