Evidence map›Paper›PMID 41044289›Full record

ReviewCognitive, affective & behavioral neuroscience2026

Insights into effortful adaptation during speech perception: The role of dopamine, norepinephrine, and prediction error.

Hannah Mechtenberg

Abstract readReview
PubMed Publisher
In one paragraph

Review in Cognitive, affective & behavioral neuroscience, 2026. 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

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

1 author.

Hannah MechtenbergDepartment of Psychological Sciences, University of Connecticut, Storrs Mansfield, CT, 06269, USA. hannah.mechtenberg@uconn.edu.

Funding

The Role of Frontal and Temporal Brain Areas in the Perception of Phonetic Category StructureR01DC013064 · NIDCD · UNIVERSITY OF CONNECTICUT STORRS · PI MYERS, EMILY B · 2014 to 2025
$4.3M
NIDCD NIH HHS R01 DC013064
6 · The paper itself

Abstract

In a highly variable world, listeners must be able to rapidly adjust to changing circumstances to prioritize comprehension. The neurobiological underpinnings of this ability are largely unspecified. This review combines insights from reinforcement learning, predictive coding, and the function of dopamine and norepinephrine to explore the viability of an effortful and error-driven system of adaptation during speech perception. Iteration, internal monitoring, error generation, and prediction are shared across multiple models of adaptation, leading to the possibility of a common mechanism. In search of this mechanism, I consider the role of error signaling within a domain-general account of perceptual adaptation. Drawing heavily from work in the neurobiology of reinforcement learning and effort, I theorize that adaptation during speech perception may be mediated by reward prediction errors wherein successful comprehension may act as its own reward. However, reward prediction error alone is likely insufficient to capture the full dynamics of rapid adaptation. I further propose that the system also leverages norepinephrine signaling to push the listener into an arousal state that allows for rapid perceptual fine-tuning. Ultimately, I argue that complementary, and parallel, error signals originating from multiple subcortical nuclei may direct effortful adaptation during challenges to speech perception.

Indexed as

Adaptation, PhysiologicalAdaptation, PsychologicalDopamineNorepinephrineSpeech PerceptionHumansReinforcement, PsychologyRewardDopamineNorepinephrineDopamineEffortNorephinephrinePerceptual adaptationPrediction errorSpeech perception

Identifiers

What OpenQuestion holds

Textmetadata
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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.