Search · four archives
Search · four archives
22 papers · ranked by Valyu relevance
Willem B. Verwey, Wouter J. Dronkers
The present study tested the hypothesis that in motor sequences, the interval between successive movements is critical for the type of representation that develops. Participants practiced two 7-key sequences in the context of a discrete sequence production (DSP) task. The 0-RSI group practiced these sequences with…
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…
Hillary Schwarb, Eric H. Schumacher
serial reaction time task Authors: ['Hillary Schwarb' 'Eric H. Schumacher'] Over the last 20 years researchers have used the serial reaction time (SRT) task to investigate the nature of spatial sequence learning. They have used the task to identify the locus of spatial sequence learning, identify situations that…
Sabine Schwager, Dennis Rünger, Robert Gaschler, Peter A. Frensch
for the generation of explicit sequence knowledge? Authors: ['Sabine Schwager' 'Dennis Rünger' 'Robert Gaschler' 'Peter A. Frensch'] In incidental sequence learning situations, there is often a number of participants who can report the task-inherent sequential regularity after training. Two kinds of mechanisms for the…
Sean P. Anderson, Tyler J. Adkins, Bradley S. Gary, Taraz G. Lee
From typing on a keyboard to playing the piano, many everyday skills require the ability to quickly and accurately perform sequential movements. It is well-known that the availability of rewards lead to increases in motivational vigor whereby people enhance both the speed and force of their movements. However, in the…
Kata Horváth, Csenge Török, Orsolya Pesthy, Dezso Nemeth + 1 more
Procedural learning facilitates the efficient processing of complex environmental stimuli and contributes to the acquisition of automatic behaviour. In the present study, we investigated two sub-components of procedural learning: statistical learning and sequence learning. The former one refers to the acquisition of…
Juliana Yordanova, Roumen Kirov, Vasil Kolev
Only some, but not all, individuals who practice tasks with dual structure, overt and covert, are able to comprehend consciously a hidden regularity. The formation of implicit representations of regularity has been proposed to be critical for subsequent awareness. However, explicit knowledge also has been predicted by…
Tejas Savalia, Anuj Shukla, Raju S. Bapi
The capacity to sequence information is central to human performance. Sequencing ability forms the foundation stone for higher order cognition related to language and goal-directed planning. Information related to the order of items, their timing, chunking and hierarchical organization are important aspects in…
Basem G. El-Barashy
Current learning algorithms face many difficulties in learning simple patterns and using them to learn more complex ones. They also require more examples than humans do to learn the same pattern, assuming no prior knowledge. In this paper, a new learning framework is introduced that is called common-description…
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…
Felix Ball, Inga Spuerck, Toemme Noesselt
While temporal expectations (TE) generally improve reactions to temporally predictable events, it remains unknown how temporal rule learning and explicit knowledge about temporal rules contribute to performance improvements and whether any contributions generalise across modalities. Here, participants discriminated the…
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…
Ghodai Abdelrahman, Qing Wang
Can machines trace human knowledge like humans? Knowledge tracing (KT) is a fundamental task in a wide range of applications in education, such as massive open online courses (MOOCs), intelligent tutoring systems, educational games, and learning management systems. It models dynamics in a student's knowledge states in…
Kristjan Kalm, Dennis Norris
We contrast two accounts of how novel sequences are learned. The first is that learning changes the signal-to-noise ratio (SNR) of existing neural representations by reducing noise or increasing signal gain. Alternatively, learning might cause the initial representation of the sequence to be recoded into more efficient…
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…
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…
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
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…
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
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…