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van Dam, J.

Publications and source records attributed to van Dam, J..

2 recordsLinked to original sources

Long-term, multi-event surprise enhances autobiographical memory

Neurobiological and psychological models of learning emphasize the importance of prediction errors (surprises) for memory formation. These effects have focused on memory for information surrounding a momentary surprising event; however, it is less clear whether surprise that unfolds across multiple events and timescales impacts memory. We asked basketball fans about their most positive and negative autobiographical memories of individual plays, games, and seasons, allowing surprise measurements spanning seconds, hours, and months. We used advanced analytics on National Basketball Association play-by-play data and betting odds spanning 17 seasons, >22K games, >5.6M plays to compute and align the estimated surprise values of each memory. We found that surprising events biased positive memories on the scale of seconds and months and negative memories across all three timescales. Critically, game and season memories could not be explained by surprise at shorter timescales, suggesting that long-term, multi-event surprise influences memory. These results expand the role of surprise in models of learning and reinforce its relevance in real-world domains.

neuroscience↗

Consistency, Inconsistency and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome Scale Metabolic Modelling

Genome scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in eleven biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.

bioinformatics↗