I am primarily a Philosopher of Neuroscience, but my research ranges in scope from questions in general philosophy of science (How does data analysis generate evidence?) to the epistemology of neuroscience (What can the evidence of neuroimaging tells us about the structure of cognition?). My work is often engaged with the cognitive neurosciences (What is the nature of the evidence produced by machine learning data analysis techniques with respect to the cognitive activities of the brain?).
I also work on teaching philosophy and the philosophy of teaching. I am an active member of the America Association of Philosophy Teachers, have presented workshops on teaching and pedagogy and actively seek new ideas, strategies and discussions on teaching and course design within and for philosophy. My current work on teaching includes the development of a method for assessing and promoting student participation that is inclusive and aligned with typical learning outcomes for philosophy courses, investigations of the role for games in teaching philosophy, and the development of graduate courses and training programs to train graduate students to be effective instructors and also prepare them for interdisciplinary research projects.
Epistemology of Neuroimaging
Philosophers have been skeptical of the use of neuroimaging technology to investigate the relationship between cognitive function and the brain (van Orden and Paap 1997; Klein 2010; Roskies 2010; Aktunc 2014). I have argued that, when the full range of data analysis techniques used with neuroimaging data are taken into consideration, the strength of the evidence generated by neuroimaging data is stronger than philosophers have appreciated (Wright forthcoming). This argument both provides grounds for optimism with respect to the use of neuroimaging in cognitive neuroscience, and general insight into the role and importance of multiple data analysis techniques for the generation of scientific knowledge.
The next stage of this project is to develop an account of evidence in neuroimaging that is sensitive to the supporting role data manipulations and analysis play in the interpretation of neuroimaging data. I am current working on such an account, drawing on the accounts of evidence offered by Jim Woodward (2003), Deborah Mayo (1996), and Julian Reiss (2015), with inspiration from work on the philosophy of data (Leonelli 2016)
Cognitive Ontology Revision
A cognitive ontology is the taxonomy of cognitive concepts and their relations which psychologists and cognitive neuroscientists use to describe cognitive function. There is a recent movement in cognitive neuroscience to revise the cognitive ontology. The revision is motivated because of persistent discoveries that cognitive functions map many-to-many onto parts of the brain (Price & Friston 2005) and because the cognitive ontology is largely inherited from psychology and has not been refined on the basis of what we know about the brain (Bunzl, Hanson & Poldrack 2010). This has led to what I call brain-first ontology revision proposals, which rely on meta-anlayses of brain data to ground ontology revision proposals (Lenartowicz et al 2010; Klein 2012; Anderson 2015). I am currently investigating the contribution that machine learning analyses play in this debate, and how, if at all, meta-analyses of neuroimaging can achieve some the goals proponents of large-scale meta-analyses have claimed it can.
Automated Database Curation
Database is novel tool used in cognitive neuroscience to perform meta-analyses of neuroimaging data. What’s novel about it is that data is entered into the database and curated automatically by an algorithm that searches neuroscience journals, extracts the relevant data points and labels the data. The automated procedure is important as it overcomes practical limitations to database development in cognitive neuroscience (Yarkoni et al 2009). However, automated curation raises interesting epistemic problems as curation is normally managed by a team of highly trained professional curators with field-relevant expertise (for example, see the IEDB curation manual
). Two questions that I am working on are: What difference does automation make with respect to the epistemically responsible use of the database? How does automated curation bear on philosophical accounts of the role databases play in science?
Teaching Philosophy through Games
“Gamification” has become yet another buzzword in teaching and pedagogy. The idea is that people have fun playing games, and people are engaged when they have fun. Classroom engagement, then, could be improved by using games to make students have fun. The problem is, games in the classroom are a learning activity and so the learning needs to be aligned
with the learning outcomes of the course or else it not actually supporting learning. I argue that ‘fun’ isn’t the primary benefit of using games in the classroom. Games provide an opportunity to give students an experience, and that experience can be tailored by the rules and components of the game. Creating an authentic game experience, then, is about creating an experience that is analogous to the lesson, debate or theory being taught. I propose that well designed game experiences can create first-hand analogies which can serve as the subject of discussion in stead of (or in addition to) traditional, abstract thought experiments.