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Difficulty as a Proxy for Measuring Intrinsic Cognitive Load Item
Minghao Cai, Guher Gorgun, Carrie Demmans Epp
https://arxiv.org/abs/2507.13235 https://…

Difficulty as a Proxy for Measuring Intrinsic Cognitive Load Item
Cognitive load is key to ensuring an optimal learning experience. However, measuring the cognitive load of educational tasks typically relies on self-report measures which has been criticized by researchers for being subjective. In this study, we investigated the feasibility of using item difficulty parameters as a proxy for measuring cognitive load in an online learning platform. Difficulty values that were derived using item-response theory were consistent with theories of how intrinsic and e…

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…

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…

Cognitive Load and Information Processing in Financial Markets: Theory and Evidence from Disclosure Complexity
Yimin Du, Guolin Tang
https://arxiv.org/abs/2507.07037 https://

Cognitive Load and Information Processing in Financial Markets: Theory and Evidence from Disclosure Complexity
We develop a theoretical framework for understanding how cognitive load affects information processing in financial markets and test it using exogenous variation in disclosure complexity. Our model distinguishes between attention allocation and cognitive processing capacity, showing that complex information creates differential effects across investor types. Using a comprehensive dataset of corporate disclosures and a novel identification strategy based on regulatory changes, we find that cogni…

On the capabilities of LLMs for classifying and segmenting time series of fruit picking motions into primitive actions
Eleni Konstantinidou, Nikolaos Kounalakis, Nikolaos Efstathopoulos, Dimitrios Papageorgiou
https://arxiv.org/abs/2507.07745

On the capabilities of LLMs for classifying and segmenting time series of fruit picking motions into primitive actions
Despite their recent introduction to human society, Large Language Models (LLMs) have significantly affected the way we tackle mental challenges in our everyday lives. From optimizing our linguistic communication to assisting us in making important decisions, LLMs, such as ChatGPT, are notably reducing our cognitive load by gradually taking on an increasing share of our mental activities. In the context of Learning by Demonstration (LbD), classifying and segmenting complex motions into primitiv…

Effects of task difficulty and music expertise in virtual reality: Observations of cognitive load and task accuracy in a rhythm exergame
Kyla Ellahiyoun, Emma Jane Pretty, Renan Guarese, Marcel Takac, Haytham Fayek, Fabio Zambetta
https://arxiv.org/abs/2507.06691

Effects of task difficulty and music expertise in virtual reality: Observations of cognitive load and task accuracy in a rhythm exergame
This study explores the relationship between musical training, cognitive load (CL), and task accuracy within the virtual reality (VR) exergame Beat Saber across increasing levels of difficulty. Participants (N=32) completed a series of post-task questionnaires after playing the game under three task difficulty levels while having their physiological data measured by an Emotibit. Using regression analyses, we found that task difficulty and gaming experience significantly predicted subjective CL,…

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…

Requirements Elicitation Follow-Up Question Generation
Yuchen Shen, Anmol Singhal, Travis Breaux
https://arxiv.org/abs/2507.02858 https://

Requirements Elicitation Follow-Up Question Generation
Interviews are a widely used technique in eliciting requirements to gather stakeholder needs, preferences, and expectations for a software system. Effective interviewing requires skilled interviewers to formulate appropriate interview questions in real time while facing multiple challenges, including lack of familiarity with the domain, excessive cognitive load, and information overload that hinders how humans process stakeholders' speech. Recently, large language models (LLMs) have exhibited s…

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…

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…

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…

Estimating Cognitive Effort from Functional Near-Infrared Spectroscopy (fNIRS) Signals using Machine Learning
Shayla Sharmin, Roghayeh Leila Barmaki
https://arxiv.org/abs/2507.13952

Estimating Cognitive Effort from Functional Near-Infrared Spectroscopy (fNIRS) Signals using Machine Learning
The estimation of cognitive effort could potentially help educators to modify material to enhance learning effectiveness and student engagement. Where cognitive load refers how much work the brain is doing while someone is learning or doing a task cognitive effort consider both load and behavioral performance. Cognitive effort can be captured by measuring oxygen flow and behavioral performance during a task. This study infers cognitive effort metrics using machine learning models based on oxyge…

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…

Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs
Xiaoxiao Yang, Chan Feng, Jiancheng Chen
https://arxiv.org/abs/2506.23458

Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs
Portable and wearable consumer-grade electroencephalography (EEG) devices, like Muse headbands, offer unprecedented mobility for daily brain-computer interface (BCI) applications, including cognitive load detection. However, the exacerbated non-stationarity in portable EEG signals constrains data fidelity and decoding accuracy, creating a fundamental trade-off between portability and performance. To mitigate such limitation, we propose MuseCogNet (Muse-based Cognitive Network), a unified joint …

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…

A Method for Rapid Area Prioritisation in Flood Disaster Response
Moritz Schneider, Lukas Halekotte, Tina Comes, Frank Fiedrich
https://arxiv.org/abs/2506.18423

A Method for Rapid Area Prioritisation in Flood Disaster Response
In flood disasters, decision-makers have to rapidly prioritise the areas that need assistance based on a high volume of information. While approaches that combine GIS with Bayesian networks are generally effective in integrating multiple spatial variables and can thus reduce cognitive load, existing models in the literature are not equipped to address the time pressure and information-scape that is typical in a flood. To address the lack of a model for area prioritisation in flood disaster resp…

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.

ImprovMate: Multimodal AI Assistant for Improv Actor Training
Riccardo Drago, Yotam Sechayk, Mustafa Doga Dogan, Andrea Sanna, Takeo Igarashi
https://arxiv.org/abs/2506.23180

ImprovMate: Multimodal AI Assistant for Improv Actor Training
Improvisation training for actors presents unique challenges, particularly in maintaining narrative coherence and managing cognitive load during performances. Previous research on AI in improvisation performance often predates advances in large language models (LLMs) and relies on human intervention. We introduce ImprovMate, which leverages LLMs as GPTs to automate the generation of narrative stimuli and cues, allowing actors to focus on creativity without keeping track of plot or character con…

Designing Visualization Widgets for Tangible Data Exploration: A Systematic Review
Haonan Yao, Lingyun Yu, Lijie Yao
https://arxiv.org/abs/2507.00775 https…

Designing Visualization Widgets for Tangible Data Exploration: A Systematic Review
We present a systematic review on tasks, interactions, and visualization widgets (refer to tangible entities that are used to accomplish data exploration tasks through specific interactions) in the context of tangible data exploration. Tangible widgets have been shown to reduce cognitive load, enable more natural interactions, and support the completion of complex data exploration tasks. Yet, the field lacks a structured understanding of how task types, interaction methods, and widget designs a…

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…