Dissertation
Systems level investigation of rat category learning
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Spring 2022
DOI: 10.25820/etd.006453
Abstract
Categorization is the process of extrapolating meaningful structure from individual experiences that have perceptual or relational similarity. This is adaptive since we can generalize learned category knowledge to novel objects and situations. The neural mechanisms underlying category learning are likely complex and involve complicated interactions across multiple neural substrates. Models of human categorization (e.g., COVIS, nSUSTAIN) have attempted to characterize these mechanisms; however, the specific predictions of these models have not been thoroughly tested empirically. Specifically, few studies have tested whether key regions are necessary for category learning and fewer studies have examined interregional interactions at different phases of learning. The set of experiments described in the current thesis is among the first to investigate category learning in rats, a model organism that is well-suited for understanding fundamental neural mechanisms. Using a touchscreen apparatus, rats were trained to learn classic categorization paradigms using training procedures and stimuli that are comparable to human experiments. Rats were trained to categorize distributions of visual stimuli that varied along two continuous dimensions (i.e., the spatial frequency and orientation of black and white gratings). For some rats, only one stimulus dimension (i.e., spatial frequency or orientation) needed to be considered to categorize the stimuli (1D tasks). These tasks encouraged a shift of attention towards the category-relevant dimension. For other rats, both stimulus dimensions (i.e., spatial frequency and orientation) were category-relevant (2D tasks). In Chapters 2-4, we selectively inactivated candidate brain regions (i.e., PL, HPC, DMS, and DLS) while rats learned to categorize either a 1D or 2D task. We found that the PL, HPC, and DMS are all necessary for rat category learning. Computational approaches were used to assess the functions of these regions during category learning. Results suggest that the HPC builds category representations that are retrieved to categorize new stimuli, the PL modulates these representations by amplifying category-relevant information, and the DMS maps these category representations to appropriate behavioral responses. In Chapter 5, in vivo single unit activity and LFPs were recorded from the PL, HPC, and DMS concurrently as rats learned a 1D category task. Category-selective signals were observed in the single units of each region. Category-selectivity in the PL was observed throughout training, whereas selectivity in the DMS and HPC developed across learning. Neural connectivity analyses characterize critical inter-regional interactions across learning. Communication increased between the PL and the HPC as well as the HPC and the DMS across training sessions. The results of these experiments are synthesized and compared in relation to current models of human category learning. Future experiments are discussed to extend this research to further understand the mechanisms underlying category learning.
Details
- Title: Subtitle
- Systems level investigation of rat category learning
- Creators
- Matthew B Broschard
- Contributors
- John H Freeman (Advisor)Edward A Wasserman (Committee Member)Mark S Blumberg (Committee Member)Ryan T Lalumiere (Committee Member)Jan R Wessel (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Psychology
- Date degree season
- Spring 2022
- Publisher
- University of Iowa
- DOI
- 10.25820/etd.006453
- Number of pages
- xv, 211 pages
- Copyright
- Copyright 2022 Matthew B. Broschard
- Language
- English
- Description illustrations
- illustrations (some color)
- Description bibliographic
- Includes bibliographical references (pages 186-211).
- Public Abstract (ETD)
Categorization organizes our world by extracting common patterns from our everyday experiences. This is advantageous and allows us to apply information we’ve learned to new situations. Multiple patient populations (e.g., Parkinson’s disease, dementia, autism, schizophrenia, etc.) have difficulty learning new categories, suggesting that the mechanisms by which the brain learns new categories are very complex. Although models exist that predict how the brain learns new categories, these models have not been thoroughly tested. The set of experiments in this thesis is among the first to examine category learning in rats, which is well-suited for understanding key neural mechanisms. Using touchscreens, rats learned to sort simple visual stimuli into two categories (i.e., category ‘A’ and category ‘B’). These stimuli contained black and white lines that, across objects, varied in their thickness and orientation. Some rats were trained to sort the stimuli along one stimulus feature (i.e., thickness or orientation; 1D tasks); other rats were trained to sort the stimuli using both stimulus features (i.e., thickness and orientation; 2D tasks). In Chapters 2-4, we tested the importance of candidate brain regions (i.e., PL, HPC, DMS, and DLS) by lesioning each region individually before rats were trained to categorize the objects. Results suggest that the HPC stores memories of previous training stimuli, the PL selects memories that are important for the current task, and the DMS learns how to connect these memories to the correct category label (i.e., category ‘A’ or category ‘B’). In Chapter 5, we recorded from single cells and LFPs from the PL, HPC, and DMS simultaneously as rats learned the category task. We found that the firing rate of cells from all regions showed information related to the category membership of the stimuli (i.e., category-selectivity). Additionally, we found that category learning was characterized by increased communication between the PL and HPC, as well as between the HPC and DMS. The results of these experiments are compared to current models of human category learning. Future experiments are discussed that extend this research.
- Academic Unit
- Psychological and Brain Sciences
- Record Identifier
- 9984271255502771
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