Distinct P300 of cognitive load during problem presentation and response evaluation in an arithmetic verification...

Area of Science:

• Cognitive Neuroscience
• Psychophysiology
• Human Performance

Background:

• Understanding how task complexity affects cognitive processes is crucial for optimizing performance.
• Individual differences in cognitive abilities can modulate responses to varying cognitive loads.
• Neural dynamics, including event-related potentials (ERPs) and oscillatory activity, offer insights into cognitive resource allocation.

Purpose of the Study:

• To investigate the influence of increasing task complexity on cognitive performance (accuracy, reaction time, inverse efficiency) and neural dynamics.
• To examine how individual differences (high-performing vs. low-performing groups) shape the effects of cognitive load.
• To explore the complementary roles of P300 event-related potentials and frequency-band oscillations in tracking neural resource allocation.

Main Methods:

• A two-stage arithmetic load verification task with varying complexity levels (low, intermediate, high).
• Behavioral measures included accuracy, reaction time (RT), and inverse efficiency scores (IES).
• Electrophysiological recordings captured P300 amplitudes and theta/alpha band power dynamics.

Main Results:

• Increasing task complexity led to reduced accuracy, slower RT, and higher IES.
• During problem presentation, P300 showed group differences (HG < LG), and LG exhibited load-dependent frontal theta/alpha decreases.
• During response evaluation, P300 amplitude decreased with cognitive load, and frontal theta/alpha power predicted accuracy under intermediate load.

Conclusions:

• Cognitive load significantly impairs performance and alters neural dynamics in a task-dependent manner.
• Electrophysiological measures (ERP and oscillations) provide complementary insights into neural resource allocation and adaptability under cognitive load.
• Findings extend cognitive load theory by detailing neural mechanisms underlying performance variations.