Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale...

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Arousal levels significantly impact task performance, with optimal levels dependent on task difficulty.
• The Yerkes-Dodson law describes an inverted U-shaped relationship between arousal and performance on difficult tasks.
• The noradrenergic locus coeruleus system is a proposed regulator of arousal effects on performance via noradrenaline release and cortical gain changes.

Purpose of the Study:

• To propose a novel mechanistic model explaining the Yerkes-Dodson effect from a sensory processing perspective.
• To investigate how arousal states differentially modulate sensory information encoding across processing hierarchies.
• To elucidate why performance decays with high arousal only in difficult tasks.

Main Methods:

• Developed a deep convolutional neural network with a global gain mechanism.
• Simulated task performance under varying arousal states and task difficulties.
• Analyzed the model's internal representations to understand information processing.

Main Results:

• The model successfully reproduced the interaction between arousal state and task difficulty observed in the Yerkes-Dodson effect.
• Global gain states differentially modulated sensory information encoding across the network's processing hierarchy.
• Differential modulation explained the varying effects of arousal on simple versus difficult tasks.

Conclusions:

• A hierarchical sensory processing account provides a mechanistic explanation for the Yerkes-Dodson effect.
• Global gain modulation in neural networks offers insights into arousal's impact on cognitive performance.
• This framework advances understanding of how arousal influences performance across different task complexities.