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  3. Deficient Executive Control In Transformer Attention.
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  3. Deficient Executive Control In Transformer Attention.

Related Experiment Video

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Deficient executive control in transformer attention.

Suketu Chandrakant Patel1, Hongbin Wang2, Jin Fan1

  • 1Department of Psychology, Queens College, The City University of NewYork, 65-30 Kissena Blvd, Queens, NY 11367, USA.

PNAS Nexus
|June 4, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Large language models (LLMs) using transformer attention show human-like performance on simple attention tasks but fail complex conflict resolution. Their attention mechanisms struggle with increasing interference, limiting artificial general intelligence development.

Keywords:
attentioncolor Stroop taskexecutive controllarge language model (LLM)transformer

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09:37

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Neuroscience

Background:

  • Transformers in LLMs utilize self-attention, revolutionizing NLP.
  • Human attention has explicit executive control for conflict resolution and adaptive behavior.
  • LLMs currently lack explicit executive control mechanisms found in human attention.

Purpose of the Study:

  • To investigate the limitations of LLM attention mechanisms in conflict resolution.
  • To compare LLM attention performance to human performance on the Stroop task.
  • To assess the impact of context length on LLM attention and conflict resolution.

Main Methods:

  • Utilized the classic color Stroop task to evaluate executive control of attention in LLMs.
  • Tested LLMs on both congruent and incongruent conditions of the Stroop task.
  • Varied the length of word lists to examine performance degradation under increasing interference.

    • Main Results:

    • LLMs exhibited a conflict effect, with reduced accuracy in incongruent conditions for short word lists, mirroring human performance.
    • Performance in incongruent conditions significantly degraded with increased word list length, approaching total collapse.
    • Accuracy remained high in congruent conditions, and word reading accuracy was near-perfect across list lengths.

    Conclusions:

    • Transformer attention mechanisms in LLMs have fundamental limitations in conflict resolution over extended contexts.
    • LLMs fail to adaptively up-regulate control under rising interference.
    • Incorporating executive control mechanisms similar to biological attention is vital for advancing artificial general intelligence.