MindPapers: 7.3c. Explanation in Neuroscience

Bechtel, William P. (online). Mental mechanisms: What are the operations? (Google | More links | Edit)

Abstract: trying to explain these reactions in terms of changes in ele- began trying to characterize physiological processes in

Bechtel, William P. (2005). The challenge of characterizing operations in the mechanisms underlying behavior. Journal of the Experimental Analysis of Behavior 84:313-325. (Google | More links | Edit)

Abstract: Neuroscience and cognitive science seek to explain behavioral regularities in terms of underlying mechanisms. An important element of a mechanistic explanation is a characterization of the operations of the parts of the mechanism. The challenge in characterizing such operations is illustrated by an example from the history of physiological chemistry in which some investigators tried to characterize the internal operations in the same terms as the overall physiological system while others appealed to elemental chemistry. In order for biochemistry to become successful, researchers had to identify a new level of operations involving operations over molecular groups. Existing attempts at mechanistic explanation of behavior are in a situation comparable to earlier approaches to physiological chemistry, drawing their inspiration either from overall psychology activities or from low-level neural processes. Successful mechanistic explanations of behavior require the discovery of the appropriate component operations. Such discovery is a daunting challenge but one on which success will be beneficial to both behavioral scientists and cognitive and neuroscientists

Coltheart, Max & Davies, Martin (2003). Inference and explanation in cognitive neuropsychology. Cortex 39 (1):188-191. (Cited by 7 | Google | More links | Edit)

Abstract: The question posed by Dunn and Kirsner (D&K) is an instance of a more general one: What can we infer from data? One answer, if we are talking about logically valid deductive inference, is that we cannot infer theories from data. A theory is supposed to explain the data and so cannot be a mere summary of the data to be explained. The truth of an explanatory theory goes beyond the data and so is never logically guaranteed by the data. This is not just a point about cognitive neuropsychology, or even about psychology in general. It is a familiar point about all science

Craver, Carl F. & Darden, Lindley (2001). Discovering mechanisms in neurobiology: The case of spatial memory. In P.K. Machamer, Rick Grush & Peter McLaughlin (eds.), Theory and Method in Neuroscience. Pittsburgh: University of Pitt Press. (Cited by 38 | Google | Edit)

Craver, Carl F. (2006). When mechanistic models explain. Synthese 153 (3):355-376. (Cited by 1 | Google | More links | Edit)

Abstract: Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what is required of an adequate mechanistic model. Mechanistic models are explanatory

Cruse, H. (2001). The explanatory power and limits of simulation models in the neurosciences. In Peter K. Machamer, Peter McLaughlin & Rick Grush (eds.), Theory and Method in the Neurosciences. University of Pittsburgh Press. (Cited by 2 | Google | Edit)

Hardcastle, Valerie Gray & Stewart, C. Matthew (2001). Theory structure in neuroscience. In Peter K. Machamer, Peter McLaughlin & Rick Grush (eds.), Theory and Method in the Neurosciences. University of Pittsburgh Press. (Google | Edit)

Hartmann, Stephan (2001). Mechanisms, coherence, and the place of psychology. In Theory and Method in the Neurosciences. Pittsburgh: University of Pitt Press. (Google | Edit)

Hartmann, Stephan (2001). Mechanisms, coherence, and theory choice in the cognitive neurosciences. In P. Machamer, P. McLaughlin & R. Grush (eds.), Theory and Method in the Neurosciences. Pittsburgh: Pittsburgh University Press. (Google | More links | Edit)

Abstract: Let me first state that I like Antti Revonsuo’s discussion of the various methodological and interpretational problems in neuroscience. It shows how careful and methodologically reflected scientists have to proceed in this fascinating field of research. I have nothing to add here. Furthermore, I am very sympathetic towards Revonsuo’s general proposal to call for a Philosophy of Neuroscience that stresses foundational issues, but also focuses on methodological and explanatory strategies.² In a footnote of his paper, Revonsuo complains – as many others do today – about what is sometimes called “physics imperialism”. This is the view that physics dominates the philosophy of science. I am not sure if this is still the case nowadays, but it is certainly historically correct that almost all work in the field of methodology centered around cases from physics. Although this has been changing, there are still plenty of special sciences philosophers did not worry about much. Admittedly, I am myself a trained physicist and not a neuroscientist and will therefore probably be biased negatively. As it is, I will discuss some examples from physics in order to illustrate my points

Hurley, Susan L. (online). The shared circuits model. How control, mirroring, and simulation can enable imitation and mind reading. (Google | Edit)

Lyons, Jack C. (2003). Lesion studies, spared performance, and cognitive systems. Cortex 39 (1):145-7. (Cited by 1 | Google | More links | Edit)

Abstract: The term ‘module’ has – to my ear – too many associations with Fodor’s (1983) seminal book, and I will concentrate here on the more general notion of a cognitive system. The latter, as I will understand the term, is – roughly – a computational mechanism which can operate independently of all other computational mechanisms (for a much fuller and more precise treatment, see Lyons, 2001). To say that there is a face recognition system, for example, is to say, at least in part, that there is a mechanism which by itself is capable of effecting a transformation from some set of inputs to face identification outputs. If there is one such system, there are likely to be several. Since systems may contain various subsystems, it is generally impossible to specify a system uniquely without specifying a set of inputs. The largest system that would count as a face recognition system would be the one that takes retinal irradiation arrays as inputs and delivers face identifications as outputs, but the last subsystem in this system would map high level representations to face identifications. For any task (where a task is construed as an input/output mapping), take away all cortical regions whose absence does not affect the ability of what is left to perform the task, and you are left with the system that performs that task

Machamer, Peter K.; Darden, Lindley & Craver, Carl F. (2000). Thinking about mechanisms. Philosophy Of Science 67 (1):1-25. (Cited by 207 | Google | More links | Edit)

Piccinini, Gualtiero (2006). Computational explanation in neuroscience. Synthese 153 (3):343-353. (Google | More links | Edit)

Abstract: According to some philosophers, computational explanation is proprietary to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that are being developed by a number of authors

Revonsuo, Antti (2001). On the nature of explanation in the neurosciences. In Peter K. Machamer, Peter McLaughlin & Rick Grush (eds.), Theory and Method in the Neurosciences. University of Pittsburgh wPress. (Cited by 5 | Google | Edit)

Skarda, S. (1986). Explaining behavior: Bringing the brain back in. Inquiry 29 (June):187-201. (Cited by 5 | Google | Edit)

von Eckardt, Barbara & Poland, Jeffrey S. (2004). Mechanism and explanation in cognitive neuroscience. Philosophy of Science 71 (5):972-984. (Cited by 2 | Google | More links | Edit)

Abstract: The aim of this paper is to examine the usefulness of the Machamer, Darden, and Craver (2000) mechanism approach to gaining an understanding of explanation in cognitive neuroscience. We argue that although the mechanism approach can capture many aspects of explanation in cognitive neuroscience, it cannot capture everything. In particular, it cannot completely capture all aspects of the content and significance of mental representations or the evaluative features constitutive of psychopathology

Wright, Cory & Bechtel, William P. (2006). Mechanisms and psychological explanation. In Paul Thagard (ed.), Philosophy of Psychology and Cognitive Science. Elsevier. (Google | Edit)

Abstract: As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve to bridge levels rather than reduce them, as well as the different ways in which mechanisms are discovered. Finally, we offer a more detailed example of an important psychological phenomenon for which mechanistic explanation has provided the main source of scientific understanding

7.3c	Explanation in Neuroscience [17]

7.3e	Neurophilosophy [31]

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