Testing Capacity-Constrained Learning

TL;DR

This paper develops a general test for capacity-constrained learning models, showing that choice data align with these models if they satisfy a specific no-switches condition, and finds empirical evidence of violations in standard perceptual tasks.

Contribution

It introduces the first comprehensive test for capacity-constrained learning models and applies it to various perceptual tasks to assess their validity.

Findings

Participants fail NIS in standard perceptual tasks

Evidence suggests incentives influence attention in some tasks

Not all perceptual tasks violate the NIS condition

Abstract

We introduce the first general test of capacity-constrained learning models. Cognitive economic models of this type share the common feature that constraints on perception are exogenously fixed, as in the widely used fixed-capacity versions of rational inattention (Sims 2003) and efficient coding (Woodford 2012). We show that choice data are consistent with capacity-constrained learning if and only if they satisfy a No Improving (Action or Attention) Switches (NIS) condition. Based on existing experiments in which the incentives for being correct are varied, we find strong evidence that participants fail NIS for a wide range of standard perceptual tasks: identifying the proportion of ball colors, recognizing shapes, and counting the number of balls. However, we find that this is not true for all existing perceptual tasks in the literature, which offers insights into settings where we do or do not expect incentives to impact the extent of attention.