Data assimilation in dynamical cognitive science.

Dynamical models make specific assumptions about cognitive processes that generate human behavior. In data assimilation, these models are tested against time-ordered data. Recent progress on Bayesian data assimilation demonstrates that this approach combines the strengths of statistical modeling of individual differences with the those of dynamical cognitive models.

A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts.

In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777-813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between-subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics.

Newly emerging pandemics like COVID-19 call for predictive models to implement precisely tuned responses to limit their deep impact on society. Standard epidemic models provide a theoretically well-founded dynamical description of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which creates challenges for containment strategies. However, spatial heterogeneity raises questions about the adequacy of modeling epidemic outbreaks on the level of a whole country. Here, we show that by applying sequential data assimilation to the stochastic SEIR epidemic model, we can capture the dynamic behavior of outbreaks on a regional level. Regional modeling, with relatively low numbers of infected and demographic noise, accounts for both spatial heterogeneity and stochasticity. Based on adapted models, short-term predictions can be achieved. Thus, with the help of these sequential data assimilation methods, more realistic epidemic models are within reach.

Modulation of additive and interactive effects by trial history revisited.

Masson and Kliegl (Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 898-914, 2013) reported evidence that the nature of the target stimulus on the previous trial of a lexical decision task modulates the effects of independent variables on the current trial, including additive versus interactive effects of word frequency and stimulus quality. In contrast, recent reanalyses of previously published data from experiments that, unlike the Masson and Kliegl experiments, did not include semantic priming as a factor, found no evidence for modulation of additive effects of frequency and stimulus quality by trial history (Balota, Aschenbrenner, & Yap, Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 1563-1571, 2013; O'Malley & Besner, Journal of Experimental Psychology: Learning, Memory, and Cognition, 34, 1400-1411, 2013). We report two experiments that included semantic priming as a factor and that attempted to replicate the modulatory effects found by Masson and Kliegl. In neither experiment was additivity of frequency and stimulus quality modulated by trial history, converging with the findings reported by Balota et al. and O'Malley and Besner. Other modulatory influences of trial history, however, were replicated in the new experiments and reflect potential trial-by-trial alterations in decision processes.