When we perform any task, we engage a diverse set of processes. These processes can be optimized with learning. While there exists substantial research that probes specific aspects of learning, there is a scarcity of research regarding interactions between different types of learning. Here, we investigate possible interactions between Perceptual Learning (PL) and Contextual Learning (CL), two types of implicit learning that have garnered much attention in the psychological sciences and that often co-occur in natural settings. PL increases sensitivity to features of task targets and distractors and is thought to involve improvements in low-level perceptual processing. CL regards learning of regularities in the environment (such as spatial relations between objects) and is consistent with improvements in higher level perceptual processes. Surprisingly, we found CL, PL for target features, and PL for distractor features to be independent. This triple dissociation demonstrates how different learning processes may operate in parallel as tasks are mastered.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1167/12.2.5 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
PathInHydro, a Set of Machine Learning Models to Identify Unbinding Pathways of Gas Molecules in [NiFe] Hydrogenases.
J Chem Inf Model
January 2025
Institute of Chemistry, Technische Universität Berlin, Straße des 17. Juni 135, Berlin 10623, Germany.
Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding pathways for the dissociation of gas molecules from [NiFe] hydrogenases, using the unbinding trajectories of CO and H from [NiFe] hydrogenase as a training set.
View Article and Find Full Text PDFEstimation of Central Aortic Pressure Waveforms by Combination of a Meta-Learning Neural Network and a Physics-Driven Method.
Int J Numer Method Biomed Eng
January 2025
College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
The accurate non-invasive detection and estimation of central aortic pressure waveforms (CAPW) are crucial for reliable treatments of cardiovascular system diseases. But the accuracy and practicality of current estimation methods need to be improved. Our study combines a meta-learning neural network and a physics-driven method to accurately estimate CAPW based on personalized physiological indicators.
View Article and Find Full Text PDFAccelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning.
ACS Sens
January 2025
Department of Physics, Chalmers University of Technology, SE-41296 Göteborg, Sweden.
Rapidly detecting hydrogen leaks is critical for the safe large-scale implementation of hydrogen technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets under technically relevant conditions. Here, we demonstrate how a tailored long short-term transformer ensemble model for accelerated sensing (LEMAS) speeds up the response of an optical plasmonic hydrogen sensor by up to a factor of 40 and eliminates its intrinsic pressure dependence in an environment emulating the inert gas encapsulation of large-scale hydrogen installations by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware.
View Article and Find Full Text PDFIncidence of fall-from-height injuries and predictive factors for severity.
J Osteopath Med
January 2025
McAllen Department of Trauma, South Texas Health System, McAllen, TX, USA.
Context: The injuries caused by falls-from-height (FFH) are a significant public health concern. FFH is one of the most common causes of polytrauma. The injuries persist to be significant adverse events and a challenge regarding injury severity assessment to identify patients at high risk upon admission.
View Article and Find Full Text PDFDecoding Salience: A Functional Magnetic Resonance Imaging Investigation of Reward and Contextual Unexpectedness in Memory Encoding and Retrieval.
Hum Brain Mapp
January 2025
Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, Magdeburg, Germany.
The present study investigated the neuromodulatory substrates of salience processing and its impact on memory encoding and behaviour, with a specific focus on two distinct types of salience: reward and contextual unexpectedness. 46 Participants performed a novel task paradigm modulating these two aspects independently and allowing for investigating their distinct and interactive effects on memory encoding while undergoing high-resolution fMRI. By using advanced image processing techniques tailored to examine midbrain and brainstem nuclei with high precision, our study additionally aimed to elucidate differential activation patterns in subcortical nuclei in response to reward-associated and contextually unexpected stimuli, including distinct pathways involving in particular dopaminergic modulation.
View Article and Find Full Text PDFWant 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!