Neurosymbolic Systems of Perception & Cognition: The Role of Attention

TL;DR

This paper proposes that cognition involves neurosymbolic information across all levels of abstraction, with attention mechanisms playing a key role in processing differences between high and low-level data, challenging the binary System-1 and System-2 model.

Contribution

It introduces a neurosymbolic perspective on perception and cognition, emphasizing the importance of attention mechanisms in processing different data abstractions.

Findings

Cognition involves neurosymbolic information at all levels.

Attention mechanisms differentiate processing of high and low-level data.

Evidence supports a non-binary view of cognitive processing.

Abstract

A cognitive architecture aimed at cumulative learning must provide the necessary information and control structures to allow agents to learn incrementally and autonomously from their experience. This involves managing an agent's goals as well as continuously relating sensory information to these in its perception-cognition information stack. The more varied the environment of a learning agent is, the more general and flexible must be these mechanisms to handle a wider variety of relevant patterns, tasks, and goal structures. While many researchers agree that information at different levels of abstraction likely differs in its makeup and structure and processing mechanisms, agreement on the particulars of such differences is not generally shared in the research community. A binary processing architecture (often referred to as System-1 and System-2) has been proposed as a model of cognitive processing for low- and high-level information, respectively. We posit that cognition is not binary in this way and that knowledge at any level of abstraction involves what we refer to as neurosymbolic information, meaning that data at both high and low levels must contain both symbolic and subsymbolic information. Further, we argue that the main differentiating factor between the processing of high and low levels of data abstraction can be largely attributed to the nature of the involved attention mechanisms. We describe the key arguments behind this view and review relevant evidence from the literature.