This year's SMiP IOPS Conference took place on 20th and 21st July 2024 in Mannheim. We welcomed many SMiP and IOPS members as well as the SMiP group's former postdoc Prof. Dr. Daniel Heck (University of Marburg) as keynote speaker.

The program included
(1) talks by SMiP and IOPS PhD Candidates,
(2) posters presented by SMiP and IOPS PhD Candidates,
(3) a keynote talk by the SMiP group's former postdoc Prof. Dr. Daniel Heck (University of Marburg), and
(4) the presentation of SMiP and IOPS awards for: the best paper 2023 / 2024, best presentation and best poster (at SMiP IOPS conference).

  • Talks

    Jan Killisch: Constructing multidimensional forced-choice tests using social desirability rankings

    Qixiang Fang: Leveraging measurement theory for natural language processing research

    Caroline Böhm: How do modifications of validated scales impact replicability in existing data?

    Caroline Streitberger: What drives the associative memory deficit in healthy aging? Mixed storage and clear retrieval findings

    Jill de Ron: Towards a general modeling framework of resource competition in cognitive development

    Steven Bißantz: Beyond human expertise: Predicting replication success with a super learner

    Pia Andresen: It's about time: Integrating time scales using measurement burst data

    Anna Neumer: Hybrid happiness: Employees' proactive efforts for psychological need satisfaction when working from home versus at the office

    Manuel Haqiqatkhah: Day-to-day dynamics, day-of-week effects, and week-to-week dynamics: Seasonal ARMA modeling of daily diary data

    Yufei Wu: Learning in a non-learning paradigm: The undiscovered dynamics

    Shanqing Gao: Associative priming and categorical priming in the word-picture paradigm: A hierarchical drift diffusion model analysis

    Julia Liss: Discriminating between biases in the first-person shooter task

    Tamás Szűcs: A systematic examination of the causal determinants of affect

    Emre Alagöz: Disentangling heterogeneity in response processes with IRTrees

  • Posters

    Lasse Elsemüller             Enabling large-scale sensitivity analyses in amortized Bayesian inference with neural networks

    Jan Failenschmid            Modeling non-linear psychological processes: Reviewing and evaluating (non‑)parametric approaches and their applicability to intensive longitudinal data

    Tugba Hato                     Lévy versus Wiener: Assessing the effects of model misspecification on diffusion model parameters

    Rebekka Kupffer             Investigating the validity of indices to detect careless responding in multidimensional forced-choice questionnaires

    Ulrich Lösener                Bayesian sample size determination for longitudinal trials with attrition

    Katja Pollak                     Testing the convergent validity of the non-decision time parameter of the diffusion model

    Khadiga Sayed               The analysis of randomized response “ever” and “last year” questions: A non- saturated multinomial model

    Nicola Schneider             Developing a reinforcement learning-drift diffusion model for probabilistic decision making. A simulation study

    Timo Seitz                      “How would you fake?”: Disentangling qualitatively different faking tendencies through mixture IRT modeling

    Lisette Sibbald                Identifying predictive risk factors of postpartum depression: A machine learning approach

    Tian Zhou                       Cognitive motor interference and aging: An analysis plan for the GAITRite database

  • Keynote Talk

    Daniel W. Heck (University of Marburg): Cognitive and psychometric modeling of truth judgments

    Factual statements are more likely to be judged as true when they are presented repeatedly. This repetition-based truth effect has been replicated many times, yet formal statistical models explaining the effect are still scarce. In order to address this, I outline different cognitive and psychometric modeling approaches for truth judgments. I highlight the benefits of precise mathematical equations instead of simulation-based models for testing theories about the moderating role of the plausibility of statements. The importance of model identifiability, auxiliary assumptions, and validation studies is illustrated in the context of competing multinomial processing tree models of the truth effect. In certain settings, such as fact checking, the actual truth status of statements may not be known. I show how cultural-consensus models provide a psychometric solution for estimating both the statements' truth status and the participants' expertise.

  • Awards

    SMiP Best Paper Award 2023 / 2024

    Elsemüller, L., Schnuerch, M., Bürkner, P.-C., & Radev, S. T. (2024). A deep learning method for comparing Bayesian hierarchical models. Psychological Methods.

    SMiP Best Presentation Award

    Killisch, J. (2024). Constructing multidimensional forced-choice tests using social desirability rankings. Talk given at the SMiP IOPS Summer Conference 2024, Mannheim.

    SMiP Best Poster Award

    Seitz, T. (2024). “How would you fake?”: Disentangling qualitatively different faking tendencies through mixture IRT modeling. Poster presented at the SMiP IOPS Summer Conference 2024, Mannheim.