Evidence map›Paper›PMID 36510102›Full record

ArticleAttention, perception & psychophysics2023

Incidental auditory category learning and visuomotor sequence learning do not compete for cognitive resources.

Yafit Gabay, Michelle Madlansacay, Lori L Holt

Abstract read
PubMed Publisher
In one paragraph

Article in Attention, perception & psychophysics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Yafit GabayDepartment of Special Education, University of Haifa, Haifa, Israel. ygabay@edu.haifa.ac.il.ORCID http://orcid.org/0000-0002-7899-3044
Michelle MadlansacayDepartment of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.
Lori L HoltDepartment of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.

Funding

National Science Foundation BCS1655126United States-Israel Binational Science Foundation 2015227
6 · The paper itself

Abstract

The environment provides multiple regularities that might be useful in guiding behavior if one was able to learn their structure. Understanding statistical learning across simultaneous regularities is important, but poorly understood. We investigate learning across two domains: visuomotor sequence learning through the serial reaction time (SRT) task, and incidental auditory category learning via the systematic multimodal association reaction time (SMART) task. Several commonalities raise the possibility that these two learning phenomena may draw on common cognitive resources and neural networks. In each, participants are uninformed of the regularities that they come to use to guide actions, the outcomes of which may provide a form of internal feedback. We used dual-task conditions to compare learning of the regularities in isolation versus when they are simultaneously available to support behavior on a seemingly orthogonal visuomotor task. Learning occurred across the simultaneous regularities, without attenuation even when the informational value of a regularity was reduced by the presence of the additional, convergent regularity. Thus, the simultaneous regularities do not compete for associative strength, as in overshadowing effects. Moreover, the visuomotor sequence learning and incidental auditory category learning do not appear to compete for common cognitive resources; learning across the simultaneous regularities was comparable to learning each regularity in isolation.

Indexed as

LearningNeural Networks, ComputerCognitionFeedbackHumansReaction TimeDual taskIncidental auditory category learningOvershadowingStatistical learningVisuomotor sequence learning

Identifiers

What OpenQuestion holds

Textmetadata
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.