Dasa Zeithamova-Demircan

Assistant Professor, Department of Psychology
Member, ION

Ph.D. University of Texas at Austin
M.A. Charles University in Prague

325 LISB


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.


Related Articles

Perceived similarity ratings predict generalization success after traditional category learning and a new paired-associate learning task.

Psychon Bull Rev. 2020 May 29;:

Authors: Ashby SR, Bowman CR, Zeithamova D

The current study investigated category learning across two experiments using face-blend stimuli that formed face families controlled for within- and between-category similarity. Experiment 1 was a traditional feedback-based category-learning task, with three family names serving as category labels. In Experiment 2, the shared family name was encountered in the context of a face-full name paired-associate learning task, with a unique first name for each face. A subsequent test that required participants to categorize new faces from each family showed successful generalization in both experiments. Furthermore, perceived similarity ratings for pairs of faces were collected before and after learning, prior to generalization test. In Experiment 1, similarity ratings increased for faces within a family and decreased for faces that were physically similar but belonged to different families. In Experiment 2, overall similarity ratings decreased after learning, driven primarily by decreases for physically similar faces from different families. The post-learning category bias in similarity ratings was predictive of subsequent generalization success in both experiments. The results indicate that individuals formed generalizable category knowledge prior to an explicit demand to generalize and did so both when attention was directed towards category-relevant features (Experiment 1) and when attention was directed towards individuating faces within a family (Experiment 2). The results tie together research on category learning and categorical perception and extend them beyond a traditional category-learning task.

PMID: 32472329 [PubMed - as supplied by publisher]

Related Articles

Spatiotemporal Dynamics of Multiple Memory Systems During Category Learning.

Brain Sci. 2020 Apr 09;10(4):

Authors: K Morgan K, Zeithamova D, Luu P, Tucker D

The brain utilizes distinct neural mechanisms that ease the transition through different stages of learning. Furthermore, evidence from category learning has shown that dissociable memory systems are engaged, depending on the structure of a task. This can even hold true for tasks that are very similar to each other, which complicates the process of classifying brain activity as relating to changes that are associated with learning or reflecting the engagement of a memory system suited for the task. The primary goals of these studies were to characterize the mechanisms that are associated with category learning and understand the extent to which different memory systems are recruited within a single task. Two studies providing spatial and temporal distinctions between learning-related changes in the brain and category-dependent memory systems are presented. The results from these experiments support the notion that exemplar memorization, rule-based, and perceptual similarity-based categorization are flexibly recruited in order to optimize performance during a single task. We conclude that these three methods, along with the memory systems they rely on, aid in the development of expertise, but their engagement might depend on the level of familiarity with a category.

PMID: 32283678 [PubMed]

Related Articles

Training set coherence and set size effects on concept generalization and recognition.

J Exp Psychol Learn Mem Cogn. 2020 Feb 27;:

Authors: Bowman CR, Zeithamova D

Building conceptual knowledge that generalizes to novel situations is a key function of human memory. Category-learning paradigms have long been used to understand the mechanisms of knowledge generalization. In the present study, we tested the conditions that promote formation of new concepts. Participants underwent 1 of 6 training conditions that differed in the number of examples per category (set size) and their relative similarity to the category average (set coherence). Performance metrics included rates of category learning, ability to generalize categories to new items of varying similarity to prototypes, and recognition memory for individual examples. In categorization, high set coherence led to faster learning and better generalization, while set size had little effect. Recognition did not differ reliably among conditions. We also tested the nature of memory representations used for categorization and recognition decisions using quantitative prototype and exemplar models fit to behavioral responses. Prototype models posit abstract category representations based on the category's central tendency, whereas exemplar models posit that categories are represented by individual category members. Prototype strategy use during categorization increased with increasing set coherence, suggesting that coherent training sets facilitate extraction of commonalities within a category. We conclude that learning from a coherent set of examples is an efficient means of forming abstract knowledge that generalizes broadly. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

PMID: 32105147 [PubMed - as supplied by publisher]