pwc-archive/papers-with-abstracts huggingface dataset:
Modulating task outcome value to mitigate real-world procrastination via noninvasive brain stimulation
Authors: Zhiyi Chen, Zhilin Ren, Wei Li, ZhenZhen Huo, ZhuangZheng Wang, Ye Liu, Bowen Hu, Wanting Chen, Ting Xu, Artemiy Leonov, Chenyan Zhang, Bernhard Hommel, Tingyong Feng
Date: 2025-06-26 00:00:00

Procrastination represents one of the most prevalent behavioral problems affecting individual health and societal productivity. Although it is often conceptualized as a form of self-control failure, its underlying neurocognitive mechanisms are poorly understood. A leading model posits that procrastination arises from imbalanced competing motivations: the avoidance of negative task aversiveness and the pursuit of positive task outcomes, yet this theoretical framework has not fully validated in real-world settings and not applied effectively to guide interventions. Here, we addressed this gap with a preregistered, double-blind, randomized controlled trial. We applied seven sessions of high-definition transcranial direct current stimulation (HD-tDCS) to the left dorsolateral prefrontal cortex (DLPFC), a key region for self-control, in chronic procrastinators. Using the intensive experience sampling method (iESM), we assessed the effect of anodal HD-tDCS on real-world procrastination behavior at offline after-effect (2-day interval) and long-term retention (6-month follow-up). We found that this neuromodulation produced a lasting reduction in real-world procrastination, with effects sustained at a 6-month follow-up. While the intervention both decreased task aversiveness and increased perceived task outcome value, causal mediation analysis revealed a striking mechanism: the increase in task outcome value uniquely and sufficiently mediated the entire behavioral improvement. In conclusion, these findings provide causal evidence that enhancing DLPFC function mitigates procrastination by selectively amplifying the valuation of future rewards, not by simply reducing negative feelings about the task. This establishes a precise, value-driven neurocognitive pathway for self-control and offers a validated, theory-driven strategy for intervention.
Predicting Readiness to Engage in Psychotherapy of People with Chronic Pain Based on their Pain-Related Narratives Saar
Authors: Saar Draznin Shiran, Boris Boltyansky, Alexandra Zhuravleva, Dmitry Scherbakov, Pavel Goldstein
Date: 2025-06-25 00:00:00
Tasks: Large Language Model, Sensitivity, Sentence, Specificity

Background. Chronic pain afflicts 20 % of the global population. A strictly biomedical mind-set leaves many sufferers chasing somatic cures and has fuelled the opioid crisis. The biopsychosocial model recognises pain subjective, multifactorial nature, yet uptake of psychosocial care remains low. We hypothesised that patients own pain narratives would predict their readiness to engage in psychotherapy. Methods. In a cross-sectional pilot, 24 chronic-pain patients recorded narrated pain stories on Painstory.science. Open questions probed perceived pain source, interference and influencing factors. Narratives were cleaned, embedded with a pretrained large-language model and entered into machine-learning classifiers that output ready/not ready probabilities. Results. The perception-domain model achieved 95.7 % accuracy (specificity = 0.80, sensitivity = 1.00, AUC = 0.90). The factors-influencing-pain model yielded 83.3 % accuracy (specificity = 0.60, sensitivity = 0.90, AUC = 0.75). Sentence count correlated with readiness for perception narratives (r = 0.54, p < .01) and factor narratives (r = 0.24, p < .05). Conclusion. Brief spoken pain narratives carry reliable signals of willingness to start psychosocial treatment. NLP-based screening could help clinicians match chronic-pain patients to appropriate interventions sooner, supporting a patient-centred biopsychosocial pathway.
Brains and language models converge on a shared conceptual space across different languages
Authors: Zaid Zada, Samuel A Nastase, Jixing Li, Uri Hasson
Date: 2025-06-25 00:00:00

Human languages differ widely in their forms, each having distinct sounds, scripts, and syntax. Yet, they can all convey similar meaning. Do different languages converge on a shared neural substrate for conceptual meaning? We used language models (LMs) and naturalistic fMRI to identify neural representations of the shared conceptual meaning of the same story as heard by native speakers of three languages: English, Chinese, and French. We found that LMs trained on entirely different languages converge onto a similar embedding space, especially in the middle layers. We then aimed to find if a similar shared space exists in the brains of different native speakers of the three languages. We trained voxelwise encoding models that align the LM embeddings with neural responses from one group of subjects speaking a single language. We then used the encoding models trained on one language to predict the neural activity in listeners of other languages. We found that models trained to predict neural activity for one language generalize to different subjects listening to the same content in a different language, across high-level language and default-mode regions. Our results suggest that the neural representations of meaning underlying different languages are shared across speakers of various languages, and that LMs trained on different languages converge on this shared meaning. These findings suggest that, despite the diversity of languages, shared meaning emerges from our interactions with one another and our shared world.
Identifying multi-compartment Hodgkin-Huxley models with high-density extracellular voltage recordings
Authors: Ian Christopher Tanoh, Michael Deistler, Jakob H. Macke, Scott W. Linderman
Date: 2025-06-25 00:00:00

Multi-compartment Hodgkin-Huxley models are biophysical models of how electrical signals propagate throughout a neuron, and they form the basis of our knowledge of neural computation at the cellular level. However, these models have many free parameters that must be estimated for each cell, and existing fitting methods rely on intracellular voltage measurements that are highly challenging to obtain in vivo. Recent advances in neural recording technology with high-density probes and arrays enable dense sampling of extracellular voltage from many sites surrounding a neuron, allowing indirect measurement of many compartments of a cell simultaneously. Here, we propose a method for inferring the underlying membrane voltage, biophysical parameters, and the neuron's position relative to the probe, using extracellular measurements alone. We use an Extended Kalman Filter to infer membrane voltage and channel states using efficient, differentiable simulators. Then, we learn the model parameters by maximizing the marginal likelihood using gradient-based methods. We demonstrate the performance of this approach using simulated data and real neuron morphologies.
The time course of visuo-semantic representations in the human brain is captured by combining vision and language models
Authors: Boyan Rong, Alessandro Thomas Gifford, Emrah Düzel, Radoslaw Martin Cichy
Date: 2025-06-24 00:00:00
Tasks: EEG

The human visual system provides us with a rich and meaningful percept of the world, transforming retinal signals into visuo-semantic representations. For a model of these representations, here we leveraged a combination of two currently dominating approaches: vision deep neural networks (DNNs) and large language models (LLMs). Using large-scale human electroencephalography (EEG) data recorded during object image viewing, we built encoding models to predict EEG responses using representations from a vision DNN, an LLM, and their fusion. We show that the fusion encoding model outperforms encoding models based on either the vision DNN or the LLM alone, as well as previous modelling approaches, in predicting neural responses to visual stimulation. The vision DNN and the LLM complemented each other in explaining stimulus-related signal in the EEG responses. The vision DNN uniquely captured earlier and broadband EEG signals, whereas the LLM uniquely captured later and low frequency signals, as well as detailed visuo-semantic stimulus information. Together, this provides a more accurate model of the time course of visuo-semantic processing in the human brain.
Increasing Efficiency of the Chain of Contagion Task
Authors: Lars OM Rothkegel, Jakob Fink-Lamotte
Date: 2025-06-24 00:00:00

The chain of contagion task (CCT) is a pychological test to measure the amount of contagious beliefs in individuals. Contagious beliefs thereby refer to the perception that certain objects, people, or substances can transmit contamination through mere contact or proximity (Rozin et al., 1986). In the CCT, a neutral object (usually a pen) is rubbed against an inherently disgusting object (e.g. a toilet paper with feces) and participants are asked how contaminated this pen is on a scale from 0 (not at all) to 100 (very contaminated). Afterwards, this pen is rubbed against another pen, and again, the experienced degree of contamination is assessed. This is repeated 12 times. The CCT has first been experimentally investigated by Tolin et al. (2004) in an in vivo procedure with real disgusting objects. The authors could show that contagious beliefs measured with the CCT show a strong bias for people with contamination-based obsessive-compulsive disorder (C-OCD) compared to anxious individuals and non-anxious controls. Fink-Lamotte et al. (2024) replicated these findings with an online version of the CCT using audio-imagery-based and video-based stimuli and instructions. Both studies used 12 pens to assess the degree of contagious beliefs. Within this brief report, we show that after 8 pens, hardly any additional variance is explained between participants and after the tenth pen, no new information is gained. Thus, we recommend only using 8 pens instead of 12 when using the CCT to assess contagious beliefs.
Longitudinal analysis of heart rate variability as it pertains to anxiety and readiness
Authors: Tucker Paron
Date: 2025-06-23 00:00:00
Tasks: Heart Rate Variability

The aim of this study is to explore the relationship between lifestyle choices, subjective experiences and objective biometric data in a single individual. The participant, at the time a male in his twenties, used the EliteHRV app to perform Heart Rate Variability Readings across twenty-six months accompanied by logs about the previous days activity as well as current emotional and physical state. The study will use a mixed-methods approach to analyze the data, including quantitative analysis of the biometric data and correlation analysis between the biometric data and subjective experience tags. Qualitative analysis of the daily logs will also be conducted to gain a deeper understanding of the participant's experiences and to identify keywords, people, or ideas that affect biometric output. The results of this study will provide insights into the relationship between subjective and objective measures, and the potential benefits or drawbacks of certain lifestyle choices and ways of thinking. The findings could have implications for the development of wearable-based personalized interventions for improving mental health and well-being.
How brains build higher order representations of uncertainty
Authors: Megan A. K. Peters, Hojjat Azimi Asrari
Date: 2025-06-23 00:00:00

Higher-order representations (HORs) are neural or computational states that are "about" first-order representations (FORs), encoding information not about the external world per se but about the agent's own representational processes -- such as the reliability, source, or structure of a FOR. These HORs appear critical to metacognition, learning, and even consciousness by some accounts, yet their dimensionality, construction, and neural substrates remain poorly understood. Here, we propose that metacognitive estimates of uncertainty or noise reflect a read-out of "posterior-like" HORs from a Bayesian perspective. We then discuss how these posterior-like HORs reflect a combination of "likelihood-like" estimates of current FOR uncertainty and "prior-like" learned distributions over expected FOR uncertainty, and how various emerging engineering and theory-based analytical approaches may be employed to examine the estimation processes and neural correlates associated with these highly under-explored components of our experienced uncertainty.
BrainSymphony: A Transformer-Driven Fusion of fMRI Time Series and Structural Connectivity
Authors: Moein Khajehnejad, Forough Habibollahi, Adeel Razi
Date: 2025-06-23 00:00:00
Tasks: Diffusion  MRI, Network Identification, Time Series

Existing foundation models for neuroimaging are often prohibitively large and data-intensive. We introduce BrainSymphony, a lightweight, parameter-efficient foundation model that achieves state-of-the-art performance while being pre-trained on significantly smaller public datasets. BrainSymphony's strong multimodal architecture processes functional MRI data through parallel spatial and temporal transformer streams, which are then efficiently distilled into a unified representation by a Perceiver module. Concurrently, it models structural connectivity from diffusion MRI using a novel signed graph transformer to encode the brain's anatomical structure. These powerful, modality-specific representations are then integrated via an adaptive fusion gate. Despite its compact design, our model consistently outperforms larger models on a diverse range of downstream benchmarks, including classification, prediction, and unsupervised network identification tasks. Furthermore, our model revealed novel insights into brain dynamics using attention maps on a unique external psilocybin neuroimaging dataset (pre- and post-administration). BrainSymphony establishes that architecturally-aware, multimodal models can surpass their larger counterparts, paving the way for more accessible and powerful research in computational neuroscience.
Perceptual multistability: a window for a multi-facet understanding of psychiatric disorders
Authors: Shervin Safavi, Danaé Rolland, Philipp Sterzer, Renaud Jardri, Pantelis Leptourgos
Date: 2025-06-22 00:00:00

Perceptual multistability, observed across species and sensory modalities, offers valuable insights into numerous cognitive functions and dysfunctions. For instance, differences in temporal dynamics and information integration during percept formation often distinguish clinical from non-clinical populations. Computational psychiatry can elucidate these variations, through two primary approaches: (i) Bayesian modeling, which treats perception as an unconscious inference, and (ii) an active, information-seeking perspective (e.g., reinforcement learning) framing perceptual switches as internal actions. Our synthesis aims to leverage multistability to bridge these computational psychiatry subfields, linking human and animal studies as well as connecting behavior to underlying neural mechanisms. Perceptual multistability emerges as a promising non-invasive tool for clinical applications, facilitating translational research and enhancing our mechanistic understanding of cognitive processes and their impairments.
The Relationship between Cognition and Computation: "Global-first" Cognition versus Local-first Computation
Authors: Lin Chen
Date: 2025-06-22 00:00:00

What fundamental research questions are essential for advancing toward brain-like AI or AGI (Artificial General Intelligence) capable of performing any intellectual task a human can? Should it be something like the Turing machine (1936), which answers the question "What is computation?" and lays the foundation for the entire field of computer science? Or should it be something like Shannon's mathematical theory of communication (1948), which answers the question "What is information?" and forms the basis for modern communication technology? We believe the key question today is the relationship between cognition and computation (RCC). For example, the widely discussed question "Will artificial intelligence replace the human mind?" is, in essence and in scientific terms, an issue concerning RCC. We have chosen to classify RCC into four categories: 1. The relationship between the primitives of cognition and the primitives of computation. 2. The relationship between the anatomical structure of neural representation of cognition and the computational architecture of artificial intelligence. 3. The relationship between emergents in cognition and emergents in computation. 4. The relationship between the mathematical foundations of cognition and computation.
Which Consciousness Can Be Artificialized? Local Percept-Perceiver Phenomenon for the Existence of Machine Consciousness
Authors: Shri Lal Raghudev Ram Singh
Date: 2025-06-22 00:00:00

This paper presents a novel paradigm of the local percept-perceiver phenomenon to formalize certain observations in neuroscientific theories of consciousness. Using this model, a set-theoretic formalism is developed for artificial systems, and the existence of machine consciousness is proved by invoking Zermelo-Fraenkel set theory. The article argues for the possibility of a reductionist form of epistemic consciousness within machines.
Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search
Authors: Nikolaus Salvatore, Qiong Zhang
Date: 2025-06-20 00:00:00
Tasks: Machine Translation, Translation

Past work has long recognized the important role of context in guiding how humans search their memory. While context-based memory models can explain many memory phenomena, it remains unclear why humans develop such architectures over possible alternatives in the first place. In this work, we demonstrate that foundational architectures in neural machine translation -- specifically, recurrent neural network (RNN)-based sequence-to-sequence models with attention -- exhibit mechanisms that directly correspond to those specified in the Context Maintenance and Retrieval (CMR) model of human memory. Since neural machine translation models have evolved to optimize task performance, their convergence with human memory models provides a deeper understanding of the functional role of context in human memory, as well as presenting new ways to model human memory. Leveraging this convergence, we implement a neural machine translation model as a cognitive model of human memory search that is both interpretable and capable of capturing complex dynamics of learning. We show that our model accounts for both averaged and optimal human behavioral patterns as effectively as context-based memory models. Further, we demonstrate additional strengths of the proposed model by evaluating how memory search performance emerges from the interaction of different model components.
Challenges in Grounding Language in the Real World
Authors: Peter Lindes, Kaoutar Skiker
Date: 2025-06-20 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

A long-term goal of Artificial Intelligence is to build a language understanding system that allows a human to collaborate with a physical robot using language that is natural to the human. In this paper we highlight some of the challenges in doing this, and propose a solution that integrates the abilities of a cognitive agent capable of interactive task learning in a physical robot with the linguistic abilities of a large language model. We also point the way to an initial implementation of this approach.
Brain-inspired interpretable reservoir computing with resonant recurrent neural networks
Authors: Mark A. Kramer
Date: 2025-06-20 00:00:00
Tasks: Classification

Traditional artificial neural networks consist of nodes with non-oscillatory dynamics. Biological neural networks, on the other hand, consist of oscillatory components embedded in an oscillatory environment. Motivated by this feature of biological neurons, we describe a reservoir computing framework with explicit damped, oscillatory node dynamics. We express the oscillatory dynamics using two history dependent terms to connect these dynamics with existing artificial neural network approaches and apply physical and stationary constraints to reduce the number of free parameters. We then optimize and illustrate reservoir performance by classifying different brain rhythms associated with epilepsy and show that reservoir elements support classification by resonating with features of the input signals. Applying the same reservoir network to visual and auditory signal types, we show the reservoir generalizes for accurate classification with few trainable parameters. Compared to existing artificial neural network approaches, the proposed resonant reservoir network (RRN) utilizes oscillatory dynamics expressed as a straightforward extension of traditional artificial neural networks, produces interpretable features for classification, avoids computationally expensive training (e.g., backpropagation), and performs well with few parameters in different classification scenarios. We propose that RRNs may serve as efficient, biologically implemented building blocks to achieve complex goals in biological and artificial neural networks.
Efficient and faithful reconstruction of dynamical attractors using homogeneous differentiators
Authors: Uros Sutulovic, Daniele Proverbio, Rami Katz, Giulia Giordano
Date: 2025-06-20 00:00:00
Tasks: EEG

Reconstructing the attractors of complex nonlinear dynamical systems from available measurements is key to analyse and predict their time evolution. Existing attractor reconstruction methods typically rely on topological embedding and may produce poor reconstructions, which differ significantly from the actual attractor, because measurements are corrupted by noise and often available only for some of the state variables and/or their combinations, and the time series are often relatively short. Here, we propose the use of Homogeneous Differentiators (HD) to effectively de-noise measurements and more faithfully reconstruct attractors of nonlinear systems. Homogeneous Differentiators are supported by rigorous theoretical guarantees about their de-noising capabilities, and their results can be fruitfully combined with time-delay embedding, differential embedding and functional observability. We apply our proposed HD-based methodology to simulated dynamical models of increasing complexity, from the Lorenz system to the Hindmarsh-Rose model and the Epileptor model for neural dynamics, as well as to empirical data of EEG recordings. In the presence of corrupting noise of various types, we obtain drastically improved quality and resolution of the reconstructed attractors, as well as significantly reduced computational time, which can be orders of magnitude lower than that of alternative methods. Our tests show the flexibility and effectiveness of Homogeneous Differentiators and suggest that they can become the tool of choice for preprocessing noisy signals and reconstructing attractors of highly nonlinear dynamical systems from both theoretical models and real data.