Tootfinder

EEG-Based Cognitive Load Classification During Landmark-Based VR Navigation
Jiahui An, Bingjie Cheng, Dmitriy Rudyka, Elisa Donati, Sara Fabrikant
https://arxiv.org/abs/2509.14056

EEG-Based Cognitive Load Classification During Landmark-Based VR Navigation
Brain computer interfaces enable real-time monitoring of cognitive load, but their effectiveness in dynamic navigation contexts is not well established. Using an existing VR navigation dataset, we examined whether EEG signals can classify cognitive load during map-based wayfinding and whether classification accuracy depends more on task complexity or on individual traits. EEG recordings from forty-six participants navigating routes with 3, 5, or 7 map landmarks were analyzed with a nested cross…

Hear Your Code Fail, Voice-Assisted Debugging for Python
Sayed Mahbub Hasan Amiri, Md. Mainul Islam, Mohammad Shakhawat Hossen, Sayed Majhab Hasan Amiri, Mohammad Shawkat Ali Mamun, Sk. Humaun Kabir, Naznin Akter
https://arxiv.org/abs/2507.15007

Hear Your Code Fail, Voice-Assisted Debugging for Python
This research introduces an innovative voice-assisted debugging plugin for Python that transforms silent runtime errors into actionable audible diagnostics. By implementing a global exception hook architecture with pyttsx3 text-to-speech conversion and Tkinter-based GUI visualization, the solution delivers multimodal error feedback through parallel auditory and visual channels. Empirical evaluation demonstrates 37% reduced cognitive load (p<0.01, n=50) compared to traditional stack-trace debugg…

Shapes of Cognition for Computational Cognitive Modeling
Marjorie McShane, Sergei Nirenburg, Sanjay Oruganti, Jesse English
https://arxiv.org/abs/2509.13288 https://

Shapes of Cognition for Computational Cognitive Modeling
Shapes of cognition is a new conceptual paradigm for the computational cognitive modeling of Language-Endowed Intelligent Agents (LEIAs). Shapes are remembered constellations of sensory, linguistic, conceptual, episodic, and procedural knowledge that allow agents to cut through the complexity of real life the same way as people do: by expecting things to be typical, recognizing patterns, acting by habit, reasoning by analogy, satisficing, and generally minimizing cognitive load to the degree si…

Simultaneous real-time multispectral fluorescence and reflectance imaging for enhanced intraoperative guidance
Kyriakos Pentarakis, George Kakavelakis, Constantinos Petridis, George Themelis
https://arxiv.org/abs/2509.15979

Simultaneous real-time multispectral fluorescence and reflectance imaging for enhanced intraoperative guidance
Intraoperative optical imaging is essential for surgical precision and patient safety, but current systems present anatomical and fluorescence information separately, causing delays and increasing cognitive load. A unified system for simultaneous visualization is critically needed to enhance guidance and efficiency. To develop and validate a multispectral imaging system that overcomes fragmented intraoperative imaging by simultaneously capturing and integrating white light reflectance and multi…

""[…] There is only one correct metric that should be counted when dealing with software, and that is the user's cognitive load. […] If my Windows/Python/Notepad setup is more ubiquitous, understandable, intuitive and replicable than your obscure Arch/Hyprland build with its hundred painstakingly typed-out customizations for every single software in it, then my setup is better and more minimalist than yours. Full stop. […]""

CLAd-VR: Cognitive Load-based Adaptive Training for Machining Tasks in Virtual Reality
Bhavya Matam, Adamay Mann, Kachina Studer, Christian Gabbianelli, Sonia Castelo, John Liu, Claudio Silva, Dishita Turakhia
https://arxiv.org/abs/2510.05249

CLAd-VR: Cognitive Load-based Adaptive Training for Machining Tasks in Virtual Reality
With the growing need to effectively support workforce upskilling in the manufacturing sector, virtual reality is gaining popularity as a scalable training solution. However, most current systems are designed as static, step-by-step tutorials and do not adapt to a learner's needs or cognitive load, which is a critical factor in learning and longterm retention. We address this limitation with CLAd-VR, an adaptive VR training system that integrates realtime EEG-based sensing to measure the learne…

Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control
Jiahui An, Chonghao Cai, Olympia Gallou, Sara Irina Fabrikant, Giacomo Indiveri, Elisa Donati
https://arxiv.org/abs/2509.21345

Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control
This paper presents a neuromorphic system for cognitive load classification in a real-world setting, an Air Traffic Control (ATC) task, using a hardware implementation of Spiking Neural Networks (SNNs). Electroencephalogram (EEG) and eye-tracking features, extracted from an open-source dataset, were used to train and evaluate both conventional machine learning models and SNNs. Among the SNN architectures explored, a minimalistic, single-layer model trained with a biologically inspired delta-rul…

Cognitive Load Limits in Large Language Models: Benchmarking Multi-Hop Reasoning
Sai Teja Reddy Adapala
https://arxiv.org/abs/2509.19517 https://arxiv.org/…

Cognitive Load Limits in Large Language Models: Benchmarking Multi-Hop Reasoning
The scaling of Large Language Models (LLMs) has exposed a critical gap between their performance on static benchmarks and their fragility in dynamic, information-rich environments. While models excel at isolated tasks, the computational limits that govern their reasoning under cognitive load remain poorly understood. In this work, we introduce a formal theory of computational cognitive load, positing that extraneous, task-irrelevant information (Context Saturation) and interference from task-sw…

Detecting Blinks in Healthy and Parkinson's EEG: A Deep Learning Perspective
Artem Lensky, Yiding Qiu
https://arxiv.org/abs/2509.04951 https://arxiv.or…

Detecting Blinks in Healthy and Parkinson's EEG: A Deep Learning Perspective
Blinks in electroencephalography (EEG) are often treated as unwanted artifacts. However, recent studies have demonstrated that blink rate and its variability are important physiological markers to monitor cognitive load, attention, and potential neurological disorders. This paper addresses the critical task of accurate blink detection by evaluating various deep learning models for segmenting EEG signals into involuntary blinks and non-blinks. We present a pipeline for blink detection using 1, 3…

Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
Bujar Raufi
https://arxiv.org/abs/2508.07283 https://arxiv.o…

Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
This study explores the intersection of electroencephalography (EEG) microstates and Large Language Models (LLMs) to enhance the assessment of cognitive load states. By utilizing EEG microstate features, the research aims to fine-tune LLMs for improved predictions of distinct cognitive states, specifically 'Rest' and 'Load'. The experimental design is delineated in four comprehensive stages: dataset collection and preprocessing, microstate segmentation and EEG backfitting, feature extraction pa…

Cognitive Load Limits in Large Language Models: Benchmarking Multi-Hop Reasoning
Sai Teja Reddy Adapala
https://arxiv.org/abs/2509.19517 https://arxiv.org/…

Cognitive Load Limits in Large Language Models: Benchmarking Multi-Hop Reasoning
The scaling of Large Language Models (LLMs) has exposed a critical gap between their performance on static benchmarks and their fragility in dynamic, information-rich environments. While models excel at isolated tasks, the computational limits that govern their reasoning under cognitive load remain poorly understood. In this work, we introduce a formal theory of computational cognitive load, positing that extraneous, task-irrelevant information (Context Saturation) and interference from task-sw…

The Impact of Simple, Brief, and Adaptive Instructions within Virtual Reality Training: Components of Cognitive Load Theory in an Assembly Task
Rebecca L. Pharmer, Christopher D. Wickens, Lucas Plabst, Benjamin A. Clegg, Leanne M. Hirshfield, Joanna E. Lewis, Jalynn B. Nicoly, Cara A. Spencer, Francisco R. Ortega
https://arxiv.org/abs/2507…

The Impact of Simple, Brief, and Adaptive Instructions within Virtual Reality Training: Components of Cognitive Load Theory in an Assembly Task
Objective: The study examined the effects of varying all three core elements of cognitive load on learning efficiency during a shape assembly task in virtual reality (VR). Background: Adaptive training systems aim to improve learning efficiency and retention by dynamically adjusting difficulty. However, design choices can impact the cognitive workload imposed on the learner. The present experiments examined how aspects of cognitive load impact training outcomes. Method: Participants learned…

Identifier Name Similarities: An Exploratory Study
Carol Wong, Mai Abe, Silvia De Benedictis, Marissa Halim, Anthony Peruma
https://arxiv.org/abs/2507.18081 https://

Identifier Name Similarities: An Exploratory Study
Identifier names, which comprise a significant portion of the codebase, are the cornerstone of effective program comprehension. However, research has shown that poorly chosen names can significantly increase cognitive load and hinder collaboration. Even names that appear readable in isolation may lead to misunderstandings in contexts when they closely resemble other names in either structure or functionality. In this exploratory study, we present our preliminary findings on the occurrence of id…

SwarmChat: An LLM-Based, Context-Aware Multimodal Interaction System for Robotic Swarms
Ettilla Mohiuddin Eumi, Hussein Abbass, Nadine Marcus
https://arxiv.org/abs/2509.16920 ht…

SwarmChat: An LLM-Based, Context-Aware Multimodal Interaction System for Robotic Swarms
Traditional Human-Swarm Interaction (HSI) methods often lack intuitive real-time adaptive interfaces, making decision making slower and increasing cognitive load while limiting command flexibility. To solve this, we present SwarmChat, a context-aware, multimodal interaction system powered by Large Language Models (LLMs). SwarmChat enables users to issue natural language commands to robotic swarms using multiple modalities, such as text, voice, or teleoperation. The system integrates four LLM-ba…

The Tournament Tree Method for preference elicitation in Multi-criteria decision-making
Diego Garc\'ia-Zamora, \'Alvaro Labella, Jos\'e Rui Figueira
https://arxiv.org/abs/2510.08197

The Tournament Tree Method for preference elicitation in Multi-criteria decision-making
Pairwise comparison methods, such as Fuzzy Preference Relations and Saaty's Multiplicative Preference Relations, are widely used to model expert judgments in multi-criteria decision-making. However, their application is limited by the high cognitive load required to complete $m(m-1)/2$ comparisons, the risk of inconsistency, and the computational complexity of deriving consistent value scales. This paper proposes the Tournament Tree Method (TTM), a novel elicitation and evaluation framework tha…

GlobalizeEd: A Multimodal Translation System that Preserves Speaker Identity in Academic Lectures
Hoang-Son Vo, Karina Kolmogortseva, Ngumimi Karen Iyortsuun, Hong-Duyen Vo, Soo-Hyung Kim
https://arxiv.org/abs/2510.11596

GlobalizeEd: A Multimodal Translation System that Preserves Speaker Identity in Academic Lectures
A large amount of valuable academic content is only available in its original language, creating a significant access barrier for the global student community. This is a challenge for translating in several subjects, such as history, culture, and the arts, where current automated subtitle tools fail to convey the appropriate pedagogical tone and specialized meaning. In addition, reading traditional automated subtitles increases cognitive load and leads to a disconnected learning experience. Thr…

Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach
Jiahui An, Sara Irina Fabrikant, Giacomo Indiveri, Elisa Donati
https://arxiv.org/abs/2509.21346

Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach
Accurately assessing mental workload is crucial in cognitive neuroscience, human-computer interaction, and real-time monitoring, as cognitive load fluctuations affect performance and decision-making. While Electroencephalography (EEG) based machine learning (ML) models can be used to this end, their high computational cost hinders embedded real-time applications. Hardware implementations of spiking neural networks (SNNs) offer a promising alternative for low-power, fast, event-driven processing…

Comparing Visual Qubit Representations in Quantum Education: The Bloch sphere Enhances Task Efficiency
Linda Qerimi, Sarah Malone, Eva Rexigel, Jochen Kuhn, Stefan K\"uchemann
https://arxiv.org/abs/2507.21721

Comparing Visual Qubit Representations in Quantum Education: The Bloch sphere Enhances Task Efficiency
Visual-graphical qubit representations offer a means to introduce abstract quantum concepts - such as quantum state, superposition, or measurement - in an accessible manner, particularly for learners with low prior knowledge. Building on a previous expert rating of mechanisms of qubit representations, this study compared two representations - Bloch sphere and Quantum Bead - in terms of learning outcomes, task performance, cognitive load, and mid-term retention. The study was conducted with N=14…

Toward Inclusive AI-Driven Development: Exploring Gender Differences in Code Generation Tool Interactions
Manaal Basha, Ivan Beschastnikh, Gema Rodriguez-Perez, Cleidson R. B. de Souza
https://arxiv.org/abs/2507.14770

Toward Inclusive AI-Driven Development: Exploring Gender Differences in Code Generation Tool Interactions
Context: The increasing reliance on Code Generation Tools (CGTs), such as Windsurf and GitHub Copilot, are revamping programming workflows and raising critical questions about fairness and inclusivity. While CGTs offer potential productivity enhancements, their effectiveness across diverse user groups have not been sufficiently investigated. Objectives: We hypothesize that developers' interactions with CGTs vary based on gender, influencing task outcomes and cognitive load, as prior research su…

A Custom-Built Ambient Scribe Reduces Cognitive Load and Documentation Burden for Telehealth Clinicians
Justin Morse, Kurt Gilbert, Kyle Shin, Rick Cooke, Peyton Rose, Jack Sullivan, Angelo Sisante
https://arxiv.org/abs/2507.17754

A Custom-Built Ambient Scribe Reduces Cognitive Load and Documentation Burden for Telehealth Clinicians
Clinician burnout has motivated the growing adoption of ambient medical scribes in the clinic. In this work, we introduce a custom-built ambient scribe application integrated into the EHR system at Included Health, a personalized all-in-one healthcare company offering telehealth services. The application uses Whisper for transcription and a modular in-context learning pipeline with GPT-4o to automatically generate SOAP notes and patient instructions. Testing on mock visit data shows that the no…

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- CLARE: Cognitive Load Assessment in REaltime with Multimodal Data
Anubhav Bhatti, et al.

From Code to Concept: Evaluating Multiple Coordinated Views in Introductory Programming
Naaz Sibia, Valeria Ramirez Osorio, Jessica Wen, Rutwa Engineer, Angela Zavaleta Bernuy, Andrew Petersen, Michael Liut, Carolina Nobre
https://arxiv.org/abs/2509.26466

From Code to Concept: Evaluating Multiple Coordinated Views in Introductory Programming
Novice programmers often struggle to understand how code executes and to form the abstract mental models necessary for effective problem-solving, challenges that are amplified in large, diverse introductory courses where students' backgrounds, language proficiencies, and prior experiences vary widely. This study examines whether interactive, multi-representational visualizations, combining synchronized code views, memory diagrams, and conceptual analogies, can help manage cognitive load and fos…

VeasyGuide: Personalized Visual Guidance for Low-vision Learners on Instructor Actions in Presentation Videos
Yotam Sechayk, Ariel Shamir, Amy Pavel, Takeo Igarashi
https://arxiv.org/abs/2507.21837

VeasyGuide: Personalized Visual Guidance for Low-vision Learners on Instructor Actions in Presentation Videos
Instructors often rely on visual actions such as pointing, marking, and sketching to convey information in educational presentation videos. These subtle visual cues often lack verbal descriptions, forcing low-vision (LV) learners to search for visual indicators or rely solely on audio, which can lead to missed information and increased cognitive load. To address this challenge, we conducted a co-design study with three LV participants and developed VeasyGuide, a tool that uses motion detection …

LitForager: Exploring Multimodal Literature Foraging Strategies in Immersive Sensemaking
Haoyang Yang, Elliott H. Faa, Weijian Liu, Shunan Guo, Duen Horng Chau, Yalong Yang
https://arxiv.org/abs/2508.15043

LitForager: Exploring Multimodal Literature Foraging Strategies in Immersive Sensemaking
Exploring and comprehending relevant academic literature is a vital yet challenging task for researchers, especially given the rapid expansion in research publications. This task fundamentally involves sensemaking - interpreting complex, scattered information sources to build understanding. While emerging immersive analytics tools have shown cognitive benefits like enhanced spatial memory and reduced mental load, they predominantly focus on information synthesis (e.g., organizing known document…