The underpinning assumption of much research on cognitive individual differences (or group differences) is that task performance indexes cognitive ability in that domain. In many tasks performance is measured by differences (costs) between conditions, which are widely assumed to index a psychological process of interest rather than extraneous factors such as speed-accuracy trade-offs (e.g., Stroop, implicit association task, lexical decision, antisaccade, Simon, Navon, flanker, and task switching). Relatedly, reaction time (RT) costs or error costs are interpreted similarly and used interchangeably in the literature. All of this assumes a strong correlation between RT-costs and error-costs from the same psychological effect. We conducted a meta-analysis to test this, with 114 effects across a range of well-known tasks. Counterintuitively, we found a general pattern of weak, and often no, association between RT and error costs (mean = .17, range -.45 to .78). This general problem is accounted for by the theoretical framework of evidence accumulation models, which capture individual differences in (at least) 2 distinct ways. Differences affecting accumulation rate produce positive correlation. But this is cancelled out if individuals also differ in response threshold, which produces negative correlations. In the models, subtractions between conditions do not isolate processing costs from caution. To demonstrate the explanatory power of synthesizing the traditional subtraction method within a broader decision model framework, we confirm 2 predictions with new data. Thus, using error costs or RT costs is more than a pragmatic choice; the decision carries theoretical consequence that can be understood through the accumulation model framework. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195302 | PMC |
http://dx.doi.org/10.1037/bul0000164 | DOI Listing |
Appl Sci (Basel)
June 2024
Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE 68182, USA.
Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2-5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series.
View Article and Find Full Text PDFACS Sens
January 2025
Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, British Columbia V6T 1Z4, Canada.
Natural gas (NG) is a promising alternative to diesel for sustainable transport, potentially reducing GHG and air quality emissions significantly. However, the GHG benefits hinge on managing methane slip, the unburned methane in the exhaust of NG engines, which carries a significant global warming potential. The CH slip from NG engines is highly dependent on engine type and operation, and effective greenhouse gas emission mitigation requires that the actual operation of real-world engines is monitored.
View Article and Find Full Text PDFVet Anim Sci
March 2025
University of Dar es Salaam, P.O. Box 35091, Dar es Salaam, Tanzania.
This study aimed to evaluate and compare Bayesian predictive models to identify and quantify the key household inputs affecting cattle milk production in Tanzania. A sample of 1,266 households with at least one milked cow was extracted from the National Panel Survey datasets, the data were collected in 2012/2013 (wave 3), 2014/2015 (wave 4), and 2020/2021 (wave 5). Two generalized linear and generalized additive mixed models were fitted using the Integrated Nested Laplace Approximation.
View Article and Find Full Text PDFData Brief
February 2025
School of Engineering and Technology, University of New South Wales, Canberra, Australia.
This dataset is generated from real-time simulations conducted in MATLAB/Simscape, focusing on the impact of smart noise signals on battery energy storage systems (BESS). Using Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), noise signals in the form of subtle millivolt and milliampere variations are strategically created to represent realistic cases of False Data Injection Attacks (FDIA). These signals are designed to disrupt the State of Charge (SoC) and State of Health (SoH) estimation blocks within Unscented Kalman Filters (UKF).
View Article and Find Full Text PDFAnn N Y Acad Sci
January 2025
School of Psychology, Shenzhen University, Shenzhen, China.
Individuals with high math anxiety (HMA) demonstrate a tendency to avoid math-related tasks, a behavior that perpetuates a detrimental cycle of limited practice, poor performance, increased anxiety, and further avoidance. This study delves into the cognitive and neural bases of math avoidance behavior in HMA through the lens of reward processing. In Experiment 1, participants reported their satisfaction level in response to the reward provided after solving an arithmetic problem.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!