The Department of Psychology and Center for Cognitive and Brain Sciences with support from The Delos D. Wickens Lectureship Fund
Psychophysical TMS: delayed fovea noise disrupts discrimination of object details in the visual periphery
Previous neuroimaging and TMS studies suggest that object category information from peripherally presented images is available in foveal retinotopic cortex and is functionally relevant, presumably due to feedback signals from high-level object sensitive cortex. We investigated this potential feedback signal by presenting dynamic noise in fovea at different SOAs while subjects performed a discrimination task on peripheral objects. Results revealed a selective impairment of performance when foveal noise was presented around 250ms following the onset of peripheral objects, and only for tasks that required spatial details. This pattern of results suggests that there is a task-dependent and temporally specific feedback signal to foveal retinotopic cortex. Furthermore, the temporal window when foveal noise disrupts the peripheral object discrimination could be shifted back when mental rotation was required as part of the peripheral objects discrimination task, indicating that the foveal retinotopic cortex is not automatically engaged at a fixed time following peripheral stimulation, rather it occurs at a stage when higher level cortical areas are ready for and requires such feedback interactions. A companion fMRI study using multi-voxel pattern analysis showed that image level information was more robustly represented than object category information in the feveal cortex. Together these findings suggest that foveal retinotopic cortex plays an important role in processing spatially detailed object information even for peripheral objects.
Using Forecasting Tournaments to Improve Intuitive Predictions And Increase Open-Mindedness
I will report the results of four forecasting tournaments that occurred between 2011 and 2015 that were designed to discover the best ways of crowd sourcing numerical forecasts and aggregating them as accurately as possible. Over 20,000 participants from all over the world estimated the probabilities of geopolitical events, from pandemics and military conflicts to international agreements and refugee flows. Participants were randomly assigned to experimental conditions that varied in terms of elicitation methods (surveys versus prediction markets), social dynamics (independent forecasters versus forecasters who worked collaboratively in teams) and training in probabilistic reasoning. Accuracy was defined by the Brier scoring rule and its variance decomposition. By measuring the cognitive, psychological, and political traits of forecasters, we learned what factors were most highly correlated with top performers. Finally, by studying the strategies of "superforecasters" - those who scored among the top 2%, we learned what contributed to their success and how they made forecasts that were more accurate than U.S. Intelligence Analysts predicting the same events but with classified information. Accuracy depends on selecting the right people, creating the best forecasting environments, and taking advantage of simple statistical aggregation methods.