Can Masked Emotion-Laden Words Prime Emotion-Label Words? An ERP Test on the Mediated Account

The present event-related potential (ERP) study explored whether masked emotion-laden words could facilitate the processing of both emotion-label words and emotion-laden words in a valence judgment task. The results revealed that emotion-laden words as primes failed to influence target emotion-label word processing, whereas emotion-laden words facilitated target emotion-laden words in the congruent condition. Specifically, decreased late positivity complex (LPC) was elicited by emotion-laden words primed by emotion-laden words of the same valence than those primed by emotion-laden words of different valence. Nevertheless, no difference was observed for emotion-label words as targets. These findings supported the mediated account that claimed emotion-laden words engendered emotion via the mediation of emotion-label words and hypothesized that emotion-laden words could not prime emotion-label words in the masked priming paradigm. Moreover, this study provided additional evidence showing the distinction between emotion-laden words and emotion-label words.

Emotion Label Enhancement via Emotion Wheel and Lexicon

Emotion Distribution Learning (EDL) is a recently proposed multiemotion analysis paradigm, which identifies basic emotions with different degrees of expression in a sentence. Different from traditional methods, EDL quantitatively models the expression degree of the corresponding emotion on the given instance in an emotion distribution. However, emotion labels are crisp in most existing emotion datasets. To utilize traditional emotion datasets in EDL, label enhancement aims to convert logical emotion labels into emotion distributions. This paper proposed a novel label enhancement method, called Emotion Wheel and Lexicon-based emotion distribution Label Enhancement (EWLLE), utilizing the affective words’ linguistic emotional information and the psychological knowledge of Plutchik’s emotion wheel. The EWLLE method generates separate discrete Gaussian distributions for the emotion label of sentence and the emotion labels of sentiment words based on the psychological emotion distance and combines the two types of information into a unified emotion distribution by superposition of the distributions. The extensive experiments on 4 commonly used text emotion datasets showed that the proposed EWLLE method has a distinct advantage over the existing EDL label enhancement methods in the emotion classification task.

Exploring Affective Priming Effect of Emotion-Label Words and Emotion-Laden Words: An Event-Related Potential Study

In order to explore the affective priming effect of emotion-label words and emotion-laden words, the current study used unmasked (Experiment 1) and masked (Experiment 2) priming paradigm by including emotion-label words (e.g., sadness, anger) and emotion-laden words (e.g., death, gift) as primes and examined how the two kinds of words acted upon the processing of the target words (all emotion-laden words). Participants were instructed to decide the valence of target words, and their electroencephalogram was recorded at the same time. The behavioral and event-related potential (ERP) results showed that positive words produced a priming effect whereas negative words inhibited target word processing (Experiment 1). In Experiment 2, the inhibition effect of negative emotion-label words on emotion word recognition was found in both behavioral and ERP results, suggesting that modulation of emotion word type on emotion word processing could be observed even in the masked priming paradigm. The two experiments further supported the necessity of defining emotion words under an emotion word type perspective. The implications of the findings are proffered. Specifically, a clear understanding of emotion-label words and emotion-laden words can improve the effectiveness of emotional communications in clinical settings. Theoretically, the emotion word type perspective awaits further explorations and is still at its infancy.

Automated Emotion Classification in Free-moving Rats

Studies involving emotion often use animal models and currently rely on manual labelling by researchers. This human-driven labelling approach leads to a number of challenges such as: long analysis times, imprecise results, observer drift, and varying correlation between observers. These problems impact reproducibility, and have contributed to our lack of understanding of fundamental mechanical questions such as how emotions arise from neuronal circuits. Recent success of machine learning models across similar problems show that it can help to mitigate these challenges while meeting or exceeding human accuracy. We developed a classifier pipeline that takes in videos and produces an emotion label. The pipeline extracts body part positions from each frame using a pose estimator and feeds them into an Artificial Neural Network (ANN) classifier built using stacked Long Short Term Memory (LSTM) layers. The data was collected by treating nine rats with Lypopolysaccharide (LPS) injections (10mg/kg). First, rats were recorded for 10 minutes under control conditions with no manipulation and no observed symptoms of stress or malaise. A week later, rats were injected with LPS and filmed for 10 minutes two hours post-injection. The classifier pipeline developed correctly labelled 78% of the 125,040 video segments from 8 test videos. When combined with a vote-based system, this led to 7 of the 8 test videos being classified correctly which was the same accuracy attained by a human expert from the lab. The test videos had varying environments and used rats that were different from the training videos, providing evidence of a degree of robustness in the model. Future work will focus on expanding the test data and incorporating models for 3D pose estimation and behavioral classification.

Affective Context and the Interpretation of Facial Expressions in Portrait Paintings

The present study examined the effects of affective context on evaluation of facial expression of emotion in portrait paintings. Pleasant, unpleasant, and neutral context photographs were presented prior to target portrait paintings. The participants’ task was to view the portrait painting and choose an emotion label that fit the subject of the painting. The results from Experiment 1 indicated that when preceded by pleasant context, the faces in the portraits were labeled as happier. When preceded by unpleasant context, they were labeled as less happy, sadder, and more fearful. In Experiment 2, the labeling effects disappeared when context photographs were presented at a subthreshold 20 ms SOA. In both experiments, context affected processing times, with times slower for pleasant context and faster for unpleasant context. The results suggest that the context effects depend on both automatic and controlled processing of affective content contained in context photographs.

Emotion Prediction with Weighted Appraisal Models – Validating a Psychological Theory of Affect

Appraisal theories are a prominent approach for the explanation and prediction of emotions. According to these theories, the subjective perception of an emotion results from a series of specific event evaluations. To validate and extend one of the most known representatives of appraisal theory, the Component Process Model by Klaus Scherer, we implemented four computational appraisal models that predicted emotion labels based on prototype similarity calculations. Different weighting algorithms, mapping the models' input to a distinct emotion label, were integrated in the models. We evaluated the plausibility of the models' structure by assessing their predictive power and comparing their performance to a baseline model and a highly predictive machine learning algorithm. Model parameters were estimated from empirical data and validated out-of-sample. All models were notably better than the baseline model and able to explain part of the variance in the emotion labels. The preferred model, yielding a relatively high performance and stable parameter estimations, was able to predict a correct emotion label with an accuracy of 40.2% and a correct emotion family with an accuracy of 76.9%. The weighting algorithm of this favored model corresponds to the weighting complexity implied by the Component Process Model, but uses differing weighting parameters.

Emotion Words Modulate Early Conflict Processing in a Flanker Task: Differentiating Emotion-Label Words and Emotion-Laden Words in Second Language

Emotion words modulate conflict processing, even at an early stage (i.e., N200). However, the previous studies implicitly mixed emotion-label words and emotion-laden words together and mostly concentrated on first language (L1) rather than on second language (L2). The current study aimed to investigate whether L2 negative emotion-label words, negative emotion-laden words, and neutral words would affect conflict processing in a flanker task by using event-related potential (ERP) measurements. Twenty Chinese-English bilinguals completed a modified flanker task to decide the color of the target words. The results revealed that only L2 negative emotion-label words elicited larger left frontal N200 in the incongruent condition than in the congruent condition. No significant difference between the two conditions was observed for L2 negative emotion-laden words or neutral words. This research demonstrated that L2 emotion words could also modulate early conflict processing, at least for L2 negative emotion-label words.