Human thought is highly flexible, achieved by evolving patterns of brain activity across groups of cells. Neuroscience aims to understand cognition in the brain by analysing these intricate patterns. We argue this goal is impeded by the time format of our dataclock time. The brain is a system with its own dynamics and regime of time, with no intrinsic concern for the human-invented second. Here, we present the Brain Time Toolbox, a software library that retunes electrophysiology data in line with oscillations that orchestrate neural patterns of cognition. These oscillations continually slow down, speed up, and undergo abrupt changes, introducing a disharmony between the brain's internal regime and clock time. The toolbox overcomes this disharmony by warping the data to the dynamics of coordinating oscillations, setting oscillatory cycles as the data's new time axis. This enables the study of neural patterns as they unfold in the brain, aiding neuroscientific inquiry into dynamic cognition. In support of this, we demonstrate that the toolbox can reveal results that are absent in a default clock time format.
Studying dynamic cognitionEveryday tasks involve a plethora of cognitive functions that operate dynamically in tandem. Something as mundane as taking notes during a meeting or battling your friend in a video game requires attention, motor activity, perception, memory, and decision-making, each evolving over time. How does the brain achieve dynamic cognition? To answer this question, neuroscientists closely study how brain activity unfolds from one moment to the next using temporally precise neuroimaging methods. These include electroencephalography (EEG), magnetoencephalography (MEG), and single and multi-unit recordingsgrouped together under the term electrophysiology.
Seconds are foreign to the brainIn a typical electrophysiology study, neuroscientists first probe cognition by introducing an experimental manipulation. For example, an attention researcher might introduce a set of moving dots. Then, to understand cognition in the brain, they perform a series of analyses on the recorded data. They might study changes in scalp topography over a second of data, apply machine learning to characterize how the representation of the dots evolves, or perform any other time-dependent analysis.Critically, from the raw output of neuroimaging devices to the analysis of recorded brain signals, time is operationalized as clock timesequences of milliseconds. We claim that clock time, with all its benefits for human affairs, is generally inappropriate for neuroscience. This is because clock time is defined by us and for us, based on how long it takes for Earth to rotate its axis. The brain itself, however, employs its own regime of time, dictated by its own dynamics. As such, the brain is indifferent to how many milliseconds, seconds, minutes, or hours have passed unless it is expressly relevant for specific behaviour, such as maintaining circadian rhythms [1] or tracking a time-dependent reward [2]. Instead, the brain is conc...
