By Michael L. Anderson
The laptop analogy of the brain has been as extensively followed in modern cognitive neuroscience as used to be the analogy of the mind as a suite of organs in phrenology. simply because the phrenologist might insist that every organ needs to have its specific functionality, so modern cognitive neuroscience is dedicated to the thought that every mind quarter should have its basic computation. In After Phrenology, Michael Anderson argues that to accomplish an absolutely post-phrenological technology of the mind, we have to think again this dedication and devise another, neuroscientifically grounded taxonomy of psychological functionality.
Anderson contends that the cognitive roles performed by means of every one area of the mind are hugely a number of, reflecting varied neural partnerships verified less than diversified situations. He proposes quantifying the sensible houses of neural assemblies when it comes to their dispositional traits quite than their computational or information-processing operations. Exploring larger-scale concerns, and drawing on proof from embodied cognition, Anderson develops an image of considering rooted within the exploitation and extension of our early-evolving means for iterated interplay with the realm. He argues that the multidimensional method of the mind he describes deals a better healthy for those findings, and a extra promising street towards a unified technology of minded organisms.
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Additional resources for After phrenology : neural reuse and the interactive brain
1988) only in the expectation that these workings would be multimodal, multidomain operators capable of contributing to task processes across perceptual modalities and cognitive domains—a conceptual and architectural reform that suggested that the operators might not look like those typically postulated by contemporary cognitive theories. Yet as I noted in that earlier work and wish to emphasize here, the evidence is equally compatible with a weaker (and thus an even less architecturally and functionally conservative) claim: local neural structures have a functional bias, a set of dispositional tendencies that capture the set of inputs to which the circuit will respond and govern the form of the resulting output.
We can make this individual visual result quantitative and general by comparing the average node overlap with the average edge overlap in a pairwise comparison of all the functional networks from the 10 cognitive domains analyzed. 1, we can easily generate predictions for the three possible functional architectures. If the brain is largely modular, then we should expect both low node overlap and low edge overlap; because regions are largely dedicated to their individual function, there is by hypothesis low node overlap, and there can not be edge overlap between nonoverlapping nodes.
These findings most certainly do not explain away the kinds of semantic inheritance from sensorimotor to higher-order cognitive systems so important to many ECS accounts of higher cognition. Quite the contrary: they urgently need to be explained. The worst effect my arguments will have on advocates of these theories is to strip away the illusion that the semantic inheritance observed in so many domains was ever actually explained by the discovery of neural overlaps. But such disillusionment should be welcomed by any scientist, as it lays down the direction of future research.