[Proposal] Predicting Hallucinations: the association between hallucinatory experiences, sleep health and cognitive control in a non-clinical sample

💡 Prolific Hub 📝 £10,000 Research Grant Competition
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Have you ever seen something move out of the corner of your eye, heard someone call out your name, or felt an insect crawling on your skin, only to realise that nothing was there?

What if your predisposition to these experiences was linked to your sleep?

The research proposed here aims to understand links between sleep, hallucinations, and cognitive control, with implications for how we understand, and design interventions for, distressing hallucinations.

Background

We spend, on average, one third of our lives asleep1. An extensive body of literature demonstrates the vital importance of this physiological process. Yet, it still seems to collectively surprise us when insufficient sleep leads to decline in many areas of our functioning, particularly within mental health. Poor sleep is, in fact, widely considered a secondary symptom to many mental health disorders 2.

A prime example is seen in schizophrenia and psychosis, in which it is well-established that sleep disruption is common2. Many people with psychosis hear frequent distressing voices3, so reports of not sleeping well are perhaps unsurprising: hearing voices may disrupt sleep. However, a series of recent experimental and intervention-based studies hinted that the directionality may be the other way round, finding that implementing an intervention for insomnia significantly reduced psychotic experiences4, while imposed sleep loss led to increased reports of said symptoms, including hallucinations5. This could have important implications for interventions designed to help people with psychosis, which do not currently tend to target sleep.

It is important, therefore, to understand the mechanisms by which sleep disruption might cause hallucinations. The same group of studies found that both sleep loss and psychotic experiences were significantly associated with cognitive impairments (e.g., cognitive disorganisation, negative affect, working memory)5. This raises the possibility that cognitive impairment may play a mediating role between sleep disruption and hallucinations – that is, poor sleep may lead to cognitive impairment, which may in turn increase the likelihood of hallucinations. A key aspect of cognition may be cognitive control, and specifically the ability to inhibit irrelevant memories or suppress intrusive cognitions, which is associated with hallucinations in psychosis patients6. There is also ample evidence that top-down processes involved in speech perception may be linked to hallucinations3,7. Separate research provides evidence for an association between sleep quality and cognitive control8, as well as between sleep quality and dysfunctional thought suppression9, but there is very little research investigating how sleep, hallucinations, and cognitive control variables might covary and interact.

Understanding these relationships is key to predicting hallucinations, and ultimately, developing and deploying new sleep and cognition orientated interventions for people who experience distressing hallucinations. It is also important to understand the potential psychiatric risks insufficient sleep might harbour to inform public health initiatives, especially given the increasing prevalence of poor sleep in the general population over the last few decades10,11.

Aims

Participants will be assessed for sleep quality and hallucinatory experiences, and complete three cognitive tasks aimed at assessing different aspects of cognitive control and top-down processing. Specifically, the aim of the project is to further establish the association between disrupted sleep and hallucinations in a non-clinical sample, and test whether specific components of cognitive control may play a mediating role.

Methodology

Participants will provide their socio-demographic background (e.g., age, gender, ethnicity) before completing several self-report measures to assess sleep disruption and frequency of hallucinatory experiences: the Sleep Health Index (SHI)12, the Multi-Modality Unusual Sensory Experiences Questionnaire (MUSEQ)13, and the Thought Control Ability Questionnaire (TCAQ)14.

Participants will also complete three cognitive tasks.

  1. The Inhibition of Currently Irrelevant Memories (ICIM) task15 consists of two stages involving presentation of a series of images. The first stage requires participants to respond when an image in the set is repeated (i.e., a continuous recognition task), while the second requires participants to respond when an image is repeated, but also instructs them to intentionally inhibit responses to images presented in the first stage. The outcome measure is the number of ‘false alarm’ responses made in the second stage (i.e., failure to inhibit previously presented images).
  2. The backwards digit span task16 presents participants with a series of digits, with trial length differing from 3 to 12, then instructs them to input these digits in reverse order. The outcome measure is the mean length of correct trials16.
  3. The auditory signal detection task15 presents participants with bursts of white noise, some of which contain embedded speech clips at threshold levels. For each burst of noise, participants indicate whether they heard a voice in the noise and provide a confidence rating for their response. The outcome measures are false alarm responses (i.e., reporting a voice when no voice was present) and associated signal detection parameters (sensitivity and bias).

Analysis will aim to determine correlations between sleep, hallucinations, and cognitive control before constructing a hierarchical regression model predicting hallucinations from cognitive control and sleep disruption variables. Comparable non-clinical research into associations between hallucinations and cognition have shown small effect sizes (r = .14)3,7. For simple correlations, detecting this effect size would require N = 434 (alpha = .05, power = .90, r = .14, one-tailed). Where significant correlations are observed, a series of mediation analyses will test whether cognitive control mediates this relationship between poor sleep and hallucinatory experiences. Using suggested guidelines17,18 a sample of N = 593 is required to detect a large direct effect (r = .39) and small indirect effects (r = .14) within a bias-corrected bootstrap mediation analysis conducted using the R package ‘mediate’19.

Expected Costs

The study will take about 45minutes to complete, and participants will receive payment based on the 2021 UK living wage (£8.91p/h)20. Using Prolific’s costing tool, we are looking to receive £6450.72 for a nationally representative sample of 650 participants (accounting for attrition).

Data Transparency

All study materials, protocol and data will be made available on the Open Science Framework (OSF | Predicting Hallucinations: the association between hallucination-like experiences, sleep health and cognitive control in a non-clinical sample). Pre-registration has been completed using AsPredicted (https://aspredicted.org/zj54c.pdf), and a pre-print will be made available on PsyArxiv before submission for publication at a peer-reviewed journal.

References

  1. Aminoff, M., Boller, F. & Swaab, D. We spend about one-third of our life either sleeping or attempting to do so. Handb. Clin. Neurol. Ed. PJ Vinken GW Bruyn 98, vii (2011).
  2. Freeman, D., Sheaves, B., Waite, F., Harvey, A. G. & Harrison, P. J. Sleep disturbance and psychiatric disorders. Lancet Psychiatry 7, 628–637 (2020).
  3. Moseley, P., Smailes, D., Ellison, A. & Fernyhough, C. The effect of auditory verbal imagery on signal detection in hallucination-prone individuals. Cognition 146, 206–216 (2016).
  4. Freeman, D. et al. The effects of improving sleep on mental health (OASIS): a randomised controlled trial with mediation analysis. Lancet Psychiatry 4, 749–758 (2017).
  5. Reeve, S., Emsley, R., Sheaves, B. & Freeman, D. Disrupting Sleep: The Effects of Sleep Loss on Psychotic Experiences Tested in an Experimental Study With Mediation Analysis. Schizophr. Bull. 44, 662–671 (2018).
  6. Badcock, J. C. & Hugdahl, K. A synthesis of evidence on inhibitory control and auditory hallucinations based on the Research Domain Criteria (RDoC) framework. Front. Hum. Neurosci. 8, (2014).
  7. Moseley, P. et al. Correlates of Hallucinatory Experiences in the General Population: An International Multisite Replication Study. Psychol. Sci. 0956797620985832 (2021) doi:10.1177/0956797620985832.
  8. Kusztor, A. et al. Sleep deprivation differentially affects subcomponents of cognitive control. Sleep 42, (2019).
  9. Harrington, M. O. & Cairney, S. A. Sleep Loss Gives Rise to Intrusive Thoughts. Trends Cogn. Sci. 0, (2021).
  10. Acquavella, J., Mehra, R., Bron, M., Suomi, J. M.-H. & Hess, G. P. Prevalence of narcolepsy and other sleep disorders and frequency of diagnostic tests from 2013–2016 in insured patients actively seeking care. J. Clin. Sleep Med. (2020) doi:10.5664/jcsm.8482.
  11. Ferrie, J. E., Kumari, M., Salo, P., Singh-Manoux, A. & Kivimäki, M. Sleep epidemiology–a rapidly growing field. Int. J. Epidemiol. 40, 1431–1437 (2011).
  12. Knutson, K. L. et al. The National Sleep Foundation’s Sleep Health Index. Sleep Health 3, 234–240 (2017).
  13. Mitchell, C. A. A. et al. The Structure and Measurement of Unusual Sensory Experiences in Different Modalities: The Multi-Modality Unusual Sensory Experiences Questionnaire (MUSEQ). Front. Psychol. 8, (2017).
  14. Luciano, J. V., Algarabel, S., Tomás, J. M. & Martínez, J. L. Development and validation of the thought control ability questionnaire. Personal. Individ. Differ. 38, 997–1008 (2005).
  15. Alderson-Day, B. et al. Intentional inhibition but not source memory is related to hallucination-proneness and intrusive thoughts in a university sample. Cortex 113, 267–278 (2019).
  16. Woods, D. L. et al. Improving digit span assessment of short-term verbal memory. J. Clin. Exp. Neuropsychol. 33, 101–111 (2011).
  17. Fritz, M. S. & MacKinnon, D. P. Required Sample Size to Detect the Mediated Effect. Psychol. Sci. 18, 233–239 (2007).
  18. Schoemann, A., Boulton, A. & Short, S. Determining Power and Sample Size for Simple and Complex Mediation Models. Soc. Psychol. Personal. Sci. 8, 194855061771506 (2017).
  19. Tingley, D., Yamamoto, T., Hirose, K., Keele, L. & Imai, K. Mediation: R Package for Causal Mediation Analysis. J. Stat. Softw. 59, (2014).
  20. GOV.UK. National Minimum Wage and National Living Wage rates. GOV.UK National Minimum Wage and National Living Wage rates - GOV.UK (2021).