Mark Lescroart, an assistant professor at the ÐÔ°®ÎåÉ«Ìì, Reno, was recognized with a prestigious CAREER Award from the National Science Foundation (NSF). Lescroart works in the Department of Psychology in the College of Science. He has been a part of the Cognitive & Brain Sciences group since 2018.
The NSF publicizes a 17% success rate in proposals for this competitive program. However, University faculty have fared much better in recent years, with success rates, on average, around 25%.
“The University’s acceptance rate in this last round of NSF CAREER Awards is amazing by any top-notch university standards,” Mridul Gautam, vice president for research and innovation, said. “When it comes to our faculty, the ÐÔ°®ÎåÉ«Ìì is in that top-notch league.”
Twenty-seven University faculty have received an NSF CAREER Award in the last five years; four awards in 2020, eight in 2021 – the most ever in a single year – seven awarded in 2022, three in the 2023 cycle, and five in 2024. Within the last 28 years, 57 University faculty have been awarded.
Lescroart’s CAREER Award is titled, “Studying the effects of task goals on brain representations of visual motion.” This new research project, funded by a $700,000 grant from the NSF, aims to uncover the complex brain mechanisms that underlie attention. Led by Lescroart, a team at the University will use advanced computational models and functional magnetic resonance imaging (fMRI) to investigate how task goals influence brain activity. This research could lead to a deeper understanding of attention-related disorders such as ADHD and schizophrenia, as well as provide insights into everyday activities like driving and sports.
Read more from Lescroart as he goes more in depth about the CAREER research project.
People do not always notice the same things when they look at the same scene, because perception depends on the goal. At the neural level, many perceptual regions in the brain do not necessarily respond the same way to the same stimuli – activity depends on both the stimulus and on task goals.
The ability to focus on task-relevant features and thereby change activity in the brain is called top-down attention. Attention is affected in many neurological disorders, including ADHD and Schizophrenia, and varies across the healthy population. Despite its importance, brain mechanisms of attention are not well understood.
One hypothesis holds that a network spanning frontal and parietal cortex supports task-general functions including attention and accumulating or weighing evidence. However, parts of this network respond to visual features of stimuli including motion and shape, and the dual influence of task and stimulus are seldom studied in the same experiments.
The goal of this project is to integrate these findings into a coherent computational model. Instead of trying to attribute one function to each parcel of the brain, we aim to quantify the degree to which brain responses across the brain vary with the stimulus, with the cognitive components of the task, and with task difficulty. The project seeks answers to these questions in a cumulative series of behavioral and functional magnetic resonance imaging (fMRI) experiments. We hope the research will determine the degree to which different tasks change the pattern of activity within and across brain regions.
Participants in the study will view moving stimuli on which they perform different visual tasks while their brains are measured with fMRI. For example, they judge whether one object will collide with another object or whether the participant – proxied by the point of view of the camera – will collide with a static object.
These tasks serve as an experimental proxy for many natural tasks. For example, driving, walking and many sports involve estimating one’s own motion relative to static obstacles and moving objects. Importantly, among stimulus-based factors, self-motion is a particularly strong influence on responses in many areas also driven by task-related factors.
We will analyze the data using cutting-edge encoding models based on labels for task conditions and motion parameters derived from deep neural networks. Models are compared with variance partitioning, which assesses the relative effect of each factor on brain responses. The models developed in this project can provide a detailed quantitative baseline that enables sensitive measurement of individual differences in attention and task processing in future studies.
Finally, since the analytic approach of this project is computationally intensive, we also aim to improve data science education at the ÐÔ°®ÎåÉ«Ìì, Reno by developing and teaching an introductory applied research computing course. This course is also intended to teach the ‘hidden curriculum’ of research computing, including use of the command line, version control and dependency management. We plan to help build a community of practice in data science at both graduate and undergraduate levels.
Professor Lescroart’s visual perception lab is to join their research. Contact Professor Lescroart to learn more about the positions available in the lab.
In preparing his proposal, Lescroart used proposal planning and review services provided by Research & Proposal Development Services, part of Research & Innovation. For proposal submission assistance, submit a .
