Searching for the Best Cause: Roles of Mechanism Beliefs, Autocorrelation, and Exploitation

• Published in: Journal of experimental psychology. Learning, memory, and cognition memory, and cognition, 2016-08, Vol.42 (8), p.1233-1256
• Main Author: Rottman, Benjamin M.
• Format: Article
• Language: English
• Subjects: Appetitive Behavior - physiology; Attitudes; Beliefs; Causal Analysis; Causal Models; Choice Behavior - physiology; Correlation; Correlation analysis; Culture; Environment; Experimental psychology; Female; Heuristics - physiology; Human; Humans; Information Seeking; Intervention; Judgment; Judgment - physiology; Learning; Male; Models, Psychological; Reasoning; Regression (Statistics); Regression Analysis; Sequential Approach; Strategies
• ISSN: 0278-7393; 1939-1285
• DOI: 10.1037/xlm0000244

When testing which of multiple causes (e.g., medicines) works best, the testing sequence has important implications for the validity of the final judgment. Trying each cause for a period of time before switching to the other is important if the causes have tolerance, sensitization, delay, or carryover (TSDC) effects. In contrast, if the outcome variable is autocorrelated and gradually fluctuates over time rather than being random across time, it can be useful to quickly alternate between the 2 causes, otherwise the causes could be confounded with a secular trend in the outcome. Five experiments tested whether individuals modify their causal testing strategies based on beliefs about TSDC effects and autocorrelation in the outcome. Participants adaptively tested each cause for longer periods of time before switching when testing causal interventions for which TSDC effects were plausible relative to cases when TSDC effects were not plausible. When the autocorrelation in the baseline trend was manipulated, participants exhibited only a small (if any) tendency toward increasing the amount of alternation; however, they adapted to the autocorrelation by focusing on changes in outcomes rather than raw outcome scores, both when making choices about which cause to test as well as when making the final judgment of which cause worked best. Understanding how people test causal relations in diverse environments is an important first step for being able to predict when individuals will successfully choose effective causes in real-world settings.