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: 

  • [PDF] Heck, D. W., & Erdfelder, E. (2017). Linking process and measurement models of recognition-based decisions. Psychological Review, 124, 442–471. https://doi.org/10.1037/rev0000063
    [Abstract] [BibTeX] [Data & R Scripts]

    When making inferences about pairs of objects, one of which is recognized and the other is not, the recognition heuristic states that participants choose the recognized object in a noncompensatory way without considering any further knowledge. In contrast, information-integration theories such as parallel constraint satisfaction (PCS) assume that recognition is merely one of many cues that is integrated with further knowledge in a compensatory way. To test both process models against each other without manipulating recognition or further knowledge, we include response times into the r-model, a popular multinomial processing tree model for memory-based decisions. Essentially, this response-time-extended r-model allows to test a crucial prediction of PCS, namely, that the integration of recognition-congruent knowledge leads to faster decisions compared to the consideration of recognition only—even though more information is processed. In contrast, decisions due to recognition-heuristic use are predicted to be faster than decisions affected by any further knowledge. Using the classical German-cities example, simulations show that the novel measurement model discriminates between both process models based on choices, decision times, and recognition judgments only. In a reanalysis of 29 data sets including more than 400,000 individual trials, noncompensatory choices of the recognized option were estimated to be slower than choices due to recognition-congruent knowledge. This corroborates the parallel information-integration account of memory-based decisions, according to which decisions become faster when the coherence of the available information increases. (PsycINFO Database Record (c) 2017 APA, all rights reserved)

    @article{heck2017linking,
title = {Linking Process and Measurement Models of Recognition-Based Decisions},
author = {Heck, Daniel W and Erdfelder, Edgar},
date = {2017},
journaltitle = {Psychological Review},
volume = {124},
pages = {442--471},
doi = {10.1037/rev0000063},
abstract = {When making inferences about pairs of objects, one of which is recognized and the other is not, the recognition heuristic states that participants choose the recognized object in a noncompensatory way without considering any further knowledge. In contrast, information-integration theories such as parallel constraint satisfaction (PCS) assume that recognition is merely one of many cues that is integrated with further knowledge in a compensatory way. To test both process models against each other without manipulating recognition or further knowledge, we include response times into the r-model, a popular multinomial processing tree model for memory-based decisions. Essentially, this response-time-extended r-model allows to test a crucial prediction of PCS, namely, that the integration of recognition-congruent knowledge leads to faster decisions compared to the consideration of recognition only—even though more information is processed. In contrast, decisions due to recognition-heuristic use are predicted to be faster than decisions affected by any further knowledge. Using the classical German-cities example, simulations show that the novel measurement model discriminates between both process models based on choices, decision times, and recognition judgments only. In a reanalysis of 29 data sets including more than 400,000 individual trials, noncompensatory choices of the recognized option were estimated to be slower than choices due to recognition-congruent knowledge. This corroborates the parallel information-integration account of memory-based decisions, according to which decisions become faster when the coherence of the available information increases. (PsycINFO Database Record (c) 2017 APA, all rights reserved)},
osf = {https://osf.io/4kv87},
keywords = {heckfirst,heckpaper,popularity\_bias}
}

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:

  • [PDF] 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. https://doi.org/10.1016/j.cogpsych.2017.05.003
    [Abstract] [BibTeX] [Data & R Scripts]

    Decision strategies explain how people integrate multiple sources of information to make probabilistic inferences. In the past decade, increasingly sophisticated methods have been developed to determine which strategy explains decision behavior best. We extend these efforts to test psychologically more plausible models (i.e., strategies), including a new, probabilistic version of the take-the-best (TTB) heuristic that implements a rank order of error probabilities based on sequential processing. Within a coherent statistical framework, deterministic and probabilistic versions of TTB and other strategies can directly be compared using model selection by minimum description length or the Bayes factor. In an experiment with inferences from given information, only three of 104 participants were best described by the psychologically plausible, probabilistic version of TTB. Similar as in previous studies, most participants were classified as users of weighted-additive, a strategy that integrates all available information and approximates rational decisions.

    @article{heck2017information,
title = {From Information Processing to Decisions: {{Formalizing}} and Comparing Probabilistic Choice Models},
author = {Heck, Daniel W and Hilbig, Benjamin E and Moshagen, Morten},
date = {2017},
journaltitle = {Cognitive Psychology},
volume = {96},
pages = {26--40},
doi = {10.1016/j.cogpsych.2017.05.003},
abstract = {Decision strategies explain how people integrate multiple sources of information to make probabilistic inferences. In the past decade, increasingly sophisticated methods have been developed to determine which strategy explains decision behavior best. We extend these efforts to test psychologically more plausible models (i.e., strategies), including a new, probabilistic version of the take-the-best (TTB) heuristic that implements a rank order of error probabilities based on sequential processing. Within a coherent statistical framework, deterministic and probabilistic versions of TTB and other strategies can directly be compared using model selection by minimum description length or the Bayes factor. In an experiment with inferences from given information, only three of 104 participants were best described by the psychologically plausible, probabilistic version of TTB. Similar as in previous studies, most participants were classified as users of weighted-additive, a strategy that integrates all available information and approximates rational decisions.},
osf = {https://osf.io/jcd2c},
keywords = {heckfirst,Polytope\_Sampling,popularity\_bias}
}