Evidence map›Paper›PMID 29209058›Full record

ArticleScientific reports2017

Learning the value of information and reward over time when solving exploration-exploitation problems.

Irene Cogliati Dezza, Angela J Yu, Axel Cleeremans, William Alexander

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

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  14. Active inference and the two-step task.Scientific reports · 2022
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  19. Revisiting the Role of Uncertainty-Driven Exploration in a (Perceived) Non-Stationary World.CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference · 2021
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Irene Cogliati DezzaCentre for Research in Cognition & Neurosciences (CRCN), Université Libre de Bruxelles, Brussels, Belgium. icogliat@ulb.ac.be.
Angela J YuDepartment of Cognitive Science, University of California San Diego, La Jolla, CA, United States.
Axel CleeremansCentre for Research in Cognition & Neurosciences (CRCN), Université Libre de Bruxelles, Brussels, Belgium.
William AlexanderDepartment of Experimental Psychology, Ghent University, Gent, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To flexibly adapt to the demands of their environment, animals are constantly exposed to the conflict resulting from having to choose between predictably rewarding familiar options (exploitation) and risky novel options, the value of which essentially consists of obtaining new information about the space of possible rewards (exploration). Despite extensive research, the mechanisms that subtend the manner in which animals solve this exploitation-exploration dilemma are still poorly understood. Here, we investigate human decision-making in a gambling task in which the informational value of each trial and the reward potential were separately manipulated. To better characterize the mechanisms that underlined the observed behavioural choices, we introduce a computational model that augments the standard reward-based reinforcement learning formulation by associating a value to information. We find that both reward and information gained during learning influence the balance between exploitation and exploration, and that this influence was dependent on the reward context. Our results shed light on the mechanisms that underpin decision-making under uncertainty, and suggest new approaches for investigating the exploration-exploitation dilemma throughout the animal kingdom.

Indexed as

Exploratory BehaviorModels, PsychologicalRewardAdultFemaleGamblingHumansMaleNontherapeutic Human ExperimentationProblem SolvingReinforcement, Psychology

Identifiers

PMID29209058
PMCPMC5717252

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.