There is a long tradition at CMU of striving for complete cognitive models to account for a wide range of phenomena using a small common set of theoretical assumptions. Our training program is the only one focused on training cognitive modeling skills. The Combined Computational and Behavioral Approaches to the Study of Cognitive Neuroscience (T32) has the specific aim of providing pre and postdoctoral trainees with the ability to formalize their theories about the mechanisms underlying normal and abnormal cognitive behavior and brain functioning. We will provide trainees with opportunities to conduct state of the art behavioral studies, neuro-imaging studies and build computational models that predict as well as account for behavioral and neuroscience data of cognitive tasks. The methodologies that the training faculty exploit to further our understanding of cognition include fMRI, ERP, psychopharmacological interventions and eye- tracking as well as reaction time and other behavioral studies in combination with computational modeling. Many of these methodologies are used in combination. In addition, the trainees will have the opportunity to rotate into the lab of a medical scientist who conducts cognitive neuroscience research concerned with one of several different types of mental disorders: autism, bi-polar, depression, or schizophrenia. The collaboration among trainee, mentor and clinical researcher will result in the opportunity to test theories of normal behavior with special populations as well as providing useful exposure for the trainees concerning the nature of cognitive and mood disorders. The goal of this proposal is primarily to train the next generation of computational modelers of cognition and cognitive brain function;however, the collaborative work on formal models of disordered cognition as a limiting case in cognitive neuroscience has the promise to bring synergy to both enterprises and move the theoretical work forward in both disciplines. The exciting aspect of this proposal is that it will not only train young researchers to appreciate clinical-cognitive issues and expose them to translational research but it promises to foster synergistic collaborations that have the potential to provide new insights into both normal functioning or treatments for patients who are suffering. We request two years of support for 2 predoctoral and 2 postdoctoral fellows appointed each year.
| Manelis, Anna; Popov, Vencislav; Paynter, Christopher et al. (2017) Cortical Networks Involved in Memory for Temporal Order. J Cogn Neurosci 29:1253-1266 |
| Jern, Alan; Lucas, Christopher G; Kemp, Charles (2017) People learn other people's preferences through inverse decision-making. Cognition 168:46-64 |
| Liu, Xiaonan L; Reder, Lynne M (2016) fMRI exploration of pedagogical benefits of repeated testing: when more is not always better. Brain Behav 6:e00476 |
| Walsh, Matthew M; Paynter, Christopher A; Zhang, Ya et al. (2016) Hitting the reset button: An ERP investigation of memory for temporal context. Brain Res 1642:524-531 |
| Schipul, Sarah E; Just, Marcel Adam (2016) Diminished neural adaptation during implicit learning in autism. Neuroimage 125:332-341 |
| Greenberg, Adam S; Rosen, Maya; Cutrone, Elizabeth et al. (2015) The effects of visual search efficiency on object-based attention. Atten Percept Psychophys 77:1544-57 |
| Carroll, Christopher D; Kemp, Charles (2015) Evaluating the inverse reasoning account of object discovery. Cognition 139:130-53 |
| Jern, Alan; Kemp, Charles (2015) A decision network account of reasoning about other people's choices. Cognition 142:12-38 |
| Manelis, Anna; Reder, Lynne M (2015) He who is well prepared has half won the battle: an FMRI study of task preparation. Cereb Cortex 25:726-35 |
| Nie, Aiqing; Griffin, Michael; Keinath, Alexander et al. (2014) ERP profiles for face and word recognition are based on their status in semantic memory not their stimulus category. Brain Res 1557:66-73 |
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