Explicit and implicit timing in older adults: Dissociable associations with age and cognitive decline

Mariagrazia Capizzi; Antonino Visalli; Alessio Faralli; Giovanna Mioni

doi:10.1371/journal.pone.0264999

. 2022 Mar 16;17(3):e0264999. doi: 10.1371/journal.pone.0264999

Explicit and implicit timing in older adults: Dissociable associations with age and cognitive decline

Mariagrazia Capizzi ^1,^*,^#, Antonino Visalli ^2,^#, Alessio Faralli ³, Giovanna Mioni ^4,^*

Editor: Bradley R King⁵

PMCID: PMC8926191 PMID: 35294473

Abstract

This study aimed to test two common explanations for the general finding of age-related changes in the performance of timing tasks within the millisecond-to-second range intervals. The first explanation is that older adults have a real difficulty in temporal processing as compared to younger adults. The second explanation is that older adults perform poorly on timing tasks because of their reduced cognitive control functions. These explanations have been mostly contrasted in explicit timing tasks that overtly require participants to process interval durations. Fewer studies have instead focused on implicit timing tasks, where no explicit instructions to process time are provided. Moreover, the investigation of both explicit and implicit timing in older adults has been restricted so far to healthy older participants. Here, a large sample (N = 85) comprising not only healthy but also pathological older adults completed explicit (time bisection) and implicit (foreperiod) timing tasks within a single session. Participants’ age and cognitive decline, measured with the Mini-Mental State Examination (MMSE), were used as continuous variables to explain performance on explicit and implicit timing tasks. Results for the explicit timing task showed a flatter psychometric curve with increasing age or decreasing MMSE scores, pointing to a deficit at the level of cognitive control functions rather than of temporal processing. By contrast, for the implicit timing task, a decrease in the MMSE scores was associated with a reduced foreperiod effect, an index of implicit time processing. Overall, these findings extend previous studies on explicit and implicit timing in healthy aged samples by dissociating between age and cognitive decline (in the normal-to-pathological continuum) in older adults.

Introduction

Age-related changes in the performance of timing tasks within the millisecond-to-second range intervals are commonly reported [1–6]. Performance of older adults on timing tasks has been mostly tested using explicit timing tasks, which overtly inform participants about the temporal nature of the task [7]. For instance, in the time bisection task (the one also used in the present study as illustrated in Fig 1A), participants first learn a “short standard” and a “long standard” duration and then classify intermediate durations as being more similar to the short or the long standard [8, 9].

Fig 1 — **(A) Schematic view of the explicit (time bisection) task.** In a first training phase, participants memorized a “short standard” (480 ms) and a “long standard” duration (1920 ms). In a subsequent testing phase, they indicated whether the interval duration elapsing from the onset of the thicker circle to the onset of the cross was closer to the previously memorized “short standard” or “long standard”. Responses were given by pressing two response keys on the computer keyboard. I.T.I. stands for Inter-Trial-Interval. **(B) Patterns of behavioural performance in the time bisection task.** A common way of looking at performance on the time bisection task is to construct a psychometric curve by plotting the proportion of trials in which participants respond “long” as function of interval duration. The black psychometric curve depicts an ideal performance in which participants never respond “long” to the short standard duration, whereas they always respond “long” to the long standard. At the intermediate duration (1200 ms), they are equally likely to respond short or long. Relative to the reference, a shift of the psychometric curve towards the left (light blue line) or the right (blue line) means over- or under-estimation, respectively. Conversely, a flatter psychometric curve (pink line) is indicative of a poorer temporal performance (i.e., participants tend to respond “short” to long durations and “long” to short durations). Although it is difficult to completely isolate clock (i.e., “temporal processing”) from memory/decision stages (i.e., “cognitive control functions”), it makes sense to hypothesize that age-related changes in clock speed should be mainly expressed by a rightward shift of the psychometric curve (i.e., a slower clock in older adults). By contrast, a flatter psychometric curve in older adults could be likely attributed to a deficit in the additional cognitive control functions (e.g., working memory) thought to be required to correctly perform on the time bisection task.

According to influential pacemaker-based models of time perception [10], performance on the time bisection task relies on the functioning of both internal “clock” and memory/decision stages. The internal clock is conceived of as a pacemaker-counter device that emits pulses accumulated in a counter (i.e., the greater the number of pulses, the longer the estimation of the interval duration). The pulses stored into the accumulator are then transferred to working memory; an additional decision stage finally compares the pulses accumulated in working memory to those already stored in a reference memory system (i.e., the short and long standards for the time bisection task) to identify an appropriate outcome.

Building up on pacemaker-based models of time, it is thus debated whether the age-related changes observed in explicit timing tasks can be genuinely attributed to a dysfunction at the level of the clock stage, hereafter referred to as “temporal processing” (e.g., a slower clock accumulating less pulses in older than younger adults), or should be rather considered as a secondary deficit at the level of memory/decision stages, hereafter referred to as “cognitive control functions” (e.g., a noisier memory representation of the short and long standards in older adults; see Fig 1B for a depiction of the behavioural patterns usually obtained in the time bisection task for both ideal and possible age-related performances). Support for a secondary cognitive deficit of older adults in timing tasks also comes from studies of pathological aged populations such as, for example, patients with Alzheimer’s disease (AD). Relative to age-matched controls, AD patients–who are known to present severe cognitive deficits [11, 12]–perform poorly on explicit timing tasks (see [13, 14] for reviews). As will become clear below, the main goal of the present study on older adults was to elucidate the role of age and cognitive decline in explicit and implicit timing tasks.

Explicit requirements to memorize or to pay attention to interval durations are instead absent in the context of implicit timing tasks, hence, making these tasks less demanding with respect to explicit timing ones. As illustration, consider a warned reaction time (RT) task (like the task used here as depicted in Fig 2A) that just entails a fast response to a target stimulus. The time interval between the warning signal (thicker circle) and the target (cross symbol), otherwise known as “foreperiod”, could assume one of different durations with equal a-priori probability at the beginning of each trial. In this kind of tasks, the probability that the target will occur at the longest foreperiods grows up with the passage of time, as formally described by the hazard function, i.e., the conditional probability that an event will occur given it has not yet occurred ([15–17]; see Fig 2B, for an illustration of the hazard rate). Participants are sensitive to the hazard function, a conclusion supported by their shorter RTs for the longer foreperiod trials, the so-called “variable foreperiod effect”, hereafter simply referred to as the foreperiod effect [18–21], which is represented by the back line in Fig 2B. The behavioural benefit afforded by elapsing time during longer foreperiod trials is interpreted as evidence for an implicit processing of time, since the foreperiod effect occurs even if participants are not explicitly instructed to pay attention to time or are uninformed about the used interval durations.

Fig 2 — **(A) Schematic view of the implicit (foreperiod) task.** The foreperiod task comprised the same stimulus material and general procedure of the time bisection task, but differed in the specific task instructions given to participants. Specifically, the participants’ task was to press the spacebar as quickly as possible whenever the cross appeared inside the circle. Thus, no instructions to memorize interval durations were provided for the implicit timing task. The interval duration (or foreperiod, FP) separating the thicker circle from the cross could randomly assume one of seven values with equal a-priori probability. I.T.I. stands for Inter-Trial-Interval. **(B) Patterns of behavioural performance in the foreperiod task.** Performance on the foreperiod task is plotted in terms of reaction time (RT) as function of interval duration. For illustrative purposes, the depicted log-RTs are in arbitrary units (a.u.). The black line shows the reference finding typically observed in the foreperiod task, with shorter RTs at longer interval durations (i.e., the foreperiod effect). The foreperiod effect is formally described by the hazard function (represented in grey), that is, the increasing conditional probability that an event will occur given it has not yet occurred. The size of the foreperiod effect is taken as evidence for an implicit processing of time, considering that participants are not explicitly instructed to memorize or use interval durations. Relative to the reference, the blue line depicts the presence of a generalized RT slowing that, however, does not imply a deficit in the processing of implicit timing, as indexed by the size of the foreperiod effect. By contrast, the light blue line depicts a smaller foreperiod effect, which is indicative of an impaired use of implicit timing (i.e., no benefit afforded by the passage of time). Assuming that implicit timing tasks rely less on cognitive resources than explicit timing tasks leads to the prediction that processing of implicit timing should be spared in older adults as compared to processing of (more demanding) explicit timing (i.e., a pattern more consistent with the blue, rather than the light blue, line).

To our knowledge, explicit and implicit timing have been thus far compared in healthy older adults only [2, 22]. As an example, Droit-Volet and colleagues [2] devised a between-participants design in which one group of older adults and one group of younger adults performed an explicit timing task (i.e., temporal generalization task), whereas different groups of older and younger adults were engaged in an implicit timing task (i.e., a variant of the foreperiod task; [23]). Participants’ performances on explicit and implicit timing tasks were also correlated to cognitive scores derived from neuropsychological tests. Results showed that older adults were as accurate as younger adults in both explicit and implicit timing tasks; however, older participants were more variable than younger ones in the explicit timing task and their performance was explained by lower attentional capacity rather than age. By contrast, older adults showed a greater reliance on the hazard function than younger adults, a result that was significantly associated with age but not cognitive scores.

Collectively, the correlational findings by Droit-Volet and colleagues [2] speak to different possible influences of age and cognitive functions in explicit and implicit timing tasks. In the present study, we aimed to advance our knowledge about performance of older adults on explicit and implicit timing tasks by considering not only healthy but also pathological older participants. This allowed capturing differences in explicit and implicit timing tasks linked to age and pathological cognitive decline, two variables that, although often correlated, are not systematically associated [24]. To this end, healthy older adults and individuals diagnosed with either Mild Cognitive Impairment (MCI) or dementia completed explicit (time bisection) and implicit (foreperiod) timing tasks in a single session. The Mini-Mental State Examination (MMSE; [25]) was used as an index of cognitive decline. The MMSE represents, indeed, one of the most routinely used screening tools in clinical practice, even if it provides only a generic assessment of cognitive decline. Of note, although unhealthy participants received a formal diagnosis of (mild-) cognitive dementia (see the Methods Section), hereafter any reference to cognitive impairment/decline in our sample is related to the MMSE score. Participants’ ages and MMSE scores were considered as continuous predictors of performance in the analyses.

Concerning implicit timing, we expected to find a significant association of performance on the foreperiod task with age rather than MMSE scores, replicating previous research [2]. As concerns explicit timing, if the poor performance of older participants on the time bisection task depends on a deficit in temporal processing, this should be reflected by a (rightward) shift of the psychometric curve. According to previous literature reporting a slowing down of the pacemaker with age (see [26]), we predicted to find a significant association between the rightward shift of the curve and age. If, conversely, changes in the performance of older participants depend on their reduced cognitive control functions, this should translate into a flatter psychometric curve. Therefore, we predicted a significant association between MMSE scores and the flattening of the curve.

Method

Participants

Ninety-one older adults recruited from different centers in the Italian territory voluntarily took part in the study. Six participants were excluded as they just completed the implicit timing task leaving a final sample of 85 older adults (this sample allows observing a correlation with a Pearson’s r of .3 with a power of .8). Twenty-three participants resided in the municipality of Padova; 14 were tested at their home and 9 at local social centers. Thirteen participants resided in the municipality of Sacile (Pordenone) and were tested at local social centers. Fifteen participants resided in the municipality of Vicenza and were tested at local social centers. Twenty-one participants resided in the municipality of Grosseto and were tested either at home (N = 14) or at a local social center (N = 7). Finally, 13 resided in the municipality of Cagliari and all were tested at home. Unhealthy participants received a diagnosis of either MCI or dementia by expert clinicians. To control for differences in the testing context (home vs. public centers), particular attention was given to the experimental setting such that all of the participants performed the temporal tasks in a quiet and normally illuminated room and all of them received the same instructions. Participants’ cognitive decline was defined according to the score (corrected for age and education) obtained on the Italian version of the Mini-Mental State Examination (MMSE, [25]; [27], for the Italian version). Of note, although in the below analyses MMSE scores were considered as a continuous variable, for completeness S1 Table also reports the clinical classifications (i.e., dementia or MCI) of our sample as commonly done according to the cut-offs used in the literature, in addition to the demographic characteristics of the participants included in this study.

All the participants had normal or corrected-to-normal vision and normal hearing. All of them signed an informed consent before participation, in accordance with the Declaration of Helsinki. The experiment was approved by the Ethics Committee of the Department of General Psychology of the University of Padova (protocol n. 3387).

Procedure and task

Participants were seated in a quiet room at an approximate distance of 60 cm from the computer screen (15.6”) that produced and recorded experimental events via Psycophy Software [28]. Explicit and implicit timing tasks comprised the same stimulus material and general procedure but differed in the specific task instructions given to participants (Figs 1A and 2A). For both tasks, stimuli consisted of a grey circle and a grey cross presented at the center of a lighter grey background screen. A thin circle was initially displayed for 500 ms (Inter-Trial-Interval, ITI), followed by a thicker circle that could assume one of the following interval durations: 480, 720, 960, 1200, 1440, 1680, or 1920 ms. After the duration had elapsed, a cross appeared in the center of the circle for 500 ms. For the explicit timing task, the experimental session started with a training phase, in which participants were instructed to memorize two standard durations: 480 (short standard) and 1920 ms (long standard), each presented 10 times. During a subsequent testing phase, participants had to indicate whether the temporal interval elapsing from the onset of the thicker circle to the onset of the cross was closer in duration to the previously memorized “short standard” or “long standard”. Responses were given by pressing two response keys (“S” and “L” on the computer keyboard), which were covered with the labels “B” and “L” (i.e., “Breve” and “Lungo”, respectively, meaning short and long in Italian); response keys were counterbalanced between participants.

In the implicit timing task, participants were instructed to press the spacebar as fast as possible whenever the cross appeared inside the thicker circle.

For both explicit and implicit timing tasks, no information about stimulus durations was given to participants. The experiment consisted of a total of 6 blocks (3 blocks for each timing task) of 42 trials each (6 repetitions for each temporal interval). Forty-four participants started with the explicit timing task, whereas 41 participants started with the implicit timing task. Explicit and implicit timing tasks were separated by a short break to allow participants a brief rest before undergoing the second task.

Statistical analyses

The same statistical approach was applied to the analysis of both explicit and implicit timing tasks by means of Mixed-Effects models, which were implemented in the R environment (http://www.R-project.org/) using functions from the lme4 library [29]. Concerning the explicit timing task, the probability of “long” responses was modelled through logistic regressions conducted with the glmer function (i.e., a generalized linear mixed model, GLMM, with probit-link function). Data from trials with missing responses were discarded from the analysis. The GLMM included “Interval duration”, “MMSE score”, “Age” variables and their interactions as fixed terms. The correlation between Age and MMSE score variables was very low (r = -.068). These continuous variables were centered and scaled to improve their interpretation and the fit of the model. Participants were treated as random effects. For the interpretation of the model terms, a significant main effect of MMSE score would indicate a change in the intercept value (since all variables were centered, the intercept is the expected value of the logistic curve at the middle interval duration, i.e., 1200 ms, when MMSE and AGE variables are at their mean value) for a 1 unit change in the MMSE score. As can be appreciated from Fig 1B, the higher the value of the psychometric curve at the middle interval duration, the higher the shift of the curve towards the left (i.e., over-estimation), and vice versa. In sum, a significant main effect of MMSE with an odds ratio greater than 1 would indicate a progressive shift of the curve towards the left with increasing MMSE score (if lower than 1, this would indicate a progressive shift of the curve towards the right with increasing MMSE score). The same logic applies to the main effect of AGE. The flattening of the curve represented in Fig 1B is captured by the interaction of MMSE score (or Age) and Interval duration. A significant odds ratio greater than 1 would indicate a significant steeping of the curve with the increase of the MMSE variable (or Age), whereas a significant odds ratio lower than 1 would indicate a significant flattening of the curve with the increase of the MMSE variable (or Age).

For the implicit timing task, linear regressions were conducted on log-transformed reaction times (RTs) by using the lmer function (i.e., a linear mixed model, LMM). As for the GLMM, a full LMM was specified including “Interval duration”, “MMSE score”, “Age” variables and their interactions as fixed terms. These continuous variables were centered and scaled to improve their interpretation and the fit of the model. Participants were treated as random effects. Data from error trials, i.e., anticipated (< 100 ms) or missing responses to the target, were not included. For the interpretation of the model terms, a significant interaction between MMSE (or Age) and Interval duration would indicate changes in the foreperiod effect associated with the MMSE score (or Age). As explained above, the foreperiod effect is the well-observed lowering of RT with increasing interval duration. This effect is captured by the negative slope of the regression line (see Fig 2B). A significant negative interaction effect would indicate, hence, a stronger foreperiod effect with increasing MMSE score (or Age). On the contrary, a significant positive interaction would indicate a progressive reduction of the foreperiod effect with increasing in the variable (MMSE or Age).

To quantify and evaluate the contribution of MMSE scores and Age in explaining the data (and, hence, to justify their inclusion in the model), two model comparisons (as implemented in the lme4 function anova) were conducted, each one including three models: (i) a simple model with just Interval duration as fixed term; (ii) a model adding in one case MMSE scores and in the other case Age (and their interaction with Interval duration); and (iii) the full model including Interval duration and both MMSE score and Age variables.

Analyses were conducted on data from all the participants (N = 85) with no exclusion criteria. However, to check for the robustness of our results, we also repeated the above-mentioned analyses by excluding participants according to the proportion of trials in which they did not provide a response. Specifically, for each participant and separately for each task, we calculated the proportion of non-given responses. Then, the highest proportion of non-given responses between tasks was taken for each participant, and analyses were repeated four times including participants with a proportion of non-given responses lower than .1, .2, .3, or .4, respectively. Overall, these control analyses confirmed the robustness of our main findings (see S2 and S3 Tables).

Results

Explicit timing task

Model comparison (Table 1) showed that the best fitting model was the full model including Interval duration, MMSE and Age variables (a summary of the model output is presented in Table 2). Fig 3 shows the finding of significant interactions between Interval duration and MMSE and between Interval duration and Age. Specifically, the shape of the psychometric curve became flatter with decreasing MMSE scores and increasing age. The main effects of MMSE and Age, which indicate differences at the middle interval duration, were not significant, implying that there were no systematic changes in temporal judgements (i.e., over- or under-estimation) modulated by MMSE or Age.

Table 1. Model comparison for the explicit and implicit timing tasks showed that the best fitting model was the full model including the interval duration and both the MMSE score and age variables.

Fixed effects	log-likelihood	χ2 (df)	p(>χ2)	AIC
Explicit timing task (a)
Interval duration	-8262.7			16531
Interval duration × MMSE	-8166.9	191.7(2)	< .001	16344
Interval duration × MMSE × Age	-8128.5	76.8(4)	< .001	16275
Explicit timing task (b)
Interval duration	-8262.7			16531
Interval duration × Age	-8220.8	83.7(2)	< .001	16452
Interval duration × MMSE × Age	-8128.5	184.7(4)	< .001	16275
Implicit timing task (a)
Interval duration	-2943.8			5896
Interval duration × MMSE	-2921.7	44.11(2)	< .001	5855
Interval duration × MMSE × Age	-2914.6	14.26(4)	.007	5849
Implicit timing task (b)
Interval duration	647.7			5896
Interval duration × Age	-2937.2	13.24(2)	.001	5886
Interval duration × MMSE × Age	-2914.6	45.13(4)	< .001	5849

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Table 2. Summary of the model outputs for the explicit timing task.

Fixed Effects	Odds ratios	β	p
(Intercept)	1.082	0.103	0.093
Interval duration	1.842	0.665	<0.001
MMSE	1.034	0.046	0.521
Age	0.987	-0.031	0.793
Interval duration × MMSE	1.245	0.149	<0.001
Interval duration × Age	0.877	-0.099	<0.001
MMSE × Age	0.923	-0.050	0.121
Interval duration × MMSE × Age	1.013	0.007	0.430
N _ID			85
Observations			14729
Marginal R² /Conditional R²			0.30 / 0.38

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Fig 3 — Panel A depicts the interaction between Interval duration and MMSE, whereas panel B depicts the interaction between Interval duration and Age. The interaction plots were obtained using the "interact_plot" function of the R package interactions, which by default plots the marginal effects of the first continuous predictor (i.e., interval duration) at 1 standard deviation above (+1SD) and below (-1SD) the mean and at the mean itself of the second predictor (i.e., MMSE and Age, respectively). The seven interval durations on the x-axis represent the actual durations used in the task.

Implicit timing task

As for the explicit timing task, model comparison (Table 1) showed that the best fitting model was again the full model including Interval duration, MMSE and Age variables. Visual inspection of the residuals showed that they were skewed. Following Baayen and Milin [30], trials with absolute standardized residuals higher than 2.5 SD were considered outliers and removed (3% of the trials). After removal of outlier trials, the full model was refitted achieving reasonable closeness to normality. A summary of the model output is presented in Table 3. Fig 4 shows an overall increase in RT with decreasing MMSE scores (MMSE main effect) and with increasing Age values (Age main effect). Concerning the foreperiod effect (i.e., shorter RTs with longer interval durations), it decreased with decreasing MMSE scores (Interval duration × MMSE interaction), but increased with greater age (Interval duration × Age interaction). The Interval duration × MMSE × Age three-way interaction was not significant.

Table 3. Summary of the model outputs for the implicit timing task.

Fixed Effects	Estimates	β	p
(Intercept)	6.119	-0.002	< .001
Interval duration	-0.064	-0.166	< .001
MMSE	-0.161	-0.328	< .001
Age	0.118	0.279	.001
Interval duration × MMSE	-0.014	-0.027	< .001
Interval duration × Age	-0.006	-0.011	.018
MMSE × Age	0.017	0.029	.632
Interval duration × MMSE × Age	0.002	0.003	.507
N _ID			85
Observations			14976
Marginal R² /Conditional R²			0.18/0.66

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Fig 4 — Panel A depicts the interaction between Interval duration and MMSE, whereas panel B depicts the interaction between Interval duration and Age. The seven interval durations on the x-axis represent the actual durations used in the task. Please refer to Fig 3 for a detailed explanation of the interaction plots.

Discussion

In this correlational study, we compared explicit and implicit timing in both healthy and pathological older participants, thus, extending recent research on explicit and implicit timing in healthy older adults [2].

As concerns explicit timing, we reasoned that a slower clock in older participants should lead to a rightward shift of the psychometric curve with increasing age. By contrast, if changes in the temporal performance of older adults depend on their reduced cognitive control functions, one would expect a flatter psychometric curve with decreasing MMSE scores. These predictions were partly supported by our results, given that a flatter psychometric curve was observed not only for decreased MMSE scores but also for greater age. Therefore, much older and more compromised participants made less precise temporal judgments in the time bisection task.

The lack of evidence for a systematic over- or under-estimation bias in the time bisection task, indicated by the non-significant main effects of MMSE and age variables, accords well with previous time bisection studies in healthy [3, 31, 32] and pathological samples [33, 34]. Consistent with this prior literature is also our observation of a flattening of the psychometric curve with a decreasing of MMSE scores and an increasing of age. A possible interpretation for the flatter psychometric curve associated with decreased MMSE scores or increased age is that more compromised or much older participants had a noisier memory representation of standard durations, which led them to respond “short” to long durations and “long” to short durations. Supporting this claim, it has been shown that similar anatomical structures underlie memory and timing functions [35]. Moreover, as stated in the Introduction, a role for memory in the performance of the time bisection task is acknowledged within pacemaker-based accounts of time perception [36–39]. Framing our results within such models, a poor memory representation of the standards would lead to a flatter psychometric curve. However, because we did not test for memory abilities, this explanation remains speculative and warrants further examination (but see [2] for relationships between performance on explicit timing and neuropsychological tests).

Overall, findings from the explicit time bisection task point to a deficit for much older or more compromised participants at the level of cognitive control functions rather than of temporal processing, as evinced by the flattening of the psychometric curve with increasing age and decreasing MMSE scores and the lack of a significant under- or over-estimation of interval durations. Future studies should administer older adults with tasks requiring judgment of different magnitudes (e.g., time, weight, brightness) to directly compare deficits in temporal and non-temporal dimensions.

In contrast to the explicit timing task, in the implicit timing one the task goal itself was non-temporal; participants had to simply respond to the onset of the cross without memorizing or providing an explicit judgment of the interval duration (or foreperiod) separating the cross from the thicker circle (see Fig 2A). The foreperiod effect (i.e., shorter RTs at longer interval durations), commonly observed in this type of RT tasks, is considered indicative of an implicit processing of time, with participants benefitting from the information afforded by the elapse of time during longer foreperiod trials [7, 20]. For the type of instructions given to participants and the task goal itself, implicit timing is believed to pose fewer demands on cognitive processes as compared to explicit timing. Accordingly, our original hypothesis was that the best predictor of performance on the implicit timing task should be the participants’ age [2]. Echoing the results from the explicit timing task, the best fitting model was again the one including interval duration, MMSE scores and age variables. However, unlike the explicit timing task, MMSE scores and age had different effects on participants’ performance. We detail these differences in what follows.

To begin with, the significant main effects of MMSE scores and age showed longer RTs for decreasing MMSE scores and greater age, in line with the presence of a generalized RT slowing in healthy and pathological older adults [40]. As alluded to above, this pattern does not reflect a deficit in the implicit processing of temporal information, which was instead operationalized by the size of the foreperiod effect. A close look at Fig 4 shows that the foreperiod effect increased with age, whereas it decreased with MMSE scores.

The presence of a larger foreperiod effect in much older participants is explained by the fact that they were slower than less older participants at shorter interval durations having, in turn, more room for improvement at longer durations. This is a simple and parsimonious explanation for the steepening of the foreperiod effect with increasing age. At the same time, a steeper foreperiod effect in much older participants indicates that the ability to implicitly process temporal information seems preserved with age, in line with previous studies [2, 41, 42]; but also see [43]). By contrast, looking at the MMSE score variable, although a decrease in the MMSE scores was associated with a slowing down of RT (as observed with increasing in Age), there was also a reduced foreperiod effect with decreased MMSE scores (differently from what observed for Age).

Taken together, our findings from the implicit timing task, showing that implicit processing of time is less efficient in participants with more cognitive decline, extend previous research in healthy aging [2] and add to other dissociations between explicit and implicit timing reported in children [44] and clinical conditions such as Parkinson’s disease [45] and schizophrenia [46]. Coupled with these studies, our findings converge to the idea that implicit timing tasks could offer important insights into processing of time in vulnerable populations because of its fewer demands in terms of cognitive control functions (i.e., no explicit instructions to pay attention to time neither to memorize durations) as compared to more demanding explicit timing tasks, where deficits in temporal processing might be masked by other executive deficits. A path that future research must surely follow is the investigation of the neural mechanisms that are in charge of preserving implicit timing in healthy aged populations. It would be also interesting to find the age point at which performance on explicit and implicit timing tasks starts to diverge to get a comprehensive picture on processing of explicit and implicit timing from a life-span perspective.

A potential caveat to our study is that we exclusively relied on the use of the MMSE and did not employ other neuropsychological tests that could have better defined the cognitive profile of our sample. Nonetheless, it should be noted that the MMSE is routinely used in clinical practice to screen for cognitive impairment because of its easy procedure and short administration time. Another concern could be that our study did not provide any information about potential differences in temporal processing between individuals diagnosed with MCI and patients with dementia. As amply discussed above, we were interested in disentangling the effects of age from those related to cognitive decline regardless of the specific neurodegenerative condition. However, our study could be used in the future as an ideal starting point to investigate whether and how neurodegenerative conditions differently affect performance on temporal tasks.

In conclusion, the present findings contribute to the understanding of deficits in explicit and implicit timing tasks in older adults by showing dissociable associations with age and cognitive decline.

Supporting information

S1 Table. Descriptive statistics (mean and standard deviation) of our sample.

(DOCX)

Click here for additional data file.^{(14.8KB, docx)}

S2 Table. Summary of the model outputs for the explicit timing task from analyses including participants with a proportion of non-given responses lower than .1, .2, .3, or .4, respectively.

(DOCX)

Click here for additional data file.^{(15.3KB, docx)}

S3 Table. Summary of the model outputs for the implicit timing task from analyses including participants with a proportion of non-given responses lower than .1, .2, .3, or .4, respectively.

(DOCX)

Click here for additional data file.^{(15.3KB, docx)}

Acknowledgments

This work was carried out within the scope of the project “Use-inspired basic research”, for which the Department of General Psychology of the University of Padova has been recognized as “Dipartimento di Eccellenza” by the Ministry of University and Research. The authors gratefully thank Diletta Piazzesi, Martina Corda, Davide Tempesta and Alessandro Vicenzotti for helping with data collection.

Data Availability

All relevant data are available from Open Science Framework database (https://osf.io/sh492/).

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0264999.r001

Decision Letter 0

Bradley R King

19 Oct 2021

PONE-D-21-29634Explicit and implicit timing in healthy and pathological aging:

Disentangling the role of age and cognitive functioning in temporal processingPLOS ONE

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Reviewer #1: This is a relatively straightforward study of timing in older participants whose MMSE scores were correlated with performance on both explicit (temporal bisection) and implicit (variable foreperiod) measures of timing. The authors conclude that explicit timing is impaired due to cognitive dysfunction but implicit timing is spared.

Major comments

1. In the Methods, authors state that they included participants with Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) as well as healthy older adults. Table 1 indicates that MMSE scores were used as cut-offs for healthy/MCI/dementia groups. However, it’s not clear whether participants had received a formal diagnosis of MCI or AD, or whether they were classified as AD or MCI purely on the basis of their MMSE scores. Please clarify whether the “pathological” aspect of the study was based on formal diagnosis, or on the MMSE scores obtained by the researchers. This is clarification is important for two reasons (1) the inclusion of pathological groups is the only thing that differentiates this study from previously published findings so precision concerning diagnoses is critical (2) dementia is not the same thing as AD. It’s possible to have low MMSE scores for reasons other than AD. So we need to know whether participants were classified as having dementia based on MMSE scores or whether there were a group of patients that had received a diagnosis of AD. If it’s the former, then all mention of participants with AD is misleading and should be removed from the manuscript.

2. Given the schematics presented in Figures 1B and 2B, the authors should analyse their explicit timing data in terms of the bisection point and slope of the bisection curve, and their implicit timing data in terms of the slope of the RT function. These measures directly index the various predictions detailed in the figures. They can be calculated on a participant-by participant level and will provide a more accurate reflection of individual differences, and how they are modulated by MMSE score or age.

3. At the end of the Introduction, the authors state that “A more direct way to disentangle the contribution of age and cognitive resources to processing of explicit and implicit timing is to include both healthy and pathological aged samples and to use participants’ age and level of cognitive decline as continuous predictors of performance.” I don’t see why this is more “direct” than the Droit-Volet study. In the current study, they using a correlational approach with age and cognitive scores as a parametric variables, which is exactly what Droit-Volet did. The only difference is that they use MMSE as a measure of cognitive function, rather than neuropsychological tests of attention and memory. The authors need to clarify why they think their approach is more “direct”.

4. A few issues concerning the novelty of the study:

- In the Abstract the authors claim to provide “the first experimental evidence that processing of implicit, but not explicit, timing is differentially affected in healthy and pathological aging”. This is a subtle point but potentially misleading – their investigation might be the first demonstration of differential effects of DEMENTIA on implicit and explicit timing but it’s not the first demonstration of this in healthy aging. This sentence should be reformulated to reflect the novel contribution of the study, which is the inclusion of participants with MMSE scores that reflect MCI or mild to moderate dementia.

- Their predictions at the end of the Intro are exactly the same as the results of the Droit-Volet study. The authors need to clarify the novel contribution of their own study, perhaps by reframing the predictions in terms of pathological decline and the four levels of dementia detailed in Table 1?

- The authors conclude that they provide the “first evidence that implicit processing of time can be spared in healthy age-related cognitive decline”. But this is NOT the first evidence of such a finding. They mention in the manuscript several studies that have already shown a steeper FP effect in older participants and in the Droit-Volet study implicit timing performance in older participants was influenced more by the hazard function than by a memorized representation of duration, again suggesting sparing of implicit timing. In addition, they conclude that explicit timing was impaired due to general cognitive dysfunction, but implicit timing was spared. This is exactly the conclusion reached by Droit-Volet et al, so it’s unclear what the novel contribution of this study is. The authors also say that it would be interesting to apply their approach to neuropsychological tests rather than the MMSE, but this is precisely what was done in the Droit-Volet study! The similarity of their findings and conclusions to those of previous studies must be mentioned and the novel contribution of the current study needs to be radically clarified. In addition, all statements to the effect of providing the “first evidence” are vastly overstated and should be removed.

- The authors find an effect of both age and MMSE on explicit timing. This was also the case in the Droit-Volet study, who addressed this issue with a hierarchical regression analysis to determine whether age or cognitive function score was the best predictor of performance. They found that age was no longer a significant predictor once attention was modelled. In the discussion the authors speculate that poor memory or poor attention could explain the effects of MMSE and age on explicit timing, but Droit-Volet tested this with a hierarchical regression analysis and found attention to be the best predictor of performance. Indeed, once attention was included in the model age no longer predicted performance. These findings are relevant for the conclusions of the current study (which used a global measure of cognitive function (MMSE) rather than scores on individual cognitive processes) and should be mentioned in the discussion.

Minor comments

1. In the Introduction, I’m not sure that the description of results in terms of the pacemaker-accumulator model is strictly necessary. It doesn’t inform either the predictions or results. I suggest removing all this part from “Such psychological operations are typically explained…” to “(Lustig and Meck, 2001)”.

2. In Figures 3 and 4, the authors present data in terms of the mean, +1SD and -1 SD. But no explanation of these categories is provided. Why was this approach chosen?

3. In the discussion the authors state that figure 4 “clearly shows” that the FP effect increased with age (and decreased with MMSE). Based on their statement I would expect a flatter slope for the -1SD MMSE group and a steeper slope for the +1SD age group. The figure does “clearly” show the main effect of age and the main effect of MMSE (upward or downward shifts in the slopes) but it doesn’t “clearly” show the strength of the FP effect, which would be indexed by a change in the SLOPE of the RT function. The slopes for the three categories all look parallel. I don’t question their statistics but I wonder if the can authors find a more descriptive/illustrative way to present the data?

Reviewer #2: The authors conducted a behavioral study on temporal processing in older adults with varying degrees of cognitive performance as assessed on the MMSE. Using two dissimilar paradigms, they distinguished explicit and implicit timing abilities, which might be differentially affected by aging, age-related changes in cognition, or both. This is a very interesting research question, and it is very informative to advance the current knowledge of differences between different aspects of timing abilities (explicit vs. implicit) and how these abilities relate to interindividual differences in cognitive status. I do have some conceptual concerns, though, and hope that my comments will help to address these.

My main concern is how cognitive functions are regarded in this paper. First, it is somehow implied that there is some kind of a mechanistic role for cognitive functions in temporal processing. This is never made very explicit, but it should be clarified that by relying on interindividual differences, it is impossible to draw conclusions regarding the mechanistic involvement of cognitive functions in temporal processing (see the paper by Borsboom and colleagues (2009) for a more detailed account, https://dx.doi.org/10.1007/978-0-387-95922-1_4). Second, the research design seems to imply a distinction between cognitive functions and temporal processing that should be subjected to critical conceptual discussion. Could it be argued that temporal processing is an aspect of cognitive functions, rather than a consequence? Is there any evidence that these are distinguishable entities at all? If not, how could one possibly “influence” the other (but see my previous comment)? Third, the assessment of cognitive functions using the MMSE screening as a single measure does not seem broad enough to draw firm conclusions. The authors address this in the limitations already, but the conclusions drawn from the study do not reflect this limitation. I would strongly suggest to temper all claims regarding “cognitive functions” such that it becomes clear at all times that this refers to a rather superficial cognitive screening, rather than an in-depth or in any way specific assessment of cognitive functioning.

In addition to that, the wording sometimes obscures the exact comparisons that were being made in the paper. Sometimes, I had the impression that the authors aimed at comparing timing abilities between two dissimilar groups (healthy vs. “pathological” aging), but the analyses refer to the entire sample. I believe that this is preferable, given that group comparisons would not be powerful enough, but the wording should be adapted accordingly, also in the Manuscript Title. In addition, the word “aging” seems inappropriate here, as there was no longitudinal or group comparison, but this was instead a cross-sectional study in a population of older adults. This should be made very clear throughout the text.

More detailed comments:

Abstract:

The wording in the results and discussion sections of the abstract should be adapted for clarity. For instance, “… processing of implicit, but not explicit, timing is differentially affected in healthy and pathological aging” seems to suggest that patterns of timing abilities significantly differ between healthy and non-healthy older adults.

Introduction:

The concept of “temporal processing” should be introduced in more detail. What does it entail, and from what related concepts does it differ? How is temporal processing distinguished from cognitive functioning in general, if at all (see above)?

Paragraph starting with “To our knowledge, … “: The second sentence referring to previous studies is somewhat ambiguous and should be revised.

Same paragraph, last portion: It is not valid to conclude that lower attentional abilities are in any way mechanistically related to temporal processing, please see the paper by Borsboom that I recommended above. This must be revised.

Same paragraph, last sentence + first sentence following paragraph: Is this a statistically meaningful difference? Is it possible that cognitive scores were just not sensible enough to show an effect, e.g. due to the typically low reliability of cognitive measures?

Paragraph starting with “Collectively, …”, third sentence: The current design is implied to provide more direct evidence for the effect of interest than previous studies, but this is not the case, as it relies on the same correlational logic, rather than on a true experimental manipulation of “age” and “cognition”. This is not a problem per se (or at least not specific to this particular paper), but it should be discussed accordingly.

Paragraph starting with “Different, …”, third sentence: Remove the comma before “which”, as it changes the meaning of the sentence.

Same paragraph, last sentence: It seems very difficult to show that two variables are “unrelated”, as this implies acceptance of the null hypothesis. Please revise the wording.

Methods:

As a general suggestion, I would recommend to walk the reader more gently through the analyses and explain which particular questions are answered by which tests and what is implied by which outcome.

General comment: MMSE-scores are not independent of participant age. Does this represent a problem for the chosen analysis approach? Consider adapting the analysis strategy, but at least, this issue should be discussed.

Regarding the strategy to deal with no-response trials: Did I understand correctly that no-response trials were included in the original analyses? Why were they not discarded on a trial-by-trial basis in that analysis, rather than discarding an entire case in a supplementary analysis? Would the results change when excluding no-response trials?

Results:

Paragraph “Implicit timing task”, second sentence: Could you please explain in more detail what it means that “the model was a bit stressed”?

Same paragraph, last sentence: This is very difficult to see from the Figure. Please provide some more guidance or adapt accordingly.

Discussion:

First paragraph: The statement “in both healthy and pathological aged populations” is misleading; cf. my earlier comment. Please revise.

Second paragraph, last sentence: The term “pathological” cognitive decline is not appropriate here, given that (1) the classification as “pathology” was merely based on a screening tool rather than on a solid clinical diagnosis, and (2) it was not part of an actual comparison. Please rephrase.

Third paragraph, sentence starting with “Another possible, …”: It is suggested here that attentional deficits might explain worse performance in relatively older adults and in relative low performers on the MMSE on the explicit timing task. Such a deficit is not definitely shown, as there was no specific test to measure attention. Even if an attention deficit had been shown to coincide with worse explicit timing performance, it should be made explicit that this does in no way allow for a mechanistic or causal interpretation of the results. Finally, if attention were indeed causally related to timing abilities, why would this effect be limited to the explicit timing task and not also the implicit timing task?

Same paragraph, sentence starting with “In any case, …”: This wording suggests that age and cognitive performance independently modulate task performance, but they are confounded. Please rephrase, if applicable (cf. my earlier comment regarding the Methods).

Same paragraph, sentence starting with “Remarkably, …”: This sentence should be rephrased. First, it is difficult to conclude that “neither age nor cognitive impairment altered timekeeping mechanisms”, as this implies the acceptance of the null hypothesis. Second, it should not be stated that either of those variables “influenced” cognitive processes to avoid suggesting a mechanistic relationship.

Fourth paragraph, sentence starting with “Considering that implicit timing poses…”: Can it actually be stated that implicit timing poses less demands on cognitive processing as compared to explicit timing? What is the basis for this claim?

Paragraph starting with “The presence of a larger…”, last sentence: Revise the wording in this sentence. It seems to suggest a comparison between “older”/ “healthy” and “impaired” / “pathological” adults (cf. my previous comments).

Paragraph starting with “Taken together, …”, third sentence: Please remove the statement regarding early markers. This cross-sectional design does not allow for the conclusion of any longitudinal effects. In addition, I understood that the authors conceptualized temporal processing to be distinct from cognitive functioning. How can this be reconciled with the viewpoint that temporal processing can serve as a marker for cognitive changes?

Same paragraph, fourth sentence: If this paradigm poses “very little demands” on cognitive resources, then why is it worth to assess a relationship with MMSE scores at all?

Same paragraph, fifth sentence: This wording implies a direct statistical comparison between explicit and implicit timing tasks, please revise. Similarly, a few sentences further, it is stated that the MMSE was “enough sensitive to differentiate performance on explicit and implicit tasks”, but this, again, would need to be shown statistically.

Last paragraph, second sentence: It seems difficult to make conclusions regarding “age-related” changes based on the present data, as the study population were older adults exclusively.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 Mar 16;17(3):e0264999. doi: 10.1371/journal.pone.0264999.r002

Author response to Decision Letter 0

6 Jan 2022

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: No

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

5. Review Comments to the Author

Reviewer #1:

This is a relatively straightforward study of timing in older participants whose MMSE scores were correlated with performance on both explicit (temporal bisection) and implicit (variable foreperiod) measures of timing. The authors conclude that explicit timing is impaired due to cognitive dysfunction but implicit timing is spared.

RESPONSE: We thank the reviewer for the positive evaluation of our work.

Major comments

RESPONSE: We apologize for the lack of clarity concerning these issues. Because our study was not aimed at investigating whether and how different neurodegenerative conditions affect performance on explicit and implicit timing tasks (as also outlined in the Introduction and Discussion Sections), any reference to specific types of dementia (e.g., Alzheimer’s disease) was deleted from the manuscript. As specified in the revised version of the manuscript (see pages 8 and 9), unhealthy participants received a formal diagnosis of MCI or dementia; accordingly, they all can be confidently considered as pathological. Nevertheless, although participants received such a diagnosis, any reference to cognitive impairment/decline in our sample refers to the MMSE score, which, for our goals, was used as a continuous predictor of performance. This aspect is now clearly reported on page 8 (“although unhealthy participants received a formal diagnosis of (mild-) cognitive dementia (see the Methods Section), hereafter any reference to cognitive impairment/decline in our sample is related to the MMSE score. Participants’ ages and MMSE scores were considered as continuous predictors of performance in the analyses”).

Finally, to avoid confusion regarding this part, namely, that no cut-offs were used to characterize our sample, we amended the legend of the Supplementary Table S1, which now reads as follows: “According to the cut-offs commonly used in the literature, a score between 30 and 28 would define healthy older adults with a normal cognitive functioning; a score between 27 and 25 would indicate the presence of Mild Cognitive Impairment (MCI); a score between 24 and 19 would indicate a mild dementia, whereas a score between 18 and 10 a moderate dementia”. Moreover, we specified on page 9 that “for completeness Supplementary table S1 also reports the clinical classifications (i.e., dementia or MCI) of our sample as commonly done according to the cut-offs used in the literature”.

RESPONSE: We thank the reviewer for the suggestion. However, if we get it correctly, the reviewer proposes to perform a two-level regression, which is a form of multilevel modelling analysis. Namely, we should fit logistic (in the explicit timing task) and linear (in the implicit timing task) regressions separately for each participant (first-level subject-specific analysis), and then use the obtained parameters for fitting models at a second (group) level including age and MMSE scores. We agree that the proposed approach is a way to test the hypotheses presented in Figures 1B and 2B. However, we would respectfully point out that this approach is already implemented in our mixed effects analyses. Our models, indeed, are another form of multilevel modelling in which the suggested first and second levels analyses are fitted simultaneously. Accordingly, and unless the reviewer meant something else, we have already implemented the suggested approach with our mixed effect analyses. Please also note that, in response to Reviewer 2, we added a more thorough explanation of the performed analyses and interpretation of the results on pages 11 and 12.

RESPONSE: We agree with the reviewer that our statement was unclear, and we deleted the term “direct” from the Introduction. Indeed, what we wanted to emphasize here is that our study extended previous investigation on processing of explicit and implicit timing in older adults by including both healthy and pathological older participants. In the light of this comment, we rephrased the paragraph as follows (see page 8): “In the present study, we aimed to advance our knowledge about performance of older adults on explicit and implicit timing tasks by considering not only healthy but also pathological older participants. This allowed capturing differences in explicit and implicit timing tasks linked to age and pathological cognitive decline, two variables that, although often correlated, are not systematically associated (Jansen et al., 2018). To this end, healthy older adults and individuals diagnosed with either Mild Cognitive Impairment (MCI) or dementia completed explicit (time bisection) and implicit (foreperiod) timing tasks in a single session. The Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975) was used as an index of cognitive decline. The MMSE represents, indeed, one of the most routinely used screening tools in clinical practice, even if it provides only a generic assessment of cognitive decline. Of note, although unhealthy participants received a formal diagnosis of (mild-) cognitive dementia (see the Methods Section), hereafter any reference to cognitive impairment/decline in our sample is related to the MMSE score”.

Moreover, we converge with the reviewer on the similarity between the two correlational approaches. However, we respectfully disagree with the reviewer in that our sample size (N= 85) is more adequate for testing correlations than the one used by Droit-Volet et al. (N=20). We clarified this point in the manuscript (page 9): “this sample allows observing a correlation with a Pearson’s r of .3 with a power of .8”.

4. A few issues concerning the novelty of the study:

4.1- In the Abstract the authors claim to provide “the first experimental evidence that processing of implicit, but not explicit, timing is differentially affected in healthy and pathological aging”. This is a subtle point but potentially misleading – their investigation might be the first demonstration of differential effects of DEMENTIA on implicit and explicit timing but it’s not the first demonstration of this in healthy aging. This sentence should be reformulated to reflect the novel contribution of the study, which is the inclusion of participants with MMSE scores that reflect MCI or mild to moderate dementia.

RESPONSE: The Abstract has been amended to incorporate the concerns raised by both reviewers. Moreover, any reference to “first evidence” has been deleted from both the Abstract and the manuscript. We think that the novel contribution of our study is overall clearer. The last paragraph of the Abstract now reads as follows: “Results for the explicit timing task showed a flatter psychometric curve with increasing age or decreasing MMSE scores, pointing to a deficit at the level of cognitive control functions rather than of temporal processing. By contrast, for the implicit timing task, a decrease in the MMSE scores was associated with a reduced foreperiod effect, an index of implicit time processing. Overall, these findings extend previous studies on explicit and implicit timing in healthy aged samples by dissociating between age and cognitive decline (in the normal-to-pathological continuum) in older adults

4.2- Their predictions at the end of the Intro are exactly the same as the results of the Droit-Volet study. The authors need to clarify the novel contribution of their own study, perhaps by reframing the predictions in terms of pathological decline and the four levels of dementia detailed in Table 1?

RESPONSE: Following this point, we clarified on page 8 that: “Concerning implicit timing, we expected to find a significant association of performance on the foreperiod task with age rather than with MMSE scores, replicating previous research (Droit-Volet et al., 2019)”.

By contrast, our predictions for the explicit timing task were not exactly the same as the results of the Droit-Volet et al.’ study. Indeed, such a previous study found no differences in accuracy performance between younger and older participants. Here, we hypothesized (please also see Figure 1B) that: “As concerns explicit timing, if the poor performance of older participants on the time bisection task depends on a deficit in temporal processing, this should be reflected by a (rightward) shift of the psychometric curve. According to previous literature reporting a slowing down of the pacemaker with age (see Turgeon, Lustig, & Meck, 2016), we predicted to find a significant association between the rightward shift of the curve and age. If, conversely, changes in the performance of older participants depend on their reduced cognitive control functions, this should translate into a flatter psychometric curve. Therefore, we predicted a significant association between MMSE scores and the flattening of the curve”. Accordingly, both scenarios (and not only the second one) are equally plausible. Please also note that, as reported in the Discussion section (pages 20-21), we were not interested in the differences between individuals diagnosed with MCI or dementia, but in considering the MMSE scores as a continuous predictor of performance. In this sense, we do not have any specific prediction as a function of the classification reported in Table 1, which was only inserted for the sake of completeness and for transparently providing the reader with all the information about our sample (see also our response to point 1).

4.3- The authors conclude that they provide the “first evidence that implicit processing of time can be spared in healthy age-related cognitive decline”. But this is NOT the first evidence of such a finding. They mention in the manuscript several studies that have already shown a steeper FP effect in older participants and in the Droit-Volet study implicit timing performance in older participants was influenced more by the hazard function than by a memorized representation of duration, again suggesting sparing of implicit timing. In addition, they conclude that explicit timing was impaired due to general cognitive dysfunction, but implicit timing was spared. This is exactly the conclusion reached by Droit-Volet et al, so it’s unclear what the novel contribution of this study is. The authors also say that it would be interesting to apply their approach to neuropsychological tests rather than the MMSE, but this is precisely what was done in the Droit-Volet study! The similarity of their findings and conclusions to those of previous studies must be mentioned and the novel contribution of the current study needs to be radically clarified. In addition, all statements to the effect of providing the “first evidence” are vastly overstated and should be removed.

RESPONSE: As reported in our response to point 4.1, all the references to “first evidence” were removed. Moreover, the paragraph concerning the administration of neuropsychological tasks in future studies was also removed. The novel contribution of the present study concerning the finding of a reduced foreperiod effect in more compromised participants was clarified on page 20: “by contrast, looking at the MMSE score variable, although a decrease in the MMSE scores was associated with a slowing down of RT (as observed with increasing in Age), there was also a reduced foreperiod effect with decreased MMSE scores (differently from what observed for Age)”.

4.4- The authors find an effect of both age and MMSE on explicit timing. This was also the case in the Droit-Volet study, who addressed this issue with a hierarchical regression analysis to determine whether age or cognitive function score was the best predictor of performance. They found that age was no longer a significant predictor once attention was modelled. In the discussion the authors speculate that poor memory or poor attention could explain the effects of MMSE and age on explicit timing, but Droit-Volet tested this with a hierarchical regression analysis and found attention to be the best predictor of performance. Indeed, once attention was included in the model age no longer predicted performance. These findings are relevant for the conclusions of the current study (which used a global measure of cognitive function (MMSE) rather than scores on individual cognitive processes) and should be mentioned in the discussion.

RESPONSE: As correctly pointed out by Reviewer 2, since we did not measure attentional abilities, all the speculations concerning the involvement of attention were removed from the manuscript. Moreover, as concerns the role of memory, we now specified (page 18) that “because we did not test for memory abilities, this explanation remains speculative and warrants further examination (but see Droit-Volet et al., 2019, for relationships between performance on explicit timing and neuropsychological tests)”.

Minor comments

RESPONSE: As reported in our response to Reviewer 2 (see point 2), the description of pacemaker-based model of time is necessary for framing the concepts of “temporal processing” and “cognitive control functions” and for better understanding the predictions illustrated in Figures 1B and 2B. We, thus, hope that the new elaboration regarding this aspect (page 3) is more informative as compared to the previous version.

2. In Figures 3 and 4, the authors present data in terms of the mean, +1SD and -1 SD. But no explanation of these categories is provided. Why was this approach chosen?

RESPONSE: We apologize for the poor explanation of the Figures. This approach is a simple and common solution to represent interactions between two continuous predictors (e.g., interval duration and age; concerning the commonality, it is the default behavior of the function interact_plot in the R package "interactions"). A real alternative would be to plot a 3D plane that would be hard to visualize (unless an interactive visualization mode is available).

The aspect of the default behavior of the function has now been explicated in the figure caption (page 15) as follows: “Figure 3. Interaction effects in the explicit timing task. Panel A depicts the interaction between Interval duration and MMSE, whereas panel B depicts the interaction between Interval duration and Age. The interaction plots were obtained using the "interact_plot" function of the R package interactions, which by default plots the marginal effects of the first continuous predictor (i.e., interval duration) at 1 standard deviation above (+1SD) and below (-1SD) the mean and at the mean itself of the second predictor (i.e., MMSE and Age, respectively). The seven interval durations on the x-axis represent the actual durations used in the task.”

RESPONSE: We understand that the figure doesn’t “clearly” show the strength of the FP effect in a very intuitive way. However, even if the slopes for the three categories all look parallel, they are not parallel. Otherwise, if the slopes were equal (parallel lines), the interaction between interval duration and age and the interaction between interval duration and MMSE would not have been significant. Said this, given that changes in slope might not be so “clearly” evident in Figure 4, we amended the sentence as follows (page 19): “A close look at Figure 4 shows that the foreperiod effect increased with age, whereas it decreased with MMSE scores.” Unfortunately, indeed, there are no other illustrative ways to present the data as have been analyzed here. We, thus, hope that our amendment is sufficient to meet the reviewer's request.

Reviewer #2:

The authors conducted a behavioral study on temporal processing in older adults with varying degrees of cognitive performance as assessed on the MMSE. Using two dissimilar paradigms, they distinguished explicit and implicit timing abilities, which might be differentially affected by aging, age-related changes in cognition, or both. This is a very interesting research question, and it is very informative to advance the current knowledge of differences between different aspects of timing abilities (explicit vs. implicit) and how these abilities relate to interindividual differences in cognitive status. I do have some conceptual concerns, though, and hope that my comments will help to address these.

RESPONSE: We thank the reviewer for the positive evaluation of our work and for the useful comments.

My main concern is how cognitive functions are regarded in this paper.

1-First, it is somehow implied that there is some kind of a mechanistic role for cognitive functions in temporal processing. This is never made very explicit, but it should be clarified that by relying on interindividual differences, it is impossible to draw conclusions regarding the mechanistic involvement of cognitive functions in temporal processing (see the paper by Borsboom and colleagues (2009) for a more detailed account, https://dx.doi.org/10.1007/978-0-387-95922-1_4).

RESPONSE: We thank the reviewer for the useful comment and for the suggested reading. As also pointed out in our response to the below issue n. 2, we hope that our reference to “temporal processing” and “cognitive control functions” is now clearer in the manuscript, and that we managed to explain that our study was not aimed at drawing any conclusion regarding the involvement of cognitive functions in temporal processing. Specifically, as reported on page 3, in framing our study, we built up on pacemaker-based models of time to address the question of whether “the age-related changes observed in explicit timing tasks can be genuinely attributed to a dysfunction at the level of the clock stage, hereafter referred to as “temporal processing” (e.g., a slower clock accumulating more pulses in older than younger adults), or should be rather considered as a secondary deficit at the level of memory/decision stages, hereafter referred to as “cognitive control functions” (e.g., a noisier memory representation of the short and long standards in older adults)”. In other words, “temporal processing” refers to the clock stage, whereas “cognitive control functions” to memory/decision stages, which, in influential pacemaker-based models of time, represent the three stages accounting for explicit temporal judgements.

Said this, we again apologize if it seemed that our goal was to account for the mechanisms involved in temporal processing and we hope that it is now clear that, by building up on pacemaker-based models of time, we rather aimed at exploring possible differences in older adults in explicit and implicit timing tasks.

2-Second, the research design seems to imply a distinction between cognitive functions and temporal processing that should be subjected to critical conceptual discussion. Could it be argued that temporal processing is an aspect of cognitive functions, rather than a consequence? Is there any evidence that these are distinguishable entities at all? If not, how could one possibly “influence” the other (but see my previous comment)?

RESPONSE: In keeping with our previous response, the distinction between “temporal processing” and “cognitive control functions” is grounded in pacemaker-based models of time. As also pointed out in the description of Figure1B, we acknowledge that “it is difficult to completely isolate clock (i.e., “temporal processing”) from memory/decision stages (i.e., “cognitive control functions”)”. This notwithstanding, it makes sense to hypothesise that “age-related changes in clock speed (namely, in temporal processing) should be mainly expressed by a rightward shift of the psychometric curve (i.e., a slower clock in older adults). By contrast, a flatter psychometric curve in older adults could be likely attributed to a deficit in the additional cognitive control functions (e.g., working memory) thought to be required to correctly perform on the time bisection task”. We hope that these clarifications allow for a better understanding of the distinction between temporal processing and cognitive control functions made in the manuscript.

3-Third, the assessment of cognitive functions using the MMSE screening as a single measure does not seem broad enough to draw firm conclusions. The authors address this in the limitations already, but the conclusions drawn from the study do not reflect this limitation. I would strongly suggest to temper all claims regarding “cognitive functions” such that it becomes clear at all times that this refers to a rather superficial cognitive screening, rather than an in-depth or in any way specific assessment of cognitive functioning.

RESPONSE: We clarified on page 8 that “the MMSE represents one of the most routinely used screening tools in clinical practice, even if it provides only a generic assessment of cognitive decline”. Moreover, we hope that it is now clear that “cognitive control functions” refer to the (non-temporal) cognitive operations acknowledged in pacemaker-based models of time (see above).

4-In addition to that, the wording sometimes obscures the exact comparisons that were being made in the paper. Sometimes, I had the impression that the authors aimed at comparing timing abilities between two dissimilar groups (healthy vs. “pathological” aging), but the analyses refer to the entire sample. I believe that this is preferable, given that group comparisons would not be powerful enough, but the wording should be adapted accordingly, also in the Manuscript Title.

RESPONSE: We implemented this suggestion throughout the manuscript (e.g., the use of “vs.” was deleted and substituted with expressions such as “much older participants and more compromised participants”). The title was also amended as follows: “Explicit and implicit timing in older adults: Dissociable associations with age and cognitive decline”.

5-In addition, the word “aging” seems inappropriate here, as there was no longitudinal or group comparison, but this was instead a cross-sectional study in a population of older adults. This should be made very clear throughout the text.

RESPONSE: Thank you for the useful clarification. The word aging has been deleted from the manuscript when used to refer to our findings.

More detailed comments:

Abstract:

RESPONSE: The Abstract has been almost rewritten to account for this and other comments raised by Reviewer 1. Specifically, the result and discussion sections of the Abstract have been amended as follows: “Results for the explicit timing task showed a flatter psychometric curve with increasing age or decreasing MMSE scores, pointing to a deficit at the level of cognitive control functions rather than of temporal processing. By contrast, for the implicit timing task, a decrease in the MMSE scores was associated with a reduced foreperiod effect, an index of implicit time processing. Overall, these findings extend previous studies on explicit and implicit timing in healthy aged samples by dissociating between age and cognitive decline (in the normal-to-pathological continuum) in older adults”.

Introduction:

1-The concept of “temporal processing” should be introduced in more detail. What does it entail, and from what related concepts does it differ? How is temporal processing distinguished from cognitive functioning in general, if at all (see above)?

RESPONSE: We thank the reviewer for this useful comment that allowed us to better frame the concept of temporal processing. At the very outset of the manuscript, we changed the first line of the Introduction by stating that: “Age-related changes in the performance of temporal tasks within the millisecond-to-second range intervals are commonly reported”. This way, it is clear that we are referring to the processing of temporal information in this specific temporal range. We took it for granted in the previous version of the manuscript. Moreover, as reported in our previous responses, we specified what “temporal processing” means within the framework of pacemaker-based models of time.

2-Paragraph starting with “To our knowledge, … “: The second sentence referring to previous studies is somewhat ambiguous and should be revised.

RESPONSE: The sentence (page 7) has been amended as follows: “To our knowledge, explicit and implicit timing have been thus far compared in healthy older adults only”. Moreover, the paragraph referring to previous studies has been amended to improve clarity and readability (“As an example, Droit-Volet and colleagues (2019) devised a between-participants design, in which one group of older adults and one group of younger adults performed an explicit timing task (i.e., temporal generalization task), whereas different groups of older and younger adults were engaged in an implicit timing task (i.e., a variant of the foreperiod task; Piras & Coull, 2011). Participants’ performances on explicit and implicit timing tasks were also correlated to cognitive scores derived from neuropsychological tests)”.

3-Same paragraph, last portion: It is not valid to conclude that lower attentional abilities are in any way mechanistically related to temporal processing, please see the paper by Borsboom that I recommended above. This must be revised.

RESPONSE: We understand the point raised by the reviewer. When we presented the study by Droit-Volet and colleagues (2019), we tried to report their conclusions as faithfully as possible. Indeed, the authors explained their results by stating that “the variability of duration judgements was greater in older than young participants, though this was directly related to older participants’ lower attentional capacity”. However, in the light of the reviewer’s comments, we deleted the paragraph stating that “performance of older participants was a consequence of their lower attentional abilities”. We now state (page 7) that “older participants were more variable than younger ones in the explicit timing task and their performance was explained by lower attentional capacity rather than age”.

4-Same paragraph, last sentence + first sentence following paragraph: Is this a statistically meaningful difference? Is it possible that cognitive scores were just not sensible enough to show an effect, e.g. due to the typically low reliability of cognitive measures?

RESPONSE: As said above, in this introductory section, we are reporting previous findings as originally presented by the authors. In particular, Droit-Volet and colleagues (2019) performed hierarchical regression analyses to determine whether age or score on the cognitive tasks was the best predictor of performance. Accordingly, although it is possible that cognitive measures had low reliability, we think it would be unfair for the authors of that previous study to question their results as they have already been peer-reviewed and published. In any case, we amended the sentence as follows: “By contrast, older adults showed a greater reliance on the hazard function than younger adults, a result that was significantly associated with age but not cognitive scores”.

5-Paragraph starting with “Collectively, …”, third sentence: The current design is implied to provide more direct evidence for the effect of interest than previous studies, but this is not the case, as it relies on the same correlational logic, rather than on a true experimental manipulation of “age” and “cognition”. This is not a problem per se (or at least not specific to this particular paper), but it should be discussed accordingly.

RESPONSE: We agree with the reviewer. Please note that a similar concern was also raised by Reviewer 1 (point 3). We now amended this part as follows (page 8): “Collectively, the correlational findings by Droit-Volet and colleagues (2019) speak to different possible influences of age and cognitive functions in explicit and implicit timing tasks. In the present study, we aimed to advance our knowledge about performance of older adults on explicit and implicit timing tasks by considering not only healthy but also pathological older participants. This allowed capturing differences in explicit and implicit timing tasks linked to age and pathological cognitive decline, two variables that, although often correlated, are not systematically associated (Jansen et al., 2018)”.

6-Paragraph starting with “Different, …”, third sentence: Remove the comma before “which”, as it changes the meaning of the sentence.

RESPONSE: Please note that this paragraph about the predictions of the study was amended. It now reads as follows: “Concerning implicit timing, we expected to find a significant association of performance on the foreperiod task with age rather than with MMSE scores, replicating previous research (Droit-Volet et al., 2019). As concerns explicit timing, if the poor performance of older participants on the time bisection task depends on a deficit in temporal processing, this should be reflected by a (rightward) shift of the psychometric curve. According to previous literature reporting a slowing down of the pacemaker with age (see Turgeon, Lustig, & Meck, 2016), we predicted to find a significant association between the rightward shift of the curve and age. If, conversely, changes in the performance of older participants depend on their reduced cognitive control functions, this should translate into a flatter psychometric curve. Therefore, we predicted a significant association between MMSE scores and the flattening of the curve”.

7-Same paragraph, last sentence: It seems very difficult to show that two variables are “unrelated”, as this implies acceptance of the null hypothesis. Please revise the wording.

RESPONSE: Revised accordingly. Specifically, the word “unrelated” has been deleted and the predictions have been framed taking into account the comments of both reviewers (see our response above).

Methods:

As a general suggestion, I would recommend to walk the reader more gently through the analyses and explain which particular questions are answered by which tests and what is implied by which outcome.

RESPONSE: We implemented this useful suggestion and added the following explanations on pages 11 and 12: “For the interpretation of the model terms, a significant main effect of MMSE score would indicate a change in the intercept value (since all variables were centered, the intercept is the expected value of the logistic curve at the middle interval duration, i.e., 1200 ms, when MMSE and AGE variables are at their mean value) for a 1 unit change in the MMSE score. As can be appreciated from Figure 1B, the higher the value of the psychometric curve at the middle interval duration, the higher the shift of the curve towards the left (i.e., over-estimation), and vice versa. In sum, a significant main effect of MMSE with an odds ratio greater than 1 would indicate a progressive shift of the curve towards the left with increasing MMSE score (if lower than 1, this would indicate a progressive shift of the curve towards the right with increasing MMSE score). The same logic applies to the main effect of AGE. The flattening of the curve represented in Figure 1B is captured by the interaction of MMSE score (or Age) and Interval duration. A significant odds ratio greater than 1 would indicate a significant steeping of the curve with the increase of the MMSE variable (or Age), whereas a significant odds ratio lower than 1 would indicate a significant flattening of the curve with the increase of the MMSE variable (or Age)”. Moreover, for the implicit timing task, we added that: “As explained above, the foreperiod effect is the well-observed lowering of RT with increasing interval duration. This effect is captured by the negative slope of the regression line (see Figure 2B). A significant negative interaction effect would indicate, hence, a stronger foreperiod effect with increasing MMSE score (or Age). On the contrary, a significant positive interaction would indicate a progressive reduction of the foreperiod effect with increasing in the variable (MMSE or Age)”.

RESPONSE: As already reported in the previous version of the manuscript (page 9), MMSE scores were corrected for age and level of education as suggested by Magni et al. 1996 for the Italian population. Moreover, although the analysis considers the unique (i.e., not shared) variance explained by each predictor, the correlation between MMSE and Age variables was negligible (r = .0676, R2 = .0046). This information is now reported in the revised version (page 11): “The correlation between Age and MMSE score variables was very low (r = -.068)”.

RESPONSE: We apologize for having forgotten to report that for the explicit timing task “data from trials with missing responses were discarded from the analysis” (now page 11). Moreover, for the implicit timing task, “data from error trials, i.e., anticipated (< 100 ms) or missing responses to the target, were not included” (page 12)”. To check for the robustness of our results, in a second step, we repeated the analyses by excluding participants according to the proportion of trials in which they did not provide a response.

Results:

Paragraph “Implicit timing task”, second sentence: Could you please explain in more detail what it means that “the model was a bit stressed”?

RESPONSE: We apologise if the sentence was unclear. In the revised version, the sentence was rephrased as follows (page 15): “Visual inspection of the residuals showed that they were skewed”.

Same paragraph, last sentence: This is very difficult to see from the Figure. Please provide some more guidance or adapt accordingly.

RESPONSE: A similar concern was also raised by Reviewer 1. As replied above, we understand that the figure doesn’t “clearly” show the strength of the FP effect in a very intuitive way. Unfortunately, however, there are no other illustrative ways to present the data as have been analyzed here. We hope that the explanation added on page 12 (“As explained above, the foreperiod effect is the well-observed lowering of RT with increasing interval duration. This effect is captured by the negative slope of the regression line (see Figure 2B). A significant negative interaction effect would indicate, hence, a stronger foreperiod effect with increasing MMSE score (or Age). On the contrary, a significant positive interaction would indicate a progressive reduction of the foreperiod effect with increasing in the variable (MMSE or Age))” is sufficient to better understand the Figure.

Discussion:

First paragraph: The statement “in both healthy and pathological aged populations” is misleading; cf. my earlier comment. Please revise.

RESPONSE: This statement now reads as follows: “In this correlational study, we compared explicit and implicit timing in both healthy and pathological older participants, thus, extending recent research on explicit and implicit timing in healthy older adults (Droit-Volet et al., 2019).

RESPONSE: As now reported in the manuscript, unhealthy participants received a diagnosis of either MCI or dementia. Accordingly, we hope that the term pathological is now justified in the manuscript. However, we understand the reviewer’s suggestion, and the expression “pathological cognitive decline” was deleted from the sentence, which now reads as follows: “Therefore, much older and more compromised participants made less precise temporal judgments in the time bisection task”.

RESPONSE: We agree with the reviewer and removed the speculation about the involvement of attention from the manuscript.

RESPONSE: This paragraph was deleted (see also below).

RESPONSE: This paragraph now reads as follows (see page 18): “Overall, findings from the explicit time bisection task point to a deficit for much older or more compromised participants at the level of cognitive control functions rather than of temporal processing, as evinced by a flatter psychometric curve and the lack of a systematic under- or over-estimation of interval durations”.

RESPONSE: We specified (page 19) that: “For the type of instructions given to participants and the task goal itself, implicit timing is believed to pose fewer demands on cognitive processes as compared to explicit timing”. This claim is based on the literature cited in the manuscript (e.g., Coull & Nobre, 2008) that divides timing tasks, and underlying temporal processes, into explicit and implicit as a function of the type of instructions given to participants. In explicit timing tasks, such as the time bisection task also employed in our study, the task goal itself is temporal, as participants are explicitly informed about the need to pay attention to elapsing time and to memorize some specific duration. It follows then that under this condition, performance on the explicit timing task is also related to the participants’ ability to correctly process interval durations and to maintain them in memory for making a subsequent decision (e.g., shorter or longer than the standard durations?).

By contrast, in implicit timing tasks, like the foreperiod task, the task goal itself is not temporal and no explicit requirements to process time are provided. Accordingly, it is assumed that implicit timing tasks pose fewer demands on cognitive processes as compared to explicit timing tasks.

RESPONSE: The sentence (see page 19) now reads as follows: “The presence of a larger foreperiod effect in much older participants is explained by the fact that they were slower than less older participants at shorter interval durations having, in turn, more room for improvement at longer durations”.

RESPONSE: Removed as suggested.

Same paragraph, fourth sentence: If this paradigm poses “very little demands” on cognitive resources, then why is it worth to assess a relationship with MMSE scores at all?

RESPONSE: Although implicit timing tasks are believed to pose fewer demands on performance than explicit timing tasks, they still require an elaboration of temporal information. Accordingly, this temporal part deserves to be investigated in relation to cognitive decline.

RESPONSE: The statement “enough sensitive to differentiate performance on explicit and implicit tasks” was removed from the manuscript.

Last paragraph, second sentence: It seems difficult to make conclusions regarding “age-related” changes based on the present data, as the study population were older adults exclusively.

RESPONSE: We avoided using the wording “age-related” changes when referring to our findings throughout the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0264999.r003

Decision Letter 1

Bradley R King

21 Feb 2022

Explicit and implicit timing in older adults: Dissociable associations with age and cognitive decline

PONE-D-21-29634R1

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PLoS One. doi: 10.1371/journal.pone.0264999.r004

Acceptance letter

Bradley R King

1 Mar 2022

PONE-D-21-29634R1

Explicit and implicit timing in older adults: Dissociable associations with age and cognitive decline

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This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Descriptive statistics (mean and standard deviation) of our sample.

(DOCX)

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S2 Table. Summary of the model outputs for the explicit timing task from analyses including participants with a proportion of non-given responses lower than .1, .2, .3, or .4, respectively.

(DOCX)

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S3 Table. Summary of the model outputs for the implicit timing task from analyses including participants with a proportion of non-given responses lower than .1, .2, .3, or .4, respectively.

(DOCX)

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Data Availability Statement

All relevant data are available from Open Science Framework database (https://osf.io/sh492/).

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PERMALINK

Explicit and implicit timing in older adults: Dissociable associations with age and cognitive decline

Mariagrazia Capizzi

Antonino Visalli

Alessio Faralli

Giovanna Mioni

Roles

Abstract

Introduction

Fig 1.

Fig 2.

Method

Participants

Procedure and task

Statistical analyses

Results

Explicit timing task

Table 1. Model comparison for the explicit and implicit timing tasks showed that the best fitting model was the full model including the interval duration and both the MMSE score and age variables.

Table 2. Summary of the model outputs for the explicit timing task.

Fig 3. Interaction effects in the explicit timing task.

Implicit timing task

Table 3. Summary of the model outputs for the implicit timing task.

Fig 4. Interaction effects in the implicit timing task.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Bradley R King

Roles

Author response to Decision Letter 0

Decision Letter 1

Bradley R King

Roles

Acceptance letter

Bradley R King

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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