EMOTION-II Model: A Theoretical Framework for Happy Emotion as a Self-Assessment Measure Indicating the Degree-of-Fit (Congruency) between the Expectancy in Subjective and Objective Realities in Autonomous Control Systems

Tam, David C.

doi:10.2174/1874110x00701010047

Cited by 18 publications

(22 citation statements)

References 14 publications

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“…The next phase of the "EMOTION-II" model presented in the next paper [5] will establish the "internal model" needed for the representation of the external world for an autonomous control system to function, and provide the derivation of principles for the emergence of happy/unhappy emotions for self-assessment and consistency-check when comparing the discrepancy between the expectancy in objective and subject realities (not just sensation, as in EMOTION-I).…”

Section: Discussionmentioning

confidence: 99%

“…It addresses the emergence of the "feel" of sensation for increasing the chance of survival as the first step in internal pre-processing of emotions. The subsequent paper [5] will focus on the second phase in developing the minimal set of basic emotions for this model: EMOTION-II. It addresses the emergence of a metric for assessing the accuracy of the internal model.…”

Section: Emotion-i Modelmentioning

confidence: 99%

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EMOTION-I Model: A Biologically-Based Theoretical Framework for Deriving Emotional Context of Sensation in Autonomous Control Systems

Tam

2007

TOCSJ

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A theoretical model for deriving the origin of emotional functions from first principles is introduced. The model, called "Emotional Model Of the Theoretical Interpretations Of Neuroprocessing", abbreviated as the "EMO-TION", derives how emotional context can be evolved from innate responses. It is based on a biological framework for autonomous systems with minimal assumptions on the system or what emotion is. The first phase of the model (EMO-TION-I) addresses the progressive abstraction of the sensory input signals within relevant context of the environment to produce the appropriate output actions for survival. It uses a probabilistic feedforward and feedback neural network with multiple adaptable gains, self-adaptive learning rate and modifiable connection weights to produce a self-organizing, selfadaptive system incorporating associative reinforcement learning rules for conditioning and fixation of circuitry into hardwire to form innate responses such that contextual feel of sensation is evolved as an emergent property known as emotional feel.

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Section: Discussionmentioning

confidence: 99%

Section: Emotion-i Modelmentioning

confidence: 99%

EMOTION-I Model: A Biologically-Based Theoretical Framework for Deriving Emotional Context of Sensation in Autonomous Control Systems

Tam

2007

TOCSJ

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“…A theoretical emotional model was proposed previously to describe how emotion is evolved to increase the survivability of an organism by providing the feedback to correct for any discrepancy between the internal predictions and the actual outcomes [1,2]. Using a set of minimal assumptions, it can be shown that the computations in emotional processing involve the assessment of the disparity between the internally generated world model (subjective reality) and the actual real world (objective reality) [3].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Happy Emotion: Dependence on Decisions

Tam

2014

PBS

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Abstract:In the previously proposed theoretical model of emotion, emotion serves as an internal feedback to assess the disparity between the internal prediction and the actual outcomes in the external world, so that congruency between the desirable wants and needs can be met by resolving the emotions. The Emotional-Gain Model predicts that the happy emotional intensity is proportional to the magnitude of the desirable gain signals, while unhappy emotional intensity is proportional to the magnitude loss signals. Using the classical Ultimatum Game (UG) experimental paradigm to elicit self-generated emotions in response to a monetary offer, we want to determine whether the emotional responses are altered in relation to the decision to accept or reject the offer. If so, then does it change the emotional baseline level or the emotional sensitivity? The results showed that the proportionality relationship between emotional intensity and offer-ratios remains the same with respect to the acceptance or rejection decision. The only difference between the decisions is that the baseline level of happiness is shifted by 40% higher for the decisions to accept the offer, compared to the decisions that rejected the offer. The emotional baseline level is changed without changing the emotional sensitivity. This is quantified by the shift in the y-intercept of the emotional stimulus-response function. The happy emotional intensity is shifted upward (toward positive emotion) for those trials that accepted the offer, compared to those who rejected the offer. The slope of the stimulus-response function does not change with respect to the decision, indicating the constancy of the emotional sensitivity. These results validated the hypothesis that happy emotion is inter-related to the decision-making process, such that the decision to accept an offer is related to a shift towards a happier emotion, while the decision to reject an offer is associated with a shift towards an unhappier emotion. This provided the quantitative assessment of how emotion is biased in relation to the decision. The decision to accept an offer is related to a shift to the emotional baseline level rather than a change in the emotional sensitivity -without altering the proportionality relationship between happiness intensity and monetary offer-ratios in UG.

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“…Toward this goal to examine the functional role of emotions in survival, a neurobiological model had been developed to explore the computations involved in emotional processing [1,2]. Using a minimal assumption approach, it can be shown that an objective model of emotion can be quantified using basic principles of survival [1,2].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Happy Emotion: Proportionality Relationship to Gain/Loss

Tam¹

2014

PBS

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Abstract:Toward understanding the role of emotion played in cognitive processing, an emotional model has been proposed to quantify the computation involved in assessing the disparity between the expected and the actual outcomes. This study provides the experimental evidence to validate the above emotional model. In this model, emotion serves as an internal feedback to assess the disparity between the internal predicted outcomes and the actual (external) outcomes in reality. It predicts that emotion provides a feedback to reduce the discrepancy between the expected (subjective) reality and actual (objective) reality. The hypothesis for this model is that the intensity of emotional response is proportional to the disparity between the expected outcome and the actual outcome (i.e., gain/loss magnitude). Happiness is an emotional feedback that indicates the congruency between the predicted and actual outcomes. In order to validate this theoretical model of emotion, the classical Ultimatum Game (UG) is used as an experimental paradigm to elicit self-generated (endogenous) emotions in response to a monetary offer, so that the emotional responses with respect to the perceived monetary gain/loss can be assessed by the stimulus-response function. The results showed that the self-reported happiness intensity is directly proportional to the magnitude of the desirable monetary gain. An empirically derived emotion stimulus-response function is shown to quantify the specific emotional biases graphically by the emotional-disparity graph. The results validated the hypothesis that the intensity of self-reported happy emotion is directly proportional to the monetary gain. The analysis also showed that the happy emotional sensitivity is also changed by the perception of fairness (whether the offer is fair or unfair), which can be represented graphically by the emotional-disparity graph.

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EMOTION-II Model: A Theoretical Framework for Happy Emotion as a Self-Assessment Measure Indicating the Degree-of-Fit (Congruency) between the Expectancy in Subjective and Objective Realities in Autonomous Control Systems

Cited by 18 publications

References 14 publications

EMOTION-I Model: A Biologically-Based Theoretical Framework for Deriving Emotional Context of Sensation in Autonomous Control Systems

EMOTION-I Model: A Biologically-Based Theoretical Framework for Deriving Emotional Context of Sensation in Autonomous Control Systems

Quantification of Happy Emotion: Dependence on Decisions

Quantification of Happy Emotion: Proportionality Relationship to Gain/Loss

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