Evidence map›Paper›PMID 42459221›Full record

ArticleComputational brain & behavior2025

Externally Provided Rewards Increase Internal Preference, but Not as Much as Preferred Ones Without Extrinsic Rewards.

Jianhong Zhu, Kentaro Katahira, Makoto Hirakawa, Takashi Nakao

Abstract read
In one paragraph

Article in Computational brain & behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jianhong ZhuGraduate School of Humanities and Social Sciences, Hiroshima University, 1-1-1 Kagamiyama, Higashihiroshima, Hiroshima 739-8524 Japan.ORCID 0000-0001-9188-1381
Kentaro KatahiraHuman Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8566 Japan.
Makoto HirakawaGraduate School of Humanities and Social Sciences, Hiroshima University, 1-1-1 Kagamiyama, Higashihiroshima, Hiroshima 739-8524 Japan.
Takashi NakaoGraduate School of Humanities and Social Sciences, Hiroshima University, 1-1-1 Kagamiyama, Higashihiroshima, Hiroshima 739-8524 Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is well known that preferences are formed through choices, known as choice-induced preference change (CIPC). However, whether value learned through externally provided rewards influences the preferences formed through CIPC remains unclear. To address this issue, we used tasks for decision-making guided by reward provided by the external environment (externally guided decision-making; EDM) and for decision-making guided by one's internal preference (internally guided decision-making; IDM). In the IDM task, we presented stimuli with learned value in the EDM and novel stimuli to examine whether the value in the EDM affects preferences. Stimuli reinforced by rewards given in the EDM were reflected in the IDM's initial preference and further increased through CIPC in the IDM. However, such stimuli were not as strongly preferred as the most preferred novel stimulus in the IDM (superiority of intrinsically learned values; SIV), suggesting that the values learned by the EDM and IDM differ. The underlying process of this phenomenon is discussed in terms of the fundamental self-hypothesis. Supplementary Information: The online version contains supplementary material available at 10.1007/s42113-024-00198-5.

Indexed as

Choice-based learning (CBL)Computational modelingReinforcement learning (RL)Reward learning taskSelf-prioritization effect

Identifiers

PMID42459221
PMCPMC13298676

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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.