A computational model of affects

Emotions and feelings (i.e. affects) are a central feature of human behavior. Due to complexity and interdisciplinarity of affective phenomena, attempts to define them have often been unsatisfactory. This article provides a simple logical structure, in which affective concepts can be defined. The set of affects defined is similar to the set of emotions covered in the OCC model, but the model presented in this article is fully computationally defined, whereas the OCC model depends on undefined concepts. Following Matthis, affects are seen as unconscious, emotions as preconscious and feelings as conscious. Affects are thus a superclass of emotions and feelings with regards to consciousness. A set of affective states and related affect-specific behaviors and strategies can be defined with unconscious affects only. In addition, affects are defined as processes of change in the body state, that have specific triggers. For example, an affect of hope is defined as a specific body state that is triggered when the agent is becomes informed about a future event, that is positive with regards to the agent’s needs. Affects are differentiated from each other by types of causing events. Affects caused by unexpected positive, neutral and negative events are delight, surprise and fright, respectively. Affects caused by expected positive and negative future events are hope and fear. Affects caused by expected past events are as follows: satisfaction results from a positive expectation being fulfilled, disappointment results from a positive expectation not being fulfilled, fears-confirmed results from a negative expectation being fulfilled, and relief results from a negative expectation not being fulfilled. Pride is targeted towards a self-originated positive event, and shame towards a self-originated negative event. Remorse is targeted towards a self-originated action causing a negative event. Pity is targeted towards a liked agent experiencing a negative event, and happy-for towards a liked agent experiencing a positive event. Resentment is targeted towards a disliked agent experiencing a positive event, and gloating towards a disliked agent experiencing a negative event. An agent is liked/loved if it has produced a net utility greater than zero, and disliked/hated if the net utility is lower than zero. An agent is desired if it is expected to produce a positive net utility in the future, and disliked if the expected net utility is negative. The above model for unconscious affects is easily computationally implementable, and may be used as a starting point in building believable simulation models of human behavior. The models can be used as a starting point in the development of computational psychological, psychiatric, sociological and criminological theories, or in e.g. computer games. [please cite using the arXiv url below.

A non-mediational approach to emotions and feelings

ABSTRACT: The present analysis proposes a non-mediational approach to the study of affective phenomena. It starts off with the common recognition that “emotion” is not a technical term. Even so, researchers often treat it as if it were, confusing ordinary language with technical language. This leads to two problems: first, a referentialist bias, according to which we assume emotions to be something unapparent that one must infer and describe; and second, the nominalist fallacy, according to which we assume that emotions have causal effects on actions by the fact of naming them. I review some proposals to solve the problem, among which are some behavioral alternatives. Although these alternatives overcome many of the problems mentioned, they do not completely avoid them. I conclude that a strict non-mediational approach is possible and necessary. It supports the analytical separation of ordinary and technical language. Technical language abstracts relevant properties of ordinary language that become relevant parameters to model certain emotions, as they are referred to in ordinary language. I present some possible parameters and examples for consideration and conclude that the non-mediational approach is a plausible alternative that can stimulate research programs to find natural regularities in affective phenomena

A Non-mediational Approach to Emotions and Feelings

The present analysis proposes a non-mediational approach to the study of affective phenomena. It starts off with the common recognition that “emotion” is not a technical term. Even so, researchers often treat it as if it were, confusing ordinary language with technical language. This leads to two problems: first, a referentialist bias, according to which we assume emotions to be something unapparent that one must infer and describe; and second, the nominalist fallacy, according to which we assume that emotions have causal effects on actions by the fact of naming them. I review some proposals to solve the problem, among which are some behavioral alternatives. Although these alternatives overcome many of the problems mentioned, they do not completely avoid them. I conclude that a strict non-mediational approach is possible and necessary. It supports the analytical separation of ordinary and technical language. Technical language abstracts relevant properties of ordinary language that become relevant parameters to model certain emotions, as they are referred to in ordinary language. I present some possible parameters and examples for consideration and conclude that the non-mediational approach is a plausible alternative that can stimulate research programs to find natural regularities in affective phenomena

Implicit emotion detection in text

In text, emotion can be expressed explicitly, using emotion-bearing words (e.g. happy, guilty) or implicitly without emotion-bearing words. Existing approaches focus on the detection of explicitly expressed emotion in text. However, there are various ways to express and convey emotions without the use of these emotion-bearing words. For example, given two sentences: “The outcome of my exam makes me happy” and “I passed my exam”, both sentences express happiness, with the first expressing it explicitly and the other implying it. In this thesis, we investigate implicit emotion detection in text. We propose a rule-based approach for implicit emotion detection, which can be used without labeled corpora for training. Our results show that our approach outperforms the lexicon matching method consistently and gives competitive performance in comparison to supervised classifiers. Given that emotions such as guilt and admiration which often require the identification of blameworthiness and praiseworthiness, we also propose an approach for the detection of blame and praise in text, using an adapted psychology model, Path model to blame. Lack of benchmarking dataset led us to construct a corpus containing comments of individuals’ emotional experiences annotated as blame, praise or others. Since implicit emotion detection might be useful for conflict-of-interest (CoI) detection in Wikipedia articles, we built a CoI corpus and explored various features including linguistic and stylometric, presentation, bias and emotion features. Our results show that emotion features are important when using Nave Bayes, but the best performance is obtained with SVM on linguistic and stylometric features only. Overall, we show that a rule-based approach can be used to detect implicit emotion in the absence of labelled data; it is feasible to adopt the psychology path model to blame for blame/praise detection from text, and implicit emotion detection is beneficial for CoI detection in Wikipedia articles

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

Semantic Clustering of Basic Emotion Sets

A plethora of words are used to describe the spectrum of human emotions, but how many emotions are there really, and how do they interact? Over the past few decades, several theories of emotion have been proposed, each based around the existence of a set of basic emotions, and each supported by an extensive variety of research including studies in facial expression, ethology, neurology and physiology. Here we present research based ona theory that people transmit their understanding of emotions through the language they use surrounding emotion keywords. Using a labelled corpus of over 21,000 tweets, six of the basic emotion sets proposed in existing literature were analysed using Latent Semantic Clustering (LSC), evaluating the distinctiveness of the semantic meaning attached to the emotional label. We hypothesise that the more distinct the language is used to express acertain emotion, then the more distinct the perception (including proprioception) of that emotion is, and thus more basic. This allows us to select the dimensions best representing the entire spectrum of emotion. We find that Ekman’s set, arguably the most frequently used for classifying emotions, is in fact the most semantically distinct overall. Next, takingall analysed (that is, previously proposed) emotion terms into account, we determine the optimal semantically irreducible basic emotion set using an iterative LSC algorithm. Our newly-derived set (Accepting, Ashamed, Contempt, Interested, Joyful, Pleased, Sleepy, Stressed) generates a 6.1% increase in distinctiveness over Ekman’s set (Angry, Disgusted, Joyful, Sad, Scared). We also demonstrate how using LSC data can help visualise emotions. We introduce the concept of an Emotion Profile and briefly analysecompound emotions both visually and mathematically

Ayahuasca in the treatment of bipolar disorder with psychotic features—A retrospective case study

Ayahuasca is a plant-based brew of indigenous Amazonian origin. It has psychedelic, anti-inflammatory, neuroprotective, cytotoxic, and anti-parasitic effects, which are primarily due to monoamine oxidase inhibitors (MAOIs) and N,N-dimethyltryptamine (DMT). This article describes the case of a woman in her late thirties with complex trauma due to severe, years-long sexual abuse in early childhood, resulting in a decades-long chronic condition involving suicidality. She was diagnosed with bipolar disorder and borderline personality disorder, but refused to accept either of them. She presented with delusional parasitosis and deep dissociation. Despite being severely psychotic in private, she appeared high-functioning in public, hiding most of her symptoms. In her mid-thirties, she participated in an ayahuasca ceremony in a legal setting. It resolved her suicidality, eliminated her social isolation, and reduced her shame related to her early trauma. Nine more ceremonies alleviated her distress further. Her abuser also participated in an ayahuasca ceremony and confirmed her memories of childhood abuse. The first interview was conducted 1.5 years after her first ceremony, and a follow-up interview 2.5 years later. She had experienced sixteen additional ceremonies, recognized the validity of her bipolar disorder diagnosis, and believed her early trauma to be its sole cause. Her core trauma remained partially unresolved, but her dissociative symptoms continued to decrease. She had observed several other instances of psychosis and bipolar disorder in which ayahuasca had resulted in positive effects. This case study contributes to a better understanding of the use of ayahuasca in bipolar disorder and severe traumatization. It also reviews the current state-of-the-art in the treatment of bipolar disorder using low-dose ayahuasca, and a case in which bipolar disorder was resolved with LSD. doi: 10.13140/RG.2.2.21294.1824