Associate Professor Wayne Wilson Ph.D.
Head of Audiology, The University of Queensland, Australia, E: [email protected].
It is widely suggested that noise impairs cognition (e.g., Ronsse & Wang, 2010; Shield & Dockrell, 2003). For this reason, we often seek to reduce noise particularly in learning environments such as schools and universities. But what if noise could benefit cognition? It sounds counter-intuitive, but it could be true for a particular type of noise, white noise (WN).
The presence of WN has been shown to improve a range of cognitive functions including verbal memory (Sikstrom et al., 2007; Soderlund et al., 2010), speed of arithmetic computations (Usher et al., 2000), and working memory for verbal stimuli (Manan et al., 2012). While this research was mostly conducted on healthy adults with normal hearing sensitivity, the finding that some of the benefits of WN were only observed in adults with reduced attentional capacity saw researchers turn to the Moderate Brain Arousal (MBA) model (Sikstrom et al., 2007) for a possible explanation of these effects.
The MBA model is a neurocomputational model that explains noise-related improvements in cognitive function via the statistical phenomenon of stochastic resonance (SR) and the modulating properties of dopamine (DA) and neural noise on neural responsivity. Stochastic resonance is the paradoxical, statistical phenomena whereby a weak, undetectable signal becomes detectable after adding random (stochastic) noise (Moss et al., 2004). In the brain, this beneficial effect is thought to be linked dopamine (DA) (Rausch et al., 2013) with optimal levels of DA needed to generate the neural noise for SR and optimal neural performance (Sikstrom et al., 2007). The MBA model also suggests that a hypodopaminergic brain might be best positioned to benefit from an increase in neural noise, with this increase to be achieved by: 1) increasing DA levels in the brain, or 2) increasing external acoustic noise by introducing noise with stochastic properties, such as WN.
Researchers at the University of Queensland, Australia, have been investigating the effects of WN on language in young, healthy adults with normal hearing sensitivity.
In their first study, these researchers found participants who listened to 70 dB(A) of WN while learning novel written words demonstrated superior recall accuracy over time compared to participants who completed the exercise in quiet (Angwin et al., 2017). The authors concluded that WN has the capacity to enhance lexical acquisition in young, healthy adults with normal hearing sensitivity.
In their second study, these researchers found participants who listened to 70 dB(A) of WN while completing a semantic priming task showed significantly reduced indirect priming effects compared to participants who completed the exercise in quiet. This effect was greater for participants with lower executive and orienting attention (Angwin et al., 2017). The authors suggested that WN could focuses automatic spreading activation in the brain, which may be driven by modulation of dopaminergic circuitry, in some young, healthy adults with normal hearing sensitivity.
In their third study, these researchers found participants who listened to 70 dB(A) of WN while learning novel written words from context demonstrated superior recognition accuracy for word meanings immediately after training compared to participants who completed the training in quiet. Interestingly, this effect was no longer present when the participants were reassessed shortly after the training. The authors concluded that WN has the capacity to facilitate meaning acquisition from context in young, healthy adults with normal hearing sensitivity, but further research is needed to clarify its capacity to improve longer-term retention of word meaning.
Overall, the research investigating the use of WN to benefit cognition continues to show promise. Further research is needed both to fully elucidate the neurophysiological mechanisms underpinning the observed behavioural effects and to determine if WN can benefit cognition in clinical populations. Efforts in this regard are currently underway at several universities around the world where researchers are using dopamine, auditory evoked potentials and neuroimaging to investigate the effects of WN in a wider range of adult populations.
References
- Angwin, A.J., Wilson, W.J., Arnott, W.L., Signorini, A., Barry, R.J., & Copland, D.A. (2017). White noise enhances new-word learning in healthy adults. Scientific Reports, 7(1), 13045.
- Angwin, A.J., Wilson, W.J., Copland, D.A., Barry, R.J., Myatt, G., & Arnott, W.L. (2018). The impact of white noise on semantic priming. Brain and Language, 180-182, 1-7.
- Angwin, A., Wilson, W.J., Ripolles, P., Rodriguez-Fornells, A., Arnott, W., Barry, R., Cheng, B., Garden, K., & Copland, D. (2019). White noise facilitates new-word learning from context. Brain and Language. 99:104699. doi: 10.1016/j.bandl.2019.104699.
- Manan et al. (2012). Hippocampal-cerebellar involvement in enhancement of performance in word-based BRT with background noise. Psychology & Neuroscience, 5, 247-256.
- Moss et al. (2004). Stochastic resonance and sensory information processing: a tutorial and review of application. Clinical Neurophysiology, 115, 267-281.
- Rausch et al. (2013). White noise improves learning by modulating activity in dopaminergic midbrain regions and right superior temporal sulcus. Journal of Cognitive Neuroscience.
- Ronsse et al. (2010). Effects of noise from building mechanical systems on elementary school student achievement. ASHRAE Transactions, 116(2), 347-354.
- Shield et al. (2003). The effects of noise on children at school: A review. Building Acoustics, 10, 97-116.
- Sikstrom et al. (2007). Stimulus-dependent dopamine release in attention-deficit/hyperactivity disorder. Psychological Review, 114, 1047-1075.
- Soderlund et al. (2010). The effects of background white noise on memory performance in inattentive school children. Behavioral and Brain Functions, 6 (55).
- Usher et al. (2000). Stochastic resonance in the speed of memory retrieval. Biological Cybernetics, 83, L11-16.
