References of "Schrater, Paul"
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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 ▲]

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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)
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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 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 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 ▲]

Detailed reference viewed: 20 (4 UL)