Holly Kular

hkular [at] ucsd [dot] edu

PhD candidate in the Perception and Cognition Lab at University of California San Diego advised by Dr. John Serences.

My research focus is perception and decision making. I apply statistical modeling to human behavioral data and use machine learning methods to train and evaluate recurrent neural network models of efficient-coding. I am interested in how populations of neurons encode and manipulate visual information for flexible decision making and behavior.

This summer (2026) I will be joining Wisk Aero as a human factors intern applying my expertise in human perception, working memory, and attention towards safety in aviation.

news

Nov 20, 2025	I presented my work Feedback promotes efficient-coding while attenuating bias in recurrent neural networks at the Society for Neuroscience Meeting 2025
May 20, 2025	I presented my work Neural networks rely on top-down representations under conditions of uncertainty at the Vision Sciences Society Conference 2025
Jul 17, 2024	Completed the Cold Spring Harbor Laboratories Computational Neuroscience: vision course
Jun 18, 2024	I presented my work Working memory errors from distraction are not influenced by sensory noise at encoding at the Vision Sciences Society Conference 2024
Jun 12, 2024	Successfully proposed my dissertation and advanced to candidacy.

selected publications

JOV

Neural networks rely on top-down representations under conditions of uncertainty

In Journal of Vision, 2025

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Statistical regularities in natural visual environments can ve used to form expectations that guide behavior. Hierarchical neural networks utilize expectations in noisy enviornments.
In Prep

Memory recall errors reflect interacting sensory and mnemonic representations

Kular, and Serences

In Prep, 2025
JOV

Working memory errors from distraction are not influenced by sensory noise at encoding

Kular, and Serences

In Journal of Vision, 2024

Abs HTML

It is unclear whether sensory uncertainty during encoding increases susceptibility to interference or whether memories are relatively stable once information is encoded. These data provide evidence that while working memory representations are prone to adaptive distortions during delay periods, these distortions are not modulated by sensory noise during encoding.