The Experts below are selected from a list of 53247 Experts worldwide ranked by ideXlab platform

Nele Russwinkel - One of the best experts on this subject based on the ideXlab platform.

  • a Cognitive Modeling approach to strategy formation in dynamic decision making
    Frontiers in Psychology, 2017
    Co-Authors: Sabine Prezenski, Susann Wolff, André Brechmann, Nele Russwinkel
    Abstract:

    Decision-making is a high-level Cognitive process based on Cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of Cognitive Modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar and a rule-based approach which incorporates perceptual-motor and metaCognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the Modeling data. However, we found a larger variance and a lower overall performance of the Modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying Cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.

  • A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making
    Frontiers Media S.A., 2017
    Co-Authors: Sabine Prezenski, Susann Wolff, André Brechmann, Nele Russwinkel
    Abstract:

    Decision-making is a high-level Cognitive process based on Cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of Cognitive Modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metaCognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the Modeling data. However, we found a larger variance and a lower overall performance of the Modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying Cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks

Glenn Gunzelmann - One of the best experts on this subject based on the ideXlab platform.

  • sleep deprivation and sustained attention performance integrating mathematical and Cognitive Modeling
    Cognitive Science, 2009
    Co-Authors: Glenn Gunzelmann, Kevin A Gluck, Joshua B Gross, David F Dinges
    Abstract:

    A long history of research has revealed many neurophysiological changes and concomitant behavioral impacts of sleep deprivation, sleep restriction, and circadian rhythms. Little research, however, has been conducted in the area of computational Cognitive Modeling to understand the information processing mechanisms through which neurobehavioral factors operate to produce degradations in human performance. Our approach to understanding this relationship is to link predictions of overall Cognitive functioning, or alertness, from existing biomathematical models to information processing parameters in a Cognitive architecture, leveraging the strengths from each to develop a more comprehensive explanation. The integration of these methodologies is used to account for changes in human performance on a sustained attention task across 88 h of total sleep deprivation. The integrated model captures changes due to time awake and circadian rhythms, and it also provides an account for underlying changes in the Cognitive processes that give rise to those effects. The results show the potential for developing mechanistic accounts of how fatigue impacts cognition, and they illustrate the increased explanatory power that is possible by combining theoretical insights from multiple methodologies.

  • using computational Cognitive Modeling to predict dual task performance with sleep deprivation
    Human Factors, 2009
    Co-Authors: Glenn Gunzelmann, Michael D Byrne, Kevin A Gluck, Richard L Moore
    Abstract:

    Objective: The effects of fatigue on multiple-task performance were explored through computational Cognitive Modeling. Background: Fatigue typically has a negative impact on human performance. Biomathematical models exist that characterize the dynamics of human alertness, but the link between alertness and in situ performance on specific tasks is tenuous. Cognitive architectures offer a principled means of establishing that link. Method: We implemented mechanisms for fatigue, which produce microlapses in Cognitive processing, into an existing model, adaptive control of thought—rational, and validated the performance predictions with Bratzke, Rolke, Ulrich, and Peters' data on fatigue and multiple-task performance. Results: The microlapse model replicated the human performance results very well with zero free parameters, although the fit was improved when we allowed two individual differences parameters to vary. Conclusion: Increased frequency of microlapses as a result of fatigue provides a parsimonious explanation for the impact of fatigue on dual-task performance and is consistent with previous research. Application: Our results illustrate how using biomathematical models of fatigue in conjunction with a Cognitive architecture can result in accurate predictions of the effects of fatigue on dual-task performance. Extending and generalizing this capability has potential utility in any safety-critical domain in which fatigue may affect performance.

Sabine Prezenski - One of the best experts on this subject based on the ideXlab platform.

  • a Cognitive Modeling approach to strategy formation in dynamic decision making
    Frontiers in Psychology, 2017
    Co-Authors: Sabine Prezenski, Susann Wolff, André Brechmann, Nele Russwinkel
    Abstract:

    Decision-making is a high-level Cognitive process based on Cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of Cognitive Modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar and a rule-based approach which incorporates perceptual-motor and metaCognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the Modeling data. However, we found a larger variance and a lower overall performance of the Modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying Cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.

  • A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making
    Frontiers Media S.A., 2017
    Co-Authors: Sabine Prezenski, Susann Wolff, André Brechmann, Nele Russwinkel
    Abstract:

    Decision-making is a high-level Cognitive process based on Cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of Cognitive Modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metaCognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the Modeling data. However, we found a larger variance and a lower overall performance of the Modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying Cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks

Michael D Lee - One of the best experts on this subject based on the ideXlab platform.

  • bayesian Cognitive Modeling a practical course
    2014
    Co-Authors: Michael D Lee, Eric-jan Wagenmakers
    Abstract:

    Part I. Getting Started: 1. The basics of Bayesian analysis 2. Getting started with WinBUGS Part II. Parameter Estimation: 3. Inferences with binomials 4. Inferences with Gaussians 5. Some examples of data analysis 6. Latent mixture models Part III. Model Selection: 7. Bayesian model comparison 8. Comparing Gaussian means 9. Comparing binomial rates Part IV. Case Studies: 10. Memory retention 11. Signal detection theory 12. Psychophysical functions 13. Extrasensory perception 14. Multinomial processing trees 15. The SIMPLE model of memory 16. The BART model of risk taking 17. The GCM model of categorization 18. Heuristic decision-making 19. Number concept development.

  • how Cognitive Modeling can benefit from hierarchical bayesian models
    Journal of Mathematical Psychology, 2011
    Co-Authors: Michael D Lee
    Abstract:

    Abstract Hierarchical Bayesian Modeling provides a flexible and interpretable way of extending simple models of Cognitive processes. To introduce this special issue, we discuss four of the most important potential hierarchical Bayesian contributions. The first involves the development of more complete theories, including accounting for variation coming from sources like individual differences in cognition. The second involves the capability to account for observed behavior in terms of the combination of multiple different Cognitive processes. The third involves using a few key psychological variables to explain behavior on a wide range of Cognitive tasks. The fourth involves the conceptual unification and integration of disparate Cognitive models. For all of these potential contributions, we outline an appropriate general hierarchical Bayesian Modeling structure. We also highlight current models that already use the hierarchical Bayesian approach, as well as identifying research areas that could benefit from its adoption.

Kenneth R Koedinger - One of the best experts on this subject based on the ideXlab platform.

  • designing knowledge scaffolds to support mathematical problem solving
    Cognition and Instruction, 2005
    Co-Authors: Bethany Rittlejohnson, Kenneth R Koedinger
    Abstract:

    We present a methodology for designing better learning environments. In Phase 1, 6th-grade students' (n = 223) prior knowledge was assessed using a difficulty factors assessment (DFA). The assessment revealed that scaffolds designed to elicit contextual, conceptual, or procedural knowledge each improved students' ability to add and subtract fractions. Analyses of errors and strategies along with Cognitive Modeling suggested potential mechanisms underlying these effects. In Phase 2, we designed an intervention based on scaffolding this prior knowledge and implemented the computer-based lessons in mathematics classes. In Phase 3, we used the DFA and supporting analyses to assess student learning from the intervention. The posttest results suggest that scaffolding conceptual, contextual, and procedural knowledge are promising tools for improving student learning.

  • toward tutoring help seeking applying Cognitive Modeling to meta Cognitive skills
    Lecture Notes in Computer Science, 2004
    Co-Authors: Vincent Aleven, Bruce M Mclaren, Ido Roll, Kenneth R Koedinger
    Abstract:

    The goal of our research is to investigate whether a Cognitive Tutor can be made more effective by extending it to help students acquire help-seeking skills. We present a preliminary model of help-seeking behavior that will provide the basis for a Help-Seeking Tutor Agent. The model, implemented by 57 production rules, captures both productive and unproductive help-seeking behavior. As a first test of the model's efficacy, we used it off-line to evaluate students' help-seeking behavior in an existing data set of student-tutor interactions, We found that 72% of all student actions represented unproductive help-seeking behavior. Consistent with some of our earlier work (Aleven & Koedinger, 2000) we found a proliferation of hint abuse (e.g., using hints to find answers rather than trying to understand). We also found that students frequently avoided using help when it was likely to be of benefit and often acted in a quick, possibly undeliberate manner. Students' help-seeking behavior accounted for as much variance in their learning gains as their performance at the Cognitive level (i.e., the errors that they made with the tutor). These findings indicate that the help-seeking model needs to be adjusted, but they also underscore the importance of the educational need that the Help-Seeking Tutor Agent aims to address.