Judgment and Decision Making

Theories of judgment and decision making are concerned with how people search for information and integrate different sources of information (often operationalized by probabilistic cues) in different environments and scenarios. Concerning the integration of information, some theories predict that people rely on a set of qualitatively different, ecologically valid heuristics, which often provide good inferences while requiring only minor effort. In contrast, other theories assume that information integration can be explained by a single, automatic evidence-accumulation process as represented by neural-network and sequential-sampling models.  

Recognition-Based Decisions

In the domain of memory-based inferences, people have to decide which of two options is larger on some criterion (e.g., which city is larger). The recognition heuristic states that people simply choose the city they know, which often provides good inferences because cities that are larger are also more likely to be recognized. To disentangle whether other sources of information (e.g., whether the known city has an airport or metro) also enter the decision, we extended a measurement model (the r-model, a specific MPT model) to response times. Thereby, we could show that people are actually faster when considering more, recognition-congruent knowledge, which cannot be explained by the recognition heuristic: 

  • Heck, D. W., & Erdfelder, E. (2017). Linking process and measurement models of recognition-based decisions. Psychological Review, 124(4), 442–471. doi:10.1037/rev0000063

Strategy Selection

In multiattribute decisions, people have to choose the better option with respect to some criterion (e.g., selecting the more successful stock) based on several probabilistic cues that indicate which option scores higher on the criterion (e.g., number of employees, country, etc.). Based on observed choice patterns, the Bayes factor provides the posterior probability that a participant has used a specific strategy given the data (as an alternative to these posterior model probabilities, NML model weights can be used). We relied on this outcome-based strategy classification to test a psychologically plausible version of take-the-best (TTB), a strategy that predicts that decisions are made only based on the most valid piece of information whereas any further information is ignored:

  • Heck, D. W., Hilbig, B. E., & Moshagen, M. (2017). From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26–40. doi:10.1016/j.cogpsych.2017.05.003