'Critical flicker fusion' test can measure the brain's processing speed

Sampling rates are important when we want to observe or record phenomena extending through time – for a stream of information of a given bit depth, we can capture a richer and more accurate portrait of whatever it is that we’re processing when we take samples or measurements more times per second. We measure the quality of an audio file in its bitrate, the performance of a monitor in its refresh rate, and the smoothness of a video by its frame rate. Now scientists from the University of Georgia have devised an elegant method of testing the visual sampling rate(Opens in a new window) of the human brain.

The researchers used a metric called critical flicker fusion to assess the sampling rate of the brain in this recent experiment involving two cohorts: college-aged (average: 21 years old) and elderly (average: 72 years old) participants. Critical flicker fusion occurs when the observer can no longer distinguish between changing visual stimuli, like two colors of light flickering at increasing frequencies, the approach used by the researchers in this experiment. Many factors act upon the sampling rate of the human eye, but the processing speed of the brain determines the rate at which it can use the information provided to it by the optic nerve. Individuals in either cohort with a higher critical flicker fusion score went on to score higher, in the second half of the experiment, on tests of executive function: cognitive tasks requiring planning, reflection, and self-control.

The principle at work here is the same principle behind audio clipping. MP3s are a container that we use to compress the large amount of information required to reproduce a complex audio waveform into a format that can fit on storage media in use today. To compress a WAV means that we use a less-exact but representative approximation of the waveform to reconstruct the original noise to an acceptable level of accuracy at a smaller file size. In low-quality audio files, this results in noise and distortion, especially the “clipping” of certain high and low frequencies that aren’t accounted for by the original approximation and therefore can’t be accurately reproduced by playing back a file compressed using that approximation. We call this “lossy” compression. Similarly, the brain is receiving a less lossy data stream when it functions at a higher processing rate. We can assess the processing rate of the brain by testing its sampling rate, as measured by the rate of critical flicker fusion.

But noise isn’t the only important implication of cognitive processing speed. Reaction time is a manifestation of latency in the neuronal networks that make up the human nervous system. Between the point when a receptor senses a stimulus and the point when we send a motor response, the information gathered by the receptor must travel through several different regions of the brain involved in perception and association. This creates an additive time delay associated with the propagation speed of signals down axons and through neuronal networks, and also with the rate at which those neurons can fire. Neurons that can fire at a higher frequency contribute less to that additive delay, and therefore allow a smaller reaction time.

Furthermore, the processing speed of the brain is important to the ongoing battle against aging-related brain disorders like Alzheimer’s. Slower cognitive processing, according to the NIH, is “a primary predictor of the cognitive declines that older adults experience.” With finer-grained methods of detecting decline in cognitive function, we have a better shot at early detection of disorders like Alzheimer’s that have cognitive symptoms. Catching such diseases sooner and more accurately could open up insight into the processes by which they develop, with hopes of an eventual cure.