The evolution of Internet addiction: A global perspective

Kimberly Young’s early work on Internet addiction (IA)has been pioneering and her early writings on the topic inspired many others to carry out research in the area. Young's (2015) recent paper on the 'evolution of Internet addiction' featured very little European research, and did not consider the main international evidence that has contributed to our current knowledge about the conceptualization, epidemiology, etiology, and course of Internet-related disorders. This short commentary paper elaborates on important literature omitted by Young that the present authors believe may be of use to researchers. We also address statements made in Young’s (2015) commentary that are incorrect (and therefore misleading) and not systematically substantiated by empirical evidence

Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal

Metric clusters in evolutionary games on scale-free networks

The evolution of cooperation in social dilemmas in structured populations has been studied extensively in recent years. Whereas many theoretical studies have found that a heterogeneous network of contacts favors cooperation, the impact of spatial effects in scale-free networks is still not well understood. In addition to being heterogeneous, real contact networks exhibit a high mean local clustering coefficient, which implies the existence of an underlying metric space. Here, we show that evolutionary dynamics in scale-free networks self-organize into spatial patterns in the underlying metric space. The resulting metric clusters of cooperators are able to survive in social dilemmas as their spatial organization shields them from surrounding defectors, similar to spatial selection in Euclidean space. We show that under certain conditions these metric clusters are more efficient than the most connected nodes at sustaining cooperation and that heterogeneity does not always favor--but can even hinder--cooperation in social dilemmas. Our findings provide a new perspective to understand the emergence of cooperation in evolutionary games in realistic structured populations

Performance Feedback, Firm Resources, and Strategic Change

Combining insights from the behavioral theory of the firm and the resource-based view we investigate the antecedents of strategic change in fast-changing environments. We hypothesize the independent and joint effects of performance feedback and of flexible and specific resources on strategic change. Using an unbalanced panel of 493 publisher-year observations we find that negative performance feedback triggers more strategic change. Further, while flexible resources have no direct influence on strategic change they weaken the negative relationship between performance feedback and strategic change. Finally, we find that larger stocks of specific resources lead to less strategic change.Performance feedback; strategic change; resource-based-view; video game industry

A systematic literature review of methodology used to measure effectiveness in digital game-based learning

In recent years, a growing number of studies is being conducted into the effectiveness of digital game-based learning (DGBL). Despite this growing interest, however, it remains difficult to draw general conclusions due to the disparities in methods and reporting. Guidelines or a standardized procedure for conducting DGBL effectiveness research would allow to compare results across studies and provide well-founded and more generalizable evidence for the impact of DGBL. This study presents a first step in this process by mapping current practices through a systematic literature review. The review included peer-reviewed journal and conference publications between 2000 and 2012. Other inclusion criteria were that (1) the study’s primary aim was effectiveness measurement of cognitive learning outcomes, (2) the focus was on digital games and (3) a pre-post design with a control group was used. Twenty-five publications were found eligible for this study. Important differences were found in the number of control groups used and the type of intervention implemented in the control group (e.g. traditional classroom teaching, use of multimedia, computer-based learning, paper exercises, other games, or no intervention). Regarding the implementation method of the DGBL intervention in the experimental group, two approaches can be distinguished: stand-alone intervention or as part of a larger program. Moreover, a wide variety of effectiveness measures was used: measures for learning outcomes were complemented with time measurements and/or with self-reported measurements for self-efficacy and motivation. Learning effect calculation also varied, introducing pre-test scores in the analysis, conducting a separate analysis on pre- and post-test scores or conducting an analysis on difference scores. Our study thus indicates that a variety of methods is being used in DGBL effectiveness research opening a discussion regarding the potential and requirements for future procedural guidelines

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Body Image Perception: Adolescent Boys and Avatar Depiction in Video Games

Research on mass media’s impact on body image has mostly been focused on females thus far. Of the little research that has been done on male body image, most of it has been focused on adult males, and therefore the effect of mass media on adolescent boys’ body image is still a relatively primitive field of knowledge. Through comparing the exposure of adolescent boys to muscular avatars in popular video games, a source of mass media that a majority of adolescent boys are exposed to, and relating it to research done on the effects of frequent ideal image exposure through other forms of mass media on males, the influence of video games on the body image of adolescent boys can be determined. This study consisted of several factors: (1) understanding the impact of constantly viewing ideal images in mass media on males’ perceptions of their own bodies, (2) reviewing the body types of the male avatars in several modern, popular video games played by adolescent boys, (3) relating the exposure of video game avatars on adolescent boys’ views of their own physiques, and (4) examining the implications of negative body image on adolescent boys’ eating and exercise strategies. Although video game avatars tend to have a slightly different body shape than those presented in most types of mass media, their unifying trait of naturally unattainable muscularity resulted a reaction among adolescent boys that was similar to that of adult males with regard to mesomorphic (muscular, V-shaped) body types in mass media. This resulting negative body image can lead to psychological disorders such as depression or such physical disorders as anabolic steroid usage, unnatural dieting, and excessive exercising