vSNE Abstract Book Contents ORAL PRESENTATION ABSTRACTS ............................................................................................................... 2 Session I: Risk, uncertainty, and delay .................................................................................................... 2 Session II: Social decision making ............................................................................................................ 5 Session IV: Valuation and value systems................................................................................................. 9 Session V: Dynamic decision making ..................................................................................................... 12 Session VI: Efficient coding and representation .................................................................................... 15 SYMPOSIUM ABSTRACTS ........................................................................................................................... 19 Symposium 1: Advances in neuroforecasting: forecasting consumer and firm choice using neural data ......................................................................................................................................................... 19 Symposium 2: Neuroeconomics meets the digital age ......................................................................... 22 POSTER ABSTRACTS.................................................................................................................................... 27 1 vSNE Abstract Book ORAL PRESENTATION ABSTRACTS Session I: Risk, uncertainty, and delay Considering what we know and what we don't know: Expectations and metacognition guide value integration during economic choice Romy Froemer¹, Frederick Callaway¹, Amitai Shenhav¹ ¹Brown University Objective: Most models of economic choice assume that choices are at their core driven by noisy samples of value, but how people accumulate these samples remains unclear. While recent work has demonstrated that overt attention can shape the way in which option values are integrated into a decision, less is known about the extent to which this integration depends on prior expectations and beliefs about those values. Here we use a novel, sequential presentation paradigm to investigate how value integration is informed by what the decision- maker knows at a given point in time (predictive inference), and by how reliable they believe this information to be (metacognition). Method: Participants (N = 30) chose between pairs of options previously rated on their value, and the participant's confidence in those values. Options alternated on the screen, and we independently varied the order, value, and duration of item presentation. Participants could choose either option at any time prior to a 5 second deadline. We compare participant behavior to simulations of a Bayesian model that optimally integrates evidence from samples whose variance depends on both attention and confidence. Results: We found that participants' choices were shaped both by predictive inference and metacognition, and that this can be accounted for by a Bayesian model of value integration. Consistent with the integration of prior information, (1) choices display hallmarks of reference- dependent processing of the options' values and of the first seen item in particular, and (2) participants selected certain over uncertain options when overall value was high, but vice versa when overall value was low. Consistent with uncertainty-weighted updating, participants were faster and more accurate when their confidence in both options' values was higher. However, participants who were more confident on average showed faster but less consistent choices. We show that this maladaptive behavior is consistent with Bayesian integration using incorrect variance estimates (i.e. faulty metacognition). Conclusion: Our findings provide a more complete understanding of the information that shapes our evaluation of choice options. Our empirical data and modeling show that this information includes prior beliefs about value distributions, as well as beliefs about the precision of value representations. In so doing, our work demonstrates how growing research on Bayesian inference across domains of psychology and neuroscience can be leveraged to offer novel insights into economic choice. 2 vSNE Abstract Book Predicting Risk Attitudes from the Precision of Neural Magnitude Representations Miguel Garcia¹, Gilles de Hollander¹, Marcus Grueschow¹, Rafael Polania², Michael Woodford³, Christian Ruff¹ ¹University of Zurich, ²ETH Zurich, ³Columbia University Risk aversion occurs when a decision maker prefers a choice option with a smaller expected monetary payoff but more outcome certainty over an alternative option with a larger expected payoff but higher outcome uncertainty. This phenomenon is classically explained as reflecting properties of outcome valuation, i.e., the concavity of the utility function mapping monetary amounts to subjective utility. However, this explanation cannot account for the prominent empirical findings that subjects are risk averse even for very small gambles, and that they often tend to choose differently across repetitions of the same choice problem. To overcome these problems, it has recently been proposed that risk aversion in small-stake gambles may not reflect properties of valuation but rather of numerical cognition - how potential payoffs are mentally represented (Khaw et al., 2019). These proposals are based on the observations that humans (a) underestimate larger magnitudes, especially under uncertainy and (b) are stochastic in decision-making about numerical magnitudes. Since risky options are typically associated with larger payoffs, this underestimation bias may lead to risk aversion simply as a result of inaccurate and noisy perception. Crucially, such a perceptual account predicts that an individual's observed risk attitude should depend on the precision of the perceptual representations of payoffs. Here, we test this novel prediction both behaviorally and neurally. Subjects performed a perceptual numerical decision-making task during fMRI as well as a risky- choice task with different presentation formats for potential payoffs, i.e., non-symbolically as a pile of coins and symbolically as Arabic numerals. We fitted a computational model of noisy logarithmic coding (NLC) of numerical magnitudes (Khaw et al., 2019) that can jointly explain behavior in both task domains and quantified noise and bias parameters. Our results show that (1) the precision of mental representations of numerical magnitudes and payoffs is correlated within subjects, across perceptual and risky choices; (2) external noise due to presentation format leads to increased risk aversion; and (3) subjects with more reliable magnitude representations in parietal cortex during the perceptual task show less variable behavior in the risky-choice paradigm and are less risk-averse. In sum, our results show that risk preferences can be predicted from the precision with which numbers are (neurally) represented. More generally, our results highlight that aspects of economic behavior may be determined by capacity limitations in perceptual processing rather than properties of neural valuation. From Value to Saliency: Neural Computations of Subjective Value under Uncertainty in Combat Veterans 3 vSNE Abstract Book Ruonan Jia¹, Lital Ruderman¹, Charles Gordon¹, Daniel Ehrlich¹, Mark Horvath¹, Serena Mirchandani¹, Clara DeFontes¹, Steven Southwick¹, John Krystal¹, Ilan Harpaz-Rotem¹, Ifat Levy¹ ¹Yale University Objective: Military personnel engaged in combat are vulnerable to Posttraumatic Stress Disorder (PTSD), following traumatic experiences in the battlefield. Prior research has mostly employed fear-related paradigms to unravel neural underpinnings of fear dysregulation related to PTSD. The ability to acquire and update fear responses depends critically on the individual?s ability to cope with uncertainty, yet the role of individual uncertainty attitudes in the development of trauma-related psychopathology has hardly been examined. Here, we investigated the association between PTSD-related alterations and the subjective valuation of uncertain outcomes during decision-making. Methods: We used a monetary gambling paradigm to explore the neural markers of both vulnerability and resilience to PTSD in combat veterans (24 with current PTSD, 34 controls). Participants chose between a certain gain (or loss), and playing a lottery which offered a larger gain (or loss) but also chance of zero outcome. Outcome probabilities for half of the lotteries were precisely known, and were ambiguous for the other half. fMRI was used to track neural activation while subjects completed 240 decisions. One choice was randomly picked for payment to ensure task engagement. We evaluated PTSD symptoms by CAPS (Clinician-Administered PTSD Scale). Results: Using a dimensional approach, we replicated our previous finding (Ruderman et al. 2016) that veterans with more severe PTSD symptoms were more averse to ambiguous losses (Pearson?s correlation r=-0.30, p<0.05). We additionally found that they were more averse to risky gains (r=-0.39, p<0.01). fMRI activity in the ventromedial prefrontal cortex (vmPFC) during valuation of uncertain options was associated with PTSD symptoms (p<0.001, corrected at FWE=0.05), an effect which was specifically driven by numbing symptoms. Moreover, the neural encoding of the subjective value