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Emotion: A Unified Mechanistic Interpretation from a Cognitive Architecture

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Abstract

This paper reviews a project that attempts to interpret emotion, a complex and multifaceted phenomenon, from a mechanistic point of view, facilitated by an existing comprehensive computational cognitive architecture—CLARION. This cognitive architecture consists of a number of subsystems: the action-centered, non-action-centered, motivational, and metacognitive subsystems. From this perspective, emotion is, first and foremost, motivationally based. It is also action-oriented. It involves many other identifiable cognitive functionalities within these subsystems. Based on these functionalities, we fit the pieces together mechanistically (computationally) within the CLARION framework and capture a variety of important aspects of emotion as documented in the literature.

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Notes

  1. Briefly, this set of hypothesized primary drives bears close relationships to Murray’s needs [30] and Reiss’s motives [35]. The prior justifications of these frameworks may be applied, to a significant extent, to this set of drives as well [25, 30, 35, 46].

  2. Some of the appraisal dimensions might contain sub-dimensions. Some of the dimensions may also represent intermediate processing steps. For example, the “cause” dimension might require a process that can associate environmental factors with beliefs about the cause(s) of those factors.

  3. The MS has been justified extensively elsewhere [46, 49]. It has been used to capture many motivationally based phenomena (e.g., [50, 52, 63]).

  4. The ACS may recommend actions using a combination of the top (explicit) and the bottom (implicit) level, but the bottom level is always activated as part of the decision-making process [44, 49].

  5. Note that for calculating action potentials, inputs to the neural networks might include drive activations but not goals (because it might be done before goal setting).

  6. Previous research has shown that the NACS captures many aspects of human reasoning, including similarity-based reasoning, rule-based reasoning, analogical reasoning, incubation, insight, and creativity [12, 53].

  7. The outcomes of deliberative appraisal from the NACS may be filtered by the MCS. This allows the MCS to select only the knowledge that is relevant to the situation. Filtering could be done based on the current input state and its relevant microfeatures (see section “A Comprehensive Framework Capable of Addressing Emotion”).

  8. In CLARION, the MCS is responsible for many regulatory processes [49], including, among others, goal setting, parameter changing, and input and output filtering. In previous work, the MCS has been shown to capture a variety of psychological phenomena (see, e.g., [63]).

  9. Prior research has extensively explored goal setting using the MCS [46, 52].

  10. These CLARION details outlined above address the processes by which coping strategies are chosen. They also suggest a possible origin for how the appraisal dimensions might be formed, that is, possibly for the sake of facilitating goal setting.

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Acknowledgments

This work has been supported in part by the ONR Grants N00014-08-1-0068 and N00014-13-1-0342.

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  1. Cognitive Sciences Department, Rensselaer Polytechnic Institute, 110 Eighth Street, Carnegie 302A, Troy, NY, 12180, USA

    Ron Sun, Nick Wilson & Michael Lynch

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  1. Ron Sun
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Correspondence to Ron Sun.

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Ron Sun, Nick Wilson, and Michael Lynch declare that they have no conflict of interest.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

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This work has been supported in part by the ONR Grants N00014-08-1-0068 and N00014-13-1-0342 to the first author.

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Sun, R., Wilson, N. & Lynch, M. Emotion: A Unified Mechanistic Interpretation from a Cognitive Architecture. Cogn Comput 8, 1–14 (2016). https://doi.org/10.1007/s12559-015-9374-4

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