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The Accuracy of Causal Learning Over Long Timeframes: An Ecological Momentary Experiment Approach
Ciara L Willett1, Benjamin M Rottman1
1Psychology Department, University of Pittsburgh.
Cognitive Science
|July 2, 2021
Summary
Learning cause-effect relationships is vital for adaptive behavior. This study found that learning over extended periods (24 days) showed similar results to traditional short-term learning, suggesting long-term memory supports causal inference.
Area of Science:
- Cognitive Psychology
- Human Learning
Background:
- Causal learning is essential for adaptive behavior.
- Traditional studies compress learning into short timeframes, relying on working memory.
- Real-world causal learning often occurs over extended periods, requiring long-term memory.
Purpose of the Study:
- To compare experience-based causal learning across short and long timeframes.
- To investigate the impact of time on the accuracy and biases of causal inference.
Main Methods:
- A smartphone study with 413 participants.
- Compared daily causal learning over 24 days versus 24 trials in one session.
- Assessed detection of generative and preventive causal relations and illusory correlations.
Main Results:
- Few differences were observed between short-term and long-term learning paradigms.
- Participants accurately detected causal relations in both conditions.
- Illusory correlations were present in both short and long timeframe tasks.
Conclusions:
- Experience-based causal learning over long timeframes shares strengths and weaknesses with short-term learning.
- Long-term causal inference appears robust across different temporal scales.
- Task complexity may impact learning effectiveness over extended periods.