Matching Items (14)
Description
For over a century, researchers have been investigating collective cognition, in which a group of individuals together process information and act as a single cognitive unit. However, I still know little about circumstances under which groups achieve better (or worse) decisions than individuals. My dissertation research directly addressed this longstanding question, using the house-hunting ant Temnothorax rugatulus as a model system. Here I applied concepts and methods developed in psychology not only to individuals but also to colonies in order to investigate differences of their cognitive abilities. This approach is inspired by the superorganism concept, which sees a tightly integrated insect society as the analog of a single organism. I combined experimental manipulations and models to elucidate the emergent processes of collective cognition. My studies show that groups can achieve superior cognition by sharing the burden of option assessment among members and by integrating information from members using positive feedback. However, the same positive feedback can lock the group into a suboptimal choice in certain circumstances. Although ants are obligately social, my results show that they can be isolated and individually tested on cognitive tasks. In the future, this novel approach will help the field of animal behavior move towards better understanding of collective cognition.
ContributorsSasaki, Takao (Author) / Pratt, Stephen C (Thesis advisor) / Amazeen, Polemnia (Committee member) / Liebig, Jürgen (Committee member) / Janssen, Marco (Committee member) / Fewell, Jennifer (Committee member) / Hölldobler, Bert (Committee member) / Arizona State University (Publisher)
Created2013
Description
The coordination of group behavior in the social insects is representative of a broader phenomenon in nature, emergent biological complexity. In such systems, it is believed that large-scale patterns result from the interaction of relatively simple subunits. This dissertation involved the study of one such system: the social foraging of the ant Temnothorax rugatulus. Physically tiny with small population sizes, these cavity-dwelling ants provide a good model system to explore the mechanisms and ultimate origins of collective behavior in insect societies. My studies showed that colonies robustly exploit sugar water. Given a choice between feeders unequal in quality, colonies allocate more foragers to the better feeder. If the feeders change in quality, colonies are able to reallocate their foragers to the new location of the better feeder. These qualities of flexibility and allocation could be explained by the nature of positive feedback (tandem run recruitment) that these ants use. By observing foraging colonies with paint-marked ants, I was able to determine the `rules' that individuals follow: foragers recruit more and give up less when they find a better food source. By altering the nutritional condition of colonies, I found that these rules are flexible - attuned to the colony state. In starved colonies, individual ants are more likely to explore and recruit to food sources than in well-fed colonies. Similar to honeybees, Temmnothorax foragers appear to modulate their exploitation and recruitment behavior in response to environmental and social cues. Finally, I explored the influence of ecology (resource distribution) on the foraging success of colonies. Larger colonies showed increased consistency and a greater rate of harvest than smaller colonies, but this advantage was mediated by the distribution of resources. While patchy or rare food sources exaggerated the relative success of large colonies, regularly (or easily found) distributions leveled the playing field for smaller colonies. Social foraging in ant societies can best be understood when we view the colony as a single organism and the phenotype - group size, communication, and individual behavior - as integrated components of a homeostatic unit.
ContributorsShaffer, Zachary (Author) / Pratt, Stephen C (Thesis advisor) / Hölldobler, Bert (Committee member) / Janssen, Marco (Committee member) / Fewell, Jennifer (Committee member) / Liebig, Juergen (Committee member) / Arizona State University (Publisher)
Created2014
Description
For interspecific mutualisms, the behavior of one partner can influence the fitness of the other, especially in the case of symbiotic mutualisms where partners live in close physical association for much of their lives. Behavioral effects on fitness may be particularly important if either species in these long-term relationships displays personality. Animal personality is defined as repeatable individual differences in behavior, and how correlations among these consistent traits are structured is termed behavioral syndromes. Animal personality has been broadly documented across the animal kingdom but is poorly understood in the context of mutualisms. My dissertation focuses on the structure, causes, and consequences of collective personality in Azteca constructor colonies that live in Cecropia trees, one of the most successful and prominent mutualisms of the neotropics. These pioneer plants provide hollow internodes for nesting and nutrient-rich food bodies; in return, the ants provide protection from herbivores and encroaching vines. I first explored the structure of the behavioral syndrome by testing the consistency and correlation of colony-level behavioral traits under natural conditions in the field. Traits were both consistent within colonies and correlated among colonies revealing a behavioral syndrome along a docile-aggressive axis. Host plants of more active, aggressive colonies had less leaf damage, suggesting a link between a colony personality and host plant health. I then studied how aspects of colony sociometry are intertwined with their host plants by assessing the relationship among plant growth, colony growth, colony structure, ant morphology, and colony personality. Colony personality was independent of host plant measures like tree size, age, volume. Finally, I tested how colony personality influenced by soil nutrients by assessing personality in the field and transferring colonies to plants the greenhouse under different soil nutrient treatments. Personality was correlated with soil nutrients in the field but was not influenced by soil nutrient treatment in the greenhouse. This suggests that soil nutrients interact with other factors in the environment to structure personality. This dissertation demonstrates that colony personality is an ecologically relevant phenomenon and an important consideration for mutualism dynamics.
ContributorsMarting, Peter (Author) / Pratt, Stephen C (Thesis advisor) / Wcislo, William T (Committee member) / Hoelldobler, Bert (Committee member) / Fewell, Jennifer H (Committee member) / Gadau, Juergen (Committee member) / Arizona State University (Publisher)
Created2018
Description
What makes living systems different than non-living ones? Unfortunately this question is impossible to answer, at least currently. Instead, we must face computationally tangible questions based on our current understanding of physics, computation, information, and biology. Yet we have few insights into how living systems might quantifiably differ from their non-living counterparts, as in a mathematical foundation to explain away our observations of biological evolution, emergence, innovation, and organization. The development of a theory of living systems, if at all possible, demands a mathematical understanding of how data generated by complex biological systems changes over time. In addition, this theory ought to be broad enough as to not be constrained to an Earth-based biochemistry. In this dissertation, the philosophy of studying living systems from the perspective of traditional physics is first explored as a motivating discussion for subsequent research. Traditionally, we have often thought of the physical world from a bottom-up approach: things happening on a smaller scale aggregate into things happening on a larger scale. In addition, the laws of physics are generally considered static over time. Research suggests that biological evolution may follow dynamic laws that (at least in part) change as a function of the state of the system. Of the three featured research projects, cellular automata (CA) are used as a model to study certain aspects of living systems in two of them. These aspects include self-reference, open-ended evolution, local physical universality, subjectivity, and information processing. Open-ended evolution and local physical universality are attributed to the vast amount of innovation observed throughout biological evolution. Biological systems may distinguish themselves in terms of information processing and storage, not outside the theory of computation. The final research project concretely explores real-world phenomenon by means of mapping dominance hierarchies in the evolution of video game strategies. Though the main question of how life differs from non-life remains unanswered, the mechanisms behind open-ended evolution and physical universality are revealed.
ContributorsAdams, Alyssa M (Author) / Walker, Sara I (Thesis advisor) / Davies, Paul CW (Committee member) / Pavlic, Theodore P (Committee member) / Chamberlin, Ralph V (Committee member) / Arizona State University (Publisher)
Created2017
Description
Variation in living systems and how it cascades across organizational levels is central to biology. To understand the constraints and amplifications of variation in collective systems, I mathematically study how group-level differences emerge from individual variation in eusocial-insect colonies, which are inherently diverse and easily observable individually and collectively. Considering collective processes in three species where increasing degrees of heterogeneity are relevant, I address how individual variation scales to colony-level variation and to what degree it is adaptive. In Chapter 2, I introduce a Markov-chain decision model for stochastic individual quorum-based recruitment decisions of rock-ant workers during house hunting, and how they determine collective speed--accuracy balance. Differences in the average threshold-dependent response characteristics of workers between colonies cause collective differences in decision-making. Moreover, noisy behavior may prevent drastic collective cascading into poor nests. In Chapter 3, I develop an ordinary differential equation (ODE) model to study how cognitive diversity among honey-bee foragers influences collective attention allocation between novel and familiar resources. Results provide a mechanistic basis for changes in foraging activity and preference with group composition. Moreover, sensitivity analysis reveals that the main individual driver for foraging allocation shifts from recruitment (communication) to persistence (independent effort) as colony composition changes. This might favor specific degrees of heterogeneity that best amplify communication in wild colonies. Lastly, in Chapter 4, I consider diversity in size, age, and task for nest defense in stingless bees. To better understand how these dimensions of diversity interact to balance defensive demands with other colony needs, I study their effect on colony size and task allocation through a demographic Filippov ODE model. Along each dimension, variation is beneficial in a certain range, outside of which colony adaptation and survival are compromised. This work elucidates how variation in collective properties emerges from nonlinear interactions between varying components in eusocial insects, but it can be generalized to other biological systems with similar fundamental characteristics but less empirical tractability. Moreover, it has the potential of inspiring algorithms that capitalize on heterogeneity in engineered systems where simple components with limited information and no central control must solve complex tasks.
ContributorsNavas Zuloaga, Maria Gabriela (Author) / Kang, Yun (Thesis advisor) / Smith, Brian H (Thesis advisor) / Pavlic, Theodore P (Committee member) / Arizona State University (Publisher)
Created2022
Description
Animals have evolved a diversity of signaling traits, and in some species, they co-occur and are used simultaneously to communicate. Although much work has been done to understand why animals possess multiple signals, studies do not typically address the role of inter-signal interactions, which may vary intra- and inter-specifically and help drive the evolutionary diversity in signals. For my dissertation, I tested how angle-dependent structural coloration, courtship displays, and the display environment interact and co-evolved in hummingbird species from the “bee” tribe (Mellisugini). Most “bee” hummingbird species possess an angle-dependent structurally colored throat patch and stereotyped courtship (shuttle) display. For 6 U.S. “bee” hummingbird species, I filmed male shuttle displays and mapped out the orientation- and-position-specific movements during the displays. With such display paths, I was able to then recreate each shuttle display in the field by moving plucked feathers from each male in space and time, as if they were naturally displaying, in order to measure each male’s color appearance during their display (i.e. the interactions between male hummingbird plumage, shuttle displays, and environment) from full-spectrum photographs. I tested how these interactions varied intra- and inter-specifically, and which of these originating traits might explain that variation. I first found that the solar-positional environment played a significant role in explaining variation in male color appearance within two species (Selasphorus platycercus and Calypte costae), and that different combinations of color-behavior-environment interactions made some males (in both species) appear bright, colorful, and flashy (i.e. their color appearance changes throughout a display), while other males maintained a consistent (non-flashing) color display. Among species, I found that plumage flashiness positively co-varied with male display behaviors, while another measure of male color appearance (average brightness/colorfulness) co-varied with the feather reflectance characteristics themselves. Additionally, species that had more exaggerated plumage features had less exaggerated shuttle displays. Altogether, my dissertation work illustrates the complexity of multiple signal evolution and how color-behavior-environment interactions are vital to understanding the evolution of colorful and behavioral display traits in animals.
ContributorsSimpson, Richard Kendall (Author) / McGraw, Kevin J. (Thesis advisor) / Rutowski, Ronald L (Committee member) / Pratt, Stephen C (Committee member) / Clark, Christopher J (Committee member) / McGuire, Jimmy A. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Chemical Reaction Networks (CRNs) provide a useful framework for modeling andcontrolling large numbers of agents that undergo stochastic transitions between a set of states
in a manner similar to chemical compounds. By utilizing CRN models to design agent control
policies, some of the computational challenges in the coordination of multi-agent systems can
be overcome. In this thesis, a CRN model is developed that defines agent control policies for a
multi-agent construction task. The use of surface CRNs to overcome the tradeoff between
speed and accuracy of task performance is explained. The computational difficulties involved
in coordinating multiple agents to complete collective construction tasks is then discussed. A
method for stochastic task and motion planning (TAMP) is proposed to explain how a TAMP
solver can be applied with CRNs to coordinate multiple agents.
This work defines a collective construction scenario in which a group of noncommunicating agents must rearrange blocks on a discrete domain with obstacles into a
predefined target distribution. Four different construction tasks are considered with 10, 20,
30, or 40 blocks, and a simulation of each scenario with 2, 4, 6, or 8 agents is performed. As the
number of blocks increases, the construction problem becomes more complex, and a given
population of agents requires more time to complete the task. Populations of fewer than 8
agents are unable to solve the 30-block and 40-block problems in the allotted simulation time,
suggesting an inflection point for computational feasibility, implying that beyond that point
the solution times for fewer than 8 agents would be expected to increase significantly. For a
group of 8 agents, the time to complete the task generally increases as the number of blocks
increases, except for the 30-block problem, which has specifications that make the task slightly
easier for the agents to complete compared to the 20-block problem. For the 10-block and 20-
block problems, the time to complete the task decreases as the number of agents increases;
however, the marginal effect of each additional two agents on this time decreases. This can be
explained through the pigeonhole principle: since there area finite number of states, when the
number of agents is greater than the number of available spaces, deadlocks start to occur and
the expectation is that the overall solution time to tend to infinity.
ContributorsKamojjhala, Pranav (Author) / Berman, Spring (Thesis advisor) / Fainekos, Gergios E (Thesis advisor) / Pavlic, Theodore P (Committee member) / Arizona State University (Publisher)
Created2022
Description
Social insects collectively exploit food sources by recruiting nestmates, creating positive feedback that steers foraging effort to the best locations. The nature of this positive feedback varies among species, with implications for collective foraging. The mass recruitment trails of many ants are nonlinear, meaning that small increases in recruitment effort yield disproportionately large increases in recruitment success. The waggle dance of honeybees, in contrast, is believed to be linear, meaning that success increases proportionately to effort. However, the implications of this presumed linearityhave never been tested. One such implication is the prediction that linear recruiters will equally exploit two identical food sources, in contrast to nonlinear recruiters, who randomly choose only one of them. I tested this prediction in colonies of honeybees that were isolated in flight cages and presented with two identical sucrose feeders. The results from 15 trials were consistent with linearity, with many cases of equal exploitation of the feeders. In addition, I tested the prediction that linear recruiters can reallocate their forager distribution when unequal feeders are swapped in position. Results from 15 trials were consistent with linearity, with many cases of clear choice for a stronger food source, followed by a subsequent switch with reallocation of foragers to the new location of the stronger food source. These findings show evidence of a linear pattern of nestmate recruitment, with implications for how colonies effectively distribute their foragers across
available resources.
ContributorsAlam, Showmik (Author) / Shaffer, Zachary (Thesis advisor) / Pratt, Stephen C (Thesis advisor) / Ozturk, Cahit (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2022
Description
An insect society needs to share information about important resources in order to collectively exploit them. This task poses a dilemma if the colony must consider multiple resource types, such as food and nest sites. How does it allocate workers appropriately to each resource, and how does it adapt its recruitment communication to the specific needs of each resource type? In this dissertation, I investigate these questions in the ant Temnothorax rugatulus.
In Chapter 1, I summarize relevant past work on food and nest recruitment. Then I describe T. rugatulus and its recruitment behavior, tandem running, and I explain its suitability for these questions. In Chapter 2, I investigate whether food and nest recruiters behave differently. I report two novel behaviors used by recruiters during their interaction with nestmates. Food recruiters perform these behaviors more often than nest recruiters, suggesting that they convey information about target type. In Chapter 3, I investigate whether colonies respond to a tradeoff between foraging and emigration by allocating their workforce adaptively. I describe how colonies responded when I posed a tradeoff by manipulating colony need for food and shelter and presenting both resources simultaneously. Recruitment and visitation to each target partially matched the predictions of the tradeoff hypothesis. In Chapter 4, I address the tuned error hypothesis, which states that the error rate in recruitment is adaptively tuned to the patch area of the target. Food tandem leaders lost followers at a higher rate than nest tandem leaders. This supports the tuned error hypothesis, because food targets generally have larger patch areas than nest targets with small entrances.
This work shows that animal groups face tradeoffs as individual animals do. It also suggests that colonies spatially allocate their workforce according to resource type. Investigating recruitment for multiple resource types gives a better understanding of exploitation of each resource type, how colonies make collective decisions under conflicting goals, as well as how colonies manage the exploitation of multiple types of resources differently. This has implications for managing the health of economically important social insects such as honeybees or invasive fire ants.
In Chapter 1, I summarize relevant past work on food and nest recruitment. Then I describe T. rugatulus and its recruitment behavior, tandem running, and I explain its suitability for these questions. In Chapter 2, I investigate whether food and nest recruiters behave differently. I report two novel behaviors used by recruiters during their interaction with nestmates. Food recruiters perform these behaviors more often than nest recruiters, suggesting that they convey information about target type. In Chapter 3, I investigate whether colonies respond to a tradeoff between foraging and emigration by allocating their workforce adaptively. I describe how colonies responded when I posed a tradeoff by manipulating colony need for food and shelter and presenting both resources simultaneously. Recruitment and visitation to each target partially matched the predictions of the tradeoff hypothesis. In Chapter 4, I address the tuned error hypothesis, which states that the error rate in recruitment is adaptively tuned to the patch area of the target. Food tandem leaders lost followers at a higher rate than nest tandem leaders. This supports the tuned error hypothesis, because food targets generally have larger patch areas than nest targets with small entrances.
This work shows that animal groups face tradeoffs as individual animals do. It also suggests that colonies spatially allocate their workforce according to resource type. Investigating recruitment for multiple resource types gives a better understanding of exploitation of each resource type, how colonies make collective decisions under conflicting goals, as well as how colonies manage the exploitation of multiple types of resources differently. This has implications for managing the health of economically important social insects such as honeybees or invasive fire ants.
ContributorsCho, John Yohan (Author) / Pratt, Stephen C (Thesis advisor) / Hölldobler, Bert (Committee member) / Liebig, Jürgen R (Committee member) / Amazeen, Polemnia G (Committee member) / Rutowski, Ronald L (Committee member) / Arizona State University (Publisher)
Created2019
Description
In a multi-robot system, locating a team robot is an important issue. If robots
can refer to the location of team robots based on information through passive action
recognition without explicit communication, various advantages (e.g. improving security
for military purposes) can be obtained. Specifically, when team robots follow
the same motion rule based on information about adjacent robots, associations can
be found between robot actions. If the association can be analyzed, this can be a clue
to the remote robot. Using these clues, it is possible to infer remote robots which are
outside of the sensor range.
In this paper, a multi-robot system is constructed using a combination of Thymio
II robotic platforms and Raspberry pi controllers. Robots moving in chain-formation
take action using motion rules based on information obtained through passive action
recognition. To find associations between robots, a regression model is created using
Deep Neural Network (DNN) and Long Short-Term Memory (LSTM), one of state-of-art technologies.
The input data of the regression model is divided into historical data, which
are consecutive positions of the robot, and observed data, which is information about the
observed robot. Historical data is sequence data that is analyzed through the LSTM
layer. The accuracy of the regression model designed using DNN can vary depending
on the quantity and quality of the input. In this thesis, three different input situations
are assumed for comparison. First, the amount of observed data is different, second, the
type of observed data is different, and third, the history length is different. Comparative
models are constructed for each case, and prediction accuracy is compared to analyze
the effect of input data on the regression model. This exploration validates that these
methods from deep learning can reduce the communication demands in coordinated
motion of multi-robot systems
can refer to the location of team robots based on information through passive action
recognition without explicit communication, various advantages (e.g. improving security
for military purposes) can be obtained. Specifically, when team robots follow
the same motion rule based on information about adjacent robots, associations can
be found between robot actions. If the association can be analyzed, this can be a clue
to the remote robot. Using these clues, it is possible to infer remote robots which are
outside of the sensor range.
In this paper, a multi-robot system is constructed using a combination of Thymio
II robotic platforms and Raspberry pi controllers. Robots moving in chain-formation
take action using motion rules based on information obtained through passive action
recognition. To find associations between robots, a regression model is created using
Deep Neural Network (DNN) and Long Short-Term Memory (LSTM), one of state-of-art technologies.
The input data of the regression model is divided into historical data, which
are consecutive positions of the robot, and observed data, which is information about the
observed robot. Historical data is sequence data that is analyzed through the LSTM
layer. The accuracy of the regression model designed using DNN can vary depending
on the quantity and quality of the input. In this thesis, three different input situations
are assumed for comparison. First, the amount of observed data is different, second, the
type of observed data is different, and third, the history length is different. Comparative
models are constructed for each case, and prediction accuracy is compared to analyze
the effect of input data on the regression model. This exploration validates that these
methods from deep learning can reduce the communication demands in coordinated
motion of multi-robot systems
ContributorsKang, Sehyeok (Author) / Pavlic, Theodore P (Thesis advisor) / Richa, Andréa W. (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020