Pyeong Whan Cho


A Modeling Study of the Effects of Surprisal and Entropy in Perceptual Decision Making of an Adaptive Agent
Pyeong Whan Cho | Richard Lewis
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Processing difficulty in online language comprehension has been explained in terms of surprisal and entropy reduction. Although both hypotheses have been supported by experimental data, we do not fully understand their relative contributions on processing difficulty. To develop a better understanding, we propose a mechanistic model of perceptual decision making that interacts with a simulated task environment with temporal dynamics. The proposed model collects noisy bottom-up evidence over multiple timesteps, integrates it with its top-down expectation, and makes perceptual decisions, producing processing time data directly without relying on any linking hypothesis. Temporal dynamics in the task environment was determined by a simple finite-state grammar, which was designed to create the situations where the surprisal and entropy reduction hypotheses predict different patterns. After the model was trained to maximize rewards, the model developed an adaptive policy and both surprisal and entropy effects were observed especially in a measure reflecting earlier processing.


Dynamic encoding of structural uncertainty in gradient symbols
Pyeong Whan Cho | Matthew Goldrick | Richard L. Lewis | Paul Smolensky
Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018)


Fractal Unfolding: A Metamorphic Approach to Learning to Parse Recursive Structure
Whitney Tabor | Pyeong Whan Cho | Emily Szkudlarek
Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2012)