Action selection appears to rely on conjunctive representations that nonlinearly integrate task-relevant features. Here, we tested a corollary of this hypothesis: that such representations are also intricately involved during attempts to stop an action-a key aspect of action regulation. We tracked both conjunctive representations and those of constituent rule, stimulus, or response features through trial-by-trial representational similarity analysis of the electroencephalogram signal in a combined rule-selection and stop-signal paradigm. Across two experiments with student participants ( = 57), we found (a) that the strength of decoded conjunctive representations prior to the stop signal uniquely predicted trial-by-trial stopping success (Experiment 1) and (b) that these representations were selectively suppressed following the onset of the stop signal (Experiments 1 and 2). We conclude that conjunctive representations are key to successful action execution and therefore need to be suppressed when an intended action is no longer appropriate.
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http://dx.doi.org/10.1177/09567976211034505 | DOI Listing |
J Chem Phys
January 2025
Department of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
Typical path integral Monte Carlo approaches use the primitive approximation to compute the probability density for a given path. In this work, we develop the pair discrete variable representation (pair-DVR) approach to study molecular rotations. The pair propagator, which was initially introduced to study superfluidity in condensed helium, is naturally well-suited for systems interacting with a pairwise potential.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, USA.
The integration of drug molecular representations into predictive models for Drug Response Prediction (DRP) is a standard procedure in pharmaceutical research and development. However, the comparative effectiveness of combining these representations with genetic profiles for DRP remains unclear. This study conducts a comprehensive evaluation of the efficacy of various drug molecular representations employing cutting-edge machine learning models under various experimental settings.
View Article and Find Full Text PDFWorld J Gastroenterol
December 2024
School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China.
Background: Wireless capsule endoscopy (WCE) has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging technology. However, the complexity of the digestive tract structure, and the diversity of lesion types, results in different sites and types of lesions distinctly appearing in the images, posing a challenge for the accurate identification of digestive tract diseases.
Aim: To propose a deep learning-based lesion detection model to automatically identify and accurately label digestive tract lesions, thereby improving the diagnostic efficiency of doctors, and creating significant clinical application value.
Cogn Neurodyn
December 2024
Research Centre of Mathematics, University of Minho, Guimarães, Portugal.
Continuous bump attractor networks (CANs) have been widely used in the past to explain the phenomenology of working memory (WM) tasks in which continuous-valued information has to be maintained to guide future behavior. Standard CAN models suffer from two major limitations: the stereotyped shape of the bump attractor does not reflect differences in the representational quality of WM items and the recurrent connections within the network require a biologically unrealistic level of fine tuning. We address both challenges in a two-dimensional (2D) network model formalized by two coupled neural field equations of Amari type.
View Article and Find Full Text PDFJ Neurosci
December 2024
Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
Real-world choice options have many features or attributes, whereas the reward outcome from those options only depends on a few features or attributes. It has been shown that humans learn and combine feature-based with more complex conjunction-based learning to tackle challenges of learning in naturalistic reward environments. However, it remains unclear how different learning strategies interact to determine what features or conjunctions should be attended to and control choice behavior, and how subsequent attentional modulations influence future learning and choice.
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