HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection

2025-10-10

AI Summary

Existing methods for personality trait recognition struggle to model cross-text contextual dependencies and exhibit insufficient feature robustness under semantic sparsity. To address these limitations, we propose a brain-inspired hierarchical framework: (1) a large language model emulates cortical semantic reasoning; (2) a prefrontal cortex–inspired dynamic memory module enables adaptive context integration; and (3) a basal ganglia–inspired lightweight expert model, incorporating prediction-error–driven feedback and winner-take-all dynamic routing, achieves selective preservation and specialized recognition of discriminative features. This architecture enhances feature abstraction capability and cross-scenario generalization. Empirical evaluation demonstrates state-of-the-art performance on both the Kaggle and Pandora personality datasets, outperforming current SOTA approaches.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Mixture of Experts (MoE)
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Detects personality traits from linguistic patterns in text
• Captures contextual information across multiple text posts
• Extracts robust features in semantically sparse environments

Innovation

• Brain-inspired hierarchical framework for personality detection
• Dynamic memory module with dopaminergic feedback mechanism
• Specialized lightweight models with winner-takes-all routing