time estimation abilities in mild cognitive impairment and alzheimer's disease

Time Estimation Abilities in Mild Cognitive Impairmentand Alzheimer’s Disease

Alicia D. Rueda and Maureen Schmitter-EdgecombeWashington State University

Accurate time estimation abilities are assumed to play an important role in efficient performance of manydaily activities. The authors investigated the role of episodic memory impairment in temporal perceptionusing a prospective verbal time estimation paradigm. Verbal time estimations were made for filledintervals both within (i.e., �30 s) and beyond the time frame of working memory. In Experiment 1, theverbal time estimates of 24 individuals with mild cognitive impairment (MCI) were comparable withthose of age-matched controls at both short and long (i.e., �30 s) intervals. The verbal time estimates ofboth older adult groups, however, deviated more significantly from true time when compared withyounger adult controls. In Experiment 2, 17 individuals with Alzheimer’s disease (AD) demonstratedgreater error and variability in their time estimates, but no disproportionate differences emerged betweenshort- and long-duration estimates when compared with age-matched controls. The findings did notsupport a noteworthy role for episodic memory impairment in temporal perception but rather elucidateda significant effect of normal aging, as well as a detrimental effect of AD on temporal perception.

Keywords: time estimation, temporal perception/temporal cognition, mild cognitive impairment,Alzheimer’s disease, episodic memory

Many daily activities are assumed to involve estimations ofshort durations (Block, Zakay, & Hancock, 1998). For example,temporal processing has been described as necessary for coordi-nating movements, localizing sound, driving, participating in goal-oriented behaviors, and planning future actions (Carrasco, Bernal,& Redolat, 2001; Coelho et al., 2004). A significant correlationbetween ability to measure time passing and ability to executeself-care and independent activities of daily living was found in astudy involving patients with Alzheimer’s disease (AD; Venable &Mitchell, 1991). It is currently well known that, as dementiaprogresses, individuals experience increased difficulties executingdaily activities (Tomaszewski Farias, Harrell, Neumann, & Houtz,2003), thereby inhibiting independence and increasing dependenceon caregivers. However, despite the potential role that temporalprocessing may play in everyday functioning, few studies haveinvestigated time estimation abilities in persons with dementia.

Several theories have been put forth to explain people’s abilityto estimate time. As an example, the scalar timing theory (Church,1984; Gibbon, Church, & Meck, 1984) proposes that a biologicalinternal clock underlies our ability to perceive time. The clock

generates neuronal pulses regulated by a pacemaker. When the“switch” is opened as a result of the individual attending to thepassage of time, the pulses accumulate in a counter and a signal israised when the number of pulses reaches some target intervalduration (Coelho et al., 2004). According to this theory, durationjudgments are made by comparing the result of the current pulsecount being held in working memory with a value stored inreference memory (Nichelli, Venneri, Molinari, Tavani, & Graf-man, 1993; Perbal, Couillet, Azouvi, & Pouthas, 2003). In additionto memory processes, attention has been implicated in temporalperception, as time estimations have been found to shorten whenattention is occupied by a cognitively demanding task (Zakay,1990). Other researchers have hypothesized a role for frontalexecutive functioning (Gunstad, Cohen, Paul, Luyster, & Gordon,2006; Shaw & Aggleton, 1994), given that frontal lobe impairmenthas been found to lead to greater overestimation of time (Nichelliet al., 1993).

In studies designed to investigate possible substrates of temporalperception, several different methods have been used. In a pro-spective time estimation paradigm, the participant is aware inadvance of his or her task to estimate time. Typically one of threemethods is used. The method of verbal estimation exposes theparticipant to a duration interval and requires that he or shesubsequently provide an estimation of its length (Licht, Morganti,Nehrke, & Heiman, 1985). This method requires the participant totranslate his or her subjective experience into objective clock time(Block et al., 1998). For the production method, the participant isasked to indicate when a stated time has elapsed (Licht et al.,1985). In this case, the participant begins with an objective timeand must translate it to a subjectively experienced duration (Blocket al., 1998). Research suggests that the methods of verbal esti-mation and production recruit similar underlying cognitive andbiological processes (Coelho et al., 2004; Craik & Hay, 1999). Incontrast, the reproduction method requires the participant to ex-

Alicia Rueda and Maureen Schmitter-Edgecombe, Department of Psy-chology, Washington State University.

This research was funded in part by an Edward R. Meyer ProjectAward. Portions of this research were presented at the 35th annualmeeting of the International Neuropsychological Society, Portland, OR,and at the 36th annual meeting of the International NeuropsychologicalSociety, Waikoloa, HI.

We thank Michelle Langill, Kimberly Lanni, and Scott Creamer for theirsupport in coordinating data collection.

Correspondence concerning this article should be addressed to MaureenSchmitter-Edgecombe, Department of Psychology, PO Box 4820, Wash-ington State University, Pullman, WA 99164-4820. E-mail: [email protected]

Neuropsychology © 2009 American Psychological Association2009, Vol. 23, No. 2, 178–188 0894-4105/09/$12.00 DOI:10.1037/a0014289

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perience a delineated interval and subsequently indicate when thesame time duration has passed, requiring a comparison with thestored representation of that interval (Shaw & Aggleton, 1994). Inthis case, no comparison of subjective time to objective clock timeis thought necessary (Zakay, 1990). Contrary to prospective par-adigms, in a retrospective time estimation design, the participant isnot told in advance that his or her task will be to estimate the timeelapsed. This method likely requires long-term memory rather thantemporal judgment processes (Zakay, 1990). In addition, the ret-rospective method is likely only valid on the first trial, as theparticipant then becomes aware of his or her task. In this study,therefore, we used a prospective verbal time estimation paradigmto investigate time perception in cognitively healthy older adults(OAs), in persons with mild cognitive impairment (MCI), and inpersons with AD.

Concerning the role of memory, working memory and episodicmemory have been implicated as necessary for accurate timeestimations for short and long durations, respectively (Kinsbourne& Hicks, 1990; Mimura, Kinsbourne, & O’Conner, 2000; Schmit-ter-Edgecombe & Rueda, 2008). In populations with episodicmemory impairments (e.g., traumatic brain injury and amnesicparticipants), researchers have found deficits in time perception fordurations outside the limits of working memory (beyond 30 s).Specifically, participants with episodic memory impairment havebeen found to significantly underestimate time durations onlywhen the intervals to be estimated exceeded the time frame ofimmediate or working memory (Kinsbourne & Hicks, 1990;Mimura et al., 2000; Schmitter-Edgecombe & Rueda, 2008). It hasbeen hypothesized that judging durations beyond the time frame ofworking memory also requires assistance from episodic or long-term memory (Kinsbourne, 2000; Mimura et al., 2000; Nichelli etal., 1993). However, one study found relatively normal perfor-mance by postviral encephalitis amnesics, suggesting that timeestimation can be independent of memory abilities (Shaw &Aggleton, 1994). As such, the role of memory in time estimationrequires further investigation. Because of prominent episodicmemory deficits, individuals with MCI and AD may further con-tribute to our understanding of the role of memory in time esti-mation. Currently, little work has evaluated the time perceptionabilities of participants with AD, and no studies of which we areaware have investigated the time estimation performance of par-ticipants with MCI.

In one of the few time perception studies conducted with ADparticipants, Papagno, Allegra, and Cardaci (2004) used a verbalestimation method and had participants estimate filled intervals ofshort (i.e., 15 s) and long (i.e., 50 s) durations. These authors foundthat the AD participants overestimated time at both intervals,compared with controls. In contrast, in a study using the produc-tion method, Carrasco, Guillem, and Redolat (2000) had partici-pants produce intervals of 5 s, 10 s, and 25 s and showed that ADparticipants overproduced time at the shortest interval duration(5 s) relative to controls. These authors further found that some ADparticipants overestimated time, whereas others underestimatedtime, at the 10-s and 20-s intervals, so that the AD group’s meandid not significantly differ from that of controls and appeared to bean accurate estimation of time. These findings suggest that ameasure of absolute error, which reflects the degree to whichestimations deviate from true clock time, may be a more sensitivemeasure than the mean for detecting timing decrements in the AD

population. Carrasco and colleagues further showed that the ADparticipants were significantly more variable in their responseswhen compared with control participants. Consistent with thesefindings, Nichelli et al. (1993) found that AD participants whomade verbal estimations of filled intervals of approximately 5-s,10-s, 20-s, and 40-s durations were more imprecise or variable intheir estimations than controls. However, contrary to Carrasco andcolleagues’ findings, the AD participants’ estimations did notdeviate from true time more greatly than those of controls.

In contrast to the typical finding of greater underestimation withincreasing interval durations found in other memory impairedpopulations (e.g., traumatic brain injury, amnestic), as discussedearlier, AD participants have been found to overestimate verbalestimations (Papagno et al., 2004), to over- or underestimateproductions (Carrasco et al., 2000), to be more variable in theirestimations (Carrasco et al., 2000; Nichelli et al., 1993), and toshow greater deviations in their estimations from the standard time(Carrasco et al., 2000). It is possible that AD participants haveadditional cognitive burden beyond episodic memory impairmentthat affects their temporal perception. We further examine the roleof memory on time estimation abilities by evaluating verbal timeestimates at two different stages of a dementing process in popu-lations with prominent episodic memory impairment (i.e., MCIand AD). According to previous findings (Kinsbourne & Hicks,1990; Mimura et al., 2000; Schmitter-Edgecombe & Rueda, 2008),individuals with episodic memory impairment would be expectedto underestimate the standard interval disproportionately whenpresented with time durations outside the bounds of workingmemory. In Experiment 1, we tested a population who had poorerepisodic memory than that of age-matched controls but who do notyet meet criteria for AD (i.e., MCI population with episodicmemory deficits). In Experiment 2, we examined an AD groupwith more profound episodic memory and cognitive deficits.

Experiment 1

The purpose of Experiment 1 was to examine time perceptionabilities in persons diagnosed with MCI. We utilized the verbaltime estimation method and required participants to verbally esti-mate the length of filled short (10 s and 25 s) and long (45 s and60 s) intervals by reporting their time estimate in seconds. Verbalestimation has been argued to be the preferable method for study-ing the sense of time passing (Kinsbourne, 2000) because it is notconfounded by impulsivity or impatience as are the production andreproduction methods (Block et al., 1998). The use of a filledinterval, rather than an empty interval in which the participant isinstructed to do nothing, will also more closely approximate ac-tivities in daily living, as individuals rarely wait idly by whencompleting a task (e.g., preparing multiple components of a meal).If intact episodic memory is important for accurate time perceptionof durations outside of working memory, then relative to OAcontrols, the MCI group should more greatly underestimate longerintervals (45 s and 60 s) but not shorter intervals (10 s and 25 s;Baddeley & Warrington, 1970; Mimura et al., 2000). To furtherunderstanding of the cognitive underpinnings of temporal percep-tion, we also examined the relationship between time estimationvariables and neuropsychological tests assessing attention, mem-ory, and executive functioning abilities.

179VERBAL TIME ESTIMATES IN MCI AND AD

Method

Participants. Twenty-four younger adult (YA) controls (12female, 12 male; mean age � 19.83), 24 OA controls (14 fe-male, 10 male; mean age � 72.54), and 24 individuals with MCI(12 female, 12 male; mean age � 70.96) were participants in thisstudy. Participants were recruited through community advertise-ments and outreach to Whitman and Spokane counties in Wash-ington State between August 2005 and May 2007. This study wasconducted as part of a larger study investigating the relationshipbetween multiple aspects of memory functioning and everydayabilities in persons with MCI (Schmitter-Edgecombe, Woo, &Greeley, 2009). Study participants completed a battery of experi-mental and standardized neuropsychological tests. The battery oftests was administered across 2 days of testing, with each testingsession lasting 2–3 hr. Participants were initially screened byphone, which included (a) a medical interview to rule out exclu-sion criteria (e.g., significant stroke, heart attack, multiple headinjuries, substance abuse, neurologic disorder), (b) the TelephoneInterview of Cognitive Status to exclude participants who weresignificantly cognitively impaired, and (c) the Clinical DementiaRating (CDR) instrument to assess dementia staging (Hughes,Berg, Danzinger, Cohen, & Martin, 1982; Morris, 1993; Morris,McKeel, & Storandt, 1991). The content of the CDR was notmodified per its administration by phone. Care was taken tointerview the informant without the participant nearby. Whenavailable, collateral medical information, including the results oflaboratory and brain imaging studies, were obtained and reviewed.Case consensus was used to establish diagnosis. All participantswere compensated for parking and were given a report document-ing their performances in exchange for their time.

Participants in the MCI group all reported experiencing cogni-tive change for a minimum of 6 months and had a CDR scoreof 0.5. Neuropsychological testing data were further used to definethe MCI group. We did not distinguish between amnestic MCIsingle domain or multidomain. Psychometric inclusion criteria forthese participants were consistent with criteria outlined byPetersen and colleagues (Petersen et al., 2001) and included thefollowing: (a) subjective memory impairment corroborated by aknowledgeable informant and confirmed by a score falling 1.5 SDbelow the mean of age- and education-matched peers on at leastone of three measures (Total List Learning, Immediate Recall, orDelayed Recall) from the Rey Auditory Verbal Learning Test(RAVLT, Lezak, 1983); (b) nonfulfillment of the Diagnostic andStatistical Manual of Mental Disorders (4th ed; DSM–IV; Amer-ican Psychiatric Association, 2000) criteria for dementia; (c) gen-eral cognitive functions within normal limits as confirmed by anormal score on the Mini-Mental State Examination (MMSE;normality cutoff score, 24; Measso, Cavarzeran, Zappala, & Leb-owitz, 1993); (d) no significant impact of the memory deficit on theparticipant’s daily activities as confirmed by a total CDR scoreof 0.5, which is consistent with a minimal change in the partici-pant’s habits; and (e) absence of severe depression as confirmed bya score above 17 on the Geriatric Depression Scale (GDS; Yesav-age et al., 1983). All of the control participants met exclusioncriteria, reported no history of cognitive complaints, had a CDRscore of 0, a GDS score above 17, and an MMSE score of atleast 26.

Participants’ vision was tested with the Rosenbaum PocketScreening assessment. Scores on this measure ranged from 20/20to 20/70. Inspection of the number reading performance of the 3participants who scored a 20/70 on the Rosenbaum assessmentrevealed only one error between them; thus, all time estimationtrials were used in the analyses. To increase the likelihood thatMCI participants’ premorbid abilities were roughly equivalent tothose of the controls, they were matched by age (t � �.59, p �.56) and education (t � .16, p � .87) to OA control participants.Descriptive data are given in Table 1.

A battery of neuropsychological tests was administered to allparticipants. The data are in Table 1 and show typical age effectsalong with decline in episodic memory performance by the MCIgroup. Compared with the YA group, the OA controls performedmore poorly on tests of attention and speeded processing (i.e., theSymbol Digit Modalities Test [SDMT]—Oral and Writtensubtests; Smith, 1991; and the Trail Making Test, Parts A and B;Reitan, 1958). In comparison with both the YA group and the OAcontrols, the MCI group performed more poorly on tests of atten-tion and speeded processing (SDMT—Oral and Written subtests),executive functioning (Trail Making Test, Part B; the Letter–Number Sequencing subtest from the Wechsler Adult IntelligenceScale—Third Edition [WAIS-III, L-N Seq.;Wechsler, 1997]), ver-bal learning and delayed memory (RAVLT Total, Immediate De-lay Recall, and Long Delay Recall measures; Majdan, Sziklas, &Jones-Gotman, 1996), and verbal fluency (the Letter Fluency andAnimal Fluency subtests from the Delis–Kaplan Executive Func-tioning System; Delis, Kaplan, & Kramer, 2001). The MCI groupperformed better than the YA controls and similarly to OA con-trols on a test of word knowledge (Shipley Institute of LivingScale; Zachary, 1991). The OA group performed significantlybetter than the YA and MCI groups on picture naming (BostonNaming Test; (BNT; Kaplan, Goodglass, & Weintraub, 1983)).

Apparatus and stimuli. IBM-compatible personal computerswere programmed with SuperLab Pro Beta Version ExperimentalLab Software (1999) to display the stimuli. All characters pre-sented during the study were 11-mm high and appeared as blackagainst a white background. Four 10-s time estimation trials wereadministered, as well as four 25-s trials, four 45-s trials, and four60-s trials. These time intervals allowed us to examine time esti-mation abilities within the range of working memory (�30 s), aswell as outside working memory span (Mimura et al., 2000). Theseintervals were presented in a random sequence of 16 trials. Duringthe interval, numbers appeared every 1–3 s regardless of partici-pants’ response rate, and the digits 1 through 9 appeared in randomorder. The entire time estimation task took approximately 20 minto administer.

Procedure. As this was a prospective verbal estimation para-digm, participants were told in advance that their task would be toestimate, in seconds, how long each trial lasted. Additionally,participants were advised that numbers would appear at randomintervals in the center of the computer screen, and they wereinstructed to read the numbers aloud. These filled durations wereutilized to prevent subvocal counting. Accuracy of number readingwas recorded by the examiner, and no feedback was given toparticipants regarding the accuracy of their number reading. Eachtrial was preceded by the question “Ready?” Subsequently, thetrial was initiated by the participants’ verbal indication of readi-ness. Interval time between trials was thus controlled by the

180 RUEDA AND SCHMITTER-EDGECOMBE

participant to ensure their readiness for the next trial. At the end ofeach trial, the question “How long did that trial last?” appeared onthe computer screen. Participants verbally responded with an es-timation of time in seconds. After completing three practice trialsand expressing an understanding of the task, participants wereadministered 16 experimental trials of verbal time estimation. Nofeedback was given regarding the correctness of their verbal timeestimates.

Results

Scoring. Four scores were derived from the verbal time esti-mation task. These scores included mean time estimates, absoluteerror values (ABS), a coefficient of variance (CV), and a durationjudgment ratio. Mean time estimates represent the raw scorescalculated for each time duration interval. The ABS were calcu-lated by taking the difference between the participant’s estimateand the true interval duration, without regard to sign (Hicks,Miller, & Kinsbourne, 1976). The ABS provided an overall mea-sure of accuracy so that if a particular participant tended to error inthe direction of both over- and underestimation, the average errorwould not tend toward zero (erroneously indicating perfect timeperception; Carrasco et al., 2000). The CV was calculated bydividing the standard deviation by the mean judgment for eachparticipant (Brown, 1997), and this measure served as an indicatorof variability in time estimates (Carrasco et al., 2001). The CVscore allowed us to evaluate how consistent participants were in

their verbal estimates of the same target interval. Duration judg-ment ratios provided an index of accuracy regardless of the size ofthe standard interval and were calculated by dividing the partici-pants’ estimate by the actual time (Licht et al., 1985). For thisvariable, a score of 1.0 indicates that the participant estimated truetime exactly, whereas scores less than and greater than 1.0 indicateunderestimation and overestimation, respectively.

Statistical analysis. Mixed-model analyses of variance (ANOVAs)were run separately for each of the four variables with group(healthy YAs vs. healthy OAs vs. MCI) as the between-partici-pants factor and interval (10 s, 25 s, 45 s, and 60 s) as thewithin-participant factor. Because Mauchley’s test of sphericitywas significant for each analysis, the more conservative Geisser–Greenhouse correction was examined to adjust the degrees offreedom for the repeated measures. In all cases, the more conser-vative Geisser–Greenhouse correction led to the same results asthose obtained when sphericity was assumed, indicating no in-creased risk of Type I error. We, therefore, report the data from thestandard univariate analysis (Myers & Well, 2003). Although theMCI and the OA control groups were closely matched in educa-tion, we were unable to match the YA control group by education,given that the YAs were in the process of attaining their collegedegrees. To determine whether we needed to account for educationin analysis of the time estimation data, we examined the relation-ship between education and mean verbal time estimates for boththe YA and OA groups. These analyses revealed that, for each

Table 1Demographic Data and Mean Summary Data for the Younger Adult Controls (YA), Older AdultControls (OA), and the MCI Group

Variable or test

M SD

YA OA MCI YA OA MCI

DemographicsAge 19.83 72.54 70.96 2.35 8.84 9.79Education 14.04 16.29b 16.17 1.23 2.85 2.58Snellen chart rating 20.21 32.27b 24.57b 1.02 16.31 7.22

Intellectual abilityShipley total score 27.70c 35.61b 33.46b 3.76 4.02 4.08

Attention/speeded processingSDMT Oral total correct 74.73 49.13b 42.00a 13.31 12.51 10.44SDMT Written total correct 59.95 42.09b 36.58a 8.21 9.49 9.12Trail Making Test, Part A (time) 30.33 39.74b 44.78b 10.95 13.81 11.14

Verbal memoryRAVLT Trials 1–5 total 52.79 48.65 34.04a 7.64 9.79 8.14RAVLT Immediate Delay 11.54 9.74b 4.63a 2.52 3.05 3.12RAVLT Long Delay Recall 10.75 9.70 4.46a 3.33 2.98 3.30

Word findingBNT total correct 53.04c 56.57b 52.70 4.06 3.17 9.40

ExecutiveTrail Making Test, Part B (time) 57.22 92.09b 136.22a 12.53 30.76 50.54WAIS-III L-N Seq. 11.09 10.13 8.08a 2.84 2.97 1.84

FluencyDKEFS Animal Fluency total 21.0 20.30 15.96a 4.96 5.51 4.87DKEFS Letter Fluency total 40.13 40.43 32.38a 11.19 10.26 13.03

Note. Unless otherwise indicated, mean scores are raw scores. MCI � mild cognitive impairment; Shipley �Shipley Institute of Living Scale; SDMT � Symbol Digit Modalities Test; RAVLT � Rey Verbal Learning Test;BNT � Boston Naming Test; WAIS-III L-N Seq. � Letter–Number Sequencing subtest of the Wechsler AdultIntelligence Scale—Third Edition; DKEFS � Delis–Kaplan Executive Functioning System.a Significant difference compared with YA and OA groups, p � .05. b Significant difference compared withYA group, p � .05. c Better performance by OA group, p � .01.


group, there was little relationship between education and verbaltime estimates, rs between �.29 and .25, ps � .18. Therefore, wedid not covary for education in our analyses.

Mean score. Analyses of the raw scores revealed a signifi-cant main effect for time interval, F(3, 207) � 251.07,MSE � 77.54, p � .001, �2 � .78. As expected, mean verbaltime estimates for each group increased as the intervals to bejudged lengthened. The main effect of group, F � 1.92, �2 �.05; and the Group � Time Interval interaction, F � 1.40, �2 �.04, did not reach statistical significance, ps � .15. Consistentwith the time perception literature, which indicates that indi-viduals typically underestimate time (e.g., Craik, & Hay, 1999;Polyukhov, 1989), the verbal estimates of both the YA and OAgroups represented an underestimation of time relative to theactual intervals (see Table 2).

Absolute error score. The ANOVA of the absolute errorscores revealed a significant main effect for interval, F(3,207) � 198.84, MSE � 25.87, p � .001, �2 � .79. As can beseen in Figure 1, participants’ estimates increasingly deviatedfrom true clock time as the intervals to be estimated lengthened.There was also a significant main effect of group, F(2,69) � 12.10, MSE � 37.28, p � .001, �2 � .26. Post hoc testingusing Tukey’s honestly significant difference (HSD) revealedthat the magnitude of the discrepancy between the verbal esti-mate and the actual time interval was larger for the MCI group(M � 16.03) and the OA group (M � 15.79) compared with theYA group (M � 8.40). There was also a significant Group �Interval interaction, F(6, 207) � 221.90, MSE � 25.87, p �.001, �2 � .20. Breakdown of the interaction revealed that,although the absolute error scores of the MCI and OA groupsdiffered from the YA group at all time intervals, Fs(2,69) � 6.31, ps � .005; the group difference at the 25-s, 45-s,and 60-s intervals was disproportionately greater than that at the10-s interval (see Figure 1). These findings clearly show that,compared with the OA control group, the MCI group did not

yield significantly greater absolute error in scores across thetime intervals. However, there was a significant impact of age,with both the MCI group and OA control group exhibiting alarger magnitude of discrepancy between the verbal estimateand the actual time interval relative to the YA controls.

CV score. The analyses of the CV scores revealed that theYAs (M � 0.19), OA controls (M � 0.21), and the MCI group(M � 0.21) did not differ in response consistency, F � 1, �2 � .01.There was also no significant interaction between group and timeinterval, F � 1.51, �2 � .04, for response consistency. Thisfinding indicates that the larger absolute error rates of the OAgroups cannot be attributed to variability in their responses. Therewas a significant main effect for interval, F(3, 207) � 6.62,MSE � .01, p � .001, �2 � .09. Breakdown of the main effectrevealed greater variability in time estimates made for the 10-sinterval (M � 0.24) compared with the 25-s (M � 0.20, F � 4.99,p � .05), 45-s (M � 0.19, F � 10.35, p � .005), and 60-s(M � 0.17, F � 12.64, p � .001) intervals. This finding isconsistent with other work that has found greater variability inverbal estimates made for shorter intervals (e.g., Schmitter-Edge-combe & Rueda, 2008; Seri, Kofman, & Shay, 2002; Wearden,2003).

Duration judgment ratio score. The ANOVA on the ratioscore revealed no significant main effect of time interval,F � 2.34, �2 � .03; or Group � Time Interval interaction,F � 1.41, �2 � .04. These findings indicate that the ratio ofestimated time to clock time remained stable across time intervalsfor all three groups. In addition, as can be seen in Table 2, for theMCI group, the ratio score for the 10-s interval was numericallythe smallest. This finding runs contrary to the hypothesis that, dueto episodic memory difficulties, the MCI participants would showsmaller ratios for intervals outside of working memory, thus indi-cating a greater magnitude of underestimation. The main effect ofgroup also failed to reach significance, F � 2.09, �2 � .06, withall three groups underestimating clock time.

Table 2Time Estimation Data for the Younger Adult Controls (YA), Older Adult Controls (OA), and theMCI Group as a Function of Time Interval

Test variable

10 s 25 s 45 s 60 s

M SD M SD M SD M SD

Mean dataMCI 6.05 2.46 16.56 8.25 30.30 16.58 42.10 24.33OA 7.17 3.89 18.13 10.21 30.97 15.70 41.03 24.32YA 7.76 2.31 21.90 6.08 37.51 9.96 51.07 13.86

Absolute errorMCI 4.36 1.84 11.31 4.62 20.95 9.18 27.48 11.76OA 4.46 1.99 11.23 4.97 19.55 8.83 27.93 12.93YA 2.78 1.68 6.23 3.51 10.36 7.42 14.24 9.43

CVMCI 0.23 0.11 0.23 0.15 0.19 0.11 0.18 0.10OA 0.25 0.14 0.19 0.10 0.22 0.15 0.18 0.13YA 0.25 0.11 0.19 0.10 0.15 0.07 0.16 0.11

RatioMCI 0.61 0.25 0.66 0.33 0.67 0.37 0.70 0.41OA 0.72 0.39 0.73 0.41 0.69 0.35 0.68 0.41YA 0.78 0.23 0.88 0.24 0.83 0.22 0.85 0.23

Note. MCI � mild cognitive impairment; CV � coefficient of variance.


Correlational analyses with neuropsychological variables.We analyzed correlations to further examine the relationship be-tween time estimation and memory, as well as other possiblecognitive variables involved in time estimation ability, such asattention and speeded processing and executive functioning (seeTable 1 for a list of the measures). For use in these analyses, totalmeasures for all time estimation variables were derived by sum-ming their means for all intervals (total mean, total ABS, total CV,and total ratio). Correlational analyses were run first for each groupseparately and then for a combined MCI and OA control groups.Because of the large number of correlations conducted, we used amore conservative p value, p � .01, in reporting significant cor-relations. For the MCI participants, the only correlation of signif-icance was between the Total Mean time estimation variable andtotal correct on the SDMT Oral subtest (r � .52, p � .01). Therewere no significant correlations between time estimation variablesand neuropsychological measures for the YA group, the OA con-trol group, or the combined MCI and OA control groups.

Discussion

Consistent with previous research (Craik & Hay, 1999; Kins-bourne & Hicks, 1990; Mimura et al., 2000; Schmitter-Edgecombe& Rueda, 2008), all groups tended to underestimate the standardintervals of time. In addition, although mean estimates did notsignificantly differ between the groups, the healthy OA controlsand MCI groups significantly differed from the YA controls inmagnitude of deviation in estimates from true clock time. Theseresults highlight an effect of age on time estimation abilities, asboth OA groups performed more poorly than the YA controls.Despite the presence of episodic memory impairment in the MCIgroup, the verbal time estimation data of the MCI and OA controlgroups did not differ. In addition, no clear differences emerged forthe MCI group in time estimates for intervals that exceeded thetime frame of immediate or working memory (i.e., more than 30 sin duration) as compared with shorter intervals. These results runcontrary to those of several studies that have found a dispropor-tionate underestimation of time for intervals outside of workingmemory in other populations with episodic memory impairment(Kinsbourne & Hicks, 1990; Mimura et al., 2000; Schmitter-Edgecombe & Rueda, 2008). Correlational analyses also revealedno consistent relationships between time estimation variables andneuropsychological measures of memory, attention, and executive

functioning. Overall, these results suggest that either episodicmemory does not play a significant role in verbal time estimationsfor the intervals examined in this study or that the degree ofepisodic memory impairment in the MCI group was not impairedenough to affect time estimations abilities beyond that of thenormal aging process. To determine whether time estimation per-formances would be affected by more significant levels of episodicmemory impairment, in Experiment 2 we compared time estimatesof a group of individuals with AD to an age- and education-matched control group.

Experiment 2

The purpose of Experiment 2 was to determine whether thesignificant episodic memory impairment and cognitive deficitspresent in an AD population would result in compromisedtemporal perception beyond that found with the normal agingprocess. A secondary purpose of Experiment 2 was to correlatetime estimation variables with neuropsychological variables,specifically with measures of attention, memory, and executivefunctioning, to better understand the cognitive underpinnings oftemporal perception.

Method

Participants. Participants were 17 individuals with AD (7female, 10 male) and 17 healthy OA controls (9 female, 8 male)matched in age, t � .55, p � .58; and education, t � �.85, p � .40.Descriptive data are presented in Table 3. Participants were re-cruited through community advertisements and outreach to Whit-man and Spokane counties in Washington State between August2005 and November 2007 or were referred by community physi-cians. Participants received parking expenses and a neuropsycholog-ical research report as compensation in return for their time. Partici-pants were considered to have AD if they met research criteria of theNational Institute of Neurological and Communicative Disorders andStroke and the Alzheimer’s Disease and Related Disorders Associa-tion (NINCDS–ADRDA). Participants were excluded from the studyif they had a history of neurological disease or if they had a history ofsignificant stroke, heart attack, multiple head injuries, or substanceabuse. We tested participants’ vision using the Rosenbaum PocketScreening assessment. Although 3 participants scored at or worse than

Figure 1. Mean absolute discrepancy scores reflecting the magnitude of the timing error across intervals for themild cognitive impairment (MCI), older adult control (older), and younger adult control (younger) groups.


20/70 on this test, an examination of number reading errors revealedonly seven reading errors in total. As such, all time estimation trialswere included in the analysis.

A battery of neuropsychological tests was administered to allparticipants. As can be seen in Table 3, the AD participants

performed more poorly than their healthy OA controls on nearlyall tests, including measures of attention and speeded processing(SDMT Oral and Written subtests; Trail Making Test, Part A),verbal learning and delayed memory (RAVLT Total, ImmediateDelay Recall, and Long Delay Recall), executive functioning(Trail Making Test, Part B, and WAIS-III L-N), verbal fluency(Animal Fluency subtest from the Delis–Kaplan Executive Func-tioning System), and confrontation naming (BNT). The groupsapproached a significant difference on the DKEFS Letter Fluencysubtest, p � .06. Summary data are presented in Table 3.

Stimuli and procedure. Stimuli and procedures were identicalto those used in Experiment 1.

Results

Statistical analysis. Mixed-model ANOVAs were run for eachof the variables separately with group (AD vs. healthy OA) as thebetween-participants factor and interval (10 s, 25 s, 45 s, and 60 s)as the within-participant factor. Mauchley’s test of sphericity wassignificant for each analysis; therefore, we examined the moreconservative Geisser–Greenhouse correction from the univariate anal-yses for each variable. In all cases, the more conservative Geisser–Greenhouse correction led to the same results as those obtainedwhen sphericity was assumed, indicating no increased risk of TypeI error; therefore, we report the data from the standard univariateanalysis (Myers & Well, 2003).

Mean score. As expected, analysis of the mean score re-vealed that verbal estimates significantly increased as the inter-vals to be judged increased, F(3, 96) � 51.61, MSE � 222.41, p �.001, �2 � .62 (see Table 4). There was no significant main effect forgroup, F � 0.92, �2 � .03, or Group � Interval, interaction F � 0.24,�2 � .01.

Absolute error score. The Group � Interval ANOVA on theabsolute error scores revealed that the discrepancy between theverbal estimates and the actual time intervals was larger for the ADgroup (M � 25.97) compared with the OA controls (M � 14.15),F(1, 32) � 5.81, MSE � 204.55, p � .05, �2 � .15. A significantmain effect for interval, F(3, 96) � 43.81, MSE � 94.47, p � .001,�2 � .58, showed that participants’ absolute errors in time esti-

Table 3Demographic Data and Mean Summary Data for the Alzheimer’sDisease (AD) and Older Adult Control (OA) Groups

Variable or test

M SD

AD OA AD OA

DemographicsAge 78.06 76.65 7.14 7.75Education 14.82 15.76 3.57 2.86Snellen chart rating 49.69 32.06 45.18 13.69

Intellectual abilityShipley total score 30.71 36.12� 7.91 2.55

Attention/speeded processingSDMT Oral total correct 32.86 50.06�� 10.14 8.65SDMT Written total correct 28.86 41.47� 10.84 9.51Trail Making Test, Part A (time) 67.00 43.88� 37.30 10.60

Verbal memoryRAVLT Trials 1–5 total 21.07 45.5�� 7.20 8.25RAVLT Immediate Delay Recall 0.71 7.76�� 1.10 2.51RAVLT Long Delay Recall 0.71 7.41�� 1.16 2.58

Word findingBNT total correct 43.06 55.65�� 11.92 2.85

ExecutiveTrail Making Test, Part B (time) 215.87 95.71�� 102.19 25.45WAIS-III L-N Seq. 6.00 9.06�� 2.85 1.52

FluencyDKEFS Animal Fluency total 11.29 19.59�� 3.24 6.23DKEFS Letter Fluency total 26.32 35.53 14.08 13.37

Note. Unless otherwise indicated, mean scores are raw scores. Shipley �Shipley Institute of Living Scale; SDMT � Symbol Digit Modalities Test;RAVLT � Rey Verbal Learning Test; BNT � Boston Naming Test;WAIS-III L-N Seq. � Letter–Number Sequencing subtest of the WechslerAdult Intelligence Scale—Third Edition; DKEFS � Delis–Kaplan Exec-utive Functioning System.� p � .05. �� p � .001.

Table 4Time Estimation Data for the Alzheimer’s Disease (AD) and Older Adult Control (OA) Groupsas a Function of Time Interval

Test variable

10 s 25 s 45 s 60 s

M SD M SD M SD M SD

Mean dataAD 12.71 21.04 27.03 26.16 45.41 41.11 56.90 49.67OA 7.93 5.54 19.37 9.57 35.29 16.27 46.94 23.09

Absolute errorAD 10.08 18.62 19.71 16.98 33.56 23.20 40.54 27.18OA 4.75 3.68 10.13 4.43 17.92 8.91 23.79 13.37

CVAD 0.40 0.26 0.37 0.25 0.38 0.27 .32 0.24OA 0.33 0.26 0.22 0.11 0.24 0.13 .19 0.10

RatioAD 1.27 2.10 1.08 1.05 1.01 0.91 0.95 0.83OA 0.79 0.55 0.77 0.38 0.78 0.36 0.78 0.39

Note. OA � older adults; CV � coefficient of variance.


mations increased as the interval to be estimated increased (seeFigure 2). There was no significant Group � Interval interaction,F � 2.58, �2 � .07.

CV score. As seen in Figure 3, the ANOVA of the CV scorerevealed that the AD group (M � 0.37) demonstrated greatervariability in their responding than the OA control group(M � 0.25), F(1, 32) � 4.86, MSE � 0.03, p � .05, �2 � .13.There was also a significant main effect for interval, F(3,96) � 2.74, MSE � 0.03, p � .05, �2 � .08. Pairwise comparisonsrevealed that the groups showed greater variability in time esti-mates made for the 10-s (M � 0.36) and 45-s (M � 0.31) intervals,compared with the 60-s interval (M � 0.25; Fs � 6.12, ps � .05).Response consistency at the 25-s interval (M � 0.29) fell betweenthe other intervals but did not significantly differ from any of theintervals. There was no significant Group � Interval interaction,F � 0.40, �2 � .01.

Duration judgment ratio score. As can be seen in Table 4,there was a large degree of variability in the ratio scores computedfor the AD group. Thus, although the AD group consistentlydemonstrated a greater ratio score than the OA control group, thisdifference did not reach significance, F � 1.05, �2 � .03. Therewas also no main effect of interval, F � 0.49, �2 � .01, indicatingthat the ratio of estimated time to clock time remained stableacross time intervals, and no group by interval interaction,F � 0.43, �2 � .01.

Correlational analyses with neuropsychological variables.Correlational analyses were run for the AD group to investigate therole of memory, as well as other possible substrates of timeestimation ability, such as attention, speeded processing, and ex-ecutive functioning (see Table 2 for a list of the measures). For usein these analyses, total measures for all time estimation variableswere derived by summing means across intervals (Total Mean,Total ABS, Total CV, and Total Ratio). Given the large number ofcorrelations conducted, we used a more conservative p value, p �.01, in reporting significant correlations. For the RAVLT Imme-diate and Long Delay, 59% and 64%, respectively, of the ADparticipants were unable to recall word list items. Therefore, nocorrelations were computed between the time estimation variablesand these RAVLT measures for the AD group. Correlations wereobserved for AD participants between time on Trail Making Test,Part A, and the Total ABS (r � .78, p � .001) and Total Ratio (r �.72, p � .002) scores. For the OA controls, total correct on the

COWA correlated with Total CV (r � .66, p � .004). No othersignificant correlations between cognitive variables and time esti-mation variables were observed for the AD group (rs between�.53 and .37) or the control group (rs between �.56 and .43).

Discussion

Although no significant difference in mean verbal time esti-mates were found between the AD group and the age- and educa-tion-matched OA controls, group differences were observed on theabsolute error measure. In comparison with those of controls, theverbal estimates of the AD participants deviated more significantlyfrom true clock time, with some AD participants significantlyunderestimating time and others significantly overestimating timeso that the mean estimate closely approximated true clock time.AD participants were also found to be more variable in estimatinginterval durations of similar lengths. These findings are consistentwith those of Carrasco et al. (2000), who found both overestima-tors and underestimators in an AD group and found that the ADgroup was less accurate and more imprecise in time estimates,compared with the control group.

The results also revealed no difference in the pattern of the ADgroup’s verbal estimates for intervals of short and long duration,suggesting that episodic memory impairment does not play adominant role in the time estimation difficulties of AD partici-pants. This is further supported by the finding of both overestima-tors and underestimators in the AD population despite the presenceof significant episodic memory impairment in all AD participants.When compared with the normal aging process, we found that thepresence of AD does appear to affect temporal perception so thatone is more variable and less accurate in estimating intervals.

General Discussion

We used a prospective, verbal time estimation task with a filledinterval to investigate the role of memory in temporal perception.By having participants estimate time intervals greater than and lessthan 30 s, we were able to evaluate time estimation abilities forintervals that would (and would not) rely significantly on episodicmemory. Specifically, it has been suggested that events longer than30 s in the past are not readily retrieved from one’s immediateexperience; rather, “episodic retrieval is necessary to supplement

Absolute Error Data

0

10

20

30

40

50

10" 25" 45" 60"

Time Intervals

Me

an

Ab

solu

te

Err

or

ADControls

Figure 2. Mean absolute discrepancy scores reflecting the magnitude of the timing error across intervals for theAlzheimer’s disease (AD) and older adult control groups.


what remains within the present” (Kinsbourne & Hicks, 1990,p. 324). We hypothesized that, if intact episodic memory is im-portant for accurate time perception of durations outside of work-ing memory, then the MCI group should show greater underesti-mation of the longer intervals (45 s and 60 s) in comparison withOA controls (Baddeley & Warrington, 1970; Kinsbourne & Hicks,1990; Mimura et al., 2000; Schmitter-Edgecombe & Rueda, 2008).The findings from Experiment 1 did not support this hypothesis, asthere was no delineation in the MCI group’s performances forintervals greater than and less than 30 s. In addition, despite thepresence of episodic memory impairment, there were no differ-ences in the time estimation performances of the MCI group andthe healthy OA control group. Instead, we found that the magni-tude of deviation in estimates from true clock time was greater forboth the MCI group and healthy OA control group when comparedwith the YA group, highlighting a significant effect of normalaging on time estimation abilities. In Experiment 2, we examinedtime perception abilities in an AD group with more profoundepisodic memory and cognitive deficits than the MCI group. Sim-ilar to Experiment 1, the AD group did not demonstrate a dispro-portionate deficit in time estimates for intervals greater than 30 s.However, when compared with age- and education-matched con-trols, the AD group was found to be more variable and lessaccurate in estimating intervals. This suggests that temporal per-ception is disrupted beyond that of the normal aging process in thelater stages of dementia.

In addition to exhibiting deficits in episodic memory, our MCIgroup also performed more poorly than controls in the area ofworking memory, which potentially could have affected theirability to estimate durations that fell within the time frame ofworking memory (i.e., less than 30 s). However, despite groupdifferences in both episodic and working memory performances,the MCI group and the OA controls did not differ on the timeestimation variables. Instead, both OA groups showed a greaterdiscrepancy between their verbal estimates and actual time inter-vals than the YA group. The results of Experiment 1 suggest thatsome aspect of the aging process other than episodic memory andworking memory plays a more critical role in the time perceptionabilities of older adults. What aspect of the aging process mightthen explain the age difference found in time perception abilities?

Correlational analyses failed to reveal any consistent pattern ofrelationship between time estimation variables and administeredneuropsychological measures for the healthy OA controls, the MCIgroup, and a combined MCI and OA control group. Thus, we wereunable to identify other cognitive processing variables that mightcontribute to the age difference found in time estimation abilities.In addition to cognitive variables, other variables that might con-tribute to age differences in time perception and remain to beexplored include personality, anxiety and mood factors, an indi-vidual’s value of time, and a change in the biological clockmechanism (Church, 1984; Licht et al., 1985).

It should be noted that the time estimation literature for healthyOAs has, to date, revealed inconsistent findings, even when re-viewing studies that used a similar method. For example, studiesusing the verbal estimation method have found greater underesti-mation by OAs when compared to YAs (Craik & Hay, 1999),whereas others have found greater overestimation (Coelho et al.,2004) or greater variability in estimations (Gunstad et al., 2006).Although not easily discernable upon reviewing the literature, suchdiscrepant findings in the literature may reflect differences be-tween study methodologies, including the time estimation vari-ables reported (e.g., mean estimate, variability, error, ratio mea-sures), the use of an empty versus filled interval, and durationlength of the intervals examined.

In this study, to prevent subvocal rehearsal, participants per-formed an easy concurrent (nontemporal) information-processingtask during the time intervals. Consistent with previous research(e.g., Perbal et al., 2003), this concurrent task was not expected toaffect attentional capacity and processing resources but rather wasused to minimize counting or other monitoring strategies thatmight allow a participant to estimate an interval without usingmemory abilities. The fact that the MCI group and the healthy OAgroup did not differ in time estimation performances further sup-ports the supposition that this concurrent task had little impact onprocessing resources. Researchers in future studies may want toconsider varying the attention demanding nature of the nontempo-ral task to increase comparability to many everyday living situa-tions in which nontemporal information is processed at a consid-erable rate. Conditions that require MCI participants to work closerto their attentional capacity limits or that involve the use of greater

Coefficient of Variance Data

0.00

0.10

0.20

0.30

0.40

0.50

10" 25" 45" 60"

Time Intervals

Me

an

CV

ADControls

Figure 3. Coefficient of variance scores reflecting the variability in verbal estimates across intervals for theAlzheimer’s disease (AD) and older adult control groups.


processing resources may reveal differences in the temporal per-ception abilities of MCI participants and normal age-matchedcontrols. Such manipulations may also contribute to our under-standing of what cognitive factors may influence time perceptionabilities.

Although mean verbal time estimates did not show significantdifferences between the AD group and matched controls, the ADparticipants differed in terms of absolute error and variability. Thatis, when compared with age-matched controls, the AD partici-pants’ estimations deviated more greatly from true clock time, andthe AD group was more inconsistent in estimating intervals of thesame length. In addition, although not significantly different, theratio scores of the AD group were also found to be higher thanthose of the controls. These findings are consistent with those ofNichelli et al. (1993) and Carrasco et al. (2000) and suggest thatAD is not a benign factor in time estimation. The presence of ADmay add cognitive burden to the participant’s ability to accuratelyand precisely estimate time. However, the exact mechanism forthis remains unclear. Correlational analyses revealed no clearpattern of relationship between time estimation variables and othermeasures of cognitive functioning for the AD group. In addition,although many of the AD participants tended to underestimatetime, a small subset of AD participants were found to greatlyoverestimate time. The combination of underestimators and over-estimators made the mean data for the AD group resemble that oftheir matched controls. As such, it appears that the absolute errormeasure is a more sensitive indicator of time estimation deficits inthe AD population.

It is unclear why some participants with AD are prone tooverestimate time whereas others are not. According to the scalartiming theory (Gibbon et al., 1990), for example, underestimationmay result if the pacemaker is slowed by incoming information orif there is a defect in working memory. A defect in the referencememory could lead to overestimation (Nichelli et al., 1996). There-fore, the disruption of different processes or the same processes indifferent combinations may account for the presence of overesti-mators and underestimators. Future research should aim to subdi-vide an AD sample by those who underestimate and those whooverestimate time to examine their unique challenges to accuratetime estimation. It may also be helpful to examine AD partici-pants’ ability to make other forms of judgments (e.g., distance,weight, height) to elucidate whether AD patients demonstrate aspecific impairment in judging time.

An important strength of this study was the inclusion of bothYA and healthy OA control groups, as this also allowed forexamination of the aging process on time estimation. In addition,we explored the progression of the disease process and memoryimpairment by including groups with MCI and with diagnosedAD. However, our study has some specific limitations. Most of ourparticipants had a high level of education, which may limit thegeneralizability of our results. In addition, it currently remainsunknown whether all participants in the MCI group will progressto AD. Moreover, our longest interval duration was 60 s in length,whereas a longer interval may generalize more readily to sometasks in everyday living. Furthermore, the paradigm used in thisstudy was concerned with time components involved in the de-tecting, storing, and recalling of temporal intervals. Given thehypothesized importance of the ability to estimate time in dailyactivities and the finding of impaired time estimates for healthy

OAs, further research should continue to examine the substrates ofthis ability. Future research would also add to the current literatureby expanding the intervals of time duration to more closely ap-proximate specific activities of daily life (i.e., �60 s) and byinvestigating the relationship between performance-based and in-formant-based measures of daily living and variables of timeestimation.

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Received March 14, 2008Revision received September 9, 2008

Accepted September 23, 2008 �


time estimation abilities in mild cognitive impairment and alzheimer's disease

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