Working Group

Toward a unified evolutionary theory of decision-making in animals

PI(s): Tamra Mendelson (University of Maryland - Baltimore County)
Mark Hauber (Hunter College, City University of New York)
Rebecca Safran (University of Colorado Boulder)
Start Date: 1-Feb-2013
End Date: 30-Nov-2014
Keywords: behavior, communication, meta-analysis, empirical studies, neurobiology

Underlying the adaptive behavior of animals is a process by which individuals must make decisions based on functionally relevant categories: who is a suitable mate, social partner, host, or prey item? Who is a competitor or a predator? Despite the ubiquitous need for animals to find a suitable mate, sort out enemies from collaborators, and correctly identify food, we lack a unifying framework of evolutionary decision theory. Here, we propose a cross-disciplinary team to establish an integrative conceptual framework with testable hypotheses for studying decision-making in an evolutionary context. Leveraging expertise from research programs in evolution, neurobiology, behavioral ecology, and comparative psychology, we aim to address questions of whether and how available information is processed by similar or different algorithms to generate decisions across individuals, species, sensory modalities, and functional contexts. We identify directions for immediate analyses within a new framework, including the role of learning and memory in shaping animal decisions, and hypotheses related to the evolution of categorical-like perception in a complex environment. We propose to systematically synthesize the primary literature incorporating data from behavioral “choice” and “recognition” trials (e.g., mate choice, host choice, kin recognition, parasite rejection), in order to generate a large, multi-taxon, publicly available database that will provide a rich source of data for future analyses of comparative patterns in decision making algorithms. Ultimately, our aim is to bring together a diversity of perspectives spanning multiple levels of analysis in order to transform our understanding of decision-making in an evolutionary context.