Human decision-making behavior varies widely across individuals and task conditions. This variability is often interpreted in terms of different suboptimal decision strategies, but the principles that govern these suboptimalities remain poorly understood. We propose that some of these suboptimalities can be understood in terms of limited-capacity, but information-efficient, inference processes that inform decision-making. We developed and used new theoretical and empirical approaches to compare the amount of information used (capacity) to the effectiveness with which it was used (accuracy) by individual participants performing simple inference tasks. Variable, suboptimal performance was explained largely by inference that had variable, limited information capacity. Across these capacity limits, and regardless of whether the inference strategy was based on optimal or heuristic principles, the information was used effectively to maximize accuracy for a given capacity. This form of flexible and efficient information bottleneck reflects fundamental capacity-accuracy tradeoffs that structure individual variability.
Parker, J. A., Filipowicz, A. L. S., Li, K., Balasubramanian, V., Kable, J. W., Gold, J. I.
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