Examining the reliability and validity of two versions of the Effort-Expenditure for Rewards Task (EEfRT)
Hanno Andreas Ohmann; Niclas Kuper; Jan Wacker · 2022 · PLoS ONE
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
The Effort-Expenditure for Rewards Task (EEfRT) has gained validity evidence from several studies. However, various modifications have been applied to the original version, which have never been compared systematically. In Study 1, we tested 120 healthy participants to directly compare two versions of the EEfRT. In Study 2, we tested a larger sample of 394 healthy participants to further examine the original EEfRT. We replicated the split-half reliability of both task versions. However, self-reported personality traits (e.g., trait BAS) correlated with only some task performance parameters in
Abstract by Hanno Andreas Ohmann; Niclas Kuper; Jan Wacker, PLoS ONE (2022) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Our Princess Is in Another Castle
Negative / Null Result ReportUsing Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled Trial
Negative / Null Result ReportHow Replicable Are Links Between Personality Traits and Consequential Life Outcomes? The Life Outcomes of Personality Replication Project
Negative / Null Result ReportThe cross-cultural validity of posttraumatic stress disorder: implications for DSM-5
Negative / Null Result ReportPsychiatric and medical correlates of DSM‐5 eating disorders in a nationally representative sample of adults in the United States
Negative / Null Result ReportRomantic relationships and the physical and mental health of college students
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1371/journal.pone.0262902