References of "Ansarinia, Morteza 50034722"
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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: 149 (5 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: 59 (3 UL)
Peer Reviewed
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)

Detailed reference viewed: 19 (0 UL)