How robust are meta-analyses to publication bias? Sensitivity analysis methods and empirical findings

Publication bias can distort meta-analytic results, sometimes justifying considerable skepticism toward meta-analyses. This talk will discuss recently developed statistical sensitivity analyses for publication bias, which enable statements such as: “For publication bias to shift the observed point estimate to the null, ‘significant’ results would need to be at least 10-fold more likely to be published than negative or ‘non-significant’ results” or “no amount of publication bias could explain away the average effect.” The methods are based on inverse-probability weighted estimators and use r

The rational use of cognitive resources

Psychologists and computer scientists have very different views of the mind. Psychologists tell us that humans are error-prone, using simple heuristics that result in systematic biases. Computer scientists view human intelligence as aspirational, trying to capture it in artificial intelligence systems. How can we reconcile these two perspectives? In this talk, I will argue that we can do so by reconsidering how we think about rational action.

Curious, cooperative, and communicative: How we learn from others and help others learn

Humans are not the only species that learns from others, but only humans learn and communicate in rich, diverse social contexts, and build repertoires of abstract, structured knowledge. What makes human social learning so distinctive, powerful, and smart?  In this talk, I argue that social learning is inferential at its core (inferential social learning); rather than copying what others do or trusting what others say, humans learn from others by drawing rich inferences from others’ behaviors, and help others learn by generating evidence tailored to others’ goals and knowledge states.

What Happened to the 'Mental' in 'Mental" Disorders

People often seek help for mental problems because they are suffering subjectively. Yet, for decades, the subjective experience of patients has been marginalized. This is in part due to the dominant medical model of mental illness, which has tended to treat subjective experience as a quaint relic of a scientifically less enlightened time. To the extent that subjective symptoms are related to the underlying problem, it is often assumed that they will be taken care of if the more objective symptoms, such as behavioral and physiological responses are treated.

Rethinking behavior in the light of evolution

Abstract: In psychology and neuroscience, the human brain is usually described as an information processing system that encodes and manipulates representations of knowledge to produce plans of action. This view leads to a decomposition of brain functions into putative processes such as object recognition, memory, decision-making, action planning, etc., inspiring the search for the neural correlates of these processes. However, neurophysiological data does not support many of the predictions of these classic subdivisions.

Integration of Personal vs. Social Information for Sustainable Decisions on Climate Action

Some of my past and current research looks at "decisions from  experience,” i.e., decisions based on the personally experienced outcomes of past choices, along the lines of reinforcement learning models and how such learning and updating is related to and differs from the way in which people and other intelligent agents use other sources of information, e.g., vicarious feedback (anecdotal/social and/or in the form of statistical distributions of outcomes) or science- or model-based outcome predictions to make “decisions from description.”  What happens when these different sources of foreca

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