AMICAI: A Method Based on Risk Analysis to Integrate Responsible Research and Innovation into the Work of Research and Innovation Practitioners

Science and Engineering Ethics - The integration of ethics into the day-to-day work of research and innovation (R&I) is an important but difficult challenge. However, with the Aachen method...     

Bewertung von Publikationsleistungen in den psychosozialen Fächern

Ohne Zusammenfassung     

On the Stability of Psychological Hardiness: A Three-Year Longitudinal Study

More than 30 years of research has established psychological hardiness as an important individual resiliency resource. One important question still remaining is whether psychological hardiness can be trained. The present study explored this question longitudinally within the context of a 3-year military academy training program. Cadets from 3 different Norwegian military academies (N = 293) completed hardiness questionnaires during the first week of their training, and then again at the end of each year, resulting in a total of 4 waves of data. Using hierarchical linear modeling, no statistically significant effect of time on hardiness scores was found. The nonsignificant growth parameter was examined further using Bayesian statistics as an indicator of the relative evidence for the null hypothesis of no change over time versus the alternative hypothesis of change. The resulting Bayes factor provided substantial support in our data for the null hypothesis of no hardiness development during the 3-year officer training programs.     

Beobachtungen des Spontanverhaltens vorschulpflichtiger Kinder über lange Zeitintervalle im Montessori-Kinderhause

Ohne Zusammenfassung     

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results.