25 papers · ranked by Valyu relevance
Ari Liu, Michael Schartner, Ila Fiete
Expectations stemming from prior knowledge have long been known to influence stimulus processing and decision-making. Empowered by brain-wide recordings during a two-choice sensory decision-making task from the International Brain Laboratory^1^, we sought to identify the form of expectation-based modulations and how…
Georgia Milne, Oris Shenyan, Laura Haye, Matteo Lisi + 1 more
Visual perception depends on both sensory input and prior knowledge, and the quality of these information sources changes across the lifespan. As people age, accumulated knowledge, and its influence on perception, tend to increase, while declines in low-level visual functions reduce input quality and bias perception…
Chenyin Wang, Yanqing Chen, Guoxia Wang, Tour Liu
Hypermedia learning environments place high demands on learners’ self-regulation, yet many learners struggle to plan, monitor, and evaluate their understanding. This study examined whether integrated metacognitive support improves self-regulated learning (SRL) processes and learning outcomes, and whether these effects…
Wen Liu, Xuanshun Zhuang, Lei Ma, Zhongliang Deng + 1 more
Zero-shot goal navigation requires an agent to locate targets in unseen environments based on object categories, reference images, or text descriptions, placing high demands on scene understanding and reasoning. Existing methods mainly rely on online observations, modality similarity, or heuristic graph matching, and…
Gleb Svinin, Enrico Glaab
Identifying causal relationships in omics data is essential for understanding underlying biological processes. However, detecting these relationships remains challenging due to the complexity of molecular networks and observational data limitations. To guide researchers, we conducted a systematic literature review of…
Yuwei Xiao, Shuai Ma, Antti Oulasvirta, Eunice Jun
- 7.1.1 There were three strategies for externalizing domain knowledge in the observable space using PriorWeaver. Participants often began with a single strategy, commonly distribution-driven or example-driven, and then flexibly switched across strategies, moving back and forth as needed to articulate their knowledge.…
Beomsu Baek, Eunyoung Jang, Youngsoon Kim, Mingon Kang
Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can…
Elena Sophia Doll, Karla Alex, Carlotta Julia Mayer, Heiko Brennenstuhl + 10 more
Purpose This study assessed general attitudes toward genomic newborn screening (gNBS) among the German public. Methods In a population-representative survey, we assessed self-rated prior knowledge of gNBS, newborn screening (NBS), and genome sequencing and perceived attitudinal and informational uncertainty and…
Moritz Herzog, Michael Grosche, Gunnar Bruns, Gino Casale
The moderation of intervention effects by intelligence and prior knowledge deserves further investigation, because they inform how to design and implement interventions. This study analyzed the moderation of the effectiveness of a computer-based mathematics intervention in 10 primary school students with low…
Shunichiro Tomura, Owen Powell, Melanie J. Wilkinson, Mark Cooper
Accurate selection of favourable crop genotypes has motivated the exploration of diverse prediction algorithms for crop breeding applications. One genomic prediction method that has not been fully explored is graph attention networks (GAT). By directly analysing graphical data with the attention mechanism, GAT can…
Authors not listed
Surfactants are widely used for industrial applications, yet more environmentally-friendly surfactants with enhanced properties are demanded. A key thermodynamic property governing the behavior of a surfactant in an aqueous solution is its critical micelle concentration (CMC). Below the CMC, increasing the surfactant…
Bruno Kopp
Cognitive biases are typically viewed as departures from normative Bayesian reasoning. Instead, we propose that such biases emerge from a hierarchical cognitive architecture that adaptively regulates the weighting of prior beliefs and incoming evidence when cognitive resources are limited and uncertainty exists. While…
Pacuit, Eric, Yang, Leo
In his seminal 1976 paper, Robert Aumann proved a fascinating result, later called the "Agreeing to Disagree" theorem [[2]]. Suppose that two agents share the same prior probability distribution and update this probability by conditioning on different private information. Aumann showed that if their posterior…
Ying Wang, Qiong Li, Xiping Liu
When collaborative learning facilitates learning, remains a central question in educational research. Grounded in cognitive load theory, this study examined whether the effectiveness of collaborative, relative to individual, learning from worked-examples is jointly moderated by learners’ experimentally induced…
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Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Tommaso Costa
In this paper, I argue that the problem of induction dissolves when recast in terms of logical coherence (understood as internal consistency of credences under updating) rather than truth. Following E. T. Jaynes, probability is interpreted not as frequency or decision rule but as the extension of deductive logic to…
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Inverse molecular design aims to generate novel chemical structures that satisfy multiple property constraints, yet reinforcement-learning (RL) fine-tuning can be sensitive to how objectives are converted into a scalar reward. Here, we systematically analyze how scalarization choices and stabilization mechanisms shape…
Dorje C. Brody, Karl J. Friston, Bernhard K. Meister, Emmanuel M. Pothos
In this paper, the phenomenon generally classified as confirmation bias is formulated on the space of square-root probabilities (or equivalently, using the structures of quantum probability). In this framework, observations are modelled by matrices, rather than random variables on a probability space. In the problem of…
Junli Jiang, Pavel Naumov, Wenxuan Zhang
Traditionally, an agent's beliefs would come from what the agent can see, hear, or sense. In the modern world, beliefs are often based on the data available to the agents. In this work, we investigate a dynamic logic of such beliefs that incorporates public announcements of data. The main technical contribution is a…
Chenlu Ding, Jiancan Wu, Yanchen Luo, Zheyuan Liu + 2 more
Large language models (LLMs) often fail to reason under temporal cutoffs: when prompted to answer from the standpoint of an earlier time, they exploit knowledge that became available only later. We study this failure through the lens of ex-ante reasoning, where a model must rely exclusively on information knowable…
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The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
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We present an updated version of a priori computational intelligence, a methodology that integrates semi-empirical Quantum Mechanics calculations with supervised machine learning to predict optimal reaction conditions without prior extensive experimental work. First, the synergy between semi-empirical calculations and…
Theofanis Aravanis, Costas D. Koutras
Belief update concerns changes in an agent's beliefs induced by changes in the underlying world. Standard Katsuno-Mendelzon update assumes that an epistemic input can be incorporated from every initially possible world, whereas credibility-limited belief update restricts, for each source world, the successor worlds…
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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…
Daniel L. Kimmel, Kimberly L. Stachenfeld, Nikolaus Kriegeskorte, Stefano Fusi + 2 more
Abstraction and generalization are essential for flexible decision-making in novel situations. Recent work in humans and monkeys has shown how abstract variables are encoded by the representational geometry of neural population activity. However, these observations—which are typically made after learning has…