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Auditory false perception in schizophrenia: Development and validation of auditory signal detection task
Accepted Manuscript Title: Auditory False Perception in Schizophrenia: Development and Validation of Auditory Signal Detection Task Author: Harleen Chhabra Selvaraj Sowmya Vanteemar S. Sreeraj Sunil V. Kalmady Venkataram Shivakumar Anekal C. Amaresha Janardhanan C. Narayanaswamy Ganesan Venkatasubramanian Additional Professor and Wellcome Trust/DBT India Alliance Senior Fellow PII: DOI: Reference:
S1876-2018(16)30212-X http://dx.doi.org/doi:10.1016/j.ajp.2016.08.006 AJP 929
To appear in: Received date: Revised date: Accepted date:
6-5-2016 13-8-2016 13-8-2016
Please cite this article as: Chhabra, Harleen, Sowmya, Selvaraj, Sreeraj, Vanteemar S., Kalmady, Sunil V., Shivakumar, Venkataram, Amaresha, Anekal C., Narayanaswamy, Janardhanan C., Venkatasubramanian, Ganesan, Auditory False Perception in Schizophrenia: Development and Validation of Auditory Signal Detection Task.Asian Journal of Psychiatry http://dx.doi.org/10.1016/j.ajp.2016.08.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
AJP_2016_181 (Revised Manuscript) Original Research
Auditory False Perception in Schizophrenia: Development and Validation of Auditory Signal Detection Task Harleen Chhabra, Selvaraj Sowmya, Vanteemar S Sreeraj, Sunil V. Kalmady,Venkataram Shivakumar, Anekal C Amaresha, Janardhanan C Narayanaswamy, Ganesan Venkatasubramanian
The Schizophrenia Clinic, Department of Psychiatry & Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore, India.
* Corresponding author Dr Ganesan Venkatasubramanian MD PhD Additional Professor and Wellcome Trust / DBT India Alliance Senior Fellow Department of Psychiatry National Institute of Mental Health And Neurosciences (NIMHANS) Bangalore 560029 Email – [email protected]
Key Words Schizophrenia, Auditory hallucinations, auditory signal detection, false alarm Acknowledgements This study is supported by the Department of Science and Technology (Government of India) Research Grant (DST/SJF/LSA-02/2014-15) and Department of Biotechnology (Government of India) Research Grant (BT/PR5322/COE/34/8/2012) to G.V. H.C. is supported by the Department of Biotechnology, Government of India (DBT/2015/NIMHANS/345). V.S. is supported by Department of Health Research, Young Scientist in Newer Research Areas: DHR/HRD/Young Scientist/Type-VI(2)/2015. S.V.K. and A.C.A are supported by the Welcome Trust/DBT India Alliance. Conflict of interest There are no potential conflicts of interest to report for any of the authors
1
Highlights Auditory False Perception in Schizophrenia: Development and Validation of Auditory Signal Detection Task
Signal detection aberrations underlie pathogenesis of auditory hallucinations in schizophrenia We developed & validated an auditory signal detection task relevant for Indian population. Schizophrenia patients showed significantly greater false alarms & decision bias Findings support impaired perceptual expectation system in the prediction of incoming stimuli in schizophrenia
Abstract Auditory hallucinations constitute an important symptom component in 70-80% of schizophrenia patients. These hallucinations are proposed to occur due to an imbalance between perceptual expectation and external input, resulting in attachment of meaning to abstract noises; signal detection theory has been proposed to explain these phenomena. In this study, we describe the development of an auditory signal detection task using a carefully chosen set of English words that could be tested successfully in schizophrenia patients coming from varying linguistic, cultural and social backgrounds. Schizophrenia patients with significant auditory hallucinations (N=15) and healthy controls (N=15) performed the auditory signal detection task wherein they were instructed to differentiate between a 5-sec burst of plain white noise and voiced-noise. The analysis showed that false alarms (p = 0.02), discriminability index (p = 0.001) and decision bias (p = 0.004) were significantly different between the two groups. 2
There was a significant
negative correlation between false alarm rate and decision bias. These findings extend further support for impaired perceptual expectation system in schizophrenia patients.
Introduction Schizophrenia is a debilitating neuropsychiatric disorder that manifests through hallucinations, delusions, behavioral disorganization, and cognitive deficits (van Os and Kapur, 2009). Among these symptoms, auditory verbal hallucinations (AVH) occur in almost 70–80% of patients leading to significant distress, functional disabilities and behavioral dysfunctions (Shergill et al., 1998; Wible et al., 2009). Of the various theoretical models explaining the neurobiology of hallucinations, source monitoring theory postulates that, hallucinating individuals misattribute internally generated speech (or sensory stimuli) to an external source (Boksa, 2009; Frith and Done, 1988). Theory of expectation-perception is another important postulated paradigm to understand the origin of hallucinations. According to this theory, aberrant expectation-perception is associated with deviant predictive coding that can lead to loss/attenuation of error prediction with resultant AVH 3
(Fletcher and Frith, 2009; Friston, 2005; Nazimek et al., 2012). This deviant predictive coding is suggested to be due to impaired coding of external input which in turn could be attributed to defective modulation of prefrontal cortex by thalamus, or abnormal functioning of auditory cortex/auditory association cortex. To further validate this theory, Dalmaan et. al (2012) conducted a context manipulation task and showed that top-down processing that is responsible for expectations during predictions are impaired in hallucinating subjects. In an fMRI study too, Horga et. al (2014) reported overlapping of auditory cortex activation while patients experienced hallucinations and also during the occurrence of prediction of error signals. These observations suggested that neural dysfunction at the level of early or secondary auditory processing is important in the mechanism of hallucinations (Woodruff, 2004). Moreover, these findings support a potential role for signal detection (Brookwell et al., 2013) abnormalities to underlie the genesis of AVH.
To tap the efficiency of auditory processing in clinical and healthy population, studies have adopted the theory of signal detection to measure the false detections. An earlier study used a specific frequency signal tone in the noise to assess for the deficits in auditory processing (Rappaport et al., 1972). In a later study, (Bentall and Slade, 1985) a signal detection task was developed, wherein participants were asked to listen to white noise and respond if they heard a voice in the signal. These tasks enabled the analysis of erroneous and correct perception. Based on this signal detection theory, various study groups reported that participants, both schizophrenia and healthy subjects who experienced hallucinations perceived greater false alarms (Barkus et al., 2011; Brookwell et al., 2013; Varese et al., 2011). These results were reported to be due to differences in response bias rather than any significant deficit towards sensitivity to the task. However, later studies have reported varying findings: enhanced 4
sensitivity in hallucinating patients (Vercammen et al., 2008) or no significant difference at all (Li et al., 2002) in participants who experienced AVHs when compared to healthy controls. These differences in the finding could be attributed to varied AVH severity profile of study patients, the nature of the stimuli presented (tones or verbal stimuli) for the task in different studies.
To the best of our knowledge, we are unaware of any report on auditory signal detection in schizophrenia patients from India. Given the multilingual culture prevalent in India, it is a challenge to develop an auditory signal detection task that can be potentially tested in a substantial proportion of schizophrenia patients in this country. In this study, we describe the development of an auditory signal detection task using a carefully chosen set of English words that could be tested successfully in schizophrenia patients that came from varying linguistic, cultural and social background.
In addition, we examined the auditory signal detection
parameters in schizophrenia patients with AVH in comparison with matched healthy subjects. Methodology Participants: In this study, we examined 15 schizophrenia patients [Age = 28.00 ±7.65 years, 9 men] and 15 healthy subjects [Age = 28.00 ±2.30 years, 8 men]. Patients recruited for the study were those attending the clinical services of the National Institute of Mental Health & Neurosciences (India), fulfilling DSM-IV-TR criteria for schizophrenia and experiencing auditory hallucinations. The diagnosis of schizophrenia was established using Mini International Neuropsychiatric Interview Plus (Sheehan et al., 1998) and the same was confirmed by at least one experienced psychiatrist through an independent clinical interview. The severity of auditory 5
hallucinations was measured by the auditory hallucinations subscales of PSYRATS (Haddock et al., 1999) and Hoffman’s Auditory Hallucination Rating scale (Hoffman et al., 2003). All subjects were ascertained to be right- handed using Edinburgh inventory (Oldfield, 1971). The participants had a minimum 7 years of formal education. None of the participants had i) substance dependence, ii) neurological disorders, iii) mental retardation, or iv) impaired hearing. Patients were receiving clozapine (N=9), risperidone (N=4), aripiprazole (N=1), amisulpride (N=1), haloperidol (N=1), olanzapine (N=4) and quetiapine (N=1) with an average chlorpromazine equivalence of 376.42±183.61 mg/day (Danivas and Venkatasubramanian, 2013; Woods, 2003). After complete description of the study to the participants, a written consent was obtained.
Recording and validation of words: 69 English neutral words (6-7 letters (6.35±0.48, 1-sec long) were chosen for validation. The words selected were those spoken commonly in day to day conversation irrespective of the local communicating language. In the first level of screening the participants (N=7, M:F= 3:4, Year of education= 8±4.15; schizophrenia patients and their care giver) were shown the list of words and were asked to report whether the word sounds familiar to them. If they were familiar with the words they were asked to explain the context of the word and were asked to rate it as having heard the word on daily basis, occasionally or not heard of it at all. Based on the responses collected, 6 words were rejected at this stage. In the next step, these 63 words were manually recorded in a sound proof room using a Sony IC Recorder Mp3 3.1.0 with three different male voices and neutral tone. After recording the words were further processed with Audacity 2.0.6 (audacityteam.org) software. Here, the words were first de-noised, normalized and then the tempo was reduced by 15% to speed down the playback of words for a better audibility. The 6
purpose of normalization was to set the similar sound gain for all the words in case they varied in loudness and also to set the maximum amplitude of -1.0 db in order to avoid any clipping of the recorded words. After processing, there was three set of words from three voices. These words were played back and based on the neutrality of tone and clarity in pronunciation the best was selected of the three set. The final one set of words was further used for validation. These words were tested for familiarity and clarity of pronunciation with 13 subjects (schizophrenia patients and their care givers), both genders (M:F = 6:7) matched for age (32.33±14.35) and year of education (9.25±3.82). Of these 63 words, 10 were rejected at this step. Remaining 53 words were matched for basic lexical characteristics such as number of letters, absolute recorded length, frequency, number of syllables and phonemes. During all the validation steps it was taken care that none of the participants had basic education level more than 12 years and English was neither their medium of education nor the language of daily communication. Finally, a total of 12 seven letter words and 28 six letters words (total of 40 unique words) were selected for designing of the auditory signal detection paradigm. The lexical characteristics of the words chosen for the paradigm were reported in accordance to the English lexical project (Balota et al., 2007). [Supplementary Table 1] Signal detection Paradigm: The signal detection task was constructed as per the previous description by Barkus et al. (2011; 2007) and Mosely et. al (2014), in which participants were asked to detect a voice stimulus embedded in white noise. To set the volume levels in the task, we conducted a pilot study (20 participants, 10 healthy controls and 10 schizophrenia patients with AVH; none of these subjects participated in the main study that compared the auditory signal detection parameters). In this pilot study, the participants listened to a continuous burst of white noise for 5sec embedded with 7
words being played at a SNR (Signal to Noise Ratio) levels varying between -24 to -2 SNR (increment of +2 SNR at each level, therefore total of 12 SNR levels). Stimuli were presented using Bose noise cancelling earphones (QuietComfort® 20 Acoustic Noise Cancelling® headphones; https://www.boseindia.com/en_in/products/headphones/earphones/quietcomfort-20acoustic-noise-cancelling-headphones.html#v=qc20_samsung_black) played using a desktop at 65 SPL. Participants were instructed to respond with a button press when they heard a voice, and each pilot participant's threshold was defined. For the final task four SNR levels were to be selected out of these 12 levels. The selection of these SNR levels was based on the finding where 100%, 75%, 50% and 25% of the total participants consistently identified the signal (detected voice) at an SNR level. The four levels chosen are hereafter referred to as volume levels1, 2, 3 and 4 respectively. The stimuli for the final task consisted of 144 bursts of white noise of 5 second duration; of these 144 white noise stimuli, 80 were embedded noise signals at volume levels 1-4 and remaining 64 were only noise. In the voiced trials, four volume levels were kept constant across all participants (a requirement of signal detection theory analysis). Each burst was followed by 3s of response period. During this time the participants were asked to indicate whether they heard a voice in the noise (yes/no) by a button press. In order to be sure that none of the five possible trial types (noise, plus four voiced volume levels) were repeated more than three times in a row, the stimuli were presented in pseudo-random fashion. Participants were informed of the four volume levels but not about their frequency of occurrence in the task. Trials were presented in 2 identical blocks of 72 bursts each (32 white noise and 40 voiced trials) with a rest of minimum 5 minutes in-between the blocks. The stimuli were presented using E-prime version 2.0.10 (Psychology Software Tools, Pittsburgh, PA) (2012). Four different versions of the task were designed and were randomised across all subjects. One-way ANOVA did not report any significant difference 8
between these versions when compared for the above stated lexical characteristics. [Supplementary Table 2] Statistical Analyses Data analysis was performed using R (https://www.r-project.org/). The following statistical tests were applied: student’s t-test (two-tailed), chi-square test, Pearson’s correlation, and general linear model based multivariate analysis of covariance (ANCOVA). On the basis of subjects hit rate and false alarm perception rate, discriminability index (d’) and response decision bias were calculated (Barkus et al., 2007). d’ is the measure of sensitivity with which the participant distinguishes voiced trials (signals) from the white noise (noises). Response bias reflects decision-making bias, participants’ willingness to accept ambiguous noise as meaningful stimuli.
Results
Descriptive statistics for various clinical parameters for schizophrenia patients are presented in the table 1. Patients did not differ significantly from controls in age. The sex ratio was comparable between patients and controls. Number of years of education was significantly greater in controls than patients. --------------------------------------------------------------------------------------------------------------------Table-1, Table-2 & Figure to appear here --------------------------------------------------------------------------------------------------------------------All four signal detection parameters were checked for normality by examining the q-q plot of residuals. Multivariate ANCOVA on signal detection parameters, controlling for the potential confounding effects of education (numbers of years) revealed a significant main effect of diagnosis [Multivariate F (4,24) = 3.35, p = 0.03, partial η2 = 0.36] for false alarm rate , decision 9
bias and discriminability index. However, there was no significant effect of diagnosis on hit rate. Also, there was a significant negative correlation between signal false alarms and decision bias among both the groups, controls (r = - 0.68, p = 0.005) and patients (r = - 0.58, p = 0.023) (Table-2 & Figure-1). No significant correlation of these signal detection parameters were observed with either clinical or demographic parameters. Also, we did not find any correlation between the antipsychotic dose and signal detection parameters.
Discussion Our results showed that schizophrenia patients experiencing AVHs were prone to commit significantly higher false alarm rate with lower discriminability index (perceptual sensitivity). Furthermore, the patients adopted a lenient response criterion as reported by lower decision bias. These findings are in consistence with the previous study results (Rappaport et al., 1972; Vercammen et al., 2008). Vercammen et. al (2008) study which compared non-hallucinating and hallucinating schizophrenia patients also reported that hallucinating patients were more sensitive to speech information. This proneness towards accepting the auditory stimuli was on account of a liberal acceptance criterion. The high sensitivity to speech perception could possibly be due to impaired top-down factors of perception in schizophrenia and indicative towards a mechanistic difference that needs to be explored to explain the altered auditory processing efficiency. However, our results differ from that of Brookwell et. al (2013) who observed no significant 10
difference in perceptual sensitivity among two groups. Our results also varied in response bias, as compared to Iskigaki et. al (1999) who reported lower decision bias in controls than patients and in the study by Li et. al (2002) were no difference between the two groups was reported. It was even though the patients showed a selection towards lenient response bias. Evidence from studies conducted with non-clinical hallucination and psychotic hallucinations (Barkus et al., 2011; Bentall and Slade, 1985) also reported that there was a significant association with a greater response bias in this population. However, it doesn’t account for any differences in perceptual sensitivity deficits. The evidence for a significant difference in response bias in our study and other similar studies might suggest that patients with AVHs are associated with a bias towards categorizing internal ambiguous percepts as external. A finding that can be interpreted as supporting the involvement of externalizing biases in hallucinatory experiences or the hallucinating patients were seen to be prone to misperceiving speech stimuli at lower threshold levels (Tracy and Shergill, 2013). The observation that patients selected a lenient response criterion, can also be seen in the light of hallucinating patients being more tuned to their auditory environment, picking up subtle sensations and interpreting them in par with their expectations. Thus attributing this difference in decision criterion to be a result of hallucination rather than its cause (Li et al., 2002). A lower perceptual sensitivity is also reported in our study which can be attributed to either deficit in the auditory perceptual mechanism (McKay et al., 2000) or deterioration in auditory attention (Hoffman et al., 1999). Taken together, altered perceptual sensitivity could implicate pathogenesis of AVHs. And response bias observed in our study might suggest the inefficient corollary discharge mechanism or the integration of signal and perceptual inputs being out of place (Li et al., 2002).
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From a signal detection theory perspective, response bias and false alarm rates are inversely related, i.e., a decrease in response bias predicts an increase in the false alarm rate. Accordingly in our results, there was a significantly decreased response bias with increased false alarm rate in schizophrenia patients as against healthy controls who had increased response bias with decreased false alarm rate. There was no difference observed in the overall hit rate between two groups though. This lack of difference in hit rate suggests that in spite of the increased propensity towards false alarm and differences in bias and sensitivity, there is no reflection of poor detection of a true signal in noise. No significant difference in hit rate also suggests that in the current study individual attention cannot be held accountable for the impaired discrimination (perceptual sensitivity) in schizophrenia patients. Various studies deployed a forced-choice detection task with patients and observed impaired detection of signals in both low and highintensity signal. Suggesting that attention was not a factor attributing to the significant findings (Bull and Venables, 1974; Li et al., 2003). In the context of the findings obtained from the study, it can be stated that altered perceptual sensitivity could implicate pathogenesis of AVHs. Response bias might suggest the inefficient corollary discharge mechanism (Reznik et al., 2014) or incongruent integration of signal and perceptual inputs (Talsma, 2015). In summary, our study supports the feasibility of implementing the auditory signal detection task in a group of minimally educated schizophrenia patients who came from varying social and cultural backgrounds. Our preliminary findings support auditory signal detection aberrations in schizophrenia. Further systematic evaluation is required using a larger sample as well as using an additional comparison group of schizophrenia patients without AVH. Moreover, the neurobiological correlates of auditory signal detection in schizophrenia need evaluation with concurring brain imaging / electrophysiological studies.
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References Balota, D.A., Yap, M.J., Hutchison, K.A., Cortese, M.J., Kessler, B., Loftis, B., Neely, J.H., Nelson, D.L., Simpson, G.B., Treiman, R., 2007. The English Lexicon Project. Behavior Research Methods 39, 445-459. Barkus, E., Smallman, R., Royle, N., Barkus, C., Lewis, S., Rushe, T., 2011. Auditory false perceptions are mediated by psychosis risk factors. Cogn Neuropsychiatry 16, 289-302. Barkus, E., Stirling, J., Hopkins, R., McKie, S., Lewis, S., 2007. Cognitive and neural processes in nonclinical auditory hallucinations. Br J Psychiatry Suppl 51, s76-81. Bentall, R.P., Slade, P.D., 1985. Reality testing and auditory hallucinations: a signal detection analysis. Br J Clin Psychol 24 ( Pt 3), 159-169. Boksa, P., 2009. On the neurobiology of hallucinations. J Psychiatry Neurosci 34, 260-262. Brookwell, M.L., Bentall, R.P., Varese, F., 2013. Externalizing biases and hallucinations in sourcemonitoring, self-monitoring and signal detection studies: a meta-analytic review. Psychol Med 43, 24652475. Bull, H.C., Venables, P.H., 1974. Speech Perception in Schizophrenia. The British Journal of Psychiatry 125, 350-354. Daalman, K., Verkooijen, S., Derks, E.M., Aleman, A., Sommer, I.E., 2012. The influence of semantic topdown processing in auditory verbal hallucinations. Schizophr Res 139, 82-86. Danivas, V., Venkatasubramanian, G., 2013. Current perspectives on chlorpromazine equivalents: Comparing apples and oranges! Indian J Psychiatry 55, 207-208. Fletcher, P.C., Frith, C.D., 2009. Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia. Nat Rev Neurosci 10, 48-58. Friston, K.J., 2005. Hallucinations and perceptual inference. Behavioral and Brain Sciences 28, 764-766. Frith, C.D., Done, D.J., 1988. Towards a neuropsychology of schizophrenia. Br J Psychiatry 153, 437-443. 13
Haddock, G., McCarron, J., Tarrier, N., Faragher, E.B., 1999. Scales to measure dimensions of hallucinations and delusions: the psychotic symptom rating scales (PSYRATS). Psychol Med 29, 879-889. Hoffman, R.E., Hawkins, K.A., Gueorguieva, R., et al., 2003. TRanscranial magnetic stimulation of left temporoparietal cortex and medication-resistant auditory hallucinations. Archives of General Psychiatry 60, 49-56. Hoffman, R.E., Rapaport, J., Mazure, C.M., Quinlan, D.M., 1999. Selective speech perception alterations in schizophrenic patients reporting hallucinated "voices". Am J Psychiatry 156, 393-399. Horga, G., Schatz, K.C., Abi-Dargham, A., Peterson, B.S., 2014. Deficits in predictive coding underlie hallucinations in schizophrenia. J Neurosci 34, 8072-8082. Ishigaki, T., Tanno, Y., 1999. The signal detection ability of patients with auditory hallucination: analysis using the continuous performance test. Psychiatry Clin Neurosci 53, 471-476. Li, C.S., Chen, M.C., Yang, Y.Y., Tsay, P.K., 2002. Altered performance of schizophrenia patients in an auditory detection and discrimination task: exploring the 'self-monitoring' model of hallucination. Schizophr Res 55, 115-128. Li, C.S., Yang, Y.Y., Chen, M.C., Chen, W.J., Liu, J.L., 2003. Auditory discrimination in female adolescents varying in schizotypal features: preliminary findings. Psychiatry Clin Neurosci 57, 391-397. McKay, C.M., Headlam, D.M., Copolov, D.L., 2000. Central auditory processing in patients with auditory hallucinations. Am J Psychiatry 157, 759-766. Moseley, P., Fernyhough, C., Ellison, A., 2014. The role of the superior temporal lobe in auditory false perceptions: a transcranial direct current stimulation study. Neuropsychologia 62, 202-208. Nazimek, J.M., Hunter, M.D., Woodruff, P.W., 2012. Auditory hallucinations: expectation-perception model. Med Hypotheses 78, 802-810. Oldfield, R.C., 1971. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9, 97-113. Rappaport, M., Hopkins, H.K., Hall, K., 1972. Auditory signal detection in paranoid and nonparanoid schizophrenics. Arch Gen Psychiatry 27, 747-752. Reznik, D., Henkin, Y., Schadel, N., Mukamel, R., 2014. Lateralized enhancement of auditory cortex activity and increased sensitivity to self-generated sounds. Nat Commun 5, 4059. Sheehan, D.V., Lecrubier, Y., Sheehan, K.H., Amorim, P., Janavs, J., Weiller, E., Hergueta, T., Baker, R., Dunbar, G.C., 1998. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry 59 Suppl 20, 22-33;quiz 34-57. Shergill, S.S., Murray, R.M., McGuire, P.K., 1998. Auditory hallucinations: a review of psychological treatments. Schizophr Res 32, 137-150. Talsma, D., 2015. Predictive coding and multisensory integration: an attentional account of the multisensory mind. Front Integr Neurosci 9, 19. Tools, P.S., 2012. Inc. [E-Prime 2.0]. Retrieved from http://www.pstnet.com. . Tracy, D.K., Shergill, S.S., 2013. Mechanisms Underlying Auditory Hallucinations-Understanding Perception without Stimulus. Brain Sci 3, 642-669. van Os, J., Kapur, S., 2009. Schizophrenia. Lancet 374, 635-645. Varese, F., Barkus, E., Bentall, R.P., 2011. Dissociative and metacognitive factors in hallucinationproneness when controlling for comorbid symptoms. Cogn Neuropsychiatry 16, 193-217. Vercammen, A., de Haan, E.H., Aleman, A., 2008. Hearing a voice in the noise: auditory hallucinations and speech perception. Psychol Med 38, 1177-1184. Wible, C.G., Preus, A.P., Hashimoto, R., 2009. A Cognitive Neuroscience View of Schizophrenic Symptoms: Abnormal Activation of a System for Social Perception and Communication. Brain Imaging Behav 3, 85-110. Woodruff, P.W., 2004. Auditory hallucinations: Insights and questions from neuroimaging. Cogn Neuropsychiatry 9, 73-91. 14
Woods, S.W., 2003. Chlorpromazine equivalent doses for the newer atypical antipsychotics. J Clin Psychiatry 64, 663-667.
Legend for Tables & Figure Table-1 Clinical characteristics of study subjects Table-2 Comparison of auditory signal detection parameters between schizophrenia patients and healthy controls* Figure-1 Comparative profile of auditory signal detection parameters between schizophrenia patients and healthy controls
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Table 1: Clinical characteristics of study subjects Characteristics Patients (Mean± SD) N 15 Age in years 28.33 ±7.65 Sex (M:F) 9:6 Age at onset 21.92 ±4.70 Illness Duration (months) 14.4±38.0 Total SAPS score 28.93±12.48 Total SANS score 23.53±10.45 SAPS total score Hallucination 12.87±4.39 PSYRATS Auditory hallucination score 29.07±5.42 Hoffman’s Auditory hallucination score 25.20±7.05 *2 Sample-test, #Chi-square test
Controls (Mean± SD) 15 28.00±2.30 8:7 -
Table 2: Auditory signal detection parameters comparison between schizophrenia patients and healthy controls* Patients Controls F partial η2 (Mean± SD) (Mean± SD) N 15 15 Signal Hit rate 2.06 0.07 0.610.22 0.510.09 Discriminability index (d’) 13.90 0.34 1.300.68 2.080.40 Signal False alarm rate 6.48 0.19 0.270.32 0.0310.06 Bias decision 9.90 0.27 4.406.40 11.516.86 *Multivariate ANCOVA was used controlling for the potential confounding effects of Years of education Characteristic
16
P
0.160 0.001 0.020 0.004
S1876-2018(16)30212-X http://dx.doi.org/doi:10.1016/j.ajp.2016.08.006 AJP 929
To appear in: Received date: Revised date: Accepted date:
6-5-2016 13-8-2016 13-8-2016
Please cite this article as: Chhabra, Harleen, Sowmya, Selvaraj, Sreeraj, Vanteemar S., Kalmady, Sunil V., Shivakumar, Venkataram, Amaresha, Anekal C., Narayanaswamy, Janardhanan C., Venkatasubramanian, Ganesan, Auditory False Perception in Schizophrenia: Development and Validation of Auditory Signal Detection Task.Asian Journal of Psychiatry http://dx.doi.org/10.1016/j.ajp.2016.08.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
AJP_2016_181 (Revised Manuscript) Original Research
Auditory False Perception in Schizophrenia: Development and Validation of Auditory Signal Detection Task Harleen Chhabra, Selvaraj Sowmya, Vanteemar S Sreeraj, Sunil V. Kalmady,Venkataram Shivakumar, Anekal C Amaresha, Janardhanan C Narayanaswamy, Ganesan Venkatasubramanian
The Schizophrenia Clinic, Department of Psychiatry & Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore, India.
* Corresponding author Dr Ganesan Venkatasubramanian MD PhD Additional Professor and Wellcome Trust / DBT India Alliance Senior Fellow Department of Psychiatry National Institute of Mental Health And Neurosciences (NIMHANS) Bangalore 560029 Email – [email protected]
Key Words Schizophrenia, Auditory hallucinations, auditory signal detection, false alarm Acknowledgements This study is supported by the Department of Science and Technology (Government of India) Research Grant (DST/SJF/LSA-02/2014-15) and Department of Biotechnology (Government of India) Research Grant (BT/PR5322/COE/34/8/2012) to G.V. H.C. is supported by the Department of Biotechnology, Government of India (DBT/2015/NIMHANS/345). V.S. is supported by Department of Health Research, Young Scientist in Newer Research Areas: DHR/HRD/Young Scientist/Type-VI(2)/2015. S.V.K. and A.C.A are supported by the Welcome Trust/DBT India Alliance. Conflict of interest There are no potential conflicts of interest to report for any of the authors
1
Highlights Auditory False Perception in Schizophrenia: Development and Validation of Auditory Signal Detection Task
Signal detection aberrations underlie pathogenesis of auditory hallucinations in schizophrenia We developed & validated an auditory signal detection task relevant for Indian population. Schizophrenia patients showed significantly greater false alarms & decision bias Findings support impaired perceptual expectation system in the prediction of incoming stimuli in schizophrenia
Abstract Auditory hallucinations constitute an important symptom component in 70-80% of schizophrenia patients. These hallucinations are proposed to occur due to an imbalance between perceptual expectation and external input, resulting in attachment of meaning to abstract noises; signal detection theory has been proposed to explain these phenomena. In this study, we describe the development of an auditory signal detection task using a carefully chosen set of English words that could be tested successfully in schizophrenia patients coming from varying linguistic, cultural and social backgrounds. Schizophrenia patients with significant auditory hallucinations (N=15) and healthy controls (N=15) performed the auditory signal detection task wherein they were instructed to differentiate between a 5-sec burst of plain white noise and voiced-noise. The analysis showed that false alarms (p = 0.02), discriminability index (p = 0.001) and decision bias (p = 0.004) were significantly different between the two groups. 2
There was a significant
negative correlation between false alarm rate and decision bias. These findings extend further support for impaired perceptual expectation system in schizophrenia patients.
Introduction Schizophrenia is a debilitating neuropsychiatric disorder that manifests through hallucinations, delusions, behavioral disorganization, and cognitive deficits (van Os and Kapur, 2009). Among these symptoms, auditory verbal hallucinations (AVH) occur in almost 70–80% of patients leading to significant distress, functional disabilities and behavioral dysfunctions (Shergill et al., 1998; Wible et al., 2009). Of the various theoretical models explaining the neurobiology of hallucinations, source monitoring theory postulates that, hallucinating individuals misattribute internally generated speech (or sensory stimuli) to an external source (Boksa, 2009; Frith and Done, 1988). Theory of expectation-perception is another important postulated paradigm to understand the origin of hallucinations. According to this theory, aberrant expectation-perception is associated with deviant predictive coding that can lead to loss/attenuation of error prediction with resultant AVH 3
(Fletcher and Frith, 2009; Friston, 2005; Nazimek et al., 2012). This deviant predictive coding is suggested to be due to impaired coding of external input which in turn could be attributed to defective modulation of prefrontal cortex by thalamus, or abnormal functioning of auditory cortex/auditory association cortex. To further validate this theory, Dalmaan et. al (2012) conducted a context manipulation task and showed that top-down processing that is responsible for expectations during predictions are impaired in hallucinating subjects. In an fMRI study too, Horga et. al (2014) reported overlapping of auditory cortex activation while patients experienced hallucinations and also during the occurrence of prediction of error signals. These observations suggested that neural dysfunction at the level of early or secondary auditory processing is important in the mechanism of hallucinations (Woodruff, 2004). Moreover, these findings support a potential role for signal detection (Brookwell et al., 2013) abnormalities to underlie the genesis of AVH.
To tap the efficiency of auditory processing in clinical and healthy population, studies have adopted the theory of signal detection to measure the false detections. An earlier study used a specific frequency signal tone in the noise to assess for the deficits in auditory processing (Rappaport et al., 1972). In a later study, (Bentall and Slade, 1985) a signal detection task was developed, wherein participants were asked to listen to white noise and respond if they heard a voice in the signal. These tasks enabled the analysis of erroneous and correct perception. Based on this signal detection theory, various study groups reported that participants, both schizophrenia and healthy subjects who experienced hallucinations perceived greater false alarms (Barkus et al., 2011; Brookwell et al., 2013; Varese et al., 2011). These results were reported to be due to differences in response bias rather than any significant deficit towards sensitivity to the task. However, later studies have reported varying findings: enhanced 4
sensitivity in hallucinating patients (Vercammen et al., 2008) or no significant difference at all (Li et al., 2002) in participants who experienced AVHs when compared to healthy controls. These differences in the finding could be attributed to varied AVH severity profile of study patients, the nature of the stimuli presented (tones or verbal stimuli) for the task in different studies.
To the best of our knowledge, we are unaware of any report on auditory signal detection in schizophrenia patients from India. Given the multilingual culture prevalent in India, it is a challenge to develop an auditory signal detection task that can be potentially tested in a substantial proportion of schizophrenia patients in this country. In this study, we describe the development of an auditory signal detection task using a carefully chosen set of English words that could be tested successfully in schizophrenia patients that came from varying linguistic, cultural and social background.
In addition, we examined the auditory signal detection
parameters in schizophrenia patients with AVH in comparison with matched healthy subjects. Methodology Participants: In this study, we examined 15 schizophrenia patients [Age = 28.00 ±7.65 years, 9 men] and 15 healthy subjects [Age = 28.00 ±2.30 years, 8 men]. Patients recruited for the study were those attending the clinical services of the National Institute of Mental Health & Neurosciences (India), fulfilling DSM-IV-TR criteria for schizophrenia and experiencing auditory hallucinations. The diagnosis of schizophrenia was established using Mini International Neuropsychiatric Interview Plus (Sheehan et al., 1998) and the same was confirmed by at least one experienced psychiatrist through an independent clinical interview. The severity of auditory 5
hallucinations was measured by the auditory hallucinations subscales of PSYRATS (Haddock et al., 1999) and Hoffman’s Auditory Hallucination Rating scale (Hoffman et al., 2003). All subjects were ascertained to be right- handed using Edinburgh inventory (Oldfield, 1971). The participants had a minimum 7 years of formal education. None of the participants had i) substance dependence, ii) neurological disorders, iii) mental retardation, or iv) impaired hearing. Patients were receiving clozapine (N=9), risperidone (N=4), aripiprazole (N=1), amisulpride (N=1), haloperidol (N=1), olanzapine (N=4) and quetiapine (N=1) with an average chlorpromazine equivalence of 376.42±183.61 mg/day (Danivas and Venkatasubramanian, 2013; Woods, 2003). After complete description of the study to the participants, a written consent was obtained.
Recording and validation of words: 69 English neutral words (6-7 letters (6.35±0.48, 1-sec long) were chosen for validation. The words selected were those spoken commonly in day to day conversation irrespective of the local communicating language. In the first level of screening the participants (N=7, M:F= 3:4, Year of education= 8±4.15; schizophrenia patients and their care giver) were shown the list of words and were asked to report whether the word sounds familiar to them. If they were familiar with the words they were asked to explain the context of the word and were asked to rate it as having heard the word on daily basis, occasionally or not heard of it at all. Based on the responses collected, 6 words were rejected at this stage. In the next step, these 63 words were manually recorded in a sound proof room using a Sony IC Recorder Mp3 3.1.0 with three different male voices and neutral tone. After recording the words were further processed with Audacity 2.0.6 (audacityteam.org) software. Here, the words were first de-noised, normalized and then the tempo was reduced by 15% to speed down the playback of words for a better audibility. The 6
purpose of normalization was to set the similar sound gain for all the words in case they varied in loudness and also to set the maximum amplitude of -1.0 db in order to avoid any clipping of the recorded words. After processing, there was three set of words from three voices. These words were played back and based on the neutrality of tone and clarity in pronunciation the best was selected of the three set. The final one set of words was further used for validation. These words were tested for familiarity and clarity of pronunciation with 13 subjects (schizophrenia patients and their care givers), both genders (M:F = 6:7) matched for age (32.33±14.35) and year of education (9.25±3.82). Of these 63 words, 10 were rejected at this step. Remaining 53 words were matched for basic lexical characteristics such as number of letters, absolute recorded length, frequency, number of syllables and phonemes. During all the validation steps it was taken care that none of the participants had basic education level more than 12 years and English was neither their medium of education nor the language of daily communication. Finally, a total of 12 seven letter words and 28 six letters words (total of 40 unique words) were selected for designing of the auditory signal detection paradigm. The lexical characteristics of the words chosen for the paradigm were reported in accordance to the English lexical project (Balota et al., 2007). [Supplementary Table 1] Signal detection Paradigm: The signal detection task was constructed as per the previous description by Barkus et al. (2011; 2007) and Mosely et. al (2014), in which participants were asked to detect a voice stimulus embedded in white noise. To set the volume levels in the task, we conducted a pilot study (20 participants, 10 healthy controls and 10 schizophrenia patients with AVH; none of these subjects participated in the main study that compared the auditory signal detection parameters). In this pilot study, the participants listened to a continuous burst of white noise for 5sec embedded with 7
words being played at a SNR (Signal to Noise Ratio) levels varying between -24 to -2 SNR (increment of +2 SNR at each level, therefore total of 12 SNR levels). Stimuli were presented using Bose noise cancelling earphones (QuietComfort® 20 Acoustic Noise Cancelling® headphones; https://www.boseindia.com/en_in/products/headphones/earphones/quietcomfort-20acoustic-noise-cancelling-headphones.html#v=qc20_samsung_black) played using a desktop at 65 SPL. Participants were instructed to respond with a button press when they heard a voice, and each pilot participant's threshold was defined. For the final task four SNR levels were to be selected out of these 12 levels. The selection of these SNR levels was based on the finding where 100%, 75%, 50% and 25% of the total participants consistently identified the signal (detected voice) at an SNR level. The four levels chosen are hereafter referred to as volume levels1, 2, 3 and 4 respectively. The stimuli for the final task consisted of 144 bursts of white noise of 5 second duration; of these 144 white noise stimuli, 80 were embedded noise signals at volume levels 1-4 and remaining 64 were only noise. In the voiced trials, four volume levels were kept constant across all participants (a requirement of signal detection theory analysis). Each burst was followed by 3s of response period. During this time the participants were asked to indicate whether they heard a voice in the noise (yes/no) by a button press. In order to be sure that none of the five possible trial types (noise, plus four voiced volume levels) were repeated more than three times in a row, the stimuli were presented in pseudo-random fashion. Participants were informed of the four volume levels but not about their frequency of occurrence in the task. Trials were presented in 2 identical blocks of 72 bursts each (32 white noise and 40 voiced trials) with a rest of minimum 5 minutes in-between the blocks. The stimuli were presented using E-prime version 2.0.10 (Psychology Software Tools, Pittsburgh, PA) (2012). Four different versions of the task were designed and were randomised across all subjects. One-way ANOVA did not report any significant difference 8
between these versions when compared for the above stated lexical characteristics. [Supplementary Table 2] Statistical Analyses Data analysis was performed using R (https://www.r-project.org/). The following statistical tests were applied: student’s t-test (two-tailed), chi-square test, Pearson’s correlation, and general linear model based multivariate analysis of covariance (ANCOVA). On the basis of subjects hit rate and false alarm perception rate, discriminability index (d’) and response decision bias were calculated (Barkus et al., 2007). d’ is the measure of sensitivity with which the participant distinguishes voiced trials (signals) from the white noise (noises). Response bias reflects decision-making bias, participants’ willingness to accept ambiguous noise as meaningful stimuli.
Results
Descriptive statistics for various clinical parameters for schizophrenia patients are presented in the table 1. Patients did not differ significantly from controls in age. The sex ratio was comparable between patients and controls. Number of years of education was significantly greater in controls than patients. --------------------------------------------------------------------------------------------------------------------Table-1, Table-2 & Figure to appear here --------------------------------------------------------------------------------------------------------------------All four signal detection parameters were checked for normality by examining the q-q plot of residuals. Multivariate ANCOVA on signal detection parameters, controlling for the potential confounding effects of education (numbers of years) revealed a significant main effect of diagnosis [Multivariate F (4,24) = 3.35, p = 0.03, partial η2 = 0.36] for false alarm rate , decision 9
bias and discriminability index. However, there was no significant effect of diagnosis on hit rate. Also, there was a significant negative correlation between signal false alarms and decision bias among both the groups, controls (r = - 0.68, p = 0.005) and patients (r = - 0.58, p = 0.023) (Table-2 & Figure-1). No significant correlation of these signal detection parameters were observed with either clinical or demographic parameters. Also, we did not find any correlation between the antipsychotic dose and signal detection parameters.
Discussion Our results showed that schizophrenia patients experiencing AVHs were prone to commit significantly higher false alarm rate with lower discriminability index (perceptual sensitivity). Furthermore, the patients adopted a lenient response criterion as reported by lower decision bias. These findings are in consistence with the previous study results (Rappaport et al., 1972; Vercammen et al., 2008). Vercammen et. al (2008) study which compared non-hallucinating and hallucinating schizophrenia patients also reported that hallucinating patients were more sensitive to speech information. This proneness towards accepting the auditory stimuli was on account of a liberal acceptance criterion. The high sensitivity to speech perception could possibly be due to impaired top-down factors of perception in schizophrenia and indicative towards a mechanistic difference that needs to be explored to explain the altered auditory processing efficiency. However, our results differ from that of Brookwell et. al (2013) who observed no significant 10
difference in perceptual sensitivity among two groups. Our results also varied in response bias, as compared to Iskigaki et. al (1999) who reported lower decision bias in controls than patients and in the study by Li et. al (2002) were no difference between the two groups was reported. It was even though the patients showed a selection towards lenient response bias. Evidence from studies conducted with non-clinical hallucination and psychotic hallucinations (Barkus et al., 2011; Bentall and Slade, 1985) also reported that there was a significant association with a greater response bias in this population. However, it doesn’t account for any differences in perceptual sensitivity deficits. The evidence for a significant difference in response bias in our study and other similar studies might suggest that patients with AVHs are associated with a bias towards categorizing internal ambiguous percepts as external. A finding that can be interpreted as supporting the involvement of externalizing biases in hallucinatory experiences or the hallucinating patients were seen to be prone to misperceiving speech stimuli at lower threshold levels (Tracy and Shergill, 2013). The observation that patients selected a lenient response criterion, can also be seen in the light of hallucinating patients being more tuned to their auditory environment, picking up subtle sensations and interpreting them in par with their expectations. Thus attributing this difference in decision criterion to be a result of hallucination rather than its cause (Li et al., 2002). A lower perceptual sensitivity is also reported in our study which can be attributed to either deficit in the auditory perceptual mechanism (McKay et al., 2000) or deterioration in auditory attention (Hoffman et al., 1999). Taken together, altered perceptual sensitivity could implicate pathogenesis of AVHs. And response bias observed in our study might suggest the inefficient corollary discharge mechanism or the integration of signal and perceptual inputs being out of place (Li et al., 2002).
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From a signal detection theory perspective, response bias and false alarm rates are inversely related, i.e., a decrease in response bias predicts an increase in the false alarm rate. Accordingly in our results, there was a significantly decreased response bias with increased false alarm rate in schizophrenia patients as against healthy controls who had increased response bias with decreased false alarm rate. There was no difference observed in the overall hit rate between two groups though. This lack of difference in hit rate suggests that in spite of the increased propensity towards false alarm and differences in bias and sensitivity, there is no reflection of poor detection of a true signal in noise. No significant difference in hit rate also suggests that in the current study individual attention cannot be held accountable for the impaired discrimination (perceptual sensitivity) in schizophrenia patients. Various studies deployed a forced-choice detection task with patients and observed impaired detection of signals in both low and highintensity signal. Suggesting that attention was not a factor attributing to the significant findings (Bull and Venables, 1974; Li et al., 2003). In the context of the findings obtained from the study, it can be stated that altered perceptual sensitivity could implicate pathogenesis of AVHs. Response bias might suggest the inefficient corollary discharge mechanism (Reznik et al., 2014) or incongruent integration of signal and perceptual inputs (Talsma, 2015). In summary, our study supports the feasibility of implementing the auditory signal detection task in a group of minimally educated schizophrenia patients who came from varying social and cultural backgrounds. Our preliminary findings support auditory signal detection aberrations in schizophrenia. Further systematic evaluation is required using a larger sample as well as using an additional comparison group of schizophrenia patients without AVH. Moreover, the neurobiological correlates of auditory signal detection in schizophrenia need evaluation with concurring brain imaging / electrophysiological studies.
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References Balota, D.A., Yap, M.J., Hutchison, K.A., Cortese, M.J., Kessler, B., Loftis, B., Neely, J.H., Nelson, D.L., Simpson, G.B., Treiman, R., 2007. The English Lexicon Project. Behavior Research Methods 39, 445-459. Barkus, E., Smallman, R., Royle, N., Barkus, C., Lewis, S., Rushe, T., 2011. Auditory false perceptions are mediated by psychosis risk factors. Cogn Neuropsychiatry 16, 289-302. Barkus, E., Stirling, J., Hopkins, R., McKie, S., Lewis, S., 2007. Cognitive and neural processes in nonclinical auditory hallucinations. Br J Psychiatry Suppl 51, s76-81. Bentall, R.P., Slade, P.D., 1985. Reality testing and auditory hallucinations: a signal detection analysis. Br J Clin Psychol 24 ( Pt 3), 159-169. Boksa, P., 2009. On the neurobiology of hallucinations. J Psychiatry Neurosci 34, 260-262. Brookwell, M.L., Bentall, R.P., Varese, F., 2013. Externalizing biases and hallucinations in sourcemonitoring, self-monitoring and signal detection studies: a meta-analytic review. Psychol Med 43, 24652475. Bull, H.C., Venables, P.H., 1974. Speech Perception in Schizophrenia. The British Journal of Psychiatry 125, 350-354. Daalman, K., Verkooijen, S., Derks, E.M., Aleman, A., Sommer, I.E., 2012. The influence of semantic topdown processing in auditory verbal hallucinations. Schizophr Res 139, 82-86. Danivas, V., Venkatasubramanian, G., 2013. Current perspectives on chlorpromazine equivalents: Comparing apples and oranges! Indian J Psychiatry 55, 207-208. Fletcher, P.C., Frith, C.D., 2009. Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia. Nat Rev Neurosci 10, 48-58. Friston, K.J., 2005. Hallucinations and perceptual inference. Behavioral and Brain Sciences 28, 764-766. Frith, C.D., Done, D.J., 1988. Towards a neuropsychology of schizophrenia. Br J Psychiatry 153, 437-443. 13
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Woods, S.W., 2003. Chlorpromazine equivalent doses for the newer atypical antipsychotics. J Clin Psychiatry 64, 663-667.
Legend for Tables & Figure Table-1 Clinical characteristics of study subjects Table-2 Comparison of auditory signal detection parameters between schizophrenia patients and healthy controls* Figure-1 Comparative profile of auditory signal detection parameters between schizophrenia patients and healthy controls
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Table 1: Clinical characteristics of study subjects Characteristics Patients (Mean± SD) N 15 Age in years 28.33 ±7.65 Sex (M:F) 9:6 Age at onset 21.92 ±4.70 Illness Duration (months) 14.4±38.0 Total SAPS score 28.93±12.48 Total SANS score 23.53±10.45 SAPS total score Hallucination 12.87±4.39 PSYRATS Auditory hallucination score 29.07±5.42 Hoffman’s Auditory hallucination score 25.20±7.05 *2 Sample-test, #Chi-square test
Controls (Mean± SD) 15 28.00±2.30 8:7 -
Table 2: Auditory signal detection parameters comparison between schizophrenia patients and healthy controls* Patients Controls F partial η2 (Mean± SD) (Mean± SD) N 15 15 Signal Hit rate 2.06 0.07 0.610.22 0.510.09 Discriminability index (d’) 13.90 0.34 1.300.68 2.080.40 Signal False alarm rate 6.48 0.19 0.270.32 0.0310.06 Bias decision 9.90 0.27 4.406.40 11.516.86 *Multivariate ANCOVA was used controlling for the potential confounding effects of Years of education Characteristic
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P
0.160 0.001 0.020 0.004