[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-293165-105":3,"doc-detail-293165-en":81,"detail-sidebar-cat-0-en-105":98},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","hippd-brain-inspired-hierarchical-information-processing-for-personality-detection","HIPPD - Brain-Inspired Hierarchical Information Processing for Personality Detection","","Personality detection from text infers an individual’s personality traits from linguistic patterns, yet existing machine learning methods often miss contextual information across multiple posts and struggle to extract robust features in semantically sparse settings. This paper introduces HIPPD, a brain-inspired framework that emulates hierarchical information processing. It uses a large language model for global semantic reasoning and deep abstraction, a dynamic prefrontal-cortex-like memory with dopaminergic prediction-error feedback, and routed lightweight basal-ganglia-like models via winner-takes-all. Experiments on Kaggle and Pandora show consistent gains over state-of-the-art baselines.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/hippd-brain-inspired-hierarchical-information-processing-for-personality-detection/293165/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/hippd-brain-inspired-hierarchical-information-processing-for-personality-detection/293165.png","ImageObject",300,407,{"name":42,"@type":43},"Anna Hans","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",11,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does the paper address in personality detection from text?","Question",{"text":63,"@type":64},"It targets the difficulty of capturing contextual information spanning multiple posts and extracting representative, robust features in semantically sparse environments.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does HIPPD emulate the human brain to perform personality detection?",{"text":68,"@type":64},"It uses a large language model to simulate cortical global semantic reasoning, a dynamic prefrontal-cortex-like memory for adaptive feature selection driven by dopaminergic prediction error feedback, and routed basal-ganglia-like lightweight models selected by a winner-takes-all mechanism.",{"name":70,"@type":61,"acceptedAnswer":71},"What evidence shows HIPPD improves performance over existing methods?",{"text":72,"@type":64},"The paper reports extensive experiments on the Kaggle and Pandora datasets, where HIPPD consistently outperforms state-of-the-art baselines.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},293165,1789660546,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":92,"language":93,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":94,"faqs":95,"seo_title":96,"seo_description":12,"update_tm":80,"read_time":97,"preview_page_count":4},5909892332657,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"HIPPD: Brain-Inspired Hierarchical Information Processing  \nfor Personality Detection  \nGuanming Chen [gc3n21@soton.ac.uk](gc3n21@soton.ac.uk)  \nLingzhi Shen [l.shen@soton.ac.uk](l.shen@soton.ac.uk)  \nXiaohao Cai [x.cai@soton.ac.uk](x.cai@soton.ac.uk)  \nShoaib Jameel [M.S.Jameel@southampton.ac.uk](M.S.Jameel@southampton.ac.uk)  \nSchool of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom  \nImran Razzak [imran.razzak@mbzuai.ac.ae](imran.razzak@mbzuai.ac.ae)  \nDepartment of Computational Biology, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates  \nEditors: Hung-yi Lee and Tongliang Liu  \nAbstract  \nPersonality detection from text aims to infer an individual’s personality traits based on linguistic patterns. However, existing machine learning approaches often struggle to capture contextual information spanning multiple posts and tend to fall short in extracting representative and robust features in semantically sparse environments. This paper presents HIPPD, a brain-inspired framework for personality detection that emulates the hierarchical information processing of the human brain. HIPPD utilises a large language model to simulate the cerebral cortex, enabling global semantic reasoning and deep feature abstraction.  \nA dynamic memory module, modelled after the prefrontal cortex, performs adaptive gating and selective retention of critical features, with all adjustments driven by dopaminergic prediction error feedback. Subsequently, a set of specialised lightweight models, emulating the basal ganglia, are dynamically routed via a strict winner-takes-all mechanism to capture the personality-related patterns they are most proficient at recognising. Extensive experiments on the Kaggle and Pandora datasets demonstrate that HIPPD consistently outperforms state-of-the-art baselines.  \nKeywords: Personality Detection; Brain-Inspired Modelling; Hierarchical Processing;  \nLarge Language Models; Winner-Take-All Networks  \n1. Introduction  \nPersonality can be likened to a fingerprint: distinct to each individual, yet displaying common patterns. Although various theories on personality exist, they commonly concur that it is shaped by a complex mix of genetic, environmental, and experiential factors (Kozlova et al. , 2024) . Among these, the Myers-Briggs Type Indicator (MBTI) (Myers et al. , 1962) is a widely used and effective personality assessment tool. It categorises individuals into 16 different personality types based on four dimensions: Introversion vs. Extraversion (I/E), Sensing vs. Intuition (S/N), Thinking vs. Feeling (T/F), and Judging vs. Perceiving (P/J) .  \nRecent detection approaches, such as EERPD (Li et al. , 2024), integrate emotion regulation with emotional features, leveraging few-shot examples and chain reasoning. However, they may have high requirements for the quality and diversity of emotional data. The PsyAttention model (Zhang et al. , 2023) improves personality detection accuracy by  \n© 2025 G. Chen, L. Shen, X. Cai, S. Jameel & I. Razzak.  \nChen Shen Cai Jameel Razzak  \neffectively encoding psychological features and reducing the number of features. However, during feature selection, some details crucial for specific application scenarios may be lost, potentially affecting the model’s adaptability across different contexts. In Hu et al. (2024), the authors propose a method that utilises large language models (LLMs) to generate augmented textual data and explanations of personality labels from raw posts, focusing on semantic, emotional, and linguistic aspects. While AI-generated data can provide rich information, it may also introduce more severe challenges (Shen et al. , 2025b), such as causing hallucinations and amplifying biases.  \nFigure 1: The visualization shows the co-occurrence matrix of the top 10 most frequent  \nwords in the real texts from the Kaggle (left) and Pandora (right) datasets.  \nA central issue highlighted in this wor","cbCaicE2rLzVVyXD","https://ap.wps.com/l/cbCaicE2rLzVVyXD","pdf",2455659,16,"English","# Introduction\n## Personality detection and existing approaches\n## Key limitations: reasoning and context modelling\n## Core intuition and hierarchical design\n## Class imbalance in personality datasets","[{\"question\":\"What problem does the paper address in personality detection from text?\",\"answer\":\"It targets the difficulty of capturing contextual information spanning multiple posts and extracting representative, robust features in semantically sparse environments.\"},{\"question\":\"How does HIPPD emulate the human brain to perform personality detection?\",\"answer\":\"It uses a large language model to simulate cortical global semantic reasoning, a dynamic prefrontal-cortex-like memory for adaptive feature selection driven by dopaminergic prediction error feedback, and routed basal-ganglia-like lightweight models selected by a winner-takes-all mechanism.\"},{\"question\":\"What evidence shows HIPPD improves performance over existing methods?\",\"answer\":\"The paper reports extensive experiments on the Kaggle and Pandora datasets, where HIPPD consistently outperforms state-of-the-art baselines.\"}]","HIPPD - Brain-Inspired Hierarchical Information Processing for Personality Detection | PDF",40,{"code":4,"msg":82,"data":99},[100,104,108,112,117,122,126,129,134,137,141],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":105,"show_sort_weight":106,"slug":107},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},"Exam",70,"exam",{"id":113,"doc_module":4,"doc_module_name":25,"category_name":114,"show_sort_weight":115,"slug":116},5,"Comic",60,"comic",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":119,"show_sort_weight":120,"slug":121},6,"Technology",50,"technology",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":97,"slug":125},7,"Healthcare","healthcare",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":127,"slug":128},30,"research-report",{"id":130,"doc_module":4,"doc_module_name":25,"category_name":131,"show_sort_weight":132,"slug":133},9,"Religion & Spirituality",20,"religion-spirituality",{"id":132,"doc_module":4,"doc_module_name":25,"category_name":135,"show_sort_weight":132,"slug":136},"World Cup","world-cup",{"id":138,"doc_module":4,"doc_module_name":25,"category_name":139,"show_sort_weight":138,"slug":140},10,"Lifestyle","lifestyle",{"id":142,"doc_module":4,"doc_module_name":25,"category_name":143,"show_sort_weight":113,"slug":144},19,"General","general"]