Computational Cognitive Modeling of Operator Behavior in Telerover Navigation

2006 ◽  
Author(s):  
David B. Kaber ◽  
Xuezhong Wang ◽  
Sang-Hwan Kim
Keyword(s):  
Cognitive Modeling ◽  
Computational Cognitive Modeling ◽  
Operator Behavior

scholarly journals Neural Correlates of Causal Confounding

2020 ◽  
Vol 32 (2) ◽  
pp. 301-314
Author(s):  
Mimi Liljeholm
Keyword(s):  
Cognitive Modeling ◽  
Causal Model ◽  
Neural Substrates ◽  
Neural Correlates ◽  
Learning Context ◽  
Causal Knowledge ◽  
Causal Induction ◽  
General Constraints ◽  
Computational Cognitive Modeling ◽  
Adults And Children

As scientists, we are keenly aware that if putative causes perfectly covary, the independent influence of neither can be discerned—a “no confounding” constraint on inference, fundamental to philosophical and statistical perspectives on causation. Intriguingly, a substantial behavioral literature suggests that naïve human reasoners, adults and children, are tacitly sensitive to causal confounding. Here, a combination of fMRI and computational cognitive modeling was used to investigate neural substrates mediating such sensitivity. While being scanned, participants observed and judged the influences of various putative causes with confounded or nonconfounded, deterministic or stochastic, influences. During judgments requiring generalization of causal knowledge from a feedback-based learning context to a transfer probe, activity in the dorsomedial pFC was better accounted for by a Bayesian causal model, sensitive to both confounding and stochasticity, than a purely error-driven algorithm, sensitive only to stochasticity. Implications for the detection and estimation of distinct forms of uncertainty, and for a neural mediation of domain-general constraints on causal induction, are discussed.

scholarly journals Towards Replication in Computational Cognitive Modeling: a Machine Learning Perspective

2019 ◽  
Vol 2 (3-4) ◽  
pp. 242-246 ◽  
Author(s):  
Chris Emmery ◽  
Ákos Kádár ◽  
Travis J. Wiltshire ◽  
Andrew T. Hendrickson
Keyword(s):  
Machine Learning ◽  
Cognitive Modeling ◽  
Computational Cognitive Modeling

scholarly journals Empirical Aesthetics, Computational Cognitive Modeling, and Experimental Phenomenology: Methodological remarks on “Shaping and Co-Shaping Forms of Vitality in Music: Beyond Cognitivist and Emotivist Approaches to Musical Expressiveness” by Jin Hyun Kim

2013 ◽  
Vol 8 (3-4) ◽  
pp. 178
Author(s):  
Uwe Seifert
Keyword(s):  
Cognitive Modeling ◽  
Emotional Processing ◽  
Affective Computing ◽  
Human Robot Interaction ◽  
Empirical Adequacy ◽  
Robot Interaction ◽  
Experimental Phenomenology ◽  
Computational Cognitive Modeling ◽  
The Mind ◽  
Near Future

The core ideas of the proposed framework for empirical aesthetics are interpreted as focusing on processes, interaction, and phenomenological experience. This commentary first touches on some methodological impediments to developing theories of processing and interaction, and emphasizes the necessity of computational cognitive modeling using robots to test the empirical adequacy of such theories. Further, the importance of developing and integrating phenomenological methods into current experimental research is stressed, using experimental phenomenology as reference. Situated cognition, affective computing, human-robot interaction research, computational cognitive modeling and social and cultural neuroscience are noted as providing relevant insight into the empirical adequacy of current theories of cognitive and emotional processing. In the near future these fields will have a stimulating impact on empirical aesthetics and research on music and the mind.

scholarly journals Simulating Spanish-English Code-Switching: El Modelo Está Generating Code-Switches

2019 ◽  
Author(s):  
Chara Tsoukala ◽  
Stefan L. Frank ◽  
Antal van den Bosch ◽  
Jorge Valdés Kroff ◽  
Mirjam Broersma
Keyword(s):  
Neural Network ◽  
Cognitive Modeling ◽  
Cognitive Model ◽  
Sentence Production ◽  
Path Model ◽  
The Other ◽  
Cognitive Mechanisms ◽  
Computational Cognitive Modeling ◽  
Possible Cause ◽  
Shed Light

Multilingual speakers are able to switch from one language to the other ("code-switch'') between or within sentences. Because the underlying cognitive mechanisms are not well understood, in this study we use computational cognitive modeling to shed light on the process of code-switching.We employed the Bilingual Dual-path model, a Recurrent Neural Network of bilingual sentence production Tsoukala et al. (2017) and simulated sentence production in simultaneous Spanish-English bilinguals. Our first goal was to investigate whether the model would code-switch without being exposed to code-switched training input.The model indeed produced code-switches even without any exposure to such input and the patterns of code-switches are in line with earlier linguistic work Poplack (1980). The second goal of this study was to investigate an auxiliary phrase asymmetry that exists in Spanish-English code-switched production. Using this cognitive model, we examined a possible cause for this asymmetry. To our knowledge, this is the first computational cognitive model that aims to simulate code-switched sentence production.

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