Lauren N Ross.

Lauren N. Ross

Associate professor of logic and philosophy of science
University of California, Irvine

Lauren N. Ross is associate professor of logic and philosophy of science at the University of California, Irvine. Her research concerns causal reasoning and explanation in the life sciences, primarily neuroscience and biology.  One main area of her research explores causal varieties—different types of causes, causal relationships and causal systems in the life sciences. Her work identifies the features characteristic of these causal varieties and their implications for how these systems are studied, how they figure in scientific explanations and how they behave. A second main area of work focuses on types of explanation in neuroscience and biology, including distinct forms of causal and noncausal explanation.

Ross’ research has received a National Science Foundation CAREER award, a Humboldt Experienced Researcher Fellowship, a John Templeton Foundation Grant, and an Editor’s Choice Award at the British Journal for the Philosophy of Science.  Recent publications include “Causation in neuroscience: Keeping mechanism meaningful” with Dani S. Bassett in Nature Reviews Neuroscience and a forthcoming book, “Explanation in Biology” (Cambridge University Press: Elements Series).

Explore more from The Transmitter

Research image of neuron organization in c elegans.

Worms help untangle brain structure/function mystery

The synaptic connectome of most animals bears little resemblance to functional brain maps, but it can still predict neuronal activity, according to two preprints that tackle the puzzle in C. elegans

By Holly Barker
29 August 2025 | 7 min read
Research image of microglia in organoids.

Microglia nurture young interneurons

The immune cells secrete a growth factor that “sets the supply of GABAergic interneurons in the developing brain.”

By Lauren Schenkman
28 August 2025 | 4 min read

Xaq Pitkow shares his principles for studying cognition in our imperfect brains and bodies

Pitkow discusses how evolution's messy constraints shape optimal brain algorithms, from Bayesian inference to ecological affordances.

By Paul Middlebrooks
27 August 2025 | 1 min read

privacy consent banner

Privacy Preference

We use cookies to provide you with the best online experience. By clicking “Accept All,” you help us understand how our site is used and enhance its performance. You can change your choice at any time. To learn more, please visit our Privacy Policy.