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How personalization guides (or distorts) learning: A model-based explanation

Giwon Bahg
Vanderbilt University ~ Psychology
Mr. Matthew Galdo
The Ohio State University ~ Psychology
Ryan Day
Ohio State University ~ Cognitive Neuroscience
Vladimir Sloutsky
The Ohio State University ~ Psychology
Dr. Brandon Turner
The Ohio State University ~ Psychology

In reality, our understanding of the world is often affected by the interaction with other (natural or artificial) cognitive agents. Personalization algorithms have been discussed as an influential component on the internet that possibly limits the pursuit of accurate knowledge, causing confirmation bias and polarization (e.g., Pariser, 2011). However, there has been no mechanistic explanation of how such personalization affects internal cognitive processes and learned knowledge. In this study, we aim to explain using a model-based approach how the interaction with personalization algorithms can hinder optimal category learning. Here, we assume that an exemplar-based learner experiences a baseline, self-directed, or curated learning sequence, while adapting one’s attention via gradient-based optimization (e.g., Krushcke, 1992; Galdo, Vladimir, & Turner, submitted). In the two experimental conditions (i.e., self-directed, curated), either the learner or the external curation algorithm had control over items and features to be learned. Simulation experiments revealed that a personalized learning process can distort the latent representations of categories and misguide learners’ attention, even when a learner is well-intentioned. This observation was generalizable to different datasets with various underlying structural forms. Lastly, when the learners’ knowledge was tested using an independent test dataset, personalized learners tend to show overconfidence about their decisions compared to their predictive accuracy.



category learning

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Cite this as:

Bahg, G., Galdo, M., Day, R. W., Sloutsky, V., & Turner, B. (2021, July). How personalization guides (or distorts) learning: A model-based explanation. Paper presented at Virtual MathPsych/ICCM 2021. Via