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Imprecise Probabilistic Inference From Sequential Data.

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Imprecise Probabilistic Inference From Sequential Data.

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Imprecise probabilistic inference from sequential data.

Arthur Prat-Carrabin¹, Michael Woodford¹

¹Department of Economics, Columbia University.

Psychological Review

|April 18, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Human subjects show systematic deviations from Bayesian inference when estimating probabilities. They exhibit underreaction to evidence early on and overreaction later, suggesting an economy of attention rather than incorrect beliefs.

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Area of Science:

Cognitive Psychology
Decision Science
Behavioral Economics

Background:

The Bayesian paradigm is a key model for human inference, but its applicability to real-world behavior is debated.
Understanding how humans update beliefs based on evidence is crucial for cognitive science.

Purpose of the Study:

To investigate systematic departures from Bayesian inference in human probability estimation.
To identify the cognitive mechanisms underlying these deviations, moving beyond simple incorrect priors or common Bayesian models.

Main Methods:

Experimental subjects estimated the probability of binary events after observing successive realizations.
Analysis focused on identifying patterns of underreaction and overreaction to evidence.
A 'noisy-counting' model was used to reproduce observed behavioral patterns.

Main Results:

Subjects demonstrated 'conservatism' (underreaction) with few observations and overreaction with longer sequences.
Autocorrelation in estimates suggested imprecise belief representations and noise propagation.
Deviations persisted even after accounting for internal imprecisions and incorrect beliefs.

Conclusions:

Human probability estimation deviates from Bayesian inference due to an economy of attention, not just incorrect beliefs.
Subjects economize on attention to information and response control while maintaining task-adapted responses.
The 'noisy-counting' model effectively captures these observed behavioral patterns, highlighting the importance of attention economy in decision-making.