Search · four archives
Search · four archives
20 papers · ranked by Valyu relevance
Willem B. Verwey
An exhaustive review is reported of over 25 years of research with the Discrete Sequence Production (DSP) task as reported in well over 100 articles. In line with the increasing call for theory development, this culminates into proposing the second version of the Cognitive framework of Sequential Motor Behavior (C-SMB…
Marius Barth, Christoph Stahl, Hilde Haider
Sequence learning in the serial response time task (SRTT) is one of few learning phenomena where researchers agree that such learning may proceed in the absence of awareness, while it is also possible to explicitly learn a sequence of events. In the past few decades, research into sequence learning largely focused on…
Li-Ann Leow, Jarrad Lum, Sara Johnson, Emily Corti + 1 more
Musicians demonstrate advantages in acquiring motor sequences, showing faster learning and better explicit sequence knowledge than non-musicians. However, it is unclear whether this advantage extends beyond acquisition to the consolidation phase, which is when newly learned skills stabilize and become resistant to…
Sarah Seiwert, Elodie Martin, Yannick Lagarrigue, David Amarantini + 3 more
The present study aimed to explore (a) the impact of the explicit knowledge of the task on PPML and (b) the links between PPML and subjective evaluation of the task and of their performance, including the levels of stress, motivation, tiredness, attention and perceived progress. Experiment 1 assessed motor sequence…
Susanne Dyck, Christian Klaes
Many motor actions we perform have a sequential nature while learning a motor sequence involves both implicit and explicit processes. In this work, we developed a task design where participants concurrently learn an implicit and an explicit motor sequence across five training sessions, with EEG recordings at sessions 1…
Noémi Éltető, Dezső Nemeth, Karolina Janacsek, Peter Dayan
Humans can implicitly learn complex perceptuo-motor skills over the course of large numbers of trials. This likely depends on our becoming better able to take advantage of ever richer and temporally deeper predictive relationships in the environment. Here, we offer a novel characterization of this process, fitting a…
Emily Cordeiro, Daniela Herrera Chaves, Nima Talei, Iván Castro + 6 more
Statistical learning (SL) has been proposed to depend on the hippocampus, but traditional neuropsychological theories of long-term memory posit that the hippocampus is only necessary for explicit memory processes, not implicit memory processes. To reconcile these two accounts, we exposed 27 temporal lobe epilepsy (TLE)…
Shuchen Wu, Mirko Thalmann, Peter Dayan, Zeynep Akata + 1 more
Concrete Sequences Authors: ['Shuchen Wu' 'Mirko Thalmann' 'Peter Dayan' 'Zeynep Akata' 'Eric Schulz'] Humans excel at learning abstract patterns across different sequences, filtering out irrelevant details, and transferring these generalized concepts to new sequences. In contrast, many sequence learning models lack…
Przemysław Stokłosa, Janusz A. Starzyk, Paweł Raif, Adrian Horzyk + 1 more
Retrieval Authors: ['Przemysław Stokłosa' 'Janusz A. Starzyk' 'Paweł Raif' 'Adrian Horzyk' 'M. Kowalik'] This paper presents a novel approach for constructing associative knowledge graphs that are highly effective for storing and recognizing sequences. The graph is created by representing overlapping sequences of…
Nina Dolfen, Amir Tal, Wim Fias, Lila Davachi
Learning temporal regularities between pairs of events has been shown to shape neural representations in the medial temporal lobe, but it remains unclear whether representational changes generalize across memory domains. Here we used fMRI and multivoxel pattern similarity analyses to examine representational shaping as…
Nadira Yusif Rodriguez, Theresa H. McKim, Debaleena Basu, Aarit Ahuja + 1 more
Monitoring sequential information is an essential component of our daily lives. Many of these sequences are abstract, in that they do not depend on the individual stimuli, but do depend on an ordered set of rules (e.g., chop then stir when cooking). Despite the ubiquity and utility of abstract sequential monitoring…
Dominik Garber, József Fiser
Transfer learning, the re-application of previously learned higher-level regularities to novel input, is a key challenge in cognition. While previous empirical studies investigated human transfer learning in supervised or reinforcement learning for explicit knowledge, it is unknown whether such transfer occurs during…
Peiran Li
MeMo proposes language models with explicit multi-layer correlation matrix memories (CMMs), where memorization, retrieval, and forgetting are architectural operations. This paper asks how such memories can reduce the need for retraining when knowledge changes. For changes expressible as MeMo memory associations, the…
Zeb Kurth‐Nelson, Timothy E.J. Behrens, Greg Wayne, Kevin Miller + 4 more
'Lennart Luettgau' 'Raymond J. Dolan' 'Yunzhe Liu' 'Philipp Schwartenbeck'] Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK Wellcome Centre for Human Neuroimaging, University College London, London, UK Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK…
Authors not listed
Perovskite solar cell performance depends on the joint configuration of materials, interfaces, and layer-specific physical parameters, forming a structured design space that is naturally sequential but rarely modeled as such. This work introduces PervoTransformer, a transformer-based framework that represents complete…
Bowen Xu
Sequence learning is an essential aspect of intelligence. In Artificial Intelligence, sequence prediction task is usually used to test a sequence learning model. In this paper, a model of sequence learning, which is interpretable through Non-Axiomatic Logic, is designed and tested. The learning mechanism is composed of…
Wout Bittremieux, Varun Ananth, William E. Fondrie, Carlo Melendez + 5 more
Protein tandem mass spectrometry data is most often interpreted by matching observed mass spectra to a protein database derived from the reference genome of the sample being analyzed. In many application domains, however, a relevant protein database is unavailable or incomplete, and in such settings de novo sequencing…
Authors not listed
Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
Authors not listed
Accurate prediction of chemical reaction yields remains essential for accelerating synthesis optimization, yet current machine learning models face critical limitations in capturing temporal dynamics, providing calibrated uncertainty estimates, and explicitly modeling reactant-to-product transformations. Here we…
Yuxuan Wu, Guangming Wang, Zhiheng Yang, Maoqing Yao + 2 more
Developing general robot intelligence in open environments requires continual skill learning. Recent Vision-Language-Action (VLA) models leverage massive pretraining data to support diverse manipulation tasks, but they still depend heavily on task-specific fine-tuning, revealing a lack of continual learning capability.…