Evidence map›Paper›PMID 37662382›Full record

ArticlebioRxiv : the preprint server for biology2025

Control adjustment costs limit goal flexibility: Empirical evidence and a computational account.

Ivan Grahek, Xiamin Leng, Sebastian Musslick, Amitai Shenhav

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Ivan GrahekDepartment of Cognitive, Linguistic, and Psychological Sciences; Carney Institute for Brain Science; Brown University; Providence, RI, USA.ORCID 0000-0002-9247-5956
Xiamin LengDepartment of Cognitive, Linguistic, and Psychological Sciences; Carney Institute for Brain Science; Brown University; Providence, RI, USA.ORCID 0000-0002-5517-7570
Sebastian MusslickDepartment of Cognitive, Linguistic, and Psychological Sciences; Carney Institute for Brain Science; Brown University; Providence, RI, USA.ORCID 0000-0002-8896-639X
Amitai ShenhavDepartment of Cognitive, Linguistic, and Psychological Sciences; Carney Institute for Brain Science; Brown University; Providence, RI, USA.ORCID 0000-0002-0222-0774

Funding

Neural and computational mechanisms of motivation and cognitive controlR01MH124849 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI SHENHAV, AMITAI · 2021 to 2025
$2.0M
Training Program for Interactionist Cognitive Neuroscience (ICoN)T32MH115895 · NIMH · BROWN UNIVERSITY · PI MICHAEL J. FRANK, STEPHANIE Ruggiano JONES · 2019 to 2026
$1.8M
Computationally modeling the failure of effort to become a secondary reinforcer in schizophreniaR21MH122863 · NIMH · UNIVERSITY OF GEORGIA · PI SHENHAV, AMITAI, STRAUSS, GREGORY P · 2020 to 2020
$441k
NIMH NIH HHS R01 MH124849NIMH NIH HHS R21 MH122863NIMH NIH HHS T32 MH115895
6 · The paper itself

Abstract

A cornerstone of human intelligence is the ability to flexibly adjust our cognition and behavior as our goals change. For instance, achieving some goals requires efficiency, while others require caution. Different goals require us to engage different control processes, such as adjusting how attentive and cautious we are. Here, we show that performance incurs control adjustment costs when people adjust control to meet changing goals. Across four experiments, we provide evidence of these costs, and validate a dynamical systems model explaining the source of these costs. Participants performed a single cognitively demanding task under varying performance goals (e.g., being fast or accurate). We modeled control allocation to include a dynamic process of adjusting from one's current control state to a target state for a given performance goal. By incorporating inertia into this adjustment process, our model accounts for our empirical finding that people under-shoot their target control state more (i.e., exhibit larger adjustment costs) when goals switch rather than remain fixed (Study 1). Further validating our model, we show that the magnitude of this cost is increased when: distances between target states are larger (Study 2), there is less time to adjust to the new goal (Study 3), and goal switches are more frequent (Study 4). Our findings characterize the costs of adjusting control to meet changing goals, and show that these costs emerge directly from cognitive control dynamics. In so doing, they shed new light on the sources of and constraints on flexibility of goal-directed behavior.

Indexed as

cognitive controldrift diffusion modelgoal flexibilityinertiamotivation

Identifiers

PMID37662382
PMCPMC10473589

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.