The Epistemology of Cognitive Science and Neuroscience
University of Pittsburgh
The goal of this series of five lectures is to introduce
students to the recent debates about the epistemology of cognitive science and
cognitive neuroscience: How do we know about cognition? Among the discovery
heuristics and confirmation strategies that are used in cognitive science and
in cognitive neuroscience, which ones are justified and which ones should be
rejected and criticized? What is the proper form of explanation in cognitive
science and neuroscience? The topics examined in this series of lectures will
intersect with more general issues in the philosophy of science (e.g., are all
explanations mechanistic?) and in the philosophy of statistics (e.g., Should we
reject classical statistics?).
Some philosophers of cognitive science
and of neuroscience have recently argued that genuine explanations in cognitive
science and in neuroscience are mechanistic, and that apparently
non-mechanistic explanations are merely incomplete mechanistic explanations or
drafts of to-be-completed mechanistic explanations. Call this view “the
mechanistic stance.” In this lecture, I will first explain what mechanistic
explanations are, then present the arguments from the mechanistic stance,
before finally assessing it.
Dissociations in neuropsychology are one
of the most important forms of evidence to identify the processes that underlie
human beings’ cognitive capacities. On the other hand, what can be inferred
from dissociations remains controversial. In this lecture, we will review the
debates about the epistemology of dissociations.
In this lecture we will examine two forms of inference used
in cognitive neuroscience: forward inference and reverse inference. Debates
about the validity of each form of inference are extremely intense. The
inferences will be presented before discussion and assessing the criticisms raised against them.
R. A. (2006). Can cognitive processes be inferred from neuroimaging data?
Trends in Cognitive Sciences, 10, 59-63.
Recently, it has been argued that neuroimagery fails to provide evidence for functional
hypotheses about brain areas or networks because it relies on significance
testing (aka, null hypothesis significance testing). If correct, this argument
would have drastic consequences and would call into question much of cognitive
neuroscience. In this lecture, we will discuss and assess this argument.
statistics (in particular, significance testing) is the most influential
statistical framework for drawing statistical inferences in psychology as in
many other sciences (epidemiology, ecology, etc.). However, it has been
extensively criticized by Bayesians. In this lecture, I will examine the claim
that classical statistics gives rise to paradoxes.