Econ Papers Daily / 期刊论文
测试能力约束下的学习
Testing capacity-constrained learning
摘要
We introduce a general test of capacity-constrained learning models. Learning has capacity constraints when the set of possible ways to learn is exogenously fixed, as in the widely used fixed-capacity versions of rational inattention (Sims, 2003) and efficient coding (Woodford, 2012). With such models, changes in incentives do not alter the extent of attention, only how individuals decide to allocate their scarce attention. 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. We further show that violations of NIS occur systematically in response to higher incentives, suggesting that incentives often expand attention beyond what capacity-constrained models allow. 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.
中文摘要
我们提出一种对容量受限学习模型的通用检验。当可能的学习方式集合外生固定时，学习便具有容量约束，正如广泛使用的理性疏忽（Sims，2003）和高效编码（Woodford，2012）的固定容量版本那样。在此类模型中，激励的变化不会改变注意的程度，只会改变个体决定如何配置其稀缺的注意力。我们证明，选择数据与容量受限学习相一致，当且仅当其满足无改进（行动或注意）切换（NIS）条件。基于现有那些改变正确性激励的实验，我们发现强有力的证据表明，参与者在广泛的标准知觉任务中未能满足NIS：识别球色比例、辨认形状以及计数球的数量。我们进一步表明，NIS的违反会系统性地响应更高的激励而发生，这表明激励往往会将注意力扩展到超出容量受限模型所允许的范围。然而，我们发现这并不适用于文献中所有现有的知觉任务，这为我们在哪些情境中预期或不预期激励会影响注意程度提供了洞见。