25 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…
Jie Deng, Washington Taylor, Simon A. Levin, Serguei Saavedra
The dynamics of ecological communities in nature are typically characterized by probabilistic, sequential assembly processes (i.e., invasion dynamics). Because of technical challenges, however, the majority of theoretical and experimental studies have focused on the outcomes derived from simultaneous assembly processes…
Ru Zhang, Yanjun Liu, James T. Townsend
An immense number of psychological tasks involve mental operations on various types of perceptual, cognitive, or action entities. Thus, questions concerning whether these operations occur in parallel (i.e., simultaneously), in serial (i.e., one at a time) or in some more complex fashion arise. Though reaching back to…
Andrew D Levy, Peter Zeidman, Karl Friston
Sequential experimental paradigms are fundamental to cognitive neuroscience, yet standard event-related response analysis struggles with the temporal variability inherent to these designs. Conventional epoching treats each event within a sequence as an independent response, discarding the temporal dependencies between…
Kevin Zhang, Neha Patki, Kalyan Veeramachaneni
The goal of this paper is to describe a system for generating synthetic sequential data within the Synthetic data vault. To achieve this, we present the Sequential model currently in SDV, an end-to-end framework that builds a generative model for multi-sequence, real-world data. This includes a novel neural…
Niek Tax, Irene Teinemaa, Sebastiaan J. van Zelst
Data of sequential nature arise in many application domains in forms of, e.g. textual data, DNA sequences, and software execution traces. Different research disciplines have developed methods to learn sequence models from such datasets: (i) in the machine learning field methods such as (hidden) Markov models and…
Anne Voormann, Jeff Miller
A common finding across numerous response time (RT) paradigms is that the mean RT in one trial depends strongly on the characteristics of the immediately preceding trial. Although such sequential effects have usually only been considered within each single paradigm in isolation from the others, there are important…
Haiyan Zhao, Björn Andersson, Boliang Guo, Tao Xin
Writing assessments are an indispensable part of most language competency tests. In our research, we used cross-classified models to study rater effects in the real essay rating process of a large-scale, high-stakes educational examination administered in China in 2011. Generally, four cross-classified models are…
Heather M. Mong, David P. McCabe, Benjamin A. Clegg
process-dissociation Authors: ['Heather M. Mong' 'David P. McCabe' 'Benjamin A. Clegg'] This paper proposes a way to apply process-dissociation to sequence learning in addition and extension to the approach used by Destrebecqz and Cleeremans ([17]). Participants were trained on two sequences separated from each other…
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…
Niels Grewe
Background Biomedical ontologies usually encode knowledge that applies always or at least most of the time, that is in normal circumstances. But for some applications like phenotype ontologies it is becoming increasingly important to represent information about aberrations from a norm. These aberrations may be…
Authors not listed
Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture…
Xueying Tang, Susu Zhang, Zhi Wang, Jingchen Liu + 1 more
Process data refer to data recorded in the log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents' response processes of solving the items. Process data analysis aims at enhancing educational assessment accuracy and serving other assessment purposes by…
Nazanin Mehrasa, Ruizhi Deng, Mohamed A. Ahmed, Bo Chang + 4 more
'Thibaut Durand' 'Marcus A. Brubaker' 'Greg Mori'] Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly models the point process distribution by utilizing normalizing flows. This approach is capable…
Bernard Costa, Marcus V. C. Baldo, Carolina Feher da Silva
Adaptive human behaviour depends on the ability to detect regularities and probabilistic structures within a noisy environment. Repeated binary choice tasks, in which individuals predict one of two possible outcomes, have long served as a fundamental tool for investigating learning, reward processing, and…
Sebastian Berger, Andrii Kravtsiv, Gerhard Schneider, Denis Jordan
Ordinal patterns are the common basis of various techniques used in the study of dynamical systems and nonlinear time series analysis. The present article focusses on the computational problem of turning time series into sequences of ordinal patterns. In a first step, a numerical encoding scheme for ordinal patterns is…
Michael A. Boemo, Luca Cardelli, Conrad A. Nieduszynski
Biological systems are made up of components that change their actions (and interactions) over time and coordinate with other components nearby. Together with a large state space, the complexity of this behaviour can make it difficult to create concise mathematical models that can be easily extended or modified. This…
Authors not listed
Sequence is the critical determinant of macromolecular function, yet current polymer design approaches often optimize monomer composition and ratios while ignoring sequence. This creates poorly defined design spaces for active learning that miss the vast combinatorial landscape of sequence possibilities. We introduce…
Anna Kalenkova, Lewis Mitchell, Matthew Roughan
Process mining is a well-established discipline of data analysis focused on the discovery of process models from information systems' event logs. Recently, an emerging subarea of process mining – stochastic process discovery has started to evolve. Stochastic process discovery considers frequencies of events in the…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
James P. Crutchfield, Antonio M. Scarfone
We show that mixtures comprising multicomponent systems typically are much more structurally complex than the sum of their parts; sometimes, infinitely more complex. We contrast this with the more familiar notion of statistical mixtures, demonstrating how statistical mixtures miss key aspects of emergent hierarchical…
Josh M. Salet, Wouter Kruijne, Hedderik van Rijn, Eckart Zimmermann + 1 more
Timing studies on statistical learning predominantly present temporal regularities in a discrete, trial-by-trial manner. This, however, is a simplified representation of nature’s complex temporal structure in which regularities are embedded in a continuous stream of interrupting, irregular events. Recent studies using…
Michael Statt, Kristopher Brown, Santosh Suram, Linda Hung + 3 more
In this work, we present DBgen, a Python library that provides a framework for defining extract-transform-load (ETL) pipelines to create and populate SQL databases. DBgen is most useful when the underlying data has complex relationships, requires multi-step analysis, is large-scale, and the type of data being collected…
Authors not listed
The lithium-ion batteries (LIBs) industry has expanded quickly despite technological constraints. Additionally, raw materials supply, end-of-life (EoL) management, and the creation of LIB manufacturing policies are receiving attention. All these concerns could be addressed simultaneously by integrating recycling of EoL…
Authors not listed
Classical molecular dynamics (MD) simulation is the most computationally efficient way to model large molecular systems atomistically for extended periods; however, due to fixed forcefield parameters, incorporating on-the-fly quantum reactions is not straightforward. Reactive Step-Based Molecular Dynamics (RSMD) is a…