Causal Modeling and the Efficacy of Action

This paper brings together Thompson's naive action explanation with interventionist modeling of causal structure to show how they work together to produce causal models that go beyond current modeling capabilities, when applied to specifically selected systems. By deploying well-justified assumptions about rationalization, we can strengthen existing causal modeling techniques' inferential power in cases where we take ourselves to be modeling causal systems that also involve actions. The internal connection between means and end exhibited in naive action explanation has a modal strength like that of distinctively mathematical explanation, rather than that of causal explanation. Because it is stronger than causation, it can be treated as if it were merely causal in a causal model without thereby overextending the justification it can provide for inferences. This chapter introduces and demonstrate the usage of the Rationalization condition in causal modeling, where it is apt for the system(s) being modeled, and to provide the basics for incorporating R variables into systems of variables and R arrows into DAGs. Use of the Rationalization condition supplements causal analysis with action analysis where it is apt

Hiddleston’s Causal Modeling Semantics and the Distinction between Forward-Tracking and Backtracking Counterfactuals

Some cases show that counterfactual conditionals (‘counterfactuals’ for short) are inherently ambiguous, equivocating between forward-tracking and backtracking counterfactu- als. Elsewhere, I have proposed a causal modeling semantics, which takes this phenomenon to be generated by two kinds of causal manipulations. (Lee 2015; Lee 2016) In an important paper (Hiddleston 2005), Eric Hiddleston offers a different causal modeling semantics, which he claims to be able to explain away the inherent ambiguity of counterfactuals. In this paper, I discuss these two semantic treatments and argue that my (bifurcated) semantics is theoretically more promising than Hiddleston’s (unified) semantics

Exploring manual asymmetries during grasping: a dynamic causal modeling approach

Recording of neural activity during grasping actions in macaques showed that grasp-related sensorimotor transformations are accomplished in a circuit constituted by the anterior part of the intraparietal sulcus (AIP), the ventral (F5) and the dorsal (F2) region of the premotor area. In humans, neuroimaging studies have revealed the existence of a similar circuit, involving the putative homolog of macaque areas AIP, F5 and F2. These studies have mainly considered grasping movements performed with the right dominant hand and only a few studies have measured brain activity associated with a movement performed with the left non-dominant hand. As a consequence of this gap, how the brain controls for grasping movement performed with the dominant and the non-dominant hand still represents an open question. A functional resonance imaging experiment (fMRI) has been conducted, and effective connectivity (Dynamic Causal Modelling, DCM) was used to assess how connectivity among grasping-related areas is modulated by hand (i.e., left and right) during the execution of grasping movements towards a small object requiring precision grasping. Results underlined boosted inter-hemispheric couplings between dorsal premotor cortices during the execution of movements performed with the left rather than the right dominant hand. More specifically, they suggest that the dorsal premotor cortices may play a fundamental role in monitoring the configuration of fingers when grasping movements are performed by either the right and the left hand. This role becomes particularly evident when the hand less-skilled (i.e., the left hand) to perform such action is utilized. The results are discussed in light of recent theories put forward to explain how parieto-frontal connectivity is modulated by the execution of prehensile movements