Cognitive Aging and Time Perception: Roles of Bayesian Optimization and Degeneracy

Abstract

This review outlines the basic psychological and neurobiological processes associated with age-related distortions in timing and time perception in the hundredths of milliseconds-to-minutes range. The difficulty in separating indirect effects of impairments in attention and memory from direct effects on timing mechanisms is addressed. The main premise is that normal aging is commonly associated with increased noise and temporal uncertainty as a result of impairments in attention and memory as well as the possible reduction in the accuracy and precision of a central timing mechanism supported by dopamine-glutamate interactions in cortico-striatal circuits. Pertinent to these findings, potential interventions that may reduce the likelihood of observing age-related declines in timing are discussed. Bayesian optimization models are able to account for the adaptive changes observed in time perception by assuming that older adults are more likely to base their temporal judgments on statistical inferences derived from multiple trials than on a single trial's clock reading, which is more susceptible to distortion. We propose that the timing functions assigned to the age-sensitive fronto-striatal network can be subserved by other neural networks typically associated with finely-tuned perceptuo-motor adjustments, through degeneracy principles (different structures serving a common function).

Keywords: attention; clock; decision-making; interval timing; memory; striatal beat-frequency model.

Figure 1

Reproduced time (s) in a peak-interval procedure with 8-s and 21-s target durations for young (19–28 years, n = 7) and aged (67–90 years, n = 12) participants as a function of 30 min blocks during a 2 h session without feedback. Participants received training with inter-trial interval feedback prior to the discontinuation of feedback during the test phase. Data are taken from test sessions described by Malapani et al. (1998) and Rakitin et al. (1998). See Lustig and Meck (2005), Lake and Meck (2013), and Yin et al. (2016a) for a review of the benefits of the peak-interval procedure.

Figure 2

Spontaneous motor tempo (SMT) across age and its associated variability at the beginning and end of testing. The top two panels show the mean coefficient of variation (CV) across the first 3 trials (beginning) and the last 3 trials (end), each CV equaling to the standard deviation (SD) of inter-tap intervals (ITI) divided by its mean on a given 30-s trial) across Age (A) and mini mental state examination (MMSE) scores (B). The bottom two panels show the SD of the mean ITI across the 3 trials at the beginning and end of the study across Age (C) and MMSE scores (D). Data represent additional analyses of the findings reported in Turgeon and Wing (2012).

Figure 3

Outline of the Striatal Beat-Frequency (SBF) model of interval timing with the incorporation of a cerebellar adjustment mechanism into the time code. (A) At the start of a to-be-timed signal a phasic pulse of dopamine from the ventral tegmental area (VTA) synchronizes cortical oscillations. Cortical oscillations in areas such as the prefrontal cortex (PFC) can be modulated with synchronous stimuli possibly through efferents from the thalamus. (B) These dispersed cortical neurons synapse onto medium spiny neurons (MSNs) within the striatum, which are activated at specific target durations based on the oscillatory activity pattern of synapsing projections. These neurons project from non-motor regions in the thalamus such as the caudal portion of ventralis lateralis, pars caudalis (VLcc) and receive extensive inputs from the cerebellar dentate nucleus (DN). (C) The DN also exhibits changes in population activity in response to synchronous stimuli, which may drive the modulation seen in cortical regions of the cerebrum. (D) The change in DN activity is likely modulated by decreases in tonic Purkinje cell activity (pauses) allowing for the precise tuning of timing mechanisms through disinhibition. Adapted from the initiation, continuation, adjustment, and termination (ICAT) model of temporal integration described by Lusk et al. (2016) and Petter et al. (submitted).