Dagmar Zeithamova

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Dagmar Zeithamova

Dagmar Zeithamova also know as Dasa Zeithamova is an associate professor at the University of Oregon in the Department of Psychology working in the brain and memory lab. She is Chair of the Undergraduate Education Committee and Co-Director of Undergraduate Studies. Dagmar Zeithamova is a postdoctoral researcher specializing in cognitive neuroscience and memory. Her lab is working on identifying the neuronal basis for human experiences using advanced brain imaging and tracking techniques through functional MRI.

Education

Dagmar Zeithamova studied at the Charles University in Prague from September 1996 - February 2003, obtaining a bachelors and masters degree in Psychology with a minor in Logic and Economics. She then earned a her doctorate degree in the Institute for Neuroscience at the University of Texas at Austin from August 2003 - August 2008, becoming a postdoctoral researcher. From 2008-2014 she conducted research at the University of Texas in the Center for Learning and Memory, focusing on the role of the medial temporal lobe in memory. She was able to do this research by utilizing fMRI to identify and evaluate neuronal patterns in episodic memory, and how this causes humans to form generalizations.

Credibility

Dr. Zeithamova has 58 scientific publications with an h-index of 24. Her work has been used in a total of 3,069 citations with 196 of those being highly influential citations. Her publications include the help of 183 co-authors and in these papers she has referenced 4,495 additional authors. Dr. Zeithamova has an above average H-index demonstrating the prestige and validity of her work. On average, associate professors may reach 6-10 as thier H-index and full professors average at 12-24. On average, male researchers have higher index's than female researchers, highlighting the importance of Dr. Zeithamova acquiring a competitive H-index for her field.

Research

Dagmar Zeithamova's research provides detailed insight on how neurons create underlying concept mechanisms that allow human brains to build schemas and stereotypes about the surrounding world. Memory allows humans to recall past lived experiences and to synthesize information from other experienced events creating new knowledge and inferences. Dr. Zeithamova and her colleagues are looking for when these conceptualizations form in the brain and how a category-biased representation is able to adapt to our sensory perceptions. Dr. Zeithamova's early research from her time at the University of Texas at Austin has particular focus on the role of the hippocampus to create conceptualized inferential reasoning during memory encoding and memory retrieval, demonstrating how the memories in the hippocampus are organized such that humans can anticipate future decisions and actions based on prior conceptions of knowledge.. Recent studies have shown that humans create a false memory when making quick generalized decisions. Integrated neuronal networks create inferences that may differ from the actual source memory. This demonstrates how on-demand retrieval may have a significant effect on all future learning experiences.

The work conducted by Dagmar Zeithamova highlights the importance of awareness. She recognizes how categorical enables the brain to form stubborn stereotypes, but also recognizes the brain's abilities to see past such categorical stereotypes. The purpose of her work is not to discourage the brains' tendency to form categories, but to recognize when categorical thinking may become misleading. Creating schemas and categories is very important for processing new information and accessing prior learned knowledge. Dr. Zeithamova and her team conduct their studies to reveal a deeper understanding of the brain. In a complex world, cognitive categorization and generalizations are highly valuable, but Dagmar Zeithamova highlights that despite this unique brain ability, humans must also be able to look past potentially stringent labels.

Dr. Zeithamova seeks further understanding as to how our ability to generalize works with our ability to retain specific sensory details. Doing so, she utilizes computer-based experiments, formal models of behavior, and advanced functional MRI methods as her primary research tools. Measuring the functional interactions of brain regions allows Dr. Zeithamova to identify idiosyncratic patterns of resting-state neuronal connections. These patterns can be used to identify and predict individual differences in clinical symptoms, cognitive abilities, and other individual factors. Idiosyncratic connectivity patterns are thought to remain the same across task states, and therefore a task-based fMRI can be used to analyze individual differences. When comparing the degree to which functional interactions that occur in the background of a task during slow event-related fMRI are similar or different from those captured during resting-state fMRI, Dr. Zeithamova and her colleagues found that the organization of large-scale cortical networks and individual's idiosyncratic connectivity patterns are preserved during task-based fMRI. These findings were important for all future research because these patterns become "functional fingerprints" that remain stable across different cognitive states allowing researchers to predict behavior and understand individual differences in cognitive ability. Dr. Zeithamova is a credible researcher thoroughly investigating methodologies, reasoning, and exact functional brain circuits to understand how memory plays a role in present, everyday learning situations. Measuring the synchronicity of brain activity allows further research into how our brain functions when deciphering new information with previously understood information. Research on task-based fMRI is very important for her future endeavors in memory research.

Dagmar Zeithamova's research is well-developed and important for human learning and perception. With a current position as an associate professor at the University of Oregon, her research will continue resulting in further understanding of how our complex brain functions, causing the social dynamic phenomenons that have occurred for centuries.

Publications

1. Monte Carlo approaches to model selection: Application to the prototype and exemplar models of categorization
2. High Coherence Among Training Exemplars Promotes Broad Generalization of Face Families
3. Monte Carlo approaches to model selection: Application to the prototype and exemplar models of categorization
4. Monte Carlo approaches to model selection: Application to the prototype and exemplar models of categorization
5. High coherence among training exemplars promotes broad generalization of face families
6. How Our Brains Create Categories: A Look Inside the Mind
7. Retrieval-based inference in the acquired equivalence paradigm
8. Differential effects of location and object overlap on new learning
9. Memory separation and integration
10. Category bias in similarity ratings: the influence of perceptual and strategic biases in similarity judgments of faces
11. A combination of restudy and retrieval practice maximizes retention of briefly encountered facts
12. Category Bias in Similarity Ratings: The Influence of Perceptual and Strategic Biases in Similarity Judgments of Faces
13. Coherent Category Training Enhances Generalization in Prototype-Based Categories
14. Evaluating methods for measuring background connectivity in slow event-related functional magnetic resonance imaging designs
15. Generalization and false memory in acquired equivalence
16. Evaluating methods for measuring background connectivity in slow event-related functional MRI designs
17. The Effects of Age on Category Learning and Prototype- and Exemplar-Based Generalization
18. Category-Biased Neural Representations Form Spontaneously during Learning That Emphasizes Memory for Specific Instances
19. Is Retrieval Practice Always Superior to Restudy?
20. Age effects on category learning, categorical perception, and generalization
21. The effects of age on prototype- and exemplar-based categorization
22. The effect of learning condition on memory for and integration of related information
23. The effect of learning condition on memory for and integration of related information
24. How do we generalize?
25. Generalization and False Memory in an Acquired Equivalence Paradigm: The Influence of Physical Resemblance Across Related Episodes
26. Coherent category training enhances generalization and increases reliance on prototype representations
27. How do we generalize?
28. Characterizing the impact of adversity, abuse, and neglect on adolescent amygdala resting-state functional connectivity
29. Generalization and the hippocampus: More than one story?
30. Generalization and source memory in acquired equivalence
31. Age effects on category learning and their relationship to deficits in memory specificity
32. Perceived similarity ratings predict generalization success after traditional category learning and a new paired-associate learning task
33. Model-based fMRI reveals co-existing specific and generalized concept representation
34. Spatiotemporal Dynamics of Multiple Memory Systems During Category Learning
35. Training Set Coherence and Set Size Effects on Concept Generalization and Recognition
36. Multivariate neural signatures for health neuroscience: Assessing spontaneous regulation during food choice
37. Differential Functional Connectivity along the Long Axis of the Hippocampus Aligns with Differential Role in Memory Specificity and Generalization
38. Brain Mechanisms of Concept Learning
39. Multivariate neural signatures for health neuroscience: Assessing spontaneous regulation during food choice
40. Generalization Following Incidental and Intentional Category Learning
41. Training typicality and set size effects on concept generalization and recognition
42. Functional connectivity between memory and reward centers across task and rest track memory sensitivity to reward
43. Abstract Representation of Prospective Reward in the Hippocampus
44. Ventromedial Prefrontal Cortex Is Necessary for Normal Associative Inference and Memory Integration
45. Choosing to regulate: Does choice enhance craving regulation?
46. Decreased Prefrontal Activation during Matrix Reasoning in Predementia Progranulin Mutation Carriers
47. Choosing to regulate: Does choice enhance craving regulation?
48. Supplementary Data
49. Abstract Memory Representations in the Ventromedial Prefrontal Cortex and Hippocampus Support Concept Generalization
50. Are child abuse and neglect uniquely associated with functional connectivity across networks? An investigation of amygdala and hippocampal connectivity at rest
51. Temporal Proximity Promotes Integration of Overlapping Events
52. Trial timing and pattern-information analyses of fMRI data
53. Repetition suppression in the medial temporal lobe and midbrain is altered by event overlap
54. CA(1) Subfield Contributions to Memory Integration and Inference
55. Distributed Hippocampal Patterns That Discriminate Reward Context Are Associated With Enhanced Associative Binding
56. Hippocampal and Ventral Medial Prefrontal Activation during Retrieval-Mediated Learning Supports Novel Inference
57. Reward Modulation of Hippocampal Subfield Activation during Successful Associative Encoding and Retrieval
58. The hippocampus and inferential reasoning: building memories to navigate future decisions
59. Prototype Learning Systems
60. The Effects of Sleep Deprivation on Dissociable Prototype Learning Systems
61. Flexible Memories: Differential Roles for Medial Temporal Lobe and Prefrontal Cortex in Cross-Episode Binding
62. Computational Models Inform Clinical Science and Assessment: An Application to Category Learning in Striatal-Damaged Patients
63. Learning Mode and Exemplar Sequencing in Unsupervised Category Learning
64. Dissociable Processes in Classification: Implications From Sleep Deprivation
65. Dissociable Prototype Learning Systems: Evidence from Brain Imaging and Behavior
66. The role of visuospatial and verbal working memory in perceptual category learning
67. Dual-task interference in perceptual category learning
68. Theories of categorization and category learning: Why a single approach cannot be sufficient to account for the phenomenon

References

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