ICCM: Bodies

This project’s purpose was to simulate human periodic motor behavior in a simple self-paced tapping task that involved period error correction and feedback processing. When humans try to tap at a certain period, their inter-tap times are normally distributed with a standard deviation that is proportional to the period. When they try to change the period of their tapping, they do so in a single tap instead of a progressive correction taking place over multiple taps. We calibrated ACT-R’s new periodic tapping motor extension based on human experimental results and showed that ACT-R can simulate human motor behavior. Future research can leverage these findings and ACT-R’s periodic tapping motor extension to simulate fast-paced skilled motor behavior in complex perceptual-motor environments.

Many studies have been conducted concerning curiosity, a type of intrinsic motivation in humans and artificial agents. However, the specifics of the correspondence between curiosity in humans and artificial agents have not yet been fully explained. This study explores this correspondence on the Adaptive Control of Thought–Rational (ACT-R) cognitive architecture by exploring situations in which curiosity effectively promotes learning. We prepared three models of path planning, representing different levels of thinking, and made them learn in multiple-breadth maze environments while manipulating the curiosity strength. The results showed that curiosity in learning an environment negatively affected the model with a shallow level of thinking. Still, it was influential in the model with a deliberative level of thinking. We consider that the results show some commonalities with human learning.

In the field of syllogistic reasoning research, a significant number of models aiming at describing the human inference processes were developed. There is profound work fitting the model's parameters and analyzing each model's ability to account for the data in order to support or disprove the underlying theories. However, the model parameters are rarely used to extract explanations and hypotheses for phenomena that go beyond the original scope of the models. In this work, we apply three state-of-the-art models, PHM, mReasoner, and TransSet, to data from reasoning experiments where participants received feedback for their conclusions. We derived hypotheses based on the models' explanations for the feedback effect and putted these to test by conducting an experiment targeting the hypotheses. The work contributes to the field in three ways: (a) the feedback effect could be replicated and was shown to be a robust effect; (b) we demonstrate the use of the model parameters in order to derive new hypotheses; (c) we present possible explanations for the feedback effect based on existing theories.

Most computational theories of cognition lack a representation of physiology. Understanding the effects of compounds present in the environment on cognition is important for explaining and predicting changes in cognition and behavior given exposure to toxins, pharmaceuticals, or the deprivation of critical compounds like oxygen. This research integrates physiologically-based pharmacokinetic (PBPK) model predictions with ACT-R's fatigue module to predict the effects of caffeine on fatigue. Parameter mapping between PBPK model parameters and ACT-R are informed by neurophysiological literature and established mappings between ACT-R modules and brain regions. Predicted caffeine concentrations in the brain are used to modulate a parameter in the fatigue module to explain caffeine's effects on multiple performance metrics.