Ego-depletion, a psychological phenomenon in which participants are less able to engage in self-control after prior exertion of self-control, has become widely popular in the scientific community as well as in the media. However, considerable debate exists among researchers as to the nature of the ego-depletion effect, and growing evidence suggests the effect may not be as strong or robust as the extant literature suggests. We examined the robustness of the ego-depletion effect and aimed to maximize the likelihood of detecting the effect by using one of the most widely used depletion tasks (video-viewing attention control task) and by considering task characteristics and individual differences that potentially moderate the effect. We also sought to make our research plan transparent by pre-registering our hypotheses, procedure, and planned analyses prior to data collection. Contrary to the ego-depletion hypothesis, participants in the depletion condition did not perform worse than control participants on the subsequent self-control task, even after considering moderator variables. These findings add to a growing body of evidence suggesting ego-depletion is not a reliable phenomenon, though more research is needed that uses large sample sizes, considers moderator variables, and pre-registers prior to data collection.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4749338PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147770PLOS

Publication Analysis

Top Keywords

task characteristics
8
characteristics individual
8
individual differences
8
task considering
8
prior data
8
data collection
8
moderator variables
8
ego-depletion
5
evidence ego-depletion
4
task
4

Similar Publications

Purpose: Mental health screening is recommended by the US Preventive Services Task Force for all patients in areas where treatment options are available. Still, it is estimated that only 4% of primary care patients are screened for depression. The goal of this study was to evaluate the efficacy of machine learning technology (Kintsugi Voice, v1, Kintsugi Mindful Wellness, Inc) to detect and analyze voice biomarkers consistent with moderate to severe depression, potentially allowing for greater compliance with this critical primary care public health need.

View Article and Find Full Text PDF

Background: The escalating global scarcity of skilled health care professionals is a critical concern, further exacerbated by rising stress levels and clinician burnout rates. Artificial intelligence (AI) has surfaced as a potential resource to alleviate these challenges. Nevertheless, it is not taken for granted that AI will inevitably augment human performance, as ill-designed systems may inadvertently impose new burdens on health care workers, and implementation may be challenging.

View Article and Find Full Text PDF

Differentiating Choroidal Melanomas and Nevi Using a Self-Supervised Deep Learning Model Applied to Clinical Fundoscopy Images.

Ophthalmol Sci

November 2024

Liverpool Ocular Oncology Research Group, Department of Eye and Vision Science, Institute of Life Course and Medical Sciences (ILCaMS), University of Liverpool, Liverpool, United Kingdom.

Purpose: Testing the validity of a self-supervised deep learning (DL) model, RETFound, for use on posterior uveal (choroidal) melanoma (UM) and nevus differentiation.

Design: Case-control study.

Subjects: Ultrawidefield fundoscopy images, both color and autofluorescence, were used for this study, obtained from 4255 patients seen at the Liverpool Ocular Oncology Center between 1995 and 2020.

View Article and Find Full Text PDF

Little is known about the effect of prior social performance feedback on face processing. Our previous study explored how equal and unequal social comparison-related outcomes modulate event-related potential (ERP) responses to subsequently-presented faces, where interests between oneself and others were independent (noncompetitive situations). Here, we aimed to extend this investigation by assessing how different unequal social comparison-related outcomes affect face processing under noncompetitive and competitive situations (i.

View Article and Find Full Text PDF

In the intelligent harvesting of eggplant, the lack of in situ identification technology makes it challenging to determine the maturity of purple eggplant fruit. The length of the fruit-setting date can determine when the eggplant is ready to be harvested. This study uses deep learning techniques to predict the date of fruit maturity.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!