Time scales in cognitive neuroscience

Abstract

Cognitive neuroscience boils down to describing the ways in which cognitive function results from brain activity. In turn, brain activity shows complex fluctuations, with structure at many spatio-temporal scales. Exactly how cognitive function inherits the physical dimensions of neural activity, though, is highly non-trivial, and so are generally the corresponding dimensions of cognitive phenomena. As for any physical phenomenon, when studying cognitive function, the first conceptual step should be that of establishing its dimensions. Here, we provide a systematic presentation of the temporal aspects of task-related brain activity, from the smallest scale of the brain imaging technique's resolution, to the observation time of a given experiment, through the characteristic time scales of the process under study. We first review some standard assumptions on the temporal scales of cognitive function. In spite of their general use, these assumptions hold true to a high degree of approximation for many cognitive (viz. fast perceptual) processes, but have their limitations for other ones (e.g., thinking or reasoning). We define in a rigorous way the temporal quantifiers of cognition at all scales, and illustrate how they qualitatively vary as a function of the properties of the cognitive process under study. We propose that each phenomenon should be approached with its own set of theoretical, methodological and analytical tools. In particular, we show that when treating cognitive processes such as thinking or reasoning, complex properties of ongoing brain activity, which can be drastically simplified when considering fast (e.g., perceptual) processes, start playing a major role, and not only characterize the temporal properties of task-related brain activity, but also determine the conditions for proper observation of the phenomena. Finally, some implications on the design of experiments, data analyses, and the choice of recording parameters are discussed.

Near-Critical Dynamics in Stimulus-Evoked Activity of the Human Brain and Its Relation to Spontaneous Resting-State Activity

“…The scale invariance of neuronal avalanches enables their detectability even at large-scale activity of the entire human cortex observed using noninvasive neuroimaging methods. Recently, using MEG, cortical activity of healthy human subjects was shown to consist of neuronal avalanches at both the sensor (Shriki et al, 2013) and the source level (Palva et al, 2013), suggesting that it is in a critical state. Here, we were able to demonstrate that the human brain maintains critical dynamics during different cognitive states, namely, rest and stimulus-evoked.…”

“…Data processing and analysis were performed using MATLAB 2011b (MathWorks) and FieldTrip open-source toolbox for Advanced MEG Analysis (Oostenveld et al, 2011).…”

“…To date, criticality in humans as indicated by the neuronal avalanche analysis has been studied mostly in the context of resting activity (Tagliazucchi et al, 2012;Palva et al, 2013;Priesemann et al, 2013;Shriki et al, 2013). As opposed to spontaneous resting-state activity, stimulus-evoked activity comprises time-locked activity induced by the specific sensory stimuli or motor responses.…”

Experimental control of scaling behavior: what is not fractal?

“…There are examples of fractals in physiology (e.g., Goldberger et al 2002;Ivanov et al 2001;Papo 2013;Peng et al 1994Peng et al , 2002; intraperson behavior (Aks et al 2002;Delignières et al 2008;Gilden et al 1995;Gilden 2001Gilden , 2009Ihlen and Vereijken 2010;Kello et al 2007;Torre et al 2007;Torre and Wagenmakers 2009;Anastas et al 2011;Valdez and Amazeen 2008, 2010Van Orden et al 2003); interperson behavior (e.g., Fine et al 2015;Gorman et al 2010;Likens et al 2014;Marmelat and Delignières 2012); and countless other non-human processes (Bak 1997;Mandelbrot 1983;Solé and Goodwin 2000). Thus, it is no wonder that some researchers investigating fractal phenomena use words like "ubiquitous" (Van Orden et al 2003;Van Orden 2010) and "pervasive" (Kello et al 2008) in favor of "idiosyncratic" (Torre and Wagenmakers 2009).…”

Information Flow in the Brain: Ordered Sequences of Metastable States

“…It was shown that the probability that a particular number of cortical areas are recruited into an OM (defined as the temporal RTP coincidences) is governed by power-law statistics [69]. Such dependency is characteristic for non-ergodic systems, thus suggesting, in contrast to a traditional understanding of the brain as an ergodic system [27], that brain functional activity is rather non-ergodic [70,71]. Further, our analysis revealed that such stabilized spatiotemporal OM configurations also have the transient dynamic which is expressed as a series of sudden transitions between OMs [22].…”