Differences in learning across the lifespan emerge via resource-rational computations.

2025/02/27 by Rasmus Bruckner, Matthew R. Nassar, Shu Li +1 · 2 voices
Computer Science · #Neural Networks and Applications #Intelligent Tutoring Systems and Adaptive Learning #Reinforcement Learning in Robotics

paper · doi:10.1037/rev0000526

Abstract

= 94) show that children and older adults display biases characteristic of a more frugal sampling policy. This is reflected in (a) more frequent perseveration when participants are required to update from previous beliefs and (b) a stronger anchoring bias when updating beliefs from an externally generated value. These results are qualitatively consistent with simulated predictions of our resource-rational model, corroborating the assumption that the identified biases originate from sampling. Our model and results provide a unifying perspective on perseverative and anchoring biases, show that they can jointly emerge from efficient belief-updating computations, and suggest that resource-rational adjustments of sampling computations can explain age-related changes in adaptive learning. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Discussions

• 🚀 New paper out in Psychological Review! How does learning change across the lifespan? We propose that resource rationality—adapting belief updating to cognitive limitations—can explain age-related [bsky, 31 points, 3 comments]
• Why are young children & older adults more prone to stick to their prior beliefs and/or to be influenced by anchors (e.g., "This costs $500.")? The authors argue it's b/c cognitive processing is more [bsky, 6 points, 3 comments]

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