Computational frameworks accurately model human behavior and personality
While numerous studies demonstrate that computational models and machine learning frameworks can successfully predict and simulate human behavior and personality traits, critical analyses highlight significant psychometric limitations, measurement artifacts, and validity concerns.
The retrieved literature contains multiple strong studies demonstrating high predictive accuracy and behavioral alignment for computational models of personality and behavior (supporting papers 0, 1, 2, 3, 4, 8, 9). However, recent methodological and critical evaluations caution that these models suffer from measurement artifacts, psychometric unreliability, and prompt sensitivity (papers 5, 7), creating a contested landscape where predictive success often clashes with deeper construct validity issues.
K. Kannadasan, Jainendra Shukla, Sridevi Veerasingam, B. Begum, N. Ramasubramanian. An EEG-Based Computational Model for Decoding Emotional Intelligence, Personality, and Emotions. 2024. https://doi.org/10.1109/TIM.2023.3347790
Demonstrates that EEG-based computational models can successfully recognize and classify human emotional intelligence and personality traits.
Lin Z. A validity-guided workflow for robust large language model research in psychology.. 2026. https://doi.org/10.3758/s13428-026-03073-2
Identifies severe measurement unreliability and statistical artifacts, termed 'measurement phantoms,' when LLMs are integrated into psychological and behavioral research.
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A. Shirvani, Stephen G. Ware, Lewis J. Baker. Personality and Emotion in Strong-Story Narrative Planning. 2023. https://doi.org/10.1109/TG.2022.3227220
Shows that computational narrative planning models incorporating emotion and personality generate believable character behavior that aligns with human expectations.
Vahid Sadiri Javadi, Fryderyk Róg, Aksa Aksa, Johanne R. Trippas, S. Vakulenko, Lucie Flek. CHARISMA: Character-Based Interaction Simulation with Multi-LLM Agents Toward Computational Social Psychology. 2026. https://doi.org/10.1145/3786304.3787921
Utilizes LLM simulation frameworks to demonstrate how agent-based models reflect real human social interactions and personality-driven behaviors.
Takahashi Y, Soda T, Tomita H, Yamashita Y. Digital Twin Brain: Generating Multitask Behavior from Connectomes for Personalized Therapy.. 2026. https://doi.org/10.34133/bmef.0231
Validates a digital twin brain framework that accurately translates individual connectomes into complex neurobehavioral dynamics and predictions.
F. Arshad, Arslan Shaukat, Seemab Latif. Personality Prediction using Multiple Textual Datasets and Deep Learning Models. 2024. https://doi.org/10.1109/FIT63703.2024.10838446
Applies natural language processing and deep learning models to predict Big Five personality traits from textual data with high accuracy.
Hussain R, Ma Z, Khandelwal R, Oltmanns J, Gupta M. Language-based personality assessment from life narratives: a focus on model interpretability and efficiency.. 2026. https://doi.org/10.3389/frai.2026.1760246
Proposes a hybrid transformer and RNN framework that effectively assesses personality traits from long-form life narratives.
Elmahalawy AR, Wei M, Wu X, Tao X, Li L. Multi-Gate Mixture-of-Experts with Explanation for Predictive Computational Personality Analysis. 2025. https://doi.org/10.21203/rs.3.rs-8203590/v1
Introduces computational mixture-of-experts architectures that successfully perform predictive personality analysis across multiple datasets.
Tshimula JM, Galekwa RM, Chikhaoui B. A critical analysis of MBTI-based personality profiling with large language models.. 2026. https://doi.org/10.3389/fncom.2026.1800284
Highlights critical psychometric limitations, methodological weaknesses, and sensitivity to dataset biases when using computational models for personality profiling.
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