Since its introduction, multivariate pattern analysis, or ‘neural decoding’, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder’s dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is (...) a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. (shrink)

Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...) considerably richer than those suggested by a nomological framework. Second, the fact that mechanisms involve organized systems of component parts and operations provides direction to both the discovery and testing of mechanistic explanations. Finally, models of mechanisms are developed for specific exemplars and are not represented in terms of universally quantified statements. Generalization involves investigating both the similarity of new exemplars to those already studied and the variations between them. (shrink)

Can purely predictive models be useful in investigating causal systems? I argue ‘yes’. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to use theory to achieve explanation (...) or insight without empirical success therefore fails, leaving us with the worst of both worlds – neither prediction nor explanation. Best go with empirical success by any means necessary. I support these methodological claims via case studies of two impressive feats of predictive modelling: opinion polling of political elections, and weather forecasting. (shrink)

The interpretation of functional imaging experiments is complicated by the pluripotency of brain regions. As there is a many-to-one mapping between cognitive functions and their neural substrates, region-based analyses of imaging data provide only weak support for cognitive theories. Price and Friston argue that we need a ‘cognitive ontology’ that abstractly categorizes the function of regions. I argue that abstract characterizations are unlikely to be cognitively interesting. I argue instead that we should attribute functions to regions in a context-sensitive manner. (...) I review recent meta-analyses that approach fMRI data in this light and argue that they have revisionary potential. (shrink)

This paper describes one style of functional analysis commonly used in the neurosciences called task-bound functional analysis. The concept of function invoked by this style of analysis is distinctive in virtue of the dependence relations it bears to transient environmental properties. It is argued that task-bound functional analysis cannot explain the presence of structural properties in nervous systems. An alternative concept of neural function is introduced that draws on the theoretical neuroscience literature, and an argument is given to show that (...) this alternative concept of may help to overcome the explanatory limitations of task-bound functional analysis. (shrink)

The paper makes three points about the role of double dissociation in cognitive neuropsychology. First, arguments from double dissociation to separate modules work by inference to the best, not the only possible, explanation. Second, in the development of computational cognitive neuropsychology, the contribution of connectionist cognitive science has been to broaden the range of potential explanations of double dissociation. As a result, the competition between explanations, and the characteristic features of the assessment of theories against the criteria of probability and (...) explanatory value, are more visible. Third, cognitive neuropsychology is a division of cognitive psychology but the practice of cognitive neuropsychology proceeds on assumptions that go beyond the subject matter of cognitive psychology. Given such assumptions, neuroscientific findings about lesion location may enhance the value of double dissociation in shifting the balance of support between cognitive theories. (shrink)

Although prediction has been largely absent from discussions of explanation for the past 40 years, theories of explanation can gain much from a reintroduction. I review the history that divorced prediction from explanation, examine the proliferation of models of explanation that followed, and argue that accounts of explanation have been impoverished by the neglect of prediction. Instead of a revival of the symmetry thesis, I suggest that explanation should be understood as a cognitive tool that assists us in generating new (...) predictions. This view of explanation and prediction clarifies what makes an explanation scientific and why inference to the best explanation makes sense in science. *Received August 2009; revised September 2009. †To contact the author, please write to: Department of Philosophy, University of Tennessee, 801 McClung Tower, Knoxville, TN 37920‐0480; e‐mail: [email protected]. (shrink)

Multifunctionality poses significant challenges for human brain mapping. Cathy Price and Karl Friston argue that brain regions perform many functions in one sense and a single function in another. Thus, neuroscientists must revise their “cognitive ontologies” to obtain systematic mappings. Colin Klein draws a different lesson from these findings: neuroscientists should abandon systematic mappings for context-sensitive ones. I claim that neither account succeeds as a general treatment of multifunctionality. I argue that brain areas, like genes or organs, are multifunctional in (...) different ways. I call this the “functional heterogeneity hypothesis.” I contend that different multifunctional parts require different mapping strategies. (shrink)

Reverse inference in cognitive neuropsychology has been characterized as inference to ‘psychological processes’ from ‘patterns of activation’ revealed by functional magnetic resonance or other scanning techniques. Several arguments have been provided against the possibility. Focusing on Machery’s presentation, we attempt to clarify the issues, rebut the impossibility arguments, and propose and illustrate a strategy for reverse inference. 1 The Problem of Reverse Inference in Cognitive Neuropsychology2 The Arguments2.1 The anti-Bayesian argument3 Patterns of Activation4 Reverse Inference Practiced5 Seek and Ye Shall (...) Find, Maybe6 Conclusion. (shrink)

The sceptical positions philosophers have adopted with respect to neuroimaging data are based on detailed evaluations of subtraction, which is one of many data analysis techniques used with neuroimaging data. These positions are undermined when the epistemic implications of the use of a diversity of data analysis techniques are taken into account. I argue that different data analysis techniques reveal different patterns in the data. Through the use of multiple data analysis techniques, researchers can produce results that are locally robust. (...) Thus, the epistemology of neuroimaging must take into consideration the details of the different data analysis techniques that are used to evaluate neuroimaging data, and the specific theoretical aims those techniques are deployed towards. _1_ Introduction _2_ Scepticism about Neuroimaging _3_ Data Analysis and Evidence _4_ Deconvolution and Pattern Classification Analysis _4.1_ Deconvolution analysis _4.2_ Region of interest selection _4.3_ Pattern classification analysis _5_ The Strength of Multiple Analyses _6_ Conclusion. (shrink)

Reverse inference is the most commonly used inferential strategy for bringing images of brain activation to bear on psychological hypotheses, but its inductive validity has recently been questioned. In this article, I show that, when it is analyzed in likelihoodist terms, reverse inference does not suffer from the problems highlighted in the recent literature, and I defend the appropriateness of treating reverse inference in these terms. 1 Introduction2 Reverse Inference3 Reverse Inference Defended3.1 Typical reverse inferences are fallacious3.2 No quick and (...) easy fix3.3 A likelihoodist defense of reverse inference3.4 An example4 Appropriateness of the Likelihoodist Approach4.1 Likelihoodist reverse inference is not applicable4.2 Cognitive neuroscientists are not interested in comparative conclusions4.3 Reverse inference and negative hypotheses4.4 Likelihoodist reverse inference may confuse cognitive neuroscientists4.5 Bayesian reverse inferences should be preferred to likelihoodist reverse inferences5 Conclusion. (shrink)

It is a widespread assumption in philosophy of science that data is what is explained by theory—that data itself is not explanatory. I draw on instances of representational and explanatory practice from mammalian chronobiology to suggest that this assumption is unsustainable. In many instances, biologists employ representations of data in explanatory ways that are not reducible to constraints on or evidence for representations of mechanisms. Data graphs are used to exemplify relationships between quantities in the mechanism, and often these representations (...) are necessary for explaining particular aspects of the phenomena under study. I argue that this kind of representation is distinct from representing laws or generalizations, and its primary purpose is to convey particular types or patterns of quantitative relationships. The benefit of the analysis is two-fold. First, it provides a more accurate account of explanatory practice in broadly mechanistic analysis in biology. Second, it suggests that there is not an explanatory “fundamental” type of representation in biology. Rather, the practice of explanation consists in the construction of different types of representations and their employment for distinct explanatory purposes. (shrink)

This article presents and discusses one of the most prominent inferential strategies currently employed in cognitive neuropsychology, namely, reverse inference. Simply put, this is the practice of inferring, in the context of experimental tasks, the engagement of cognitive processes from locations or patterns of neural activation. This technique is notoriously controversial because, critics argue, it presupposes the problematic assumption that neural areas are functionally selective. We proceed as follows. We begin by introducing the basic structure of traditional “location-based” reverse inference (...) and discuss the influential lack of selectivity objection. Next, we rehearse various ways of responding to this challenge and provide some reasons for cautious optimism. The second part of the essay presents a more recent development: “pattern-decoding reverse inference”. This inferential strategy, we maintain, provides an even more convincing response to the lack of selectivity charge. Due to this and other methodological advantages, it is now a prominent component in the toolbox of cognitive neuropsychology. Finally, we conclude by drawing some implications for philosophy of science and philosophy of mind. (shrink)

Neuroscience constrains psychology, but stating these constraints with precision is not simple. Here I consider whether mechanistic analysis provides a useful way to integrate models of cognitive and neural structure. Recent evidence suggests that cognitive systems map onto overlapping, distributed networks of brain regions. These highly entangled networks often depart from stereotypical mechanistic behaviors. While this casts doubt on the prospects for classical mechanistic integration of psychology and neuroscience, I argue that it does not impugn a realistic interpretation of either (...) type of model. Cognitive and neural models may depict different, but equally real, causal structures within the mind/brain. (shrink)

In some recent papers, Max Coltheart has questioned the ability of neuroimaging techniques to tell us anything interesting about the mind and has thrown down the gauntlet before neuroimagers, challenging them to prove he is mistaken. Here I analyze Coltheart ’s challenge, show that as posed its terms are unfair, and reconstruct it so that it is addressable. I argue that, so modified, Coltheart ’s challenge is able to be met and indeed has been met. In an effort to delineate (...) the extent of neuroimaging’s ability to address Coltheart ’s concerns, I explore how different brain structure‐function relationships would constrain the ability of neuroimaging to provide insight about psychological questions. †To contact the author, please write to: Department of Philosophy, Dartmouth College, Hanover, NH 03755; e‐mail: [email protected]. (shrink)

Since its introduction, multivariate pattern analysis, or ‘neural decoding’, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder’s dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is (...) a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. 1Introduction 2A Brief Primer on Neural Decoding: Methods, Application, and Interpretation 2.1What is multivariate pattern analysis? 2.2The informational benefits of multivariate pattern analysis 3Why the Decoder’s Dictum Is False 3.1We don’t know what information is decoded 3.2The theoretical basis for the dictum 3.3Undermining the theoretical basis 4Objections and Replies 4.1Does anyone really believe the dictum? 4.2Good decoding is not enough 4.3Predicting behaviour is not enough 5Moving beyond the Dictum 6Conclusion. (shrink)

Functional localization has historically been one of the primary goals of neuroscience. There is still debate, however, about whether it is possible, and if so what kind of theories succeed at localization. I argue for a contextualist approach to localization. Most theorists assume that widespread contextual variability in function is fundamentally incompatible with functional decomposition in the brain, because contextualist accounts will fail to be generalizable and projectable. I argue that this assumption is misplaced. A properly articulated contextualism can ground (...) successful theories of localization even without positing completely generalizable accounts. Via a case study from perceptual neuroscience, I suggest that there is strong evidence for contextual variation in the function of perceptual brain areas. I then outline a version of contextualism that is empirically adequate with respect to this data, and claim that it can still distinguish brain areas from each other according to their functional properties. Finally, I claim that the view does not fail the norms for good theory in the way that anticontextualists suppose. It is true that, on a contextualist view, we will not have theories that are completely generalizable and predictive. We can, however, have successful partial generalizations that structure ongoing investigation and lead to novel functional insight, and this success is sufficient to ground the project of functional localization. (shrink)