לוח שנה משולב

Our brain relies on internal representations to support perception, action, and decision-making. Internal representations are usually rich, multidimensional, and cannot be directly observed. How can these internal representations be characterized? How are they affected by experience? My work develops adaptive behavioral paradigms that integrate human decisions into computer algorithms via human-in-the-loop experiments. I combine these paradigms with a data-intensive expansion of the scale and scope of behavioral research by means of massive online experiments and cross-cultural comparative research. This talk presents “adaptive sampling,” a type of experimental paradigm inspired by Monte Carlo Markov Chain techniques. Each successive stimulus depends on a subject's response to the previous stimulus. This process allows us to sample from the complex and high-dimensional joint distribution associated with internal representations and obtain high-resolution maps of perceptual spaces. After introducing these methods and describing their implementation via large-scale online experiments and field experiments around the world, I demonstrate how they can be applied to fundamental questions in the understanding of the
human mind. Specifically, I examine how biology and culture influence internal representations and how semantics influence perception.