1989 — 1992 |
Dyer, Fred |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Visual Orientation in Honey Bees @ Michigan State University
Many animals, from insects to human beings, can learn the locations of several important sites in the environment, such as feeding places, watering holes, nests, shelters, or mating grounds. For example, foraging honey bees, like many other insects, may range widely from their nest, traveling to any of several familiar flower patches as far away as 10 kilometers. Over such a distance, the flowers and the landmarks around them cannot be seen by a bee setting out, yet the bee can find its way using familiar landmarks it sees at the starting point and along the way. Little is known about how bees learn landmarks on this scale. Equally remarkable, on returning home the bee can communicate the location of the food using the famous "dance language," a series of precise body movements observed by nestmates. The dance language itself relies on the bee's ability to learn about the landscape. A dancer indicates the angle of its flight path relative to the sun's compass direction; on cloudy days the bee can determine the direction of the unseen sun by referring to a memory, stored previously in sunny weather, of its position relative to familiar landmarks. It is still a riddle how bees form this memory in the first place, and how they update it as they encounter new landmarks or as the sun changes its pattern of movement seasonally. To study how bees use visual information stored in memory to perform these feats of orientation, Dr. Dyer will observe the responses of experienced bees when visual cues are altered in specific ways. Such experiments can reveal which of many features of the environment (e.g., the size, shape, distance, and color of landmarks, or the sun's position and rate of movement) provide spatial information to bees, how the relationships among features are remembered, how memories develop over time, and what happens to previously stored information when a bee encounters new information. Thus, the goal is to uncover general processing strategies underlying the use of visual spatial information for orientation. The visual orientation of a bee in a familiar environment is controlled by a brain little larger than the head of a pin. The larger brains of vertebrates, and especially of human beings, are obviously capable of processing that is considerably more complex. However, a difference in complexity does not necessarily imply that there is a dramatic difference in the basic neural processes. A recurrent theme in biology, in fact, is that general principles can sometimes be more easily elucidated by studying simple organisms. Hence, in addition to throwing light on how bees learn about their environment, these studies may help cognitive scientists refine their hypotheses regarding the mechanisms of spatial orientation in more complex organisms, including man.
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0.915 |
1990 — 1994 |
Smith, Deborah Dyer, Fred |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Reu: Dance Language, Eusociality, and the Systematics of Apis and the Apidae @ Michigan State University
Honey bees exhibit two complex social behaviors which have long fascinated human observers: the "dance language," a series of body movements whereby successful scout bees precisely communicate to their nestmates the locations of flowers as far away as 10 kilometers; and highly organized, perennial colonies. Little is known about how the behaviors we see in honey bees alive today emerged from the simpler capabilities of their solitary-living ancestors. Since behavior, unlike structure, is not fossilized, the historical sequences of changes cannot be observed directly. Instead, it must be inferred from comparisons of closely related living species. Since equivalent traits in related species can be assumed to have descended from a common ancestor, one can infer the characteristics of the ancestral trait, and the modifications that occurred during descent, from the similarities and differences seen among species now alive. There are 6-8 species of honey bees (genus Apis which all have dance languages, but which differ in the details of the dances. All the Apis species have advanced societies, but bees in other genera exhibit varying degrees of social organization. These patterns have already been used to formulate competing hypotheses about the evolution of these behaviors. Ambiguities remain because no set of independent traits has satisfactorily resolved the phylogenetic relationships among the honey bee species, and among the different bee genera. The present project will undertake the first comprehensive study of molecular variation among bees, though an analysis of sequences of nucleotides (subunits of the DNA molecule) in a gene found in all the species. Specimens will be collected in several locations in Asia and Central America, the gene extracted and sequenced, and the phylogenetic relationships among the species determined from variations in the sequences. At the same time, further detailed studies will be done to obtain a clearer picture of the pattern of behavioral variation among the species. Together these approaches should lead to more satisfying answers to two questions: how the dance was assembled from a set of simpler behaviors, and whether advanced sociality originated uniquely in honey bees or was derived from an ancestor which also gave rise to other highly social bees. Apart from leading to a better understanding of the biology of a fascinating and ecologically important group of organisms, this project will also provide a model for combining molecular and behavioral approaches to reconstruct the evolution of complex behavior in other animals. Additionally, the patterns of variation among different honey bee populations in Southeast Asia, which is fragmented into islands separated by a range of distances and for varying periods of time, could lead to insights into the processes governing the behavioral and genetic divergence of animal populations evolving in isolation. Finally, given the prominent role played by the honey bees in the pollination of trees in tropical forests, these studies could aid future attempts to understand the ecology of this important ecosystem.
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0.915 |
1993 — 1995 |
Dyer, Fred |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Visual-Spatial Memory in An Invertebrate @ Michigan State University
Foraging honey bees learn the locations of dozens of flower patches within a 10-kilometer radius of their nest, and perform a series of body movements referred to as a "dance language" to communicate to nestmates the locations of these sites. Memory of landmarks, and of the relationship between the landscape and the sun's course, plays a prominent role in these behavioral feats. Dr. Dyer's previous studies have clarified what bees learn about the relationships among landmarks seen in different parts of their foraging range, and about the course of the sun relative to the landscape. The present project will further investigate the contents of spatial memory in experienced honey bees, and will also attempt to uncover the processes whereby naive bees acquire information about important spatial relationships in the environment. Honey bees are extremely attractive subjects for such studies because their behavior poses some intriguing problems, given the enormous scale and flexibility of the bees' movements and the small size of their brain. At the same time, the behavior is highly amenable to experimental exploration of the underlying processes. To the extent that many other animals face navigational problems parallel to those faced by honey bees, these studies can contribute to the development of general theories about how nervous systems may be equipped to make use of inevitably limited processing capacity.
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0.915 |
1995 — 1997 |
Dyer, Fred |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Dissertation Research: the Development of Spatial Memory in Honeybees @ Michigan State University
Dyer, Fred IBN-9423576 Non Technical Summary Many animals, ranging from insects to human beings, face the problem of using features of a landscape to find places to feed or nest, and spatial orientation has been studied intensively by generations of biologists and psychologists. Honey bees have long been an important model organism for studies of how visual features of the environment can be used for orientation. Worker bees move over an enormous space, traveling up to 10 km from the nest in search of food, using landmarks and celestial cues to find their way. Furthermore, they do this with a very small brain. Thus they present an extreme version of a problem that arises on some level for all animals: given that there are limits to the capacity of a nervous system to store and process information,, how can the brain be designed to produce appropriate behavior in a complex environment? Previous work on how bees use features of the landscape for orientation has focused of the specific features of the landscape that bees learn about. In this project, the complementary issue of how the bees' spatial memory develops is addressed. The research will examine what bees learn as a consequence of their first short (less than 10 minutes) orientation flight in a new landscape. By studying the homing ability of bees that have been displaced from the hive following their first orientation flights, the experiments will address questions about how widely and uniformly bees learn about a landscape around the nest, whether the landscape to be learned influences the extent of the bees' exploration of the landscape, and what role the sun plays in learning landmarks during the orientation flight. This work should pave the way for studies of the neural mechanisms of spatial learning in bees and other insects. Also, to the extent that other animals face navigational problems parallel to those faced by honey bees, these studies may contribute to the d evelopment of general theories about how spatial memory is generated.
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0.915 |
1998 — 2002 |
Henderson, John [⬀] Mahadevan, Sridhar (co-PI) [⬀] Dyer, Fred |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Kdi: Sequential Decision Making in Animals and Machines @ Michigan State University
9873531 Henderson Mobile organisms make accurate behavioral decisions with extraordinary speed and flexibility in real-world environments despite incomplete knowledge about the state of the world and the effects of their actions. This ability must be shared by artificial agents such as mobile robots if they are to operate flexibly in similar environments. The main goal of the research is to undertake a detailed interdisciplinary study of sequential decision making across animals and robots, with a focus on real time learning and control of information gathering and navigational behaviors.
The project will take a comparative approach, combining psychophysical and cognitive research techniques from the study of human eye movement control, behavioral research techniques from the study of insect navigation, and computational methods from the study of mobile robots. All of these systems provide experimentally tractable test-beds for studying real-time decision making in partially observable environments.
The research is guided by a class of sequential decision making models called Markov decision processes (MDP). These models are attractive because they provide a formal framework for computing optimal behavior in uncertain environments. However, these models do not fully capture the complexity of decision making in organisms. We will explore extensions of the MDP framework using insights gained from the study of behavior in organisms and algorithms in artificial agents. This synergy will lead both to a better theoretical understanding of sequential decision making in biological organisms, and to the development of efficient algorithms for artificial agents.
A major outcome of the project will be to show how the design of artificial creatures (robots) can be guided by, and serve as a guide for, the study of sequential behavior in animals. Understanding the challenges that robot designers face, and the formal framework that they have developed to tackle these challenges, leads to novel questions about organisms behavior. Similarly, insights gained from organisms will help suggest ways for improving algorithms for building intelligent artificial agents. ***
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0.915 |
2001 — 2008 |
Getty, Thomas (co-PI) [⬀] Dyer, Fred Henderson, John (co-PI) [⬀] Ferreira, Fernanda (co-PI) [⬀] Mahadevan, Sridhar (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Igert: a Unified Approach to Sequential Decision-Making in Cognitive Science @ Michigan State University
This IGERT project examines the problem of sequential decision-making as a unifying framework for the study of several central topics in cognitive science: selective attention, navigation, language processing, and the coordination of action in multiple-agent groups. The overarching question our students are trained to investigate is the following: how is it possible for an agent to decide what actions to take to achieve long-term goals? We recognize that decision-making in complex environments is a sequential process, involving a series of episodes in which an agent, based on information available through its senses and stored in memory, selects the action appropriate for its goals. The problem is made difficult by perceptual uncertainty arising from sensory limitations and environmental complexity, by the challenge of sorting through the large space of actions available, and by inherent delays in feedback about the long-term consequences of actions. A wide variety of fundamental cognitive tasks can be cast as sequential decision-making problems. Understanding how such problems may be solved will be a critical component of a general theory of intelligent behavior in organisms, and will be essential for the design of truly intelligent machines. To study these problems, we adopt a comparative approach, combining insights from a range of model systems, including humans, non-human animals, robots, and intelligent software agents. This multidisciplinary framework will enable students to integrate ideas and methods from different fields that have been concerned with the study of sequential decision-making (psychology, behavioral biology, linguistics, and computer science), but that have so far remained largely separate. The training program is designed to create a new generation of scientists trained in this innovative, multidisciplinary approach. Graduate training will be focused on fundamental disciplinary education, a common set of courses focused on the sequential decision-making framework, and a strong emphasis on mentored, interdisciplinary research activities that span each student's entire graduate program.
IGERT is an NSF-wide program intended to meet the challenges of educating Ph.D. scientists and engineers with the multidisciplinary backgrounds and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing new, innovative models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries. In the fourth year of the program, awards are being made to twenty-two institutions for programs that collectively span all areas of science and engineering supported by NSF. The intellectual foci of this specific award reside in the Directorates for Social, Behavioral, and Economic Sciences; Computer and Information Science and Engineering; Engineering; Biological Sciences; and Education and Human Resources.
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0.915 |
2002 — 2004 |
Dyer, Fred |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Dissertation Research: Modulation of Active Learning Behavior in the Context of Foraging @ Michigan State University
Modulation of active learning behavior in the context of foraging
Fred C. Dyer, Principal Investigator, Professor of Zoology Cynthia A. Wei, Doctoral Candidate in Zoology
Abstract
Honeybees possess the remarkable ability to repeatedly return to specific flower patches that they have discovered. Essential to this ability is a behavior known as a learning flight. These flights, which are performed upon departure from a food source, allow a bee to collect visual information about landmarks nearby the food, which then help the bee find her way back. The performance of these flights is a modulated behavior; bees typically perform longer flights during the first few visits, and in subsequent visits, the durations of the flights decline. This raises a fundamental question: How do bees determine when and for how long to perform these flights? Answers to this question will provide greater understanding of learning as an active decision making process. The dissertation work of Cynthia Wei, under the guidance of Fred Dyer, has so far documented precise ways in which various factors influence the modulation of learning flight duration. The work supported by this award will expand upon these prior findings by studying the modulation of learning flight duration in a more natural foraging context. The specific goals are 1) to characterize the occurrence of learning flights in response to a change in nectar quality availability in a natural flower patch, 2) to determine the influence of nectar volume and concentration on the induction of learning flights, and 3) to determine the effect of observed occurrences of learning flights on subsequent spatial patterns of foraging. This research will further illuminate the processes by which animals make decisions to learn in response to changing needs for spatial information. It will also lead to a better understanding of how learning affects changes in spatial foraging patterns and the resulting implications for foraging success. Collectively, this work will help develop a clearer picture of how mechanisms of learning have been shaped by an animal's foraging ecology.
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0.915 |