Neocortical neurons in mammals (including primates and rodents) consume 20 times more energy per neuron than do cerebellar neurons (Herculano-Houzel 2011); the neocortex, unlike the cerebellum, is necessary for storing conscious/declarative information (Tehovnik, Hasanbegović, Chen 2024). The cerebellum, on the other hand, stores information related to converting declarative information into executable code to move the body. The high energy consumption of neocortical neurons may be required to maintain consciousness during wakefulness by supporting synaptic resilience (Attwell and Laughlin 2001; Herculano-Houzel 2011; Richards 2002; Sibson et al. 1998; Shulman et al. 2009). We have estimated (based on the number of functional synapses) that for humans the information storage capacity of neocortex is 1.6 x 10^14 bits, and the information storage capacity of the cerebellum is 2.8 x 10^14 bits (Tehovnik, Hasanbegović, Chen 2024). Whether the storage of information in the cerebellum is dependent on an intact neocortex is not known, but if so, then what would be stored by the cerebellum? Perhaps, neurons of neocortex are attached to a string of cerebellar pathways and synapses (like a puppeteer attached to his or her puppets) to facilitate the efficient execution of a previously learned act (Hasanbegović 2024).
The purpose of automaticity is to reduce the energy demand of the brain (and the body) for task performance. In the brain this is done by reducing the number of synapses required for task execution. There is overwhelming evidence for this idea as observed for the frontal cortex, the basal ganglia, and the cerebellum of human subjects overtrained (for one month) on a sequence of finger-tapping routines (Lehericy et al. 2005). Using fMRI, it was found that the activity of the frontal cortex (including the premotor and supplementary motor areas and the anterior cingulate), the basal ganglia (including the striatum, substantia nigra, and subthalamic nucleus), and the cerebellum (including the pons and the cerebellar cortex and nuclei) all exhibited a systematic diminution of activity over the one-month period of overtraining. Furthermore, in the case of express saccades (which are highly automated ocular responses to a visual stimulus by primates) there is evidence that the number of synapses to execute such saccades is greatly reduced by excluding synaptic pathways/synapses of the frontal lobes and extrastriate cortex to focus the activity on a minimal V1 to brainstem channel for task execution (Schiller and Tehovnik 2015).
Automatic behavior, therefore, is designed to reduce the number of synapses used for task execution, which minimizes energy consumption since synapses are very expensive to sustain (Attwell and Laughlin 2001; Richards 2002; Sibson et al. 1998; Shulman et al. 2009). Additionally, such a reduction will make the neocortex available for new learning, which always starts declaratively and transitions into an automated state that depends on the cerebellum (Tehovnik, Hasanbegović, Chen 2024). The amount of energy expended by the brain per (bits per second transferred) will need to be assessed both for new learning of a task and for the automatic performance of an overlearned task. This metric should yield a measure of the energy reduction by the brain once behaviors become automated, and this metric can be used to evaluate the energy reduction by species in general and per task performed by a given species.
Multicellular organisms have evolved to bring about a reduction in energy consumption per cell that occurs as a function of increased body mass or number of cells comprising an organism (which is called Kleiber’s Law: Delong et al. 2010; Moses et al. 2016; Wells 2007). That energy reduction has been factored into the evolutionary process should not be a surprise. For survival, major portions of the brain such as the cingulate and orbital cortices and their subcortical targets, especially the hypothalamus, are dedicated to energy regulation (Katsumi et al. 2021). It has been suggested by Katsumi et al. that emotionality as mediated by the brain may have evolved to regulate an animal’s energy consumption. In a like manner, therefore, it should not be a surprise that automaticity has evolved for regulating an animal’s energy budget.
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