Automaticity: Componential, Causal, and Mechanistic Explanations.

It is widely assumed that mechanistic explanations are causal explanations. Many prominent new mechanists endorse interventionism as the correct analysis of explanatory causal models in biology and other fields. This article argues that interventionism is not entirely satisfactory in this regard. A case study of Jacob and Monod’s operon model shows that at least some important mechanistic explanations in biology present significant contrasts with the interventionist account. This result motivates a more inclusive approach to mechanistic explanation, allowing for noncausal aspects.

Kaplan and Craver (Philos Sci 78(4):601–627, 2011) and Piccinini and Craver (Synthese, 183(3):283–311, 2011) argue that only mechanistic explanations of cognition are genuine causal explanations, because only evidence of mechanisms reveals the causal structure of cognition. I first argue that this claim is grounded in a commitment to the mechanistic account of causality, which cannot be endorsed by a defender of causal-nonmechanistic explanations. Then, I defend the epistemic theory of causality, which holds that causal explanations are not genuine to the extent that they reveal mechanistic causal structure, but, rather, to the extent that they have evidential support and yield successful prediction, explanation, and control inferences. Finally, I enact an epistemic unification of causal explanation in cognitive science, according to which both mechanistic and nonmechanistic explanations of cognition can be genuine causal explanations.

The nature of explanation is an important area of inquiry in philosophy of science. Consensus has been that explanation in the cognitive and brain sciences is typically a special case of causal explanation, specifically, mechanistic explanation. But recently there has been increased attention to computational explanation in the brain sciences and to whether that can be understood as a variety of mechanistic explanation. After laying out the stakes for a proper understanding of scientific explanation, we consider the status of computational explanation in the brain sciences by comparing the mechanistic proposal to computational accounts advanced by Piccinini, Milkowski, Cao, Chirimuuta and Ross. We argue that many of these accounts of computational explanation in neuroscience can satisfy the same explanatory criteria as causal explanations, but not all. This has implications for interpretation of those computational explanations that satisfy different criteria.

Mechanisms are ubiquitous in sociological explanation. Recent theoretical work has sought to extend mechanistic explanation further still: into cultural and interpretative analysis. Yet it is not clear that the concept of mechanism can coherently unify interpretation and causal explanation within a single explanatory framework. We note that sociological mechanistic explanation is marked by a crucial disjuncture. Specifically, we identify two conflicting mechanistic approaches: Modular mechanism models depict counterfactual dependence among independent causal chains, whereas meaningful mechanism models depict relational interdependence among semiotic assemblages. This disjuncture, we argue, is grounded in incompatible causal foundations and entails mechanistic models with distinct and conflicting evidentiary standards. We conclude by proposing a way forward: a sociological pluralism that is attentive to the productive incongruity of our distinct explanatory models.

Research in Organic Chemistry education has revealed students’ challenges in mechanistic reasoning. When solving mechanistic tasks, students tend to focus on explicit surface features, apply fragmented conceptual knowledge, rely on rote-memorization and, hence, often struggle to build well-grounded causal explanations. When taking a resource perspective as a lens, students’ difficulties may arise from either an unproductive or a missing activation of cognitive resources. Instructors’ explanations and their guidance in teaching situations could serve as a lynchpin to activate these resources. Compared to students’ challenges in building mechanistic explanations in Organic Chemistry, little is known about instructors’ explanations when solving mechanistic tasks and how they shape their targeted explanations for students in terms of the construction and embedding of cause–effect rationales. This qualitative study aims to contribute to the growing research on mechanistic reasoning by exploring instructors’ explanatory approaches. Therefore, we made use of the framing construct, intended to trigger certain frames with explicit instruction. Ten Organic Chemistry instructors (university professors and lecturers) were asked to solve case comparison tasks while being prompted in two scenarios: an expert frame and a teaching frame. Our analysis shows that there is a shift from instructors’ mechanistic explanations in the expert frame towards more elaborated explanations in the teaching frame. In the teaching frame, contrary to what might be expected, complete cause–effect relationships were not always established and instructors differed in how they shaped their explanations. Additional explanatory elements were identified in both frames and their shift in use is discussed. Comparing approaches between frames sheds light on how instructors communicate mechanistic explanations and allows us to derive implications for teaching Organic Chemistry.

Quran And Causal Mechanisms In Explaining Social Harms Applied To The Proposition Of The Influence Of Worldliness On Sin

The previous chapters have been largely focused on mechanism and causal explanations. Tackling mechanisms and the ways they work can also, as we have mentioned, bring to the fore the notion of function. As clearly expressed, for instance, in the definition by Bechtel and Abrahamsen, “a mechanism is a structure performing a function in virtue of its component parts, component operations and their organization. The orchestrated functioning of the mechanism is responsible for one or more phenomena” (2005, p. 423, italics added). Functions can actually be at the core of scientific explanations directly centered on functions, and have been largely discussed with respect to their meanings and uses in different contexts, especially in the biological sciences.

What science is and its foundation have been explored by philosophers for centuries, including Bacon, Hume and Popper, and what knowledge is and its foundation for over two and a half millennia, including Plato, Aristotle and Wittgenstein. Philosophers have addressed central questions of science of science longer than researchers in any other subfield of science of science. Major debates that have dominated philosophy of science include paradigm shifts, justification, induction, demarcation and realism. Paradigm shifts refer to fundamental changes in the theories of a scientific field. Justification deals with principles such as falsification and verification to justify our theories of the world. Induction addresses the question of whether observations we make can or cannot justify generalising about the observations in other contexts or in the future. Demarcation involves defining criteria for what is and is not science. Realism concerns whether scientific theories provide a reliable approximation and true description of reality, for observable and not directly observable phenomena. We can also better understand science by comprehending the metaphysical aspects underlying science: causal and mechanistic explanations, measurement of causes, the nature of scientific regularities and laws, and the metaphysical assumptions of science inherent in research, namely in methods, definitions, concepts and theories.

In this article, I develop an account of the use of intentional predicates in cognitive neuroscience explanations. As pointed out by Maxwell Bennett and Peter Hacker, intentional language abounds in neuroscience theories. According to Bennett and Hacker, the subpersonal use of intentional predicates results in conceptual confusion. I argue against this overly strong conclusion by evaluating the contested language use in light of its explanatory function. By employing conceptual resources from the contemporary philosophy of science, I show that although the use of intentional predicates in mechanistic explanations sometimes leads to explanatorily inert claims, intentional predicates can also successfully feature in mechanistic explanations as tools for the functional analysis of the explanandum phenomenon. Despite the similarities between my account and Daniel Dennett's intentional-stance approach, I argue that intentional stance should not be understood as a theory of subpersonal causal explanation, and therefore cannot be used to assess the explanatory role of intentional predicates in neuroscience. Finally, I outline a general strategy for answering the question of what kind of language can be employed in mechanistic explanations.

In this chapter, the authors trace a tug of war between adopting more descriptive versus more conjectural approaches to the study of psychopathology from the seventeenth century onward. The contrast between descriptive versus conjectural is somewhat relative. Statements which rely on shared background assumptions tend to be viewed as descriptions, while those that do not are viewed as conjectural. Throughout the history of psychopathology attempts to offer causal explanations have relied on conjecture hoping to extend our knowledge, but when conjecture was seen as going too far, there were calls to return to descriptions, on which there is more consensus. Recently, the descriptive psychopathology paradigm that was introduced in the DSM-III has been challenged by renewed calls for explaining disorders with respect to etiology and pathogenesis. Turning to causal explanations, the authors contrast production versus regularity accounts of causality. Production accounts tend to favor reducing mind to brain, whereas regularity accounts do not place a priori restrictions on which levels of analysis have genuine casual power. The authors also look at a recently proposed nonreductive perspective in the philosophy of mind, the 4E framework, in which the mind is seen as embodied, embedded, enactive, and extended. The chapter closes by examining the contrast between scientific realism and scientific anti-realism in the study of psychopathology, emphasizing the common ground between scientific realism and factor analytic models and describing some alternative anti-realist views.

To explain phenomenon R by showing how mechanism M yields output R each time it is triggered by circumstances C, is to give a causal explanation of R. This paper analyses what mechanistic analysis can contribute to our understanding of causation in general and of downward causation in particular. It is first shown, against Glennan, that the concept of causation cannot be reduced to that of mechanism. Second it is shown, against Craver and Bechtel, that mechanistic explanation allows us to make sense of causal processes that cut across levels, either in bottom-up direction where a change in a part of a system causes a change in the whole, or in downward direction where a change at the level of the system causes a change at the level of its parts. I suggest construing a decision’s influence on molecules in muscle cells as a global constraint. Microscopic laws determine the detailed evolution of muscle cells and glucose molecules, but this evolution is constrained by the fact that it must be compatible with the action caused by the decision.

How the brain generates conscious experiences remains profoundly mysterious. Pharmacological interventions that alter the state of consciousness have been proposed as a tool to investigate the neural mechanisms of consciousness. However, we have recently demonstrated that the sedative Propofol influences both conscious and unconscious neural processing. Altered arousal, and other pharmacological effects, therefore cannot be assumed a priori to provide information specifically on conscious neural processes. Instead, effects on both conscious and unconscious processes need to be considered. Here we investigated the role of noradrenergic activity in conscious and unconscious visuospatial processing. In Study 1 we used Dexmedetomidine, a sedative that specifically targets α2A noradrenergic receptors. In Study 2, we used sleep deprivation as a natural state of altered arousal, which exerts partially overlapping effects on noradrenaline levels. Unlike Propofol, both Dexmedetomidine and sleep deprivation selectively altered brain activity (fMRI BOLD signal change) during conscious processing. However, the two methods produced distinct effects on visuospatial bias during low arousal: while Dexmedetomidine reduced leftward bias, sleep deprivation increased leftward bias. These differential effects on spatial bias were explained by an unexpected increase in sympathetic drive, as indexed by increased activity in the central autonomic network and in heart rate, from sleep deprivation, that indicate increased rather than decreased noradrenaline levels during task performance. Together, these findings emphasize noradrenergic activity as a target for pharmacological manipulations of consciousness, which could open a window to its neurophysiological underpinnings.

Implicit learning (IL) occurs unconsciously and without intention. Perceptual fluency is the ease of processing elicited by previous exposure to a stimulus. It has been assumed that perceptual fluency is associated with IL. However, the role of perceptual fluency following IL has not been investigated in temporal pattern learning. Two experiments by Schultz, Stevens, Keller, and Tillmann demonstrated the IL of auditory temporal patterns using a serial reaction-time task and a generation task based on the process dissociation procedure. The generation task demonstrated that learning was implicit in both experiments via motor fluency, that is, the inability to suppress learned information. With the aim to disentangle conscious and unconscious processes, we analyze unreported recognition data associated with the Schultz et al. experiments using the sequence identification measurement model. The model assumes that perceptual fluency reflects unconscious processes and IL. For Experiment 1, the model indicated that conscious and unconscious processes contributed to recognition of temporal patterns, but that unconscious processes had a greater influence on recognition than conscious processes. In the model implementation of Experiment 2, there was equal contribution of conscious and unconscious processes in the recognition of temporal patterns. As Schultz et al. demonstrated IL in both experiments using a generation task, and the conditions reported here in Experiments 1 and 2 were identical, two explanations are offered for the discrepancy in model and behavioral results based on the two tasks: 1) perceptual fluency may not be necessary to infer IL, or 2) conscious control over implicitly learned information may vary as a function of perceptual fluency and motor fluency.

Causal and non-causal explanations in code biology

The role of conscious and unconscious processes in second language learning is one of the problems under dispute in applied linguistics. Richard Schmidt argues, in his article: The Role of Consciousness in Second Language Learning (1990), that the notion of consciousness is both useful and possibly necessary in second language learning. Conscious processes are important in second language learning, but unconscious processes should not be neglected in language comprehension and production, both of which contribute to second language learning. He concludes that much more research is needed on learners’ noticing, which becomes intake when combined with input, on incidental learning, on implicit learning, and on what learners are conscious of as they learn a second language. This paper presents how Schmidt’s theory of the Noticing Hypothesis and L2 conscious processes have influenced other research and aroused many advocates and criticisms. The argument on the role of conscious and unconscious processes in SLL will last and needs more research. This theory lays a new theoretical foundation for constructing the theories of foreign language learning, and provides the theoretical support for renewing teaching ideas, improving teaching methods and learning strategies in English teaching and learning of China. However, whether and how the Noticing Hypothesis and L2 conscious processes are appropriate for English language education in China deserves more discussion and research.