Evidence map›Paper›PMID 36711762›Full record

ArticlebioRxiv : the preprint server for biology2025

Phantom controllers: Misspecified models create the false appearance of adaptive control during value-based choice.

H Ritz, R Frömer, A Shenhav

Open access · greenAbstract 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. Not yet cited in PubMed.

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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 3 countries.

H Ritz *Cognitive, Linguistic, and Psychological Sciences, Brown University.ORCID 0000-0003-3011-2946
R Frömer *Cognitive, Linguistic, and Psychological Sciences, Brown University.ORCID 0000-0002-9468-4014
A ShenhavDepartment of Psychology, University of California, Berkeley.ORCID 0000-0002-0222-0774
Allen Institute for Brain Science · US

Funding

The neural network of OCD, linking NHP and human studiesP50MH106435 · NIMH · UNIVERSITY OF ROCHESTER · PI Suzanne N Haber · 2015 to 2026
$28.9M
Neural and computational mechanisms of motivation and cognitive controlR01MH124849 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI SHENHAV, AMITAI · 2021 to 2025
$2.0M
Brain Science Compute ClusterS10OD025181 · OD · BROWN UNIVERSITY · PI SANES, JEROME N · 2019 to 2019
$600k
NIH HHS S10 OD025181NIMH NIH HHS P50 MH106435NIMH NIH HHS R01 MH124849
6 · The paper itself

Abstract

Decision scientists have grown increasingly interested in how people adaptively control their decision making. Researchers have demonstrated that parameters governing the accumulation of evidence towards a choice, such as the decision threshold, are shaped by information available prior to or in parallel with one's evaluation of an option set (e.g., recent outcomes or choice conflict). A recent account has taken a bold leap forward in this approach, suggesting that adjustments in decision parameters can be motivated by the value of the options under consideration. This motivated control account predicts that when faced with difficult choices (similarly valued options) under time pressure, people will adaptively lower their decision threshold to ensure that they make a choice in time. This account was supported by drift diffusion modeling of a deadlined choice task, demonstrating that decision thresholds decrease for difficult relative to easy choices. Here, we reanalyze the data from this experiment, and show that evidence for this novel account does not hold up to further scrutiny. Using a more systematic and comprehensive modeling approach, we show that this previously observed threshold adjustment disappears (or even reverses) under a more complete model of the data. Importantly, we further show how this and other apparent evidence for motivated control arises as an artifact of model (mis)specification, where one model's putatively controlled decision process (e.g., value-driven threshold adjustments) can mimic another model's stimulus-driven decision processes (e.g., accumulator competition or collapsing bounds). Collectively, this work reveals crucial insights and constraints in the pursuit of understanding how control guides decision-making, and when it doesn't.

Identifiers

PMID36711762
PMCPMC9882254
OpenAlexW4317600122

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.