The Faculty of Psychology and Educational Sciences of the University of Coimbra, Portugal, is seeking applications for 3 Post-Doctoral positions in Cognitive Science and Cognitive Neuroscience as part of the ERA Chair grant CogBooster. The positions are aimed at contributing to the renewal of Psychological Sciences in Portugal and involve working with Alfonso Caramazza and Jorge Almeida. The selected applicants will be based in Coimbra with opportunities to spend time at Harvard University in Alfonso Caramazza’s laboratory. The positions are focused on: 1) lexical processing, visual object recognition, reading, or action recognition; 2) visual object recognition and how object knowledge is organized and represented; 3) object dimensionality and dimensional mapping using population receptive field analysis/connective field modeling.

The Faculty of Psychology and Educational Sciences of the University of Coimbra Portugal (FPCE-UC) is looking for doctoral students with expertise in Cognitive Science and Cognitive Neuroscience to work on a transformative ERA Chair grant CogBooster from the European Union. The selected applicants will work directly with Alfonso Caramazza and Jorge Almeida and will be based in Coimbra. The research areas include lexical processing, visual object recognition, reading, action recognition, and how object knowledge is organized and represented neurally and cognitively.

In the first part of this talk I will present work showing that recurrent neural networks can replicate broad behavioral patterns associated with dynamic visual object recognition in humans. An analysis of these networks shows that different types of recurrence use different strategies to solve the object recognition problem. The similarities between artificial neural networks and the brain presents another opportunity, beyond using them just as models of biological processing. In the second part of this talk, I will discuss—and solicit feedback on—a proposed research plan for testing a wide range of analysis tools frequently applied to neural data on artificial neural networks. I will present the motivation for this approach as well as the form the results could take and how this would benefit neuroscience.

Learning depends on both the learning mechanism and the regularities in the training material, yet most research on human and machine learning focus on the discovering the mechanisms that underlie powerful learning. I will present evidence from our research focusing on the statistical structure of infant visual learning environments. The findings suggest that the statistical structure of those learning environments are not like those used in laboratory experiments on visual learning, in machine learning, or in our adult assumptions about how teach visual categories. The data derive from our use of head cameras and head-mounted eye trackers capturing FOV experiences in the home as well as in simulated home environments in the laboratory. The participants range from 1 month of age to 24 months. The observed statistical structure offers new insights into the developmental foundations of visual object recognition and suggest a computational rethinking of the problem of visual category formation. The observed environmental statistics also have direct implications for understanding the development of cortical visual systems.