Lost in time: rethinking duration estimation outside the brain.

Our perception of the passage of time can suffer from significant distortions as time flies when we are busy and drags when we are bored. A prominent mechanistic model proposes that this perceptual volatility reflects changes in the activity dynamics of distributed neuronal ensembles referred to as population clocks because they encode time. In this framework, time is understood similarly to space (both can be segmented in seconds or centimeters) and duration estimation is primarily internal (the brain tells time). Here, I challenge this framework from the angle of Bergson’s proposal that the inner experience of time is unlike space because it is ever-changing and indivisible (2 successive seconds are not experienced equivalently). Quantifying and communicating this inner experience requires its externalization and immobilization through distance measurements derived from stereotyped movements and spatial metaphors (“short/long” durations; time “flies/drags”), which explains the habit of thinking time like space. In support of Bergson’s proposal, humans and animals heavily rely on movements in a variety of duration estimation tasks and the neural underpinnings of duration estimates overlap those of motor control and spatial navigation. Thus, philosophical and empirical arguments question whether duration estimation is fundamentally internal. Rather than being explained by ad hoc changes in the speed of population clocks, the puzzle of the volatility of time perception might resolve itself by considering that living beings lack the ability to internally measure time, which they compensate through interactions with regularities afforded by the world and symbolic representation drawn from space.

Does Sound Influence Perceived Duration of Visual Motion?

Multimodal perception is a key factor in obtaining a rich and meaningful representation of the world. However, how each stimulus combines to determine the overall percept remains a matter of research. The present work investigates the effect of sound on the bimodal perception of motion. A visual moving target was presented to the participants, associated with a concurrent sound, in a time reproduction task. Particular attention was paid to the structure of both the auditory and the visual stimuli. Four different laws of motion were tested for the visual motion, one of which is biological. Nine different sound profiles were tested, from an easier constant sound to more variable and complex pitch profiles, always presented synchronously with motion. Participants’ responses show that constant sounds produce the worst duration estimation performance, even worse than the silent condition; more complex sounds, instead, guarantee significantly better performance. The structure of the visual stimulus and that of the auditory stimulus appear to condition the performance independently. Biological motion provides the best performance, while the motion featured by a constant-velocity profile provides the worst performance. Results clearly show that a concurrent sound influences the unified perception of motion; the type and magnitude of the bias depends on the structure of the sound stimulus. Contrary to expectations, the best performance is not generated by the simplest stimuli, but rather by more complex stimuli that are richer in information.

The Blursday Database: Individuals’ Temporalities in Covid Times

The Covid-19 pandemic and associated lockdowns triggered worldwide changes in the daily routines of human experience. The Blursday database provides measures of subjective time and related processes from more than 2,800 participants (over 9 countries) tested on 14 questionnaires and 15 behavioral tasks during the Covid-19 pandemic. The easy-to-process database and all data collection tools are made fully accessible to researchers interested in studying the effects of social isolation on temporal information processing, time perspective, decision-making, sleep, metacognition, attention, memory, self-perception, and mindfulness. Blursday also includes vital quantitative statistics such as sleep patterns, personality traits, psychological well-being, and lockdown indices. Herein, we exemplify the use of the database with novel quantitative insights on the effects of lockdown (stringency, mobility) and subjective confinement on time perception (duration, passage of time, temporal distances). We show that new discoveries are possible as illustrated by an inter-individual central tendency effect in retrospective duration estimation.

Temporal Alignment but not Complexity of Audiovisual Stimuli Influences Crossmodal Duration Percepts

Reliable duration perception is an integral aspect of daily life that impacts everyday perception, motor coordination, and subjective passage of time. The Scalar Expectancy Theory (SET) is a common model that explains how an internal pacemaker, gated by an external stimulus-driven switch, accumulates pulses during sensory events and compares these accumulated pulses to a reference memory duration for subsequent duration estimation. Second-order mechanisms, such as multisensory integration (MSI) and attention, can influence this model and affect duration perception. For instance, diverting attention away from temporal features could delay the switch closure or temporarily open the accumulator, altering pulse accumulation and distorting duration perception. In crossmodal duration perception, auditory signals of unequal duration can induce perceptual compression and expansion of durations of visual stimuli, presumably via auditory influence on the visual clock. The current project aimed to investigate the role of temporal (stimulus alignment) and nontemporal (stimulus complexity) features on crossmodal, specifically auditory over visual, duration perception. While temporal alignment revealed a larger impact on the strength of crossmodal duration percepts compared to stimulus complexity, both features showcase auditory dominance in processing visual duration.

Machine learning - based framework for construction delay mitigation

The construction industry, for many decades, has been underperforming in terms of the success of project delivery. Construction delays have become typical of many construction projects leading to lawsuits, project termination, and ultimately dissatisfied stakeholders. Experts have highlighted the lack of adoption of modern technologies as a cause of underproductivity. Nevertheless, the construction industry has an opportunity to tackle many of its woes through Construction 4.0, driven by enabling digital technologies such as machine learning. Consequently, this paper describes a framework based on the application of machine learning for delay mitigation in construction projects. The key areas identified for machine learning application include "cost estimation", "duration estimation", and "delay risk assessment". The developed framework is based on the CRISP-DM graphical framework. Relevant data were obtained to implement the framework in the three key areas identified, and satisfactory results were obtained. The machine learning methods considered include Multi Linear Regression Analysis, K-Nearest Neighbours, Artificial Neural Networks, Support Vector Machines, and Ensemble methods. Finally, interviews with professional experts were carried out to validate the developed framework in terms of its applicability, appropriateness, practicality, and reliability. The main contribution of this research is in its conceptualization and validation of a framework as a problem-solving strategy to mitigate construction delays. The study emphasized the cross-disciplinary campaign of the modern construction industry and the potential of machine learning in solving construction problems.

PSM: A New Approach to Probabilistic Scheduling

A new scheduling method, where probability values can be assigned to activity durations, is proposed in this thesis. Probabilistic Scheduling Method (PSM) accepts activity durations tagged with probability or confidence intervals. Tests were carried out using examples of 3,7, and 9 activities to evaluate PSM's practical capability. The comparisons of PSM to Critical Path Method (CPM), Performance Evaluation and Review Technique (PERT), and Monte Carlo application to PERT (MC PERT) conclude that PSM results in similar most probable duration estimation. Further tests were implemented to evaluate PSM's capability to project schedule revision on an ongoing project. A microsoft Excel application was used to organize tests data and calculations. PSM computations are more industry friendly. They allow for a range of duration associated with a range of probabilites. PSM provides flexibility and simplicity, and also dependency information that will benefit its user in decision making

PSM: A New Approach to Probabilistic Scheduling

A new scheduling method, where probability values can be assigned to activity durations, is proposed in this thesis. Probabilistic Scheduling Method (PSM) accepts activity durations tagged with probability or confidence intervals. Tests were carried out using examples of 3,7, and 9 activities to evaluate PSM's practical capability. The comparisons of PSM to Critical Path Method (CPM), Performance Evaluation and Review Technique (PERT), and Monte Carlo application to PERT (MC PERT) conclude that PSM results in similar most probable duration estimation. Further tests were implemented to evaluate PSM's capability to project schedule revision on an ongoing project. A microsoft Excel application was used to organize tests data and calculations. PSM computations are more industry friendly. They allow for a range of duration associated with a range of probabilites. PSM provides flexibility and simplicity, and also dependency information that will benefit its user in decision making

Serial Dependence in Temporal Perception Reveals the Dynamics of Constructing an Internal Reference Frame

Temporal perception is crucial to cognitive functions. To better estimate temporal durations, the observers need to construct an internal reference frame based on past experience and apply it to guide future perception. However, how this internal reference frame is constructed remains largely unclear. Here we showed the dynamics of the internal reference construction from the perspective of serial dependence in temporal reproduction tasks. We found the current duration estimation is biased towards both perceived and reproduced durations in previous trials. Moreover, this effect is regulated by the variability of sample durations. The influence of previous trials was stronger when the observers were exposed to context with more variable durations, which is inconsistent with previous theories that the similarity between successive stimuli induces serial dependence. We proposed a Bayesian model with an adaptive reference updated continuously after each observation, which can better explain the serial dependence observed in temporal perception.