Understanding greater male variability in general intelligence: The role of hemispheric independence and lateralization

Abstract

Deary et al. (2003) present data, further analysed by Johnson et al. (2008), showing a sex difference in general intelligence across a whole population: males were more variable than females and were over-represented at the lower and upper ends of the distribution. We propose a single-factor explanation based on hemisphericity, the degree of hemispheric independence and lateralization of function. We demonstrate this argument with a conceptual analysis and with neural network simulations of hemispheric interaction. We discuss the nature of abstraction in computational cognitive modelling. Greater male variance in general intelligence can be at least partially understood as emerging from increased hemisphericity, underlining the latter’s critical role in the evolution, development and functioning of human cognition.

Mirror Neurons, Prediction and Hemispheric Coordination: The Prioritizing of Intersubjectivity Over ‘Intrasubjectivity’

“…There is an inverse relation between brain lateralization and callosal connectivity in males (Aboitiz et al 1992;Dorion et al 2000;Witelson and Goldsmith 1991), suggesting that we should see in males a greater variability at both the upper and lower ends of the range of cognitive performance, which is exactly what we do see (Johnson et al 2008). (See Shillcock et al 2018, for the relevant cognitive modelling.) (10) Cognitive scientists have conventionally prioritized 'mindreading'/intersubjectivity over metacognition/'intrasubjectivity', even deriving the latter from the former (e.g.…”

“…The fields of computational cognitive modelling, statistical physics and machine learning contain implementations of some of the principles considered above. There is a history of neural net simulations with a 'hemispheric split' (Anninos and Cook 1988;Monaghan et al 2004;Reggia et al 2013;Shillcock and Monaghan 2001;Monaghan and Pollmann 2003;Shillcock et al 2018;Shkuro et al 2000), in which typically the two halves of the model are encapsulated, except for a set of 'callosal' connections between the sets of hidden units. One half of the model 'second guesses' the other half.…”