Environmental Noise Alters Neural Regulation Without Behavioral Impairment: A Pilot EEG Study
Morina, E.
Show abstract
Environmental noise is a pervasive stressor that impairs attention and increases arousal, whereas natural soundscapes are linked to restoration and improved well-being. This pilot study tested whether exposure to natural auditory environments can buffer the neural strain induced by noise. Twelve healthy adults completed five cognitive tasks (DotProbe, Stroop, GoNoGo, NBack, Visual Search) under three auditory conditions: Noise (urban traffic), Nature (ambient natural sounds), and Control (silence with earplugs), while 32-channel EEG recorded ongoing activity. Behavioral accuracy remained high across all tasks (89.9 to 99.5 %), differing only for the Nback, which improved under Nature relative to Noise and Control. In contrast, EEG revealed robust environment-dependent modulation. Mixed-effects models (FDR-corrected p < .05) identified 17 significant condition effects across frequency bands and cortical regions. Nature increased baseline-corrected{delta} , {beta}, and{gamma} power, elevated /{beta} ratios, and enhanced the Engagement Index in posterior networks, all signatures of relaxed yet alert cortical states. Noise, by contrast, amplified{delta} and{theta} activity and raised{theta} / ratios in parieto-occipital regions, indicating higher cognitive load and compensatory effort. Reliability analyses confirmed moderate within-condition stability of EEG measures (mean ICC = 0.53 to 0.61) and higher consistency for ratio-based indices. Together, these findings reveal that natural soundscapes promote efficient, low-effort neural organization, whereas urban noise elicits energetically costly activation despite preserved behavioral performance. The results establish electrophysiological markers of environmental stress and restoration, supporting biophilic design strategies for healthier and more cognitively sustainable work environments.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Decreased alertness reconfigures cognitive control networks 95%
- Neural alpha oscillations and pupil size differentially index cognitive demand under competing audio-visual task conditions 95%
- Dissociating Contributions of Theta and Alpha Oscillations from Aperiodic Neural Activity in Human Visual Working Memory 95%
Similar papers in this journal
- Impoverished auditory cues limit engagement of brain networks controlling spatial selective attention 95%
- The impact of 1/f activity and baseline correction on the results and interpretation of time-frequency analyses of EEG/MEG data: A cautionary tale 95%
- Natural Music Evokes Correlated EEG Responses Reflecting Temporal Structure and Beat 95%
Similar papers in this journal
- Dissociable neural information dynamics of perceptual integration and differentiation during bistable perception 95%
- Irrelevant Predictions: Distractor Rhythmicity Modulates Neural Encoding in Auditory Cortex 94%
- Expectations boost the reconstruction of auditory features from electrophysiological responses to noisy speech 94%
Similar papers in this journal
- Bayesian Prior Uncertainty and Surprisal Elicit Distinct Neural Patterns During Sound Localization in Dynamic Environments 95%
- Disentangling the Functional Roles of Pre-Stimulus Oscillations in Crossmodal Associative Memory Formation via Sensory Entrainment 95%
- EEG and behavioral correlates of attentional processing while walking and navigating naturalistic environments 95%
Similar papers in this journal
- Characterising time-on-task effects on oscillatory and aperiodic EEG components and their co-variation with visual task performance. 94%
- 40 Hz Audiovisual Stimulation Improves Sustained Attention and Related Brain Oscillations 94%
- The infant brain rapidly entrains to visual statistical regularities during stimulus exposure 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.