Clinical and Non-Clinical Populations May Experience Hallucinations Induced by Reality Monitoring Deficits
Background: Previous research demonstrates that hallucinations typically occur in clinical populations and can be attributed to cognitive mechanisms including Reality Monitoring (RM) deficits. Similarly, non-clinical populations can be prone to hallucinations as a result of RM deficits. Consequently, RM tasks may provide a possible screening tool to identify individuals at risk of psychosis (Simons et al., 2017) and de-stigmatise the experience of hallucinations (Brookwell et al., 2013; van Os et al., 2009).
Aim: To determine whether RM deficits underpin hallucination proneness in a non-clinical sample of 56 university students (n = 39 female and n = 17 male).
Method: RM was assessed using a modified version of an established source monitoring task (Garrison et al., 2017) consisting of two phases: a study and test phase. RM deficits were indicated by obtaining lower RM and/or higher externalising error scores. Measures of hallucination proneness were obtained using 3 separate self-report scales.
Results: There was no significant relationship between hallucination proneness and (1) RM or (2) externalising errors. This was the case for all three measures of hallucination proneness.
Conclusion: RM deficits which underlie hallucinations in the clinical population do not lead to hallucination proneness in a non-clinical sample of 56 university students.
Keywords: reality monitoring, externalising error, hallucination proneness, non-clinical, clinical
ARTICLE CONTENTS & REFERENCES
Natural Sciences: Deciding what is real, what is not: Reality monitoring and predictive coding in the brain (Chang, E.)
Social Anthropology: The politics of reality and unreality: Anthropological approaches to questions of hallucination and psychosis (Stuart, E. B.)
PSYCHOLOGICAL & BEHAVIOURAL SCIENCES
Corpus Christi College, University of Cambridge
Volume 1, Issue 2, pp. 76–88
Received: August 6, 2022
Revision recieved: December 28, 2022
Accepted: December 30, 2022
Published: January 20, 2023
Renmiu, S. (2023). Clinical and non-clinical populations may experience hallucinations induced by reality monitoring deficits. Cambridge Journal of Human Behaviour, 1(2), 76–88. https://www.cjhumanbehaviour.com/pbs0012
© Sara Renmiu. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License.
RM, externalising errors, and hallucinations
Reality Monitoring (RM) can be defined as the ability to discriminate between internally and externally-generated stimuli (Humpston et al., 2017; Simons et al., 2017; Johnson et al., 1993; Johnson & Raye, 1981). To achieve this, RM uses cognitive faculties to accurately identify sensations derived from experiences within compared to those outside of the self.
In particular, externalising errors refer to incorrectly attributing an internally generated event as being externally generated (Humpston et al., 2017; Brookwell et al., 2013; Woodward & Menon, 2013; Waters et al., 2012) which can result in hallucinations; for example: an individual reporting they hear voices that are not there because they mistake imagined internal voices, they have generated themselves, for real external voices.
Hallucinations can be defined as sensory experiences that occur in the absence of corresponding external stimuli which elicit a particular sensory response (Woodward & Menon, 2013; Slade & Bentall, 1988). In other words, hallucinations are sensory misperception—instances of perceiving stimuli that are not actually there—that occur when self-generated stimuli are taken as being externally generated. Additionally, hallucinations can occur in any sensory modality including auditory, visual, olfactory, gustatory, or tactile (Waters et al., 2014; Shergill, 2001; Kopala et al., 1994; Mueser et al., 1990), although, most are auditory or visual (Waters et al, 2014). Even within one particular sensory modality, hallucinations can come in many different forms. For instance, visual hallucinations may result in seeing people, shapes, flashes of light, and colours that are not actually there.
The Source Monitoring Framework
A salient theoretical explanation outlining how RM deficits may lead to hallucinations is the Source Monitoring Framework (Johnson et al., 1993; Johnson & Raye, 1981). This theory posits that decision-making processes performed when remembering are evaluated and attributed to particular sources, thereby allowing us to determine the origin of information as either being internal or external. In this sense, RM is viewed as a product of judgement processes which allow us to encode information about experiences. Examples of this include encoding sensory details, contextual information (spatial and temporal), semantic content (e.g., tendency for internally generated stimuli to relate to the self) and the type of cognitive processes engaged (e.g., imagery; Johnson & Raye, 1981). These characteristics of memories provide a sense of reality and are similar to ongoing perceptions that occur in real-time (Garrison et al., 2017; Johnson & Raye, 1981).
Essentially, information that we use to conceptualise an experience is woven into a composite of integrated information which makes up a memory, or our perception of an event. Each piece of information has different respective “weights” which are evaluated against criteria to determine the probability of stimuli being self or other generated. However, errors in RM can occur when one source of information is weighted as being greater than another through biassed cognitive processing or due to impairments in cognitive operations (Garrison et al., 2017). Not meeting the ascribed criteria along a single dimension may result in RM deficits when judgement processes suggest that an event is between the “cut-off” for an internally and externally generated stimulus. Therefore, difficulty in determining the origin of a stimulus can lead to RM deficits as this makes it hard to distinguish between internal and external stimuli. Consequently, the Source Monitoring Framework proposes that flexible criteria used in judgement processes are prone to errors which may compromise an individual’s ability to discern the origin of stimuli, leading to RM deficits.
More recent theories, such as misattribution models, characterise RM deficits leading to hallucinations in a similar way. Woodward and Menon (2013) focus on two important dimensions of hallucinations: (1) the self-generated/non-self-generated and (2) the inner/outer dimension (Stephane et al., 2003). The former dimension relates to the origin of a specific cognitive event, whilst the latter relates to the spatial location of a cognitive event (Woodward & Menon, 2013). This is an extension of the Source Monitoring Framework explanation of hallucinations with the addition of an “inner/outer” spatial dimension, which assumes that hallucinations result from certain cognitive operations leading to externalising error.
On the other hand, current computational models offer an account of hallucinations which incorporate the role of top-down processes (e.g., the influence of expectations and prior beliefs) to a larger extent. This theory seems to converge with the Source Monitoring Framework in that features used to generate a memory tend to be driven by the interaction between top-down and bottom-up processes, with a failure to determine the origin of stimuli generating hallucinatory experiences (Garrison et al., 2019; Powers et al., 2017).
The Continuum Model of Psychosis
Hallucinations are known to occur in both clinical and non-clinical populations. This suggests that there must be some form of continuum between these populations rather than them being categorically opposed. The dimensional nature of phenomena such as hallucinations can be conceptualised through a continuum ranging between psychosis, characteristic of clinical populations, to the lack of psychosis in non-clinical populations (van Os et al., 2009; Johns & van Os, 2001). That is to say, hallucinatory experiences occur in clinical (Toh et al., 2016; Ditman & Kuperberg, 2005) and non-clinical populations (Powers et al., 2017; Sommer et al., 2010; Serper et al., 2005). However, hallucinations are more likely to be transitory (Johns et al., 2014; Linscott & van Os, 2013; Bartels-Velthuis et al., 2011) and are experienced much less frequently in non-clinical populations (Sommer et al., 2010; Badcock et al., 2008; Paulik et al., 2006). This means that hallucinatory experiences are unevenly distributed throughout the general population (Siddi et al., 2019; Garrison et al., 2017).
This paper will place an emphasis on individuals with schizophrenia as hallucinations are a key symptom required for diagnosis, as defined by the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2013), affecting between 60% and 80% of patients (Waters et al., 2012). However, there is dispute over the extent to which a pure continuum exists between clinical and non-clinical populations (Baumeister et al., 2017) as the underlying mechanisms for hallucinations may differ between these populations.
Support for the continuum model of psychosis comes from van Os and colleagues’ (2009) seminal systematic review of psychotic experiences and symptoms in the non-clinical population. The authors presented evidence for the (1) distributional, (2) psychopathological, and (3) epidemiological validity of the continuum model. First, the model of a psychosis continuum has distributional validity because the statistical distribution for a single genetic defect does not directly map onto the distribution of psychotic symptoms. This suggests that hallucinations are caused by multiple interacting factors. Thus, it is most appropriate to take a holistic approach incorporating multiple genes and environmental factors that may contribute to the continuum between health and psychosis rather than looking at these concepts as two opposing categories. Second, there is psychopathological validity to the model as scales used to measure subclinical symptoms (e.g., schizotypy scales) seem to be in parallel with symptoms used to identify whether an individual has schizophrenia, although these tend to be less severe (Lewandowski et al., 2006; Mata et al., 2003). Third, the high epidemiological validity of the model extends from its ability to account for the discrepancy between the (a) prevalence of psychosis-like experiences and (b) number of individuals diagnosed with psychosis in the general population. Specifically, continuum models assume that non-clinical populations are able to experience hallucinations in addition to clinical populations unlike categorical models of “illness” and “health” which do not allow for this. Given these fundamental assumptions, the continuum model can explain population data showing that the number of psychosis-like experiences exceeds the number of patients with psychosis.
Extending from this, recent models of psychosis such as the “Transdiagnostic Psychosis Spectrum” have posited that symptoms can be represented as unique to psychosis (e.g., affective dysregulation) or not (e.g., hallucinations; van Os & Reininghaus, 2016). This is supported by empirical work which has demonstrated that some characteristics of hallucinatory experiences are common between clinical and non-clinical populations (e.g., low-level acoustic features, content, frequency of hallucinations) whilst other characteristics are not (e.g., negative affective responses to hallucinations, less perceived control over hallucinations; Powers et al., 2017). For this reason, there may be utility to incorporating both categorical and continuum models when tailoring interventions to clinical or non-clinical populations facing hallucinations.
RM and hallucinations in individuals with schizophrenia
RM has been applied to inform our understanding of hallucinations in clinical groups including those with schizophrenia. In this section, I will review the behavioural evidence comparing individuals with schizophrenia who hallucinate to those who do not and then turn to the neural evidence to determine whether RM deficits underlie hallucinations in those with schizophrenia.
Hallucinations in patients with schizophrenia may arise due to RM deficits which result in externalising errors. A noteworthy meta-analysis by Brookwell and colleagues (2013) examined whether auditory hallucinations stem from externalising errors using data from 15 clinical studies between 1985 to 2012. The researchers found that hallucinating patients with schizophrenia (n = 240) displayed increased tendencies to misattribute internally generated events as having been externally generated compared to non-hallucinating patients (n = 249). This provides evidence that the presence of hallucinations in patients with schizophrenia may be related to impaired RM abilities which result in externalising errors. An important critique of this study, and the research field more generally, is that sample sizes tend to be very small. In this particular meta-analysis, a mean of 23 participants were included in each study (Moseley et al., 2021) which could limit our ability to detect statistically significant results when they are present due to low statistical power.
Subsequent research suggests that externalising errors are linked to the experience of hallucinations (Damiani et al., 2022; Simons et al., 2017). One review paper found that patients with schizophrenia who had experienced hallucinations (n = 119) were more likely to misidentify internally generated stimuli as having been externally generated than patients without hallucinations (n = 135; Simons et al., 2017). A range of tasks were used to assess RM abilities including semantic association, word recognition, word-stem completion, and sentence completion tasks. A methodological critique of using multiple measures of RM is that what qualifies as “internally” and “externally” generated stimuli differs across the studies reviewed. This may have resulted in differences in the effect sizes calculated, therefore we must carefully interpret the findings. For instance, the word recognition task (Brunelin et al., 2006) generated a large effect size. Perhaps this happened because for those who already struggle with RM it is harder to remember the source of stimuli that are presented artificially (i.e., words taken from a verbal fluency task with the same emotional valence and length), compared to stimuli in everyday life situations. Converging findings from a very recent systematic review and meta-analysis of 41 studies suggest that hallucinating patients with schizophrenia or who were on the schizophrenia spectrum were more likely to make externalising errors compared to patients who did not report hallucinating, of moderate effect size (Damiani et al., 2022). This provides further evidence to suggest that RM deficits leading to externalising errors underlie hallucinations in those with schizophrenia.
Neural evidence supports the above findings by demonstrating that brain morphology is linked to RM and hallucinations in those with schizophrenia. In terms of hallucinations, reduced length of the left paracingulate sulcus (PCS) has been shown in patients with schizophrenia who hallucinate compared to those who do not (Rollins et al., 2020; Garrison et al., 2015; Yucel et al., 2002). The PCS is a structure that lies in the dorsal anterior cingulate portion of the medial prefrontal cortex (Garrison et al., 2019). Garrison and colleagues (2015), found that left hemisphere PCS length was significantly reduced in patients with schizophrenia who experienced hallucinations compared to patients who did not. These findings had good predictive validity as left PCS length could accurately predict hallucinations in 70.8% of patients with schizophrenia. Specifically, reducing the left PCS length by 1 cm increased the likelihood of hallucinations by 19.9% in patients. A strength of the logistic regression model used to predict hallucinations is that several confounding demographic and clinical variables were controlled for, thereby increasing our confidence in the internal validity of the findings. Support for this comes from Rollins and colleagues (2020) who found that, in a cross-cultural data set of patients with schizophrenia from the UK and Shanghai, those who experienced hallucinations had significantly shorter left PCS lengths than those who did not. This suggests that brain morphology may be linked to hallucinations in those with schizophrenia. To complement these findings, a recent study has linked the PCS to RM. Perret and colleagues (2021) found that reduced length of the right PCS in individuals with schizophrenia was significantly associated with RM deficits including decreased RM accuracy and increased externalising errors. Thus, the PCS may be linked to RM deficits leading to hallucinations in those with schizophrenia.
In summary, RM deficits which result in externalising errors may underlie hallucinations in patients with schizophrenia because they are more likely to exhibit externalising errors than their non-hallucinating counterparts. Neural evidence seems to confirm the behavioural findings by demonstrating a link between brain morphology, RM, and hallucinations in individuals with schizophrenia. This presents a compelling narrative that RM deficitsunderlie the experience of hallucinations in schizophrenia.
RM and hallucination proneness in non-clinical populations
RM deficits may elucidate our understanding of hallucination proneness in the non-clinical population given the potential link between RM deficits and hallucinations in the clinical population. Hallucination proneness refers to subclinical symptoms which increase the likelihood of an individual hallucinating. This is most commonly measured using scales such as the Launay Slade Hallucination Scale (LSHS) and the Launay Slade Hallucination Scale Revised (LSHS-R; Launay & Slade, 2012). In this section, I will outline behavioural evidence supporting and refuting the link between RM deficits and hallucination proneness in the non-clinical population. Then, I will discuss how the neural evidence mirrors these mixed findings.
The evidence that RM deficits underlie hallucination proneness in non-clinical populations is mixed. On one hand, empirical studies have shown that hallucination-prone participants are more likely to have deficits in RM such as externalising errors (Brookwell et al., 2013; Larøi et al., 2004). Larøi and colleagues (2004) found that hallucination prone participants (n = 25) tended to misidentify self-generated words as having been spoken by the experimenter more than their non-hallucination prone counterparts (n = 25). However, an important methodological critique of Larøi and colleagues’ (ibid.) study is the lack of experimental control over the type of words internally generated by participants in response to being shown a standard stimulus. Self-generated words with increased salience may become more embedded in memory. Thus, participants who produced more meaningful words in response to a standard stimulus were less likely to produce externalising errors, reducing our ability to detect externalising errors accurately.
Converging findings from Brookwell and colleagues’ (2013) meta-analysis demonstrated that hallucination prone individuals were more likely to mistake internally generated events as having been externally generated compared to non-hallucination prone individuals. However, Brookwell and colleagues’ meta-analysis included 2/9 studies that had not been made available in the public domain (“unpublished observations”) and only 2/7 of the remaining studies used a source monitoring task to directly assess RM, with other measures being indirect (ibid.). This suggests that we should approach these results with caution, given such limitations reduce our confidence in the internal validity of the findings.
On the other hand, empirical studies have shown that RM deficits may not underlie hallucination proneness in non-clinical populations (Moseley et al., 2021; Alderson-Day et al., 2019; Garrison et al., 2017; McKague et al., 2012). In both McKague and colleagues’ (2012) and Garrison and colleagues’ (2017) studies, participants were made to distinguish between whether auditory and visual information had been self or other generated. A synthesis of the data reveals no significant differences in externalising errors exhibited by hallucination prone participants in comparison to their non-hallucination prone counterparts, thus providing consistent evidence that RM abilities are intact in hallucination prone individuals. A strength of Garrison and colleagues’ study is that, in contrast to Larøi and colleagues’ (2004), there was tighter control over confounding variables such as participants’ age, language skills, and the presence of general memory deficits which increases our confidence in the internal validity of the results. Most recently, Moseley and colleagues’ (2021) large-scale pre-registered multisite study (n = 1,394) found no relationship between RM and hallucination proneness in the non-clinical population. A major strength of this study is the large sample size used, which provides greater statistical power for the analysis thereby increasing our confidence in the internal validity of the results.
Neural evidence linking brain morphology findings to RM and hallucination proneness in the non-clinical population is also mixed, mirroring the behavioural evidence (Garrison et al., 2019; Buda et al., 2011). Garrison and colleagues (2019) found no significant difference in the length of the PCS in either hemisphere for hallucination prone (n = 50) and non-prone (n = 50) controls. This suggests that in the non-clinical population, differences in brain morphology are not linked to hallucination proneness. Turning now to findings on RM, in their seminal paper Buda and colleagues (2011) classified a small sample of 52 healthy volunteers into 4 groups based on the presence or absence of the PCS in the left or right hemisphere. The researchers found that participants with an absent PCS in both hemispheres showed significantly reduced RM performance compared to all other participants for recollection of self/experimenter status, but not for perceived/imagined items. This suggests that brain morphology may be related to RM in the non-clinical population. Taking the evidence together, there are mixed findings on whether RM deficits underlie hallucination proneness in non-clinical populations. Arguably, studies which have suggested that RM deficits do not underlie hallucination proneness in non-clinical populations (Moseley et al., 2021; Garrison et al., 2017; McKague et al., 2012) have improved on the methodological limitations of previous studies to the contrary (Brookwell et al., 2013; Larøi et al., 2004). However, further replication is needed to confirm whether inconsistent findings result from methodological differences or chance. Therefore, RM deficits may or may not underlie hallucination proneness in the non-clinical population.
Taking the evidence together, there are mixed findings on whether RM deficits underlie hallucination proneness in non-clinical populations. Arguably, studies which have suggested that RM deficits do not underlie hallucination proneness in non-clinical populations (Moseley et al., 2021; Garrison et al., 2017; McKague et al., 2012) have improved on the methodological limitations of previous studies to the contrary (Brookwell et al., 2013; Larøi et al., 2004). However, further replication is needed to confirm whether inconsistent findings result from methodological differences or chance. Therefore, RM deficits may or may not underlie hallucination proneness in the non-clinical population.
The current study
The present study aims to expand the current literature assessing the association between RM and hallucination proneness in the non-clinical population. To achieve this, an existing dataset collected by previous Part II Psychological and Behavioural Sciences students at the University of Cambridge was used. The analysis used a non-directional hypothesis in line with the mixed findings of previous research.
First, the research will examine whether there is a relationship between RM accuracy and hallucination proneness in a non-clinical sample of university students. Here, RM accuracy refers to one’s ability to correctly determine the origin of a source as having been perceived or imagined. Second, the study will identify whether externalising errors are related to hallucination proneness in a non-clinical sample of university students. In particular, externalising errors will be assessed by focusing on items presented as “imagined” that are incorrectly reported by participants as “perceived”.
Two alternate hypotheses will be addressed through the present study. This is because RM deficits are indicated by low RM accuracy and/or high externalising error scores. In the case of low RM accuracy, individuals have difficulty distinguishing between internally and externally generated stimuli whilst high externalising error suggests a tendency to mistake internal stimuli for external stimuli, both of which indicate RM deficits.
- H1: There is a relationship between RM accuracy and hallucination proneness in a non-clinical sample of university students.
- H2: There is a relationship between externalising errors and hallucination proneness in a non-clinical sampel of university students.
The sample consisted of 56 university students (n = 39 female and n = 17 male) with an average age of 21 (M = 21, SD = 2.332) whose first language was English and who had lived in the UK for most of their lives. Participants were recruited using social media and by word of mouth. After giving participants a full description of the study, written informed consent was obtained. Ethical approval was acquired from the University of Cambridge Psychology Research Ethics Committee before the study commenced.
A within-groups design and counterbalancing was used to prevent order effects. For example, on-screen reminders were used to indicate when participants needed a break from the study due to boredom/fatigue with follow-through from the experimenter. Participants were asked to read and sign the consent form, fill in a questionnaire in approximately 8 minutes, and then engage in a computerised source monitoring test for 40 minutes.
The memory task was conducted using E-Prime 1.2 software (Psychology Software Tools) on a computer. Participants’ responses were indicated by pressing specific keys on their keyboards. Before starting the task, participants were given a brief description of the task via an information sheet. Any questions they had were answered by the experimenter.
This task was modified from the original (Garrison et al., 2017) in order to reduce its length due to time constraints. This meant focusing solely on visual hallucination proneness rather than auditory and visual. The task consisted of two key phases: (1) study and (2) test phases. Overall, there were 8 study blocks which were each followed by a test block. These blocks were presented in 2 combinations with an equal number of stimuli being presented as Perceived and as Imagined.
In the study phase (Figure 1), participants were given 3 seconds to study each word pair and say these aloud. The second of the two words in the word pair was either present (Perceived) or absent. In cases where the second word of the pair was absent, there would be a single letter indicating the beginning of a word. Participants were asked to complete this word (Imagined) with what they thought it would have been. In total, 24 word-pair stimuli were studied.
Diagram of the Study Phase
In the test phase (Figure 2), recognition memory was tested by showing participants 36-word pairs including 24 “old” word pairs that had been previously studied and 12 “new” unstudied word pairs. Words were displayed for 4 seconds each time. Then, participants were asked to indicate whether they had perceived or imagined the word being presented by pressing a key on their computer keyboard. To choose the first stated option (“Perceived”) participants pressed the “1” key whilst to choose the second option (“Imagined”) they pressed the “2” key. When the participant thought the word presented was New, they pressed the “3” key.
Diagram of the Test Phase
All participants in the present study were able to complete the tasks in the time given, so no participant data was excluded.
Hallucination proneness scores were obtained using three self-report scales that were completed in the same order by all participants. First, a modified version of the Launay-Slade Hallucination Scale (LSHS; Launay & Slade, 2012) called the LSHS-R, which includes 5 items, was used (Bentall & Slade, 1985). This scale directly measures susceptibility to auditory hallucinations, with total scores ranging from 0 to 20. Second, the Varieties of Inner Speech Questionnaire (VISQ) is an indirect measure of hallucination proneness (McCarthy-Jones & Fernyhough, 2011) which measures the phenomenological properties of inner speech. Of these characteristics, 4 items were used to measure the Dialogicality component, with total scores ranging from 0 to 24. Third, a modified version of the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE; Mason et al., 1995) called the Brief O-LIFE scale (OLIFE-B+; Mason & Claridge, 2006) was used to measure schizotypy which indicates subclinical symptoms of psychosis. This scale acts as an indirect measure of hallucination proneness, with 15 items where individuals are given a total score ranging from 0 to 15. Higher scores on any of the 3 self-report scales used indicated higher levels of hallucination proneness.
Data analyses were conducted using JASP software version 0.16.0 (JASP Team 2022). The measures of hallucination proneness used in the present study including LSHS-R, OLIFE-B+, and Dialogicality scores all had acceptable internal consistency with each other, given the Cronbach’s alpha value obtained was above 0.7 (α = 0.720).
RM accuracy refers to the proportion of old items recognised in the test phase, whose origin was correctly identified. This is calculated as the number of words correctly identified as imagined or perceived divided by the total number of responses minus words incorrectly recognised as “new”.
Externalising errors are a specific deficit in RM abilities that tend to be associated with hallucinations. Externalising errors were calculated for items that were presented as “imagined” but reported by participants as “perceived”:
RM accuracy is given as a proportion out of the total with higher scores indicating a higher degree of accuracy. Similarly, externalising error scores indicate the proportion out of the total of imagined items that were incorrectly judged as perceived. Higher scores represent a greater number of externalising errors.
RM accuracy and externalising errors
Descriptive statistics were obtained for RM accuracy and externalising errors (Table 1) to provide an indication of participant performance during the RM task. The average RM accuracy and externalising error scores were fairly high, with means of 0.766 and 0.632 respectively. RM accuracy scores tended to be clustered around the mean (SD = 0.139) to a larger extent than externalising error scores (SD = 0.206).
Descriptive Statistics for RM Accuracy and Externalising Errors
RM accuracy and hallucination proneness
The data was checked to see whether RM accuracy and hallucination proneness were normally distributed. Q-Q plots revealed that RM accuracy was not normally distributed, given scores produced a left-skewed distribution (Appendix A). Measures of hallucination proneness did not follow a normal distribution, with some of the data showing a right-skewed distribution such as for LSHS-R and OLIFE-B+ scores and other data showing a left-skewed distribution such as for Dialogicality (Appendix B). Given that the data was not normally distributed, a non-parametric Spearman’s correlation test was used.
Spearman’s correlation revealed that there was no significant relationship between RM accuracy and hallucination proneness for p < 0.05 (Figure 3). This was the case for all three measures of hallucination proneness including LSHS-R (p = 0.650), OLIFE-B+ (p = 0.846), and Dialogicality (p = 0.870) scores.
Scatterplot of the Relationship Between RM Accuracy and Hallucination Proneness
Externalising errors and hallucination proneness
Q-Q plots revealed that externalising errors were approximately normally distributed (Appendix C). As previously mentioned, measures of hallucination proneness did not follow a normal distribution (Appendix B). Therefore, a non-parametric Spearman’s correlation test was used to investigate whether there was a relationship between externalising errors and hallucination proneness.
Spearman’s correlations showed that there was no significant association between externalising errors and hallucination proneness for p < 0.05 (Figure 4). This was consistent across all three measures of hallucination proneness used (LSHS-R: p = 0.998, OLIFE-B+: p = 0.761; and Dialogicality: p = 0.339).
Scatterplot of the Relationship Between Externalising Error and Hallucination Proneness
The present study sought to investigate whether RM deficits which lead to hallucinations in clinical populations also lead to hallucination proneness in non-clinical populations. To examine whether this is the case, two main areas were explored: (1) whether RM abilities were related to hallucination proneness and (2) whether externalising errors were related to hallucination proneness in a non-clinical sample of university students.
The results revealed that there was no significant relationship between hallucination proneness and (1) RM accuracy or (2) externalising errors in the non-clinical population. This was the case for all three measures of hallucination proneness. Therefore, the present study provides evidence to suggest that RM deficits are not related to hallucination proneness in the non-clinical population.
These findings are consistent with the literature suggesting that RM deficits do not underlie hallucination proneness in the non-clinical population (Moseley et al., 2021; Alderson-Day et al., 2019; Garrison et al., 2017; McKague et al., 2012) which have improved on the methodological limitations of previous studies to the contrary (Larøi et al., 2004). As research findings in this area are mixed there is also evidence suggesting that RM deficits do underlie hallucination proneness in the non-clinical population (Aynsworth et al., 2017; Brookwell et al., 2013; Brunelin et al., 2007; Larøi et al., 2004). Possible explanations for the inconsistency of findings include methodological differences, the inclusion or exclusion of confounds, and small samples as outlined in the introduction.
A strength of the present study is the use of counterbalancing of study block pairs across different formats. This meant that becoming accustomed to one study block pair format was unlikely to have affected the participant’s performance in subsequent study block pair tests. Order effects including boredom and fatigue were minimised through ensuring that participants were provided with breaks when required. Moreover, the use of target words which probe memory in particular ways meant that responses could be predetermined. Therefore, participants producing words with differing levels of salience were unlikely to have affected their recall as in Larøi and colleagues’ (2004) study. Overall, the study design implemented the use of counterbalancing, minimising order effects, and using particular target words strengthening our confidence in the internal validity of the results.
Limitation and directions for future research
One limitation of the present study and the literature in this area is the use of small samples (Alderson-Day et al., 2019; Garrison et al., 2017; Brookwell et al., 2013; McKague et al., 2012; Brunelin et al., 2007; Larøi et al., 2004). This is problematic because it may mean statistical power is too low to detect an effect when one is present (Moseley et al., 2021). Therefore, Type II errors may be more common, in turn compromising the internal validity of the study. To improve on this in the future, digital platforms could be used as a means of gathering big data about RM deficits in non-clinical populations (e.g., through the use of advertising on social media or online forums to recruit participants).
However, a limitation of using social media to recruit participants, as was the case in the present study, is that this method is prone to selection bias. This is because individuals recruited are likely to share similar characteristics which may make the sample unrepresentative of the target population. For instance, the societal stigma associated with hallucinations and psychosis may mean individuals in the non-clinical population who are not confident in their RM abilities may be less willing to take part in the study. Therefore, it is crucial to balance the need to recruit a large sample against the need for random sampling techniques to minimise the likelihood that sampling characteristics affect the internal validity of the results.
Another weakness of the present study and literature is that undergraduate samples are often used (Collignon et al., 2005). This means that the results generated are unrepresentative of hallucination proneness at later stages of life (Thompson et al., 2021) and instead focus on an age group which broadly corresponds to the typical onset period of psychosis. An emerging field has begun to look at the effect of RM abilities on hallucination proneness in conjunction with Parkinson’s and Alzheimer’s disease, which are more frequent in later life (Marques et al., 2022). This may contribute to our understanding of whether results can be applied across the lifespan in due course. Moreover, future research should include participants of a diverse age range in their samples to ensure effect sizes are not exaggerated as a result of focusing on a developmentally sensitive period.
The sample of the current study is gender-biased as many more females participated than males (n = 39 females, n = 17 males). This compromises the validity of the results because gender differences, which may act as a confound, were not controlled. The use of samples where the proportion of females and males is equal will be crucial for reducing gender bias in the future, although previous research which controlled for gender tends to suggest that this does not play a major role in RM deficits and hallucinations.
A methodological critique of the literature is that the task used to measure RM focuses on events that have been recalled. For example, in the present study, during the test phase participants were asked to recall whether a word had been perceived or imagined. This presents a challenge to the external validity of the results because the immediacy of an experience is not the same in memory as when it is happening in real-time. This means that contemporary methods may have greater difficulty detecting a statistically significant relationship between RM deficits and hallucination proneness in healthy participants. Where a significant relationship is found, the effect size may be smaller because the vividness of an experience fades in memory whilst it is preserved in real-time. However, as it is difficult to directly address this methodological issue, the use of larger samples may provide a more practical way to address concerns about the ability to detect significant results. Interestingly, a recent paper has used pseudo-hallucinations induced in the lab (Konigsmark et al., 2021) allowing the researchers to measure RM related to hallucinations which occur in real-time. Therefore, there may be some merit in adapting our current procedures to investigate whether real-time hallucinatory experiences differ from recalled ones.
In conclusion, the present study found no significant relationships between (1) RM accuracy and hallucination proneness or (2) externalising errors and hallucination proneness. This suggests that RM deficits, including externalising error, do not lead to hallucination proneness in a non-clinical sample of university students. However, there is an urgent need for replication and larger samples to improve our confidence in the internal validity of these results (Moseley et al., 2021). In terms of the broader question then, the results indicate that RM deficits which underlie hallucinations in clinical populations may not lead to hallucination proneness in the non-clinical population. The implications of this finding for our current conceptualisation of hallucinations within the non-clinical population is that it suggests mechanisms other than RM deficits may be driving hallucinations.
Ethical approval for the data collected in the present study was obtained from the University of Cambridge Psychology Research Ethics Committee. Participants gave informed consent prior to the start of the study. Participant data used in the current analysis was anonymised given that each participant was referred to by number rather than by name, and all other information included could not be used to identify the participant’s identity. In addition, participation in the study was voluntary as participants were able to leave the study at any time without penalty. Given the RM tasks that participants engaged in, no physical or psychological distress was caused by taking part in the study. The risk of potential boredom or fatigue that participants might have faced was addressed as through the experimenter giving participants breaks throughout the study.
Appendix A | Q-Q Plots of RM
Appendix B | Q-Q Plots of Hallucination Proneness Measures
Appendix C | Q-Q Plots of Externalising Error
Appendix D | Relationship Between Hallucination Proneness and RM
Appendix E | Relationship Between Hallucination Proneness and Externalising Error
Deciding what is real, what is not: Reality montioring and predictive coding in the brain
Wolfson College, University of Cambridge
In this issue, Renmiu reports on a behavioural study into the relationship between deficits in reality monitoring (RM) and the propensity of individuals in a non-clinical population towards auditory verbal hallucinations (AVHs). This commentary introduces a complementary account of RM deficits from a cognitive computational perspective where RM is modelled as a hierarchy of Bayesian inference processes, similar to other types of decisions that we make in the face of uncertainty. Using these frameworks to interpret behavioural and neuroimaging data, neuroscientists and psychologists hope to understand the emergence, progression, and persistence of sensory hallucinations.
Decisions based on noisy sensory information shape our interactions with the environment and engage behaviours critical for survival. For example, deciding that an ambiguous visual stimulus is an approaching predator triggers different motor and physiological responses than if it is deemed neutral. These inferences rely onevidence accumulated by integrating sensory signals,as well as context-dependent predictions based on prior experience and learning; for instance, “lions commonly hunt in this part of the savannah at this time of year, so I predict that I could see a predator”.
This model of perceptual decision-making has been applied to account not only for our interpretation of the contents of a stimulus, but also its source; that is, who or what caused the perceived change in the environment (Griffin & Fletcher, 2017). As Renmiu discusses, reality monitoring (RM) is a specific case of this problem, where the key information to be inferred is “is this stimulus part of reality, or is it imagined?” (Johnson et al., 1993; Johnson & Raye, 1981). Disruptions to this process, leading to the misattribution of self-generated stimuli to external forces or agents, offer a compelling explanation for the origin of sensory hallucinations.
Characterising the mechanism underlying these inferences is therefore critical for understanding the cognitive and behavioural symptoms of schizophrenia, as well as other psychotic conditions marked by hallucination, and how these differ from hallucinations experienced by non-clinical individuals. The concept of predictive coding has the potential to link a range of symptoms to a common aetiology and bridge the explanatory gap between clinical observations and models of perception (Sterzer et al., 2019). The following commentary aims to contextualise the study presented by Renmiu within this view of schizophrenia as a disorder of predictive processing and review current models of how RM deficits might arise within this framework.
Predictive coding: Internal models, prediction errors, and precision
Diagram of Predictive Coding Account for Deciding Stimulus Content
Note. An analogous framework can be applied to the inference of stimulus source, where predictions concern the sensory effects of self-generated actions and hyperpriors concern the causes of prediction errors. A, predictions from multiple levels of a Bayesian hierarchy are integrated with the outputs of early sensory processing to generate perception of a stimulus. B, when a sensory input of high precision relative to the corresponding prior generates a prediction error, the internal model should be updated to reflect the altered information. C, sensory data that is imprecise should be downweighted or altogether ignored in favour of maintaining the internal model. (see Griffin & Fletcher, 2017)
According to predictive coding accounts, perceiving a stimulus is far from an objective process of receiving, transmitting, and processing sensory signals incident on our receptors (Helmholtz, 1867). Top-down inputs from brain areas high up in a processing hierarchy convey information about subjective expectation and preference to lower levels, which carry out basic sensory processing (Figure 1.A; Friston & Kiebel, 2009; Lee & Mumford, 2003). Prior beliefs thus shape the process of evidence accumulation into a coherent interpretation of the most probable cause of a mental experience. In the formal language of Bayesian inference, predictive priors are synthesised with sensory data likelihoods and decisions about the meaning of stimuli are based on the posterior probability distribution of an interpretation being correct, given the evidence (Figure 1.A).
Colour perception provides a familiar example. Signals from long wavelength-sensitive cone cells in the retina to higher components in the visual processing pathway do not intrinsically carry information about the colour “red”; rather this property is constructed from a previously learnt association between a stimulus feature and abstract concept. These low-level predictions feed into a hierarchy where more abstract beliefs and stimulus associations, for example, “a red stimulus in the context of a road environment means stop moving”, are encoded at higher levels. We derive our entire set of predictions from an all-encompassing internal model of the world and our relationship to it.
Crucially, when comparisons between predictions and sensory signals yield prediction errors, a rational decision-maker should resolve this discrepancy by weighting priors and likelihoods against each other according to their precision(Figure 1.B, 1.C; Feldman & Friston, 2010). As the inverse of variability, precision provides a measure of confidence in an information source. The resulting balance is critical for ensuring that perceptual decisions are both robust to noise and appropriately sensitive to changes in the environment. Its disruption is proposed to be the specific computational change which predisposes an individual towards experiencing hallucinations (Friston, 2005).
In models of sensorimotor processing, it is well established that a specific type of prediction concerning the sensory consequences of a self-generated movement is important for attaching a sense of agency to our actions. Such predictions—encoded by efference copy of motor commands (von Holst & Mittelstaedt, 1950), corollary discharge (Sperry, 1950), or proprioceptive feedback—are conveyed by feedforward internal signals to a comparator, where they serve to ‘cancel out’ sensory signals produced as a result of one’s own movement. We can conceive that failure of this attenuation process invokes a hyperprior, a prediction about the precision of sensory evidence, which dictates that large prediction errors must indicate unpredicted sensory signals, which cannot result from self-generated actions (Corlett et al., 2019). This mechanism could apply equally to inner speech as to movement, accounting for auditory verbal hallucinations (AVHs) investigated in the present study.
Based on this model, we might speculate that impairments in the neural circuits which carry out this egocentric processing are the root cause of reality monitoring deficits (Crapse & Sommer, 2008). With reference to the predictive coding framework, this outcome could arise specifically if the precision of low-level predictions were reduced. Behavioural and neuroimaging studies provide support for this deficit in schizophrenia patients. For example, lowered sensitivity to visual illusions which rely on prior influences, such as preference for perceiving faces,compared to healthy controls is well-documented; patients report more veridical interpretations of the illusion (Brown et al., 2013; Notredame et al., 2014). Furthermore, Dynamic Causal Modelling (Friston et al., 2003) has been used to characterise the different causal relationships between activation signals measured by functional magnetic imaging within visual processing networks inschizophrenia patients and healthy controls (Dima et al., 2009). In this study, illusionary perception in controls coincided with activation of feedback connections from parietal cortex to object-processing visual areas, whereas in schizophrenia patients, the pattern of activation emphasised feedforward connections from primary visual cortex to these areas. These findings are consistent with the reduced influence of prior learned beliefs in favour of a greater emphasis on bottom-up, stimulus-driven perception in these individuals.
Reconciling findings with the “Strong Priors” account
The findings discussed above support the notion thatthe results of self-performed actions are poorly predicted in schizophrenia patients. However, a separate body of evidence points towards overly strong, rather than weak, prior predictions predisposing an individual towards hallucination (for review, see Corlett et al., 2019). Early studies demonstrated that people who experience hallucinations are more susceptible to mere suggestions which influence their expectation of stimulus content (Barber & Calverley, 1964; Mintz & Alpert, 1972), as well as to the phenomenon of Pavlovian conditioned perception of one stimulus by a paired stimulus (Kot & Serper, 2002). More recently, the strength of these conditioned stimulus-stimulus associations was inferred from neuroimaging data recorded whilst subjects were asked to decide whether or not they heard a near-threshold auditory stimulus upon presentation of the paired visual stimulus, and how confident they were in their decision (Powers et al., 2017). The investigators found that prior predictions were more strongly weighted and robust to updating in non-clinical and clinical groups who reported experiencing AVHs, compared with a non-clinical group who did not.
Here, it is important to distinguish alterations in function at different levels of the predictive hierarchy. It is conceivable that in schizophrenia patients, less precise predictions at lower levels allow errors to propagate further up the processing hierarchy, rather than being attenuated. To generate Bayes-optimal perceptual inferences, higher level beliefs must be updated and assigned high precision to account for these errors, thus giving rise to perceptions which are dissociated from sensory evidence (Corlett et al., 2019). In the case of reality monitoring, the hyperprior dictating that external forces or agents must be the cause of large prediction errors could be one such prior invoked with overly high precision. The coexistence of both inappropriately under- and over-weighted priors gives rise to the conviction that experienced hallucinations must have an externally identifiable source, and thus be part of reality.
Experimental paradigms which attempt to dissociate low- and high-level predictive processing provide preliminary support for this view. For example, the precision of low-level predictions can be measured via the probability of perceiving an intermittently presented stimulus in between presentations. In this way, it was found that delusion-prone and schizophrenic individuals show evidence of weaker low-level priors (Schmack et al., 2013, 2015). The precision of high-level predictions was separately probed by inducing expectations about the motion of a visual stimulus in a placebo-like experimental manipulation. The investigators found a positive correlation between the strength of the belief-induced perceptual bias and delusion proneness, and furthermore found that this effect was strongest in the same individuals who showed evidence for weak sensory predictions (Schmack et al., 2013, 2015).
As highlighted by Renmiu, RM deficits have been proposed to underlie hallucinations in both clinical and non-clinical individuals. Behavioural assessments such as the RM task presented in their study are therefore important for testing computational models of this mechanism and identifying its neurobiological correlates. The author’s findings should be interpreted in the context of a predictive coding hierarchy for RM, where inappropriate handling of errors across any level could potentially create a perceptual system biased towards hallucination.
The politics of reality and unreality: Anthropological approaches to questions of hallucination and psychosis
Eleanor Burnett Stuart
Christ’s College, University of Cambridge
The presented article defines reality monitoring as “the ability to discriminate between internally and externally generated stimuli”. Reality, therefore, is a kind of order in which the external is “real” and the internal is “imagined” (and, it follows, unreal). This point is not the significant culmination of the author’s argument; it is an assumption from which the subsequent sum total of the argument follows. In order to continue to read, the reader must accept uncritically that there is an absolute distinction between the individual’s internal subjective experiences and an external objective reality.
In the wake of the 1970’s “writing culture” critiques of anthropology and the more recent movement to ontology, the first instinct of the contemporary anthropologist may be to condemn this dichotomy entirely as unfairly privileging the ‘expert’ perspective, whether anthropologist or psychologist, over the lived experience of their subjects. But this dichotomy is not necessarily limited to the analytical terms of the outsider expert, but also present in the lives of interlocutors, many of whom in the present day are fluent in biomedical discourses of mental illness. Interlocutors themselves distinguish on a daily basis between reality and hallucination, biomedical mental illness and genuine religious experiences, madness and possession. When anthropologists pay attention to the ways in which these distinctions are made, they are also paying attention to the production and distribution of knowledge.
Early anthropologists were not always so uncomfortable with imposing the hard line between the real and the imagined. Nineteenth century evolutionists and their early-20th century functionalist successors treated anthropology as the “science of non-science” (de Castro, 2003), and anthropologists as, if not a scientist, a member of the scientific project. The anthropologist had privileged access to the tradition of Western Enlightenment thought or “reality”; their non-Western subjects were subjects of interest to social evolutionists such as Tylor (1871) precisely because they had not yet accessed this reality. Non-Western religious and ritual practice were not treated as madness, necessarily, but they were treated as an indication that their practitioners had a primitive understanding of the “way the world worked”, reflecting a wider paradigm in anthropology which took the existence of a scientifically objective and true perspective for granted (Candea, 2016).
In the 1970s, a cascade of “writing culture” and Edward Said-inspired critiques dealt a protracted series of blows to this paradigm by drawing attention to the implication of the anthropologist in producing representations of non-Western people which served to legitimise colonialism (Laidlaw, 2018). In critically examining the dynamics of power between an anthropologist and their subjects, this movement fundamentally problematised the treatment of the anthropologist as the impartial arbiter of reality (ibid.).
The ongoing (and controversial) development of ontological anthropology takes this point even further. Championed by Eduardo Vivieros de Castro (2015), ontological anthropology exhorts anthropologists to “take seriously” the possibility that there are not multiple perspectives of a singular reality, but many realities. This is an anthropology which almost exclusively favours what Clifford Geertz (1974) referred to as “experience near” concepts—how the interlocutor themselves conceptualises their experiences—over “experience distant” concepts—how the specialist would conceptualise their experiences. An “experience near” perspective is less likely to reject an experience the interlocutor understands as immediately real as a hallucination. For instance, in Rane Willerslev’s (2012) account of Yukaghir deer hunting, Willerslev does not describe the experiences that hunters have on hunting trips of living among their deer prey and eating moss in their houses as hallucinations, but as subjectively real “worlds” which his interlocutors objectively experienced. This is an anthropology which would largely reject a distinction between “internal” and “external” experiences. But whether or not an anthropologist embraces the ontological approach in its entirety, most contemporary anthropologists would be at least leary of making the distinction in ethnography that this paper makes in psychology.
Nevertheless, the distinction between “internal” and “external” experiences is substantively “real” for no small number of the people whom anthropologists are studying, and so it is not up to anthropologists to reject this distinction entirely. Indeed, it is this distinction which is often utilised to distinguish the genuine spiritual or magical experience from the unreal or imagined one. In her ethnography of evangelical Christians in the United States, Tanya Luhrmann (2012) points out that one of the distinguishing factors for the congregants between hearing the voice of God and psychotic auditory hallucinations was their recognition of these auditory experiences as “bizarre”, and their hedging of these experiences in a way that acknowledged how out of the ordinary they were. The middle class, largely White, professionals at the church Luhrmann attended generally did subscribe to biomedical discourses of mental illness; it was precisely their awareness of these discourses of mental health, and their ability to distinguish themselves from it, that they used to display the validity of their experiences to Luhrmann. And indeed among the Yukaghir who Willerslev was studying, hunters remarked on the difference between starvation-induced hallucinations and real experiences of transformation, as marked by evidence such as lost time; when a hunter believed he was only gone a week but had actually been gone several weeks, this was taken as a sign his transformation into a deer had been ‘real’, rather than a hallucination induced by starvation (Willerslev, 2012).
Asaf Sharabi (2021) illuminates the fruitfulness of engaging with where the line is drawn between the authentic and the imagined in her case of three sibling mediums in the Western Himalayas who were eventually institutionalised by the state. Whilst the possession of humans by Hindu gods (darśan) is treated as essentially normative in this area, and the siblings initially attracted widespread admiration from both locals and visitors, this specific case of mediumship came to be seen as “deviant” by powerful community members. Sharabi argues that this was because the siblings gave vitriolic speeches against the Hindu establishment and linked caste system (e.g. encouraging their base of followers to donate to the poor rather than the temples), which “challenged the entire social structure” (2021, p. 182), leading to the deployment of biomedical discourses of “madness” (cf. Foucault, 1988). Accusing the siblings of being clinically mentally ill was effective insofar as it not only discredited the siblings, but also resulted in their being forcibly removed to a mental hospital where they could not continue to communicate with their remaining followers.
An anthropologist paying attention to what Sharabi (2021) refers to as the “politics of madness” recognises biomedical discourses of hallucinations as just one of a myriad of potential discourses, rather than an unambiguously true explanation. This does not mean that an anthropologist is attempting to unmask or discredit this discourse any more than they are attempting to unmask or discredit any other discourse; this is not the objective of anthropology. But by treating psychological paradigms such as the division between “internal” imagination and “external” reality as not scientific fact but as an interesting social phenomenon, the anthropologist can appear to be implicitly discrediting the psychologist’s vision of the world, the authority of which rests on its singularity. There is an uneasiness between the anthropological and psychological approaches to hallucinations, one which I am not at all sure can be resolved.
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