A Flexible Scheme to Model the Cognitive Influence on Emotions in Autonomous Agents

2018 ◽  
Vol 12 (4) ◽  
pp. 81-100
Author(s):  
Sergio Castellanos ◽  
Luis-Felipe Rodríguez
Keyword(s):  
Computational Models ◽  
Autonomous Agents ◽  
Computational Simulation ◽  
Cognitive Model ◽  
Human Information Processing ◽  
Appraisal Theory ◽  
Cognitive Mechanisms ◽  
Integrative Framework ◽  
Cognitive Components ◽  
Cognitive Information

Autonomous agents (AAs) are designed to embody the natural intelligence by incorporating cognitive mechanisms that are applied to evaluate stimuli from an emotional perspective. Computational models of emotions (CMEs) implement mechanisms of human information processing in order to provide AAs for a capability to assign emotional values to perceived stimuli and implement emotion-driven behaviors. However, a major challenge in the design of CMEs is how cognitive information is projected from the architecture of AAs. This article presents a cognitive model for CMEs based on appraisal theory aimed at modeling AAs' interactions between cognitive and affective processes. The proposed scheme explains the influence of AAs' cognition on emotions by fuzzy membership functions associated to appraisal dimensions. The computational simulation is designed in the context of an integrative framework to facilitate the development of CMEs, which are capable of interacting with cognitive components of AAs. This article presents a case study and experiment that demonstrate the functionality of the proposed models.

Modulation Scheme for Biasing the Emotional Process of Autonomous Agents

2020 ◽  
pp. 339-361
Author(s):  
Sergio Castellanos ◽  
Luis-Felipe Rodríguez ◽  
J. Octavio Gutierrez-Garcia
Keyword(s):  
Computational Models ◽  
Autonomous Agents ◽  
Cognitive Architecture ◽  
Modulation Scheme ◽  
Membership Functions ◽  
Real World Data ◽  
Emotional Process ◽  
Fuzzy Membership Functions ◽  
Cognitive Components ◽  
Cognitive Information

Autonomous agents (AAs) are capable of evaluating their environment from an emotional perspective by implementing computational models of emotions (CMEs) in their architecture. A major challenge for CMEs is to integrate the cognitive information projected from the components included in the AA's architecture. In this chapter, a scheme for modulating emotional stimuli using appraisal dimensions is proposed. In particular, the proposed scheme models the influence of cognition on appraisal dimensions by modifying the limits of fuzzy membership functions associated with each dimension. The computational scheme is designed to facilitate, through input and output interfaces, the development of CMEs capable of interacting with cognitive components implemented in a given cognitive architecture of AAs. A proof of concept based on real-world data to provide empirical evidence that indicates that the proposed mechanism can properly modulate the emotional process is carried out.

A Global Workspace Model Implementation and its Relations with Philosophy of Mind

2021 ◽  
pp. 1-28
Author(s):  
Eduardo C. Garrido-Mercháin ◽  
Martín Molina ◽  
Francisco M. Mendoza-Soto
Keyword(s):  
Philosophy Of Mind ◽  
Autonomous Agents ◽  
Cognitive Architecture ◽  
Cognitive Model ◽  
Autonomous Agent ◽  
Cognitive Mechanisms ◽  
Global Workspace ◽  
The One ◽  
Past Experiences ◽  
Global Workspace Theory

This work seeks to study the beneficial properties that an autonomous agent can obtain by imitating a cognitive architecture similar to that of conscious beings. Throughout this document, a cognitive model of an autonomous agent-based in a global workspace architecture is presented. We hypothesize that consciousness is an evolutionary advantage, so if our autonomous agent can be potentially conscious, its performance will be enhanced. We explore whether an autonomous agent implementing a cognitive architecture like the one proposed in the global workspace theory can be conscious from a philosophy of mind perspective, with a special emphasis on functionalism and multiple realizability. The purposes of our proposed model are to create autonomous agents that can navigate within an environment composed of multiple independent magnitudes, adapting to its surroundings to find the best possible position according to its inner preferences and to test the effectiveness of many of its cognitive mechanisms, such as an attention mechanism for magnitude selection, possession of inner feelings and preferences, usage of a memory system to storage beliefs and past experiences, and incorporating the consciousness bottleneck into the decision-making process, that controls and integrates information processed by all the subsystems of the model, as in global workspace theory. We show in a large experiment set how potentially conscious autonomous agents can benefit from having a cognitive architecture such as the one described.

scholarly journals An Interoperable Framework for Computational Models of Emotion

2022 ◽  
Vol 16 (1) ◽  
pp. 1-15
Author(s):  
Enrique Osuna ◽  
Sergio Castellanos ◽  
Jonathan Hernando Rosales ◽  
Luis-Felipe Rodríguez
Keyword(s):  
Intelligent Agents ◽  
Computational Models ◽  
Ad Hoc ◽  
Communication Process ◽  
Software Systems ◽  
Cognitive Agent ◽  
Agent Architectures ◽  
Cognitive Components ◽  
Cognitive Information ◽  
Computational Models Of Emotion

Computational models of emotion (CMEs) are software systems designed to emulate specific aspects of the human emotions process. The underlying components of CMEs interact with cognitive components of cognitive agent architectures to produce realistic behaviors in intelligent agents. However, in contemporary CMEs, the interaction between affective and cognitive components occurs in ad-hoc manner, which leads to difficulties when new affective or cognitive components should be added in the CME. This paper presents a framework that facilitates taking into account in CMEs the cognitive information generated by cognitive components implemented in cognitive agent architectures. The framework is designed to allow researchers define how cognitive information biases the internal workings of affective components. This framework is inspired in software interoperability practices to enable communication and interpretation of cognitive information and standardize the cognitive-affective communication process by ensuring semantic communication channels used to modulate affective mechanisms of CMEs

scholarly journals Neural Components of Reading Revealed by Distributed and Symbolic Computational Models

2020 ◽  
Vol 1 (4) ◽  
pp. 381-401
Author(s):  
Ryan Staples ◽  
William W. Graves
Keyword(s):  
Neural Activity ◽  
Neural Coding ◽  
Computational Models ◽  
Ann Model ◽  
Distributed Representations ◽  
Neural Data ◽  
Cognitive Components ◽  
Components Of Reading ◽  
Artificial Neural Network Ann ◽  
Models Of Reading

Determining how the cognitive components of reading—orthographic, phonological, and semantic representations—are instantiated in the brain has been a long-standing goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit nonsymbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling–to–sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded to neural activity. However, the ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

Diagnosis as a Detriment: A Cognitive Approach to Understanding the Depressive Experience

2015 ◽  
Vol 17 (2) ◽  
pp. 125-134 ◽  
Author(s):  
Evan Hy Einstein
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Theoretical Model ◽  
Mental Health Care ◽  
Cognitive Model ◽  
Clinical Applications ◽  
Psychotropic Medications ◽  
Cognitive Mechanisms ◽  
Cognitive Approach ◽  
The Brain

Depression is currently understood within a biomedical paradigm. This paradigm is an example of reductionism; people are clinically diagnosed and categorized based on behavior and affect, while they are then prescribed psychotropic medications based on an inconclusively correlated neurotransmitter imbalance in the brain. In this article, clinical diagnosis and labeling are explored with respect to their detrimental potential. A framework of embodied cognition is used to conceptualize a cognitive model of depressive experience. This theoretical model explores the potentially self-reinforcing cognitive mechanisms behind a depressive experience, with the goal of highlighting the possibility of diagnosis as a detrimental influence on these mechanisms. The aim of this article is to further a discussion about our current mental health care paradigm and provide an explanation as to how it could cause harm to some. Clinical applications of the model are also discussed pertaining to the potential of rendering formal dichotomist diagnoses irrelevant to the ultimate goal of helping people feel better.

Creativity from paradoxical experience: a theory of how individuals achieve creativity while adopting paradoxical frames

2019 ◽  
Vol 23 (3) ◽  
pp. 397-418 ◽  
Author(s):  
Goran Calic ◽  
Sebastien Hélie ◽  
Nick Bontis ◽  
Elaine Mosakowski
Keyword(s):  
Cognitive Processes ◽  
Computational Simulation ◽  
Current Theory ◽  
Superior Performance ◽  
Cognitive Mechanisms ◽  
Content Type ◽  
Important Benefit ◽  
Paradox Theory ◽  
Stored Information ◽  
The Relationship

PurposeExtant paradox theory suggests that adopting paradoxical frames, which are mental templates adopted by individuals in order to embrace contradictions, will result in superior firm performance. Superior performance is achieved through learning and creativity, fostering flexibility and resilience and unleashing human capital. The creativity mechanism of paradox theory is limited by a few propositions and a rough underlying theoretical logic. Using the extant theoretical base as a platform, the paper aims to develop a more powerful theory using a computational simulation.Design/methodology/approachThis paper relies on a psychologically realistic computer simulation. Using a simulation to generate ideas from stored information, one can model and manipulate the parameters that have been shown to mediate the relationship between paradoxes and creative output – defined as the number of creative ideas generated.FindingsSimulation results suggest that the relationship between paradoxical frames and creative output is non-monotonic – contrary to previous studies. Indeed, findings suggest that paradoxical frames can reduce, rather than enhance, creative output, in at least some cases.Originality/valueAn important benefit of adopting paradoxical frames is their capacity to increase creative output. This assumption is challenging to test, because one cannot measure private cognitive processes related to knowledge creation. However, they can be simulated. This allows for the extension of current theory. This new theory depicts a more complete relationship between paradoxical frames and creativity by accounting for subjective differences in how paradoxical frames are experienced along two cognitive mechanisms – differentiation and integration.

scholarly journals Population Dynamics Based on Resource Availability & Founding Effects: Live & Computational Models

2016 ◽  
Vol 78 (5) ◽  
pp. 396-403 ◽  
Author(s):  
Samuel Potter ◽  
Rebecca M. Krall ◽  
Susan Mayo ◽  
Diane Johnson ◽  
Kim Zeidler-Watters ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Computer Literacy ◽  
Computational Models ◽  
Computational Simulation ◽  
School Science ◽  
Simulation Software ◽  
High School Science ◽  
Initial Population ◽  
College Level ◽  
Factors Affecting

With the looming global population crisis, it is more important now than ever that students understand what factors influence population dynamics. We present three learning modules with authentic, student-centered investigations that explore rates of population growth and the importance of resources. These interdisciplinary modules integrate biology, mathematics, and computer-literacy concepts aligned with the Next Generation Science Standards. The activities are appropriate for middle and high school science classes and for introductory college-level biology courses. The modules incorporate experimentation, data collection and analysis, drawing conclusions, and application of studied principles to explore factors affecting population dynamics in fruit flies. The variables explored include initial population structure, food availability, and space of the enclosed population. In addition, we present a computational simulation in which students can alter the same variables explored in the live experimental modules to test predictions on the consequences of altering the variables. Free web-based graphing (Joinpoint) and simulation software (NetLogo) allows students to work at home or at school.

scholarly journals A Neurocomputational Theory of Nightmares: The role of formal properties of nightmare images

2021 ◽  
Author(s):  
Patrick McNamara ◽  
Wesley J Wildman ◽  
George Hodulik ◽  
David Rohr
Keyword(s):  
Individual Difference ◽  
Parameter Space ◽  
Computational Models ◽  
Computational Simulation ◽  
Time Varying ◽  
Individual Difference Variables ◽  
Image Characteristics ◽  
Nightmare Disorder ◽  
Formal Properties

Abstract Study Objectives To test and extend Levin & Nielsen’s (2007) Affective Network Dysfunction (AND) model with nightmare disorder (ND) image characteristics, and then to implement the extension as a computational simulation, the Disturbed Dreaming Model (DDM). Methods We used AnyLogic V7.2 to computationally implement an extended AND model incorporating quantitative effects of image characteristics including valence, dominance, and arousal. We explored the DDM parameter space by varying parameters, running approximately one million runs, each for one month of model time, varying pathway bifurcation thresholds, image characteristics, and individual-difference variables to quantitively evaluate their combinatory effects on nightmare symptomology. Results The DDM shows that the AND model extended with pathway bifurcations and image properties is computationally coherent. Varying levels of image properties we found that when nightmare images exhibit lower dominance and arousal levels, the ND agent will choose to sleep but then has a traumatic nightmare, whereas, when images exhibit greater than average dominance and arousal levels, the nightmares trigger sleep-avoidant behavior, but lower overall nightmare distress at the price of exacerbating nightmare effects during waking hours. Conclusions Computational simulation of nightmare symptomology within the AND framework suggests that nightmare image properties significantly influence nightmare symptomology. Computational models for sleep and dream studies are powerful tools for testing quantitative effects of variables affecting nightmare symptomology and confirms the value of extending the Levin & Nielsen AND model of disturbed dreaming/ND.

A Computational Simulation of the Cognitive Process of Children Knowledge Acquisition and Memory Development

2012 ◽  
pp. 111-127
Author(s):  
Jeff Bancroft ◽  
Yingxu Wang
Keyword(s):  
Knowledge Representation ◽  
Knowledge Acquisition ◽  
Computational Simulation ◽  
Simulation Tool ◽  
Memory Development ◽  
Cognitive Mechanisms ◽  
Children's Memory ◽  
Mechanisms Of Memory ◽  
The Mathematical Model ◽  
The Brain

The cognitive mechanisms of knowledge representation, memory establishment, and learning are fundamental issues in understanding the brain. A basic approach to studying these mental processes is to observe and simulate how knowledge is memorized by little children. This paper presents a simulation tool for knowledge acquisition and memory development for young children of two to five years old. The cognitive mechanisms of memory, the mathematical model of concepts and knowledge, and the fundamental elements of internal knowledge representation are explored. The cognitive processes of children’s memory and knowledge development are described based on concept algebra and the object-attribute-relation (OAR) model. The design of the simulation tool for children’s knowledge acquisition and memory development is presented with the graphical representor of memory and the dynamic concept network of knowledge. Applications of the simulation tool are described by case studies on children’s knowledge acquisition about family members, relatives, and transportation. This work is a part of the development of cognitive computers that mimic human knowledge processing and autonomous learning.

scholarly journals Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 3 (L1V3)

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Frank T. Bergmann ◽  
Jonathan Cooper ◽  
Matthias König ◽  
Ion Moraru ◽  
David Nickerson ◽  
...  
Keyword(s):  
Time Course ◽  
Computational Models ◽  
Simulation Experiment ◽  
Computational Simulation ◽  
Biological Research ◽  
Markup Language ◽  
Formal Representation ◽  
Post Process ◽  
Experiment Description ◽  
Level 1

AbstractThe creation of computational simulation experiments to inform modern biological research poses challenges to reproduce, annotate, archive, and share such experiments. Efforts such as SBML or CellML standardize the formal representation of computational models in various areas of biology. The Simulation Experiment Description Markup Language (SED-ML) describes what procedures the models are subjected to, and the details of those procedures. These standards, together with further COMBINE standards, describe models sufficiently well for the reproduction of simulation studies among users and software tools. The Simulation Experiment Description Markup Language (SED-ML) is an XML-based format that encodes, for a given simulation experiment, (i) which models to use; (ii) which modifications to apply to models before simulation; (iii) which simulation procedures to run on each model; (iv) how to post-process the data; and (v) how these results should be plotted and reported. SED-ML Level 1 Version 1 (L1V1) implemented support for the encoding of basic time course simulations. SED-ML L1V2 added support for more complex types of simulations, specifically repeated tasks and chained simulation procedures. SED-ML L1V3 extends L1V2 by means to describe which datasets and subsets thereof to use within a simulation experiment.

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