Assistant Professor, Department of Psychology
Ph.D. University of Texas at Austin
M.A. Charles University in Prague
Research Interests: Cognitive-Neuroscience, Memory
Overview: Memory allows us to use past experiences to navigate novel situations and inform future decisions. Because every event is unique, we need to use memory flexibly, drawing upon multiple relevant experiences to anticipate future judgments. Brain and Memory Lab studies how memories are formed and how they are linked to each other to create internal representations of the world that can guide our behavior. We investigate how different memory systems are implemented in the brain, how they represent information, and how they interact. In the quest for discovery, we rely on computer-based experiments, cognitive models of behavior, and advanced functional MRI methods.
My research focuses on how we build complex knowledge representations—such as schemas, cognitive maps or concepts—from simple learning experiences. Stacking memories as building blocks, we form knowledge that transcend direct experience, allowing us to use the memory for the past to guide behavior in the future. I am especially interested how the hippocampus—a brain structure critical for memory for individual events in our lives—interacts with the prefrontal cortex and other memory systems to support the flexible use of experience. My primary research tools include computer-based experiments, formal models of behavior, and advanced functional MRI methods.
Trial timing and pattern-information analyses of fMRI data.
Neuroimage. 2017 Apr 11;:
Authors: Zeithamova D, de Araujo Sanchez MA, Adke A
Pattern-information approaches to fMRI data analysis are becoming increasingly popular but few studies to date have investigated experimental design optimization for these analyses. Here, we tested several designs that varied in the number of trials and trial timing within fixed duration scans while participants encoded images of animals and tools. Trial timing conditions with fixed onset-to-onset timing ranged from slow 12-second trials with two repetitions of each item to quick 6-second trials with four repetitions per item. We also tested a jittered version of the quick design with 4-8second trials. We assessed the effect of trial timing on three dependent measures: category-level (animals vs. tools) decoding accuracy using a multivoxel pattern analysis, item-level (e.g., cat vs. dog vs. lion) information estimates using pattern similarity analysis, and memory effects comparing pattern similarity scores across repetitions of individual items subsequently remembered vs. forgotten. For single trial estimates, category decoding was equal across all trial timing conditions while item-level information and memory effects were better detected using slow trial timing. When modeling events on an item-by-item basis across all repetitions of a given item, a larger number of quick, regularly spaced trials provided an advantage over fewer slow trials for category decoding while item-level information was comparable across conditions. Jittered and non-jittered versions of the quick trial timing did not differ significantly in any analysis. These results will help inform experimental design choices in future studies planning to employ pattern-information analyses and demonstrate that design optimization guidelines developed for univariate analyses of a few conditions are not necessarily optimal for pattern-information analyses and condition-rich designs.
PMID: 28411155 [PubMed - as supplied by publisher]