Previous research has demonstrated that attitudes are a primary determinant of intention to gamble on electronic gaming machines (EGMs) consistent with the Theory of Reasoned Action. This paper aims to address how biases in judgment can contribute to attitudes and subsequently behavior, including maladaptive problematic gambling behavior. We take a novel approach by viewing overconfidence in one's understanding of how outcomes are determined on EGMs as an indication of cognitive distortions. The novelty of this paper is further increased as we compare attitudes to existing EGMs with novel EGMs which include a skill component, referred to as skill-based gaming machines (SGMs), which enables a better controlled comparison between actual and perceived skill. In Study 1, 232 US-based participants were recruited online who were shown various slot machines and SGMs and asked a series of questions about perceived skill and chance in determining outcomes to assess their understanding, then were asked their confidence in their understanding, attitudes toward the machines and they completed the Problem Gambling Severity Index. In Study 2, 246 Australian participants were recruited through community and university student samples; they attended a laboratory where they were randomly allocated to play a real EGM or SGM without money and completed the same measures as in Study 1. In Study 2, participants were randomly told that the outcomes on the machine they would play were determined entirely by chance, skill, or a mixture of both. In both studies, our findings suggest that there are more extreme values in overconfidence in how EGMs work, whereas individuals are more similar in their confidence in understanding SGMs. We also find a relationship between overconfidence in EGM understanding and positive attitudes toward EGMs, but no such relationship with SGMs. There was no impact from controlling for demographics, problem gambling severity, or labeling of machines on these relationships.
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http://dx.doi.org/10.3389/fpsyg.2020.609731 | DOI Listing |
J Neuroeng Rehabil
January 2025
Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Vita Stråket 12, Floor 4, 41346, Gothenburg, Sweden.
Background: Myoelectric pattern recognition (MPR) combines multiple surface electromyography channels with a machine learning algorithm to decode motor intention with an aim to enhance upper limb function after stroke. This study aims to determine the feasibility and preliminary effectiveness of a novel intervention combining MPR, virtual reality (VR), and serious gaming to improve upper limb function in people with chronic stroke.
Methods: In this single case experimental A-B-A design study, six individuals with chronic stroke and moderate to severe upper limb impairment completed 18, 2 h sessions, 3 times a week.
Otolaryngol Head Neck Surg
January 2025
Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Objective: Facial trauma volume is difficult to predict accurately. We aim to understand the capacity of climate and regional events to predict daily facial trauma volume. This can provide epidemiologic understanding and subsequently tailor workforce distribution and scheduling.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer Science and Engineering, Southeast University, Nanjing, China.
The rapid urbanization has led to the loss of natural spaces and a subsequent disconnection between humans and nature, negatively affecting residents' well-being and environmental awareness. There is a a growing interest in leveraging technology to address this gap in Human-Computer Interaction. This article introduces GoChirp, an AI-powered wearable device for enhancing nature relatedness within urban landscapes.
View Article and Find Full Text PDFJMIR Serious Games
December 2024
Department of Psychology, Lund University, Lund, Sweden.
Data Brief
December 2024
School of Computing, Dublin City University, Dublin, Ireland.
Research in field sports often measures the performance of players during competitive games with individual and time-based descriptive statistics. Data is generated using GPS technologies, capturing simple data such as time (seconds) and position (latitude and longitude). While the data capture is highly granular and in relatively high volumes, the raw data are unsuited to any form of analysis or machine learning functions.
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