Many systems neuroscientists want to understand neurons in terms of mediation; we want to understand how neurons are involved in the causal chain from stimulus to behavior. Unfortunately, most tools are inappropriate for that while our language takes mediation for granted. Here we discuss the contrast between our conceptual drive toward mediation and the difficulty of obtaining meaningful evidence.

An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN research program of human vision.

In cognitive science, there is a tacit norm that phenomena such as cultural variation or synaesthesia are worthy examples of cognitive diversity that contribute to a better understanding of cognition, but that other forms of cognitive diversity (e.g., autism, attention deficit hyperactivity disorder/ADHD, and dyslexia) are primarily interesting only as examples of deficit, dysfunction, or impairment. This status quo is dehumanizing and holds back much-needed research. In contrast, the neurodiversity paradigm argues that such experiences are not necessarily deficits but rather (...) are natural reflections of biodiversity. Here, we propose that neurodiversity is an important topic for future research in cognitive science. We discuss why cognitive science has thus far failed to engage with neurodiversity, why this gap presents both ethical and scientific challenges for the field, and, crucially, why cognitive science will produce better theories of human cognition if the field engages with neurodiversity in the same way that it values other forms of cognitive diversity. Doing so will not only empower marginalized researchers but will also present an opportunity for cognitive science to benefit from the unique contributions of neurodivergent researchers and communities. (shrink)

When we learn how to throw darts we adjust how we throw based on where the darts stick. Much of skill learning is computationally similar in that we learn using feedback obtained after the completion of individual actions. We can formalize such tasks as a search problem; among the set of all possible actions, find the action that leads to the highest reward. In such cases our actions have two objectives: we want to best utilize what we already know (exploitation), (...) but we also want to learn to be more successful in the future (exploration). Here we tested how participants learn movement trajectories where feedback is provided as a monetary reward that depends on the chosen trajectory. We mathematically derived the optimal search policy for our experiment using decision theory. The search behavior of participants is well predicted by an ideal searcher model that optimally combines exploration and exploitation. (shrink)