The entropic brain: effortful speech reflcted in organisation of the network graph
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Uncertainty of incoming information increases the amount of mental effort. Scientists might have been interested in quantifying the amount of this mentaleffort, as evidenced by the great amount of research devoted to computing theenergy used to deal with the uncertainty of an event. One of these measuresis Shannon’s entropy, which was initially used as a measure of the level ofuncertainty with respect to the outcome of an event (Shannon, 1948). Asdefined by Shannon, entropy is expected to increase as the level of disorder(uncertainty or task complexity) increases. Therefore, to evaluate how the braindeals with entropy, one could either locate the focal area showing activity thatis positively correlated with the degree of uncertainty or consider informationtransfer. We speculated that a functionally effective brain would modulate its network to fit to the varying entropy, since computation in the brain isprobabilistic. Therefore, we put forward two hypotheses: 1) the functionalbrain architecture will reflect the varying entropy, and 2) the entropy willbe modality-specific. In order to test these hypotheses, we estimated severalnetwork properties of the magnetoencephalography time-series signalsobtained from healthy monolingual listeners. We particularly focused onwhether entropy reflcts the modality-specifi processing load, especially whenthe degrees of the processing load of the two events were similar in terms ofthe rate of accuracy and the length of response time between tasks. In order tomanipulate both entropy and modality, we varied the processing complexity(easy vs. diffiult) in either a linguistic or non-linguistic (pitch change detectionvs. word intelligibility test) task. Using graph-theoretical measures, the globalorganisation measures of the network, such as its small-worldness, correlationcoeffiient, global effiiency, and characteristic path-lengths, were comparedwithin the network extracted as a set of 78 brain atlas nodes. The resultsshowed a significant main effect of task complexity on the brain networkproperties, demonstrating that task load is indeed ubiquitous regardless oftask modality. Equally importantly, we also found a pronounced task-specifidifference in the network properties between linguistic and non-linguisticmodalities. Regardless of modality, in the effortful tasks, the characteristicpath-length and the correlation-coefficient were significantly larger, whereasthe linguistic tasks resulted in signifiantly higher small-worldness, with thehubs located at the usual language nodes. Our findings collectively suggestthat task load is ubiquitous but is also modality-specifi in the brain networkproperties, as evidenced by the specifi network graph measures.
Abstract
Uncertainty of incoming information increases the amount of mental effort. Scientists might have been interested in quantifying the amount of this mentaleffort, as evidenced by the great amount of research devoted to computing theenergy used to deal with the uncertainty of an event. One of these measuresis Shannon’s entropy, which was initially used as a measure of the level ofuncertainty with respect to the outcome of an event (Shannon, 1948). Asdefined by Shannon, entropy is expected to increase as the level of disorder(uncertainty or task complexity) increases. Therefore, to evaluate how the braindeals with entropy, one could either locate the focal area showing activity thatis positively correlated with the degree of uncertainty or consider informationtransfer. We speculated that a functionally effective brain would modulate its network to fit to the varying entropy, since computation in the brain isprobabilistic. Therefore, we put forward two hypotheses: 1) the functionalbrain architecture will reflect the varying entropy, and 2) the entropy willbe modality-specific. In order to test these hypotheses, we estimated severalnetwork properties of the magnetoencephalography time-series signalsobtained from healthy monolingual listeners. We particularly focused onwhether entropy reflcts the modality-specifi processing load, especially whenthe degrees of the processing load of the two events were similar in terms ofthe rate of accuracy and the length of response time between tasks. In order tomanipulate both entropy and modality, we varied the processing complexity(easy vs. diffiult) in either a linguistic or non-linguistic (pitch change detectionvs. word intelligibility test) task. Using graph-theoretical measures, the globalorganisation measures of the network, such as its small-worldness, correlationcoeffiient, global effiiency, and characteristic path-lengths, were comparedwithin the network extracted as a set of 78 brain atlas nodes. The resultsshowed a significant main effect of task complexity on the brain networkproperties, demonstrating that task load is indeed ubiquitous regardless oftask modality. Equally importantly, we also found a pronounced task-specifidifference in the network properties between linguistic and non-linguisticmodalities. Regardless of modality, in the effortful tasks, the characteristicpath-length and the correlation-coefficient were significantly larger, whereasthe linguistic tasks resulted in signifiantly higher small-worldness, with thehubs located at the usual language nodes. Our findings collectively suggestthat task load is ubiquitous but is also modality-specifi in the brain networkproperties, as evidenced by the specifi network graph measures.
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