25 papers · ranked by Valyu relevance
Hiemke K. Schmidt, Martin Rothgangel, Dietmar Grube
Prior knowledge is known to facilitate learning new information. Normally in studies confirming this outcome the relationship between prior knowledge and the topic to be learned is obvious: the information to be acquired is part of the domain or topic to which the prior knowledge belongs. This raises the question as to…
Kathleen A. Hansen, Sarah F. Hillenbrand, Leslie G. Ungerleider
Humans use prior knowledge to bias decisions made under uncertainty. In this fMRI study we predicted that different brain dynamics play a role when prior knowledge is added to decisions made under perceptual vs. categorical uncertainty. Subjects decided whether shapes belonged to Category S - smoother - or Category B -…
Oded Bein, Niv Reggev, Anat Maril
What does it mean to say, “a new association is learned”? And how is this learning different when adding new information to already-existing knowledge? Here, participants associated pairs of faces while undergoing fMRI, under two different conditions: a famous, highly-familiar face with a novel face or two novel faces.…
Cécile Gal, Ioana Țincaș, Vasile V. Moca, Andrei Ciuparu + 4 more
Recognising objects is a vital skill on which humans heavily rely to respond quickly and adaptively to their environment. Yet, we lack understanding on the role visual information sampling plays in this process, and its relation to the individual’s priors. To bridge this gap, the eye-movements of 18 adult participants…
Semir Zeki, Oliver Y. Chén
We discuss here what we feel could be an improvement in future discussions of the brain operating as a Bayesian-Laplacian system, by distinguishing between two classes of priors on which the brain’s inferential systems operate. In one category are biological priors (ß priors) and in the other artefactual ones (α…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Claire Chambers, Hugo Fernandes, Konrad Paul Kording
If the brain abstractly represents probability distributions as knowledge, then the modality of a decision, e.g. movement vs perception, should not matter. If on the other hand, learned representations are policies, they may be specific to the task where learning takes place. Here, we test this by asking if a learned…
Marcelo Gleiser, Damian Sowinski
Science is a constructed narrative of the natural world based on information gathering and its subsequent analysis. In this essay, we develop a novel approach to the epistemic foundations of the scientific narrative, as based on our experiential interactions with the natural world. We first review some of the basic…
Claire Field
In this paper, I discuss Whiting’s ([19]) account of rational belief and discuss some unresolved issues arising from its reliance on epistemic possibility and, by extension, perspective-relative aprioricity.
Jonas M. Mikhaeil, Donald P. Green
How much does a research study contribute to a scientific literature? We propose a learning metric to quantify how much a research community learns from a given study. To do so, we adopt a Bayesian perspective and assess changes in the community's beliefs once updated with a new study's evidence. We recommend the…
Ronald R. Yager
We introduce the operation of possibility qualification and show how this modal-like operator can be used to represent "typical" or default I knowledge in a theory of nonmonotonic reasoning. We investigate the representational power of this ·approach I by· looking at a number of prototypical problems from the…
Norman C. Dalkey
The form �nd justification of inductive inference rules depend strongly on the represent�tion of uncertainty. This paper examines one generic representation, namely, incomplete information. The notion can be formalized by presuming that the re}�vant probabilities rn a decision problem are known only to the extent that…
Authors not listed
One purpose -- quite a few thinkers would say the main purpose -- of seeking knowledge about the world is to enhance our ability to make good decisions. An item of knowledge that can make no conceivable difference with regard to anything we might do would strike many as frivolous. Whether or not we want to be…
Kan Hatakeyama-Sato, Seigo Watanabe, Naoki Yamane, Yasuhiko Igarashi + 1 more
Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current…
Luís M. Augusto
The concept of unconscious knowledge is fundamental for an understanding of human thought processes and mentation in general; however, the psychological community at large is not familiar with it. This paper offers a survey of the main psychological research currently being carried out into cognitive processes, and…
Kathryn Blackmond Laskey
A framework is presented for a computational theory of probabilistic argument. The Probabilistic Reasoning Environment encodes knowledge at three levels. At the deepest level are a set of schemata encoding the system's domain knowledge. This knowledge is used to build a set of second-level arguments, which are…
Authors not listed
Identifying synthesis routes from knowledge graphs poses challenges beyond retrosynthesis, including path–finding artifacts and data issues. We introduce “SynGPS”, a novel algorithm that overcomes these limitations by identifying viable routes even with common artifacts. SynGPS can resolve nonsensical cycles…
Constanza Fierro, Ruchira Dhar, Filippos Stamatiou, Nicolas Garneau + 1 more
'Anders Søgaard'] Knowledge claims are abundant in the literature on large language models (LLMs); but can we say that GPT-4 truly "knows" the Earth is round? To address this question, we review standard definitions of knowledge in epistemology and we formalize interpretations applicable to LLMs. In doing so, we…
Jürgen Jost
We investigate the basic principles of structural knowledge. Structural knowledge underlies cognition, and it organizes, selects and assigns meaning to information. It is the result of evolutionary, cultural and developmental processes. Because of its own constraints, it needs to discover and exploit regularities and…
Jonathan Najenson, Nir Fresco
Knowledge-how is the kind of knowledge implicated in skill employment and acquisition. Intellectualists claim that knowledge-how is a special type of propositional knowledge. Anti-intellectualists claim that knowledge-how is not propositional. We argue that both views face two open challenges. The first challenge…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…
Ravi Kashyap
| 1 | Abstract | | 2 | | --- | --- | --- | --- | | 2 | | Knowledge for What Sake? | 3 | | 3 | | Questions & Answers, Q&A, Definitions and Assumptions, D&A, in our DNA | 5 | | | 3.1 | Related Literature | 6 | | | 3.2 | Framework (D&A) for Knowledge Valuation | 8 | | 4 | | Switching on Knowledge Machines | 12 | | | 4.1 |…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…
Authors not listed
In philosophy and science, a first principle is a basic proposition or assumption that cannot be deduced from any other proposition or assumption. Ancient Greek philosophy Aristotle defined the first principle as “the first basis from which a thing is known.” First principles thinking (or reasoning from first…