I push back on McGilchrist frequently because I’m aware of his agenda: 1. hemisp

I push back on McGilchrist frequently because I’m aware of his agenda:
1. hemispheric bias … just bias, not exclusive specialization. Also, in the visual cortex (back of the brain, largely no. Sex differences do appear here in amplitude and tas evoked activation. once we move beyond v1 (think of the back of the brain as an archery target with rings radiating out from the center) we start to see some predicted biases such as right face bias vs left word-form. These are matters of degree only.
2. The bias becomes more obvious in connectivity: male specialization within hemispheric connection, vs female generalization which connects the hemispheres. It’s actually hemispherically integrated (f/r/more-slow/in-time) vs hemispherically specialized (m/l/less-fast/over-time)
3. This results in systematizing (over time) vs empathizing (in time). Brain organization constrains bias in processing. Brain is organized in utero and in early development.
4. Correct model that reflects universal sex differences in behavior is predator (m/left) bias vs prey (f/right) bias.

All that said, defeating McGilchrist’s ‘fictionalism’ is rather easy: (via CurtGPT)
1. Category error: hemispheric lateralization is an implementation feature of distributed networks; it is not a pair of epistemic agents. Treating hemispheres as agents confuses mechanism with narrative.
2. Level conflation: claims about civilization-scale “modes of being” cannot be inferred from circuit-level lateralization without a bridging model that specifies intermediate levels (policy, incentives, institutions). Without that bridge, it is a storytelling leap.
3. Non-uniqueness: even where lateralization exists, multiple architectures can implement the same policy. Therefore “left vs right” cannot be the explanatory primitive. Incentives and loss functions must be.