Abstract
Fluctuations with power-law scaling and long-range temporal correlations (LRTCs) are characteristic to human psychophysical performance. Systems operating in a critical state exhibit such LRTCs, but phenomenologically similar fluctuations and LRTCs may also be caused by slow decay of the system’s memory without the system being critical. Theoretically, criticality endows the system with the greatest representational capacity and flexibility in state transitions. Without criticality, however, slowly decaying system memory would predict inflexibility. We addressed these contrasting predictions of the ‘criticality’ and ‘long-memory’ candidate mechanisms of human behavioral LRTCs by using a Go/NoGo task wherein the commission errors constitute a measure of cognitive flexibility. Response time (RT) fluctuations in this task exhibited power-law frequency scaling, autocorrelations, and LRTCs. We show here that the LRTC scaling exponents, quantifying the strength of long-range correlations, were negatively correlated with the commission error rates. Strong LRTCs hence parallel optimal cognitive flexibility and, in line with the criticality hypothesis, indicate a functionally advantageous state. This conclusion was corroborated by a positive correlation between the LRTC scaling exponents and executive functions measured with the Rey-Osterrieth Complex Figure test. Our results hence support the notion that LRTCs arise from critical dynamics that is functionally significant for human cognitive performance.
Highlights
Human psychophysical performance fluctuates in time scales from seconds to tens or hundreds of seconds so that similar behavioral outcomes are much more likely to appear in clusters than expected by chance[1,2,3,4]
The presence of slow fluctuations and power-law long-range temporal correlations (LRTCs) in response time (RT) time series was observed with detrended fluctuation analysis (DFA) (Fig. 1f) that is a robust indicator of scale-free dynamics
We addressed whether critical-state or long-memory dynamics was a more likely explanation for the 1/f -like fluctuations and LRTCs in human behavioral time series
Summary
Human psychophysical performance fluctuates in time scales from seconds to tens or hundreds of seconds so that similar behavioral outcomes are much more likely to appear in clusters than expected by chance[1,2,3,4]. Power-law scaling and LRTCs are suggestive of the underlying neuronal systems operating in or near a critical state[6, 16,17,18,19] Such scale-free dynamics, could be explained without criticality[20,21,22,23] by the system having a slowly decaying memory and the past neuronal dynamics influencing the future with a long-range memory based continuity. Scale-free dynamics can arise from a persistent, slowly decaying dependence of the current dynamics on the dynamics in the past[20,21,22,23, 26] This ‘long-memory’ hypothesis would predict LRTCs to be negatively correlated with dynamic flexibility because the greater the system’s dependency of its past is, the more difficult it is to undergo flexible reconfigurations. In the criticality context, the attenuation of LRTCs during sensory stimulation has been attributed to disruption of the endogenous critical dynamics[4]
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