R ev is ed Pr oo f Synthese DOI 10.1007/s11229-015-0783-4 S.I . : NEUROSCIENCE AND ITS PHILOSOPHY The cognitive neuroscience revolution Worth Boone1 * Gualtiero Piccinini2 Received: 22 September 2014 / Accepted: 22 May 2015 © Springer Science+Business Media Dordrecht 2015 Abstract We outline a framework of multilevel neurocognitive mechanisms that1 incorporates representation and computation. We argue that paradigmatic explanations2 in cognitive neuroscience fit this framework and thus that cognitive neuroscience con-3 stitutes a revolutionary break from traditional cognitive science. Whereas traditional4 cognitive scientific explanations were supposed to be distinct and autonomous from5 mechanistic explanations, neurocognitive explanations aim to be mechanistic through6 and through. Neurocognitive explanations aim to integrate computational and repre-7 sentational functions and structures across multiple levels of organization in order to8 explain cognition. To a large extent, practicing cognitive neuroscientists have already9 accepted this shift, but philosophical theory has not fully acknowledged and appre-10 ciated its significance. As a result, the explanatory framework underlying cognitive11 neuroscience has remained largely implicit. We explicate this framework and demon-12 strate its contrast with previous approaches.13 Keywords Cognitive neuroscience * Multilevel mechanisms * Explanation *14 Integration * Computation * Representation15 The authors are listed alphabetically-the paper was thoroughly collaborative. Otávio Bueno graciously arranged for the double blind refereeing of this paper; thanks to him and the anonymous referees for helpful comments. Thanks to our audiences at Georgia State University, Washington University in St. Louis, the 2014 Society for Philosophy and Psychology meeting, 2014 Central APA meeting and to our APA commentator, Robert Rupert. Thanks to Sergio Barberis, Mazviita Chirimuuta, and Corey Maley for helpful comments. Thanks to Elliott Risch for editorial assistance. This material is based on work supported in part by a University of Missouri research award to Gualtiero Piccinini. B Gualtiero Piccinini piccininig@umsl.edu 1 University of Pittsburgh, Pittsburgh, USA 2 University of Missouri – St. Louis, St. Louis, MO, USA 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese 1 Introduction16 The traditional framework of cognitive science included (aspects of) six disciplines:17 psychology, computer science, linguistics, anthropology, neuroscience, and philos-18 ophy. These six disciplines were supposed to work together towards understanding19 cognition in accordance with a neat division of labor, to which many practitioners20 conformed. On one side stood psychology, with the help of computer science, linguis-21 tics, anthropology, and philosophy; on the other side stood neuroscience. Psychology22 etc. studied the functional or cognitive level, or-in Marr's terminology-the compu-23 tational and algorithmic levels; neuroscience investigated the neural, mechanistic, or24 implementation level. Explanations at these two levels were considered distinct and25 autonomous from one another.26 This division of labor leaves no room for cognitive neuroscience. Indeed, from27 this perspective, the very term "cognitive neuroscience" is almost an oxymoron,28 because neuroscience is supposed to deal with the mechanisms that implement cog-29 nitive processes, not with cognition proper. Yet cognitive neuroscience has emerged30 as the new mainstream approach to studying cognition. What gives?31 In this paper, we argue that cognitive science as traditionally conceived is on its way32 out and is being replaced by cognitive neuroscience, broadly construed. Cognitive neu-33 roscience is still an interdisciplinary investigation of cognition. It still includes (aspects34 of) the same six disciplines (psychology, computer science, linguistics, anthropology,35 neuroscience, and philosophy). But the old division of labor is gone, because the strong36 autonomy assumption that supported it has proven wrong.37 The scientific practices based on the old two-level view (functional/cognitive/38 computational vs. neural/mechanistic/implementation) are being replaced by scien-39 tific practices based on the view that there are many levels of mechanistic organization.40 No one level has a monopoly on cognition proper. Instead, different levels are more or41 less cognitive depending on their specific properties. The different levels and the dis-42 ciplines that study them are not autonomous from one another. Instead, the different43 disciplines contribute to the common enterprise of constructing multilevel mecha-44 nistic explanations of cognitive phenomena. In other words, there is no longer any45 meaningful distinction between cognitive psychology and the relevant portions of46 neuroscience-they are merging to form cognitive neuroscience. Or so we will argue.47 By contrast, many philosophers still insist that psychological explanation is distinct48 and autonomous from neuroscientific explanation. Some argue that psychological49 explanations can be satisfactory without being mechanistic (e.g., Weiskopf 2011,50 but see Povich forthcoming for a rejoinder). Others argue that representational and51 computational explanations of cognition belong in an autonomous psychology not in52 neuroscience (Fodor 1998; Burge 2010). A somewhat independent view, which also53 stands in contrast to our framework, is that computational explanation is not mechanis-54 tic (Rusanen and Lappi 2007; Shagrir 2010a; Chirimuuta 2014). In addition, there are55 scientists who argue that current neuroscience is wrong-headed and should be refor-56 mulated in light of a rigorous computational psychology (Gallistel and King 2009).57 While the latter view may be seen as consistent with our integrationist framework, in58 our opinion it underestimates the extent to which current neuroscience is empirically59 well grounded and should constrain our cognitive explanations.60 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese We have two primary, closely related goals. The first is to explicate the explanatory61 framework underlying contemporary cognitive neuroscience, contrasting it with tra-62 ditional cognitive scientific explanation. The second is to soften current resistance to63 the mechanistic integration of psychology and neuroscience. We proceed as follows.64 After reconstructing the received view of explanation in cognitive science (Sect. 2),65 we briefly indicate why traditional responses to the received view fail to square with66 cognitive neuroscience as we understand it (Sect. 3). We then articulate a framework67 of multilevel neurocognitive mechanisms (Sect. 4) and the levels that constitute them68 (Sect. 5). We conclude by highlighting three important aspects of cognitive neuro-69 science that illustrate our framework: the incorporation of experimental protocols from70 cognitive psychology into neuroscience experiments, the development and evolution71 of functional neuroimaging, and the movement toward biological realism in compu-72 tational modeling (Sect. 6). One important consequence of the picture we advance is73 that neither structures nor functions have primacy in individuating the scientific kinds74 of cognitive neuroscience. The upshot is that explanation in cognitive neuroscience is75 multilevel, mechanistic, computational, and representational.76 2 Cognitive science as traditionally conceived77 The cognitive revolution of the 1950s is most often juxtaposed against the behaviorist78 program it supplanted. By contrast with behaviorism's methodology and metaphysics,79 which is widely assumed to reject the postulation of cognitive states and processes,80 cognitive science explicitly postulates internal cognitive states and processes to explain81 intelligent capacities. An important motivation for this approach came from the anal-82 ogy between cognitive systems and digital computers. Computers possess internal83 states and processes that contribute to their capacities, some of which-playing chess,84 solving problems, etc.-are capacities that require intelligence in humans. Since it's85 patently legitimate to explain a computer's capacities in terms of its internal states and86 processes, cognitive scientists argued that it is equally legitimate to explain human87 cognition in terms of internal states and processes. More importantly, the internal88 states and processes of computers are representations and computations, which are89 typically considered cognitive notions. Thus, the argument continues, it is legitimate90 to explain human cognition in terms of computations and representations. Indeed,91 in this tradition cognition is often identified with some form of computation-more92 specifically, some form of digital computation over representations (e.g., Newell and93 Simon 1976; Anderson 1983; Johnson-Laird 1983; Pylyshyn 1984).94 This focus on the contrast between behaviorism and cognitive psychology often95 obscures some of the substantive commitments that came out of the cognitive rev-96 olution. At all stages of Western history, available technology has constrained the97 analogies used to think about the operations of the human mind and body. For instance,98 water technologies-pumps, fountains, etc.-provided the dominant metaphor behind99 the ancient Greek concept of the soul-the 'pneuma'-and the humorist theories that100 dominated Western medicine for 2000 years (Vartanian 1973); the gears and springs of101 clocks and wristwatches played a similar role for early mechanist thinking during the102 enlightenment (e.g., La Mettrie's L'Homme Machine, 1748); hydraulics for Freud's103 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese concept of libido; telephone switchboards for behaviorist theories of reflexes; and so104 on.1 It is no coincidence that the cognitive revolution co-occurred with the advent of105 computers.106 Whenever technology guides thinking about the human mind or body, there is107 risk that the analogy is taken too far. While it may be true that cognition involves108 transitions between internal states analogous to computations of some kind, the com-109 mitments of traditional cognitive science go far beyond this basic point. Specifically,110 the analogy between cognition and computation has been taken to imply that cogni-111 tion may be studied independently of the nervous system. The main rationale for this112 autonomy is that digital computers-more specifically, universal, program-controlled113 digital computers-reuse the same hardware for the different programs (i.e., software)114 they execute. Each particular program explains a specific capacity, while the hardware115 remains the same. By the same token, a widespread assumption of traditional cognitive116 science is that the brain is a universal, program-controlled digital computer; therefore,117 cognition can be studied simply by figuring out what programs run on such a computer,118 without worrying over the details of the wetware implementation of those programs119 (e.g., Fodor 1968b; Newell and Simon 1976; Pylyshyn 1984). Additionally, many120 who thought of the brain simply as some kind of digital computer (without assuming121 that it is universal and program-controlled) nonetheless agreed that cognition could122 be explained independently of neuroscience (e.g., Cummins 1983).2123 A close ally of this computer analogy and its rationale for the autonomy of psychol-124 ogy is the view that psychological explanation is different in kind from neuroscientific125 explanation. According to this view, psychological explanation captures cognitive126 functions and functional relations between cognitive states and capacities, whereas127 neuroscientific explanation aims at the structures that implement cognitive functions.128 The two types of explanation are supposed to place few constraints on one another129 with the upshot that each can proceed independently from the other.130 The resulting picture of cognitive science is that psychology studies cognition in131 functional terms, which are autonomous from the non-cognitive mechanisms studied132 by neuroscience. Aspects of this two-level picture can be found in the writings of many133 philosophers of cognitive science. Here are a few stark examples:134 The conventional wisdom in the philosophy of mind is that psychological states135 are functional and the laws and theories that figure in psychological explanations136 are autonomous (Fodor 1997, p. 149).137 [I]n the language of neurology ..., presumably, notions like computational state138 and representation aren't accessible (Fodor 1998, p. 96).139 1 See Daugman (1990) for more detailed discussion of the role of technology and metaphor in the study of the human mind and body. 2 A computer is universal just in case it can compute any computable function until it runs out of memory and time. A computer is program-controlled just in case it computes different functions depending on which program it executes. Contemporary digital computers are both universal and program-controlled. Different kinds of analogies may be drawn between digital computers and brains, some of which are stronger than others (cf. Piccinini 2008, Sect. 5 for a more detailed discussion). At the same time, it was widely recognized that there are significant architectural and performance differences between artificial digital computers and natural cognitive systems. 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese We could be made of Swiss cheese and it wouldn't matter (Putnam 1975, p. 291).140 [F]unctional analysis [which includes psychological explanation] puts very141 indirect constraints on componential analysis [i.e., mechanistic explanation]142 (Cummins 1983, p. 29; 2000, p. 126).143 [E]xplanation in terms of distinctively psychological representational notions is,144 as far as we now know, basic and ineliminable (Burge 2010, p. 27).145 These philosophers were, and in some cases still are, trying to capture what cognitive146 scientists were doing at the time. And while cognitive scientists were perhaps less147 explicit about the two-level picture, something similar to this view can be found in148 many landmark works that came out during the heyday of classical cognitive science149 (e.g., Newell and Simon 1976; Newell 1980; Marr 1982; Anderson 1983; Johnson-150 Laird 1983; Pylyshyn 1984).151 3 Traditional responses to cognitive science152 This traditional two-level picture of cognitive science fails to capture explanation in153 contemporary cognitive neuroscience. Cognitive neuroscience strives to explain cog-154 nition on the basis of neural mechanisms and thus involves integration, not autonomy,155 between psychology and neuroscience. After the cognitive revolution, the mechanis-156 tic integration of psychology and neuroscience amounts to another paradigm shift:157 the cognitive neuroscience revolution. In later sections we will argue that this new158 revolution requires a different way of thinking about levels, cognitive explanation,159 representation, and computation. The resulting explanatory framework, multilevel160 neurocognitive mechanisms, is what we aim to articulate in this paper.161 In seeking an account of explanation in cognitive neuroscience, let's begin with two162 traditional responses to the two-level picture-reduction and elimination. While we163 lack the space for detailed treatment, we briefly argue that these traditional responses164 to cognitive science fail to adequately capture the kind of integration found in cognitive165 neuroscience. These arguments will motivate our positive proposal (Sect. 4).166 One traditional alternative to autonomy is to eliminate the theoretical constructs167 posited by psychology in favor of the theoretical constructs posited by neuroscience.168 The model for such eliminativism is the past elimination of theoretical constructs, such169 as epicycles, phlogiston, or the ether, from past scientific theories. Just as those theo-170 retical posits were eventually eliminated from our scientific theories of, respectively,171 planetary motion, heat, and the transmission of radiation through space, so the theo-172 retical posits of psychology, such as the language-like mental representations posited173 by classical cognitive psychologists, should be eliminated in favor of posits that are174 more amenable to neuroscience (Churchland 1981, 1986).175 If eliminativism is construed radically enough-that is, as the literal elimination176 of any science of cognition other than neuroscience-it offers a partial solution to177 the problem at hand. That problem is to understand how the disciplines that study178 cognition fit together and how cognition ought to be explained. If any discipline other179 than neuroscience is eliminated, the first half of the problem is solved: since the other180 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese disciplines no longer exist, we don't need to worry about how they fit together with181 neuroscience. But this radical construal is hardly a solution to the most interesting part182 of the problem-how to explain cognition.183 Contemporary cognitive neuroscience aims to explain cognition on the basis of184 neural computation over neural representations (more on this below). If the elimi-185 nativist approach implies that cognition itself-and all "cognitive" theoretical posits,186 such as representation, computation, or information processing-should be eliminated187 or at least deflated (cf. Ramsey 2007), then we are faced with a solution that is anti-188 thetical to cognitive neuroscience.189 Another alternative to traditional (two-level) cognitive science is to reduce psy-190 chological theoretical posits to neuroscientific theoretical posits. The models for this191 reductionist strategy come from examples from physics, such as the reduction of clas-192 sical thermodynamics to statistical mechanics or the reduction of Newton's theory of193 gravitation to a special case of Einstein's theory of General Relativity. The main dif-194 ficulty for this reductionist approach in cognitive neuroscience is that, even assuming195 that it works for some physical theories (which has been debated), psychological and196 neuroscientific explanations lack the appropriately general mathematical formulation197 to be conducive to such reductions (cf. Cummins 2000).198 Nevertheless, some have argued that when we can intervene on molecular struc-199 tures in the brain and affect some cognitive behavior, the specific molecular events200 "directly explain" the behavioral data, we thereby reduce the relevant cognitive capac-201 ity to the relevant molecular events, and we thereby obviate the need for intermediate202 levels of explanation (cf. Bickle 2003, 2006). The main problem with this form of203 reductionism is that specific molecular events are at best only partial explanations204 of cognitive phenomena. It is one thing to correlate specific molecular events with205 cognitive phenomena via some specific intervention; to actually explain a cognitive206 phenomenon on the basis of molecular events requires determining the ways in which207 the molecular events are causally relevant to the production of the phenomenon of208 interest. Molecular events are only relevant to the extent that they occur within spe-209 cific neural structures, and locating the relevant neural structures requires more than210 purely molecular neuroscience. In addition, even identifying a molecular event within211 a neural structure that contributes to a cognitive behavior falls short of a full expla-212 nation. A full explanation requires identifying how molecular events contribute to213 relevant neural events, how relevant neural events contribute to circuit and network214 events, how those in turn contribute to relevant systems-level events, and finally how215 the relevant systems, appropriately coupled with the organism's body and environ-216 ment, produce the behavior. These intermediate links in the causal-mechanistic chain217 are crucial to connecting molecular events to cognitive phenomena in a way that is218 explanatory, as opposed to merely correlational. And identifying these intermediate219 level structures and their causal contributions requires going well beyond molecular220 neuroscience (cf. Craver 2007; Bechtel 2008).221 In spite of their respective limitations, both eliminativism and reductionism put222 pressure on the received view of cognitive science-most helpfully, by pointing out223 that cognitive scientists who ignore neuroscience do so at their peril and by pushing224 towards the integration of psychology and neuroscience. But neither eliminativism225 nor reductionism offers a satisfactory framework for explanation in cognitive neuro-226 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese science: the former insofar as it neglects cognition altogether; the latter because it227 offers only partial explanations that lack the necessary contextual factors provided by228 intermediate levels of analysis.229 4 Multilevel neurocognitive mechanisms230 Cognitive neuroscience stands in stark contrast to the traditional two-level picture of231 cognitive science. Broadly, cognitive neuroscience is the scientific study of how neural232 activity explains cognition and the behavior it gives rise to. Cognitive neuroscientists233 study nervous systems using many techniques at many levels. They study how cortical234 areas and other neural systems contribute to various cognitive capacities, how the235 capacities of those systems are explained by the operations of the neural subsystems236 that compose them (columns, nuclei), how networks and circuits contribute to their237 containing systems, how neurons contribute to networks and circuits, and how sub-238 neuronal structures contribute to neuronal capacities. Analyzing systems across such239 varied levels involves coordinating techniques ranging from molecular neuroscience240 and genetics to neurophysiology, neuroimaging, mathematical analysis, computational241 modeling, and a wide range of behavioral tasks.242 Cognitive neuroscience thus strives to explain cognitive phenomena by appealing to243 and analyzing (both separately and conjointly) multiple levels of organization within244 neural systems. Multilevel mechanisms have recently been proposed as a framework for245 thinking about the relations between these levels of organization. A multilevel mech-246 anism is a system of component parts and wholes in which the organized capacities247 of the component parts constitute (and thus mechanistically explain) the capacities of248 the whole (e.g., Craver 2007). Some mechanists prefer to define mechanisms in terms249 of operations, activities, or interactions rather than capacities (e.g., Glennan 2002;250 Bechtel 2008). We see these different formulations as equivalent for the purposes at251 hand because operations, activities, and interactions can be seen as manifestations252 of capacities (Piccinini unpublished). Note that it may take multiple capacities orga-253 nized in specific ways to bring about specific operations, activities, or interactions.254 In this section, we expand this framework, arguing for a specific understanding of255 cognitive neuroscience as a science directed at integrated multilevel neurocognitive256 mechanisms.3257 Multilevel neurocognitive mechanisms have an iterative structure: at any level,258 each component of the mechanism is in turn another mechanism whose capacities259 are explained by the organized capacities of its components; and each whole mech-260 anism is itself a component part that contributes to the capacities of a larger whole.261 This multilevel iterative structure tops off in the capacities of whole organisms and262 their interactions with other organisms, which are studied by social neuroscience263 and neuroeconomics; it bottoms out in structures-such as the atoms that compose264 3 Some argue that at least some explanations in cognitive neuroscience are not mechanistic but are instead "dynamical" (e.g., Chemero and Silberstein 2008). We lack the space to discuss this putative alternative to mechanistic explanation, except to point out that mechanistic explanations are often dynamical in the relevant sense (cf. Bechtel and Abrahamsen 2013) and thus are consistent with describing the dynamics of a system, whereas dynamical descriptions may or may not be explanatory in the relevant sense (cf. Kaplan and Craver 2011). 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese neurotransmitters-that fall outside the disciplinary boundaries of cognitive neuro-265 science.266 Cognitive neuroscience is not the only science that explains mechanistically, but it267 is one of the few whose mechanisms perform computations over representations (cf.268 Bechtel 2008, 2015). There is a large literature on what constitutes computation and269 representation and we cannot do justice to these topics here. For present purposes, it270 will suffice to sketch an account of computation and representation that squares with271 the framework of multilevel neurocognitive mechanisms.272 A vehicle carries semantic information about a source just in case it reliably cor-273 relates with the states of the source (Dretske 1981; Piccinini and Scarantino 2011;274 Scarantino 2015). For instance, the spike trains generated by neurons in cortical area275 V1 reliably correlate with the presence and location of edges in the visual environ-276 ment; thus, they carry semantic information about the presence and location of edges277 in the visual environment. But correlation alone is insufficient for representation.278 A vehicle represents a source just in case it has the function of carrying information279 about the source (Dretske 1988; Morgan 2014). For a vehicle to have such a function,280 the information it carries must be used by some part of the system in which it is281 embedded. The information is used by the system to the extent that it's causally282 relevant to other operations of the system. In our example, the spike trains generated by283 neurons in V1 have the function of carrying information about the visual environment284 because this information is used by downstream areas for further processing of visual285 stimuli-i.e., it is causally relevant to the operations of those downstream areas. Thus,286 in the relevant sense, V1 neurons represent the presence and locations of the edges287 with which they correlate.288 Finally, a system performs computations just in case it manipulates vehicles in289 accordance with rules that are sensitive to inputs and internal states and are defined290 in terms of differences between different portions of the vehicles it manipulates.291 Which computations are performed by a system depends on its specific mechanis-292 tic properties-its component types, its vehicle type, and the rules it follows. That293 is, computation here is defined non-semantically based on the mechanistic proper-294 ties of the system and the vehicles it manipulates. Although computation can occur295 in the absence of representation, processing representations is a form of mechanistic296 computation (Piccinini and Scarantino 2011; cf. Fresco 2014; Milkowski 2013).297 A distinctive feature of neural systems is that they pick up information from the298 environment and organism, transmit it through the system via appropriate signals299 (neural representations), and process such signals in conjunction with pre-existing300 representations and rules of manipulation (neural computation) in order to generate301 further signals that regulate the organism's behavior. This appeal to representation and302 computation distinguishes mechanistic explanations in cognitive neuroscience from303 mechanistic explanations in many other sciences.304 The above account of computation is diametrically opposed to persistent views of305 computation that draw a stark contrast between computational and mechanistic expla-306 nations.4 Such views maintain that computations are abstract or mathematical in a307 4 A recent example: "My key claim is that the use of the term 'normalization' in neuroscience retains much of its original mathematical-engineering sense. It indicates a mathematical operation-a computation-not 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese way that evades mechanistic explanation. While it's true that computation can be math-308 ematically characterized, however, the physical computations performed by nervous309 systems (and artificial computers, for that matter) are functions performed by concrete310 mechanisms.5 Like other functions, information processing via neural computation is311 performed by mechanisms-specifically, neurocomputational mechanisms. With this312 said, an important caveat is that computing mechanisms, like all mechanisms, can be313 characterized at different levels of abstraction. This is an integral aspect of multilevel314 mechanistic explanation, though one that has been a source of recent controversy.315 The mechanistic framework has recently been construed as a call for maximal316 detail in explanation and a rejection of abstraction. A number of recent criticisms have317 been developed along these lines (Barberis 2013; Barrett 2014; Levy and Bechtel318 2013; Levy 2013; Chirimuuta 2014; Weiskopf 2011). A common idea behind these319 objections is that the multilevel mechanistic framework is committed to the claim320 that the explanatory power of a model is primarily a function of the amount of detail321 contained in its description of a particular mechanism-viz. the more detail, the better322 the explanation. Thus, according to this interpretation, mechanistic integration eschews323 any valuable role for abstraction in explanation.6324 As these critics point out, many forms of explanation in cognitive and systems325 neuroscience proceed through systematic abstraction away from the particular details326 of a target system. This is, for instance, how neuroscientists come to characterize327 something like lateral inhibition as a general type of organization of neural circuitry328 found in different brain regions-e.g., peripheral somatosensory and visual processing329 both exhibit this kind of organization. The details-the particular kind of excitatory330 cell, inhibitory interneuron, number and strength of synapse, etc.-are often irrelevant331 to understanding why lateral inhibition is a useful form of circuitry and why it crops332 up in so many circumstances in which these details do in fact differ. It is thus easy333 to see why a view in which explanatory power is tied solely to detail of description334 would face serious problems in cognitive neuroscience.335 But it would be a mistake to conclude that when an explanation intentionally336 excludes some details, the explanation is thereby rendered non-mechanistic. To337 Footnote 4 continued a biological mechanism" (Chirimuuta 2014, p. 124). Chirimuuta also cites some neuroscientists who draw a similar contrast between computations and mechanisms. 5 Not all mathematical models in cognitive neuroscience ascribe computations to the nervous system; only those that explain phenomena through computations performed by the target systems do so. 6 In fairness to the critics, some mechanists may give the impression of advocating such a view: "the more accurate and detailed the model is for a target system or phenomenon the better it explains that phenomenon, all other things being equal" (Kaplan 2011, p. 347). Kaplan points out that some details may be omitted from a model either for reasons of computational tractability or because they are unknown. Similarly, Craver writes: "Between sketches and complete descriptions lies a continuum of mechanism schemata whose working is only partially understood" (Craver 2007, p. 114). To drive this point home, Craver aligns the sketch-schema-mechanism axis with the epistemic axis of "how possibly-plausibly-actually": "Progress in building mechanistic explanations involves movement along both the possibly-plausibly-actually axis and along the sketch-schema-mechanism axis" (Craver 2007, p. 114). Contrary to what Craver appears to imply, progress may consist in abstracting away from irrelevant details to construct an appropriate schema, and in some epistemic contexts even a mechanism sketch may provide all the explanatory information that is needed (more on this in this section). And in fairness to Craver and Kaplan, we should note that there are also passages where they accept that abstraction and idealization play legitimate roles in explanation. 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese the contrary, proponents of the mechanistic framework have often pointed out that338 abstracting away from irrelevant details is as important to mechanistic explanation339 as including relevant details (e.g., Piccinini and Craver 2011; see Boone and Pic-340 cinini unpublished for a more detailed treatment). Which details ought to be included341 and excluded depends on various features of explanatory context. The concepts of342 mechanism sketches and schemata were designed to capture this aspect of mechanis-343 tic explanations (Machamer et al. 2000). Mechanism sketches involve omissions of344 as yet unknown details; mechanism schemata involve deliberate omissions of detail,345 capturing the bare relevant causal structure of a system.346 Examples of schemata abound in neuroscience. A much-discussed example, which347 is particularly relevant to the present context, is the Hodgkin-Huxley model of the348 action potential (Hodgkin and Huxley 1952). The Hodgkin-Huxley model explains349 the voltage profile of the action potential in terms of a neural membrane's changing350 voltage conductivity. Lower-level mechanistic details about how changes in membrane351 permeability arise were omitted from the model, initially because they were unknown352 (Hodgkin and Huxley 1952, p. 541), but also later because this omission affords353 the model greater generality (Schaffner 2008; Levy 2013; Chirimuuta 2014, p. 141).354 The Hodgkin-Huxley model has been described as non-explanatory (Bogen 2005), as355 providing a non-mechanistic explanation (Weber 2005, 2008), and as a mere sketch356 because it omits information about the role of ion channels in allowing membrane357 permeability (Craver 2007). In our view, none of these characterizations fully hit358 the mark. Rather, the HH model is an example of a mechanism sketch that evolved359 into a mechanism schema-it explains a phenomenon (the action potential) at one360 mechanistic level (changes in membrane conductivity) while abstracting away from361 lower mechanistic levels (ion channels and their components).362 As the preceding example illustrates, mechanistic explanations-particularly those363 that involve computations and representations in the sense outlined above-are often364 presented in the form of (interpreted) mathematical or computational models. Typi-365 cally, such models become analytically insoluble and computationally intractable if366 they include too much detail about their target systems. As such, issues relating to367 solubility and tractability provide another motivation for the exclusion of detail from368 models of neurocomputational mechanisms. Issues regarding tractability are ubiqui-369 tous in computational neuroscience given the vast array of biological detail that could370 potentially figure into modeling scenarios.371 For instance, one controversial but extremely common assumption among com-372 putational neuroscientists is that individual neurons can be treated as integrating a373 linear sum of dendritic inputs, and then initiating an action potential when that sum374 reaches a threshold. The dynamics of actual neurons are more complex than this model375 suggests, which in turn has led to the development of more complex models-e.g.376 Waxman (1972) provides an alternative model, which introduces nonlinearities into377 the branching regions of the dendritic (input) and axonal (output) trees, rather than378 treating those regions as, respectively, collecting and distributing charge linearly. But379 the basic, linear treatment of dendritic input integration has been a powerful tool in a380 wide variety of modeling contexts. One explanation for the success of these simplified381 modeling strategies is that they capture important aspects of neural responses that are382 adequate for particular epistemic purposes.383 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese While this topic raises a number of interesting issues, one clear takeaway is that384 an important skill of mathematical and computational modelers is to capture all and385 only those features of the systems they study that are needed to explain phenom-386 ena of interest, often by introducing appropriate idealizations and simplifications.387 Those idealizations and simplifications allow modelers to represent systems to desired388 degrees of approximation while maintaining mathematical solubility or computational389 tractability (cf. Humphreys 2004; Piccinini 2007; Winsberg 2010; Weisberg 2013).7390 Explanations of computational or information processing mechanisms often require391 these forms of detail omission. What is crucial to appreciate here is that computation392 and information processing do not lead outside the multilevel mechanistic framework393 but are instead best seen as a special case of it.8394 Relatedly, as a matter of methodology, we are often interested in one aspect (some395 components or capacities) of a mechanism at the expense of other aspects (other396 components or capacities). This is one type of mechanism sketch, or partial (elliptical)397 mechanistic explanation. Consider what it takes to explain why a mechanism functions398 differently than it normally does. Explaining a deviation from normal functioning may399 require simply pointing out what's different in the relevant case, while omitting the rest400 of the mechanism (Van Eck and Weber 2014). For instance, to explain why certain401 patients have left-side hemineglect (roughly: inattention to and unawareness of the402 left side of visual space) it may be enough to point out that such patients suffered403 damage to the contralateral (right-side) cortical areas responsible for spatial attention,404 without providing details about the mechanisms involved in normal spatial attention405 and consciousness.406 Special cases of this type of mechanism sketch are descriptions of computational407 (the function computed and why it is adequate to the task, cf. Shagrir 2010b) and408 algorithmic (the computational operations and representations) levels of a system,409 which omit details about the components that carry out the algorithm. There is cer-410 tainly value to such approaches in cognitive neuroscience, particularly in the context411 of discovery. Marr (1982) argued that a neural "algorithm" is "likely to be understood412 more readily by understanding the nature of the problem being solved than by exam-413 ining the mechanism (and the hardware) in which it is embodied" (Marr 1982, p. 27).414 Marr's arguments have often been cited in defense of autonomist views of cognitive415 7 Issues related to tractability and solubility of mathematical models quickly get into deeper philosophical water than can be adequately treated here. Such issues spread across most domains of scientific inquiry. For instance, foundational work in continuum mechanics-i.e. the Navier–Stokes equations-developed around failures to model the flows of fluids through containers as trajectories of point particles; rather, the Navier–Stokes equations describe velocity fields at given points in space and time (see Batterman 2013 for an extended discussion). The extent to which the successes of these "top-down" modeling strategies can be treated merely as idealizations and approximations rather than reflecting more fundamental differences in the phenomena under investigation and our understanding of those phenomena at different levels of analysis is currently a topic of rich philosophical debate. 8 This is not to say that all analyses of neural computation or information-processing are mechanistic. Some focus only on the information content and efficiency of a neural code without saying anything about the processing mechanisms (Dayan and Abbott 2001, xiii; Chirimuuta 2014, p. 143ff). These models are not especially relevant here because they do not provide the kind of constitutive explanations that are the present topic, and that functional analysis and mechanistic explanation are competing accounts of. 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese science.9 We have a different take, consistent with seeing computational and algorith-416 mic accounts (in Marr's sense) as mechanism sketches (or schemata to the extent that417 underlying details are known but deliberately omitted).418 Understanding the capacities of a system often requires looking "up" to situate419 the system within some higher-level mechanism or environmental context as much420 as looking "down" to understand how those capacities are implemented by the lower421 level components, their capacities, and organization. In addition, more may be known422 about the mechanistic or environmental context of a system than its components and423 their operations. In such cases, investigators may be forced to constrain their models424 primarily by examining the problem being solved rather than the components and their425 operations, even though the likely result of such a method is a "how-possibly" model426 that falls short of explaining how the system actually works. Much of Marr's work427 belongs in this how-possibly category. We certainly face some of the same problems428 in contemporary cognitive neuroscience, but the field has developed to the point where429 integration, rather than autonomy, is the appropriate framework. The computational-430 level descriptions Marr and others sought are best construed as a valuable step along431 the way to integrated multilevel mechanistic explanations. It is no longer enough to432 simply home in on ways in which problems might be solved in the brain; contemporary433 cognitive neuroscience aims to understand how those problems are actually solved in434 the brain.435 5 Neurocognitive levels436 A primary motivation behind the traditional autonomist picture of cognitive science437 is the idea that functions can be understood independently from the structures that438 perform them. Therein lies the putative distinction between the "functional" level,439 which is cognitive, representational, and computational, and the "structural" level,440 which is non-cognitive, mechanistic, and implementational. Our account of multilevel441 neurocognitive mechanisms adopts a different notion of neurocognitive levels, which442 undermines this traditional dichotomy.443 Contrary to the received view, there is no single "functional," "cognitive," or "repre-444 sentational/computational" level of explanation, standing in opposition to a single (or445 even multiple) "structural," "neural," or "implementational" level(s).10 In this section446 we analyze each of these concepts from the perspective of neurocognitive mecha-447 nisms in order to highlight how our integrationist framework improves upon traditional448 autonomist and reductionist views.449 In the first place, every level of a multilevel mechanism is both functional and450 structural, because every level contains structures performing functions. This stands in451 stark contrast to traditional views that maintain that structural analyses and functional452 analyses are distinct and autonomous from one another. Traditional reductionists-453 9 Bechtel and Shagrir (forthcoming) is a good entry into the extensive literature on Marr's levels, including how they might fit within a mechanistic framework. We cannot do justice to that debate here. 10 This point is reminiscent of Lycan's underappreciated critique of "two-levelism" (Lycan 1990). But Lycan lacked the accounts of mechanistic explanation and computational explanation that have been developed in detail in the past decade, and that provide the foundation that we are building upon. 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese e.g. type physicalists (Smart 1959)-strove to identify mental types with physical454 types. As a result, they may be interpreted as focusing on structural properties at455 the expense of functional properties, relegating the latter to "second order states"456 of physical types (Smart 2007). Traditional functionalists do the opposite: they give457 primacy to functional properties at the expense of structural properties (e.g., Putnam458 1967; Fodor 1968a). This is a somewhat unorthodox way of characterizing these views,459 so some brief unpacking is in order.460 Classical reductionist views of the mind-brain relation, specifically type identity461 theorists, look to identify higher-order kinds (e.g. mental kinds, like "pain") with462 corresponding physical kinds. These reductive views were developed in contrast to463 dualism: the view that the mind and brain are distinct kinds of substance. Dualists have464 a notoriously difficult time specifying the means by which these distinct substances465 interact; type identity theorists provide a dissolution of this problem. To say that water466 is H2O just is to identify a higher-level kind with a lower-level physical kind-an467 arrangement of atoms. With such an identity in hand, it is illegitimate to wonder how468 water and H2O interact. In a similar vein, type identity theorists argued that mental469 kinds, like pain, could be identified with particular neural kinds, like C-fibers firing.470 This identification dissolves dualistic concerns about how mental states interact with471 bodily states. What is noteworthy for present purposes is that the defining features of the472 kinds that figure into higher-level analyses just are the lower-level physical features473 common to instances of those kinds. This identification with lower-level physical474 features downplays the role of functional features of those higher-level kinds. Water475 is not individuated by its ability to quench thirst, nourish plants, etc., nor pain with its476 role in avoidance of noxious stimuli, protecting injured body parts, etc. Instead both477 kinds are identified with particular physical types, which possess the relevant causal478 powers that are, incidentally, associated with these functions.479 Classic functionalist views turn this story on its head: the defining features of480 higher-level kinds, according to such views, are their functional features while the481 structural features are incidental. The crux of this disagreement between functionalists482 and reductionists turns on multiple realizability-i.e., the claim that the same function483 can be realized in distinct physical substrates. Carburetors provide a classic example:484 they are defined by their function in internal combustion engines (mixing fuel and air);485 they retain this function over variations in the stuff they're made of (e.g. cast iron, zinc,486 aluminum, plastic) and the types of engine they're found in (e.g. car, motorcycle, lawn487 mower). Putnam's original arguments for autonomy in the 1960s were based on this488 insight (e.g. Putnam 1967). Fodor took up the torch and used multiple realizability to489 argue for the general autonomy of the special sciences from lower-level sciences-490 physics, in particular (Fodor 1968a, b, 1974). The idea behind these arguments is that,491 while higher-level states are token identical to particular lower-level physical states,492 there is no single lower-level physical kind for the higher-level states to be identified493 with. Rather, when higher-level kinds are realized, the underlying physical kinds will494 form unruly disjunctions (e.g. cast iron OR zinc OR aluminum OR plastic); the only495 thing tying this disjoint set of physical features together is the higher-level kind itself496 (e.g. the function, "mixing fuel and air"). Thus nothing is added to the higher-level497 analysis by looking at its realizers.498 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese Neither of these approaches adequately captures the main thrust of work in cogni-499 tive neuroscience, because that work is aimed at understanding the complex interplay500 between structure and function. By contrast, the multilevel mechanistic framework we501 are advocating adequately captures this aspect of cognitive neuroscientific explana-502 tions; in our framework, functions constrain structures and vice versa. Functions cum503 contextual factors-i.e. mechanistic context-constrain the range of structures that504 can perform those functions. Similarly, structures cum contextual factors determine505 the range of functions those structures can perform. In this framework, neither struc-506 tures nor functions are given primacy over the other; neither can explain cognition507 without the other.508 Any given structure is only capable of performing a restricted range of functions. For509 an everyday example, consider again the functions that can be associated with water.510 Structural facts about the chemical composition of water both enable and restrict its511 ability to perform certain functions. For instance, the facts that water is liquid at phys-512 iological temperatures and is composed of hydrogen (positively charged) and oxygen513 (negatively charged) make it appropriate for dissolving ionic compounds into ions514 essential for normal cell function. Contextual factors-like ambient temperature and515 available compounds-combine with structural factors to determine the appropriate516 range of functions. In the context of cognitive science, similar observations abound.517 Consider for instance that neurons have a refractory period, during which they cannot518 fire. This refractory period restricts a neuron's maximum firing rate to about 1000519 Hz, which in turn limits the kind of codes by which the brain can encode and transmit520 information. The structural properties that determine the recovery period of a neuron-521 blocks that prohibit influx of sodium ions through voltage-gated channels-limit the522 encoding and signaling functions neurons can perform.523 In the other direction, any function can only be performed by a restricted range of524 structures. For an everyday example, reconsider the example of a carburetor. While525 it's true that carburetors can be made from many different materials, the appropriate526 materials are severely restricted once mechanistic context and desired function are527 considered. A plastic carburetor from a lawn mower engine will cease to function as528 a carburetor in the context of a Ford F150 engine. The function and the context in529 which the function is embedded determine the range of structures that can implement530 that function. In the context of cognitive science, consider the function of storing531 information long term in a read/write, addressable form similar to the way memory532 works in digital computers (Gallistel and King 2009). Fulfilling this function requires533 memory registers whose states persist over a sufficiently long time, which must be534 appropriately connected to the processing components; it also requires a system of535 addresses that are stored in memory components and manipulated by an appropriate536 control structure. None of this comes for free by positing a certain function; for a537 functional hypothesis to prove correct, the structures that perform that function within538 the nervous system must be identified.539 The upshot is that cognition cannot be explained without accounting for the ways540 in which structures constrain functions and vice versa. In the long run, the mutual541 constraints between structures and functions lead cognitive psychologists and neuro-542 scientists to look to each other's work to inform their analyses. At any given level of543 organization, the goal is to identify both what structures are in play and what functions544 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese are performed. The more we know about functions and the context in which they are545 embedded, the more we can formulate sensible hypotheses about which structures546 must be involved. Similarly, the more we know about structures and the contextual547 factors that influence them, the more we can formulate sensible hypotheses about548 which functions they perform. The best strategy is to investigate both structures and549 functions simultaneously. As we will illustrate in the next section, this is the main550 driving force between the merging of neuroscience and cognitive psychology into551 cognitive neuroscience.552 Building on these observations about the relations between structures and func-553 tions, similar points can be made about implementation (or realization): there is no554 single implementation (or realization) level. Every system of organized components555 implements (realizes) the capacities of the whole it composes. Every capacity of a556 whole is implemented (realized) by its organized components. Implementation is thus557 relative to level. In other words, every level of a multilevel mechanism is implementa-558 tional relative to the level above it. The only exceptions to this occur at the (somewhat559 arbitrary) boundaries of cognitive neuroscientific inquiry-e.g., the whole behaving560 organism need not implement anything (at least as far as cognitive neuroscience is561 concerned).11562 Relatedly, every level of a neurocognitive mechanism is neural-or more precisely,563 every level is either (at least partially) composed of neurons or is a component of a neu-564 ron. The fact that neurocognitive mechanisms "bottom out" in components of neurons565 is a contingent feature of the disciplinary boundaries of cognitive neuroscience. The566 crucial point for present purposes, in terms of deviation from the classical autonomist567 view, is that there is no "non-mechanistic" level of explanation to be added to the568 mechanistic ones.569 Whether a level of a neurocognitive mechanism is representational or compu-570 tational depends on whether it contains representations or performs computations571 (in accord with the above definitions of these terms). Many cortical areas and other572 large neural systems contain representations and perform computations in the rele-573 vant sense, so they are representational and computational. Many of their components574 (columns, nuclei) also contain representations and perform more limited computations575 over them; the computations they perform are component processes of the computa-576 tions performed by their containing systems. Therefore, large neural components are577 representational and computational, and the same holds for their components (e.g. net-578 works and circuits). Again, the computations performed by smaller components are579 constituents of the larger computations performed by their containing systems, and that580 11 Here we depart from Craver (2007, pp. 212ff.), who distinguishes between levels of mechanistic organization and levels of realization. Craver adopts the view that realization is a relation between two properties of one and the same whole system, not to be confused with the relation that holds between levels of mechanistic organization. (According to Craver, as according to us, levels of mechanistic organization are systems of components, their capacities, and their organizational relations, and they are related compositionally to other levels of mechanistic organization.) We reject the account of realization adopted by Craver; we hold that each level of mechanistic organization realizes the mechanistic level above it and is realized by the mechanistic level below it (Piccinini and Maley 2014). Realization, in its most useful sense, is precisely the relation that obtains between two adjacent mechanistic levels in a multi-level mechanism and is thus a compositional relation. 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese is how the computations of their containing systems are mechanistically explained.581 At a still lower level, the response profiles of some single neurons reliably correlate582 with specific variables and it appears to be their function to correlate in this way; if583 this is correct, then they are representational in the relevant sense. Whether individual584 neurons perform computations over these representations is a matter of debate that can585 be left open. Sub-neuronal structures may or may not contain representations and per-586 form computations depending on the extent to which they satisfy the relevant criteria.587 At some point, we reach explanations that are no longer computational but instead are588 purely biophysical. Here certain biophysical mechanisms explain how certain neural589 systems register and transmit information.12 These purely biophysical (and lower)590 levels are no longer representational and computational in the relevant sense.591 Finally, whether a level of a neurocognitive mechanism is cognitive depends on592 whether and how it contributes to a cognitive capacity. Given our account in the593 previous section, to the effect that explaining cognitive capacities involves neural594 computation and representation, a neurocognitive level is cognitive depending on the595 extent to which the components of that level perform computations over representa-596 tions in a way that is relevant to explaining some cognitive capacity. As above, the597 lowest-level neural computations are explained purely biophysically. In some simple598 organisms, these simple computations may be sufficient to explain the organism's599 behaviors. In more complex organisms, however, these simple computations combine600 with other simple neural computations to constitute higher level neural computations,601 which in turn constitute still higher level neural computations, and so on, until we602 reach the highest level neural computations, which explain cognitive capacities.603 An example of such an explanatory strategy would be the following sketch of an604 account of vision. Individual cells in V1 selectively respond to particular line orienta-605 tions from the visual scene. Several of these cells in conjunction form an orientation606 column, which provide the basis for edge detection in the visual scene. These orien-607 tation columns combine to constitute V1, which computes the boundaries of visual608 objects. V1 then operates in conjunction with downstream parietal and temporal areas609 to constitute the different "streams" of visual processing and visual object represen-610 tation.13611 The resulting framework for explaining cognition is a mechanistic version of612 homuncular functionalism, whereby higher-level cognitive capacities are iteratively613 explained by lower-level cognitive capacities until we reach a level at which the lower-614 level capacities are no longer cognitive in the relevant sense (Attneave 1961; Fodor615 1968b; Dennett 1978; Lycan 1981; Maley and Piccinini 2013). The rise of cognitive616 neuroscience illustrates how this framework has developed and been applied (and con-617 tinues to develop and be applied) in scientific practice. In the next section, we highlight618 three aspects of cognitive neuroscience that demonstrate the development and appli-619 12 The purely biophysical level is reached when our explanation of the processes no longer appeals solely to differences between different portions of the vehicles along relevant dimensions of variation-which in the case of neural vehicles are mostly spike frequency and timing-in favor of the specific biophysical properties of neurons, such as the flow of specific ions through their cell membranes. 13 We are not committed to the adequacy of this particular explanation of visual processing, just to its exemplifying the explanatory strategy of iterated computational mechanisms that we are explicating here. 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese cation of this framework: the incorporation of experimental protocols from cognitive620 psychology into neuroscience experiments, the evolution of functional neuroimaging,621 and the movement toward biological realism in computational modeling in cognitive622 science.623 6 How cognitive neuroscience exhibits multilevel mechanistic integration624 Cognitive neuroscience emerged as a discipline in the late 1980s. Prior to that time, cog-625 nitive science and neuroscience had developed largely in isolation from one another.626 Cognitive science developed between the 1950s and the 1970s as an interdisciplinary627 field comprised primarily of aspects of psychology, linguistics, and computer science.628 In linguistics, this involved the development of generative grammars aimed at explain-629 ing the syntax structuring human linguistic behavior. In psychology, researchers began630 developing information processing accounts aimed at explaining capacities like prob-631 lem solving and memory encoding. In computer science, researchers began developing632 computational models, involving discrete state-transitions, in order to model psycho-633 logical capacities like reasoning and problem solving. The development of cognitive634 science accelerated through the 1960s and 1970s, with these approaches proceeding635 on their own terms with little contact with neuroscience. While during this period636 the hypothesis space for cognitive functions was constrained, the lack of contact637 with neuroscientific evidence contributed to a significant underdetermination of these638 hypotheses by available evidence (cf. Anderson 1978).639 Meanwhile, neuroscience developed as an interdisciplinary field investigating both640 normal and abnormal functioning of the nervous system. Neurophysiological investi-641 gations had been carried out since at least the 1890s, at a time when neuroscience and642 psychology were seen as disciplines that should be integrated (e.g., Freud 1895/1966;643 James 1890/1983). But the term "neuroscience" was only coined in the 1960s with the644 development of new techniques for investigating the cellular and molecular levels of645 nervous systems and for relating those investigations to systems and behavioral lev-646 els. As a result, early neuroscience illuminated candidate structures for implementing647 cognitive functions, but it did so with little connection to functional context, thereby648 making limited progress towards explaining cognitive functions.649 Throughout the development of both fields in the 1960s and 1970s, neuroscience650 and cognitive science dealt with domains with a great degree of overlap. In principle,651 they could have merged; in practice, they tended to exclude one another. Concep-652 tual motivation for this exclusion was rooted in views already discussed: autonomist653 commitments (both implicit and explicit) among practicing cognitive scientists ver-654 sus reductionist commitments among many practicing neuroscientists. Meanwhile,655 practical motivation that reinforced the exclusion was rooted in the pace of early656 developments that shaped both fields. In neuroscience, techniques for investigation at657 the cellular and molecular level developed at a pace that outstripped and overshad-658 owed work at the systems level. In cognitive science, rapid developments in computer659 science and artificial intelligence in the 1970s provided a computational framework660 in which processes were decomposed into specific operations performed on symbolic661 (language-like) structures. This framework fostered a gulf between cognitive analyses662 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese and neural analyses because there was no obvious way for these symbolic computa-663 tional units to be realized in neural tissue.664 The differences between the fields began to abate in the 1980s. Bechtel (2001)665 cites two chief contributors: the need for more sophisticated behavioral protocols in666 neuroscience, and the related development of functional neuroimaging techniques.667 The former developed naturally as neuroscience researchers shifted focus toward668 determining specific functions performed by recently discovered cellular and mole-669 cular structures, attempting to link those structures to particular behaviors. In order670 to draw these links and target higher-level functions, neuroscientists needed more671 sophisticated behavioral protocols. Cognitive psychologists had developed relatively672 sophisticated behavioral protocols in order to obtain informative data from a limited673 range of dependent variables. At the time, most experiments in cognitive psychology674 involved inference to some cognitive hypothesis based on patterns in two dependent675 variables: characteristic patterns of error human subjects exhibited on some task (error676 rate), and the typical amount of time taken for those subjects to perform the task (reac-677 tion time).678 As neuroscientists began to shift their explanatory ambitions, they ran up against the679 same limited range of dependent variables. Rather than reinventing the wheel, they680 began incorporating behavioral protocols from cognitive psychology and applying681 those protocols to experimental setups in which neural activity could be monitored682 in both humans and model organisms. These more sophisticated behavioral protocols683 allowed neuroscientists to form and test hypotheses about the contributions of cellular684 and molecular structures to higher-level functions.685 This disciplinary shift demonstrates how, in practice, functions constrain structures:686 sophisticated behavioral protocols provided the functional context necessary to con-687 strain the search for the structures involved in performing those functions. Of course,688 many of these protocols were subsequently revised in a give-and-take between the689 incoming physiological data and the existing functional models that motivated the690 protocols. But with the integration of these techniques and protocols, the underdeter-691 mination of structure-function mapping became a tractable empirical issue rather than692 a conceptual one.693 The other main contributor to the practical integration of psychology and neu-694 roscience has been the development of functional neuroimaging techniques, which695 allow measurement of physiological changes in large neural structures in response696 to performance of particular tasks. The first functional neuroimaging technique to697 be developed was Positron Emission Tomography (PET). PET involves injecting a698 radioactive tracer into a subject's bloodstream, which can then be imaged as it decays699 to illuminate blood flow to different brain regions. In a seminal study, Fox et al. (1986)700 used PET to measure hemodynamic response in particular brain areas during different701 cognitive tasks-their results correlated sensory and motor tasks with increased blood702 flow in primary sensory and motor areas, respectively.703 Prior to the development of neuroimaging, the primary way to attribute specific704 cognitive functions to neural systems and thereby to relate neural activity to behavior705 (in humans) was through the study of behavioral deficits resulting from lesions due706 to some form of traumatic brain injury. While these lesion studies remain an integral707 part of cognitive neuroscience to this day, there are a number of potential confounding708 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese factors involved in extrapolating from these data to brain function in non-pathological709 cases (see, e.g., Kosslyn and Van Kleeck 1990). Brain imaging assuages some of these710 concerns, and as a result the early research into the applications of PET for functional711 brain imaging set the stage for the explosion of research in cognitive neuroscience712 precipitated by the development of even more powerful and noninvasive imaging713 techniques like functional Magnetic Resonance Imaging (fMRI).714 The ability to correlate activity in different brain regions with specific tasks has715 improved our ability to map cognitive functions to neural structures. Underdetermi-716 nation problems remain, as cognitive functions cannot be simply read off of tasks and717 functional neuroimaging still has limits on spatial and temporal resolution that place718 corresponding limits on fine-grained attribution of functions to lower-level neural719 structures (see, e.g., Roskies 2009 for further discussion). Nonetheless, these neu-720 roimaging techniques provide valuable data to constrain structure-function mapping721 by situating putative functions within mechanistic context.722 This mechanistic context needs to be supplemented by further modeling in order723 to provide fully integrated explanations of how cognitive phenomena relate to neural724 activity. For instance, the recent trend toward model-based fMRI studies, in which725 models from computational neuroscience are incorporated into traditional fMRI exper-726 imental designs, demonstrates one way in which these integrated explanations are727 currently being approached (e.g., O'Doherty et al. 2007; cf. Egan and Matthews 2006;728 Povich forthcoming). These model-based imaging techniques illustrate, with partic-729 ular clarity, the applications of the multilevel mechanistic framework we have been730 advancing. At a relatively coarse-grained level, neuroimaging allows identification of731 the cortical and subcortical networks that are active in particular tasks. In order to deter-732 mine more precisely the functions performed by these intermediate-level networks,733 researchers look to modeling efforts in computational neuroscience that are highly734 constrained by the neurophysiology of the particular regions involved (more on this735 below). This strategy facilitates integration between different mechanistic levels and736 in so doing allows more precise identification of the functions involved in cognitive737 processes and the specific structures performing those functions. The proliferation of738 neuroimaging studies over the past two decades and, in particular, the current trend739 toward model-based approaches provide further evidence that cognitive neuroscience740 is indeed a science concerned with the complexities of structure-function mapping,741 rather than a science predicated on giving primacy to one over the other.742 Finally, the evolution of computational modeling in cognitive science also exem-743 plifies the shift from autonomist cognitive science to cognitive neuroscience. After744 McCulloch and Pitts (1943) introduced the first model of neural computation, three745 main modeling research programs developed. First, there is classical symbolic com-746 putationalism, which strives to explain cognitive capacities in terms of symbolic747 computation in putative autonomy from neuroscience (e.g., Newell and Simon 1972;748 Anderson 1983). Second is connectionism, which strives to explain cognitive capaci-749 ties in terms of neural network computations, though such neural networks are artificial750 models that are minimally (if at all) constrained by what is known about actual neural751 systems (e.g., Rosenblatt 1962; Feldman and Ballard 1982). The third modeling752 research program is computational neuroscience, which explains cognitive capaci-753 ties by building models of neural systems that are explicitly constrained by known754 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese neuroanatomical and neurophysiological evidence (e.g., Hodgkin and Huxley 1952;755 Caianiello 1961; Stein 1965; Knight 1972; Wilson and Cowan 1972). The critical dif-756 ference is that while classicist and connectionist models cannot be mapped onto neural757 structures in any direct way, models from computational neuroscience target specific758 neural structures and form hypotheses about the specific functions they perform and759 how those functions contribute to cognitive behaviors (cf. Kaplan 2011). Thus, com-760 putational neuroscience models exhibit the integration of functions and structures that761 we have argued characterizes cognitive neuroscience.762 For much of their history, these three traditions developed largely independently763 from one another. Classical computationalism gained a solid footing in the 1970s and764 was based on the idea (outlined above in Sect. 2) that the brain is a universal, program-765 controlled digital computer. The idea behind this modeling paradigm was that cognitive766 processes can be seen as the software that is implemented on such computers and thus767 can be studied independently from the hardware/wetware implementing the software.768 But this analogy between natural cognitive systems and digital computers is problem-769 atic for two reasons. First, whether nervous systems are universal, program-controlled770 digital computers is an empirical question; such a question cannot be settled indepen-771 dently of neuroscience. Further, and more importantly, evidence from neuroscience772 suggests that neural computation, in the general case, is in fact not a form of digital com-773 putation (Piccinini and Bahar 2013). Since digital computation is a necessary (though774 insufficient) condition for a system to be a universal, program-controlled digital com-775 puter, current best evidence suggests that nervous systems are in fact not such systems.776 In the 1980s, connectionism re-emerged and challenged the hardware/software777 analogy in favor of "neurally inspired" network models (Rumelhart et al. 1986, p. 131).778 But typical neo-connectionist psychology was not grounded in known neural processes779 and mechanisms. Connectionists made largely arbitrary assumptions about the number780 of neurons, number of layers, connectivity between neurons, response properties of781 neurons, and learning methods. Connectionist psychology made such assumptions782 in order to model and explain psychological phenomena. Since these assumptions783 were not grounded in neuroscience, connectionists were merely developing a different784 take on the standard computer analogy, replete with their own commitment to the785 autonomy of psychology from neuroscience. Thus, while connectionism pushed in786 the right direction, it fell short of actually integrating psychology and neuroscience.787 From the point of view of cognitive neuroscience, this kind of connectionism was788 more on the side of classical, autonomist cognitive science than it was on the side of789 neuroscience. As a result, both classical computationalism and connectionism foster790 models of cognitive systems that are autonomous from structural (neuroscientific)791 constraints (cf. Weiskopf 2011).792 While philosophers were captivated by the divide between classical computation-793 alism and connectionism, computational neuroscientists developed powerful tools for794 modeling and explaining cognitive phenomena in terms of actual biological processes.795 They imported theoretical tools from mathematics and physics and took advantage of796 the exponentially increasing power of modern computers. By now, there are many797 highly sophisticated research programs developing detailed models of how specific798 neural structures perform cognitive functions at various levels of organization (e.g.,799 Dayan and Abbott 2001; Ermentrout and Terman 2010; Eliasmith 2013).800 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese The field has matured to a point where connectionism is disappearing as an inde-801 pendent research tradition, instead merging into computational cognitive neuroscience802 (O'Reilly and Munakata 2000; O'Reilly et al. 2014). Most of the classicist research803 programs are also being shaped by this emergence of computational neuroscience.804 While we lack space for more detailed treatment, recent pronouncements of some key805 figures in classical and connectionist modeling indicate that the field is undergoing a806 deep transformation.807 Early attempts at building classical cognitive architectures were based on produc-808 tion systems (Anderson 1983; Laird and Newell 1987). Production systems model809 cognitive processes as software packages specifying a series of "if... then..." state-810 ments (rules) taking inputs to outputs. Initially, these quintessentially symbolic models811 were in no way constrained by neural data. Nevertheless, their proponents expressed812 great confidence: "Cognitive skills are realized by production rules. This is one of813 the most astounding and important discoveries in psychology and may provide a base814 around which to come to a general understanding of human cognition" (Anderson815 1993, p. 1). More recently, work on these same cognitive architectures has evolved to816 respect multiple levels of computational organization that are constrained by evidence817 from neuroscience. A stark transition can be seen, in particular, in Anderson's work,818 where his initial ambitions for his ACT-R production system architecture (as a univocal819 model for cognition) have been replaced by the acknowledgement that hybrid archi-820 tectures are more promising. In a recent paper, Anderson et al. argue that "theories at821 different levels of detail and from different perspectives are mutually informative and822 constraining, and furthermore no single level can capture the full richness of cognition"823 (Jilk et al. 2008). Similarly, Laird's most recent presentation of his Soar architecture824 advocates constraint by evidence from neuroscience (as well as from psychology and825 AI): "we have found that combining what is known in psychology, in neuroscience,826 and in AI is an effective strategy to building a comprehensive cognitive architecture"827 (Laird 2012, p. 22).828 Similar transitions can be seen in the works of other leading cognitive scientists.829 Stephen Kosslyn, a pioneer of mental imagery and the view that mental imagery830 involves a special, pictorial representational format, went from a traditional theory831 based primarily on behavioral data (Kosslyn 1980) to a thoroughly cognitive neu-832 roscientific framework (Kosslyn 1994; Kosslyn et al. 2006). Kosslyn's trajectory is833 a good illustration of the process of deepening explanations via the investigation of834 underlying mechanisms (Thagard 2007), which is the hallmark of cognitive neuro-835 science. Kosslyn's early theory of mental imagery faced skeptical resistance from836 defenders of a non-pictorial alternative (Pylyshyn 1981). By appealing to fMRI and837 neuropsychological evidence, Kosslyn later gained widespread acceptance for his pic-838 torial theory. The debate over the format of mental images is not entirely over, but the839 way to resolve it is not to reject neuroscientific evidence as irrelevant or insufficient840 (cf. Pylyshyn 2002, 2003). The way to resolve it is to learn even more about how the841 brain realizes and processes mental images.842 An analogous shift from traditional cognitive science to cognitive neuroscience843 can be seen in Anne Treisman's landmark work on attention (Treisman and Gelade844 1980; Treisman 1996, 2009). James McClelland, who pioneered the neo-connectionist845 models that were developed autonomously from neuroscience (Rumelhart et al. 1986),846 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese subsequently co-founded the Center for the Neural Basis of Cognition (a collaboration847 between Carnegie-Mellon University and the University of Pittsburgh) and has become848 a computational cognitive neuroscientist (e.g., McClelland and Lambon Ralph 2013).849 Michael Posner's authoritative treatment of the subtractive method employed in cogni-850 tive psychology (Posner 1976) became the basis for the rigorous use of neuroimaging851 methods, beginning with PET, that are the backbone of much cognitive neuroscience852 (Posner and Raichle 1994).853 Because we are still in the midst of this interdisciplinary shift toward the integration854 of psychology and neuroscience, it is easy to miss how revolutionary it is. The old855 view of psychology as autonomous from neuroscience (as well as the faith in the856 reductionist program, from the other direction) has been effectively supplanted by a857 new framework where multilevel integration rules the day.858 7 Conclusion859 The cognitive neuroscience revolution consists in rejecting the scientific practices860 stemming from the traditional two-level view of cognitive science and replacing them861 with a fully integrated science of cognition. The traditional two-level view maintained862 a division of labor between the sciences of cognition proper (psychology, linguistics,863 anthropology, AI, and philosophy) and sciences of implementation (neuroscience).864 This framework has fallen by the wayside as cognitive neuroscience has risen to865 prominence.866 The old two-level picture fell apart for several reasons. First, new modeling and867 empirical techniques-including the emergence of neuroimaging methods-have pro-868 vided more sophisticated ways to link cognitive capacities to the activities of specific869 neural systems. Second, the dubious assumptions about the nervous system that bol-870 stered the received view, such as the assumption that the nervous system is a universal,871 program-controlled digital computer, simply have not panned out. Third, the received872 view of cognitive explanation, according to which there is one privileged cognitive873 level and one distinctive and autonomous explanatory style-functional analysis-has874 turned out to be faulty.875 We have argued that philosophy of cognitive science should take heed. In place876 of the eliminative/reductive and classical functionalist/autonomist views of cognitive877 science, we have proposed the framework of integrated, multilevel, representational,878 and computational neural mechanisms as capturing the essence of successful expla-879 nation in cognitive neuroscience. Any discipline that studies cognition can fruitfully880 contribute to this project by characterizing one or more neurocognitive level(s) using881 the various empirical and analytical techniques at its disposal. In addition to avoid-882 ing the problems of the old two-level view, this framework also avoids the pitfalls883 of both reduction and elimination by retaining a role for organization within each884 neurocognitive level. While much work remains to be done in order to more fully885 understand the implications, applications, and limitations of this framework, the first886 step lies in accepting the revolutionary shift in our understanding of the physical bases887 of cognition that has already taken place.888 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese References889 Anderson, J. R. (1978). Arguments concerning representations for mental imagery. Psychological Review,890 85, 249–277.891 Anderson, J. R. (1983). The architecture of cognition. Cambridge: Harvard University Press.892 Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Erlbaum.893 Attneave, F. (1961). In defense of homunculi. In W. Rosenblith (Ed.), Sensory communication (pp. 777–894 782). Cambridge, MA: MIT Press.895 Barberis, S. D. (2013). Functional analyses, mechanistic explanations, and explanatory tradeoffs. Journal896 of Cognitive Science, 14(3), 229–251.897 Barrett, D. (2014). Functional analysis and mechanistic explanation. Synthese. doi:10.1007/898 s11229-014-0410-9.899 Batterman, R. (2013). The tyranny of scales. In R. W. Batterman (Ed.), The Oxford handbook of philosophy900 of physics (pp. 255–286). New York: oxford University Press.901 Bechtel, W. (2015). Investigating neural representations: The tale of place cells. Synthese.902 Bechtel, W. (2001). Cognitive neuroscience: Relating neural mechanisms and cognition. In P. Machamer, P.903 McLaughlin, & R. Grush (Eds.), Philosophical reflections on the methods of neuroscience. Pittsburgh,904 PA: University of Pittsburgh Press.905 Bechtel, W. (2008). Mental mechanisms: Philosophical Perspectives on cognitive neuroscience. London:906 Routledge.907 Bechtel, W., & Abrahamsen, A. (2013). Thinking dynamically about biological mechanisms: Networks of908 coupled oscillators. Foundations of Science, 18, 707–723.909 Bechtel, W., & Shagrir, O. (forthcoming). The non-redundant contributions of Marr's three levels of analysis910 for explaining information processing mechanisms. Topics in Cognitive Science.911 Bickle, J. (2003). Philosophy and neuroscience: A ruthlessly reductive account. Dordrecht: Kluwer.912 Bickle, J. (2006). Reducing mind to molecular pathways: Explicating the reductionism implicit in current913 cellular and molecular neuroscience. Synthese, 151, 411–434.914 Bogen, J. (2005). Regularities and causality: Generalizations and causal explanations. Studies in History915 and Philosophy of Biological and Biomedical Sciences, 36, 397–420.916 Boone, W., & Piccinini, G. (unpublished). Mechanistic abstraction.917 Burge, T. (2010). Origins of objectivity. Oxford: Oxford University Press.918 Caianiello, E. R. (1961). Outline of a theory of thought processes and thinking machines. Journal of919 Theoretical Biology, 1(2), 204–235.920 Chemero, A., & Silberstein, M. (2008). After the philosophy of mind: Replacing scholasticism with science.921 Philosophy of Science, 75, 1–27.922 Chirimuuta, M. (2014). Minimal models and canonical neural computations: The distinctness of computa-923 tional explanation in neuroscience. Synthese, 191(2), 127–154.924 Churchland, P. M. (1981). Eliminative materialism and the propositional attitudes. Journal of Philosophy,925 78, 67–90.926 Churchland, P. S. (1986). Neurophilosophy: Toward a unified science of the mind/brain. Cambridge, MA:927 MIT Press.928 Craver, C. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford: Oxford929 University Press.930 Cummins, R. (1983). The nature of psychological explanation. Cambridge, MA: MIT Press.931 Cummins, R. (2000). 'How does it work?' vs. 'What are the laws?' Two conceptions of psychological expla-932 nation. In K. F. C. & W. R. A. (Eds.), Explanation and cognition. Cambridge: Cambridge University933 Press.934 Daugman, J. G. (1990). Brain metaphor and brain theory. In E. L. Schwartz (Ed.), Computational neuro-935 science (pp. 9–18). Cambridge, MA: MIT Press.936 Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling937 of neural systems. Cambridge, MA: MIT Press.938 Dennett, D. C. (1978). Brainstorms. Cambridge, MA: MIT Press.939 Dretske, F. I. (1981). Knowledge and the flow of information. Cambridge, MA: MIT Press.940 Dretske, F. I. (1988). Explaining behavior: Reasons in a world of causes. Cambridge, MA: MIT Press.941 Egan, F., & Matthews, R. (2006). Doing cognitive neuroscience: A third way. Synthese, 153, 377–391.942 Ermentrout, G. B., & Terman, D. H. (2010). Mathematical foundations of neuroscience. New York: Springer.943 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese Eliasmith, C. (2013). How to build a brain: A neural architecture for biological cognition. Oxford: Oxford944 University Press.945 Feldman, J. A., & Ballard, D. H. (1982). Connectionist models and their properties. Cognitive Science, 6,946 205–254.947 Fodor, J. A. (1968a). Psychological explanation. New York: Random House.948 Fodor, J. A. (1968b). The appeal to tacit knowledge in psychological explanation. Journal of Philosophy,949 65, 627–640.950 Fodor, J. A. (1974). Special sciences. Synthese, 28, 77–115.951 Fodor, J. A. (1997). Special sciences: Still autonomous after all these years. In J. Tomberlin (Ed.), Philo-952 sophical perspectives 11: Mind, causation, and world (pp. 149–163). Boston: Blackwell.953 Fodor, J. A. (1998). Concepts. Oxford: Clarendon Press.954 Fox, P. T., Minton, M. A., Raichle, M. E., Miezin, F. M., Allman, J. M., & Van Essen, D. C. (1986). Mapping955 human visual cortex with positron emission tomography. Nature, 323, 806–809.956 Fresco, N. (2014). Physical computation and cognitive science. New York: Springer.957 Freud, S. (1895/1966). Project for a scientific psychology. In E. Jones (Ed.) & J. Strachey (Trans.), The958 standard edition of the complete psychological works of Sigmund Freud (Vol. 1, pp. 295–397). London:959 Hogarth Press.960 Gallistel, R. G., & King, A. P. (2009). Memory and the computational brain: Why cognitive science will961 transform neuroscience. New York: Wiley/Blackwell.962 Glennan, S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69(3), S342–S353.963 Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application964 to conduction and excitation in nerve. Journal of Physiology, 117, 500–544.965 Humphreys, P. (2004). Extending ourselves: Computational science, empiricism, and scientific method.966 New York: Oxford University Press.967 James, W. (1890/1983). The principles of psychology. Cambridge, MA: Harvard University Press.968 Jilk, D., Lebiere, C., O'Reilly, R., & Anderson, J. (2008). SAL: An explicitly pluralistic cognitive architec-969 ture. Journal of Experimental and Theoretical Artificial Intelligence, 20(3), 197–218.970 Johnson-Laird, P. N. (1983). Mental models: Towards a cognitive science of language, inference and con-971 sciousness. New York: Cambridge University Press.972 Kaplan, D. M. (2011). Explanation and description in computational neuroscience. Synthese, 183(3), 339–973 373.974 Kaplan, D. M., & Craver, C. F. (2011). The explanatory force of dynamical models. Philosophy of Science,975 78(4), 601–627.976 Knight, B. W. (1972). Dynamics of encoding in a population of neurons. Journal of General Physiology,977 59, 734–766.978 Kosslyn, S. (1980). Image and mind. Cambridge, MA: Harvard University Press.979 Kosslyn, S. (1994). Image and brain: The resolution of the imagery debate. Cambridge, MA: MIT Press.980 Kosslyn, S., & Van Kleeck, M. H. (1990). Broken brains and normal minds: Why humpty–dumpty needs a981 skeleton. In E. L. Schwartz (Ed.), Computational neuroscience. Cambridge, MA: MIT Press.982 Kosslyn, S., Thompson, W. L., & Ganis, G. (2006). The case for mental imagery. New York: Oxford983 University Press.984 Laird, J. E. (2012). The soar cognitive architecture. Cambridge, MA: MIT Press.985 Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). Soar: An architecture for general intelligence. Artificial986 Intelligence, 33, 1–64.987 Levy, A. (2013). What was Hodgkin and Huxley's achievement? British Journal for Philosophy of Science.988 doi:10.1093/bjps/axs043.989 Levy, A., & Bechtel, W. (2013). Abstraction and the organization of mechanisms. Philosophy of Science,990 80(2), 241–261.991 Lycan, W. (1981). Form, function, and feel. Journal of Philosophy, 78, 24–50.992 Lycan, W. (1990). The continuity of levels of nature. In W. Lycan (Ed.), Mind and cognition. Malden, MA:993 Blackwell.994 Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67(1),995 1–25.996 Maley, C., & Piccinini, G. (2013). Get the latest upgrade: Functionalism 6.3.1. Philosophia Scientiae, 17(2),997 135–149.998 Marr, D. (1982). Vision. San Francisco: W. H. Freeman and Company.999 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese McClelland, J. L., & Lambon Ralph, M. A. (Eds.). (2013). Cognitive neuroscience: Emergence of mind1000 from brain. The biomedical & life sciences collection. London: Henry Stewart Talks Ltd.1001 McCulloch, W., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin1002 of Mathematical Biophysics, 5, 115–133.1003 Milkowski, M. (2013). Explaining the computational mind. Cambridge, MA: MIT Press.1004 Morgan, A. (2014). Representations gone mental. Synthese, 191(2), 213–244.1005 Newell, A. (1980). Physical symbol systems. Cognitive Science, 4, 135–183.1006 Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs: Prentice-Hall.1007 Newell, A., & Simon, H. A. (1976). Computer science as an empirical enquiry: Symbols and search.1008 Communications of the ACM, 19, 113–126.1009 O'Doherty, J., Hampton, A., & Kim, H. (2007). Model-based fMRI and its application to reward learning1010 and decision making. Annals of the New York Academy of Sciences, 1104, 35–53.1011 O'Reilly, R. C., & Munakata, Y. (2000). Computational explorations in cognitive neuroscience: Under-1012 standing the mind by simulating the brain. Cambridge, MA: MIT Press.1013 O'Reilly, R. C., Munakata, Y., Frank, M. J., Hazy, T. E., & Contributors. (2014). Computational cognitive1014 neuroscience. Wiki Book (2nd ed.). http://ccnbook.colorado.edu.1015 Piccinini, G. (unpublished). Activities are manifestations of causal powers.1016 Piccinini, G. (2007). Computational modeling vs. computational explanation: Is everything a turing machine,1017 and does it matter to the philosophy of mind? Australasian Journal of Philosophy, 85(1), 93–115.1018 Piccinini, G. (2008). Computers. Pacific Philosophical Quarterly, 89(1), 32–73.1019 Piccinini, G., & Bahar, S. (2013). Neural computation and the computational theory of cognition. Cognitive1020 Science, 34, 453–488.1021 Piccinini, G., & Craver, C. (2011). Integrating psychology and neuroscience: Functional analyses as mech-1022 anism sketches. Synthese, 183(3), 283–311.1023 Piccinini, G., & Maley, C. (2014). The metaphysics of mind and the multiple sources of multiple realizability.1024 In M. Sprevak & J. Kallestrup (Eds.), New waves in the philosophy of mind. New York: Palgrave1025 Macmillan.1026 Piccinini, G., & Scarantino, A. (2011). Information processing, computation, and cognition. Journal of1027 Biological Physics, 37(1), 1–38.1028 Posner, M. I. (1976). Chronometric explorations of mind. Hillsdale, NJ: Lawrence Erlbaum Associates.1029 Posner, M. I., & Raichle, M. E. (1994). Images of mind. New York: Scientific American Books.1030 Povich, M. (forthcoming). Mechanisms and model-based fMRI. Philosophy of Science.1031 Putnam, H. (1967). Psychological predicates. Art, philosophy, and religion. Pittsburgh, PA: University of1032 Pittsburgh Press.1033 Putnam, H. (1975). Philosophy and our mental life. In H. Putnam (Ed.), Mind, language and reality:1034 Philosophical papers (Vol. 2, pp. 291–303). Cambridge: Cambridge University Press.1035 Pylyshyn, Z. W. (1981). The imagery debate: Analogue media versus tacit knowledge. Psychological Review,1036 88, 16–45.1037 Pylyshyn, Z. W. (1984). Computation and cognition. Cambridge, MA: MIT Press.1038 Pylyshyn, Z. W. (2002). Mental imagery: In search of a theory. Behavioral and Brain Sciences, 25(2),1039 157–237.1040 Pylyshyn, Z. W. (2003). Return of the mental image: Are there really pictures in the head? Trends in1041 Cognitive Science, 7(3), 113–118.1042 Ramsey, W. M. (2007). Representation reconsidered. Cambridge: Cambridge University Press.1043 Rosenblatt, F. (1962). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms.1044 Washington, DC: Spartan.1045 Roskies, A. (2009). Brain–mind and structure–function relationships: A methodological response to Colt-1046 heart. Philosophy of Science, 76(5), 927–939.1047 Rumelhart, D. E., McClelland, J. M., & The PDP Research Group. (1986). Parallel distributed processing:1048 Explorations in the microstructure of cognition. Cambridge, MA: MIT Press.1049 Rusanen, A.-M., & Lappi, O. (2007). Th limits of mechanistic explanation in cognitive science. In S. Vos-1050 niadou, D. Kayser, & A. Protopapas (Eds.), Proceedings of the European cognitive science conference1051 2007 (pp. 284–289). Howe: Lawrence Erlbaum Associates.1052 Scarantino, A. (2015). Information as a probabilistic difference maker. Australian Journal of Philosophy.1053 doi:10.1080/00048402.2014.993665.1054 Schaffner, K. F. (2008). Theories, models, and equations in biology: The heuristic search for emergent1055 simplifications in neurobiology. Philosophy of Science, 75, 1008–1021.1056 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X R ev is ed Pr oo f Synthese Shagrir, O. (2010a). Brains as analog-model computers. Studies in History and Philosophy of Science, 41,1057 271–279.1058 Shagrir, O. (2010b). Marr on computational-level theories. Philosophy of Science, 77, 477–500.1059 Smart, J. J. C. (1959). Sensations and brain processes. The Philosophical Review, 68(2), 141–156.1060 Smart, J. J. C. (2007). The mind/brain identity theory. In E. N. Zalta (Ed.), The Stanford encyclopedia of1061 philosophy (Summer 2007 ed.). http://plato.stanford.edu/archives/sum2007/entries/mind-identity/.1062 Stein, R. (1965). A theoretical analysis of neuronal variability. Biophysical Journal, 5(2), 173–194.1063 Thagard, P. (2007). Coherence, truth, and the development of scientific knowledge. Philosophy of Science,1064 74, 28–47.1065 Treisman, A. (1996). The binding problem. Current Opinion in Neurobiology, 6(2), 171–178.1066 Treisman, A. (2009). Attention: Theoretical and psychological perspectives. In M. Gazzaniga (Ed.), The1067 cognitive neurosciences (4th ed., pp. 189–204). Cambridge, MA: MIT Press.1068 Treisman, A., & Gelade, G. (1980). A feature integration theory of attention. Cognitive Psychology, 12,1069 97–136.1070 Van Eck, D., & Weber, E. (2014). Function ascription and explanation: Elaborating an explanatory utility1071 desideratum for ascriptions of technical functions. Erkenntnis. doi:10.1007/s10670-014-9605-1.1072 Vartanian, A. (1973). In P. P. Wiener (Ed.), Dictionary of the history of ideas: Studies f selected pivotal1073 ideas. New York: Scriners.1074 Waxman, S. (1972). Regional differentiation of the axon: A review with special reference to the concept of1075 the multiplex neuron. Brain Research, 47, 269–288.1076 Weber, M. (2005). Philosophy of experimental biology. Cambridge: Cambridge University Press.1077 Weber, M. (2008). Causes without mechanisms: Experimental regularities, physical laws, and neuroscien-1078 tific explanation. Philosophy of Science, 75(5), 995–1007.1079 Weisberg, M. (2013). Simulation and similarity: Using models to understand the world. Oxford: Oxford1080 University Press.1081 Weiskopf, D. (2011). Models and mechanisms in psychological explanation. Synthese, 183(3), 313–338.1082 Wilson, H. R., & Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of1083 model neurons. Biophysical Journal, 12, 1–24.1084 Winsberg, E. (2010). Science in the age of computer simulation. Chicago: University of Chicago Press.1085 123 Journal: 11229-SYNT Article No.: 0783 TYPESET  DISK LE CP Disp.:2015/6/5 Pages: 26 Layout: Small-X