Abstract
Human decisions are based on finite information, which makes them inherently imprecise. But what determines the degree of such imprecision? Here, we develop an efficient coding framework for higher-level cognitive processes in which information is represented by a finite number of discrete samples. We characterize the sampling process that maximizes perceptual accuracy or fitness under the often-adopted assumption that full adaptation to an environmental distribution is possible, and show how the optimal process differs when detailed information about the current contextual distribution is costly. We tested this theory on a numerosity discrimination task, and found that humans efficiently adapt to contextual distributions, but in the way predicted by the model in which people must economize on environmental information. Thus, understanding decision behavior requires that we account for biological restrictions on information coding, challenging the often-adopted assumption of precise prior knowledge in higher-level decision systems.
Highlights
It has been suggested that the rules guiding behavior are not arbitrary, but follow fundamental principles of acquiring information from environmental regularities in order to make the best decisions. These principles should incorporate strategies of information coding in ways that minimize the costs of inaccurate decisions given biological constraints on information acquisition, an idea known as efficient coding (Attneave, 1954; Barlow, 1961; Niven and Laughlin, 2008; Sharpee et al, 2014)
While early applications of efficient coding theory have primarily been to early stages of sensory processing (Laughlin, 1981; Ganguli and Simoncelli, 2014; Wei and Stocker, 2015), it is worth considering whether similar principles may shape the structure of internal representations of higher-level concepts, such as the perceptions of value that underlie economic decision making (Louie and Glimcher, 2012; Polanıa et al, 2019; Rustichini et al, 2017)
We report new experimental data on numerosity discrimination by human participants, where we find that our data are most consistent with an efficient coding theory for which the performance measure is the frequency of correct comparative judgments, and where people economize on the costs associated to learn about the statistics of the environment
Summary
It has been suggested that the rules guiding behavior are not arbitrary, but follow fundamental principles of acquiring information from environmental regularities in order to make the best decisions. Implementational architectures proposed in previous work assume a population coding scheme in which different neurons have distinct ’preferred’ stimuli (Ganguli and Simoncelli, 2014; Wei and Stocker, 2015). While this is clearly relevant for some kinds of low-level sensory features such as orientation, it is not obvious that this kind of internal representation is used in representing higher-level concepts such as economic values. We instead develop an efficient coding theory for a case in which an extensive magnitude (something that can be described by a larger or smaller number) is represented by a set of
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