Despite recent interest in pupillometry as a psychophysiological measure, it remains unclear what construct the physio-logical measure is assessing in cognitive control tasks: task load or mental exertion. This debate is of particular interestas cognitive effort remains an elusive construct partly due to the difficulty in empirically quantifying mental exertion.The current research aims to differentiate these disparate accounts by leveraging rewards as motivation for effort exertion.Using an individual differences approach, a sample of 80 undergraduate students performed a cognitive control taskTaskswitching. Critically, monetary incentives were used to motivate participants to exercise cognitive control, and found toimprove overall performance. Pupillary responses were found to increase in response to trials requiring more cognitivecontrol, and relate to performance improvements in the rewarded conditions. The present findings provide some supportfor the effort account, and suggest that pupillometry may be a viable index of cognitive effort.

While anticipated rewards have been shown to impart enhancements on memory performance, it remains unclear whetherthese benefits reflect improved encoding or more cautious decision-making. In two experiments, participants (N=47, each)encoded complex videos depicting everyday episodes and were tested for their memory of various details. Importantly,participants were informed that each video was associated with either high (25 cents) or low (1 cent) reward at eitherencoding or retrieval. We found participants were more accurate for questions relating to high reward videos only whenreward information was presented at encoding. Memory performance and response-times were modeled using a driftdiffusion model to assess the effects of reward on decision parameters. The drift rate was found to be significantly largerfor high reward videos when compared to low reward videos, only when reward was presented at encoding. These resultssuggest that reward at encoding enhances memory selectivity for detailed episodic information.

Although understood that time perception is subjective, the underlying cognitive mechanisms are not well described. Eventsegmentation theories propose that spatial information serves to segment experienced information in discrete units whichthen can be used to estimate time. Based on this theory, we explored whether subjective time perception is influenced bythe amount of perceived spatial information. A group of young participants viewed short videos of episodes that includeda spatial change (e.g., moving through doorways) or no spatial change. In one experiment, participants were asked toestimate a given time duration while viewing the video and in a second experiment, participants estimated the time of thevideo after viewing. Across experiments, videos with spatial change were associated with more accurate time perceptionestimates than those without spatial changes. These results highlight the important role of spatial processing in directingthe experience of time.

A wealth of experimental evidence shows that, contrary to normative models of choice, people’s preferences are markedlyswayed by the context in which options are presented. In this work, we present the first resource-rational, mechanisticaccount of the decoy effect—a major contextual effect in risky decision making. Our model additionally explains a related,well-known behavioral departure from expected utility theory: violation of betweenness. We demonstrate that, contrary towidely held views, these effects can be accounted for by a variant of normative expected-utility maximization—sample-based expected utility model (SbEU; Nobandegani et al., 2018)—which acknowledges cognitive limitations. Our work isconsistent with two empirically well-supported hypotheses: (i) In probabilistic reasoning and judgment, a cognitive sys-tem accumulates information through sampling, and (ii) People engage in pairwise comparisons when choosing betweenmultiple alternatives.

A wealth of experimental evidence shows that, contrary to normative models of choice, peoples preferences are markedlyswayed by the context in which options are presented. Particularly, there exist a well-known triad of effects, dubbed thecontextual effects, which consistently show that preferences change depending on the availability of other options: theattraction effect, the similarity effect, and the compromise effect. In this work, we present the first resource-rational,process-level account of these three contextual effects by extending Nobandegani et al.’s (2018) sample-based expectedutility model to the realm of multi-attribute value-based decision-making. Importantly, our work is consisted with twoempirically well-supported findings: (1) People tend to draw only a few samples in their probabilistic judgment anddecision-making, and (2) People tend to overestimate the probability of extreme events in their judgment.

Human rationality is predominantly evaluated by the extent to which the mind respects the tenets of normative formalismslike logic and probability theory, and is often invoked by appealing to the notion of optimality. Drawing on bounded ratio-nality, there has been a surge in the understanding of human rationality with respect to the mind’s limited computationaland cognitive resources. In this work, we focus on a fairly underappreciated, yet crucial, facet of rationality, robustness:insensitivity of a model’s performance to miscalculations of its parameters. We argue that an integrative pursuit of threefacets (optimality, efficient use of limited resources, and robustness) would be a fruitful approach to understanding humanrationality. We present several novel formalizations of robustness and discuss a recently proposed metacognitively-rationalmodel of risky choice (Nobandegani et al., 2018) which is surprisingly robust to under- and over-estimation of its focalparameter, nicely accounting for well-known framing effects in human decision-making under risk.