Minds and Machines on Causality and the Brain June 2018, Volume 28, Issue 2,

This volume of Minds and Machines is the product of a conference, which seems largely to have determined the contributions.  Although purportedly about science, the essays are often principally directed at those philosophers of science who do not understand the banalities of the sciences they write about or are interested in. (Scientists tend to like this kind of stuff, because it is people saying what the scientists know or think. Everyone likes cheerleading.) Only one of the essays, Romeijin and Williamson's, makes any contribution a brain scientist could conceivably use.


Romeijin and Williamson, Intervention and Identification in Latent Variable Modeling


The authors actually do something.  They show that if X, Y, L are binary, and L is the common cause of X, Y, and X, Y are measured and L is unmeasured and there and there are no other causal relations between X and Y, then an exogenous perturbation of the distribution of L allows identification of p(X | L) and p(Y | L) (and of course, p(X,Y | L) for all values of L, without knowledge of the distributions of L before and after perturbation except that the distributions are different. 

Of course, it isn't true if the relation between X, Y and L is linear, or if besides the common cause, X influences Y, or if L has more than two values, etc.

The authors give no empirical example that realizes their result. Still, they did something.

Colombo and Naftali, Discovering Brain Mechanisms Using Network Analysis and Causal Modeling

This is a book--or rather multi-paper--report.  While there is nothing new in it, the essay is sensible and measured.  Unfortunately, it is not up-to-date on search methods for causal signaling relations between brain regions estimated by fMRI or EEG, not even close. Things are happening, fast.

Perhaps philosophers writing what are essentially judgemental review essays ought to talk first with some of the people actually doing the work?

Winning and Bechtel, Rethinking Causality in Biological and Neural Mechanisms: Constraints and Control

Aside from a foray into the "causality power" i (read "oomph") bit of metaphysics, this essay, like many of Bechtel's, is a paradigm of saying the scientifically banal without furthering anything. Banalities, of course, are generally true.

Gottfried Vosgerau and Patrice Soom, 

Reduction Without Elimination: Mental Disorders as Causally Efficacious Properties
Here is the upshot:

"our proposal is to analyze mental disorders as higher-level dispositional properties that cause specific symptoms under specific conditions, and that are token-identical to complex physical states. This proposal secures the causal efficacy of mental disorders and their crucial role in explanations, while specifying the systematic relation to lower levels of descriptions as found in neurology and neurochemistry". 

That's nice. Thank you Donald Davidson.

Matthew Baxendale and Garrett Mindt, Intervening on the Causal Exclusion Problem for Integrated Information Theory

In gyrations through discussions of the mental and the physical, I look for the takeaway. Here is theirs:

"According to IIT there is an identity between phenomenological properties of experience and informational properties of physical systems…The maximally irreducible conceptual structure (MICS) generated by a complex of elements is identical to its experience… An experience is thus an intrinsic property of a complex of mechanisms in a state."

Speaking of thoughts of complex mechanisms, I wonder what Pluto is thinking now that it's not a planet but still a complex of mechanisms.

I did not read the paper. 

Sebastian Wallot and Damian G. Kelty-Stephen, Interaction-Dominant Causation in Mind and Brain, and Its Implication for Questions of Generalization and Replication

I am tired, but just in case you want to read it, you will learn again that that lots of variables affect what people do, so generalization in psychology is hard.