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Childhood facial emotion recognition and psychosis-like symptoms in anonclinical population at 12 years of age: Results from the ALSPAC birthcohortAndrew Thompsonab; Sarah Sullivana; Jon Heronc; Kate Thomasc; Stanley Zammitad; Jeremy Horwoodc;David Gunnellc; Chris Hollise; Glyn Lewisa; Dieter Wolkef; Glynn Harrisona

a The Academic Unit of Psychiatry, University of Bristol, Bristol, UK b ORYGEN Research Centre,Department of Psychiatry, University of Melbourne, Melbourne, Australia c Department of SocialMedicine, University of Bristol, Bristol, UK d Department of Psychological Medicine, CardiffUniversity, Cardiff, UK e Division of Psychiatry, University of Nottingham, Nottingham, UK f

Department of Psychology, University of Warwick, Warwick, UK

First published on: 05 October 2010

To cite this Article Thompson, Andrew , Sullivan, Sarah , Heron, Jon , Thomas, Kate , Zammit, Stanley , Horwood, Jeremy, Gunnell, David , Hollis, Chris , Lewis, Glyn , Wolke, Dieter and Harrison, Glynn(2010) 'Childhood facial emotionrecognition and psychosis-like symptoms in a nonclinical population at 12 years of age: Results from the ALSPAC birthcohort', Cognitive Neuropsychiatry,, First published on: 05 October 2010 (iFirst)To link to this Article: DOI: 10.1080/13546805.2010.510040URL: http://dx.doi.org/10.1080/13546805.2010.510040

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Childhood facial emotion recognition and psychosis-like

symptoms in a nonclinical population at 12 years of age:

Results from the ALSPAC birth cohort

Andrew Thompson1,2, Sarah Sullivan1, Jon Heron3,Kate Thomas3, Stanley Zammit1,6, Jeremy Horwood3,

David Gunnell3, Chris Hollis4, Glyn Lewis1, Dieter Wolke5,and Glynn Harrison1

1The Academic Unit of Psychiatry, University of Bristol, Bristol, UK,2ORYGEN Research Centre, Department of Psychiatry, University of

Melbourne, Melbourne, Australia, 3Department of Social Medicine, University

of Bristol, Bristol, UK, 4Division of Psychiatry, University of Nottingham,

Nottingham, UK, 5Department of Psychology, University of Warwick,

Warwick, UK, 6Department of Psychological Medicine, Cardiff University,

Cardiff, UK

Introduction. Nonclinical psychotic symptoms (for example, low intensity or low

frequency psychotic symptoms such as ideas of reference or single word auditory

hallucinations) are common in adolescents and may be associated with an increased

risk of developing a psychotic disorder in adulthood. Those at high risk of developing

a psychotic disorder appear to perform poorly on facial emotion recognition tasks

Correspondence should be addressed to Andrew Thompson, Orygen Youth Health and

Research Centre, 35 Poplar Rd, Parkville, VIC 3205, Australia. E-mail: andy.thompson@mh.

org.au

We are extremely grateful to all the families who took part in this study, the midwives for

their help in recruiting them, and the whole ALSPAC team, which includes interviewers,

computer and laboratory technicians, clerical workers, research scientists, volunteers, managers,

receptionists, and nurses. AT designed the study along with GL and SS. KT and JH assisted with

analysis of the data along with AT and provided statistic support. GH was principal investigator

on the grant and provided supervision on all aspect of the study. All other authors were involved

in the design and collection of the primary outcome measure and provided comments and

feedback on the manuscript. All authors report no competing interests. All the authors had full

access to all of the data in the study and take responsibility for the integrity of the data and the

accuracy of the data analysis. The UK Medical Research Council, the Wellcome Trust, and the

University of Bristol provide core support for ALSPAC. The Wellcome Trust funded this

particular research (Grant No. 072043). DG and GL are NIHR Senior Investigators. This

publication is the work of the authors who will serve as guarantors for the contents of this paper.

COGNITIVE NEUROPSYCHIATRY

0000, 00 (00), 1�22

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but the relationship between facial emotion recognition and nonclinical ‘‘psychosislike symptoms’’ (PLIKS) in children is unclear. We aimed to examine the associationbetween childhood facial emotion recognition and PLIKS in adolescents.Methods. Longitudinal study using a large birth cohort. 6455 subjects completed asemistructured clinical assessment for psychotic symptoms (the PLIKSi) at the meanage of 12.9 (SD� 0.23). Facial emotion recognition (using the DANVA) waspreviously assessed at the age of 8 in the cohort.Results. There was no increase in odds of reporting any PLIKS either in relation tothe total score on the measure of facial emotion recognition or for the individualemotion scores of fear, sadness, anger, and happiness. Similar results were also foundwhen examining more intense and/or more frequently experienced psychoticsymptoms.Conclusions. Deficits in facial emotion recognition in 8-year-olds do not appearto predict later reporting of nonclinical psychotic symptoms in early adolescence.The results do not support the proposal that recognition of emotion is a traitphenomenon in those individuals at increased risk for psychosis. However, furtherresearch is warranted in older children/adolescents when more subtle emotionrecognition deficits can be investigated.

Keywords: Cognition; Cohort studies; Populations at risk; Psychotic disorders;

Social cognition.

INTRODUCTION

Several studies have found that nonclinical psychotic symptoms (‘‘psychosis-

like symptoms’’; PLIKS) are relatively common in both the general

community adult (Bijl, Ravelli, & van Zessen, 1998; Johns et al., 2004;

Kendler, Gallagher, Abelson, & Kessler, 1996; Kessler et al., 1994) and childand adolescent populations (Laurens et al., 2007; Poulton et al., 2000;

Yoshizumi, Murase, Honjo, Kaneko, & Murakami, 2004). The reporting of

PLIKS in these populations has been associated with an increased risk of

later developing a psychotic disorder (Hanssen, Bak, Bijl, Vollebergh, & van

Os, 2005; Poulton et al., 2000); PLIKS are thought to represent the less

severe end of a continuum of psychosis (van Os, Hanssen, Bijl, & Ravelli,

2000). There has therefore been increased interest in examining the

relationship between known or putative risk factors for schizophrenia andPLIKS as evidence of a possible intermediate phenotype (Cannon et al.,

2002; Horwood et al., 2008).

Social cognition, defined as a domain of cognition that involves the

perception, interpretation, and processing of social information (Ostrum,

1984) has received increased attention within schizophrenia research in the

past decade. One of the most widely studied of the proposed social cognitive

skills has been the perception and the recognition of emotions (Edwards,

Jackson, & Pattison, 2002; Penn, Corrigan, Bentall, Racenstein, & Newman,1997). Two main modalities for receiving information regarding emotion

2 THOMPSON ET AL.


perception and recognition are: visual, specifically facial expressions; and

auditory in the form of prosody (the emotional state of speech). Authors

have shown deficits in the recognition of emotions in both these domains

in patients with schizophrenia (Edwards et al., 2002) and those in the

first episode of psychosis (Edwards, Pattison, Jackson, & Wales, 2001;

Kucharska-Pietura, David, Masiak, & Phillips, 2005), with stronger evidencefor facial emotion recognition deficits (Edwards et al., 2002; Kohler, Walker,

Martin, Healey, & Moberg, 2009). Although often a global deficit is seen in

facial emotion recognition in schizophrenia, the most consistent findings

have been for negative emotions especially the recognition of fear and

sadness (Gaebel & Wolwer, 1992) as well as anger (Mandal, Pandey, &

Prasad, 1998).

There is emerging evidence that those in putative ‘‘high-risk’’ groups for

developing psychosis may also have difficulties recognising and discriminat-ing emotions (Phillips & Seidman, 2008). Deficits in facial emotion

recognition have been demonstrated in individuals at genetic high risk (on

the basis of family history) in the majority of studies (Eack et al., 2009; Kee,

Horan, Mintz, & Green, 2004; McCown, Johnson, Austin, & Shefsky, 1988),

in those at clinical high risk (‘‘at risk mental state’’) for psychosis

(Addington, Penn, Woods, Addington, & Perkins, 2008; Schloegelhofer,

Schaefer, Papageorgiou, Werneck-Rohrer, & Amminger, 2006), and in

individuals with schizotypy (Poreh, Whitman, Weber, & Ross, 1994; Shean,Bell, & Cameron, 2007; Williams, Henry, & Green, 2007), although often the

degree of deficit is less pronounced compared to those with schizophrenia.

This has led some authors to suggest that emotion recognition may be a

possible trait marker or endophenotype for schizophrenia (Eack et al., 2009;

Edwards et al., 2001; Phillips & Seidman, 2008) and represent a potential

vulnerability to psychosis (Penn et al., 1997). However, not all studies in

these groups have demonstrated differences between ‘‘at risk’’ individuals

and control subjects (Bolte & Poustka, 2003; Shean et al., 2007; Toomey,Seidman, Lyons, Faraone, & Tsuang, 1999).

Children appear to recognise emotions in faces from an early age (Bruce

et al., 2000; Camras & Allison, 1985; Mondloch, Geldart, Maurer, & Le

Grand, 2003). The ability to recognise different emotional states may occur at

different stages of development especially during adolescence. Although the

basic emotion recognition building blocks appear to be present at primary

school age (Harrigan, 1984; Kirouac, Dore, & Gosselin, 1985), they continue

to develop past this age (Thomas, de Bellis, Graham, & LaBar, 2007;Tremblay, Kirouac, & Dore, 2001). It is not known whether such basic facial

affect recognition skills are related to, or risk factors for, the development of

psychotic symptoms or psychotic disorder. There have been no prospective

studies of early emotion recognition skills and either schizophrenia or PLIKS

as a potential, intermediate phenotype (or ‘‘at risk’’ group) for schizophrenia.

FACIAL EMOTION RECOGNITION AND PLIKS 3


Our study uses a large population-based cohort to investigate the

relationship between performance on a facial emotion recognition task in

childhood and the presence of PLIKS in early adolescence. The specific

research questions were: (1) what is the association between the facial

emotion recognition of a child at the age of 8 and PLIKS at the age of 13

in the ALSPAC cohort?; (2) are specific negative emotions more or lessassociated with risk of PLIKS?; (3) are any associations stronger with stricter

definitions of PLIKS?; and (4) are any associations affected by confounding

factors?

METHODS

Participants

The study examined longitudinal data from the Avon Longitudinal Study of

Parents and Children (ALSPAC) birth cohort (www.alspac.bris.ac.uk). The

cohort consists of children born to residents of the former Avon Health

Authority area in South West England who had an expected date of delivery

between 1 April 1991 and 31 December 1992. The former County of Avon

includes both urban and rural areas and the population is broadlyrepresentative of children in the United Kingdom (Golding, Pembrey, &

Jones, 2001). Parents of the study children have completed regular postal

questionnaires about their child’s health and development since birth, and all

the children have been invited to attended annual assessment clinics since the

age of 7 years. A range of face-to-face interviews and psychological and

physical assessments were carried out at these clinics. The cohort has been

described in detail previously (Golding et al., 2001). The ALSPAC birth

cohort consists of 14,541 pregnancies that resulted in 14,062 live births:13,988 infants were still alive at 1 year. The current study is based on the

6455 children who completed the PLIKS interview as part of the annual

ALSPAC assessment clinic at mean age 12.9 (95% CI, 12.5�13.3 years).

Outcome variable: Psychosis-like symptoms

Psychosis-like symptoms (PLIKS) were measured at the ALSPAC clinic

using the PLIKS semistructured face-to-face interview (Horwood et al.,

2008). The PLIKS interview (PLIKSi) consists of 12 core questions covering

past 6-month occurrence of hallucinations (visual and auditory); delusions(delusions of being spied on, persecution, thoughts being read, reference,

control, grandiose ability, and other unspecified delusions); and experiences

of thought interference (thought broadcasting, insertion, and withdrawal).

For these 12 core items, seven stem questions were derived from DISC-IV

4 THOMPSON ET AL.


(Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000) (modified slightly

after piloting), and five stem questions from Section 17 of the Schedules

for Clinical Assessment in Neuropsychiatry (SCAN) version 2.0 (World

Health Organisation, 1994). Definitions of all items followed the glossary

definitions in SCAN, and clinical cross-questioning and probing was used

to establish the presence or absence of symptoms. Interviewers ratedsymptoms as either not present, suspected, or definitely present. Interviewers

(psychology graduates) underwent initial training with experienced SCAN

trainers and attended regular booster training sessions and monthly

workshops with two of the authors (GH and AT). The average kappa value

for interrater reliability was .72 and test�retest reliability was .48 (Horwood

et al., 2008).

Our study examined three primary PLIKSi outcomes reflecting increasing

‘‘severity’’ of psychotic symptoms: (1) presence of any suspected or definitesymptoms (broad PLIKS), (2) a narrower outcome of definite symptoms

only (narrow PLIKS), and (3) an even narrower outcome of any definite

symptoms occurring monthly or more frequently (strict PLIKS).

We also examined associations using a fourth PLIKS outcome: those with

third-person auditory hallucinations and ‘‘first-rank’’ delusions (delusions of

control or delusions of thought broadcast, insertion, or control) (Schneider,

1959). These symptoms were selected because they are frequently thought of

as more characteristic of schizophrenia, in concordance with both DSM-IVand ICD-10 criteria. These two groups of symptoms were combined as a

‘‘bizarre PLIKS’’ outcome. With this outcome we aimed to examine whether

the associations were similar for those with more ‘‘schizophrenia-like’’

symptoms.

Exposure variable: Facial emotion recognition

Previous information on facial expression recognition was available in the

cohort. This was collected using the DANVA (Diagnostic Analysis of Non-

Verbal Accuracy) child faces subtest at the age of 8 (Nowicki & Carton,

1993; Nowicki & Duke, 1994). The faces subtest comprises 24 photos of

child faces, with each face showing one of four emotions: fear, happiness,

sadness, and anger. The faces were colour photos of both male and female

children of primary school age. The photos are presented to the child for 2 s

and then the child has to respond by indicating which emotion is displayedin the photo. The pictures shown are classified as either high or low intensity

(i.e., the emotion displayed was easier to identify or a little harder). The

test was performed as part of the assessment clinic at age 8 and was

computerised to aid completion, with the tester providing only minimal

prompts to the child throughout the testing procedure.



The DANVA child faces test gives a total score out of 24 for facial emotion

recognition, a score for emotion misattribution, and individual summary

scores for the four emotions and low and high intensity emotions. The total

DANVA score and the scores for each of these individual summary scores

were used as continuous variables. A binary variable, based on the upper 20th

percentile, was also created and used in the analysis (DANVA total binary)(Nowicki, personal communication). This was chosen as the scores on the

DANVA were somewhat right skewed and this would give a measure of the

more extreme responders on the measure. Similar binary variables using 20th

percentile cutoffs (or as near as possible) were created for the individual

emotions and these were analysed separately. The DANVA has been shown to

have good internal consistency, test�retest reliability, and convergent validity

and construct validity (Nowicki & Carton, 1993; Nowicki & Duke, 1994).

Potential confounders

A number of sociodemographic variables were included as potential

confounders. Previous studies have demonstrated associations between

psychosis-like symptoms and social class, ethnicity, and parental education

(Horwood et al., 2008; Laurens, West, Murray, & Hodgins, 2008). Ques-

tionnaires completed by the parents during the antenatal period providedinformation on child’s ethnic background (nonwhite vs. white), parental social

class based on the higher of the mother or partner’s occupational social class

(using the 1991 Office of Population Census and Statistics classification; Dale

& Marsh, 1993; OPCS, 1991), and maternal education (O-level or more vs. less

than O-level; O-levels were the standard school-leaving qualifications taken

around age 16 years until recently in the United Kingdom).

We thought that additional factors in the child’s development including

IQ and previous childhood psychiatric disorder may also act as potentialconfounders to any relationship as they may affect both facial affect

recognition and PLIKS. This was based on previous research demonstrating

a potential association with these factors and development of psychotic-like

symptoms (Cannon et al., 2002; Horwood et al., 2008; Kim Cohen et al.,

2003). An abbreviated form of the Wechsler Intelligence Scale for Children

(WISC)-III (United Kingdom version) was used to derive an overall

Intelligence Quotient (IQ) of the children at the ALSPAC 8-year clinic

(Wechsler, 1991). DSM-IV psychiatric diagnoses were made at 91 months(7.6 years) using the Developmental and Wellbeing Assessment (DAWBA;

Goodman, Ford, Richards, Gatward, & Meltzer, 2000) based on parent and

teacher reports. The presence of any Axis 1 diagnosis of attention deficit/

hyperactivity disorder, conduct disorder, oppositional defiant disorder,

depression, or anxiety versus no diagnosis was used in the analysis.

6 THOMPSON ET AL.


Statistical analysis

All analyses were carried out using STATA version 9.0 (Stata Corporation,

2005). The sample that attended the PLIKS interview was compared with the

rest of the cohort. Logistic regression was used to calculate odds ratios (ORs)

and 95% confidence intervals (CIs) for PLIKS, with total score on the DANVA

and each of the six subscores as independent variables. The crude associations

(without adjustment for possible confounding variables) between the DANVAscores (independent variables) and broad, narrow, strict, and bizarre PLIKS

(dependent variables) were computed. The analyses were repeated controlling

for potential confounders in multiple logistic regression analyses. This was

done to determine the extent that these variables may have on the observed

associations. The first set of analyses included parents’social class, educational

level, and child’s sex, ethnicity, and age at PLIKS interview. Further

adjustment was made for IQ (WISC score) and presence of one or more

DAWBA diagnoses (psychiatric diagnosis) in a second analysis. Finally, theanalysis was repeated including all the potentially confounding variables.

For continuous dependent variables, linear regression models with mean

differences (MDs) were used.

Because information on the confounding factors was not available for all

participants for each set of confounders, we calculated the association based

on the reduced sample size for which information was available (controlled

for age and gender). In a separate step we then also added the respective

confounding variables.

Ethical approval

Ethical approval for the study was obtained from the ALSPAC Law and

Ethics Committee and the Local Research Ethics Committees. Informed

consent was obtained from the parents of the children after explanation of

the nature of the study.

RESULTS

6455 children completed the PLIKSi during the annual ALPSAC assessment

clinic at mean age 12.9 years. There were 880 children (13.7% of all those

interviewed) who were rated as having ‘‘broad’’ PLIKS, 363 (5.6% of those

interviewed) ‘‘narrow’’ PLIKS, and 164 (2.5% of those interviewed) childrenwith ‘‘strict’’ PLIKS. There were 233 children (3.6%) who were rated as

having suspected or definite ‘‘bizarre’’ PILKS.

6703 children in the total cohort completed the DANVA at the age of 8,

of these children 5267 (78.6%) also had complete data for the PLIKSi at



mean age 12.9. Overall the mean number of errors on the DANVA was 4.61

(range�0�22, SD�2.73). There was no difference in the overall score on

the DANVA faces between those who completed the PLIKSi and those who

did not (4.61 for completers compared to 4.63 for noncompleters).

A summary of potential confounders in relation to total DANVA score is

presented in Table 1 (DANVA and confounders dichotomised only for thepurpose of this table) along with the outcome variables by high/low total

DANVA scores. The high scorers on total DANVA errors were more likely to

be male, white, of a lower social class, have a mother with a lower level of

education, a lower individual IQ, and have at least one previous psychiatric

diagnosis.

Associations between PLIKS outcomes and DANVAemotion recognition scores

Table 2 shows the associations between each of the PLIKS outcomes and the

total and individual subscores of the DANVA. There was no suggestion of

a relationship between total errors on the DANVA and PLIKS at anydefinition of PLIKS. For the individual emotions subscores there was no

suggestion that worse performance on recognition of faces of negative

emotions (fear, sadness, and anger) were more associated with PLIKS. The

majority of the odds ratios were less than one. In fact those who reported

the strictest definition of PLIKS (‘‘strict’’) appeared to perform better on

recognition of fearful faces than those without PLIKS (OR�0.86, 95%

CI�0.75�1.00, p-value�.045). However, such a relationship was not seen

for other PLIKS variables with this emotion. There was also no relationshipbetween performance with regard to the intensity (high/low) of the facial

emotions displayed and reporting of PLIKS of any definition.

Given that some of the scores on the DANVA were right skewed, the

analysis was repeated with DANVA scores dichotomised into a binary

variable on the basis of scores being above or below the 80th percentile.

Again there were no consistent associations between both total DANVA

score and reporting of PLIKS nor individual emotion sub-scores and PLIKS.

Associations adjusting for potential confounders

Table 3 summarises the multivariable logistic regression models investigating

the impact of a series of potential confounding factors on the association ofDANVA individual emotions with ‘‘narrow’’ PLIKS only.

None of the sets of confounders were responsible for any substantial

change in the association between DANVA scores and PLIKS, although

all confounders reduced the odds ratios. The same was true for all of the

8 THOMPSON ET AL.


TABLE 1Frequency of potential confounders and primary outcome (PLIKS scores) by total DANVA errors binary score (dichotomised into

below and above the 80th centile) with corresponding odds ratios (ORs), 95% confidence intervals, and p-values

Low total DANVA errors score High total DANVA errors score Low vs. high total DANVA errors score

Binary confounding variables N (%) N (%) OR (95% CI) p-value

Gender

Male 2524 (75.1) 839 (24.9) 1 (reference) B.001

Female 2655 (79.5) 684 (20.5) 0.77 (0.69 to 0.87)

Social class

Low 1838 (75.9) 585 (24.1) 1 (reference) .007

High 2974 (78.8) 800 (21.2) 0.85 (0.75 to 0.95)

Maternal education

Below O level 1031 (73.4) 373 (26.6) 1 (reference) B.001

Above O level 3976 (78.5) 1088 (21.5) 0.76 (0.66 to 0.87)

Ethnicity

White 4722 (77.2) 1397 (22.8) 1 (reference) .03

Nonwhite 204 (83.3) 41 (16.7) 0.68 (0.48 to 0.96)

DAWBA

No diagnosis 4151 (77.8) 1184 (22.2) 1 (reference) .02

One or more diagnoses 281 (72.4) 107 (27.6) 1.33 (1.06 to 1.68)

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TABLE 1 (Continued)

Low total DANVA errors score High total DANVA errors score Low vs. high total DANVA errors score

Binary confounding variables N (%) N (%) OR (95% CI) p-value

None 2726 (77.7) 783 (22.3) 1 (reference) .53

At least one 398 (79.0) 106 (21.0) 1.06 (0.88 to 1.30)

Narrow PLIKS

None 2959 (77.8) 845 (22.2) 1 (reference) .66

At least one 165 (79.0) 44 (21.0) 1.07 (0.80 to 1.43)

Strict PLIKS

None 3050 (77.8) 869 (22.2) 1 (reference) .51

At least one 74 (78.7) 20 (21.3) 1.16 (0.74 to 1.81)

Bizarre PLIKS

None 3949 (77.5) 1149 (22.5) 1 (reference) .68

At least one 141 (78.8) 38 (21.2) 1.08 (0.75 to 1.55)

Continuous confounding

variables Mean (SD) Mean (SD) OR (95% CI) p-value

Age in days* 4689 (80.1) 4693 (73.0) 1.00 (1.00 to 1.00) .19

IQ# 105.7 (15.7) 99.4 (17.6) 0.98 (0.97 to 0.98) B.001

N�number; OR�odds ratio; CI�confidence interval; DAWBA�diagnostic assessment of wellbeing; IQ� intelligence quotient; DANVA�diagnostic

analysis of nonverbal accuracy; PLIKS�psychosis-like symptoms. *N�5289. #N�6068.

Outcome variables

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TABLE 2Crude odds ratios (ORs) (with associated 95% confidence intervals and p-values) of reporting PLIKS of different definitions for total

DANVA error score and error scores for individual emotions and intensity ratings

DANVA measure Broad PLIKS (N�5267) Narrow PLIKS (N�5267) Strict PLIKS (N�5267) Bizarre PLIKS (N�5267)

Total score

OR (95% CI) 0.98 (0.95�1.01) 0.99 (0.95�1.03) 0.96 (0.90�1.03) 0.99 (0.93�1.04)

p-value .120 .612 .270 .609

High intensity faces

OR (95% CI) 0.96 (0.91�1.02) 1.00 (0.92�1.09) 0.98 (0.86�1.11) 1.00 (0.90�1.11)

p-value .211 .940 .707 .987

Low intensity faces

OR (95% CI) 0.97 (0.92�1.01) 0.97 (0.90�1.04) 0.97 (0.90�1.04) 0.96 (0.88�1.05)

p-value .149 .381 .146 .419

Fearful faces

OR (95% CI) 0.96 (0.90�1.01) 0.99 (0.91�1.08) 0.86 (0.75�1.00) 0.96 (0.86�1.08)

p-value .139 .802 .045 .495

Happy faces

OR (95% CI) 1.00 (0.87�1.14) 1.06 (0.88�1.28) 1.14 (0.88�1.48) 1.17 (0.94�1.45)

p-value .947 .545 .331 .155

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TABLE 2 (Continued)

DANVA measure Broad PLIKS (N�5267) Narrow PLIKS (N�5267) Strict PLIKS (N�5267) Bizarre PLIKS (N�5267)

Sad faces

OR (95% CI) 1.00 (0.92�1.09) 0.96 (0.84�1.10) 0.97 (0.80�1.18) 0.93 (0.78�1.09)

p-value .972 .554 .789 .362

Angry faces

OR (95% CI)

p-value 0.95 (0.89�1.01) 0.97 (0.89�1.06) 0.97 (0.85�1.11) 0.98 (0.88�1.10)

.100 .525 .660 .723

The odds ratios represent the change in odds of reporting PLIKS per unit change in DANVA score. Odds ratios of less than 1 indicate that the odds of

reporting PLIKS are reduced when the number of DANVA errors increase; an odds ratio of greater than 1 indicates that the odds of reporting PLIKS increase

as the number of DANVA errors increase.

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potential confounders combined. Table 3 shows these associations for the

narrow outcome of PLIKS. This finding was the same for all of the other

definitions of PLIKS investigated (‘‘broad’’, ‘‘strict’’, and ‘‘bizarre’’) (results

not shown but available on request).

DISCUSSION

Poor facial emotion recognition at aged 8 was not associated with an

increased risk of PLIKS in early adolescence. This was the case for overall

performance in facial emotion recognition, different intensity emotions, aswell as both positive and negative emotional states. The relationship

remained similar after adjusting for potential confounders.

This is contrary to our hypotheses and in contrast to most of studies in

individuals deemed to be at high risk for schizophrenia or psychosis (family

history of schizophrenia, individuals with schizotypy, and those with ‘‘at risk

mental states’’) (Phillips & Seidman, 2008). However, not all studies have

consistently shown deficits in these groups and the evidence base is not

substantial. There are a number of possible explanations for this negativefinding that warrant further discussion.

First, it is possible that the assessment of emotion recognition used is not

sensitive enough to detect any subtle differences in emotion recognition.

Although this is possible, the DANVA has good validity and reliability in

this age group as a brief measure (Nowicki & Carton, 1993; Nowicki &

Duke, 1994; Nowicki & Mitchell, 1998). Recent studies using the DANVA

have demonstrated facial affect recognition deficits in schizotypy (Shean

et al., 2007), as well as those with ‘‘at risk’’ for bipolar illness (Brotman et al.,2008), providing some support for the face validity of the DANVA in similar

populations. Deficits in facial affect recognition using the DANVA have also

been found in schizophrenia patients (Hooker & Park, 2002; Pan, Chen,

Chen, & Liu, 2009). Rather than being chosen on the basis of any particular

theory of emotional development or on ‘‘anatomically objective’’ criteria

such as the position of facial features or specific facial muscle tensions (e.g.,

Ekman & Friesen, 1975; Izard, 1971), DANVA test items were selected

primarily on empirical-normative grounds (Nowicki & Duke, 1974). How-ever, it does not test all of the six basic emotions (disgust and surprise are

not included in the DANVA) suggested by Ekman (Ekman, 1994; Ekman,

Freisen, & Ellsworth, 1972) that are often used in emotion recognition

tests in the schizophrenic population (Edwards et al., 2001). Authors have

suggested that disgust and surprise may be less developed in 8-year-old

children (Camras & Allison, 1985) and neither of these emotions have been

implicated as important deficits in those individuals with schizophrenia

(Edwards et al., 2002).



TABLE 3Crude and adjusted ORs (95% confidence interval and p-values) for reporting ‘‘narrow’’ PLIKS by the DANVA faces emotion

recognition task as a continuous measure

Investigating the effects of controlling for specific confounders

Markers of socioeconomicstatus IQ, 8 years DAWBA, 8 years All confounders

Outcomemeasure A B C B C B C B C

Total DANVA error scoreN 5267 4878 4878 4792 4792 4706 4706 4013 4013Odds ratio(95% CI)

0.99(0.95 to 1.03)

0.98(0.95 to 1.01)

0.98(0.95 to 1.01)

1.00(0.96 to 1.05)

0.98(0.93 to 1.03)

0.99(0.94 to 1.04)

0.99(0.94 to 1.03)

0.99(0.94 to 1.04)

0.97(0.92 to 1.03)

p .612 .272 .181 .931 .371 .588 .556 .728 .319

High intensity faces errorsN 5267 4878 4878 4792 4792 4706 4706 4013 4013Odds ratio(95% CI)

1.00(0.92 to 1.09)

1.01(0.93 to 1.11)

1.00(0.92 to 1.10)

1.03(0.94 to 1.12)

0.98(0.90 to 1.07)

1.00(0.91 to 1.09)

1.00(0.91 to 1.09)

1.00(0.91 to 1.10)

0.97(0.88 to 1.07)

p .940 .743 .941 .563 .698 .856 .989 .951 .564

Low intensity faces errorsN 5267 4878 4878 4792 4792 4706 4706 4013 4013Odds ratio(95% CI)

0.97(0.90 to 1.04)

0.97(0.90 to 1.05)

0.97(0.90 to 1.04)

0.99(0.91 to1.06)

0.96(0.89 to 1.03)

0.97(0.89 to 1.04)

0.96(0.89 to 1.04)

0.97(0.90 to 1.06)

0.95(0.88 to 1.04)

p .381 .488 .400 .730 .267 .370 .353 .544 .267

Happy faces errorsN 5267 4878 4878 4792 4792 4706 4706 4013 4013Odds ratio(95% CI)

1.08(0.90 to 1.31)

1.09(0.89 to 1.33)

1.08(0.88 to 1.32)

1.06(0.86 to 1.29)

1.00(0.82 to 1.23)

1.10(0.90 to 1.34)

1.09(0.89 to 1.33)

1.08(0.87 to 1.36)

1.04(0.83 to 1.30)

p .408 .396 .459 .607 .974 .367 .383 .486 .719

14

TH

OM

PS

ON

ET

AL.


TABLE 3 (Continued)

Investigating the effects of controlling for specific confounders

Markers of socioeconomicstatus IQ, 8 years DAWBA, 8 years All confounders

Outcomemeasure A B C B C B C B C

Angry faces errorsN 5267 4878 4878 4792 4792 4706 4706 4013 4013Odds ratio(95% CI)

0.98(0.90 to 1.07)

0.98(0.89 to 1.08)

0.98(0.89 to 1.07)

1.00(0.92 to 1.11)

0.99(0.89 to 1.08)

0.97(0.88 to 1.07)

0.96(0.88 to 1.06)

0.98(0.88 to 1.09)

0.97(0.87 to 1.08)

p .669 .667 .626 .849 .767 .497 .469 .692 .535

Fearful faces errorsN 5267 4878 4878 4792 4792 4706 4706 4013 4013Odds ratio(95% CI)

0.99(0.90 to 1.08)

0.99(0.90 to 1.09)

0.98(0.89 to 1.07)

0.99(0.90 to 1.09)

0.95(0.87 to 1.05)

0.98 (0.90 to1.08)

0.98(0.90 to 1.08)

0.97(0.87 to 1.08)

0.94(0.85 to 1.05)

p .766 .834 .635 .820 .309 .732 .725 .587 .256

Sad faces errorsN 5267 4878 4878 4792 4792 4706 4706 4013 4013Odds ratio(95% CI)

0.96(0.85 to 1.10)

0.97(0.85 to 1.12)

0.97(0.84 to 1.11)

1.00(0.87 to 1.14)

0.95(0.83 to 1.10)

0.95(0.83 to 1.10)

0.95(0.82 to 1.09)

0.99(0.85 to 1.16)

0.96(0.82 to 1.12)

p .586 .706 .628 .985 .494 .503 .475 .936 .597

Model A�The model adjusts for age at PLIKS interview and gender. B�The model is based on participants with available data for the potential

confounder of interest (as indicated by the column header), adjusting for age at PLIKS interview and gender. C�The model adjusts for age at PLIKS

interview, gender, and the potential confounder. N�number; OR�odds ratio; CI�confidence interval; PLIKS�psychosis-like symptoms; IQ�intelligence

quotient; DAWBA�developmental and Wellbeing Assessment; DANVA�diagnostic analysis of nonverbal accuracy.

FA

CIA

LE

MO

TIO

NR

EC

OG

NIT

ION

AN

DP

LIK

S15


Second, it is possible that the DANVA was prone to ceiling effects in

performance. Older children and adolescents might be able to recognise and

label emotions, but they might not be as sensitive to nuances in facial

expression conveyed in blends of emotions or emotions of lesser intensity.

From our data it does appear that the mean number of errors is relatively

low (4.6 out of a possible 24) so we cannot discount this possibility. Moredifficult tasks such as those of morphing neutral faces to emotional faces

(Norton, McBain, Holt, Ongur, & Chen, 2009) might be considered in future

studies.

Third, and related to the previous suggestion, is the possibility that more

subtle social cognitive skills develop later on in adolescence and that any

deficits in these more subtle processes might be related to PLIKS, but were

not apparent at the age of 8. Research on facial affect recognition in early

childhood has suggested that such skills are fully developed in earlychildhood (Bruce et al., 2000; Camras & Allison, 1985; Mondloch et al.,

2003). However, recently others have suggested that more subtle skills,

especially relating to negative emotions such as anger and fear, may be

developed later, in adolescence (Camras & Allison, 1985; Kolb, Wilson, &

Taylor, 1992; Thomas et al., 2007). This could be a reason why we do not

find an association in this study. The finding of poorer performance on the

DANVA in an 18-year-old college population in another ‘‘at risk’’ group

with schizotypy (Shean et al., 2007) supports this suggestion.Further to this, authors have suggested that emotion recognition skills for

particular emotions may have different developmental trajectories from

adolescence to adulthood (Thomas et al, 2007). Evidence suggests that these

may be more likely to be the negative emotions such as fear and anger, that

are often poorly recognised by patients with psychotic symptoms (Edwards

et al., 2002; Mandal et al., 1998). The neuroanatomical changes and synaptic

pruning in structures believed to be involved in processing social information

are subject to relative change during early and late adolescence (Blakemore,2005). For example, there are substantial age-related changes in the

amygdala volume between 7.5 years and 18 years (Schumann et al., 2004)

and significant growth in the prefrontal cortex including grey matter growth

in the dorsolateral prefrontal cortex during adolescence/early adulthood

(Giedd, 2004). Therefore, early age deficits in social cognition may be of less

importance in the development of psychosis or psychotic symptoms than

those acquired in adolescent/early adulthood, a time of greatest risk for

developing such a disorder. Our results would suggest that ‘‘gross’’ deficits insocial cognition, that may be more apparent at the age of 8, are not

associated with early psychotic symptoms.

Fourth, it could be that the emotion recognition skills in those at risk for

developing schizophrenia are acquired normally in early childhood and

then lost in later adolescence/adulthood. There is currently no evidence to

16 THOMPSON ET AL.


support this hypothesis. However, authors have suggested that pronounced

emotional recognition problems may appear at a relatively late stage in the

developmental course of a mental disorder, or may only be associated with

more severe levels of schizophrenia-spectrum symptomatology (Toomey &

Schuldberg, 1995).

Last, it is possible that measures of psychotic like symptoms are not astrong marker for being at risk for psychosis which therefore explains why

we did not find an association. Although initial studies showed a strong

correlation between PLIKS developing a schizophreniform illness (Poulton

et al., 2000), and association between a number of putative risk factors

for schizophrenia and PLIKS (Cannon et al., 2002), recent evidence from

our group has suggested that PLIKS may not represent such a strong

intermediate phenotype for schizophrenia as first suspected, but rather an

additional risk factor that is more closely linked to other expressions ofemotional distress such as depression (Schreier et al., 2009; Thomas et al.,

2009). However, this study examined the 6-month prevalence of these

symptoms, whereas others have looked at the lifetime prevalence of such

symptoms (Kelleher, Harley, Murtagh, & Cannon, 2009; Laurens et al.,

2007). Research in this cohort and other such longitudinal approaches will

allow us to explore these hypotheses further.

Strengths and limitations

There are several strengths to our study. First, ALSPAC is a large

population-based cohort and because of the large sample size (in spite of

attrition from the original sample), we believe we had sufficient power to

detect potentially important associations. Second, the PLIKSi was derived

from widely used assessment tools for psychotic symptoms, and interviewers

were carefully trained and the interrater reliability thoroughly assessed andwas very good (average kappa�.72) (Horwood et al., 2008) and derived

similar rates of symptoms to previously used assessments in this population

(Cannon et al., 2002). Last, as the cohort study has collected information on

a number of factors in both child and the parent, we were able to control for

a number of important potential confounders in the analysis.

With regard to the limitations of the study, it is acknowledged that there

was substantial attrition in the ALSPAC cohort and likely selection biases in

the sample of participants (49.3%) that completed the PLIKS interviewcompared to the total birth cohort and were therefore included as the

primary outcome in this study. These selection biases have been investigated

in previous papers using the PLIKS interview (Horwood et al., 2008).

However, these levels of losses to follow up are similar to those seen in other

large-scale longitudinal studies (Callaway et al., 2007; Plewis, Calderwoof,



Hawkes, & Nathan, 2004). In particular, lower social classes and ethnic

minorities were under represented in this sample compared to the whole

cohort but the overall symptom levels obtained show minimal change when

adjusting for these differences. Previous research using the DANVA has

suggested that both lower social classes and certain ethnic minorities may

perform more poorly on facial emotion recognition (Collins & Nowicki,2001; Nowicki & Duke, 1994). As there was no measure of PLIKS taken at

age 8, we cannot rule out the issue of reverse causality. However, this appears

to be less of a problem, as our results show a lack of association.

In conclusion, using data from a large birth cohort, there appears to be no

specific relationship between emotion recognition deficits at the age of 8 and

an increased risk of reporting psychotic symptoms in early adolescence. Our

findings do not lend support to the proposal that facial emotion recognition

is a trait or risk factor for developing a psychotic disorder. Althoughthere was no relationship found in this cohort, other measures of either

emotion recognition (such as prosody) or social cognition were not

investigated. Other related social cognition deficits such as ‘‘theory of

mind’’ and attributional bias in particular have been suggested as other

vulnerability markers for psychotic illnesses. We plan to investigate such

skills in this cohort to further explore the relationship between social

cognitive abilities and the development of both PLIKS and psychotic

illnesses.

Manuscript received 15 December 2009

Revised manuscript received 20 April 2010

First published online month/year

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Childhood facial emotion recognition and psychosis-like symptoms in a nonclinical population at 12...

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