From Cognitive Modeling to Robotics: How Research on Human Cognition and Computational Cognitive Architectures can be Applied to Robotics Problems

2018 ◽  
pp. 273-279 ◽  
Author(s):  
Troy Dale Kelley ◽  
Christian Lebiere
Keyword(s):  
Cognitive Modeling ◽  
Human Cognition ◽  
Cognitive Architectures

Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources

2019 ◽  
Vol 43 ◽  
Author(s):  
Falk Lieder ◽  
Thomas L. Griffiths
Keyword(s):  
Cognitive Skills ◽  
Cognitive Modeling ◽  
Psychological Theory ◽  
Human Mind ◽  
Human Cognition ◽  
Cognitive Architectures ◽  
Rational Analysis ◽  
Cognitive Constraints ◽  
Rational Models ◽  
And Linguistics

Abstract Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis. The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about human rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irrationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.

Creative Stimulation in Conceptual Design

2002 ◽  
Author(s):  
Oren Benami ◽  
Yan Jin
Keyword(s):  
Conceptual Design ◽  
Design Process ◽  
Cognitive Processes ◽  
Cognitive Modeling ◽  
Protocol Analysis ◽  
Cognitive Model ◽  
Human Cognition ◽  
Creative Design ◽  
New Ideas ◽  
The Relationship

Conceptual design is a process of creating functions, forms and behaviors. Although cognitive processes are utilized in the development of new ideas, conventional methodologies do not take human cognition into account. However, it is conceivable that if one could determine how cognitive processes are stimulated, then more effective conceptual design methods could be developed. In this paper, we develop a Cognitive Model of Creative Conceptual Design to capture the relationship between the properties that stimulate cognitive processes and the design operations that facilitate cognitive processes. Through cognitive modeling, protocol analysis, and cognitive experiments, this research showed that designers exhibit patterns of creative design behavior, and that these patterns can be captured and instilled into the design process, to promote creativity.

The Next Generation of Cognitive Modeling Tools: Opportunities, Challenges and Basic Needs

2007 ◽  
Vol 51 (12) ◽  
pp. 799-801
Author(s):  
Michael L. Bernard ◽  
J. Chris Forsythe ◽  
Laurel Allender ◽  
Joseph Cohn ◽  
Gabriel Radvansky ◽  
...  
Keyword(s):  
Cognitive Modeling ◽  
Model Development ◽  
Cognitive Model ◽  
Human Cognition ◽  
Next Generation ◽  
Wide Spread ◽  
Modeling Tools ◽  
Practical Applications ◽  
The Past ◽  
The Future

In the past twenty or so years the scientific community has made impressive advancements in the modeling and simulation of general human cognition. This progress has led to the beginnings of wide-spread applications and use. In fact, we are now at a point where the community can begin to make fairly accurate predictions as to how this technology will be used in the next twenty–plus years. Accordingly, the purpose of this panel is to engage the community at large regarding the future needs and requirements associated with building cognitive models for various scientific and engineering endeavors. Specifically, this panel will discuss and make recommendations with regard to the future functionality of cognitive modeling that could be encompassed in next-generation capabilities. To do this, we will concentrate on four different domain areas. These are: academic use of cognitive modeling, cognitive model development, neuroscience-related issues, and practical applications of cognitive modeling.

Wild Architecture

2020 ◽  
pp. 791-807
Author(s):  
Vincent T. Cialdella ◽  
Emilio J. C. Lobato ◽  
J. Scott Jordan
Keyword(s):  
Systems Theory ◽  
Time Scales ◽  
Human Cognition ◽  
Cognitive Architectures

In this chapter, the authors focus on cognitive architectures that are developed with the intent to explain human cognition. The authors first describe the mission of cybernetics and early cognitive architectures and recount the popular criticism that these perspectives fail to provide genuine explanations of cognition. Moving forward, the authors propose that there are three pervasive problems that modern cognitive architectures must address: the problem of consciousness, the problem of embodiment, and the problem of representation. Wild Systems Theory (Jordan, 2013) conceptualizes biological cognition as a feature of self-sustaining embodied context that manifests itself at multiple, nested, time-scales. In this manner, Wild Systems Theory is presented as a particularly useful framework for coherently addressing the problems of consciousness, embodiment, and representation.

scholarly journals Natural Language Understanding (NLU, not NLP) in Cognitive Systems

AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 43-56 ◽  
Author(s):  
Marjorie McShane
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Cognitive Modeling ◽  
Cognitive Biases ◽  
Cooperative Behavior ◽  
Holistic Approach ◽  
Natural Language Understanding ◽  
Human Cognition ◽  
Language Understanding ◽  
Cognitive Agents

Developing cognitive agents with human-level natural language understanding (NLU) capabilities requires modeling human cognition because natural, unedited utterances regularly contain ambiguities, ellipses, production errors, implicatures, and many other types of complexities. Moreover, cognitive agents must be nimble in the face of incomplete interpretations since even people do not perfectly understand every aspect of every utterance they hear. So, once an agent has reached the best interpretation it can, it must determine how to proceed – be that acting upon the new information directly, remembering an incomplete interpretation and waiting to see what happens next, seeking out information to fill in the blanks, or asking its interlocutor for clarification. The reasoning needed to support NLU extends far beyond language itself, including, non-exhaustively, the agent’s understanding of its own plans and goals; its dynamic modeling of its interlocutor’s knowledge, plans, and goals, all guided by a theory of mind; its recognition of diverse aspects human behavior, such as affect, cooperative behavior, and the effects of cognitive biases; and its integration of linguistic interpretations with its interpretations of other perceptive inputs, such as simulated vision and non-linguistic audition. Considering all of these needs, it seems hardly possible that fundamental NLU will ever be achieved through the kinds of knowledge-lean text-string manipulation being pursued by the mainstream natural language processing (NLP) community. Instead, it requires a holistic approach to cognitive modeling of the type we are pursuing in a paradigm called OntoAgent.

scholarly journals Towards a model-theoretic framework for describing the semantic aspects of cognitive processes

2020 ◽  
Vol 8 (4) ◽  
pp. 83-96
Author(s):  
Sergio Miguel Tomé
Keyword(s):  
Mathematical Logic ◽  
Cognitive Processes ◽  
Cognitive Abilities ◽  
Cognitive Theory ◽  
Mathematical Formulation ◽  
Human Cognition ◽  
Cognitive Architectures

Semantics is one of the most challenging aspects of cognitive architectures. Mathematical logic, or linguistics, highlights that semantics is essential to human cognition. The Cognitive Theory of True Conditions (CTTC) is a proposal to implement cognitive abilities and to describe the semantics of symbolic cognitive architectures based on model-theoretic semantics. This article focuses on the concepts supporting the mathematical formulation of the CTTC, its relationship to other proposals, and how it can be used as a framework for designing cognitive abilities in agents.

Modeling Interruption and Resumption in a Smartphone Task: An ACT-R Approach

i-com ◽  
2015 ◽  
Vol 14 (2) ◽  
Author(s):  
Maria Wirzberger ◽  
Nele Russwinkel
Keyword(s):  
Cognitive Processing ◽  
Cognitive Modeling ◽  
Cognitive Architecture ◽  
Human Cognition ◽  
Mental Demand ◽  
Model Predictions ◽  
Mental Demands ◽  
Human Participants ◽  
Shed Light ◽  
Respective Number

AbstractThis research aims to inspect human cognition when being interrupted while performing a smartphone task with varying levels of mental demand. Due to its benefits especially in the early stages of interface development, a cognitive modeling approach is used. It applies the cognitive architecture ACT-R to shed light on task-related cognitive processing. The inspected task setting involves a shopping scenario, manipulating interruption via product advertisements and mental demands by the respective number of people shopping is done for. Model predictions are validated through a corresponding experimental setting with 62 human participants. Comparing model and human data in a defined set of performance-related parameters displays mixed results that indicate an acceptable fit – at least in some cases. Potential explanations for the observed differences are discussed at the end.

Wild Architecture

2017 ◽  
pp. 41-62
Author(s):  
Vincent T. Cialdella ◽  
Emilio J. C. Lobato ◽  
J. Scott Jordan
Keyword(s):  
Systems Theory ◽  
Time Scales ◽  
Human Cognition ◽  
Cognitive Architectures

In this chapter, the authors focus on cognitive architectures that are developed with the intent to explain human cognition. The authors first describe the mission of cybernetics and early cognitive architectures and recount the popular criticism that these perspectives fail to provide genuine explanations of cognition. Moving forward, the authors propose that there are three pervasive problems that modern cognitive architectures must address: the problem of consciousness, the problem of embodiment, and the problem of representation. Wild Systems Theory (Jordan, 2013) conceptualizes biological cognition as a feature of self-sustaining embodied context that manifests itself at multiple, nested, time-scales. In this manner, Wild Systems Theory is presented as a particularly useful framework for coherently addressing the problems of consciousness, embodiment, and representation.

scholarly journals Cognitive Modeling of Task Switching in Discretionary Multitasking Based on the ACT-R Cognitive Architecture

2021 ◽  
Vol 11 (9) ◽  
pp. 3967
Author(s):  
Hyungseok Oh ◽  
Yongdeok Yun ◽  
Rohae Myung
Keyword(s):  
Human Behavior ◽  
Task Switching ◽  
Cognitive Modeling ◽  
Cognitive Architecture ◽  
Cognitive Model ◽  
Difficulty Level ◽  
Cognitive Architectures ◽  
Symbolic Structure ◽  
And Performance ◽  
Insight Into

Discretionary multitasking has emerged as a prevalent and important domain in research on human–computer interaction. Studies on modeling based on cognitive architectures such as ACT-R to gain insight into and predict human behavior in multitasking are critically important. However, studies on ACT-R modeling have mainly focused on concurrent and sequential multitasking, including scheduled task switching. Therefore, in this study, an ACT-R cognitive model of task switching in discretionary multitasking was developed to provide an integrated account of when and how humans decide on switching tasks. Our model contains a symbolic structure and subsymbolic equations that represent the cognitive process of task switching as self-interruption by the imposed demands and a decision to switch. To validate our model, it was applied to an illustrative dual task, including a memory game and a subitizing task, and the results were compared with human data. The results demonstrate that our model can provide a relatively accurate representation, in terms of task-switching percent just after the subtask, the number of task-switching during the subtask, and performance time depending on the task difficulty level; it exhibits enhanced performance in predicting human behavior in multitasking and demonstrates how ACT-R facilitates accounts of voluntary task switching.

Language as a mental travel guide

2020 ◽  
Vol 43 ◽  
Author(s):  
Charles P. Davis ◽  
Gerry T. M. Altmann ◽  
Eiling Yee
Keyword(s):  
Cognitive Science ◽  
Human Cognition ◽  
Travel Guide

Abstract Gilead et al.'s approach to human cognition places abstraction and prediction at the heart of “mental travel” under a “representational diversity” perspective that embraces foundational concepts in cognitive science. But, it gives insufficient credit to the possibility that the process of abstraction produces a gradient, and underestimates the importance of a highly influential domain in predictive cognition: language, and related, the emergence of experientially based structure through time.

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