The Development and Accuracy of Time Perception

Post by Lani Cupo

The adage “time flies when you’re having fun” may be a cliché, but most people would agree that their favorite activities seem to fly by, while events they dread seem to pass by slowly. Holding a plank, for example, for 90 seconds may feel like forever while browsing social media for ten minutes can pass in the blink of an eye. How does our time perception develop and how accurate are we in perceiving time?

How does the perception of time develop?

Time perception is an essential part of human life and survival and infants begin learning about time from birth. Psychological studies demonstrate that infants can detect changes in repetitive sequences of stimuli at predictable intervals. For example, in a study from the 1970s, infants placed in a dark room were exposed to light every 20 seconds for 4 seconds, triggering a constriction in their pupils. After a learning phase, their pupils continued to constrict after 20 seconds, even when the light did not change, suggesting the infants already have an internal mechanism for keeping track of time. The emotion of surprise can be measured in infants by assessing their gaze because they will spend more time looking at surprising events. Using this approach, researchers found longer gaze time when event duration changed (such as the puppet opening its mouth for 4 seconds instead of 2), further suggesting that infants keep track of the passage of time and recognize when the duration of a rhythmic event changes.

An infant’s sense of time develops over childhood. Children and adults are able to successfully judge when a second stimulus matches the duration of the first, and accuracy at this task improves between the ages of 3 and 10. A “time bisection” has been used to measure time perception. In this task, participants learn two “anchor” stimuli, one short and one long, and are then exposed to new stimuli of various lengths. Participants must decide which “anchor” stimuli match the novel stimuli most. This allows researchers to study participants’ sense of time. Newborns can differentiate between stimuli with ratios of 1:2 (such as 5 seconds and 10 seconds), but older infants (10 months) can distinguish between more difficult ratios of 2:3 (such as 6 seconds and 9 seconds). While some adults can distinguish closer ratios, there is considerable inter-subject variability. These findings suggest that our time perception abilities improve throughout the first decade of life.

As children get older, they develop explicit knowledge of the concept of time. Time distortions, like perceiving the passage of time as slower or faster than in reality, are more common among young children. One potential explanation is that an explicit understanding of time is linked to several other psychological processes which are still being developed in children. For example, working memory capacity increases between childhood and adulthood, which may contribute to decreased time distortions. In fact, a recent study associated both working memory capacity and concentration capacity with improved performance on a time bisection task across ages. This suggests as the capacity for working memory develops, so does the precision of time perception. As children develop cognitive capabilities, they become less susceptible to time distortions. Some experiments suggest the accuracy of time perception peaks in adolescence when compared to children and adults.

How accurate is our time perception?

Recent research suggests that healthy adults may perceive time as passing slower than it actually does, which may not be a bad thing. Compared to patients with orbitofrontal cortex lesions and borderline personality disorder, healthy participants in one study overestimated the duration of 60 and 90-second time intervals, whereas the patient groups were much more accurate. Results of this study suggest individuals with greater impulsivity or frustration perceive time as moving more quickly than neurotypical individuals.

There is some evidence to suggest that time perception is subject to Weber’s Law, which states that the noticeable change in a stimulus is a constant related to the original value of the stimulus. For example, if you start with a one-pound weight and add another one-pound weight, the difference between the weights will be drastic. However, if you start with a fifty-pound weight, adding another pound will be much less noticeable. In the same way, adding two seconds to a duration of ten seconds is noticeable, however adding two seconds to a duration of 90 seconds may not be.

But, if healthy adults perceive time as passing more slowly than it really does, what is the explanation for “time flying by”? Emotions and time are also intricately connected. For example, research shows that emotional events that sustain our attention speed up time, while more neutral events that are emotionally distracting can slow down our perception of time. Research has also shown that while shorter time increments, such as hours, days, or weeks were perceived similarly across ages, decades were seen as passing more quickly with age. One explanation could be that pressures and responsibilities increase with age, such as professional activities and family duties. Further, time often seems to pass more slowly during periods of learning, which are often concentrated before the mid-20s. However, there are certain limitations to using interviews to understand the perception of time, such as the limited experience younger participants have with “past decades.” 

Additionally, approach vs. withdrawal motivation has been implicated in altering the perception of time. One study examined the impact of movie viewing on time perception, finding scary movies slowed down time perception, but sad movies did not. This suggests that some emotions like fear, eliciting motivation to withdraw, could serve as an evolutionary advantage in slowing down time perception, giving people more time to react to dangerous situations.

Experimental stimuli in research have been shown to impact time perception. In the laboratory, the modality of a presented stimulus impacts the assessment of duration. For example, visual stimuli are judged to be shorter than equal-length auditory stimuli. The reason behind this discrepancy is still subject to debate, however, one hypothesis is related to a theory about the internal representation of time. The model relies on an internal clock mechanism with a “pacemaker”, switch, and accumulator. While trying to keep track of the time the switch is flipped, the pacemaker ticks time by, and these ticks are summed in the accumulator. However, in this model, ticks accumulate more quickly for auditory stimuli than visual stimuli.

What brain regions underly systems of time perception?

One recent hypothesis links the perception of time with motor processes, as time perception provides important information on when and how to move. Functional magnetic resonance imaging (fMRI) acquired during visual and auditory rhythm tasks revealed temporal processing was associated with activity in premotor regions, the supplementary motor areas (SMA), as well as the basal ganglia. Regardless of the duration estimated, other studies have provided evidence for the role of the preSMA, the anterior cingulate cortex, the premotor cortex, and the basal ganglia. It’s also been suggested that activation of these regions measured with fMRI during time perception can be associated with different aspects of the internal clock, with the basal ganglia and SMA involved in time-keeping and more frontal regions recruited for attention, regardless of the task. Further, the insula is known to be involved in the emotional aspects of time-keeping and is thought to support the mediation between emotion and time perception.

What’s next?

Time perception begins at a young age, but the concept of external time is established over the span of childhood. As we age, how we perceive the passage of time continues to change. Lifestyle and life events can impact time perception, however more temporary changes, such as emotional state also impact how quickly time seems to pass. More research is needed to solidify theories surrounding time perception and to better determine causal links between our internal and external environments and how we perceive time. Further, research is needed to understand the real-world implications of how time perception can affect our everyday lives. Ultimately, time perception is a dynamic process that evolves and changes over the lifespan, and we still have a lot to learn about how the brain processes and experiences time.

References +

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Does the Placebo Effect Work When You Know It’s a Placebo?

Post by Christopher Chen

The takeaway

Placebos can provide emotional and physiological benefits, but their use raises ethical questions due to the use of deception. New evidence suggests off-label placebos (OLPs) which do not rely on deception, may also have positive health benefits.

What's the science?

Placebos are a form of patient treatment that have no inherent therapeutic value (e.g., sugar pills, saline injection). However, studies time and time again have validated a “placebo effect,” where people who take placebos may still undergo physiological changes that enhance overall health. For example, functional brain imaging reveals placebos can modulate brain circuits associated with emotional regulation and pain, resulting in a painkilling effect. However, ethical concerns surround the use of traditional placebos because they are given to patients who believe the placebo has therapeutic value. To avoid this, researchers have turned to the use of open-label placebos (OLPs), or placebos that patients know have no therapeutic value. Recent studies show patients taking OLPs report symptomatic relief from maladies like depression, anxiety, and irritable bowel syndrome. However, whether OLPs elicit neurological changes like conventional placebos remains unclear. In a recent article in Neuropsychopharmacology, Schaefer et al. reveal that people taking OLPs exhibited enhanced activity in regions associated with emotional regulation and pain, suggesting OLPs do elicit neurological changes even when people know they are placebos.

How did they do it?

Researchers ran two experiments: one tested the effects of OLPs on participant mood and the other tested OLP effects on brain activity. In the first experiment, participants were divided into two groups. One group was told a nasal spray they were taking was an OLP and potentially had health benefits, while the other group was told the nasal spray was a necessary part of the experiment. Following the administration of the nasal spray, both groups went through the same visual task of ranking a series of pictures depicting neutral images or images designed to elicit strong negative feelings. Following the presentation of each picture, participants described their emotional state. The second experiment played out similar to the first, but participants were instead shown the images while inside an MRI (magnetic resonance imaging) machine undergoing an fMRI protocol designed to gauge blood flow as a measure of brain activity. In this experiment, the description of emotional state was given after the MRI portion was complete.

What did they find?

In the first experiment, researchers found that participants from both groups responded similarly to the neutral images, but that participants who were told of the health benefits of OLPs reacted less strongly to the emotional images, suggesting placebos can still help people emotionally regulate even when they know it is a placebo. The second experiment went a step further, showing that the OLP group had greater activation in two regions also known to be activated by traditional placebos, the periaqueductal gray (PAG) and anterior cingulate cortex (ACC). Interestingly, there were two additional key differences in the neural signature of OLPs: 1) activation in the hippocampus, a brain region not known to be activated by normal placebos, and 2) no activation in the prefrontal cortex, a region known to be activated in normal placebos.

What's the impact?

The present study reveals that OLPs elicit activation patterns in the brain that are distinct from patterns associated with traditional placebos. The authors suggest that the lack of PFC activation in OLP treatments may indicate that the PFC is somehow linked to brain processing of deception. The activation of the hippocampus in OLP treatment but not conventional placebo treatments may also indicate how removing deception from the placebo effect may activate more hippocampal-driven processing of emotion and pain. While more research is needed to further validate these findings, this work suggests that the placebo effect holds, even when we know it’s a placebo.

Decoding Hippocampal Activity in Spatial Learning

Post by Elisa Guma

The takeaway

Animals need to learn how to find food, water, or other rewarding stimuli in their environment. To learn how to navigate to these rewards, the CA1 region of the hippocampus uses a common learning algorithm by which synchronized activity activates reinforcement learning in both navigational and non-navigational contexts.

What's the science?

Neural activity in the dorsal CA1 region of the hippocampus is critical for our ability to successfully navigate through space, but also for creating cognitive maps of our environment. These neurons are active during navigation, but they also show bursts of brief synchronous activity in non-navigational contexts (e.g., while animals are at rest or asleep), which may reflect memory recall or consolidation, and may suggest a common algorithm for coding reward in both navigational and non-navigational contexts. This week in Nature Neuroscience, Jiang and colleagues investigate the role of the hippocampus in a navigational and non-navigational foraging task by imaging neural activity in the CA1 region of the mouse hippocampus, while they perform these tasks.

How did they do it?

The authors trained mice in either a navigational or non-navigational spatial foraging task while recording neural activity of the dorsal hippocampal CA1 neurons.  Mice expressing a fluorescent calcium sensory (CGamp6f) - which fluoresces in the presence of calcium, a proxy for neural activity - were mounted with a miniscope over the dorsal hippocampus, which allowed the authors to record cell activity (fluorescence) during each task. 

The navigational task required freely moving mice to run to an unmarked target location within a few centimeters of a reward collection area. In the non-navigational task, head-fixed mice had to displace a spring-loaded joystick from a center position to a target distance. In both tasks, reward delivery was dissociated via movement into the target location. In the navigational task, this was accomplished by delivering the reward via a water port at a specific home location (20 cm away from the target location). In the non-navigational task, this was achieved by delivering the water reward with a 1-second delay after movement to the target area.

They examined cell activity during each of the foraging tasks and asked whether responses of individual dorsal CA1 neurons aligned to movements triggered by the water reward. To determine whether patterns of neural activity in the dorsal CA1 region of the hippocampus could predict behaviour, they trained a continuous-time linear decoder (machine learning model) to examine whether movement trajectories could be decoded in both tasks.

The authors identified a specific population of neurons that were synchronously active during non-movement portions of the task, so they wanted to investigate their role in task performance. To test their causal role in consolidating learning in the navigational and non-navigational tasks, the authors used optogenetics to inhibit neural activity in the dorsal CA1 region, at precisely the time that the synchronous population events were observed. A disruption of learning behaviour in response to this manipulation would indicate that the synchronous firing of those neurons was critical for this type of learning.

What did they find?

Using the head-mounted miniscope to measure neural activity in the dorsal CA1, the authors were able to detect place field activity in the dorsal CA1 cells along the foraging trajectories, as expected. In both the freely moving and the head fixed tasks, they found that the reliability of activity was high during movement and low during the inter-trial periods. The neural decoder they had trained was successfully able to predict the foraging trajectory of mice based on the activity of the dorsal hippocampus in the freely moving navigational task. However, in the head-fixed task, it was not able to decode forelimb activity based on CA1 activity, suggesting that these cells may be creating a spatial map of reward locations, not possible in the head-fixed task.

In addition to the cell activity observed during the movement portion of the task, the authors identified a subset of neurons that were synchronously active in the absence of movement. In the freely moving navigational foraging task, these events were observed at the end of a foraging run as the mouse approached the reward collection area, and they were more correlated with correct vs. incorrect attempts (although not significantly so). In contrast, in the non-navigational head-fixed task, these synchronous population events were occurring at the initiation of a trial, and they were highly correlated with the quality of task performance in that trial.

Inactivation of dorsal CA1 cells using optogenetics during the navigation tasks did not interfere with the behaviours. However, if the cells were inactivated during the time at which the synchronous population events were observed (i.e., at the end of the navigational task), trial performance was decreased. In contrast, in the non-navigational task, inactivation with optogenetics substantially reduced the probability of initiating a joystick movement, but if mice were indeed able to initiate movement, they were still capable of making coordinated movements of the joystick to trigger reward. This suggests that these circuits may be more important for navigational learning.

What's the impact?

This study provides evidence for different mechanisms by which the dorsal CA1 region of the hippocampus regulates reinforcement learning in navigational and non-navigational contexts.  Activation of synchronous bursts of activity in this region thought to underlie cognitive processing, was associated with successful trial completion if they occurred at the end of the navigational task and at the beginning of the non-navigational task. Interestingly, inactivation of the dorsal hippocampus at the end of a navigational task impaired reinforcement-dependent learning but did not have the same effects in the non-navigational task. This study provides a computational approach to better understand the neural mechanisms underlying spatial navigation and foraging behaviours.

Access the original scientific publication here.