Evidence for a Frequency Heuristic in Experience-based Decision-Making
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Most decision models focus on the role of expected value. However, gain-loss frequency, that is how often gains versus losses are experienced, is another important aspect of choice behavior. In decision from experience paradigms, people make choices and receive a series of gains and/or losses as feedback, and hence gain-loss frequency is salient in this decision context. Also, much research indicates that people are highly sensitive to frequency information. Thus, people might rely on gain-loss frequency to make decisions. This work examined whether and how people use frequency information in experience-based decision-making and further investigated some important psychological and developmental aspects of using frequency information. In Study 1, a frequency heuristic where people track the frequency of gains and losses and choose the option with frequent gains and rare losses was formalized, and an Expectancy-Frequency-Perseveration (EFP) model which accounts for this frequency heuristic was developed. In different decision-making paradigms and on various model performance criteria, EFP models consistently performed well and often outperformed other models without the frequency value component in terms of fitting human choice behavior, suggesting a crucial role of frequency information and the pervasiveness of the frequency heuristic in experience-based decision-making. Study 2 investigated the role of working memory (WM) in the use of the frequency heuristic. This study manipulated WM load and employed a decision-making task where the frequency heuristic is counterproductive. Behavioral results showed that participants with intact WM resources were biased towards options with frequent gains and rare losses (but with lower expected values), compared to those under WM load, indicating that WM load reduces reliance on gain-loss frequency. Consistent with the behavioral results, computational modeling results suggest that WM load diminishes attention to the frequency information. Thus, Study 2 provides evidence that WM contributes to the use of the frequency heuristic. Study 3 replicated these main results from Study 2. Furthermore, Study 3 reveals that at least one role of WM is to contribute towards making accurate gain-loss frequency judgments, which in turn could form a basis for applying this heuristic. Study 4 revealed a life-span trajectory of the use of the frequency heuristic, that is, people tend to utilize the frequency heuristic more with advancing age. Hence, it appears that the WM demand for using the frequency heuristic is not so strong that normal (healthy) age-related cognitive decline would constrain the use of it. These seemingly contradictory findings suggest a moderate WM demand for applying this heuristic. The “irregular” position of the frequency heuristic on the map of the dual-process models and its implications are discussed.
Pang, Bo (2017). Evidence for a Frequency Heuristic in Experience-based Decision-Making. Doctoral dissertation, Texas A & M University. Available electronically from