Tracking the contribution of inductive bias to individualized internal models

Abstract

Internal models are central to understand how human behaviour is adapted to the statistics of, potentially limited, environmental data. Such internal models contribute to rich and flexible inferences and thus adapt to varying task demands. However, the right internal model is not available for observers, instead approximate and transient internal models are recruited. To understand learning and momentary inferences, we need tools to characterise these approximate, yet rich and potentially dynamic models through behaviour. We used a combination of non-parametric Bayesian methods and probabilistic programming to infer individualised internal models from human response times in an implicit visuomotor learning task. Using this Cognitive Tomography approach we predict response times on a trial-by-trial basis and validate the internal model by showing its invariance across tasks and sensitivity to stimulus statistics. By tracking the performance of participants for multiple days, individual learning curves revealed transient subjective internal models and pronounced inductive biases.

The complexity of measuring reliability in learning tasks: An illustration using the Alternating Serial Reaction Time task

“…Only the data from the learning session is analysed in this study. Results from this sample have been previously reported in Fanuel et al (2020); Kóbor et al (2017); Quentin et al (2021); Török et al (2017Török et al ( , 2020.…”

The complexity of measuring reliability in learning tasks: An illustration using the Alternating Serial Reaction Time Task

“…Thus, if it is assumed that there are individual differences in both, then the correlation between effects in RTs and errors can be small or entirely absent. Further work using such models, as well as recent computational models of ASRT learning performance (Éltető et al, In press;Török et al, 2021) will be crucial in understanding the origins of RT-and accuracyderived learning scores and exploring the factors affecting the presence or absence of correlations between the two.…”