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Award Detail

Doing Business As Name:University of Maryland Baltimore County
  • Tamra C Mendelson
  • (410) 455-2267
  • Julien RENOULT
Award Date:07/10/2020
Estimated Total Award Amount: $ 740,479
Funds Obligated to Date: $ 740,479
  • FY 2020=$740,479
Start Date:09/01/2020
End Date:08/31/2023
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.074
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Pattern preferences, information theory, and the evolution of signal design
Federal Award ID Number:2026334
DUNS ID:061364808
Parent DUNS ID:003256088
Program:Animal Behavior
Program Officer:
  • Patrick Abbot
  • (703) 292-7820

Awardee Location

Street:1000 Hilltop Circle
Awardee Cong. District:07

Primary Place of Performance

Organization Name:University of Maryland Baltimore County
Street:1000 Hilltop Circle
Cong. District:07

Abstract at Time of Award

Courtship behavior in animals often involves elaborate visual signals, so understanding the forces that shape signal design is a central question in animal behavior. The central hypothesis of this project is that courtship signal design is driven by pattern preferences that are in turn shaped by patterns in the local habitat. This is an expanded version of sensory drive, a robust hypothesis that describes how the environment influences animal sensory systems and therefore preferences. To date, sensory drive has emphasized the detectability of courtship signals, whereas the proposed research emphasizes the efficiency with which signals can be processed by the visual system. The research plan combines behavioral studies and leading-edge computational image manipulation to test the central hypothesis that signal patterns and pattern preferences are shaped by local habitats. The focal animal is the Rainbow Darter (Etheostoma caeruleum), an abundant and visually striking fish species in eastern North America. Broader impacts of the project include international collaboration for students and a postdoctoral associate, training of students from underrepresented groups, a workshop for signal design researchers, and development of upper-level laboratory modules in animal behavior at UMBC. The proposed research addresses a novel hypothesis for signal design, framing the powerful model of sensory drive in the broader context of information theory rather than signal detection theory alone. Whereas traditional sensory drive posits detectability as the driving constraint on animal signaling, sensory drive based on information theory emphasizes more broadly the efficiency with which patterns are processed by sensory systems. A bias for efficiently processed information has been demonstrated in humans, who generally prefer patterns that match the spatial statistics of natural scenes. Sensory drive based on processing bias therefore predicts that animals will prefer signaling patterns that match the statistics of local habitat. Importantly, an alternative hypothesis of camouflage makes the same prediction. Camouflage and sexual signaling traditionally epitomize the antagonistic relationship between natural and sexual selection; however, from an information theoretic perspective, the designs that serve these functions can be mutually reinforcing. Experiments will use generative artificial intelligence, a rapidly developing field of deep machine learning, to empirically test mutually inclusive, alternative hypotheses of animal signal design. This method offers an exciting technological innovation that could dramatically improve our ability to explore receiver psychology and signal preferences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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