Jerry Scott Fisher

About Me



I am a researcher and teacher. I received my PhD in 2020 from the University of Notre Dame in the area of Cognition, Brain, and Behavior. My advisor was Gabriel Radvansky.

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Education Philosophy

I hold the view that learning is most effective when it is engaging, interactive, and rooted in real-world examples. I find the psychological sciences to be intrinsically interesting-- I believe any student can find something insightful and engaging in the field, and I enjoy finding ways to foster this insight and engagement. In my lectures, I make an effort to convey why the course material is interesting and how it can elucidate common experience. Additionally, I strive to convey complex principles as clearly as possible (e.g., with visualizations and annotations) and to provide students a scaffold with which to integrate these principles together within the broader science. Please email me for my full Teaching Statement.

Theoretical Interests

My research is in human memory and event cognition. Specifically, I am interested in the structure, segmentation, organization, and retrievability of episodic memory traces across time. My goal in research is to contribute to fundamental questions such as: What is the representational nature of episodic memory? How can we define and delimit individual memory traces within the episodic store? Do all episodic traces share common dimensions and characteristics? How are these dimensions and characteristics differentially affected by memory processes such as encoding, consolidation, and retrieval? How does the episodic memory system interact with other memory and cognitive systems?

My approach to the above research questions is largely rooted in mental model (Johnson-Laird, 1983) and event cognition theory (Radvansky & Zacks, 2014). Under this framework, I view episodic memory traces as having embodied characteristics and being organized according to an event structure. This event structure serves to segment episodic traces from one another, and it defines the dimensions (e.g., theme, causation, time, place, entity, and associations) upon which information is represented. Further, I hold the view that episodic traces often consist of multiple levels of representation, and these levels of representation likely have differential retention and retrieval processes.

The bulk of my research work assessed retention functions across a variety of learning, retrieval, and interval conditions. Through two distinct research projects, I found reliable exceptions to the common Ebbinghaus pattern of retention, particularly when memoranda are amenable to an event model level of representation (Fisher & Radvansky, 2018; Fisher & Radvansky, 2019). Under the guidance of my graduate advisor, Gabriel Radvansky, I have created the RAFT (Retention Accuracy from Fragmented Traces) computational simulation to explain such exceptions while still preserving the common assumption of exponential decay of information. My dissertation included four experiments that further explored these retention patterns and the specific methodological and theoretical factors that contribute to them (see Fisher & Radvansky, 2021).

Research Background

I am a mixed-methods researcher with an expertise in human memory. As a postdoctoral scholar and assistant professor, I have managed multiple research projects and collaborations. I have several publications and have presented my research at national and international conferences (for writing samples, please see the publication links).

Experimental Design

I have designed a variety of research experiments to answer a variety of questions in cognitive science. My dissertation work involved a series of quantitative research experiments that assessed patterns of memory retention across time and across various learning and retrieval conditions. Many of these experiments involved factorial designs with multiple Independent and Dependent variables. I use JavaScript to program these experiments, which allows them to take place either in person or online (e.g., via Amazon Mechanical Turk).

Data Representation and Statistical Inference

My research projects have involved datasets with sample sizes ranging from around 50 to 500 participants. I have written multiple aggregation and scoring programs to analyze these datasets. I prefer using Excel and R for statistical analyses, and am familiar with other software (e.g., SPSS, Jasp). Please see the publication links for more details of the statistical tests used in my research.

Usability

I believe learning and memory science can be applied successfully to usability research. Many products have an initial learning curve. This learning process could be studied and optimized. Additionally, an individual's general interactions with a product are supported by memory and experience.