Under what conditions do humans systematically deviate from rational decision making? Here we show that pupillary indices of low neural gain are associated with strong and consistent biases across six different extensively-studied decision making tasks, whereas indices of high gain are associated with weak or absent biases. Lower susceptibility to biases, however, comes at the cost of indecisiveness, or alternatively, prolonged deliberation time. We explain the association between low gain and strong biases as reflecting a broader information integration process that gives greater weight to weak biasing influences. The findings underscore the role of pupil-linked brain states in the generation of decision making biases.

One can point to a variety of historical milestones for gender equality in STEM (science, technology, engineering, and mathematics), however, the practical effects are gradual and ongoing. It is important to quantify gender differences in subdomains of scientific work in order to detect potential biases and to monitor progress. In this work, we studied the relevance of gender in scientific collaboration patterns in the Institute for Operations Research and the Management Sciences (INFORMS), a professional organization with sixteen peer-reviewed journals. We constructed a large temporal bipartite network between authors and publications, using the organization's publication data from 1952 to 2016, and augmented the author nodes with gender labels. We characterized differences in several basic statistics of this network over time, highlighting how they change with respect to relevant historical events. We found a steady increase in participation by women (e.g., fraction of authorships by women and of new women authors) starting ∼1980. However, women still comprise less than 25% of the INFORMS society, and are additionally underrepresented among authors with many publications. Finally, we describe a methodology for quantifying differences in the role that authorships by women and men play in the overall connectivity of the network. Specifically, we propose a degree-preserving temporal and geometric null model with emergent communities. We use two measures of edge importance related to diffusion throughout the network, namely effective resistance and edge contraction importance to quantify gender differences in collaboration patterns that go beyond differences in local statistics.

Under what conditions do humans systematically deviate from rational decision making? Here we show that pupillary indices of low neural gain are associated with strong and consistent biases across six different extensively-studied decision making tasks, whereas indices of high gain are associated with weak or absent biases. Lower susceptibility to biases, however, comes at the cost of indecisiveness, or alternatively, prolonged deliberation time. We explain the association between low gain and strong biases as reflecting a broader information integration process that gives greater weight to weak biasing influences. The findings underscore the role of pupil-linked brain states in the generation of decision making biases. Significance"Framing effects" are demonstrations that people's decisions can be biased by the way a decision problem is presented, and consequently, people can make decisions that violate the principles of rationality. Using a set of classic decision-making tasks, we show that pupil dilation, previously linked to levels of the neuromodulator norepinephrine and to a tradeoff between narrowly focused and broadly integrative modes of information processing, distinguishes between people who are consistently biased and people who are relatively immune to these effects. Our findings suggest that norepinephrine may drive these individual differences, and that a narrowly focused mode of information processing confers relative immunity to these decision making biases, whereas the integration of a wider range of information results in greater susceptibility.peer-reviewed)

Positive and negative affective states are respectively associated with optimistic and pessimistic expectations regarding future reward. One mechanism that might underlie these affect-related expectation biases is attention to positive- versus negative-valence stimulus features (e.g., attending to the positive reviews of a restaurant versus its expensive price). Here we tested the effects of experimentally induced positive and negative affect on feature-based attention in 120 participants completing a compound-generalization task with eye-tracking. We found that participants' reward expectations for novel compound stimuli were modulated by the affect induction in an affect-congruent way: positive affect increased reward expectations for compounds, whereas negative affect decreased reward expectations. Computational modelling and eye-tracking analyses each revealed that these effects were driven by affect-congruent changes in participants' allocation of attention to high- versus low-value features of compound stimuli. These results provide mechanistic insight into a process by which affect produces biases in generalized reward expectations.

While making plans, people have to decide how far out into the future they want to plan: days, months, years, or even longer. Overly short-sighted planning can harm people's well-being in important life domains, such as health, finances, and academics. While self-report scales exist to measure people's planning, people's answers to such questions may be distorted by their desire to make a good impression and conform to norms and expectations. Here, we introduce a method for objectively quantifying people's propensity to plan into the future. Our method combines a process-tracing method with Bayesian inverse reinforcement learning to measure how prone an individual is to plan multiple steps ahead. To infer this from a person's process-tracing data, our method inverts a new resource-rational model of individual differences in planning. This model assumes that subjective planning costs are captured by a cost function with two parameters: a mental effort cost and a planning depth cost. Upon showing that our model of planning explains individual participants' planning behavior better than the best previous models, we validate our method on simulated data and real data from a large online experiment where the cost of planning was manipulated within participants. Our results show that our method can infer individual differences in the planning depth cost. Our model provides a mechanistic account for why some people plan too shortsightedly. The subjective planning costs inferred by our method can be used as an objective, non-self-report measure of individual differences in people's propensity to plan into the future.