Explicit and implicit timing in healthy and pathological aging: Disentangling the role of age and cognitive functioning in temporal processing

This study aimed to test two common explanations for the general finding of age-related changes in temporal processing. The first one is that older adults have a real difficulty in processing temporal information as compared to younger adults. The second one is that older adults perform poorly on timing tasks because of their reduced cognitive functioning. These explanations have been mostly contrasted in explicit timing tasks, where participants are overtly informed about the temporal nature of the task. Fewer studies have instead focused on age-related differences in implicit timing tasks, where no explicit instructions to process time are provided. Moreover, the comparison of both explicit and implicit timing in older adults has been restricted to healthy aging only. Here, a large sample (N= 85) of healthy and pathological older participants completed explicit (time bisection) and implicit (foreperiod) timing tasks. Participants’ age and general cognitive functioning, measured with the Mini-Mental State Examination (MMSE), were used as continuous variables to explain performance on explicit and implicit timing tasks. Results showed a clear dissociation between the effects of healthy cognitive aging and pathological cognitive decline on processing of explicit and implicit timing. Whereas age and cognitive decline similarly impaired the non-temporal cognitive processes (e.g., memory for and/or attention to durations) involved in explicit temporal judgements, processing of implicit timing survived normal age-related changes. These findings carry important theoretical and practical implications by providing the first experimental evidence that processing of implicit, but not explicit, timing is differentially affected in healthy and pathological aging.

Explicit and Implicit Timing of Short Time Intervals: Using the Same Method

Up until now, there has been no study conducted in the field of time perception using very short intervals for a direct comparison between implicit and explicit timing tasks in order to uncover plausibly different underlying mechanisms. Therefore, the aim of this study was to compare human time estimation during implicit and explicit timing tasks with short intervals and the same method. A total of 81 adults were divided into three groups and completed two tasks with one of three different intervals: 500, 1,000, and 2,000 ms. The results revealed an overestimation for all three intervals of the implicit timing task, while participants overestimated 500 ms but underestimated 1,000 and 2,000 ms intervals of the explicit timing task. Moreover, explicit time estimation was more precise than implicit time estimation. We observed the opposite pattern as compared to a few previous studies with long intervals: Short intervals were perceived longer in the implicit timing task as compared to the explicit timing task. We concluded that nontemporal contents represent passing time during the implicit timing task but unlike temporal dimension during the explicit timing task. Therefore, even the same method of measurement led to a different performance in implicit and explicit timing tasks.

Dissociable influences of implicit temporal expectation on attentional performance and mind wandering

Mind wandering at critical moments during a cognitive task degrades performance. At other moments, mind wandering could serve to conserve task-relevant resources, allowing a brief mental respite. Recent research has shown that, if target timing is predictable, mind wandering episodes coincide with moments of low target likelihood. Conversely, mind wandering can be avoided at moments when targets are expected. In the current study, we tested whether mind wandering can be guided by implicit temporal expectations when target timing is less predictable. In two experiments (Experiment 1: N = 37, Experiment 2: N = 61), participants performed a sustained attention task in which target events were preceded by a variable pre-target interval (foreperiod). As time passes over the foreperiod duration, implicit target expectation increases, given that it has not yet appeared. In Experiment 1, all foreperiod durations were equally probable (uniform distribution: 2-10 seconds). This resulted in faster responses when targets were preceded by long compared to short foreperiods (foreperiod-effect). In contrast, mind wandering, assessed by thought probes inserted following short or long foreperiods, did not follow this pattern. In Experiment 2, alterations in the foreperiod distribution (left or right-skewed) resulted in changes in the behavioral foreperiod-effect, but mind wandering was unaffected. Our findings indicate that implicit timing strongly affects behavioral response to target events, but has no bearing on the mind wandering. Contrastingly, mind wandering did correlate with performance deterioration due to fatigue (time-on-task), suggesting that the thought probe method was sufficiently sensitive to behaviorally relevant changes in mental state.

An integral role for timing in interception

Timing is critical for myriad behaviors in dynamic environments. For example, to intercept an object, the brain must compute a reliable estimate of time-to-contact (TTC). Prior work suggests that humans compute TTC using kinematic information such as distance and speed without explicitly relying on temporal cues, just as one would do in a physics classroom using kinematic equations. Considering the inherent uncertainty associated with estimates of speed and distance and the ability of human brain to combine different sources of information, we asked whether humans additionally rely on temporal cues. We found that humans actively integrate speed information with both explicit and implicit timing cues. Analysis of behavior in relation to a Bayesian model revealed that the additional temporal information helps subjects optimize their performance in the presence of measurement uncertainty. These findings suggest that brain’s timing mechanisms are actively engaged while interacting with dynamic stimuli.

Research on Current Situations for Functional Classification of Timing

Time is a fundamental variable that must be quantified by organisms to survive. Depending on the previous functional definition, timing can be divided into explicit timing and implicit timing. For an explicit timing task, the estimation of the stimulus duration is given in the form of perceptual discrimination (perceptual timing) or a motor response (motor timing). For implicit timing, participants can subconsciously (exogenous) or consciously (endogenous) establish temporal expectation. However, the ability of humans to explicitly or implicitly direct attention in time varies with age. Moreover, specific brain mechanisms have been suggested for temporal processing of different time scales (microseconds, hundreds of milliseconds, seconds to minutes, and circadian rhythms). Furthermore, there have been numerous research studies on the neural networks involved in explicit timing during the measurement of sub-second and supra-second intervals.