From information processing to decisions: Formalizing and comparing psychologically plausible choice models

- Decision strategies explain how people draw inferences based on probabilistic cues.
- We propose and test a probabilistic version of take-the-best.
- We compare model selection by Bayes factor and normalized maximum likelihood.
- Both model-selection methods perform well in a simulation.
- In an experiment, most participants integrated cues weighted by validity.

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.