Mind meets machine: Unravelling GPT-4's cognitive psychology

TL;DR

This paper evaluates GPT-4's performance on cognitive psychology datasets, demonstrating its high accuracy and potential to bridge human and machine reasoning in understanding cognition.

Contribution

It provides a comprehensive assessment of GPT-4 across multiple cognitive psychology tasks using established datasets, highlighting its advanced reasoning capabilities.

Findings

GPT-4 achieves high accuracy on cognitive psychology tasks

GPT-4 outperforms prior state-of-the-art models

Results suggest GPT-4 can bridge gaps in AI reasoning

Abstract

Cognitive psychology delves on understanding perception, attention, memory, language, problem-solving, decision-making, and reasoning. Large language models (LLMs) are emerging as potent tools increasingly capable of performing human-level tasks. The recent development in the form of GPT-4 and its demonstrated success in tasks complex to humans exam and complex problems has led to an increased confidence in the LLMs to become perfect instruments of intelligence. Although GPT-4 report has shown performance on some cognitive psychology tasks, a comprehensive assessment of GPT-4, via the existing well-established datasets is required. In this study, we focus on the evaluation of GPT-4's performance on a set of cognitive psychology datasets such as CommonsenseQA, SuperGLUE, MATH and HANS. In doing so, we understand how GPT-4 processes and integrates cognitive psychology with contextual information, providing insight into the underlying cognitive processes that enable its ability to generate the responses. We show that GPT-4 exhibits a high level of accuracy in cognitive psychology tasks relative to the prior state-of-the-art models. Our results strengthen the already available assessments and confidence on GPT-4's cognitive psychology abilities. It has significant potential to revolutionize the field of AI, by enabling machines to bridge the gap between human and machine reasoning.