19 papers · ranked by Valyu relevance
Paul I. Jaffe, Russell A. Poldrack, Robert J. Schafer, Patrick G. Bissett
Response time (RT) data collected from cognitive tasks are a cornerstone of psychology and neuroscience research, yet existing models of these data either make strong assumptions about the data generating process or are limited to modeling single trials. We introduce task-DyVA, a deep learning framework in which…
Aishwarya Shaji, S. Lakshmi Kruthika, Chandresh Prakash, S. Abinaya
The cognitive state modeling (CSM) problem is typically formulated as a classification problem, limiting the application of the CSM for adaptive real world applications, where the desired outputs are cognitive states to be desired and the inferred ones have to be used for decision making. While conventional methods…
Sabine Prezenski, André Brechmann, Susann Wolff, Nele Russwinkel
Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying…
Gunther Eysenbach, José M Cogollor, Susanne Dirks, Ana Lúcia Faria + 2 more
'Maria Salomé Pinho' 'Sergi Bermúdez i Badia'] Background Cognitive impairments after stroke are not always given sufficient attention despite the critical limitations they impose on activities of daily living (ADLs). Although there is substantial evidence on cognitive rehabilitation benefits, its implementation is…
Drew E. Winters
Studying flexible, adaptive transitions between cognitive tasks and serial-parallel processing under changing task demands has been a central focus for understanding human cognition. Advances in neuroimaging analysis have improved the ability to link cognition with brain function, providing a foundation for developing…
Milena Rmus, Akshay K. Jagadish, Marvin Mathony, Tobias Ludwig + 1 more
'Eric Schulz'] Computational cognitive models, which formalize theories of cognition, enable researchers to quantify cognitive processes and arbitrate between competing theories by fitting models to behavioral data. Traditionally, these models are handcrafted, which requires significant domain knowledge, coding…
Nikolaus Kriegeskorte, Pamela K. Douglas
To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments. Cognitive science has developed computational models of human cognition, decomposing task performance into computational components.…
Daniel V. Holt, Magda Osman
Much of human decision making occurs in dynamic situations where decision makers have to control a number of interrelated elements (dynamic systems control). Although in recent years progress has been made toward assessing individual differences in control performance, the cognitive processes underlying exploration and…
Sandeep S. Nair, Vignayanandam R. Muddapu, C Vigneswaran, Pragathi P. Balasubramani + 3 more
Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational…
Woo-Young Ahn, Nathaniel Haines, Lei Zhang
Reinforcement learning and decision-making (RLDM) provide a quantitative framework, which allows us to specify psychiatric conditions with basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assess and potentially diagnose psychiatric patients, and there is growing enthusiasm on RLDM and…
Simon R. Steinkamp, Gereon R. Fink, Simone Vossel, Ralph Weidner
Understanding how brain activity translates into behavior is a grand challenge in neuroscientific research. Simultaneous computational modeling of both measures offers to address this question. The extension of the dynamic causal modeling (DCM) framework for BOLD responses to behavior (bDCM) constitutes such a modeling…
Tim Schürmann, Philipp Beckerle
Cognitive modeling of human behavior has advanced the understanding of underlying processes in several domains of psychology and cognitive science. In this article, we outline how we expect cognitive modeling to improve comprehension of individual cognitive processes in human-agent interaction and, particularly…
Wilka Carvalho, Andrew K. Lampinen
How should cognitive science pursue generalizable theories? We argue that progress in Artificial Intelligence (AI) offers opportunities to embrace naturalistic experiments and computational models that can accommodate them. We first review literature suggesting that building generalizable theories may require a broader…
Fei Han, Christopher Reardon, Lynne E. Parker, Hao Zhang
In order for cooperative robots ("co-robots") to respond to human behaviors accurately and efficiently in human-robot collaboration, interpretation of human actions, awareness of new situations, and appropriate decision making are all crucial abilities for corobots. For this purpose, the human behaviors should be…
Brendan Conway-Smith, Robert L. West
Attempts to import dual-system descriptions of System-1 and System-2 into AI have been hindered by a lack of clarity over their distinction. We address this and other issues by situating System-1 and System-2 within the Common Model of Cognition. Results show that what are thought to be distinctive characteristics of…
Baihan Lin
Emerging research frontiers and computational advances have gradually transformed cognitive science into a multidisciplinary and data-driven field. As a result, there is a proliferation of cognitive theories investigated and interpreted from different academic lens and in different levels of abstraction. We formulate…
Nicholas T. Franklin, Kenneth A. Norman, Charan Ranganath, Jeffrey M. Zacks + 1 more
Humans spontaneously organize a continuous experience into discrete events and use the learned structure of these events to generalize and organize memory. We introduce the Structured Event Memory (SEM) model of event cognition, which accounts for human abilities in event segmentation, memory, and generalization. SEM…
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Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…
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Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…