The role of risk aversion in non-conscious decision making

To what extent can people choose advantageously without knowing why they are making those choices? This hotly debated question has capitalized on the Iowa Gambling Task (IGT), in which people often learn to choose advantageously without appearing to know why. However, because the IGT is unconstrained in many respects, this finding remains debated and other interpretations are possible (e.g., risk aversion, ambiguity aversion, limits of working memory, or insensitivity to reward/punishment can explain the finding of the IGT). Here we devised an improved variant of the IGT in which the deck-payoff contingency switches after subjects repeatedly choose from a good deck, offering the statistical power of repeated within-subject measures based on learning the reward contingencies associated with each deck. We found that participants exhibited low confidence in their choices, as probed with post-decision wagering, despite high accuracy in selecting advantageous decks in the task, which is putative evidence for non-conscious decision making. However, such a behavioral dissociation could also be explained by risk aversion, a tendency to avoid risky decisions under uncertainty. By explicitly measuring risk aversion for each individual, we predicted subjects’ post-decision wagering using Bayesian modeling. We found that risk aversion indeed does play a role, but that it did not explain the entire effect. Moreover, independently measured risk aversion was uncorrelated with risk aversion exhibited during our version of the IGT, raising the possibility that the latter risk aversion may be non-conscious. Our findings support the idea that people can make optimal choices without being fully aware of the basis of their decision. We suggest that non-conscious decision making may be mediated by emotional feelings of risk that are based on mechanisms distinct from those that support cognitive assessment of risk

The Possibilities and Limitations of Private Prediction Markets

We consider the design of private prediction markets, financial markets designed to elicit predictions about uncertain events without revealing too much information about market participants' actions or beliefs. Our goal is to design market mechanisms in which participants' trades or wagers influence the market's behavior in a way that leads to accurate predictions, yet no single participant has too much influence over what others are able to observe. We study the possibilities and limitations of such mechanisms using tools from differential privacy. We begin by designing a private one-shot wagering mechanism in which bettors specify a belief about the likelihood of a future event and a corresponding monetary wager. Wagers are redistributed among bettors in a way that more highly rewards those with accurate predictions. We provide a class of wagering mechanisms that are guaranteed to satisfy truthfulness, budget balance on expectation, and other desirable properties while additionally guaranteeing epsilon-joint differential privacy in the bettors' reported beliefs, and analyze the trade-off between the achievable level of privacy and the sensitivity of a bettor's payment to her own report. We then ask whether it is possible to obtain privacy in dynamic prediction markets, focusing our attention on the popular cost-function framework in which securities with payments linked to future events are bought and sold by an automated market maker. We show that under general conditions, it is impossible for such a market maker to simultaneously achieve bounded worst-case loss and epsilon-differential privacy without allowing the privacy guarantee to degrade extremely quickly as the number of trades grows, making such markets impractical in settings in which privacy is valued. We conclude by suggesting several avenues for potentially circumventing this lower bound

Are errors detected before they occur? Early error sensations revealed by metacognitive judgments on the timing of error awareness

Errors in choice tasks are not only detected fast and reliably, participants often report that they knew that an error occurred already before a response was produced. These early error sensations stand in contrast with evidence suggesting that the earliest neural correlates of error awareness emerge around 300 ms after erroneous responses. The present study aimed to investigate whether anecdotal evidence for early error sensations can be corroborated in a controlled study in which participants provide metacognitive judgments on the subjective timing of error awareness. In Experiment 1, participants had to report whether they became aware of their errors before or after the response. In Experiment 2, we measured confidence in these metacognitive judgments. Our data show that participants report early error sensations with high confidence in the majority of error trials across paradigms and experiments. These results provide first evidence for early error sensations, informing theories of error awareness

GAMBLING DISORDER AS AN ADDICTIVE DISORDER AND CREATIVE PSYCHOPHARMACOTHERAPY

Addiction does not mean "addiction to substances" only. At the core of the definition of substance dependence is the loss of control. Gambling addiction belongs to non-substance / non-chemical addictions or behavioral/behavioral addictions. The concept of behavioral addictions is new and revolutionary in psychiatry. Gambling addiction, formerly pathological or problematic gambling occurs due to loss of control over gambling. There is growing evidence to suggest that behavioral addictions resemble substance addictions in many domains, including phenomenology, tolerance, comorbidity, overlapping genetic contribution, neurobiological mechanisms, and response to treatment. Behavioral addiction has been proposed as a new class in the Diagnostic Statistical Manual Fifth Revision (DSM-5), but the only category included is gambling addiction. The prevalence of gambling disorders in adolescence is very high and for certain disorders (especially related to the use of the Internet) it becomes more pronounced over time. In this paper, we presented a comprehensive overview of gambling disorders from definition, epidemiology, manifestations, comorbidities, assessment, treatment options, and existing forms of treatment. Given the complexity of the approach to the treatment of gamblers, a creative individualized integrative approach is necessary, which is the basis of creative psychopharmacotherapy. Due to the possibility of the emergence of problem gambling and other impulse-control deficits we need to be very careful when commencing a patient on dopamine replacement therapy or therapy with aripiprazole

No-Regret Online Prediction with Strategic Experts

We study a generalization of the online binary prediction with expert advice framework where at each round, the learner is allowed to pick $m\geq 1$ experts from a pool of $K$ experts and the overall utility is a modular or submodular function of the chosen experts. We focus on the setting in which experts act strategically and aim to maximize their influence on the algorithm's predictions by potentially misreporting their beliefs about the events. Among others, this setting finds applications in forecasting competitions where the learner seeks not only to make predictions by aggregating different forecasters but also to rank them according to their relative performance. Our goal is to design algorithms that satisfy the following two requirements: 1) $\textit{Incentive-compatible}$: Incentivize the experts to report their beliefs truthfully, and 2) $\textit{No-regret}$: Achieve sublinear regret with respect to the true beliefs of the best fixed set of $m$ experts in hindsight. Prior works have studied this framework when $m=1$ and provided incentive-compatible no-regret algorithms for the problem. We first show that a simple reduction of our problem to the $m=1$ setting is neither efficient nor effective. Then, we provide algorithms that utilize the specific structure of the utility functions to achieve the two desired goals

Categorical evidence, confidence and urgency during the integration of multi-feature information

Includes bibliographical references.2015 Summer.The present experiment utilized a temporally-extended categorization task to investigate the neural substrates underlying our ability to integrate information over time and across multiple stimulus features. Importantly, the design allowed differentiation of three important decision functions: 1) categorical evidence, 2) decisional confidence (the choice-independent probability that a decision will lead to a desirable state), and 3) urgency (a hypothetical signal representing a growing pressure to produce a behavioral response within each trial). In conjunction with model-based fMRI, the temporal evolution of these variables were tracked as participants deliberated about impending choices. The approach allowed investigation of the independent effects of urgency across the brain, and also the investigation of how urgency might modulate representations of categorical evidence and confidence. Representations associated with prediction errors during feedback were also investigated. Many cortical and striatal somatomotor regions tracked the dynamical evolution of categorical evidence, while many regions of the dorsal and ventral attention networks (Corbetta and Shulman, 2002) tracked decisional confidence and uncertainty. Urgency influenced activity in regions known to be associated with flexible control of the speed-accuracy trade-off (particularly the pre- SMA and striatum), and additionally modulated representations of categorical evidence and confidence. The results, therefore, link the urgency signal to two hypothetical mechanisms underling flexible control of decision thresholding (Bogacz et al., 2010): gain modulation of the striatal thresholding circuitry, and gain modulation of the integrated categorical evidence