2004 — 2009 |
Love, Bradley |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Career: Flexible Learning Inside and Outside the Classroom @ University of Texas At Austin
Imagine a child who encounters a sea lion for the first time and learns that it is a mammal. What do they retain and abstract from this experience? How do they use this knowledge to make sense of future examples? With the funding of an NSF CAREER Award, Dr. Bradley Love will develop a general model of human learning to account for learning from examples and direct instruction. The model represents human knowledge in terms of natural bundles of information, referred to as clusters. For instance knowledge of mammals might consist of several clusters to include primates, four-legged mammals, sea lions and whales, bats, and so on. The models learning procedures alter clusters to suit learners' variety of goals. For instance the goals with respect to sea lions of a marine biologist and fishing-vessel captain can be quite different and knowledge is organized to reflect such differences. Behavioral studies involving undergraduate and primary school students will test the model's predictions. The integration of research and education figures prominently in this CAREER project. Dr. Love's undergraduate research courses will center on the actual experiments that involve primary school students. For instance classroom lessons in experimental design will involve considering and critiquing the designs of these primary school studies. Undergraduates will assist in the actual research and undergraduate students will participate in outreach activities to provide primary school students, teachers, and parents with an understanding of psychology as a science. These combined activities are all aimed to advance our understanding of human learning, increase the effectiveness of classroom learning, and educate undergraduates and the community at large about the science of learning.
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0.915 |
2009 — 2012 |
Love, Bradley Waller, S. Travis |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Predicting Disrupted Network Behavior @ University of Texas At Austin
As people interact at a large-scale within infrastructure systems (such as roadway systems), the area of network modeling is often employed to characterize the impact of the resulting behavior. Traditionally, the concept of network equilibrium has been employed which models the long-term steady state behavior of many individuals each acting in their own self interest. While equilibrium has been critical for infrastructure planning and management, it requires several key assumptions such as familiarity and rationality which may not hold true in high stress disruptive situations. This research project addresses new models for network behavior when significant disruptions occur which upset the expected network state. The primary hypotheses of this research is that individuals can transform and adapt previous expectations based on their perception of the disruption as well as information learned en-route and that in unfamiliar cases network users place greater weight on system and context-specific characteristics such as route and road geometry, risk preference, and travel constraints (e.g., when unfamiliar with the true expected cost, users may select a longer path simply because it moves them closer to the destination initially). This research will discern these new individual behaviors through psychological experiments and then develop novel mathematical formulations for the resulting network impacts.
By adopting the new problem characteristics noted in the previous paragraph, fundamentally new mathematical system descriptions and predictions can be developed for large-scale networks subjected to disruptions. By achieving superior prediction capabilities, substantial societal improvements are achievable by being able to better prepare for disaster and evacuation possibilities. Furthermore, by better understanding non-equilibrium behavior even substantial near-daily non-extreme improvements are achievable such as mitigating the impact of non-recurrent congestion and traffic incidents (both areas which have long complicated transportation planning). Numerous broader benefits will also be seen beyond transportation systems. As this research addresses the fundamental problem of network behavior, numerous fields which employ network models can adopt aspects of the new behavioral models. Educationally, substantial benefits will result from the closer consideration of network modeling with psychological behavior. Further, outreach efforts will be conducted by both Co-PIs and in conjunction with programs sponsored by UT Austin to introduce students to research and practice, with an emphasis on recruiting a diverse mix of undergraduate and graduate students. Through such programs (including the Advanced Institute and US Intern program at UT-Austin combined with the NSF REU program) the PIs have been repeatedly successful in the past in recruiting such a diverse mix of students and are committed to forming a closely cooperative interdisciplinary research effort.
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0.915 |
2011 — 2012 |
Love, Bradley C Preston, Alison R. [⬀] |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Model-Based Fmri of Dynamic Category Learning: the Memory and Attention Interface @ University of Texas, Austin
DESCRIPTION (provided by applicant): Judging a person as a friend or foe, a mushroom as edible or poisonous, or a sound as an \l\ or \r\ are examples of categorization problems. One key aspect of learning is discerning the relevant stimulus dimensions that determine category membership and the value and costs (in terms of time, cognitive efort, and dollars) associated with gathering such information. Many category learning models employ selective attention mechanisms that learn which stimulus dimensions are most critical to performance. However, these models make the unrealistic assumption that all stimulus dimensions will be encoded, and, thus, fail to address challenges that arise from limited processing resources, both cognitive and neural. Improved models are required to understand the interplay between attentional allocation and memory. By recasting category learning as a dynamic decision process, we develop a model that selectively encodes information during learning as a function of the learner's goals, task demands, and knowledge state. To capture the required interplay between attention, memory, and executive function, our model consists of two primary components: one that determines the value of potential sources of information based on the decision maker's goals and assumptions about the world and a second component that reflects the decision maker's current knowledge. Current knowledge represented by the second learning component is utilized by the first value component to direct information gathering. The learning component of the model is updated by the information selected by the first component, completing the cycle of mutual influence. A central goal of the proposal is to develop models that make realistic assumptions about human capacity limitations and to characterize how individuals'mental machinery and behavioral outcomes deviate from rational principles. A second goal is to combine our novel model-based approach with eye tracking and functional magnetic resonance imaging (fMRI) to determine the neural mechanisms that support goal-directed attention and learning. Model-based analyses of fMRI data have the power to go beyond conventional analysis methods to reveal complex dynamics between neural systems that give rise to cognitive competencies. In two proposed studies, participants must decide which information sources to sample, taking into account the conflicting needs of (1) minimizing information cost, (2) making the correct decision, and (3) learning more about the categories and information sources with the aim of increasing performance on future trials. By fitting our model to individuals'information seeking and classification behavior, we can calculate a number of regressors that track unobservable mental states that are predictive of subsequent behavior and critical for determining the brain basis of the dynamic decision making processes that support category learning. Advancing our knowledge of the brain processes that underlie these powerful aspects of cognition may have real-world consequences by providing knowledge about optimal learning strategies as well as providing insight into disorders that affect learning and memory. PUBLIC HEALTH RELEVANCE: Impairments in learning, memory, and attention deficits accompany a number of psychiatric (e.g., schizophrenia, major depression, ADHD) and neurological disorders (e.g., Alzheimer's disease, epilepsy). Accordingly, understanding the neural mechanisms of attention and memory in the healthy brain promises to advance neurobiological theory and may lead to new developments that bear on the diagnosis and treatment of such conditions.
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1 |
2016 — 2020 |
Love, Bradley C |
P01Activity Code Description: For the support of a broadly based, multidisciplinary, often long-term research program which has a specific major objective or a basic theme. A program project generally involves the organized efforts of relatively large groups, members of which are conducting research projects designed to elucidate the various aspects or components of this objective. Each research project is usually under the leadership of an established investigator. The grant can provide support for certain basic resources used by these groups in the program, including clinical components, the sharing of which facilitates the total research effort. A program project is directed toward a range of problems having a central research focus, in contrast to the usually narrower thrust of the traditional research project. Each project supported through this mechanism should contribute or be directly related to the common theme of the total research effort. These scientifically meritorious projects should demonstrate an essential element of unity and interdependence, i.e., a system of research activities and projects directed toward a well-defined research program goal. |
Linking Brain, Behavior, and Genes Across Species and Development: Evaluation of Integrative Category Learning Models
Summary Judging a person as a friend or foe, a mushroom as edible or poisonous, or a sound as an \l\ or \r\ are examples of categorization problems. Because people never encounter the same stimulus twice, they must develop categorization schemes that capture the useful regularities in their environment. Key research challenges include how humans acquire and represent categories. This project tackles the broader challenge of elucidating the nature of the learning system or systems, these systems' neural underpinnings, how such systems develop, how they differ across species, and how they interact. To answer these fundamental questions, a space of category learning models is defined to allow for formal evaluation of the theories these models encode. Although no one study can explicate the nature, developmental trajectory, evolution, and neural underpinnings of all category learning mechanisms, the results from numerous studies can when coupled with powerful analysis techniques. By defining a space of models, data from numerous studies (developmental - Project 1, as well as comparative and neuroscientific - Project 2) can be jointly evaluated to determine the most likely theories given the data. This approach incorporates key task variables, such as proposed relationships between formal mechanisms and brain regions, and how various system capacities and biases can vary across development and evolution. Thus, the developed theories (in the form of formal models) not only specify computational mechanisms, but how these mechanisms change over development, vary across species, their neural underpinnings, how genetic variations shape individual differences, and how task variables (e.g., secondary task load) affect their operation. Bayesian Model Selection (BMS) procedures will evaluate candidate models on a vast array of data collected within Projects 1 and 2 to determine which models are most likely to be valid (i.e., generalize to novel studies). Preliminary results will help guide efforts in Projects 1 and 2 to determine the most theoretically informative study designs. Model fits may prove useful for gauging what constitutes normal development and for directing interventions for populations suffering from disease or other difficulties.
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0.963 |