1983 — 1988 |
Reder, Lynne |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Strategies For Question-Answering: a Three-Stage Model @ Carnegie-Mellon University |
1 |
1988 — 1990 |
Reder, Lynne |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Reu: Components of Initial Skill Learning: Development of Effective Examples and Training Procedures @ Carnegie-Mellon University
This research is concerned with how to best train technical, coginitive skills. The approach is based on a three-component model of skill acquisition. This model is developed and tested, by generating and applying diagnostic tests to determine whether a given component of a skill has been acquired by the learner. The particular skill used for experimentation is based on the command language of an electronic spreadsheet. The model is further intended to be used as part of an intelligent tutoring system, capable of predicting students' performance as they learn a technical skill. The importance of this research is that it not only sheds light on general models of cognitive processing, but has practical application to the development of advanced intelligent tutoring systems.
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1 |
1990 — 1994 |
Reder, Lynne |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Tests of a Model of Question-Answering @ Carnegie-Mellon University
How do people know whether they can give an answer before actually searching memory for it? This research will explore the role of "feeling of knowing" in strategy selection: Do humans have a feeling of knowing process that operates fast enough to indicate whether desired information is likely to be stored in memory? Does this process affect whether people search memory for a specific fact, resort to reasoning processes, or simply give up with an "I don't know" response? In order to understand how the feeling of knowing process works, this research will also investigate whether activation and priming of memory affect feeling of knowing judgments and whether activation enhances memory retrieval itself, or merely biases search processes. Finally, the research will address how plausible reasoning is done when search for a fact is not the strategy of choice. The research will develop in three stages: (a) five experiments will ask what factors affect feeling of knowing and whether priming enhances memory retrieval, (b) five experiments will ask how feeling of knowing affects question-answering strategy selection, and (c) six experiments will ask what affects plausible reasoning and whether it is done automatically during reading. To answer these questions, the research will use a number of methodologies. These will include a gameshow paradigm, in which people must determine rapidly whether they can answer a question prior to attempting to answer it, a deadline experiment, in which people must select rapidly the type of question- answering strategy preferred to answer a specific question, a variable deadline paradigm, in which people are trained to respond immediately after a signal that can come at any time after the presentation of the stimulus (and the dependent measure is thus accuracy), plausibility ratings, and simple two- alternative, choice-reaction-time studies. A complete model of question answering and strategy choice may suggest important improvements in data base search and query systems for very large artificial data bases. Work that attempts to understand feeling of knowing processes will be important for understanding clinical cases of memory impairment that include impaired feeling of knowing processes and those that do not.
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1 |
1996 — 2008 |
Reder, Lynne M |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Exploring the Sac Model of Memory @ Carnegie-Mellon University
[unreadable] DESCRIPTION (provided by applicant): The goal of this proposal is to advance a unified model of implicit and explicit memory phenomena that spans both higher-level conceptual and lower-level perceptual effects. The model, called Source of Activation Confusion (SAC), will be tested and challenged in several ways. Specifically, experiments are proposed that test SAC's novel predictions (i.e., not necessarily expected by other accounts) and data sets will be modeled at a detailed level. The proposed research will provide ever more stringent tests of SAC's predictions and will extend the range of inquiry concerning cognitive processing. The framework makes several controversial claims. For example, it claims that implicit memory is not a separate system from (or independent of) explicit memory, and that there are two processes for recognition: one based on familiarity, the other based on recollection. The proposal includes tests of the extent to which memory enhancement from the re-instatement of (ostensibly irrelevant) perceptual cues can be attributed to effects at retrieval versus at encoding. It is proposed that within the human cognitive system there is a trade-off between familiarity and distinctiveness. Familiarity eases encoding but is also responsible for habituation or inattention. Distinctiveness challenges encoding but once distinctive features are represented in memory, they serve as excellent retrieval cues. Familiar cues are less useful for retrieval (due to contextual interference); however, the memory system compensates by allowing familiarity based responding. Part of the proposed extension of the theory to encoding involves an attempt to further understand what determines whether a partial match is accepted, resulting in a distortion going undetected, and what determines whether a mismatch is considered salient. [unreadable] [unreadable]
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0.958 |
1998 — 2002 |
Reder, Lynne M |
T32Activity Code Description: To enable institutions to make National Research Service Awards to individuals selected by them for predoctoral and postdoctoral research training in specified shortage areas. |
Computational &Behavioral Approaches to Cognition @ Carnegie-Mellon University |
0.958 |
2000 — 2003 |
Reder, Lynne Lovett, Marsha (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Computational Modeling of Individual Differences in Working Memory and Strategy Adaptivity @ Carnegie-Mellon University
Lynne Reder
Abstract
Performance on cognitive tasks varies among individuals. This project is a of study two of the sources of these individual differences; namely, working memory capacity (people's limited resources for retrieving and maintaining information during cognitive processing) and strategy adaptivity (people's ability to change their approach to a task in order to achieve greater success). The first goal of this project is to develop, test, and refine a theory of how individual differences in working memory capacity impact performance across multiple tasks. This theory will be developed as a computational model that can make accurate predictions of individual subjects' performances across multiple tasks at a fine-grained, quantitative level. Specifically, the computational model will enable the estimation of an individual's working memory capacity from one task and then use that estimate to make predictions of performance on the second task. The parameters can be interpreted to represent stable differences between subjects and can be used to predict the same individual's performance on other tasks. Predicting individuals' performances in this way has not been achieved before now and will be a major contribution of this research. The second goal of this project is to explore how differences in strategy adaptivity can be understood in terms of differences in working memory capacity. In sum, the project will result in several unique achievements: 1) the development of a new way to understand individual differences in working memory capacity and strategy adaptivity; 2) provision of a mechanistic account of these differences; and 3) a determination of whether computational modeling can be used to predict performance in terms of zero parameter model fitting.
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1 |
2003 — 2007 |
Reder, Lynne M |
T32Activity Code Description: To enable institutions to make National Research Service Awards to individuals selected by them for predoctoral and postdoctoral research training in specified shortage areas. |
Computational and Behavioral Approaches to Cognition @ Carnegie-Mellon University
[unreadable] DESCRIPTION (provided by applicant): The purpose of the proposed predoctoral and postdoctoral programs is to train the next generation of cognitive psychologists both to develop formal computational models and to test and refine these models, by rigorously comparing the simulation data to carefully collected empirical data. The field is ready to benefit from formal, computational models of cognitive processes. The tools are being developed that enable this formalism, and the end product will not only deepen the empirical and conceptual basis of cognitive psychology, but will also provide stronger links between psychology, neuroscience, and the treatment of problems in mental health. Carnegie Mellon is especially suited to provide this next generation of cognitive scientists with these tools. There is a long tradition at CMU to strive for complete cognitive models to account for a wide range of phenomena using a small common set of theoretical assumptions. The proposed program would be our first focused on training modeling skills. One of the distinctive features of psychological research at CMU is the dual concern for experimental methodology and theoretical models, not just each in isolation. We have promoted the development of both production system (symbolic), connectionist (sub-symbolic) and hybrid models of the human information processing architecture as well as many specific models of performance in particular tasks. In all cases, the researchers have tested and refined their models based on behavioral and physiological data collected here at CMU and elsewhere. Methodologies that have been developed and refined within by our department include: the automatic coding of verbal protocols, the analysis of eye fixations while thinking and problem solving, and functional MRI measurements of higher cognitive processes. Some of these models address the data at the grain size of individual responses, with few subject-specific parameters. The program's goal is to develop skilled researchers who are both competent and comfortable combining the approaches of behavioral research with development of computationally implemented models of cognitive performance. Participation in research, both empirical and modeling, is a fundamental component of helping students achieve this goal. Formal courses and seminars play an important role as well. We will formally instruct and demonstrate the skills of comparing the data derived from a simulation to the human data collected from behavioral research, providing students with the skills to evaluate the quality of the fit and the sensitivity to know when and how to revise one's model based on these comparisons. In addition we will ensure that trainees are conversant in multiple computational approaches and recognize the strengths and weaknesses of each. [unreadable] [unreadable]
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0.958 |
2008 — 2012 |
Reder, Lynne Marie |
T32Activity Code Description: To enable institutions to make National Research Service Awards to individuals selected by them for predoctoral and postdoctoral research training in specified shortage areas. |
Combining Computational and Empirical Methods in Cognitive Neuroscience @ Carnegie-Mellon University
DESCRIPTION (provided by applicant): There is a long tradition at CMU of striving for complete cognitive models to account for a wide range of phenomena using a small common set of theoretical assumptions. Our training program is the only one focused on training cognitive modeling skills. The Combined Computational and Behavioral Approaches to the Study of Cognitive Neuroscience (T32) has the specific aim of providing pre and postdoctoral trainees with the ability to formalize their theories about the mechanisms underlying normal and abnormal cognitive behavior and brain functioning. We will provide trainees with opportunities to conduct state of the art behavioral studies, neuro-imaging studies and build computational models that predict as well as account for behavioral and neuroscience data of cognitive tasks. The methodologies that the training faculty exploit to further our understanding of cognition include fMRI, ERP, psychopharmacological interventions and eye- tracking as well as reaction time and other behavioral studies in combination with computational modeling. Many of these methodologies are used in combination. In addition, the trainees will have the opportunity to rotate into the lab of a medical scientist who conducts cognitive neuroscience research concerned with one of several different types of mental disorders: autism, bi-polar, depression, or schizophrenia. The collaboration among trainee, mentor and clinical researcher will result in the opportunity to test theories of normal behavior with special populations as well as providing useful exposure for the trainees concerning the nature of cognitive and mood disorders. The goal of this proposal is primarily to train the next generation of computational modelers of cognition and cognitive brain function; however, the collaborative work on formal models of disordered cognition as a limiting case in cognitive neuroscience has the promise to bring synergy to both enterprises and move the theoretical work forward in both disciplines. The exciting aspect of this proposal is that it will not only train young researchers to appreciate clinical-cognitive issues and expose them to translational research but it promises to foster synergistic collaborations that have the potential to provide new insights into both normal functioning or treatments for patients who are suffering. We request two years of support for 2 predoctoral and 2 postdoctoral fellows appointed each year.
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0.958 |