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Considerable delays between causes and effects are commonly found in real life. However, previous studies have only investigated how well people can learn probabilistic relations with delays on the order of seconds. In the current study we tested whether people can learn a cause-effect relation with delays of 0, 3, 9, or 21hours, and the study lasted 16 days. We found that learning was slowed with longer delays, but by the end of 16 days participants had learned the cause-effect relation in all four conditions, and they had learned the relation about equally well in all four conditions. This suggests that in real-world situations people may still be fairly accurate at inferring cause-effect relations with delays if they have enough experience. We also discuss ways that delays may interact with other real-world factors that could complicate learning.
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Causal Learning with Two Causes over Weeks
When making causal inferences, prior research shows that people are capable of controlling for alternative causes. These studies, however, utilize artificial inter-trial intervals on the order of seconds; in real-life situations people often experience data over days and weeks (e.g., learning the effectiveness of two new medications over multiple weeks). In the current study, participants learned about two possible causes from data presented in a traditional trial-by-trial paradigm (rapid series of trials) versus a more naturalistic paradigm (one trial per day for multiple weeks via smartphone). Our results suggest that while people are capable of detecting simple cause-effect relations that do not require controlling for another cause when learning over weeks, they have difficulty learning cause-effect relations that require controlling for alternative causes.
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- Award ID(s):
- 1651330
- PAR ID:
- 10237620
- Editor(s):
- Denison, S; Mack, M; Xu, Y; null
- Date Published:
- Journal Name:
- Proceedings of the Annual Conference of the Cognitive Science Society
- ISSN:
- 1069-7977
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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