I spent some time many, many years ago in wondering what effective pre-scientific predicting was like. The predictions I was wondering about were not omens from entrails and the like, but good prediction of natural things made without the understanding that we now use. How did ancients forecast the weather for example?
At some point I ran across some person using the biblical phrase ye can discern the face of the sky; but can ye not discern the signs of the times? That phrase ‘discern the face of the sky’ is either as old as the original biblical texts or a medieval idiom used in early English translations. In either case it was from before knowledge of clouds, winds, air pressure etc. So the thought came if you are trying to predict a system that has very complex patterns of elements are not understood if you personify the system and try to get to feel for the patterns in terms of facial expressions, tones of voice, emotions, intentions, personalities then you can use all the mental abilities that we have evolved for understanding and predicting people. Then you can learn how to predict weather, keep getting better at it for a life time, share/compare with others, and teach the skill to your children. You get to know the west wind’s personality. You get to see when the sky intends to freeze.
Deric Bownds’ blog (here) has a abstract that reminded me of ‘the face of the sky’. The paper is Bilalic, Langner, Ulrich, Grodd (2011) Many Faces of Expertise: Fusiform Face Area in Chess Experts and Novices, The Journal of Neuroscience:
The fusiform face area (FFA) is involved in face perception to such an extent that some claim it is a brain module for faces exclusively. The other possibility is that FFA is modulated by experience in individuation in any visual domain, not only faces. Here we test this latter FFA expertise hypothesis using the game of chess as a domain of investigation. We exploited the characteristic of chess, which features multiple objects forming meaningful spatial relations. In three experiments, we show that FFA activity is related to stimulus properties and not to chess skill directly. In all chess and non-chess tasks, experts’ FFA was more activated than that of novices’ only when they dealt with naturalistic full-board chess positions. When common spatial relationships formed by chess objects in chess positions were randomly disturbed, FFA was again differentially active only in experts, regardless of the actual task. Our experiments show that FFA contributes to the holistic processing of domain-specific multipart stimuli in chess experts. This suggests that FFA may not only mediate human expertise in face recognition but, supporting the expertise hypothesis, may mediate the automatic holistic processing of any highly familiar multipart visual input.
Unfortunately, I am not able to access the paper. And there was another paper that I also found interesting in the abstract but could not access – Tong, Joyce, Cottrell (2008) Why is the fusiform face area recruited for novel categories of expertise? A neurocomputational investigation:
What is the role of the Fusiform Face Area (FFA)? Is it specific to face processing, or is it a visual expertise area? The expertise hypothesis is appealing due to a number of studies showing that the FFA is activated by pictures of objects within the subject’s domain of expertise (e.g., cars for car experts, birds for birders, etc.), and that activation of the FFA increases as new expertise is acquired in the lab. However, it is incumbent upon the proponents of the expertise hypothesis to explain how it is that an area that is initially specialized for faces becomes recruited for new classes of stimuli. We dub this the “visual expertise mystery.” One suggested answer to this mystery is that the FFA is used simply because it is a fine discrimination area, but this account has historically lacked a mechanism describing exactly how the FFA would be recruited for novel domains of expertise. In this study, we show that a neurocomputational model trained to perform subordinate-level discrimination within a visually homogeneous class develops transformations that magnify differences between similar objects, in marked contrast to networks trained to simply categorize the objects. This magnification generalizes to novel classes, leading to faster learning of new discriminations. We suggest this is why the FFA is recruited for new expertise. The model predicts that individual FFA neurons will have highly variable responses to stimuli within expertise domains.
Their question ‘exactly how the FFA would be recruited for novel domains of expertise’ may be that we use a person (or group of persons as in chess) as a sort of default alternative and this allows all of the power of our understanding of others to be used for novel domains. Not a bad trick.