@article{TimmeHutchinsonRegoriusetal.2022,
  author    = {Timme, Sinika and Hutchinson, Jasmin and Regorius, Anton and Brand, Ralf},
  title     = {The influence of affective priming on the affective response during exercise},
  series = {Journal of sport \& exercise psychology},
  volume    = {44},
  journal   = {Journal of sport \& exercise psychology},
  number    = {4},
  publisher = {Human Kinetics Publishers},
  address   = {Champaign},
  issn      = {0895-2779},
  doi       = {10.1123/jsep.2022-0025},
  pages     = {286 -- 294},
  year      = {2022},
  abstract  = {The affective response during exercise is an important factor for long-term exercise adherence. Pottratz et al. suggested affective priming as a behavioral intervention for the enhancement of exercise-related affect. The present paper aims to replicate and extend upon these findings. We conducted a close replication with 53 participants completing a brisk walking task in two conditions (prime vs. no prime). Affective valence was assessed during exercise, and exercise enjoyment and remembered/forecasted pleasure were assessed postexercise. We could not replicate the findings of Pottratz et al., finding no evidence for positive changes in psychological responses in the priming condition. However, linear mixed models demonstrated significant interindividual differences in how participants responded to priming. These results demonstrate that affective priming during exercise does not work for everyone under every circumstance and, thus, provide an important contribution to the understanding of boundary conditions and moderating factors for priming in exercise psychology.},
  language  = {en}
}
@article{SeeligRabeMalemShinitskietal.2020,
  author    = {Seelig, Stefan A. and Rabe, Maximilian Michael and Malem-Shinitski, Noa and Risse, Sarah and Reich, Sebastian and Engbert, Ralf},
  title     = {Bayesian parameter estimation for the SWIFT model of eye-movement control during reading},
  series = {Journal of mathematical psychology},
  volume    = {95},
  journal   = {Journal of mathematical psychology},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {0022-2496},
  doi       = {10.1016/j.jmp.2019.102313},
  pages     = {32},
  year      = {2020},
  abstract  = {Process-oriented theories of cognition must be evaluated against time-ordered observations. Here we present a representative example for data assimilation of the SWIFT model, a dynamical model of the control of fixation positions and fixation durations during natural reading of single sentences. First, we develop and test an approximate likelihood function of the model, which is a combination of a spatial, pseudo-marginal likelihood and a temporal likelihood obtained by probability density approximation Second, we implement a Bayesian approach to parameter inference using an adaptive Markov chain Monte Carlo procedure. Our results indicate that model parameters can be estimated reliably for individual subjects. We conclude that approximative Bayesian inference represents a considerable step forward for computational models of eye-movement control, where modeling of individual data on the basis of process-based dynamic models has not been possible so far.},
  language  = {en}
}