Emrah Aktunc

Three new versions of Wason’s 2-4-6 rule discovery task incorporating error rates or feedback of uncertainty reduction, inspired by the error-statistical account in philosophy of science, were employed. In experiments 1 and 2, participants were instructed that some experimenter feedback would be erroneous (control was original 2-4-6 without error). The results showed that performance was impaired when there was probabilistic error. In experiment 3, participants were given uncertainty reduction feedback as they generated different number triples and the negative effects of probabilistic error were not observed. These findings are informative not only about rule discovery tasks in general but also about contexts of inference under uncertainty.

Duhem’s problem arises especially in scientific contexts where the tools and procedures of measurement and analysis are numerous and complex. Several philosophers of cognitive science have cited its manifestations in fMRI as grounds for skepticism regarding the epistemic value of neuroimaging. To address these Duhemian arguments for skepticism, I offer an alternative approach based on Deborah Mayo’s error-statistical account in which Duhem's problem is more fruitfully approached in terms of error probabilities. This is illustrated in examples such as the use of probabilistic brain atlases, comparison of different preprocessing protocols with respect to their error characteristics, and statistical modeling of fMRI data. These examples demonstrate the ways in which we can better understand and formulate the general methodological problem and direct the way toward alternative approaches to neuroimaging in philosophy of cognitive science, in which we can be more balanced and productive in our scrutiny of the epistemic value of neuroimaging studies.

Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and control for the types of methodological and inferential errors. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition.

Considerable methodological difficulties abound in neuroimaging, and several philosophers of science have recently called into question the potential of neuroimaging studies to contribute to our knowledge of human cognition. These skeptical accounts suggest that functional hypotheses are underdetermined by neuroimaging data. I apply Mayo’s error-statistical account to clarify the evidential import of neuroimaging data and the kinds of inferences it can reliably support. Thus, we can answer the question “What can we reliably learn from neuroimaging?” and make sense of how this knowledge can contribute to novel construals of cognition.

This is a work in the epistemology of functional neuroimaging (fNI) and it applies the error-statistical (ES) philosophy to inferential problems in fNI to formulate and address these problems. This gives us a clear, accurate, and more complete understanding of what we can learn from fNI and how we can learn it. I review the works in the epistemology of fNI which I group into two categories; the first category consists of discussions of the theoretical significance of fNI findings and the second category discusses methodological difficulties of fNI. Both types of works have shortcomings; the first category has been too theory-centered in its approach and the second category has implicitly or explicitly adopted the assumption that methodological difficulties of fNI cannot be satisfactorily addressed. In this dissertation, I address these shortcomings and show how and what kind of experimental knowledge fNI can reliably produce which would be theoretically significant. I take fMRI as a representative fNI procedure and discuss the history of its development. Two independent trajectories of research in physics and physiology eventually converge to give rise to fMRI. Thus, fMRI findings are laden in the theories of physics and physiology and I propose how this creates a kind of useful theory-ladenness which allows for the representation of and intervention in the constructs of cognitive neuroscience. Duhemian challenges and problems of underdetermination are often raised to argue that fNI is of little, if any, epistemic value for psychology. I show how the ES notions of severe tests and error probabilities can be applied in epistemological analyses of fMRI. The result is that hemodynamic hypotheses can be severely tested in fMRI experiments and I demonstrate how these hypotheses are theoretically significant and fuel the growth of experimental knowledge in cognitive neuroscience. Throughout this dissertation, I put the emphasis on the experimental knowledge we obtain from fNI and argue that this is the fruitful approach that enables us to see how fNI can contribute to psychology. In doing so, I offer an error-statistical epistemology of fNI, which hopefully will be a significant contribution to the philosophy of psychology.