The stability of time estimation in older adults.

INT’L. J. AGING AND HUMAN DEVELOPMENT, Vol. 78(3) 259-276, 2014

THE STABILITY OF TIME ESTIMATION IN OLDER ADULTS

JONATHAN W. ANDERSON Eastern Washington University ALICIA RUEDA VA Northern California Health Care System MAUREEN SCHMITTER-EDGECOMBE Washington State University

ABSTRACT

The ability to correctly estimate time is important for many daily activities, such as cooking and driving. This study investigated the stability time estimation in healthy older adults and compared them to healthy younger adults. Participants were tested and retested across the duration of 1 year. Using a prospective paradigm, verbal estimates were provided for intervals of 10, 25, 45, and 60 seconds. Although the older adults demonstrated a greater magnitude of error in their time estimates than younger adults, their time estimates remained stable across the 1-year duration. This suggests that instability in time estimates across two time points is unlikely to account for the discrepant task findings in the aging and verbal time estimation literature.

The ability to correctly judge a time duration is important for multiple activities of daily living, such as cooking and taking medication. For example, when taking time sensitive medications, individual must estimate how long it has 259 Ó 2014, Baywood Publishing Co., Inc. doi: http://dx.doi.org/10.2190/AG.78.3.c http://baywood.com

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been before they take their next dose. Perhaps because of its importance, researchers continue to study the concept of time (e.g., Anderson & SchmitterEdgecombe, 2011; Block, Hancock, & Zakay, 2010). Although research has investigated time estimation in older adults (e.g., Espinosa-Fernández, Miró, Cano, & Buela-Casal, 2003; Fraisse, 1984; Schroots & Birren, 1990), no study could be found that evaluated the stability of time estimates in older adults. To investigate time estimation, both retrospective and prospective methodologies have been used (Hicks, 1992). As first noted by James (1890), the distinction between the two methods depends on whether participants are cued before (prospective paradigm) or after (retrospective paradigm) a task to estimate a time interval (Hicks, Miller, & Kinsbourne, 1976). A meta-analysis (Block & Zakay, 1997) investigating both paradigms found prospective time judgments to be more accurate and less variable than retrospective judgments. In addition, prospective methods usually use a dual-task procedure, which involves an individual simultaneously attending to two tasks: a temporal task and a nontemporal task (Zakay & Block, 2004). Past research has argued that this dual-task procedure more closely parallels many real-life tasks (Taatgen, van Rijn, & Anderson, 2007). Researchers investigating prospective time estimations have utilized three primary methods (Underwood, 1966): verbal estimates, production, and reproduction. The verbal estimation methodology exposes the participant to a duration interval and requires that they subsequently provide a numerical label for the exposed duration (Licht, Morganti, Nehrke, & Heiman, 1985). In the production method, participants are instructed to generate a specific duration of time (Licht et al., 1985). In the reproduction method, a given duration is demonstrated and individuals reproduce the given duration or indicate when the same amount of time has passed (Buhusi & Meck, 2005; Shaw & Aggleton, 1994). It has been argued that verbal estimates are the preferable procedure to obtain duration judgments (Kinsbourne, 2000). Although the reproduction method results in normal distribution around the criterion duration (Buhusi & Meck, 2005), it does not rely on conventional units of time (e.g., seconds; Bindra & Waksberg, 1956; Block, Zakay, & Hancock, 1998; Cahoon, 1969) and may not be sensitive enough to reveal individual differences (Block et al., 1998). In addition, while the production method is thought to provide an avenue to assess “cognitive tempo” (Kinsbourne, 2000), it is susceptible to confounding variables, such as impatience (Block et al., 1998) and a propensity to terminate a task early (Block, Zakay, & Hancock, 1999; Kinsbourne, 2000), as is the reproduction method. One theory to explain prospective time judgments is the executive-gate model (EGM; Block et al., 2010). Evolving from the attentional-gate model (Zakay & Block, 1996, 1997; Zakay, Block, & Tsal, 1999), the EGM embraces

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a pulsemaker-accumulator model (for a thorough review, please see Balci, Meck, Moore, & Brunner, 2009) but emphasizes the importance of the central executive (Dutke, 2005), which is a limited capacity attentional system (Baddeley, 2000, 2002). Specifically, this theory proposes an accumulator that collects pulses generated by a pacemaker and is then compared to standard durations held in reference memory (Jones & Wearden, 2003; Nichelli, Venneri, Molinari, Tavani, & Grafman, 1993). However, this model proposes that the more individuals allocate central executive resources to nontemporal information, the less pulses are accumulated. The comparison between these accumulated pulses and stored standard durations then determines a participant’s time estimate (e.g., overestimate, underestimate). Past research investigating the effect of aging on duration judgments has found that verbal estimates become less accurate as individuals get older (Espinosa-Fernández et al., 2003; Rueda & Schmitter-Edgecombe, 2009; Schroots & Birren, 1990). However, research has been contradictory regarding how age impacts accuracy of time estimates. Specifically, older adults have been found to provide a greater degree of underestimation (Craik & Hay, 1999), overestimation (Coehlo et al., 2004), greater magnitude of errors in timing regardless of direction (i.e., absolute error; Rueda & SchmitterEdgecombe, 2009), or greater timing variability compared to younger adults (Gunstad, Cohen, Paul, Luyster, & Gordon, 2006, but see Carrasco, Bernal, & Redolat, 2001). Discrepant findings in the literature may reflect differences between study methodologies, including the time estimation variables reported (e.g., mean estimate, variability, error, ratio measures), the use of a filled interval (e.g., completing calculations; Brown, 1997) versus an empty interval (e.g., self-counting seconds; Polyukhov, 1989), and duration length of the intervals examined. It is also possible that older adults may show less stability than younger adults in time estimates made at different time points (e.g., estimating longer at one time point and shorter at another) contributing to discrepancies between studies. In a study from our laboratory investigating recovery of time estimation ability following traumatic brain injury (Anderson & Schmitter-Edgecombe, 2011), we assessed time judgments across 1 year in neurologically intact adults who were less than 55 years of age. We used a prospective paradigm in which participants provided verbal time estimates for 10 s, 25 s, 45 s, and 60 s time intervals. All participants were tested approximately 1 year after initial testing. We found that neurologically intact adults underestimated each time interval and the magnitude of underestimation remained stable across the testing sessions for each time interval. In addition, the response consistency for each of the time intervals remained constant across testing sessions. This suggests that for neurologically normal younger and middle-aged adults, time


estimation is a relatively stable ability. However, no study could be found that looked at the stability of older adults’ time estimation abilities. Using a procedure similar to the Anderson and Schmitter-Edgecombe study (2011), older adults in the present study completed a prospective time estimation paradigm and provided verbal estimates for filled intervals. In the present study, we tested the older adults’ time estimation abilities across a year’s duration against a group of neurologically normal younger adults (less than 30 years old). Based on past research (Craik & Hay, 1999; Perbal, Droit-Volet, Isingrini, & Pouthas, 2002), we expect that the older adult group will be less accurate estimating time than younger adults across all time intervals. We were especially interested in whether time estimates would remain stable in the healthy older adults in a fashion similar to what has been found for younger and middle-aged adults (Anderson & Schmitter-Edgecombe, 2011). METHODS Participants Twenty neurologically normal older adults (9 female; 11 male) and 20 neurologically normal younger adults (7 female; 13 male) participated in this study. The 20 younger adult participants’ age range was 16 to 29 (M = 19.95, SD = 3.40). The younger adult participants were recruited from the community through the use of advertisements and received parking expenses plus monetary compensation in return for their time. The younger adults had no medical history that impacted neurological functioning and an absence of depression as measured by the Depression Anxiety Stress Scales (DASS; Lovibond & Lovibond, 1995; also see Crawford & Henry, 2003). The older adult participants were recruited via community advertisements and outreach to Whitman and Spokane counties in Washington State, and received parking expenses and a neuropsychological research report as compensation. The age of the 20 older adult participants was from 58 to 90 (M = 73.90, SD = 8.16). Participants in this study were part of a larger study and completed a battery of experimental and standardized neuropsychological tests lasting approximately 2 to 3 hours during the initial visit (see Schmitter-Edgecombe, Woo, & Greeley, 2009) and approximately 1 hour during the follow-up visit. Participants underwent an initial screening interview via phone. The older adults had: (a) no medical history such as significant stroke, heart attack, multiple head injuries, substance abuse, or neurologic disorder that would affect cognitive performance; (b) a Clinical Dementia Rating (CDR) score of 0 (Hughes, Berg, Danzinger, Coben, & Martin, 1982; Morris, 1993; Morris, McKeel, & Storandt, 1991); and (c) a score on the Telephone Interview of Cognitive Status (TICS) that fell within normal limits.


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Upon testing in the laboratory, all of the control participants met exclusion criteria for Mild Cognitive Impairment (MCI; Petersen, Doody, Kurz, Mohs, Morris, Rabins, et al., 2001) and dementia (American Psychiatric Association, 2000), had an absence of severe depression as assessed by the Geriatric Depression Scale (Yesavage, Brink, Rose, Lum, Huang, Adey, et al., 1983), and scored within normal limits on the Mini Mental State Exam (MMSE; Measso, Cavarzeran, Zappalà, & Lebowitz, 1993). At retest, all older adult participants reported no significant change in cognitive status or health during the past year. Apparatus and Stimuli Personal IBM computers were programmed with SuperLab Pro Beta Version Experimental Lab Software (1999) to display the stimuli. All characters were presented in 64 pt Times New Roman, black bold font on a white background. The same time estimation task was used at test and retest. Procedure In this prospective verbal estimation paradigm, participants were instructed in advance that the goal of the task was to estimate how long they perceived trials to last. Participants were told that the numbers 1 through 9 would appear non-sequentially at random intervals (i.e., 1 to 3 seconds) in the center of the computer screen, and they were to read the numbers aloud as they appeared on the computer screen. All participants received the same sequence of numbers to read for each trial (see Appendix 1). This simple type of dual-task procedure has commonly been used in research on time perception (Rattat & Droit-Volet, 2012) to decrease use of counting strategies and other monitoring strategies that would allow a participant to estimate an interval without using memory abilities (Williams, 1988). Errors in number reading were made on less than .05% of the time estimation trials. All individuals participated in three practice trials to ensure understanding of the task requirements. After indicating understanding, the experiment continued to the experimental trials. If not, the experimenter reviewed the instructions and provided additional practice trials. Before each trial, the question “Ready?” was presented on the computer screen to ensure the participant was prepared to proceed. The experimental task consisted of four time intervals (i.e., 10 s, 25 s, 45 s, and 60 s) for a total of 16 experimental trials. The same time interval was not presented more than two times in a row. At the end of each time interval, the question, “How long did that trial last?” appeared on the computer screen. Participants provided estimates in seconds regarding how long they perceived the time interval lasted.


The examiner recorded the participant’s time estimate without feedback regarding the accuracy of their duration judgment. The time estimation task took approximately 20 minutes to administer. Retest was approximately 1 year after initial test (YA: Mdays = 378.70, SD = 23.01, range = 356-451; OA: Mdays = 414.65, SD = 39.13, range = 351-505). This project was reviewed and approved by the Washington State University Institutional Review Board and the Spokane Institutional Review Board. RESULTS Time Estimation Scoring

Four scores were derived from the time estimation task: (a) raw scores, (b) absolute error scores, (c) duration-judgment ratio scores, and (d) coefficient of variation. Unlike the raw score, the absolute error score reflects the magnitude of the participant’s errors in timing regardless of direction (i.e., overand underestimation). Higher scores reflect greater error in timing estimates. The duration-judgment ratio score is computed by dividing the participants’ estimate by actual time (Licht et al., 1985). This provides a measure of the direction and magnitude of the error without regard for the size of the time interval. This statistic does not allow participants who over- and underestimate time intervals to average to incorrectly suggest accurate time perception (Carrasco et al., 2001). A score of 1.00 equals a perfect estimation, while a score above 1.00 reflects overestimation, and a score below 1.00 reflects underestimation. With this statistic, a 1-second error for a 10-second interval is equivalent to a 6-second error on a 60-second trial. The coefficient of variation score was computed by dividing the standard deviation by the mean judgment (Brown, 1997) and was used to assess variability in verbal estimates of the same time interval (Carrasco et al., 2001). Statistical Analysis

Mixed-model analyses of variance (ANOVA) were run separately for each of the four variables (raw scores, absolute error, duration-judgment ratio scores, and coefficient of variation scores) with group (older adults and younger adults) as the between subjects factor and time interval (10 s, 25 s, 45 s, and 60 s) and time of testing (test and retest) as the within-subjects factors. For all analyses in which the condition of sphericity was not met, the Greenhouse-Geisser correction was used to make the F test more conservative (Tabachnick & Fidell, 2001). The Greenhouse-Geisser adjustment factor was still significant in all


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cases, suggesting no increased risk of type I error; therefore, we report the standard univariate analysis data (Myers & Well, 2003). Raw scores: The group by time interval by testing time mixed model ANOVA on the raw scores revealed that both groups provided increasingly larger time duration estimates as the length of time to be estimated increased, F(3, 114) = 254.11, MSE = 199.58, p < .001, h2 = .87 (see Figure 1). There were no other significant main effects, Fs < 1, and the two-way interactions, Fs < 3.0, and three-way interaction, F < 1, were not significant. Consistent with prior finding showing that individuals typically underestimate time (e.g., Craik, & Hay, 1999; Perbal et al., 2002), the verbal estimates of both the younger and older adult groups represented an underestimation of time relative to the actual intervals. Absolute error scores: The mixed model ANOVA on the absolute error scores revealed a significant main effect for time interval, F(3, 114) = 47.83, MSE = 130.18, p < .001, h2 = .56 (see Figure 2). The analysis also revealed that the older adults (M = 15.04) had a greater magnitude of error in timing than younger adults (M = 9.16), F(1, 38) = 10.29, MSE = 269.82, p < .01, h2 = .21.

Figure 1. Mean raw score for time intervals at initial test and retest for the younger adults (YA) and older adults (OA). Note: OA = Older Adults; YA = Young Adults. Error bars represent standard error.


Figure 2. Mean absolute error scores for time intervals at initial test and retest for the younger adults (YA) and older adults (OA). Note: OA = Older Adults; YA = Young Adults. Error bars represent standard error.

The main effect of time of testing, F = 1.8, was not significant, nor were any of the two-way interactions, Fs < 1.8, or the three-way interaction, F = 1.8. These findings indicate that the older adult group exhibited a larger magnitude of discrepancy between verbal estimates and the actual time intervals relative to the younger adults at both test and retest. Because no significant differences were found between groups for raw verbal time estimates, this suggests that some older adult participants’ underestimated time while others overestimated time causing the raw estimate to more closely approximate true clock time. Duration-judgment ratio score: The mixed model ANOVA on the durationjudgment ratio scores revealed that the main effects of time interval, group, and time of testing were not significant, Fs < 1. The two-way interactions between time of testing and group, F = 1.2, and time of testing and time interval, F = 1.2, were also not significant. A significant two-way interaction emerged between time interval and group, F(3, 114) = 4.57, p = .01, h2 = .11, that was modified by a significant three-way interaction between group, interval, and time of testing, F(3, 114) = 4.71, p = .01, h2 = .11 (see Figure 3). To break down the


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Figure 3. Mean duration-judgment ratio scores for time intervals at test and 12-month retest for the older adults (OA) and younger adults (YA). Note: OA = Older Adults; YA = Young Adults. Error bars represent standard error.

three-way interaction, separate time of testing by time interval ANOVAs were conducted for the older adult and younger adult groups. For older adults, neither the main effect for time interval, F = 2.5, nor time of testing, F < 1, reached significance. The two-way interaction between time of testing and time interval also did not reach significance, F < 1. This finding indicates that the ratio of estimated time to clock time remained stable across time intervals and time of testing for the older adult group. The ANOVA for the younger adults revealed no significant main effects for time of testing, F < 1, or time interval, F = 2.2. However, a significant time of testing by time interval interaction emerged, F(3, 57) = 8.12, MSE = .01, p < .001, h2 = .30. In comparison to retest, the younger adults demonstrated a greater magnitude of underestimation during the initial testing session for the 10 s interval (t = –3.02, p < .01). However, there was not a statistical difference in the direction and magnitude of error in time estimates for the 25 s, 45 s, and 60 s time intervals (ts < 1). This suggests that the younger adult group statistically adjusted their 10 s interval to be more accurate during retest, and their time estimates remained statistically similar for the other time intervals across testing sessions.


Coefficient of variation score: The mixed model ANOVA of the coefficient of variation score revealed that response consistency was similar between groups, F < 1, across different time intervals, F = 2.8, and across testing sessions, F < 1. This finding indicates that the larger absolute error rates of the older adult groups cannot be attributed to variability in their responses. There were also no significant two-way interactions, F < 1.6. A significant three-way interaction emerged between group, time of testing, and time interval, F(3, 114) = 4.04, p < .05, h2 = .10 (see Figure 4). To break down the three-way interaction, interval by testing session ANOVAs were conducted separately for the younger adult and older adult groups. The ANOVA for the older adults revealed no significant main effects, Fs < 2.3, or interaction, F = 1.7. These findings indicate that the response consistency of the older adult group remained statistically consistent across time intervals and testing sessions. In the younger group, although the main effects of time interval, F = 2.4, and time of testing, F < 1, were not statistically significant, a time of testing by time interval interaction emerged, F(3, 57) = 4.80, MSE = 0.01, p < .01,

Figure 4. Mean coefficient of variation scores for time intervals at test and 12-month retest for the older adults (OA) and younger adults (YA). Note: OA = Older Adults; YA = Young Adults. Error bars represent standard error.


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h2 = .20. The younger participants demonstrated greater variability of time estimates at the 10 s interval at retest (M = .23, SD = .18) compared to initial testing, M = .14, SD = .09; t(19) = –3.03, p < .01. Response consistency across the 25 s, 45 s, and 60 s time intervals were statistically similar for the younger adult group, ts < 1.2. This finding is consistent with other research, which has found greater variability in verbal estimates made for shorter intervals (e.g., Schmitter-Edgecombe & Rueda, 2008; Wearden, 2003). Supplemental Analysis To further assess stability of time in older adults between testing sessions, Spearman’s rho correlations were conducted between test and retest raw time estimates for each time interval. The older adults’ time estimates at test and retest showed statistically significant positive correlations at the 25 s (r = .51, p = .02), 45 s (r = .49, p = .03), and 60 s (r = .55, p = .02) time intervals. The correlation between test and retest at the 10 s interval did not reach significance, r = .33. This finding is not surprising giving past research that has found greater variability in time estimates of shorter intervals (e.g., Wearden, 2003). DISCUSSION The purpose of the present study was to investigate the stability of time estimation in neurological normal older adults. While past research suggests that verbal estimates are less accurate in older adults, there have been contradictory findings regarding the underlying cause for the poorer verbal time estimates. We were interested in whether these discrepant findings might reflect poorer stability in time estimation made at different time points by older adults. Consistent with prior research (Craik & Hay, 1999; Kinsbourne & Hicks, 1990; Rueda & Schmitter-Edgecombe, 2009), we found that both the younger and older adult groups tended to underestimate the standard intervals of time. In addition, although mean estimates did not significantly differ between the age groups, similar to a previous study from our laboratory (Rueda & Schmitter-Edgecombe, 2009), older adults in the present study demonstrated a greater magnitude of error in time estimates regardless of direction (i.e., under- or overestimation) than younger adults. Of most interest for this study was the finding that the older adults exhibited stability in their time estimates across the test and re-test interval of 1 year. Past research has found older adults to underestimate (Craik & Hay, 1999) and overestimate time intervals (Coehlo, Ferreira, Dias, Sampiao, Martins, &


Castro-Caldas, 2004), as well as provide greater magnitude of errors in time estimates (Rueda & Schmitter-Edgecombe, 2009) and greater variability than younger adults (Gunstad et al., 2006). One idea is that older adults may have more variability in the time estimates they provide across different time points. However, similar to a prior study with neurologically intact adults less than 55 years of age (Anderson & Schmitter-Edgecombe, 2011), we found that older adults provided statistically similar verbal estimates after 1 year as they did during initial testing. Correlational analyses also revealed that older adults who provide longer time estimates during the initial testing session were also more likely to provide longer time estimates during retest. This suggests that older adults showed stability in their estimates of conventional units of time (e.g., seconds) that were made at two different time points. Accuracy of verbal estimates is thought to depend on how subjective time relates to experienced time (Wearden, 2003). The executive-gate model (EGM; Block et al., 2010) proposes that a pacemaker generates pulses that pass through a gate, controlled by the central executive, and are captured by an accumulator. These accumulated pulses are then compared with learned time labels (Nichelli et al., 1993; Perbal, Coullet, Azouvi, & Pouthas, 2003). While we cannot rule out that older adults may have compensated for some experienced changes in time estimation abilities (e.g., adjusted attentional resources, adjusted time labels, etc.), the current data suggests that instability in time estimates across two points in time is unlikely to account for the discrepant task findings in the aging and verbal time estimation literature. It appears that older adults who provided longer time estimates during initial testing continued to estimate time in the same way at follow-up. This study has several limitations. Our study consisted predominately of Caucasian, highly educated individuals who reside in the Pacific Northwest. Therefore, results from the present study may not generalize well outside this specific demographic. In addition, participants were followed across a 1-year timeframe. Future research is encouraged to follow older adults across both shorter retest and a longer time period. Furthermore, the dual-task procedure used in the present study can be considered a simple task as participants read numbers aloud that were presented on a computer screen. Future studies should consider using methods of varying degree of cognitive load to assess the impact of age on time estimation (e.g., Perbal et al., 2002). Past research (Craik & Hay, 1999) has suggested that individuals typically underestimate time more when the intervening interval is filled with a resource-demanding task (e.g., completing calculations; Brown, 1997). In addition, the current research used only a verbal estimate method (Wearden, 2003; Zakay & Block, 1997). Future research is encouraged to utilize multiple time estimation


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methods to assess prospective time estimations. Given that verbal estimates and production may tap similar processes (Block et al., 1998), it is recommended that future research utilize one of those methods in combination with the reproduction method. It has been argued that the reproduction procedure is less influenced by the presence of an internal clock and more heavily influenced by cognitive functions (Ulbrich, Churan, Fink, & Wittmann, 2007), specifically working memory (Baudouin, Vanneste, Isingrini, & Pouthas, 2006; Craik & Hay, 1999). In conclusion, the present study used a prospective verbal time estimation paradigm to investigate the stability of time estimation in older adults across two time points that were approximately 1 year apart. Past research has found older adults to be less accurate estimating time than younger adults (e.g., Craik & Hay, 1999; Perbal et al., 2002; Rueda & Schmitter-Edgecombe, 2009); however, the nature of this age-related differences is unclear. In the present study, we found that verbal estimates between age groups were similar across two time points, although older adults provided a greater magnitude of error in their time estimates than younger adults. In addition, older adults demonstrated stability in their verbal estimates across a 1-year timeframe. That is, older adults who provided longer time estimates at initial testing were also likely to provide longer estimates at re-test. This suggests that, similar to young and middle-aged populations, time estimation is a stable ability in older adults and that instability in time estimates across two time points is unlikely to account for the discrepant task findings in the aging and verbal time estimation literature.

APPENDIX 1: Time Estimation Task Instructions: During this task, I will be asking you to estimate how long you think a trial has lasted. The beginning of each trial will be signaled by the question “Ready?” Once you indicate that you are ready to proceed, I will begin the trial. During each trial, you will be required to read numbers aloud. The numbers will appear at random intervals in the center of the computer screen. Please say each number aloud so I can record the accuracy of your response. At the end of each trial, the question “How many seconds did that trial take?” will appear on the computer screen. When this question appears, you are to estimate for me, in seconds, how long you think the trial took. Do you understand your task? We will now go through several practice trials to help you get comfortable with the task.


PRACTICE TRIALS

Correct? 1 = yes, 0 = no

Actual Time

452125437299622645

50

9756263225494

35

834664

15

EXPERIMENTAL TRIALS 199168293

25

14346989585978

45

15363447417994446373

60

5942

10

8319

10

61427411977894377953

60

524694993225473

45

258554176

25

2177

10

251657875292224

45

493956995

25

7883944396667613128

60

89342267

25

3185361977195928683

60

193

10

765293862299741

45

Estimated Time


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