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Efficient sampling and noisy decisions.

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.

SUBMITTER: Heng JA

PROVIDER: S-EPMC7492090 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Efficient sampling and noisy decisions.

Heng Joseph A JA Woodford Michael M Polania Rafael R

eLife 20200915

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 th ...[more]

PMID: 32930663

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Project description:BackgroundThe experience sampling method (ESM) is an intensive longitudinal research method. Participants complete questionnaires at multiple times about their current or very recent state. The design of ESM studies is complex. People with psychosis have been shown to be less adherent to ESM study protocols than the general population. It is not known how to design studies that increase adherence to study protocols. A lack of typology makes it is hard for researchers to decide how to collect data in a way that allows for methodological rigour, quality of reporting, and the ability to synthesise findings. The aims of this systematic review were to characterise the design choices made in ESM studies monitoring the daily lives of people with psychosis, and to synthesise evidence relating the data completeness to different design choices.MethodsA systematic review was conducted of published literature on studies using ESM with people with psychosis. Studies were included if they used digital technology for data collection and reported the completeness of the data set. The constant comparative method was used to identify design decisions, using inductive identification of design decisions with simultaneous comparison of design decisions observed. Weighted regression was used to identify design decisions that predicted data completeness. The review was pre-registered (PROSPERO CRD42019125545).ResultsThirty-eight studies were included. A typology of design choices used in ESM studies was developed, which comprised three superordinate categories of design choice: Study context, ESM approach and ESM implementation. Design decisions that predict data completeness include type of ESM protocol used, length of time participants are enrolled in the study, and if there is contact with the research team during data collection.ConclusionsThis review identified a range of design decisions used in studies using ESM in the context of psychosis. Design decisions that influence data completeness were identified. Findings will help the design and reporting of future ESM studies. Results are presented with the focus on psychosis, but the findings can be applied across different mental health populations.

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Efficient sampling and noisy decisions.

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Efficient sampling and noisy decisions.

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