by Isabella Grabski
figures by Allie Elchert

There are many ways in which our brains track, process, and use time to help us function. One mechanism by which they do so is motor timing. Motor timing relates to our ability to carry out any physical task where time estimation, often done unconsciously, is needed to successfully coordinate our movements. Take, for instance, the task of playing a musical instrument. The musician has to execute many different physical actions with their breath and fingers at varying intervals of time, or else the song will not sound correct.

Beyond music, many of our daily movements, such as driving, walking, and speaking, all necessitate moving different parts of our body at the appropriate rhythm and time intervals. We don’t stare at a stopwatch to figure out when to pick up our foot for our next step, so how do our brains keep track of time while coordinating motor tasks? Although neuroscientists still aren’t completely sure how this works, two key competing types of models, known as dedicated and intrinsic models, exist, and recent compelling evidence has begun to point to a mechanism.

Dedicated models: scalar expectancy theory

Dedicated models are the older and more traditional class of theories to aim to explain motor timing. These models posit that there is a dedicated structure of the brain – be it a brain region, group of neurons, or something else entirely – responsible for processing the passage of time.

One traditional idea aligned with this class of models is called scalar expectancy theory. This theory posits the existence of a dedicated structure, hypothesized to consist of three components: a pacemaker, a switch, and an accumulator (Figure 1). The pacemaker continuously creates impulses analogous to the way a clock ticks off seconds. At the beginning of an action, such as singing a song or taking a step, the switch closes to indicate the start of timing; then, the number of impulses from the pacemaker are recorded by the accumulator. The longer the switch remains closed, the more impulses are recorded by the accumulator. The amount of impulses can then be used to represent how much time has passed during the interval, which can in turn inform when to take the next action.

Scalar expectancy theory is considered among the most influential models of timing. However, it was based primarily on evidence from early behavioral experiments investigating how consistently people perform time-sensitive actions, rather than from more advanced imaging studies or measurements of brain activity during such actions. As a result, descriptions of the various components of the dedicated structure – the pacemaker, switch, and accumulator – were typically abstract, without corresponding candidate brain structures or types of neural mechanisms that would drive them.

Figure 1: Under scalar expectancy theory, our brains track time with three components: a pacemaker creating impulses, a switch to begin timing, and an accumulator to count how much time has passed.

As technology has progressed and different types of experimental evidence have become available, various brain regions, such as the hippocampus or cerebellum, have been suggested as possible homes for this dedicated structure. However, this new evidence has also given rise to another class of models, known as intrinsic models. 

Intrinsic models: population clocks

Intrinsic models paint a completely different picture of how time is processed for motor tasks. Under these models, there is no single part of the brain that serves as our internal clock. Instead, they suggest that time is instead an inherent part of how neurons operate. It is the dynamic behavior of these neurons that underlies timing for the various physical actions we perform everyday. 

One example of an intrinsic model is known as the population clock model. This model posits that in the brain regions responsible for motor tasks, time is encoded in the behavior of populations of neurons. Unlike dedicated models, there is not a single, central structure responsible for keeping time in the entire brain. Rather, many different neuronal populations localized throughout the entire brain are responsible for time processing associated with various motor activities.

In these populations, each neuron’s activity – such as whether or not it is firing (that is, sending signals to other neurons), or the rate at which it fires – changes over time, so from one moment to the next, the overall pattern of activity across the entire population is changing. As a simple example, suppose the population consists of just three neurons. At one moment in time, one neuron may be firing while the other two are not. At another moment, all three neurons might be firing, but the second is firing much faster than the other two. Each of these could be considered a different overall pattern of neuronal activity. Neurons outside this population can recognize such patterns and use these to time their corresponding actions. Since these patterns change dynamically over time, the end result is a set of actions taken in sequence to correspond to particular timing. This allows time to be naturally encoded into the changing patterns of neuronal populations.

There is experimental support for the existence of these population clocks. One such study measured neuronal activity in the zebra finch, a type of bird that sings a song with very complex timing patterns. The research team identified a population of neurons in which a different neuron or set of neurons fires at each measured point in time. Each such pattern of firing activity triggers a process creating a different vocalization, resulting in a song that follows a consistent timing pattern every time it’s sung, despite its complexity (Figure 2).

Figure 2: Under the population clock model, the pattern of activity in populations of neurons changes over time, triggering different actions such as singing various musical notes in response to each pattern.

Although the population clock model is an improvement when it comes to our understanding of motor timing, many questions about this phenomenon are still left unanswered. For example, how exactly these time-dependent patterns are created among neuronal populations is still unknown. What ensures that these patterns can be reproduced in roughly the same way every time the task is carried out is also an open question. Additionally, it is not always clear how the neurons outside the population know what to do in response to the different patterns, nor how many population clocks might exist in the brain. 

Answering these questions and validating the population clock model as a whole will require more hypothesizing and experiments. The work that has been done so far, however, points to theories like the population clock model as a promising direction for understanding the very complex processes underlying our brain’s ability to measure, process, and use time. Aside from deepening our insight into the brain, creating a better picture of motor timing can also help us learn how to help people with brain injuries or neurological disorders that result in deficits in motor timing and coordination. If we better understand how motor timing works, we may be able to help create a path forward to treat such debilitating conditions.

Isabella Grabski is a 4th year PhD student in the Biostatistics department. 

Allie Elchert is a second-year Ph.D. student in the Biological and Biomedical Sciences program at Harvard Medical School.

Cover Image: “Clock Gears” by gamp is licensed under CC BY-NC-SA 2.0

For More Information:

  • For a broader overview of time perception at large, check out this review article.
  • This overview of dedicated and intrinsic models gives a deeper dive into the two classes of theories.
  • More detailed information and examples of population clocks can be found in this paper.

One thought on “How Our Brains Estimate Time

  1. An interesting summary. It does seem odd that the piece fails no mention ecological models of timing (tau)l. Additionally, nothing is stated about Bernstein’s problem.

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