The Ubiquity of Time in Latent-cause Inference

• Published in: Journal of cognitive neuroscience 2024-11, Vol.36 (11), p.2442-2454
• Main Authors: Mirea, Dan-Mircea, Shin, Yeon Soon, DuBrow, Sarah, Niv, Yael
• Format: Article
• Language: English
• Subjects: Adult; Bayes Theorem; Bayesian analysis; Female; Generalization, Psychological - physiology; Humans; Individuality; Information processing; Male; Mathematical models; Microorganisms; Models, Psychological; Neuroimaging; Thinking - physiology; Time Factors; Young Adult
• ISSN: 0898-929X; 1530-8898; 1530-8898
• DOI: 10.1162/jocn_a_02231

Humans have an outstanding ability to generalize from past experiences, which requires parsing continuously experienced events into discrete, coherent units, and relating them to similar past experiences. Time is a key element in this process; however, how temporal information is used in generalization remains unclear. Latent-cause inference provides a Bayesian framework for clustering experiences, by building a world model in which related experiences are generated by a shared cause. Here, we examine how temporal information is used in latent-cause inference, using a novel task in which participants see “microbe” stimuli and explicitly report the latent cause (“strain”) they infer for each microbe. We show that humans incorporate time in their inference of latent causes, such that recently inferred latent causes are more likely to be inferred again. In particular, a “persistent” model, in which the latent cause inferred for one observation has a fixed probability of continuing to cause the next observation, explains the data significantly better than two other time-sensitive models, although extensive individual differences exist. We show that our task and this model have good psychometric properties, highlighting their potential use for quantifying individual differences in computational psychiatry or in neuroimaging studies.