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      Changes in Learning From Social Feedback After Web-Based Interpretation Bias Modification: Secondary Analysis of a Digital Mental Health Intervention Among Individuals With High Social Anxiety Symptoms

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          Abstract

          Background

          Biases in social reinforcement learning, or the process of learning to predict and optimize behavior based on rewards and punishments in the social environment, may underlie and maintain some negative cognitive biases that are characteristic of social anxiety. However, little is known about how cognitive and behavioral interventions may change social reinforcement learning in individuals who are anxious.

          Objective

          This study assessed whether a scalable, web-based cognitive bias modification for interpretations (CBM-I) intervention changed social reinforcement learning biases in participants with high social anxiety symptoms. This study focused on 2 types of social reinforcement learning relevant to social anxiety: learning about other people and learning about one’s own social performance.

          Methods

          Participants (N=106) completed 2 laboratory sessions, separated by 5 weeks of ecological momentary assessment tracking emotion regulation strategy use and affect. Approximately half (n=51, 48.1%) of the participants completed up to 6 brief daily sessions of CBM-I in week 3. Participants completed a task that assessed social reinforcement learning about other people in both laboratory sessions and a task that assessed social reinforcement learning about one’s own social performance in the second session. Behavioral data from these tasks were computationally modeled using Q-learning and analyzed using mixed effects models.

          Results

          After the CBM-I intervention, participants updated their beliefs about others more slowly ( P=.04; Cohen d=−0.29) but used what they learned to make more accurate decisions ( P=.005; Cohen d=0.20), choosing rewarding faces more frequently. These effects were not observed among participants who did not complete the CBM-I intervention. Participants who completed the CBM-I intervention also showed less-biased updating about their social performance than participants who did not complete the CBM-I intervention, learning similarly from positive and negative feedback and from feedback on items related to poor versus good social performance. Regardless of the intervention condition, participants at session 2 versus session 1 updated their expectancies about others more from rewarding ( P=.003; Cohen d=0.43) and less from punishing outcomes ( P=.001; Cohen d=−0.47), and they became more accurate at learning to avoid punishing faces ( P=.001; Cohen d=0.20).

          Conclusions

          Taken together, our results provide initial evidence that there may be some beneficial effects of both the CBM-I intervention and self-tracking of emotion regulation on social reinforcement learning in individuals who are socially anxious, although replication will be important.

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          Most cited references62

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          Mastering the game of Go with deep neural networks and tree search.

          The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
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            PsychoPy2: Experiments in behavior made easy

            PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of interface; users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. Here we describe the features that have been added over the last 10 years of its development. The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future.
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              Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

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                Author and article information

                Contributors
                Journal
                JMIR Form Res
                JMIR Form Res
                JFR
                JMIR Formative Research
                JMIR Publications (Toronto, Canada )
                2561-326X
                2023
                9 August 2023
                : 7
                : e44888
                Affiliations
                [1 ] Center for Behavioral Intervention Technologies Northwestern University Feinberg School of Medicine Chicago, IL United States
                [2 ] Department of Psychology University of Virginia Charlottesville, VA United States
                Author notes
                Corresponding Author: Miranda L Beltzer miranda.beltzer@ 123456northwestern.edu
                Author information
                https://orcid.org/0000-0003-0846-9682
                https://orcid.org/0000-0002-4371-2740
                https://orcid.org/0000-0001-9438-4524
                https://orcid.org/0000-0002-9031-9343
                Article
                v7i1e44888
                10.2196/44888
                10448289
                37556186
                451d4aaf-3b8b-404a-bf1a-7fe4c8c0f309
                ©Miranda L Beltzer, Katharine E Daniel, Alexander R Daros, Bethany A Teachman. Originally published in JMIR Formative Research (https://formative.jmir.org), 09.08.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

                History
                : 8 December 2022
                : 12 April 2023
                : 26 May 2023
                : 20 June 2023
                Categories
                Original Paper
                Original Paper

                social anxiety,reinforcement learning,cognitive bias modification,interpretation bias,reward learning,probabilistic learning,q-learning,digital intervention

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