Increased Timing Variability in Schizophrenia and Bipolar
Amanda R. Bolbecker1,2,3, Daniel R. Westfall3, Josselyn M. Howell3, Ryan J. Lackner1, Christine A. Carroll1,
Brian F. O’Donnell1,2,3, William P. Hetrick1,2,3*
1Department of Psychological & Brain Sciences, Indiana University, Bloomington, Indiana, United States of America, 2Department of Psychiatry, Indiana University School
of Medicine, Indianapolis, Indiana, United States of America, 3Larue D. Carter Memorial Hospital, Indianapolis, Indiana, United States of America
Theoretical and empirical evidence suggests that impaired time perception and the neural circuitry underlying internal
timing mechanisms may contribute to severe psychiatric disorders, including psychotic and mood disorders. The degree to
which alterations in temporal perceptions reflect deficits that exist across psychosis-related phenotypes and the extent to
which mood symptoms contribute to these deficits is currently unknown. In addition, compared to schizophrenia, where
timing deficits have been more extensively investigated, sub-second timing has been studied relatively infrequently in
bipolar disorder. The present study compared sub-second duration estimates of schizophrenia (SZ), schizoaffective disorder
(SA), non-psychotic bipolar disorder (BDNP), bipolar disorder with psychotic features (BDP), and healthy non-psychiatric
controls (HC) on a well-established time perception task using sub-second durations. Participants included 66 SZ, 37 BDNP,
34 BDP, 31 SA, and 73 HC who participated in a temporal bisection task that required temporal judgements about auditory
durations ranging from 300 to 600 milliseconds. Timing variability was significantly higher in SZ, BDP, and BDNP groups
compared to healthy controls. The bisection point did not differ across groups. These findings suggest that both psychotic
and mood symptoms may be associated with disruptions in internal timing mechanisms. Yet unexpected findings emerged.
Specifically, the BDNP group had significantly increased variability compared to controls, but the SA group did not. In
addition, these deficits appeared to exist independent of current symptom status. The absence of between group
differences in bisection point suggests that increased variability in the SZ and bipolar disorder groups are due to alterations
in perceptual timing in the sub-second range, possibly mediated by the cerebellum, rather than cognitive deficits.
Citation: Bolbecker AR, Westfall DR, Howell JM, Lackner RJ, Carroll CA, et al. (2014) Increased Timing Variability in Schizophrenia and Bipolar Disorder. PLoS
ONE 9(5): e97964. doi:10.1371/journal.pone.0097964
Editor: Trevor Bruce Penney, National University of Singapore, Singapore
Received March 3, 2014; Accepted April 27, 2014; Published May 21, 2014
Copyright: ? 2014 Bolbecker et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by a 2007 National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Grant awarded
to Amanda R. Bolbecker and National Institute of Mental Health grants R01 MH074983 and 2MH074983 to William P. Hetrick. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
For good reasons, an NIMH initiative is encouraging the
establishment of a domain-based, dimensional classification of
psychiatric illness , thus moving away from categorical
diagnostic classification systems. It is hoped that such an approach
will lead to enhanced and accelerated treatment options for
psychiatric disorders since gene variants or brain circuit abnor-
malities are more closely linked to cognitive or behavioral
abnormalities than to psychiatric diagnostic categories . Interval
timing deficits are implicated in several developmental and
psychiatric disorders including schizophrenia, ADHD, autism.
For each of these diagnostic categories there is some evidence of
shared genetic risk [3,4]. These disorders share impairments in the
temporal organization of thoughts and behavior that interfere with
adaptive behavior. Interestingly, cognitive processes that are
deficient in these disorders are also associated with interval timing
deficits including social cognition [5,6], understanding of causality
, and language processing . In addition the linkage of interval
timing to specific cognitive domains and its presence in several
debilitating psychiatric disorders, interval timing is a particularly
attractive because it can be used as a translational vehicle in
animals to understand pharmacological mechanisms and neural
circuits underlying temporal processing in humans . Such
knowledge is crucial for the development of novel and effective
Few studies have examined interval timing across psychiatric
diagnostic categories to date. An exception is a recent study by
Penney & Meck  indicating that individuals at high risk for
schizophrenia versus affective disorders may have modality-
specific differences in clock speed relative to controls; specifically,
the high risk schizophrenia group showed larger differences
between auditory and visual clock speed on a temporal bisection
task relative to controls, whereas the high risk affective disorder
group fell between the two groups but did not statistically differ
from either. The question this paper poses is whether interval
timing deficits are related to symptom dimensions that cross
diagnostic categories. One possible dimension is psychosis.
Temporal processing abnormalities have been reported in
schizophrenia over timescales ranging from milliseconds to several
minutes and across a range of explicit timing tasks [11–18], and
the associated temporal fragmentation of conscious experience and
the corresponding lack of temporal organization in thoughts and
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behavior has been suggested to be an important contributor to the
pathophysiology of schizophrenia . Because of its diverse
connections and uniform architecture, the cerebellum is believed
to play an important role in temporal coordination of information
between multiple brain regions [20,21]. This putative temporal
modulation by the cerebellum has been theorized to be disturbed
in schizophrenia and could account for a multitude of symptoms of
the disorder [19,22]. With respect to psychotic symptoms
specifically, temporal dys-coordination of incoming neural signals
could result in translation errors that contribute to hallucinatory
experiences and delusional interpretations of cerebellar output
signals. For example, temporal distortions in cerebellar auditory
output may cause internally generated thoughts to be interpreted
as originating from an external source, resulting in an auditory
Recent research has suggested that perceptual timing abnor-
malities may be a feature of psychosis more generally rather than
of schizophrenia specifically . Consistent with this idea,
schizophrenia and psychotic affective disorders, including schizo-
affective disorder and psychotic bipolar disorder, show more
severe neurocognitive impairment than non-psychotic bipolar
disorder [24,25], which suggests that psychosis symptoms are
better predictors of neurocognitive impairments than DSM
To follow up on this suggestion, we examined whether
perceptual timing deficits differentiate bipolar disorder with
psychotic features from bipolar disorder without psychosis and
to what extent mood symptoms independently contribute to
timing abnormalities. Investigations of time perception in bipolar
disorder for short durations, i.e., those in the second or millisecond
range, are scarce. Two reports, both from the same group [26,27],
included bipolar disorder patients and compared them with a
control group on time perception and in both cases comparisons
have been for the supra-second range. However, mood disorders
in these samples were not restricted to bipolar disorder partic-
ipants only and psychotic symptoms were not explicitly examined,
making results difficult to interpret. Specifically, Bschor et al. 
reported that depressed (meeting criteria for DSM-IV major
depressive episode) and manic (DSM-IV manic) patients did not
differ from controls on either a 7-second time production or an 8-
second time-estimation task. In a subsequent study by the same
group using the same criteria , depressed patients over-
reproduced a 6-second interval, and no differences between
patients and controls were observed in a 1-second time reproduc-
tion task. To our knowledge, our recent study examining
performance on a paced finger tapping task in bipolar disorder
 was the first to examine explicit timing in bipolar disorder in
the sub-second time domain; we found that bipolar disorder
patients tapped faster and had increased variability that could be
attributed to clock rather than motor timing variance; tapping
variables were not correlated with mood symptoms, but psychosis
history was not explicitly examined. Importantly, the milliseconds
durations are the time range in which sensory perceptions are
linked to internal cognitive and motor programs .
The present study set out to address the question of whether
perceptual timing aberrations are associated with psychotic
symptoms by studying psychosis-related phenotypes in relation
to clinical populations without psychotic symptoms and to healthy
controls. An additional goal was to determine whether perceptual
timing deficits on this task were apparent in bipolar disorder in the
absence of psychotic symptoms, given earlier evidence from our
group suggesting altered clock speed and increased variability in a
paced finger tapping task . Specifically, the present study
compared a sample of individuals with schizophrenia, schizoaf-
fective disorder, bipolar disorder with and without psychotic
features, and healthy neurotypical controls on an auditory
temporal bisection task. In this task, participants classify test tones
of intermediate durations to ‘‘short’’ or ‘‘long’’ anchor tones. The
primary advantage of this task compared to other standard time
estimation tasks is that the source of differences between groups
can be attributed to either perceptual timing, i.e. ‘‘clock’’ variables,
or to cognitive, i.e. mnemonic, factors. We hypothesized that
schizophrenia, schizoaffective disorder, and bipolar disorder with
psychotic features groups have significantly increased variability
compared to controls and that the bipolar disorder group without
psychotic features would not be significantly different from the
The study procedures were approved by the Indiana University-
Purdue University Indianapolis Internal Review Board, and the
study was conducted in accordance with the Declaration of
Helsinki (Edinburgh amendments). Written informed consent was
obtained from all participants. Obtaining informed consent
involved the following steps. First, one of the investigators
discussed the research study with the individual and ensured that
the potential participant understood the procedures, risks, and
benefits. Once an individual agreed to participate and signed the
consent form, he or she was reassured once again that
participation was voluntary and that it could be ended at any
time without consequence. Second, prior to actually entering the
study, an assessor who was not a member of the research staff once
again reviewed the study and underscored the voluntary nature of
participation. Inpatients who had been involuntarily committed
because the severity of psychiatric symptoms had impaired their
ability to manage daily affairs and impaired insight into his or her
illness were not approached for the study until the symptoms had
responded to treatment as judged by the patient’s physician and
the lack of delusions on diagnostic interview.
There were 73 healthy controls (HC; 29M:44F), 34 bipolar
disorder with psychotic features (BDP; 14M:20F), 37 bipolar
disorder without psychotic features (BDNP; 16M:21F), 66
schizophrenia (SZ; 41M:25F)
12M:19F). Patients were recruited through physician referrals
from clinics affiliated with the Indiana University School of
Medicine in Indianapolis, Indiana, USA. Control participants
were recruited using flyers and advertisements. Exclusion criteria
for all participants included a history of neurological or
cardiovascular disease, clinically documented hearing loss, head
injury resulting in loss of consciousness, electroconvulsive therapy,
diagnosis of alcohol or other substance dependence within 3
months, and intelligence quotient (IQ) below 70. For control
participants, exclusion criteria also included a history of substance
abuse or dependence, a diagnosis of any current or past DSM-IV
mood or psychotic disorder, or first-degree relatives with BD or
Table 1 shows clinical information for each group. Sex was
unevenly distributed across groups, X2(4)
primarily due to the difference in proportion in the ratio of males
to females in the schizophrenia group compared to the other 3
clinical groups (BDNP, BDP, SA). Age did not differ across groups
(F(4, 234)=0.47, p=0.76).
Diagnostic status for the schizophrenia group was determined
using the Structured Clinical Interview for Diagnostic and
Statistical Manual of Mental Disorders-IV Axis I Disorders,
Increased Timing Variability in Schizophrenia and Bipolar Disorder
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Patient Version (SCID-I/P)  sections for mood disorders,
psychotic disorders, and substance abuse disorders, as well as chart
review. Kappa inter-rater reliability in our lab has been 0.95 for
schizophrenia versus mood disordered, or other diagnoses in
patients who have been prescreened for showing psychosis.
Control participants were interviewed using the non-patient
version of SCID-I/NP  sections for mood, psychotic, and
substance abuse and the SCID II  to exclude psychiatric
disorders. All diagnostic and clinical interviews occurred within 14
days of the temporal bisection task. Mood symptoms in
participants with bipolar disorder were assessed using the Young
Mania Rating Scale (YMRS; available for 28 BDP and 31 BDNP)
 and the Montgomery-Asberg Depression Rating Scale
(MADRS; available for 27 BDP and 31 BDNP) . Schizophre-
nia and schizoaffective participants’ symptoms were assessed using
the Positive and Negative Syndrome Scores (PANSS; available for
51 SZ and 22 SA) . Finally, WASI IQ was available for 67
HC, 32 BDP, 37 BDNP, 61 SZ, and 30 SA. Clinical and IQ
information can be found in Table 1. The number and percentage
of each group taking the major classes of psychotropic medication
are listed in Table 2. Complete medication information was not
available for 2 BDP, 2 SZ, and 1 SA.
Tone stimuli (880 hz) consisted of anchor tones with durations
of 300 and 600 ms, along with five arithmetically spaced
intermediate durations of 350, 400, 450, 500, and 550 ms.
During the test phase of the experiment, tones were classified
according to their perceived similarity to the short (i.e., 300 ms) or
long (i.e., 600 ms) anchor values. To address potential difficulties
related to task comprehension, a concrete procedural context
related to bird classification was adapted from Elveva ˚g and
The task procedure was divided into training, practice, and test
phases. The experiment began with a training phase in which the
short and long anchors were paired with a small (1.8461.92 in.)
and a large (3.6063.78 in.) bird silhouette, respectively. To ensure
that participants had learned the anchor durations, six presenta-
tions of each anchor were randomly administered within a 12-trial
practice block in the absence of the associated bird silhouette.
Following each presentation, participants received on-screen
instructions to press the ‘‘Short’’ key if the sound was made by
the small bird and to press the ‘‘Long’’ key if the sound belonged
to the big bird. Visual feedback (i.e., ‘‘correct’’ or ‘‘incorrect’’) was
provided after each response, and correct responses were
associated with a monetary bonus of 10 cents. A 1 s inter-trial
interval separated feedback offset and stimulus presentation. The
practice phase was repeated in 12-trial blocks until an accuracy
level of 75% or greater was reached: the session was aborted if
75% accuracy had not been achieved after three practice blocks.
The test phase of the experiment was presented in three blocks
of 35 trials each (five presentations of each auditory duration).
Participants were asked to classify each auditory stimulus as either
‘‘Short’’ or ‘‘Long’’ based on their perceived similarity to the
sounds made by the small or large bird. Because the bisection task
is an assessment of subjective time perception, response accuracy
could only be determined for the anchor durations during the test
phase. Each correct classification of the short and long anchors
earned participants a reward of 10 cents, for a possible bonus of
$3.00. To help ensure that the participants understood the task, a
practice block consisting of one presentation of each stimulus was
administered immediately prior to the test phase to allow
Table 1. Clinical and IQ information for each group.
PANSS total score56.5 (12.6) 54.4 (11.8)—
Positive 15.3 (5.6) 14.9 (4.5)—
Negative 14.2 (5.0)12.2 (3.8)—
General26.9 (6.5) 27.3 (6.4)—
YMRS total score8.5 (10.0)15.9 (14.1)
MADRS total score9.6 (9.7) 9.6 (11.6)
WASI IQ108 (16)102 (14) 92 (14)95 (14) 110 (14)
Table 2. Numbers and percentages of major psychotropic medications prescribed across groups.
BDNP (N=37) BDP (N=32)SZ (N=64) SA (N=30)
No psychotropic medication14% (N=5) 22% (N=7)9% (N=6)13% (N=4)
Atypical antipsychotic68% (N=25)72% (N=23) 77% (N=49)80% (N=24)
Typical antipsychotic 3% (N=1)9% (N=3) 23% (N=15) 7% (N=2)
Anticonvulsant27% (N=10)38% (N=12)11% (N=7) 23% (N=7)
Antidepressant16% (N=6) 28% (N=9)23% (N=15) 40%(N=12)
Anticholinergic 0% (N=0)0% (N=0)11% (N=7) 5% (N=5)
*No control participants were taking psychotropic medication. Medication information was not available for 2 BDP, 2 SZ, and 1 SA.
Increased Timing Variability in Schizophrenia and Bipolar Disorder
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participants to ask questions and become familiar with the
Each test block was preceded by a short (,5 min) rest period.
To minimize memory demands for the anchor values, the short
and long anchors were presented and paired with the small and
big bird silhouettes, respectively, prior to the commencement of
each of the three test blocks.
Behavioral data and analysis
The proportion of long responses, p(long), made to the anchors
and intermediate signals were quantified separately for each
participant and duration condition. The proportional data can be
plotted as a function of signal duration to yield a psychometric
response curve that is typically sigmoidal in form, indicating a near
absence of long responses to signals that fall close to the short
anchor value, to a predominance of long responses as signals come
to approach the duration of the long anchor. Sigmoidal functions
were fit to the proportional response data from each participant
using the regression feature of SigmaPlot 9.0 (Systat Software, Inc.,
San Jose, CA), which employs a least squares method to estimate
equation parameters and identify the durations that correspond to
p(long) values of 0.25, 0.50, and 0.75 from the fitted sigmoidal
The duration at which the proportion of long responses was
equivalent to 0.50 for each duration condition was identified as the
bisection point, or the duration at which short and long responses
occurred with equal probability. In addition to the bisection point,
the values derived from the fitted sigmoidal functions were used to
calculate the difference limen (DL) and Weber fraction (WF),
which represent the slope of the psychometric response curves and
can be interpreted as an index of timing variability. The DL is
calculated as one-half the difference between the durations
corresponding to p(long) =0.75 and p(long) =0.25 ((0.75–0.25)/
2), where smaller values indicate steeper slopes and greater
temporal precision. The WF is computed by dividing the DL by
the bisection point, which normalizes the DL values with respect
to the timed durations. Thus, the WF provides an index of
Weber’s Law (i.e., a constant coefficient of variation of subjective
time across various temporal durations) by allowing for a direct
comparison of timing variability across various anchor pairs.
All statistical analyses were conducted using SPSS 21.0. Non-
parametric Kruskal-Wallis tests with Group (SZ, SA, BDP, BDNP,
HC) as a between-subjects factor were used to analyze differences
between groups when assumptions of traditional analysis of
variance (ANOVA) were violated, i.e. the Levene’s test for
homogeneity of variance and the Shapiro-Wilks test for normality
were significant (p,0.05), as was true for all dependent variables
for the temporal bisection task; moreover, nonparametric tests are
more resilient to unequal sample sizes such as those in the present
study. Results were considered significant if they were below p,
0.05. Planned Mann-Whitney post-hoc tests comparing clinical
groups with the control group were conducted using a Bonferroni
corrected alpha level of 0.013 (0.05/4=0.0125=0.013).
The effects of clinical symptoms were evaluated using planned
bivariate Pearson correlations of primary dependent variables
(bisection point, DL and WF) with YMRS and MADRS scores for
the bipolar disorder groups and with the PANSS for the
schizophrenia spectrum groups (schizophrenia and schizoaffective
All participants were able to differentiate between the ‘‘short’’
and ‘‘long’’ anchor tones with a 75% accuracy rate by the end of
the practice session. Practice accuracy did not differ across groups,
H(4)=4.27, p=0.371. However, there were differences between
groups with respect to how many participants took 2 practice
blocks to achieve the 75% criterion, X2(4)=12.28, p=0.02; this
effect was driven by the SZ group, the only group whose observed
count exceeded the expected count (expected:observed=5:11).
The proportion of Long responses for each of the test durations
and the resulting psychophysical response functions for each of the
5 groups are plotted in Figure 1, where they are largely
overlapping. Likewise, the mean bisection point for each group,
plotted in the left panel of Figure 2, is similar across groups.
Consistent with these observations, the groups did not differ
statistically on bisection point (H(4)=1.61, p=0.81).
Examination of the 4 panels on the right side of Figure 1 shows
that in general the psychophysical functions the clinical groups had
flatter slopes compared to controls. This impression was confirmed
by a significant effect of Group on DL (H(4)=17.53, p=0.002).
Post-hoc tests using Mann-Whitney tests showed increased
variability in the SZ (p,0.001), BDP (p=0.009) and BDNP
(p=0.003) groups compared to controls; however, the SA group
did not differ statistically from HC (p=0.136). There was also a
significant effect of Group on WF (H(4)=15.64, p=0.004), which
compares variability for each group across different durations.
Follow-up Mann-Whitney tests showed the same pattern of
significantly increased variability in the SZ (p,0.001), BDP
(p=0.013), and BDNP (p=0.007) groups compared to HC; the
SA group was not statistically different from controls (p=0.058).
Clinical symptom measures, IQ, and planned correlations
At the time of testing, mean YMRS and MADRS ratings for the
bipolar disorder groups were not significantly different (p.0.05).
Within the entire bipolar group, the YMRS and MADRS were
not significantly correlated with the bisection point, DL, or DF.
Likewise, PANSS scores for the schizophrenia spectrum groups
(SZ and SA) were not significantly different on total score, nor
were the positive, negative, and general symptom dimensions
significantly different (all p.0.05). PANSS positive, negative,
general, and total scores were not significantly correlated with any
of the primary dependent variables (all p.0.05).
WASI IQ showed main effect of Group, F(4)=16.48, with post-
hoc test indicating that significant differences existed between
controls and BDP, SZ, and SA (all p,0.013) but not BDNP
(p=0.07). All WASI IQ group means were well within the normal
range, however (see Table 1). Within-group correlations of IQ
with bisection point, DL, and WF were not significant (all ,0.05).
The hypothesis of this study was that schizophrenia, schizoaf-
fective disorder, and bipolar disorder with psychotic features (but
not bipolar disorder without psychosis), would show increased
temporal variability compared to healthy controls. Results
partially supported this hypothesis. The results indicated increased
temporal variability on both the DL (difference limen) and WF
(Weber Fraction) in schizophrenia and in bipolar disorder. Two
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Figure 1. Psychometric response curves for each group composed of the proportion of long responses as a function of signal
duration. Smaller insets to the right show each clinical group individually as compared to the control group.
Figure 2. Means and standard deviations for each group for the bisection point (left panel), difference limen, (center panel) and
Weber Fraction (right panel). No differences existed between groups on bisection point, although for all groups the perceived bisection point
occurred earlier than the mathematical bisection point. SZ, BDP, and BDNP had increased temporal variability on both the difference limen and the
Weber Fraction compared to controls. The SA group was not significantly different from controls.
Increased Timing Variability in Schizophrenia and Bipolar Disorder
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unexpected results emerged. First, increased variability was
apparent in the BD group irrespective of whether a history of
psychotic features was present. Second, the schizoaffective group
was not statistically different from controls on either variability
measure. These results indicate that factors other than the simple
presence or absence of psychotic features contribute to timing
variability. Furthermore, the fact that the schizophrenia group
showed the largest increase in variability but the schizoaffective
group did not have significantly increased variability compared to
controls suggests that temporal perception is relatively more
preserved in this phenotype.
In recent years evidence has accumulated that psychosis is a
defining feature that contributes to cognitive impairment, crosses
diagnostic boundaries to encompass schizophrenia spectrum and
psychotic affective disorders, and differentiates psychotic from
nonpsychotic bipolar disorder [24,36]. It has also been reported
that individuals with schizoaffective disorder have more cognitive
impairment and poorer functional outcomes compared to bipolar
disorder without psychotic features . The current results found
significantly increased variability in timing in bipolar disorder
whether psychosis was present or not, which, along with the
absence of significantly increased variability in schizoaffective
disorder, suggests a more nuanced view of the relative contribu-
tions of psychotic versus affective symptoms to brain mechanisms
underlying temporal processing, which has been argued to be an
important substrate of neurocognitive functioning .
The lack of between group differences in bisection point is
important because it strongly suggests that perceptual factors were
likely the primary factor contributing to the observed differences in
timing variability. This inference derives from the fact that,
because temporal bisection requires the comparison of two tones,
alterations in clock speed would cause the relative perceptions of
the duration of the two comparison tones to be rescaled according
to each individual’s internal clock. Hence, clock speed differences
alone should not affect the location of the bisection point. If the
bisection point were different across groups it would suggest that
cognitive factors could have influenced results. For example, if
memory of anchor durations were impaired, the temporal
relationships of anchor-probe comparisons would be distorted
and bisection point would be shifted. This similarity of bisection
points across groups suggests that perceptual alterations were the
primary factor contributing to increased variability in bipolar
disorder and schizophrenia.
With respect to mechanisms underlying the observed increases
in variability, it is generally accepted that the frontal cortex, basal
ganglia, and cerebellum are integrally involved in time perception,
with a consensus emerging that different timescales utilize different
neural circuits [38,39]. A recent meta-analysis of 41 functional
neuroimaging studies of perceptual and motor timing used a
robust activation likelihood estimation algorithm and found strong
support for the theory that sub-second and supra-second durations
depend on somewhat distinct neural networks, with the former
more likely to recruit subcortical structures such as the basal
ganglia and cerebellum and the latter more likely to activate
cortical structures such as the supplementary motor area and
prefrontal cortex; moreover, activation of the cerebellum was
consistent across motor and perceptual timing tasks . The
conclusions from that meta-analysis support the proposal that the
cerebellum serves as a timekeeper for brief durations . The
finding that the cerebellum is activated predominantly during sub-
second tasks is consistent with other suggestions that this structure
may be critical for the encoding of sub-second time intervals
The foregoing evidence fits well with the influential cognitive
dysmetria theory of schizophrenia put forward by Nancy
Andreasen  which highlights the potentially important role
of the cerebellum by suggesting that disturbances in temporal
processing stemming from dysfunction of the cerebellar node of
the cortico-cerebellar-thalamic-cortical circuit may provide a
unitary model of schizophrenia that could produce the diverse
symptoms of schizophrenia ranging from hallucinations to
cognitive impairment. This model has been suggested with
schizophrenia specifically in mind, and a growing literature
supports cerebellar abnormalities in this disorder. For example,
postmortem and imaging studies report reduced volume of the CB
in chronic [42–45], neuroleptic-naı ¨ve , adolescent , first-
episode [46,48,49], and childhood-onset  SZ, as well as
reduced bilateral hemispheric volume in first-episode SZ .
Postmortem studies also have found reduced size and density of
Purkinje cells in SZ [52–54]. In addition, functional neuroimaging
studies have reported abnormal CB blood flow at rest [55–57],
and during cognitive tasks [58–60] in SZ patients.
Importantly, it should also be noted that the cerebellum is also
increasingly implicated in bipolar disorder, including findings of
neurotransmitter alterations [61,62], and reduced white  and
gray  matter. Moreover, lesions to the cerebellum can cause
disturbances in mood including mania, depression, and mood
lability . Finally, our group has documented abnormalities in
bipolar disorder on several tasks for which the cerebellum is
critical including classical delay eyeblink conditioning , paced
finger tapping , and postural sway . This evidence
supports a possible role for the cerebellum in bipolar disorder
and suggests that dysfunctional cerebellar circuitry may contribute
to timing deficits observed in the present study.
Strengths of the current study include relatively large sample
sizes and the inclusion of 4 clinical diagnostic categories with
differing degrees of psychotic versus affective symptoms. We found
no relationships of timing variability to mood symptoms in bipolar
disorder or PANSS ratings in schizophrenia spectrum disorders.
However, with respect to clinical symptoms at the time of testing, a
weakness of our study is the lack of concurrent psychosis and mood
state information. Specifically, bipolar disorder patients had
YMRS and MADRS information available that provided an
index of their current mania and depression symptoms, respec-
tively; schizophrenia and schizoaffective individuals had PANSS
data available which assessed their current psychosis-related
symptoms. However, a mixture of psychotic and mood symptoms
of differing magnitudes affect these disorders, so we were unable to
completely assess the contributions of these symptom domains
within each group.
Overall, the reported results suggest that psychotic and mood
symptoms contribute to increased timing variability and that
performance differences on timing tasks for these disorders can be
attributed to perceptual, or ‘‘clock’’ alterations rather than
differences in memory performance, as indicated by the lack of
between groups differences on bisection point. Timing abnormal-
ities in the range reported here are consistent with abnormalities in
circuits in which the cerebellum participates, although multiple
brain regions are likely to be involved. In future work, complete
information about the presence or absence of manic, depressive,
and psychotic symptoms across these diagnostic groups could
provide important information about the relative contributions of
each of these symptom domains to timing variability. However,
the lack of correlations between timing variability and the ratings
that were available suggest that more enduring traits and patterns
of symptoms over time, rather than transient symptoms, are more
likely to be associated with temporal processing abnormalities.
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We are grateful to Colleen Merrill, Mallory Klaunig, and Jennifer Forsyth
for their assistance in collecting temporal bisection data. We also thank the
clinical research team at Larue D. Carter Memorial Hospital and the
Indiana University Neuroscience Clinical Research Center for their
Conceived and designed the experiments: WPH CAC. Performed the
experiments: ARB DRW JMH. Analyzed the data: ARB DRW RJL.
Wrote the paper: ARB WPH BFO CAC.
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