24 papers · ranked by Valyu relevance
Yuchen Xiao, Chien-Chen Chou, Garth Rees Cosgrove, Nathan E Crone + 8 more
Cognitive control involves flexibly combining multiple sensory inputs with task-dependent goals during decision making. Several tasks have been proposed to examine cognitive control, including Stroop, Eriksen-Flanker, and the Multi-source interference task. Because these tasks have been studied independently, it…
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.…
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…
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…
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…
Sunandini Sanyal, Ashish Ramayee Asokan, Suvaansh Bhambri, Akshay Kulkarni + 2 more
'Akshay Kulkarni' 'Jogendra Nath Kundu' 'R. Venkatesh Babu'] Conventional Domain Adaptation (DA) methods aim to learn domain-invariant feature representations to improve the target adaptation performance. However, we motivate that domain-specificity is equally important since indomain trained models hold crucial…
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…
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…
El-Mahdi El-Mhamdi, Lê-Nguyên Hoang, Mariame Tighanimine
With the rise of large multi-modal AI models, fuelled by recent interest in large language models (LLMs), the notion of artificial general intelligence (AGI) went from being restricted to a fringe community, to dominate mainstream large AI development programs. In contrast, in this paper, we make a case for…
Thomas Donoghue, Runnan Cao, Claire Z Han, Cameron M Holman + 3 more
Investigations into how individual neurons encode behavioral variables of interest have revealed specific representations in single neurons, such as place and object cells, as well as a wide range of cells with conjunctive encodings or mixed selectivity. However, as most experiments examine neural activity within…
Mareike Hartmann, Alexander Koller
Goal-directed interactive agents, which autonomously complete tasks through interactions with their environment, can assist humans in various domains of their daily lives. Recent advances in large language models (LLMs) led to a surge of new, more and more challenging tasks to evaluate such agents. To properly…
Authors not listed
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…
Mengqiao Chai, Ana F. Palenciano, Ravi Mill, Michael W. Cole + 1 more
'Senne Braem'] Rapidly learning new tasks, such as using new technology or playing a new game, is ubiquitous in our daily lives. Previous studies suggest that our brain relies on different networks for rapid task learning versus retrieving known tasks from memory, and behavioral studies have shown that novel versus…
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…
Authors not listed
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…
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…
Florian Schmitz, Raimund J. Krämer
The task-switching paradigm is deemed a measure of cognitive flexibility. Previous research has demonstrated that individual differences in task-switch costs are moderately inversely related to cognitive ability. However, current theories emphasize multiple component processes of task switching, such as task-set…
Markus Wolfgang Hermann Spitzer, Sebastian Musslick, Janina Janz, Andrea Kiesel + 1 more
'Andrea Kiesel' 'David Dignath'] Humans are remarkably flexible in adapting their behavior to current demands. It has been suggested that the decision which of multiple tasks to perform is based on a variety of factors pertaining to the rewards associated with each task as well as task performance (e.g., error rates…
Marcel Kurtz, Stefan Scherbaum, Moritz Walser, Philipp Kanske + 1 more
'Marcus Möschl'] In the present study, we used mouse tracking to investigate two processes underlying prospective memory (PM) retrieval: First, we aimed to explore to what extent spontaneous retrieval of already completed PM intentions is supported by reflexive-associative and discrepancy-plus-search processes. Second…
Tommaso Costa, Franco Cauda
Network and Methodological Rigor Authors: ['Tommaso Costa' 'Franco Cauda'] The accurate assessment of neuroimaging specificity is critical for advancing our understanding of brain disorders. Current methodologies often rely on frequentist approaches and limited crosspathology comparisons, leading to potential…
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…
Authors not listed
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…
Authors not listed
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…
Authors not listed
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…