Evidence map›Paper›PMID 33381687›Full record

ArticlePerspectives on behavior science2020

Motivating Operations and Negative Reinforcement.

Timothy L Edwards, Alan Poling

Open access · greenAbstract read
In one paragraph

Article in Perspectives on behavior science, 2020. 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
0.3field-weighted citation impact, top 40% of its field
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, 5 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 2 institutions in 2 countries.

Timothy L EdwardsSchool of Psychology, University of Waikato, Private Bag 3105, Hamilton, 3240 New Zealand.ORCID https://orcid.org/0000-0002-1569-5656
Alan PolingWestern Michigan University, Kalamazoo, MI USA.
University of Waikato · NZWestern Michigan University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The motivating operations concept has improved the precision of our approach to analyzing behavior; it serves as a framework for classifying events that alter the reinforcing and punishing effectiveness of other events. Nevertheless, some aspects of the concept are seriously flawed, thereby limiting its utility. We contend in this article that the emphasis it places on the onset of some stimuli (putative motivating operations) making their offset a reinforcer in the absence of a learning history (i.e., in the case of unconditioned motivating operations), or because of such a history (i.e., in the case of reflexive conditioned motivating operations), is of no value in predicting or controlling behavior. It is unfortunate that this pseudo-analysis has been widely accepted, which has drawn attention away from actual motivating operations that are relevant to negative reinforcement, and led to conceptually flawed explanations of challenging human behaviors that are escape-maintained. When used appropriately, the motivating operations concept can help to clarify the conditions under which a stimulus change (in particular, stimulus termination) will function as a negative reinforcer. From both a theoretical and a practical perspective, rethinking the application of the motivating operations concept to negative reinforcement is advantageous. Herein, we explore the implications of doing so with the aim of encouraging relevant research and improving the practice of applied behavior analysis.

Indexed as

Abolishing operationsAvoidanceEscapeEstablishing operationsMotivationNegative reinforcement

Identifiers

PMID33381687
PMCPMC7724015
OpenAlexW3050225963

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.