HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection
This work addresses personality detection from text, a domain-specific problem, with an incremental approach that combines existing methods in a novel brain-inspired architecture.
The paper tackles personality detection from text by proposing HIPPD, a brain-inspired framework that addresses limitations in capturing contextual information and extracting robust features, achieving consistent outperformance over state-of-the-art baselines on Kaggle and Pandora datasets.
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.