Key Publications

A common assumption of many established models for decision making is that information is searched according to some pre-specified search rule. While the content of the information influences the termination of search, usually specified as a stopping rule, the direction of search is viewed as being independent of the valence of the retrieved information. We propose an extension to the parallel constraint satisfaction network model (iCodes: integrated coherence-based decision and search), which assumes—in contrast to pre-specified search rules—that the valence of available information influences search of concealed information. Specifically, the model predicts an attraction search effect in that information search is directed towards the more attractive alternative given the available information. In three studies with participants choosing between two options based on partially revealed probabilistic information, the attraction search effect was consistently observed for environments with varying costs for information search although the magnitude of the effect decreased with decreasing monetary search costs. We also find the effect in reanalyses of five published studies. With iCodes, we propose a fully specified formal model and discuss implications for theory development within competing modeling frameworks.
Psychological Review, 125, 744–768, 2018

Whereas classic work in judgment and decision making has focused on the deviation of intuition from rationality, more recent research has focused on the performance of intuition in real-world environments. Borrowing from both approaches, we investigate to which extent competing models of intuitive probabilistic decision making overlap with choices according to the axioms of probability theory and how accurate those models can be expected to perform in real-world environments. Specifically, we assessed to which extent heuristics, models implementing weighted additive information integration (WADD), and the parallel constraint satisfaction (PCS) network model approximate the Bayesian solution and how often they lead to correct decisions in a probabilistic decision task. PCS and WADD outperform simple heuristics on both criteria with an approximation of 88.8% and a performance of 73.7%. Results are discussed in the light of selection of intuitive processes by reinforcement learning.
Synthese, 198, 147–160, 2012

One major statistical and methodological challenge in Judgment and Decision Making research is the reliable identification of individual decision strategies by selection of diagnostic tasks, that is, tasks for which predictions of the strategies differ sufficiently. The more strategies are considered, and the larger the number of dependent measures simultaneously taken into account in strategy classification (e.g., choices, decision time, confidence ratings; Glöckner, 2009), the more complex the selection of the most diagnostic tasks becomes. We suggest the Euclidian Diagnostic Task Selection (EDTS) method as a standardized solution for the problem. According to EDTS, experimental tasks are selected that maximize the average difference between strategy predictions for any multidimensional prediction space. In a comprehensive model recovery simulation, we evaluate and quantify the influence of diagnostic task selection on identification rates in strategy classification. Strategy classification with EDTS shows superior performance in comparison to less diagnostic task selection algorithms such as representative sampling. The advantage of EDTS is particularly large if only few dependent measures are considered. We also provide an easy-to-use function in the free software package R that allows generating predictions for the most commonly considered strategies for a specified set of tasks and evaluating the diagnosticity of those tasks via EDTS; thus, to apply EDTS, no prior programming knowledge is necessary.
Judgment and Decision Making, 6, 782–799, 2011

Most Recent Publications

More Publications

(2019). How to teach open science principles in the undergraduate curriculum - the Hagen Cumulative Science Project. Psychology Learning & Teaching.

Preprint PDF

(2019). Empirical content as a criterion for evaluating models. Cognitive Processing, 20, 273-275.

Preprint PDF

(2019). Perceived biological and social characteristics of a representative set of German first names. Social Psychology.

PDF Code Dataset

Projects

Open Science

Open Science Initiative at the FernUniversität in Hagen

Coherence based reasoning and rationality

DFG funded project (2011-2014) in the SPP1516 New Frameworks of Rationality

Apps

PCS Risky Choice Online App

Derive predictions for the PCS model of risky choice

MM-ML Online App

Apply the Multiple-Measure Maximum-Likelihood Strategy Classification Method

PCS-DM Online App

Derive predictions for the PCS-DM model

Interpretation von Testergebnissen Online App

z-Skala, T-Skala, IQ-Skala, Reliabilität, Messfehler, Konfidenzintervall

Optional stopping Online App

Optional stopping and alpha-error inflation

Psychophysik Online App

Funktionen Stevens und Weber-Fechner

Rescorla-Wagner Modell Online App

Funktionen des Rescorla-Wagner Modells

Statistische Power Online App

Power und Verteilung von p-Werten

Teaching

I supervised about 250 bachelor and 10 master-theses at FernUniversität in Hagen; I supervised master- and bachelor-theses at University of Göttingen and at University of Bonn.

I teach/taught the following courses:

Bachelor level

University of Cologne

  • Attitude and attitude change
  • Scientific method
  • Sensation and perception
  • Diagnostics for school teachers

FernUniversität in Hagen

  • Preparing a bachelor-thesis
  • Statistical analyses and open science link to slides
  • Artificial neuronal networks in psychology link to slides
  • Computational modeling of learning

University of Göttingen

  • Test-theory

University of Bonn

  • Computer assisted data analysis
  • Introduction to R
  • Empirical-experimental practical
  • Introduction to psychology
  • Psychology of emotions

Master level

University of Cologne

  • Social cognition

University of Göttingen

PhD level

  • Workshop (invited) on panel data analysis / complex regression models at 3rd EADM JDM Summer School (together with Andreas Glöckner) [course language: English] link to slides
  • Workshop (invited) on spreading activation network models in decision making at the international summer school on Theories and Methods in Judgment and Decision Making Research organized by the DFG research unit Contextualized Decision Making [course language: English]
  • Workshop on cognitive modeling in decision making at 8th Annual Judgment and Decision Making Workshop for Early-Career Researchers [course language: English]
  • Three short lectures (invited) on cognitive modeling of decision making at University of Erfurt
  • Workshop Rewards of Simulations in Psychology at 5th Judgment and Decision Making Workshop for Young Researchers [course language: Englisch]
  • Advanced workshops (invited) on modeling cognitive and social processes in R at University of Mannheim, University of Marburg, and University of Landau

Contact

  • marc.jekel@mail.de
  • marc.jekel
  • Institut für Psychologie, Sozialpsychologie, Richard Strauss Straße 2, 50931 Köln, Germany
  • email me for an appointment