23 papers · ranked by Valyu relevance
Roselyne Chauvin, Maarten Mennes, Jan Buitelaar, Christian Beckmann
In recent years, several large-scale neuroimaging efforts have been launched in an attempt to tackle a potential lack of power in the context of small effect sizes. However, within these large-scale efforts different cognitive tasks are typically treated independently, while the availability of such large databases…
Pritam Kadasi, Abhishek Upperwal, Mayank Singh
We propose the Task–Specificity Score (TSS) to quantify how much an instruction matters for predicting its output, by contrasting the true instruction against plausible alternatives for the same input. We further introduce TSS++, which uses hard alternatives and a small quality term to mitigate easynegative effects.…
Florian Kattner, Christopher R. Cox, C. Shawn Green, Etsuro Ito
While learning is often highly specific to the exact stimuli and tasks used during training, there are cases where training results in learning that generalizes more broadly. It has been previously argued that the degree of specificity can be predicted based upon the learning solution(s) dictated by the particular…
Nathalie Liegel, Daniel Schneider, Edmund Wascher, Laura-Isabelle Klatt + 1 more
In some situations, e.g., when we expect to gain a reward in case of good performance, goal-driven top-down attention is particularly strong. Little is known about the task specificity of such increases of top-down attention due to environmental factors. To understand to what extent performance-contingent reward…
Kristina Meyer, Werner Sommer, Andrea Hildebrandt
The study of socio-cognitive abilities emerged from intelligence research, and their specificity remains controversial until today. In recent years, the psychometric structure of face cognition (FC)-a basic facet of socio-cognitive abilities-was extensively studied. In this review, we summarize and discuss the…
Apoorva Bhandari, Haley Keglovits, Emily Chicklis, David Badre
How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use deep sampling fMRI and pattern analyses to reveal the detailed structure of lPFC representational…
Tommaso Lamarra, Caterina Villani, Marianna M. Bolognesi
Concrete concepts (banana) are processed faster and more accurately than abstract ones (belief). This phenomenon, supported by empirical studies, is known as the concreteness effect. However, recent research indicates that controlling certain psycholinguistic variables can mitigate or reverse this effect. We introduce…
Nathalie Liegel, Daniel Schneider, Edmund Wascher, Laura‐Isabelle Klatt + 1 more
'Laura‐Isabelle Klatt' 'Stefan Arnau'] Title: Abstract In some situations, for example, when we expect to gain a reward in case of good performance, goal-driven top-down attention is particularly strong. Little is known about the task specificity of such increases of top-down attention due to environmental factors. To…
Wei-Jen Ko, Greg Durrett, Junyi Jessy Li
Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse. While this information is useful for many downstream applications, specificity prediction systems predict very coarse labels (binary or ternary) and are trained on and tailored toward specific…
Tamen Jadad-Garcia, Alejandro R. Jadad
Task Automation for the Integration of Artificial Intelligence into Existing Workflows Authors: ['Tamen Jadad-Garcia' 'Alejandro R. Jadad'] Driven by the rapid ascent of artificial intelligence (AI), organizations find themselves at the epicenter of a seismic shift, facing a crucial question: How can AI be successfully…
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In recent years, the development of large language models (LLMs) has revolutionized various fields of natural science, yet their application in molecular data processing remains constrained due to the reliance on single-modality inputs and outputs. To bridge the gap between experimenters and computational tools, we…
Shivashankar Subramanian, Trevor Cohn, Timothy Baldwin
Many pledges are made in the course of an election campaign, forming important corpora for political analysis of campaign strategy and governmental accountability. At present, there are no publicly available annotated datasets of pledges, and most political analyses rely on manual analysis. In this paper we collate a…
Albert Gatt, Nicolás Marı́n, Gustavo Rivas-Gervilla, Daniel Sánchez
In this paper we study empirically the validity of measures of referential success for referring expressions involving gradual properties. More specifically, we study the ability of several measures of referential success to predict the success of a user in choosing the right object, given a referring expression.…
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Large Language Models (LLMs) based on transformer architectures excel at internet-scale tasks. However, real-world scientific scenarios—such as synthetic chemistry laboratories and autonomous experimental setups—typically involve incremental data generation in batches as new chemical reactions are conducted, unlike…
Luca Lugini, Diane Litman
High quality classroom discussion is important to student development, enhancing abilities to express claims, reason about other students' claims, and retain information for longer periods of time. Previous small-scale studies have shown that one indicator of classroom discussion quality is specificity. In this paper…
Hosein Fooladi, Steffen Hirte, Johannes Kirchmair
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These…
Mengqiao Chai, Iris Ikink, Stefania Mattioni, Ricardo Alejandro Benavides + 5 more
People can dynamically and adaptively update task goals in the face of task uncertainty. In this fMRI study, we aimed to investigate the modulation of neural task representations that subserve such flexible task control. On each trial, we asked people to perform one of nine image categorization tasks. During task…
Lydia Barnes, Dragan Rangelov, Jason B. Mattingley, Alexandra Woolgar
Many everyday tasks require us to integrate information from multiple steps to make a decision. Dominant accounts of flexible cognition suggest that we are able to navigate such complex tasks by attending to each step in turn, yet few studies measure how we direct our attention to immediate and future task steps. Here…
Connor Taylor, Kobi Felton, Daniel Wigh, Mohammed Jeraal + 4 more
Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations need to be executed in the presence of polar functionality necessary for fragment-protein binding. New technologies such as high-throughput experimentation and…
Stanisław H. Czyż
Decision-making is a complex action requiring efficient information processing. Specifically, in movement in which performance efficiency depends on reaction time, e.g., open-loop controlled movements, these processes may play a crucial role. Information processing includes three distinct stages, stimulus…
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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…
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Accurate prediction of ADME (Absorption, Distribution, Metabolism, and Excretion) properties is a key challenge in drug discovery. In the Polaris Antiviral ADME Prediction Challenge, we developed and benchmarked multi-task directed message passing neural network (D-MPNN) models using ChemProp, trained exclusively on a…
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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…