Felipe De Brigard (Duke University)
Although cognitive systems are commonly posited in explanations in psychology and cognitive neuroscience, there is uncertainty as to how to precisely characterize them. Recently, I (De Brigard, 2017) argued against a prominent characterization of cognitive system put forth by Rupert (2009; 2011), on account that it cannot capture the fact that brains exhibit both functional stability and diachronic dynamicity, as manifested, for instance, by changes in brain dynamics as a function of age without concomitant changes in task performance. In this talk I suggest a solution to the challenge of characterizing the notion of cognitive system in a way that allows for both functional stability and diachronic dynamicity. To that end, I build upon recent developments in topology and network analysis to offer a characterization of what may be called “dynamic cognitive systems”. Roughly, my suggestion is that a dynamic cognitive system can be seen as a neural network in which time is parametrized. More specifically, I argue that recent algorithms from temporaldynamic network analyses of task-based neuroimaging data can provide us with models of brain mechanisms that can exhibit stability in performance (i.e., functional stability) while at the same time capture underlying structural changes through time (i.e., diachronic dynamicity). I conclude with the admonition that any attempt to explain cognition mechanistically through neuroscience would require rethinking the notion of cognitive system in dynamic terms.
