Validation and Comparison of Non-Stationary Cognitive Models: A Diffusion Model Application
arxiv(2023)
摘要
Cognitive processes undergo various fluctuations and transient states across
different temporal scales. Superstatistics are emerging as a flexible framework
for incorporating such non-stationary dynamics into existing cognitive model
classes. In this work, we provide the first experimental validation of
superstatistics and formal comparison of four non-stationary diffusion decision
models in a specifically designed perceptual decision-making task. Task
difficulty and speed-accuracy trade-off were systematically manipulated to
induce expected changes in model parameters. To validate our models, we assess
whether the inferred parameter trajectories align with the patterns and
sequences of the experimental manipulations. To address computational
challenges, we present novel deep learning techniques for amortized Bayesian
estimation and comparison of models with time-varying parameters. Our findings
indicate that transition models incorporating both gradual and abrupt parameter
shifts provide the best fit to the empirical data. Moreover, we find that the
inferred parameter trajectories closely mirror the sequence of experimental
manipulations. Posterior re-simulations further underscore the ability of the
models to faithfully reproduce critical data patterns. Accordingly, our results
suggest that the inferred non-stationary dynamics may reflect actual changes in
the targeted psychological constructs. We argue that our initial experimental
validation paves the way for the widespread application of superstatistics in
cognitive modeling and beyond.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要