Neural decoding reveals dynamic patterns of visual chunk memory processes

Abstract

Chunk memory constitutes the basic unit that manages long-term memory and converts it into immediate decision-making processes, it remains unclear how to interpret and organize incoming information to form effective chunk memory. This paper investigates electroencephalography (EEG) patterns from the perspective of time-domain feature extraction using chunk memory in visual statistical learning and combines time-resolved multivariate pattern analysis (MVPA). The GFP and MVPA results revealed that chunk memory processes occurred during specific time windows in the learning phase. These processes included attention modulation (P1), recognition and feature extraction (P2), and segmentation for long-term memory conversion (P6). In the decision-making stage, chunk memory processes were encoded by four ERP components. Scene processing correlated with P1, followed by feature extraction facilitated by P2, encoding process (P4), and segmentation process (P6). This paper identifies the early process of chunk memory through implicit learning and applies univariate and multivariate approaches to establish the neural activity patterns of the early chunk memory process, which provides ideas for subsequent related studies.

Keywords

• Electroencephalography
• Visual statistical learning
• Chunk memory
• Machine learning