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7.2c. Explanation in Neuroscience (Explanation in Neuroscience on PhilPapers)

See also:
Abraham, Tara H. (2003). From theory to data: Representing neurons in the 1940s. Biology and Philosophy 18 (3).   (Google)
Abstract:   Recent literature on the role of pictorial representation in the life sciences has focused on the relationship between detailed representations of empirical data and more abstract, formal representations of theory. The standard argument is that in both a historical and epistemic sense, this relationship is a directional one: beginning with raw, unmediated images and moving towards diagrams that are more interpreted and more theoretically rich. Using the neural network diagrams of Warren McCulloch and Walter Pitts as a case study, I argue that while in the empirical sciences, pictorial representation tends to move from data to theory, in areas of the life sciences that are predominantly theoretical, when abstraction occurs at the outset, the relationship between detail and abstraction in pictorial representations can be of a different character
Allen, Colin (ms). Macaque mirror neurons.   (Google)
Abstract: Primatologists generally agree that monkeys lack higher-order intentional capacities related to theory of mind. Yet the discovery of the so-called “mirror neurons” in monkeys suggests to many neuroscientists that they have the rudiments of intentional understanding. Given a standard philosophical view about intentional understanding, which requires higher-order intentionality, a paradox arises. Different ways of resolving the paradox are assessed, using evidence from neural, cognitive, and behavioral studies of humans and monkeys. A decisive resolution to the paradox requires substantial additional empirical work and perhaps a rejection of the standard philosophical view
Amos, A. J. & Wynne, C. D. L. (2000). The organization of organization: Neuronal scaffold or cognitive straitjacket? Behavioral and Brain Sciences 23 (4):533-534.   (Google)
Abstract: We praise Arbib et al.'s Neural organization for its support of the integration of different levels of analysis, while noting that it does not always achieve what it advocates. We extend this approach into an area of neuropsychological activity in need of the structure offered by Organization at the intersection of the conflated fields of executive function and frontal lobe function
Arbib, Michael A. (1989). Modularity, schemas and neurons: A critique of Fodor. In Peter Slezak (ed.), Computers, Brains and Minds. Kluwer.   (Annotation | Google)
Aronson, Jerrold L. (1976). Some dubious neurological assumptions of radical behaviourism. Journal for the Theory of Social Behaviour 6 (1):49–60.   (Google | More links)
Atmanspacher, Harald, Interpreting neurodynamics: Concepts and facts.   (Google)
Abstract: The dynamics of neuronal systems, briefly neurodynamics, has developed into an attractive and influential research branch within neuroscience. In this paper, we discuss a number of conceptual issues in neurodynamics that are important for an appropriate interpretation and evaluation of its results. We demonstrate their relevance for selected topics of theoretical and empirical work. In particular, we refer to the notions of determinacy and stochasticity in neurodynamics across levels of microscopic, mesoscopic and macroscopic descriptions. The issue of correlations between neural, mental and behavioral states is also addressed in some detail. We propose an informed discussion of conceptual foundations with respect to neurobiological results as a viable step to a fruitful future philosophy of neuroscience
Bechtel, William P. (1983). A bridge between cognitive science and neuroscience: The functional architecture of mind. Philosophical Studies 44 (November):319-30.   (Cited by 6 | Annotation | Google | More links)
Bechtel, William P. (2002). Aligning multiple research techniques in cognitive neuroscience: Why is it important? Proceedings of the Philosophy of Science Association 2002 (3):548-558.   (Cited by 3 | Google | More links)
Abstract: The need to align multiple experimental procedures and produce converging results so as to demonstrate that the phenomenon under investigation is real and not an artifact is a commonplace both in scientific practice and discussions of scientific methodology (Campbell and Stanley 1963; Wimsatt 1981). Although sometimes this is the purpose of aligning techniques, often there is a different purpose—multiple techniques are sought to supply different perspectives on the phenomena under investigation that need to be integrated to answer the questions scientists are asking. After introducing this function, I will illustrate it by considering three of the major techniques in cognitive neuroscience for linking cognitive function with neural structure
Bechtel, William P. (2001). Cognitive neuroscienec: Relating neural mechanisms and cognition. In Peter K. Machamer, Peter McLaughlin & Rick Grush (eds.), Theory and Method in the Neurosciences. University of Pittsburgh Press.   (Google)
Bechtel, William P. & Mundale, Jennifer (1996). Integrating neuroscience, psychology, and evolutionary biology through a teleological conception of function. Minds And Machines 6 (4):481-505.   (Google)
Abstract: The idea of integrating evolutionary biology and psychology has great promise, but one that will be compromised if psychological functions are conceived too abstractly and neuroscience is not allowed to play a contructive role. We argue that the proper integration of neuroscience, psyychology, and evolutionary biology requires a telelogical as opposed to a merely componential analysis of function. A teleological analysis is required in neuroscience itself; we point to traditional and curent research methods in neuroscience, which make critical use of distinctly teleological functional considerations in brain cartography. Only by invoking teleological criteria can researchers distinguish the fruitful ways of identifying brain components from the myriad of possible ways. One likely reason for reluctance to turn to neuroscience is fear of reduction, but we argue that, in the context of a teleological perspective on function, this concern is misplaced. Adducing such theoretical considerations as top-down and bottom-up constraints on neuroscientific and psychological models, as well as existing cases of productive, multidisciplinary cooperation, we argue that integration of neuroscience into psychology and evolutionary biology is likely to be mutually beneficial. We also show how it can be accommodated methodologically within the framework of an interfield theory
Bechtel, William P. (online). Mental mechanisms: What are the operations?   (Google | More links)
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)
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
Blumenthal, Terry & Schirillo, James (1999). Biological neuroscience is only as radical as the evolution of mind. Behavioral and Brain Sciences 22 (5):831-831.   (Google)
Abstract: A biological neuroscientific theory must acknowledge that the function of a neurological system is to produce behaviors that promote survival. Thus, unlike what Gold & Stoljar claim, function and behavior are the province of neurobiology and cannot be relegated to the field of psychological phenomena, which would then trivialize the radical doctrine if accepted. One possible advantage of adopting such a (correctly revised) radical doctrine is that it might ultimately produce a successful, evolutionarily based, theory of mind
Boyle, Noel (2008). Neurobiology and phenomenology: Towards a three-tiered intertheoretic model of explanation. Journal of Consciousness Studies 15 (3):34-58.   (Google)
Abstract: Analytic and continental philosophies of mind are too long divided. In both traditions there is extensive discussion of consciousness, the mind-body problem, intentionality, subjectivity, perception (especially visual) and so on. Between these two discussions there are substantive disagreements, overlapping points of insight, meaningful differences in emphasis, and points of comparison which seems to offer nothing but confusion. In other words, there are the ideal circumstances for doing philosophy. Yet, there has been little discourse. This paper invites expanding discourse between these two philosophical traditions. The first part briefly describes the existing literature which works across the analytic- phenomenology divide, situating my work within it as a focus on analytic physicalism and phenomenal explanation. In the longer second part, I sketch a model for explanation embedded simultaneously in both traditions. Hopefully, a theoretical framework emerges that the unlikely combination of Maurice Merleau- Ponty and Patricia Churchland could accept. In the third part, I apply the three-tiered model to a discussion of plasticity and suggest that the model both reflects existing research across three levels of analysis and can be a fruitful way to approach future research. My suggestion for a three-tiered model is quite tentative. Much less tentative is my claim that constructive dialogue between phenomeno- logical and physicalist study of consciousness is long-overdue, illuminating, and practical
Brothers, Leslie (1999). The logic of interests in neuroscience. Behavioral and Brain Sciences 22 (5):831-832.   (Google)
Abstract: Logical problems inherent in claims that biological neuroscience can ultimately explain mind are not anomalous: They result from underlying social interests. Neuroscientists are currently making a successful bid to fill a vacuum of authority created by the demise of Freudian theory in popular culture. The conflations described in the Gold & Stoljar target article are the result of alliances between certain apologist-philosophers, neuroscientists, and institutions, for the purpose of commanding authority and resources. Social analysis has a role to play in addressing logical issues in the philosophy of neuroscience
Carlos, & René, Campis (2008). DID I DO IT? -YEAH, YOU DID! Reduction and Elimination in Philosophy and the Sciences:34- 37.   (Google)
Abstract: In this paper we analyze Libet’s conclusions on «free will» (FW), rejecting his view of the concept and defending a partially aligned view with Wittgenstein’s early remarks on FW. First, the concept of Readiness Potential (RP) and Libet’s view are presented. Second, we offer an account of Wittgenstein´s point of view. Third, a dual-domain analysis is proposed; finally, we offer our conclusions. This article´s conclusion is part of an ongoing research.
Cliff, D. (1990). Computational Neuroethology: A Provisional Manifesto. In Jean-Arcady Meyer & Stewart W. Wilson (eds.), From Animals to Animats: Proceedings of The First International Conference on Simulation of Adaptive Behavior (Complex Adaptive Systems). Cambridge University Press.   (Cited by 103 | Annotation | Google | More links)
Coltheart, Max & Davies, Martin (2003). Inference and explanation in cognitive neuropsychology. Cortex 39 (1):188-191.   (Cited by 7 | Google | More links)
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
Coulter, Jeff (1995). The informed neuron: Issues in the use of information theory in the behavioral sciences. Minds and Machines 5 (4):583-96.   (Cited by 4 | Google | More links)
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)
Craver, Carl F. (2007). Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience. Oxford University Press, Clarendon Press ;.   (Google)
Craver, Carl F. (2002). Interlevel experiments and multilevel mechanisms in the neuroscience of memory. Philosophy of Science Supplemental Volume 69 (3):S83-S97.   (Cited by 17 | Google | More links)
Craver, Carl F. (2008). Physical law and mechanistic explanation in the Hodgkin and Huxley model of the action potential. Philosophy of Science 75 (5).   (Google)
Abstract: Hodgkin and Huxley’s model of the action potential is an apparent dream case of covering‐law explanation in biology. The model includes laws of physics and chemistry that, coupled with details about antecedent and background conditions, can be used to derive features of the action potential. Hodgkin and Huxley insist that their model is not an explanation. This suggests either that subsuming a phenomenon under physical laws is insufficient to explain it or that Hodgkin and Huxley were wrong. I defend Hodgkin and Huxley against Weber’s heteronomy thesis and argue that explanations are descriptions of mechanisms. †To contact the author, please write to: Department of Philosophy, Philosophy‐Neuroscience‐Psychology Program, Washington University in St. Louis, One Brookings Drive, Wilson Hall, St. Louis, MO 63130; e‐mail: ccraver@artsci.wustl.edu
Craver, Carl F. (2001). Role functions, mechanisms, and hierarchy. Philosophy of Science 68 (1):53-74.   (Google | More links)
Craver, Carl F. & Bechtel, William (2007). Top-down causation without top-down causes. Biology and Philosophy 22 (4).   (Google)
Abstract:   We argue that intelligible appeals to interlevel causes (top-down and bottom-up) can be understood, without remainder, as appeals to mechanistically mediated effects. Mechanistically mediated effects are hybrids of causal and constitutive relations, where the causal relations are exclusively intralevel. The idea of causation would have to stretch to the breaking point to accommodate interlevel causes. The notion of a mechanistically mediated effect is preferable because it can do all of the required work without appealing to mysterious interlevel causes. When interlevel causes can be translated into mechanistically mediated effects, the posited relationship is intelligible and should raise no special philosophical objections. When they cannot, they are suspect
Craver, Carl F. (2003). The making of a memory mechanism. Journal of the History of Biology 36 (1):153-95.   (Cited by 6 | Google | More links)
Craver, Carl, Why the Hodgkin and huxely model does not explain the action potential.   (Google)
Abstract: Hodgkin and Huxley’s 1952 model of the action potential is an apparent dream case of covering-law explanation. The model appeals to general laws of physics and chemistry (specifically, Ohm’s law and the Nernst equation), and the laws, coupled with details about antecedent and background conditions, entail many of the significant properties of the action potential. However, Hodgkin and Huxley insist that their model falls short of an explanation. This historical fact suggests either that there is more to explaining the action potential than subsuming it under a general laws or that Hodgkin and Huxley were wrong about the explanatory import of their model. In this paper, I defend Hodgkin and Huxley’s view that their model alone does not explain the action potential (contra Weber 2005). I argue further that neuroscientists lacked crucial explanatory details about the action potential until they could describe the molecular and ionic mechanisms by virtue of which their model holds (see Bogen 2005). Mathematical generalizations are important epistemic tools for assessing mechanistic explanations, but they are neither necessary nor sufficient for adequate explanations, even at the lowest levels of organization where biological phenomena are integrated with physics and chemistry
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)
Delcomyn, Fred (2001). Biorobotic models can contribute to neurobiology. Behavioral and Brain Sciences 24 (6):1056-1057.   (Google)
Abstract: The idea that biorobots can be used as a testbed for the evaluation of hypotheses about how an animal functions is supported. Generation of realistic feedback is a major advantage of biorobotic models. Nevertheless, skeptics can only be convinced that this approach is valid if significant biological insights are generated from its application
den Bosch, & P., M. (2005). Structures in neuropharmacology. Poznan Studies in the Philosophy of the Sciences and the Humanities 84 (1):343-359.   (Google)
Abstract: This paper explores structuralism as a way to model theories from scientific practice. As a case study I analyzed a theory about the dynamics of the basal ganglia, a part of the brain that is involved in Parkinson's disease. After introducing the case study I explore how to structurally represent qualitative assumptions about disease, intervention and dynamical systems in general. I further explicate the structure of the basal ganglia theory in detail, how it explains Parkinson's disease and how it implies treatments. I close with a consideration of how a structuralist representation could be useful in practice to explore and develop theories with the aid of a computer
Elliott, T. & Shadbolt, N. R. (1997). Neurotrophic factors, neuronal selectionism, and neuronal proliferation. Behavioral and Brain Sciences 20 (4):561-562.   (Google)
Abstract: Quartz & Sejnowski (Q&S) disregard evidence that suggests that their view of dendrites is inadequate and they ignore recent results concerning the role of neurotrophic factors in synaptic remodelling. They misrepresent neuronal selectionism and thus erect a straw-man argument. Finally, the results discussed in section 4.2 require neuronal proliferation, but this does not occur during the period of neuronal development of relevance here. Footnotes1 Address correspondence to TE at te@proteus.psyc.nott.ac.uk
Freeman, Walter J. (1997). Nonlinear neurodynamics of intentionality. Journal of Mind and Behavior 18 (2-3):291-304.   (Cited by 9 | Google | More links)
Gerrans, Philip & Stone, Valerie E. (2008). Generous or parsimonious cognitive architecture? Cognitive neuroscience and theory of mind. British Journal for the Philosophy of Science 59 (2).   (Google)
Abstract: Recent work in cognitive neuroscience on the child's Theory of Mind (ToM) has pursued the idea that the ability to metarepresent mental states depends on a domain-specific cognitive subystem implemented in specific neural circuitry: a Theory of Mind Module. We argue that the interaction of several domain-general mechanisms and lower-level domain-specific mechanisms accounts for the flexibility and sophistication of behavior, which has been taken to be evidence for a domain-specific ToM module. This finding is of more general interest since it suggests a parsimonious cognitive architecture can account for apparent domain specificity. We argue for such an architecture in two stages. First, on conceptual grounds, contrasting the case of language with ToM, and second, by showing that recent evidence in the form of fMRI and lesion studies supports the more parsimonious hypothesis. Theory of Mind, Metarepresentation, and Modularity Developmental Components of ToM The Analogy with Modularity of Language Dissociations without Modules The Evidence from Neuroscience Conclusion CiteULike Connotea Del.icio.us What's this?
Gerrans, Philip (2003). Nativism and neuroconstructivism in the explanation of Williams syndrome. Biology and Philosophy 18 (1):41-52.   (Cited by 4 | Google | More links)
Abstract:   Nativists about syntactic processing have argued that linguisticprocessing, understood as the implementation of a rule-basedcomputational architecture, is spared in Williams syndrome, (WMS)subjects – and hence that it provides evidence for a geneticallyspecified language module. This argument is bolstered by treatingSpecific Language Impairments (SLI) and WMS as a developmental doubledissociation which identifies a syntax module. Neuroconstructivists haveargued that the cognitive deficits of a developmental disorder cannot beadequately distinguished using the standard gross behavioural tests ofneuropsychology and that the linguistic abilities of the WMS subject canbe equally well explained by a constructivist strategy of neurallearning in the individual, with linguisitic functions implemented in anassociationist architecture. The neuroconstructivist interpretation ofWMS undermines the hypothesis of a double dissociation between SLI andWMS, leaving unresolved the question of nativism about syntax. Theapparent linguistic virtuosity of WMS subjects is an artefact ofenhanced phonological processing, a fact which is easier to demonstratevia the associationist computational model embraced byneuroconstructivism
Gerrans, Philip (2002). Nativism, neuroconstructivism, and developmental disorder. Behavioral and Brain Sciences 25 (6):757-758.   (Google)
Abstract: Either genetically specified modular cognitive architecture for syntactic processing does not exist (neuroconstructivism), or there is a module but its development is so abnormal in Williams syndrome (WS) that no conclusion can be drawn about its normal architecture (moderate nativism). Radical nativism, which holds that WS is a case of intact syntax, is untenable. Specific Language Impairment and WS create a dilemma that radical nativism cannot accommodate
Gold, Ian & Stoljar, Daniel (1999). Interpreting neuroscience and explaining the mind. Behavioral and Brain Sciences 22 (5):856-866.   (Google)
Abstract: Although a wide variety of questions were raised about different aspects of the target article, most of them fall into one of five categories each of which deals with a general question. These questions are (1) Is the radical neuron doctrine really radical? (2) Is the trivial neuron doctrine really trivial? (3) Were we sufficiently critical of the radical neuron doctrine? (4) Is there a distinction to be drawn at all between the two doctrines? and (5) How does our argument bear on related issues in the ontology of mind? Our replies to the objections and observations presented are organized around these five questions
Hartmann, Stephan (2001). Mechanisms, coherence, and the place of psychology. In Theory and Method in the Neurosciences. Pittsburgh: University of Pitt Press.   (Google)
Hardcastle, Valerie Gray (2008). Review of Carl F. Craver, Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience. Notre Dame Philosophical Reviews 2008 (1).   (Google)
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)
Heinke, D. (2000). A dynamical system theory approach to cognitive neuroscience. Behavioral and Brain Sciences 23 (4):543-543.   (Google)
Abstract: Neural organization contains a wealth of facts from all areas of brain research and provides a useful overview of physiological data for those working outside the immediate field. Furthermore, it gives a good example that the approach of dynamical system theory together with the concepts of cooperative and competitive interaction can be fruitful for an interdisciplinary approach to cognition
Hurley, Susan L. (online). The shared circuits model. How control, mirroring, and simulation can enable imitation and mind reading.   (Google)
Keil, Frank (ms). The seductive allure of neuroscience explanations.   (Google)
Abstract: & Explanations of psychological phenomena seem to genervs. with neuroscience) design. Crucially, the neuroscience inate more public interest when they contain neuroscientific..
Krebs, Peter R., Models of cognition: Neurological possibility does not indicate neurological plausibility.   (Google)
Abstract: Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Cognitive functions, like learning to speak, or discovering syntactical structures in language, have been modeled and these models are the basis for many claims about human cognitive capacities. Artificial neural nets (ANNs) have had some successes in the field of Artificial Intelligence, but the results from experiments with simple ANNs may have little value in explaining cognitive functions. The problem seems to be in relating cognitive concepts that belong in the `top-down' approach to models grounded in the `bottom-up' connectionist methodology. Merging the two fundamentally different paradigms within a single model can obfuscate what is really modeled. When the tools (simple artificial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced. The ability to learn functions from data-points makes ANNs very attractive analytical tools. These tools can be developed into valuable models, if the data is adequate and a meaningful interpretation of the data is possible. The problem is, that with appropriate data and labels that fit the desired level of description, almost any function can be modeled. It is my argument that small networks offer a universal framework for modeling any conceivable cognitive theory, so that neurological possibility can be demonstrated easily with relatively simple models. However, a model demonstrating the possibility of implementation of a cognitive function using a distributed methodology, does not necessarily add support to any claims or assumptions that the cognitive function in question, is neurologically plausible
Levy, Arnon (2009). Explaining what? Review of explaining the brain: Mechanisms and the mosaic unity of neuroscience by Carl F. Craver. Biology and Philosophy 24 (1).   (Google)
Abstract: Carl Craver’s recent book offers an account of the explanatory and theoretical structure of neuroscience. It depicts it as centered around the idea of achieving mechanistic understanding, i.e., obtaining knowledge of how a set of underlying components interacts to produce a given function of the brain. Its core account of mechanistic explanation and relevance is causal-manipulationist in spirit, and offers substantial insight into casual explanation in brain science and the associated notion of levels of explanation. However, the focus on mechanistic explanation leaves some open questions regarding the role of computation and cognition
Lyons, Jack C. (2003). Lesion studies, spared performance, and cognitive systems. Cortex 39 (1):145-7.   (Cited by 1 | Google | More links)
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)
Piccinini, Gualtiero (2006). Computational explanation in neuroscience. Synthese 153 (3):343-353.   (Google | More links)
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)
Skarda, S. (1986). Explaining behavior: Bringing the brain back in. Inquiry 29 (June):187-201.   (Cited by 5 | Google)
Stinson, Catherine (2009). Searching for the Source of Executive Attention. PSYCHE 15 (1):137-154.   (Google)
Abstract: William James presaged, and Alan Allport voiced criticisms of cause theories of executive attention for involving a homunculus who directs attention. I review discussions of this problem, and argue that existing philosophical denials of the problem depend on equivocations between different senses of “Cartesian error”. Another sort of denial tries to get around the problem by offering empirical evidence that such an executive attention director exists in prefrontal cortex. I argue that the evidence does not warrant the conclusion that an executive director can be localized in prefrontal cortex unless dubious assumptions are made, and that computational models purporting to support these assumptions either beg the question, or fail to model executive attention in terms of cause theories.
Tyler, Lorraine K. & Moss, Helen E. (2001). Concepts and categories: What is the evidence for neural specialisation? Behavioral and Brain Sciences 24 (3):495-496.   (Google)
Abstract: Humphreys and Forde argue that semantic memory is divided into separate substores for different kinds of information. However, the neuro-imaging results cited in support of this view are inconsistent and often methodologically and statistically unreliable. Our own data indicate no regional specialisation as a function of semantic category or domain and support instead a distributed unitary account
Poland, Jeffrey S. & Von Eckardt, Barbara (2004). Mechanism and explanation in cognitive neuroscience. Philosophy of Science 71 (5):972-984.   (Cited by 2 | Google | More links)
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 D. & Bechtel, William P. (2007). Mechanisms and psychological explanation. In Paul Thagard (ed.), Philosophy of Psychology and Cognitive Science. Elsevier.   (Google)
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