Journal
COGNITIVE PSYCHOLOGY
Volume 125, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cogpsych.2020.101360
Keywords
Computational cognition; Data-driven cognitive modeling; Gaussian process; Active learning; Optimal experimental design; Delay discounting; Nonparametric Bayesian methods
Categories
Funding
- Air Force Office of Scientific Research (AFOSR) [FA9550-16-1-0053]
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Interest in computational modeling of cognition and behavior is growing. We propose a data-driven nonparametric framework called GPAL, which combines Gaussian Processes and active learning. By applying GPAL to a delay discounting task, we found that it shows high sensitivity and can identify novel patterns missed by ADO.
Interest in computational modeling of cognition and behavior continues to grow. To be most productive, modelers should be equipped with tools that ensure optimal efficiency in data collection and in the integrity of inference about the phenomenon of interest. Traditionally, models in cognitive science have been parametric, which are particularly susceptible to model misspecification because their strong assumptions (e.g. parameterization, functional form) may introduce unjustified biases in data collection and inference. To address this issue, we propose a data-driven nonparametric framework for model development, one that also includes optimal experimental design as a goal. It combines Gaussian Processes, a stochastic process often used for regression and classification, with active learning, from machine learning, to iteratively fit the model and use it to optimize the design selection throughout the experiment. The approach, dubbed Gaussian process with active learning (GPAL), is an extension of the parametric, adaptive design optimization (ADO) framework (Cavagnaro, Myung, Pitt, & Kujala, 2010). We demonstrate the application and features of GPAL in a delay discounting task and compare its performance to ADO in two experiments. The results show that GPAL is a viable modeling framework that is noteworthy for its high sensitivity to individual differences, identifying novel patterns in the data that were missed by the model-constrained ADO. This investigation represents a first step towards the development of a data-driven cognitive modeling framework that serves as a middle ground between raw data, which can be difficult to interpret, and parametric models, which rely on strong assumptions.
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