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See detailTraining Cognition with Video Games
Cardoso-Leite, Pedro UL; Ansarinia, Morteza UL; Schmück, Emmanuel UL et al

in Cohen Kadosh, Kathrin (Ed.) The Oxford Handbook of Developmental Cognitive Neuroscience (2021)

This chapter reviews the behavioral and neuroimaging scientific literature on the cognitive consequences of playing various genres of video games. The available research highlights that not all video ... [more ▼]

This chapter reviews the behavioral and neuroimaging scientific literature on the cognitive consequences of playing various genres of video games. The available research highlights that not all video games have similar cognitive impact; action video games as defined by first- and third-person shooter games have been associated with greater cognitive enhancement, especially when it comes to top-down attention, than puzzle or life-simulation games. This state of affairs suggests specific game mechanics need to be embodied in a video game for it to enhance cognition. These hypothesized game mechanics are reviewed; yet, the authors note that the advent of more complex, hybrid, video games poses new research challenges and call for a more systematic assessment of how specific video game mechanics relate to cognitive enhancement. [less ▲]

Detailed reference viewed: 133 (4 UL)
See detailA Mixture of Generative Models Strategy Helps Humans Generalize across Tasks
Herce Castañón, Santiago; Cardoso-Leite, Pedro UL; Altarelli, Irene et al

E-print/Working paper (2021)

What role do generative models play in generalization of learning in humans? Our novel multi-task prediction paradigm—where participants complete four sequence learning tasks, each being a different ... [more ▼]

What role do generative models play in generalization of learning in humans? Our novel multi-task prediction paradigm—where participants complete four sequence learning tasks, each being a different instance of a common generative family—allows the separate study of within-task learning (i.e., finding the solution to each of the tasks), and across-task learning (i.e., learning a task differently because of past experiences). The very first responses participants make in each task are not yet affected by within-task learning and thus reflect their priors. Our results show that these priors change across successive tasks, increasingly resembling the underlying generative family. We conceptualize multi-task learning as arising from a mixture-of-generative-models learning strategy, whereby participants simultaneously entertain multiple candidate models which compete against each other to explain the experienced sequences. This framework predicts specific error patterns, as well as a gating mechanism for learning, both of which are observed in the data. [less ▲]

Detailed reference viewed: 32 (5 UL)
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See detailTackling educational inequalities using school effectiveness measures
Levy, Jessica UL; Mussack, Dominic UL; Brunner, Martin et al

Scientific Conference (2020, November 11)

Detailed reference viewed: 80 (12 UL)
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See detailGames for enhancing cognitive abilities
Cardoso-Leite, Pedro UL; Joessel, Augustin; Bavelier, Daphne

in Plass, Jan; Mayer, Richard E; Homer, Bruce D (Eds.) Handbook of Game-based Learning (2020)

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See detailContrasting Classical and Machine Learning Approaches in the Estimation of Value-Added Scores in Large-Scale Educational Data
Levy, Jessica UL; Mussack, Dominic UL; Brunner, Martin et al

in Frontiers in Psychology (2020), 11

There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical ... [more ▼]

There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical methods: linear regression and multilevel models. These models have the advantage of being relatively transparent and thus understandable for most researchers and practitioners. However, these statistical models are bound to certain assumptions (e.g., linearity) that might limit their prediction accuracy. Machine learning methods, which have yielded spectacular results in numerous fields, may be a valuable alternative to these classical models. Although big data is not new in general, it is relatively new in the realm of social sciences and education. New types of data require new data analytical approaches. Such techniques have already evolved in fields with a long tradition in crunching big data (e.g., gene technology). The objective of the present paper is to competently apply these “imported” techniques to education data, more precisely VA scores, and assess when and how they can extend or replace the classical psychometrics toolbox. The different models include linear and non-linear methods and extend classical models with the most commonly used machine learning methods (i.e., random forest, neural networks, support vector machines, and boosting). We used representative data of 3,026 students in 153 schools who took part in the standardized achievement tests of the Luxembourg School Monitoring Program in grades 1 and 3. Multilevel models outperformed classical linear and polynomial regressions, as well as different machine learning models. However, it could be observed that across all schools, school VA scores from different model types correlated highly. Yet, the percentage of disagreements as compared to multilevel models was not trivial and real-life implications for individual schools may still be dramatic depending on the model type used. Implications of these results and possible ethical concerns regarding the use of machine learning methods for decision-making in education are discussed. [less ▲]

Detailed reference viewed: 87 (11 UL)
See detailMedia use, attention, mental health and academic performance among 8 to 12 year old children
Cardoso-Leite, Pedro UL; Buchard, Albert; Tissieres, Isabel et al

E-print/Working paper (2020)

Detailed reference viewed: 24 (4 UL)
See detailThe Structure of Behavioral Data
Defossez, Aurélien; Ansarinia, Morteza UL; Clocher, Brice UL et al

E-print/Working paper (2020)

For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend ... [more ▼]

For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend past results. Although behavioral data is fundamental to many scientific fields, there is currently no widely adopted standard for formatting, naming, organizing, describing or sharing such data. This lack of standardization is a major bottleneck for scientific progress. Not only does it prevent the effective reuse of data, it also affects how behavioral data in general are processed, as non-standard data calls for custom-made data analysis code and prevents the development of efficient tools. To address this problem, we develop the Behaverse Data Model (BDM), a standard for structuring behavioral data. Here we focus on major concepts in behavioral data, leaving further details and developments to the project's website (https://behaverse.github.io/data-model/). [less ▲]

Detailed reference viewed: 57 (3 UL)
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See detailA Formal Framework for Structured N-Back Stimuli Sequences
Ansarinia, Morteza UL; Mussack, Dominic UL; Schrater, Paul et al

Scientific Conference (2019, September 15)

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See detailA generalizable performance evaluation model of driving games via risk-weighted trajectories
Flemming, Rory; Schmück, Emmanuel UL; Mussack, Dominic UL et al

in Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019) (2019)

Efficient learning experiences require content to dynamically match a learner's skill; this assumes a fast and accurate assessment of the learner's skill and the ability to update content accordingly ... [more ▼]

Efficient learning experiences require content to dynamically match a learner's skill; this assumes a fast and accurate assessment of the learner's skill and the ability to update content accordingly. Effective personalized learning therefore involves deriving a performance-predictive mapping between behavioral and environmental factors. Once learned, this relationship can be used to generate new content and to update skill estimates based on the learner's interactions in an adaptive system. To provide proof of concept: (1) We develop a fast-paced driving video game where the player skillfully navigates a cluttered environment comprising obstacles and collectibles. Game content is generated procedurally and player behavior is recorded in the game-this provides an ideal test-bed for a method aiming to learn such a performance-predictive mapping. (2) Using blurred occupancy maps of the game's segments, we generate risk-weighted trajectory profiles for each user and segment of the game. Here, we show that these profiles can be used in a regression model to predict in-game performance both within and between game segments. Additionally, these profiles themselves reveal a trade-off between in-game rewards and risks. Successful identification of predictive environmental units within the game provides insight into the mapping between environmental features and performance, while facilitating the process of procedurally generating new, appropriate content in our adaptive system. We show that rapidly assessed measures of risk are highly predictive of both driving performance and reward rate, providing proof-of-concept evidence for the feasibility of a personalized adaptive learning system for this game. [less ▲]

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See detailPrinciples underlying the design of a cognitive training game as a research framework
Schmück, Emmanuel UL; Flemming, Rory; Schrater, Paul et al

in 2019 11th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games) (2019)

Action video games have great potential as cognitive training instruments for their data collection efficiency over standard testing, their natural motive power, and as they have demonstrated benefits for ... [more ▼]

Action video games have great potential as cognitive training instruments for their data collection efficiency over standard testing, their natural motive power, and as they have demonstrated benefits for broad aspects of cognition. However, commercial video games do not allow researchers full control over games' unique features and parameters while presently available scientific games violate key criteria, generally lack appeal, and do not collect enough data for principled exploration of the game design space. To capitalize on the benefits of action video games and facilitate a systematic, scientific exploration of video games and cognition, we propose the Cognitive Training Game Framework (CTGF). The CTGF addresses criteria that we believe are important for gamifying an experimental environment, such as modularity, accessibility, adaptivity, and variety. By offering the potential to collect large data sets and to systematically explore scientific hypotheses in a controlled environment, the resulting framework will make significant contributions to cognitive training research. [less ▲]

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See detailTowards discovering problem similarity through deep learning: combining problem features and user behavior.
Mussack, Dominic UL; Flemming, Rory; Schrater, Paul et al

in Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019) (2019)

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See detailThe impact of action video game training on mathematical abilities in adults
Libertus, Melissa E.; Liu, Allison; Pikul, Olga et al

in AERA Open (2017), 3(4), 2332858417740857

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See detailTechnology consumption and cognitive control: Contrasting action video game experience with media multitasking.
Cardoso-Leite, Pedro UL; Kludt, Rachel; Vignola, Gianluca et al

in Attention, perception & psychophysics (2016), 78(1), 218-41

Technology has the potential to impact cognition in many ways. Here we contrast two forms of technology usage: (1) media multitasking (i.e., the simultaneous consumption of multiple streams of media, such ... [more ▼]

Technology has the potential to impact cognition in many ways. Here we contrast two forms of technology usage: (1) media multitasking (i.e., the simultaneous consumption of multiple streams of media, such a texting while watching TV) and (2) playing action video games (a particular subtype of video games). Previous work has outlined an association between high levels of media multitasking and specific deficits in handling distracting information, whereas playing action video games has been associated with enhanced attentional control. Because these two factors are linked with reasonably opposing effects, failing to take them jointly into account may result in inappropriate conclusions as to the impacts of technology use on attention. Across four tasks (AX-continuous performance, N-back, task-switching, and filter tasks), testing different aspects of attention and cognition, we showed that heavy media multitaskers perform worse than light media multitaskers. Contrary to previous reports, though, the performance deficit was not specifically tied to distractors, but was instead more global in nature. Interestingly, participants with intermediate levels of media multitasking sometimes performed better than both light and heavy media multitaskers, suggesting that the effects of increasing media multitasking are not monotonic. Action video game players, as expected, outperformed non-video-game players on all tasks. However, surprisingly, this was true only for participants with intermediate levels of media multitasking, suggesting that playing action video games does not protect against the deleterious effect of heavy media multitasking. Taken together, these findings show that media consumption can have complex and counterintuitive effects on attentional control. [less ▲]

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See detailOn the impact of new technologies on multitasking
Cardoso-Leite, Pedro UL; Green, C. Shawn; Bavelier, Daphne

in Developmental Review (2015), 35

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See detailMethods to test visual attention online.
Yung, Amanda; Cardoso-Leite, Pedro UL; Dale, Gillian et al

in Journal of visualized experiments : JoVE (2015), (96),

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative data samples than can typically be collected on ... [more ▼]

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative data samples than can typically be collected on college campuses. However, before such methods can be widely adopted, a number of technological challenges must be overcome--in particular in experiments where tight control over stimulus properties is necessary. Here we present methods for collecting performance data on two tests of visual attention. Both tests require control over the visual angle of the stimuli (which in turn requires knowledge of the viewing distance, monitor size, screen resolution, etc.) and the timing of the stimuli (as the tests involve either briefly flashed stimuli or stimuli that move at specific rates). Data collected on these tests from over 1,700 online participants were consistent with data collected in laboratory-based versions of the exact same tests. These results suggest that with proper care, timing/stimulus size dependent tasks can be deployed in web-based settings. [less ▲]

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See detailVideo game play, attention, and learning: how to shape the development of attention and influence learning?
Cardoso-Leite, Pedro UL; Bavelier, Daphne

in Current opinion in neurology (2014), 27(2), 185-91

PURPOSE OF REVIEW: The notion that play may facilitate learning has long been touted. Here, we review how video game play may be leveraged for enhancing attentional control, allowing greater cognitive ... [more ▼]

PURPOSE OF REVIEW: The notion that play may facilitate learning has long been touted. Here, we review how video game play may be leveraged for enhancing attentional control, allowing greater cognitive flexibility and learning and in turn new routes to better address developmental disorders. RECENT FINDINGS: Video games, initially developed for entertainment, appear to enhance the behavior in domains as varied as perception, attention, task switching, or mental rotation. This surprisingly wide transfer may be mediated by enhanced attentional control, allowing increased signal-to-noise ratio and thus more informed decisions. SUMMARY: The possibility of enhancing attentional control through targeted interventions, be it computerized training or self-regulation techniques, is now well established. Embedding such training in video game play is appealing, given the astounding amount of time spent by children and adults worldwide with this media. It holds the promise of increasing compliance in patients and motivation in school children, and of enhancing the use of positive impact games. Yet for all the promises, existing research indicates that not all games are created equal: a better understanding of the game play elements that foster attention and learning as well as of the strategies developed by the players is needed. Computational models from machine learning or developmental robotics provide a rich theoretical framework to develop this work further and address its impact on developmental disorders. [less ▲]

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See detailSummation versus suppression in metacontrast masking: On the potential pitfalls of using metacontrast masking to assess perceptual-motor dissociation.
Cardoso-Leite, Pedro UL; Waszak, Florian

in Attention, perception & psychophysics (2014), 76(5), 1403-13

A briefly flashed target stimulus can become "invisible" when immediately followed by a mask-a phenomenon known as backward masking, which constitutes a major tool in the cognitive sciences. One form of ... [more ▼]

A briefly flashed target stimulus can become "invisible" when immediately followed by a mask-a phenomenon known as backward masking, which constitutes a major tool in the cognitive sciences. One form of backward masking is termed metacontrast masking. It is generally assumed that in metacontrast masking, the mask suppresses activity on which the conscious perception of the target relies. This assumption biases conclusions when masking is used as a tool-for example, to study the independence between perceptual detection and motor reaction. This is because other models can account for reduced perceptual performance without requiring suppression mechanisms. In this study, we used signal detection theory to test the suppression model against an alternative view of metacontrast masking, referred to as the summation model. This model claims that target- and mask-related activations fuse and that the difficulty in detecting the target results from the difficulty to discriminate this fused response from the response produced by the mask alone. Our data support this alternative view. This study is not a thorough investigation of metacontrast masking. Instead, we wanted to point out that when a different model is used to account for the reduced perceptual performance in metacontrast masking, there is no need to postulate a dissociation between perceptual and motor responses to account for the data. Metacontrast masking, as implemented in the Fehrer-Raab situation, therefore is not a valid method to assess perceptual-motor dissociations. [less ▲]

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See detailHuman perceptual decision making: disentangling task onset and stimulus onset.
Cardoso-Leite, Pedro UL; Waszak, Florian; Lepsien, Jöran

in Human brain mapping (2014), 35(7), 3170-87

The left dorsolateral prefrontal cortex (ldlPFC) has been highlighted as a key actor in human perceptual decision-making (PDM): It is theorized to support decision-formation independently of stimulus type ... [more ▼]

The left dorsolateral prefrontal cortex (ldlPFC) has been highlighted as a key actor in human perceptual decision-making (PDM): It is theorized to support decision-formation independently of stimulus type or motor response. PDM studies however generally confound stimulus onset and task onset: when the to-be-recognized stimulus is presented, subjects know that a stimulus is shown and can set up processing resources-even when they do not know which stimulus is shown. We hypothesized that the ldlPFC might be involved in task preparation rather than decision-formation. To test this, we asked participants to report whether sequences of noisy images contained a face or a house within an experimental design that decorrelates stimulus and task onset. Decision-related processes should yield a sustained response during the task, whereas preparation-related areas should yield transient responses at its beginning. The results show that the brain activation pattern at task onset is strikingly similar to that observed in previous PDM studies. In particular, they contradict the idea that ldlPFC forms an abstract decision and suggest instead that its activation reflects preparation for the upcoming task. We further investigated the role of the fusiform face areas and parahippocampal place areas which are thought to be face and house detectors, respectively, that feed their signals to higher level decision areas. The response patterns within these areas suggest that this interpretation is unlikely and that the decisions about the presence of a face or a house in a noisy image might instead already be computed within these areas without requiring higher-order areas. [less ▲]

Detailed reference viewed: 14 (0 UL)