22 papers · ranked by Valyu relevance
Anthony J. Onwuegbuzie
The original definition of qualitizing-the conversion of quantitative data into qualitative form-has made an important contribution to mixed methods research but presented a reductionist view that overlooked its interpretive and integrative dimensions. Building on Onwuegbuzie and Leech’s (2019) meta-framework, the…
Ryota Kanai, Ryota Takatsuki, Ippei Fujisawa
In this study, we explore how the notion of meta-representations in higher-order theories (HOT) of consciousness can be implemented in computational models. HOT suggests that consciousness emerges from meta-representations, which are representations of first-order sensory representations. However, translating this…
Marie Minet, Amila Beganovic, Shusruto Rishik, Elisa Michaeli + 10 more
During defined developmental windows in Drosophila, controlled re-replication generates physiological gene amplification. Although gene amplification has also been observed during human stem cell differentiation, re-replication in human cells has largely been linked to tumor-associated genome instability. Here, we…
Juliette Boscheron, Pepijn Schoenmakers, Arthur Trivier, Florian Lance + 4 more
Episodic autobiographical memory (EAM) relies on reactivations of cortical regions engaged during event encoding. While reinstatement of sensory regions is well documented, the role of internal signals from the person’s body has received less attention. Here, we investigated whether motor representations from encoding…
Mikhail Inyushin
George A. Miller’s classic discussion of memory capacity and Sidney Smith’s recoding experiments demonstrated that cognitive limits depend more strongly on the number of active representational units (“chunks”) than on the total amount of raw information being processed. Here, we reinterpret Smith’s experiments from…
Sebastijan Veselic, Nour Mohsen, Lennart Luettgau, Elena Gutierrez + 5 more
Reasoning flexibly composes known elements to solve novel problems. Recent theories suggest the brain uses the axis of time to compose elements for reasoning. In this view, elements are packaged into fast neural sequences, with each sequence exploring the implications of a different composition. Using…
Shunsuke Onoo, Yoshihiro Nagano, Yukiyasu Kamitani
Sensory representation is typically understood through a hierarchical-causal framework where progressively abstract features are extracted sequentially. However, this causal view fails to explain misrepresentation, a phenomenon better handled by an informational view based on decodable content. This creates a tension…
Jaskirat Singh, Boyang Zheng, Zongze Wu, Richard Zhang + 2 more
Representation Autoencoders (RAE) replace traditional VAE with pretrained vision encoders. In this paper, we systematically investigate several design choices and find three insights which simplify and improve RAE. First, we study a generalized formulation where the representation is defined as sum of the last k…
Roger Orpwood
There have been many very promising theories published concerning the generation of consciousness. These theories mostly link the emergence of consciousness to neural activity, but very few attempt to show how that neural activity specifically causes experience to occur. This article explores this problem at the level…
Authors not listed
Recent years have seen a growing interest in machine learning approaches for chemical tasks. The best existing methods focus on building base models that combine molecular graphs (“2D structures”) with atomic coordinates in 3D to predict molecular properties, typically through pre-training followed by fine-tuning on…
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Machine learning is increasingly used to predict reaction properties such as barrier heights, reaction energies, rates, or yields, as well as the underlying molecular geometries, including transition state structures. While such predictions have the potential to provide mechanistic insight for high-impact applications…
Ben Baker, Richard D. Lange, Andrew Richmond, Nikolaus Kriegeskorte + 3 more
Representations play a central role in the study of both biological and artificial intelligence, as well as philosophy of mind. Across neuroscience, computer science, and philosophy, a recurring theme is that representations not only carry information but should be ``useful'' for or ``usable'' by an agent in some…
Emily R. E. Evans, Niels van Poecke, Hanneke W. M. van Laarhoven, Esther Helmich + 1 more
Stories play an important role in making meaning of illness experiences and (re)establishing self-identity. Yet, telling and investigating stories about living with incurable cancer may be challenging, as people may perceive their illness experiences as hard to communicate. Visual research methods are increasingly…
Jiaojiao Guan, Jiayu Shang, Cheng Peng, Yanni Sun
Viruses play indispensable roles in ecosystems and human health, yet deciphering their molecular functions remains challenging. Many viral protein annotations are incomplete or poorly characterized. Existing tools typically predict functional categories without linking to verifiable evidence, hindering the credibility…
Mariano Martín-Villuendas
Model organisms have long occupied a central place in the life sciences, driving major discoveries and stabilizing knowledge about biological mechanisms conserved across taxa. However, their dominance has often eclipsed experimental model organisms-specimens chosen for their capacity to illuminate local hypotheses…
Mengya Zhang, Qing Yu
One ubiquitous feature of human intelligence is the ability to flexibly switch between multiple tasks. Abstract task representations provide a basis for task learning, switching, and generalization, yet how the brain coordinates multiple task representations to support multitasking and task-switching remains poorly…
Tarana Nigam, Andrea F. Campos-Pérez, Pierre Mégevand, Juan R. Vidal + 6 more
A hallmark of human intelligence is the ability to perform multiple tasks immediately upon instruction. Yet, the neural processes that implement such flexibility remain unclear. Using intracranial electrophysiology in epilepsy patients, we examined how representational geometries evolve as participants switched among…
Jinwen Wang, Youfang Lin, Xiaobo Hu, Qian Xu + 3 more
Visual Reinforcement Learning (VRL) has achieved considerable success in solving control tasks. However, generalizing learned policies to new environments remains a major challenge, as agents often overfit to task-irrelevant features in the training environment. To solve this problem, we introduce the concept of…
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Infrared (IR) spectroscopy provides rich structural information but interpreting spectra at scale remains challenging. Here we introduce j-IR-vis, a vision-based neural model that learns chemically interpretable representations directly from IR spectra for functional-group prediction and downstream molecular…
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Modeling of chemical reactions is essential for understanding kinetic mechanisms and predicting possible outcomes of reacting systems. Quantum mechanical calculations are accurate but often prohibitively expensive. Deep learning has emerged as a faster alternative, but progress is slowed by a fragmented software…
Authors not listed
Conventional molecular graphs often are unable to reliably encode stereochemistry, especially for symmetric molecules, non-tetrahedral centers, and transition states. To overcome this, we present StereoMolGraph, an open source Python library implementing a stereochemistry-aware graph representation for molecules and…
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The Polytope Formalism provides a rigorous and unifying mathematical framework for representing all possible molecular configurations and their interrelationships. Extending its application from stereoisomerism to molecular constitution reveals that both arise from a common structural foundation linking discrete and…