Jacob A. Parker

Computational and Theoretical Neuroscientist

PhD Candidate in Neuroscience

University of Pennsylvania

jacob.parker
@getwreckedmailscraperslmao-pennmedicine.upenn.edu

Welcome to my website! My name is Jake and I’m currently completing my PhD in Neuroscience at the University of Pennsylvania. My thesis work is advised by Josh Gold and Joe Kable, and is supported by an NSF Graduate Research Fellowship.

I study the principles governing inference in the brain, with an emphasis on explaining the high degree of individual variability we observe both behaviorally and neurally. In my current work, I find that a lot of this variability reflects flexible tradeoffs between maximizing performance and minimizing the use of costly cognitive resources. Two ways people appear to minimize the cost of inference are (roughly speaking) 1) reducing the computational precision of inference, and 2) using simpler (heuristic) inference strategies. I use theory, computational modeling, and experiments to explore these and other ideas, which you can read more about here.

In addition to my research work, I engage in various forms of science communication and outreach. I was co-chair of the Penn Neuroscience Public Lecture Series from 2022-2024. As a writer for PennNeuroKnow, I have written four original articles about topics in neuroscience for a non-scientific audience. In 2022, I co-authored a science policy memo and met with state and federal legislators to discuss its content. I have been a mentor for Project Short, a non-profit that provides pro-bono application support (essay reviews, mock interviews, etc) to those facing barriers to applying for grad school. Through these and other activities, I aim to increase public scientific literacy and make academia more accessible.

I obtained my B.Sc. in neuroscience from New York University in 2017. As an undergraduate research assistant in the Carrasco Lab, I developed an approach to modeling pupil responses evoked by cognitive events. After graduating, I spent four years studying the pathophysiology of movement disorders as an NIH postbac in the lab of Mark Hallett.

Outside of my work, I enjoy Olympic-style weightlifting, photography, spending time with friends, and learning new things.

selected publications

Behav. Res.

Modeling Pupil Responses to Rapid Sequential Events

Rachel N. Denison^*, Jacob A. Parker^*, and Marisa Carrasco

Behavior Research Methods, Mar 2020

*equal contribution

Abs DOI

Pupil size is an easily accessible, noninvasive online indicator of various perceptual and cognitive processes. Pupil measurements have the potential to reveal continuous processing dynamics throughout an experimental trial, including anticipatory responses. However, the relatively sluggish (~2 s) response dynamics of pupil dilation make it challenging to connect changes in pupil size to events occurring close together in time. Researchers have used models to link changes in pupil size to specific trial events, but such methods have not been systematically evaluated. Here we developed and evaluated a general linear model (GLM) pipeline that estimates pupillary responses to multiple rapid events within an experimental trial. We evaluated the modeling approach using a sample dataset in which multiple sequential stimuli were presented within 2-s trials. We found: (1) Model fits improved when the pupil impulse response function (PuRF) was fit for each observer. PuRFs varied substantially across individuals but were consistent for each individual. (2) Model fits also improved when pupil responses were not assumed to occur simultaneously with their associated trial events, but could have non-zero latencies. For example, pupil responses could anticipate predictable trial events. (3) Parameter recovery confirmed the validity of the fitting procedures, and we quantified the reliability of the parameter estimates for our sample dataset. (4) A cognitive task manipulation modulated pupil response amplitude. We provide our pupil analysis pipeline as open-source software (Pupil Response Estimation Toolbox: PRET) to facilitate the estimation of pupil responses and the evaluation of the estimates in other datasets.