HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection

Guanming Chen, Lingzhi Shen, Xiaohao Cai, Imran Razzak, Shoaib Jameel
Proceedings of the 17th Asian Conference on Machine Learning, PMLR 304:910-925, 2025.

Abstract

Personality 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. A 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v304-chen25c, title = {HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection}, author = {Chen, Guanming and Shen, Lingzhi and Cai, Xiaohao and Razzak, Imran and Jameel, Shoaib}, booktitle = {Proceedings of the 17th Asian Conference on Machine Learning}, pages = {910--925}, year = {2025}, editor = {Lee, Hung-yi and Liu, Tongliang}, volume = {304}, series = {Proceedings of Machine Learning Research}, month = {09--12 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v304/main/assets/chen25c/chen25c.pdf}, url = {https://proceedings.mlr.press/v304/chen25c.html}, abstract = {Personality 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. A 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.} }
Endnote
%0 Conference Paper %T HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection %A Guanming Chen %A Lingzhi Shen %A Xiaohao Cai %A Imran Razzak %A Shoaib Jameel %B Proceedings of the 17th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Hung-yi Lee %E Tongliang Liu %F pmlr-v304-chen25c %I PMLR %P 910--925 %U https://proceedings.mlr.press/v304/chen25c.html %V 304 %X Personality 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. A 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.
APA
Chen, G., Shen, L., Cai, X., Razzak, I. & Jameel, S.. (2025). HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection. Proceedings of the 17th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 304:910-925 Available from https://proceedings.mlr.press/v304/chen25c.html.

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