Metacognitive Monitoring: A Human Ability Beyond Generative Artificial Intelligence

• URL: http://arxiv.org/abs/2410.13392v1
• Date: Thu, 17 Oct 2024 09:42:30 GMT
• Title: Metacognitive Monitoring: A Human Ability Beyond Generative Artificial Intelligence
• Authors: Markus Huff, Elanur Ulakçı,
• Abstract summary: Large language models (LLMs) have shown impressive alignment with human cognitive processes. This study investigates whether ChatGPT possess metacognitive monitoring abilities akin to humans.
• Score: 0.0
• License:
• Abstract: Large language models (LLMs) have shown impressive alignment with human cognitive processes, raising questions about the extent of their similarity to human cognition. This study investigates whether LLMs, specifically ChatGPT, possess metacognitive monitoring abilities akin to humans-particularly in predicting memory performance on an item-by-item basis. We employed a cross-agent prediction model to compare the metacognitive performance of humans and ChatGPT in a language-based memory task involving garden-path sentences preceded by either fitting or unfitting context sentences. Both humans and ChatGPT rated the memorability of these sentences; humans then completed a surprise recognition memory test. Our findings reveal a significant positive relationship between humans' memorability ratings and their actual recognition performance, indicating reliable metacognitive monitoring. In contrast, ChatGPT did not exhibit a similar predictive capability. Bootstrapping analyses demonstrated that none of the GPT models tested (GPT-3.5-turbo, GPT-4-turbo, GPT-4o) could accurately predict human memory performance on a per-item basis. This suggests that, despite their advanced language processing abilities and alignment with human cognition at the object level, current LLMs lack the metacognitive mechanisms that enable humans to anticipate their memory performance. These results highlight a fundamental difference between human and AI cognition at the metacognitive level. Addressing this gap is crucial for developing AI systems capable of effective self-monitoring and adaptation to human needs, thereby enhancing human-AI interactions across domains such as education and personalized learning.

Related papers

• The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks [17.5336703613751]
  This study benchmarks leading large language models and vision language models against human performance on the Wechsler Adult Intelligence Scale (WAIS-IV) Most models demonstrated exceptional capabilities in the storage, retrieval, and manipulation of tokens such as arbitrary sequences of letters and numbers. Despite these broad strengths, we observed consistently poor performance on the Perceptual Reasoning Index (PRI) from multimodal models.
  arXiv  Detail & Related papers  (2024-10-09T19:22:26Z)
• Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation [70.52558242336988]
  We focus on predicting engagement in dyadic interactions by scrutinizing verbal and non-verbal cues, aiming to detect signs of disinterest or confusion. In this work, we collect a dataset featuring 34 participants engaged in casual dyadic conversations, each providing self-reported engagement ratings at the end of each conversation. We introduce a novel fusion strategy using Large Language Models (LLMs) to integrate multiple behavior modalities into a multimodal transcript''
  arXiv  Detail & Related papers  (2024-09-13T18:28:12Z)
• Generative AI as a metacognitive agent: A comparative mixed-method study with human participants on ICF-mimicking exam performance [0.0]
  This study investigates the metacognitive capabilities of Large Language Models relative to human metacognition in the context of the International Coaching Federation ICF exam. Using a mixed method approach, we assessed the metacognitive performance of human participants and five advanced LLMs. The results indicate that LLMs outperformed humans across all metacognitive metrics, particularly in terms of reduced overconfidence, compared to humans.
  arXiv  Detail & Related papers  (2024-05-07T22:15:12Z)
• GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing [74.68232970965595]
  Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks.
  arXiv  Detail & Related papers  (2024-03-09T13:56:25Z)
• Towards a Psychology of Machines: Large Language Models Predict Human Memory [0.0]
  Large language models (LLMs) are excelling across various tasks despite not being based on human cognition. This study examines ChatGPT's ability to predict human performance in a language-based memory task.
  arXiv  Detail & Related papers  (2024-03-08T08:41:14Z)
• Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot [52.277579221741746]
  Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-09T13:01:21Z)
• Humans and language models diverge when predicting repeating text [52.03471802608112]
  We present a scenario in which the performance of humans and LMs diverges. Human and GPT-2 LM predictions are strongly aligned in the first presentation of a text span, but their performance quickly diverges when memory begins to play a role. We hope that this scenario will spur future work in bringing LMs closer to human behavior.
  arXiv  Detail & Related papers  (2023-10-10T08:24:28Z)
• Human-Like Intuitive Behavior and Reasoning Biases Emerged in Language Models -- and Disappeared in GPT-4 [0.0]
  We show that large language models (LLMs) exhibit behavior that resembles human-like intuition. We also probe how sturdy the inclination for intuitive-like decision-making is.
  arXiv  Detail & Related papers  (2023-06-13T08:43:13Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• AGENT: A Benchmark for Core Psychological Reasoning [60.35621718321559]
  Intuitive psychology is the ability to reason about hidden mental variables that drive observable actions. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. We present a benchmark consisting of procedurally generated 3D animations, AGENT, structured around four scenarios.
  arXiv  Detail & Related papers  (2021-02-24T14:58:23Z)
• SensAI+Expanse Emotional Valence Prediction Studies with Cognition and Memory Integration [0.0]
  This work contributes with an artificial intelligent agent able to assist on cognitive science studies. The developed artificial agent system (SensAI+Expanse) includes machine learning algorithms, empathetic algorithms, and memory. Results of the present study show evidence of significant emotional behaviour differences between some age ranges and gender combinations.
  arXiv  Detail & Related papers  (2020-01-03T18:17:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.