In the simulation of human crowd behavior including evacuation planning, transportation management, and safety engineering in architecture design, the development of pedestrian model for higher behavior fidelity is an important task. To construct plausible facsimiles of real crowd movements, simulations should exhibit human behaviors for navigation, pedestrian decision-making, and social behaviors such as grouping and crowding. The research field is quite mature in some sense, with a large number of approaches that have been proposed to path finding, collision avoidance, and visually pleasing steering behaviors of virtual humans. However, there is still a clear disparity between the variety of approaches and the quality of crowd behaviors in simulations. Many social science field studies inform us that crowds are typically composed of multiple social groups (James, 1953; Coleman and James, 1961; Aveni, 1977). These observations indicate that one component of the complexity of crowd dynamics emerges from the presence of various patterns of social interactions within small groups that make up the crowd. Hence, realism in a crowd simulation may be enhanced when virtual characters are organized in multiple social groups, and exhibit human-like coordination behaviors. Motivated by the need for modeling groups in a crowd, we present a multi-agent model for large crowd simulations that incorporates socially plausible group behaviors. A computational model for multi-agent coordination and interaction informed by well- established Common Ground theory (Clark, 1996; Clark and Brennan, 1991) is proposed. In our approach, the task of navigation in a group is viewed as performing a joint activity which requires maintaining a state of common ground among group members regarding walking strategies and route choices. That is, group members communicate with, and adapt their behaviors to each other in order to maintain group cohesiveness while walking. In the course of interaction, an agent may present gestures or other behavioral cues according to its communicative purpose. It also considers the spatiotemporal conditions of the agent-group's environment in which the agent interacts when selecting a kind of motions. With the incorporation of our agent model, we provide a unified framework for crowd simulation and animation which accommodates high-level socially-aware behavioral realism of animated characters. The communicative purpose and motion selection of agents are consistently carried through from simulation to animation, and a resulted sequence of animated character behaviors forms not merely a chain of reactive or random gestures but a socially meaningful interactions. We conducted several experiments in order to investigate the impact of our social group interaction model in crowd simulation and animation. By showing that group communicative behaviors have a substantial influence on the overall distribution of a crowd, we demonstrate the importance of incorporating a model of social group interaction into multi-agent simulations of large crowd behaviors. With a series of perceptual user studies, we show that our model produces more believable behaviors of animated characters from the viewpoint of human observers. / Ph. D.

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Previous research identifies significant differences in children's problem behavior across various family structures, particularly noting more challenging behaviors in children from single parent families. Utilizing a pre-existing data set of Behavioral Objective Sequence (BOS) scores, this study investigated teacher ratings of social behavioral skills in students with mild disabilities, focusing on differences between groups of students from a variety of family compositions. Results indicated no significant difference in social behavioral skills among children from different family groups, taking into account the student's age. The covariate of age was significant on each of six BOS subscales, suggesting that children develop and improve social behavioral skills as they age, regardless of family composition.

Recently researchers have identified a group of children with language impairment (LI) whose IQ scores are below the typical IQ cutoff of 85 for specific language impairment (SLI) but above the IQ cutoff of 70 for intellectual disability (Weismer et al., 2000). This group is referred to as having nonspecific language impairment (NLI). Eleven children with NLI, eleven same-age peers with SLI, and eleven same-age typical peers were compared on withdrawn and sociable behaviors demonstrated in the classroom setting using the Teacher Behavior Rating Scale (Hart & Robinson, 1996). Three subtypes of withdrawal (solitary-active, solitary-passive, reticent) and two subtypes of sociable behavior (impulse control/likability and prosocial) were examined. The children were compared on the severity of their ratings as well as the pattern of item responses for each behavioral subtype. Teachers rated the group with NLI significantly more poorly than the typical group on the reticence, impulse control/likability, and prosocial subtypes. Teachers rated the group with SLI significantly more poorly than the typical group on the impulse control/likability subtype. Participants were added to the typical group and the group with SLI to increase statistical power. With additional participants, the children with SLI were also rated significantly more poorly than the typical children on the reticence, likability, and prosocial subtypes. The group with NLI was consistently rated more poorly than the group with SLI except on the solitary-active subtype. However, there were no significant differences between the groups with LI, even with additional participants. The pattern of item responses was similar between the groups with NLI and SLI on all behavioral subtypes. In addition, the pattern of item responses for the groups with LI also matched the pattern of the typical group on the solitary-active withdrawal, impulse control/likability, and prosocial subtypes. These data indicate that the differences between the groups with NLI and SLI may be quantitative but not qualitative.

The accessory olfactory bulb (AOB) processes pheromonal signals which in turn drive social behaviors. Here we identify a tract of aromatase-expressing (arom+) fibers in the dorsal lateral olfactory tract (dLOT) which terminate in the granule cell layer (GCL) of the AOB. We utilized a retrograde tracer in aromatase reporter animals to delineate the source of these fibers. We show that these input fibers emerge almost exclusively from a contiguous population of arom+ neurons that spans the bed nucleus of the accessory olfactory tract (BAOT) and posterioventral subnucleus of the medial amygdala (MeApv). This population of neurons expresses the estrogen receptor alpha and contains more aromatase neurons in male mice than female mice. Thus, this population of feedback neurons can detect neuroendocrine changes and modulate the output of AOB projection neurons in a way that is sexually dimorphic and could influence every downstream target of the AOB.

Although behavioral science, due to its emphasis on the use of single-subject research design, appears to focus solely on individual behaviors, behavioral scientists have a long history of lamenting the trajectory of humans, societies, and the discipline itself. Some scholars, for instance, called for our attention to expand our focus beyond individual behaviors to generate solutions for societal issues that we face. When we attempt to develop solutions for issues that require multi-level analysis, we must be cognizant of how institutional contingencies operate at the individual level. The current study analyzed triadic interactions using an exchange task in six triads. The result of this study showed that one common pattern of interactions among participants across triads was direct reciprocation between two participants. The implications of such findings, how they inform social behavior and metacontingency experiments, and future directions are discussed.

Across the animal kingdom, predation is a ubiquitous and omnipresent selective agent for a variety of traits. I aimed to address gaps in our knowledge pertaining to how predation shapes animal behavior. Many species of fish naturally occur in drainages that differ in the density of predators and exhibit obvious population divergence, making them ideal study organisms to investigate predator-driven behavioral evolution. In Chapter 1, I conducted a systematic review of the literature. The purpose of this review was to determine if predation acted as a stronger or weaker selective agent on particular behavioral traits (e.g., foraging, mating, antipredator etc.) across fish. This review showed that predation does not always drive behavior in predictable ways, and that some behavioral traits more consistently diverge than others. It was evident that antipredator behaviors are extremely variable but were typically measured in response to a visual stimulus. Investigations on intraspecific variation pertaining to how fish acquire, process, and respond to information across other sensory modalities are needed. To address this, I focused on a Neotropical fish, Brachyrhaphis rhabdophora (Poeciliidae), from Costa Rica that occur in distinct predation environments. For Chapter 2, I evaluated whether males and females exhibit differential responses to conspecific chemical alarm cues. Chemical alarm cues are released when a prey is injured by a predator and are an honest indicator of risk. It was clear that B. rhabdophora responded to alarm cues, but that males and females sometimes employed different antipredator strategies depending on what predation environment they were from. However, we know that in group-living species, such as B. rhabdophora, risk information can also be acquired indirectly through social cues. There are tradeoffs associated with relying on direct vs. indirect information, and these sources of information may sometimes conflict. For Chapter 3 I considered how B. rhabdophora integrates conflicting information to elicit antipredator behavior. I again exposed fish directly to chemical alarm cues and measured how their antipredator responses changed when visually observing conflicting or reinforcing social information. I found that individuals integrated personal and social information differently based on their evolutionary history with predators. Further, we found evidence that even a single observer fish is able to influence group behavior. Finally, for Chapter 4, I evaluated sex-specific variation in brain size across predation environments. According to the "expensive-tissue hypothesis" there should only be investment in brain tissue when there is sufficient selection for enhanced cognitive abilities. Prey under elevated selection from predators should invest more in cognitive traits to enhance survival, but how sex interplays with this effect is unclear. I found that females had higher relative total brain volumes than males, but males exhibited more variation across predation environments in the relative volumes for certain brain regions. This work as a whole suggests that, yes, evolutionary history matters for a variety of sensory-related traits in B. rhabdophora.