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Neural Mechanisms Underlying Intrinsic and Extraneous Cognitive Loads in Numerical Inductive Reasoning
Feng Xiao1,2, Xiuchen Zheng1, Na Xiao1
1Department of Psychology, Key Laboratory of Brain Function and Brain Disease Prevention and Treatment of Guizhou Province, Guizhou Normal University, Guiyang, China.
Psychophysiology
|August 18, 2025
Summary
This study reveals how intrinsic and extraneous cognitive loads impact numerical inductive reasoning. Findings show distinct neural mechanisms for each load, with competition for resources under complex conditions.
Area of Science:
- Cognitive Psychology
- Neuroscience
Background:
- Cognitive load theory posits that limited cognitive resources are shared between intrinsic load (task difficulty) and extraneous load (irrelevant stimuli).
- The interaction between these loads during inductive reasoning remains poorly understood, particularly concerning numerical tasks.
Purpose of the Study:
- To investigate the distinct and interactive effects of intrinsic and extraneous cognitive loads on numerical inductive reasoning.
- To explore the neural mechanisms underlying these cognitive loads using event-related potentials (ERPs).
Main Methods:
- Participants performed a numerical inductive reasoning task involving rule identification from digit sequences.
- Intrinsic cognitive load was manipulated via rule complexity (simple vs. hierarchical).
- Extraneous cognitive load was manipulated using a dot memory task with varying executive demands (low vs. high). ERPs were recorded.
Main Results:
- Behavioral data indicated independent effects of intrinsic and extraneous cognitive loads on reasoning performance.
- ERP analysis showed intrinsic load modulated N200, LPC, and LNC components (pattern detection, memory updating, integration).
- Extraneous load increased P200 amplitudes (attentional allocation), and interaction on N400 suggested resource competition under high extraneous load with complex rules.
Conclusions:
- Intrinsic and extraneous cognitive loads influence numerical inductive reasoning via separate but interacting neural pathways.
- Evidence suggests competition for semantic integration resources when processing complex rules under high extraneous cognitive load.
- Findings contribute to cognitive load theory and have implications for educational strategies to optimize learning.
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