Evidence in Neuroimaging: Towards a Philosophy of Data Analysis

Neuroimaging technology is the most widely used tool to study human cognition. While originally a promising tool for mapping the content of cognitive theories onto the structures of the brain, recently developed tools for the analysis, handling and sharing of data have changed the theoretical landscape of cognitive neuroscience. Even with these advancements philosophical analyses of evidence in neuroimaging remain skeptical of the promise of neuroimaging technology. These views often treat the analysis techniques used to make sense of data produced in a neuroimaging experiment as one, attributing the inferential limitations of analysis pipelines to the technology as a whole. Situated against the neuroscientists own critical assessment of their methods and the limitations of those methods, this skepticism appears based on a misunderstanding of the role data analysis techniques play in neuroimaging research. My project picks up here, examining how data analysis techniques, such as pattern classification analysis, are used to assess the evidential value of neuroimaging data. The project takes the form of three papers. In the first I identify the use of multiple data analysis techniques as an important aspect of the data interpretation process that is overlooked by critics. In the second I develop an account of inferences in neuroimaging research that is sensitive to this use of data analysis techniques, arguing that interpreting neuroimaging data is a process of isolating and explaining a variety of data patterns. In the third I argue that the development and uptake of new techniques for analyzing data must be accompanied by changes in research practices and standards of evidence if they are to promote knowledge generation. My approach to this work is both traditionally philosophical, insofar as it involves reading and analyzing the work of philosophers and neuroscientists, and embedded insofar as most of the research was conducted while engaged in attending lab meetings and participating in the work of those scientists whose work is the object of my research

Quantum Field Theory: Motivating the Axiom of Microcausality

Axiomatic quantum field theory is one approach to the project of merging the special theory of relativity with that of ordinary quantum mechanics. The project begins with the postulation of a set of axioms. Axioms should be motivated by reasonable physical principles in a way that illustrates how a given axiom is true. Motivations are often grounded in the principles of the parent theories: ordinary quantum mechanics or the theory of special relativity. Amongst the set of axioms first proposed by Haag and Kastler in 1963 is the axiom of microcausality. Microcausality requires the observables of regions at space-like separation to commute. This thesis seeks to answer the question ‘What principles from the special theory of relativity or ordinary quantum mechanics motivate, or justify, accepting microcausality as an axiom?’ The first chapter will provide the necessary background to investigate this question and the second chapter will undertake that investigation. In conclusion, microcausality cannot be well-motivated by individual principles rooted in the special theory of relativity or ordinary quantum mechanics

Participation in the Classroom: Classification and Assessment Techniques

Class participation and active engagement are both critical components for student success in a variety of classroom settings. This is especially true in philosophy classrooms where students are expected to develop and refine their ability to critically and productively engage with the literature being studied while also participating in conversations with their peers. Including participation expectations in classes is a common strategy, used by teachers, for developing and honing important skills in students. In requiring participation in their respective courses, teachers seek to refine and cultivate critical thinking and communication skills in their students. This workshop begins with an overview of research that, on the one hand, examines different strategies for effectively encouraging student participation and, on the other, also provides recommendations for broadening our definition of student participation in the classroom. Following the literature review, I will provide an active learning, self-assessment tool for evaluating course participation (this tool can be adapted to larger classroom settings and contexts, as well) that, I suggest, will be of benefit to teachers and students alike

Saving Data Analysis: Epistemic Friction and Progress in Neuroimaging Research

Data must be manipulated for their evidential import to be assessed. However, data analysis is regarded as a source of inferential errors by scientists and critics of neuroscience alike. In this chapter I argue that of data analysis is epistemically challenged in part because data are causally separated from the events that they are intended to provide evidence for claims about. Experimental manipulations place researchers in epistemically advantageous positions by making contact with the objects and phenomena of interest. Data manipulations, on the other hand, are applied to material objects that are not in causal contact with the events they are used to learn about. I then propose that some of the inferential liabilities that go along with data manipulation are partly overcome through the occurrence of epistemic friction. I consider two forthcoming contributions to network neuroscience to illustrate the benefits, and risks, of the data analyst’s reliance on epistemic friction

Inductive Risk, Science, and Values: A Reply to MacGillivray

The Argument from Inductive Risk (AIR) is perhaps the most common argument against the value-free ideal of science. Brian MacGillivray (2019) rejects the AIR (at least as it would apply to risk assessment) and embraces the value-free ideal. We clarify the issues at stake and argue that MacGillivray’s criticisms, although effective against some formulations of the AIR, fail to overcome the essential concerns which motivate the AIR. There are inevitable tradeoffs in scientific enquiry which cannot be resolved with any formal methods or general rules. Choices must be made, and values will be involved. It is best to recognize this explicitly. Even so, there is more work to be done developing methods and institutional support for these choices

How landmark suitability shapes recognition memory signals for objects in the medial temporal lobes

© 2017 Elsevier Inc. A role of perirhinal cortex (PrC) in recognition memory for objects has been well established. Contributions of parahippocampal cortex (PhC) to this function, while documented, remain less well understood. Here, we used fMRI to examine whether the organization of item-based recognition memory signals across these two structures is shaped by object category, independent of any difference in representing episodic context. Guided by research suggesting that PhC plays a critical role in processing landmarks, we focused on three categories of objects that differ from each other in their landmark suitability as confirmed with behavioral ratings (buildings \u3e trees \u3e aircraft). Participants made item-based recognition-memory decisions for novel and previously studied objects from these categories, which were matched in accuracy. Multi-voxel pattern classification revealed category-specific item-recognition memory signals along the long axis of PrC and PhC, with no sharp functional boundaries between these structures. Memory signals for buildings were observed in the mid to posterior extent of PhC, signals for trees in anterior to posterior segments of PhC, and signals for aircraft in mid to posterior aspects of PrC and the anterior extent of PhC. Notably, item-based memory signals for the category with highest landmark suitability ratings were observed only in those posterior segments of PhC that also allowed for classification of landmark suitability of objects when memory status was held constant. These findings provide new evidence in support of the notion that item-based memory signals for objects are not limited to PrC, and that the organization of these signals along the longitudinal axis that crosses PrC and PhC can be captured with reference to landmark suitability

Distributed category-specific recognition-memory signals in human perirhinal cortex.

Evidence from a large body of research suggests that perirhinal cortex (PrC), which interfaces the medial temporal lobe with the ventral visual pathway for object identification, plays a critical role in item-based recognition memory. The precise manner in which PrC codes for the prior occurrence of objects, however, remains poorly understood. In the present functional magnetic resonance imaging (fMRI) study, we used multivoxel pattern analyses to examine whether the prior occurrence of faces is coded by distributed patterns of PrC activity that consist of voxels with decreases as well as increases in signal. We also investigated whether pertinent voxels are preferentially tuned to the specific object category to which judged stimuli belong. We found that, when no a priori constraints were imposed on the direction of signal change, activity patterns that allowed for successful classification of recognition-memory decisions included some voxels with decreases and others with increases in signal in association with perceived prior occurrence. Moreover, successful classification was obtained in the absence of a mean difference in activity across the set of voxels in these patterns. Critically, we observed a positive relationship between classifier accuracy and behavioral performance across participants. Additional analyses revealed that voxels carrying diagnostic information for classification of memory decisions showed category specificity in their tuning for faces when probed with an independent functional localizer in a nonmnemonic task context. These voxels were spatially distributed in PrC, and extended beyond the contiguous voxel clusters previously described as the anterior temporal face patch. Our findings provide support for proposals, recently raised in the neurophysiological literature, that the prior occurrence of objects is coded by distributed PrC representations. They also suggest that the stimulus category to which an item belongs shapes the organization of these distributed representations

neurostuff/NiMARE: 0.2.0rc3

What's Changed Exciting New Features Remove resample argument from IBMA estimators by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/823 Add IBMAWorkflow by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/817 Make torch optional by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/836 ### Bug Fixes Addresses new RTD configuration file requirements by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/829 ### Other Changes Fix the NeuroLibre badge by @tsalo in https://github.com/neurostuff/NiMARE/pull/824 [FIX] handle null values in metadata by @jdkent in https://github.com/neurostuff/NiMARE/pull/831 Add badges and citations for Aperture Neuro article by @tsalo in https://github.com/neurostuff/NiMARE/pull/834 Remove pytorch warning message by @yifan0330 in https://github.com/neurostuff/NiMARE/pull/828 [FIX] handle index errors by @jdkent in https://github.com/neurostuff/NiMARE/pull/839 Full Changelog: https://github.com/neurostuff/NiMARE/compare/0.2.0rc2...0.2.0rc