scholarly journals Regulating mirroring of emotions A social-specific mechanism?

2021 ◽  
pp. 174702182110497
Author(s):  
Sophie Sowden ◽  
Divyush Khemka ◽  
Caroline Catmur
Keyword(s):  
Individual Differences ◽  
Response Times ◽  
Emotional Responses ◽  
Emotional States ◽  
Interference Effects ◽  
Specific Mechanism ◽  
Brain Responses ◽  
Current Task ◽  
Human Actors ◽  
Experienced Emotion

There is evidence that humans mirror others’ emotional responses: brain responses to observed and experienced emotion overlap, and reaction time costs of observing others’ pain suggest that others’ emotional states interfere with our own. Such emotional mirroring requires regulation to prevent personal distress. However, currently it is unclear whether this “empathic interference effect” is uniquely social, arising only from the observation of human actors, or also from the observation of non-biological objects in “painful” states. Moreover, the degree to which this interference relates to individual differences in self-reported levels of empathy is yet to be revealed. We introduce a modified pain observation task, measuring empathic interference effects induced by observation of painful states applied to both biological and non-biological stimuli. An initial validation study ( N = 50) confirmed that painful states applied to biological stimuli were rated explicitly as more painful than non-painful states applied to biological stimuli, and also than both painful and non-painful states applied to non-biological stimuli. Subsequently, across two independent discovery ( N = 83) and replication ( N = 80) samples, the task elicited slowing of response times during the observation of painful states when compared to non-painful states, but the magnitude of this effect did not differ between biological and non-biological stimuli. Little evidence was found for reliable relationships between empathic interference and self-reported empathy. Caution should therefore be taken in using the current task to pursue an individual differences approach to empathic interference, but the task shows promise for investigating the specificity of the mechanism involved in regulating emotional mirroring.

Individual Differences in Mental Rotation

2013 ◽  
Vol 60 (3) ◽  
pp. 164-171 ◽  
Author(s):  
Peter Khooshabeh ◽  
Mary Hegarty ◽  
Thomas F. Shipley
Keyword(s):  
Individual Differences ◽  
Mental Rotation ◽  
Response Times ◽  
Task Demands ◽  
Mental Rotation Task ◽  
Imagery Ability ◽  
Alternative Strategies

Two experiments tested the hypothesis that imagery ability and figural complexity interact to affect the choice of mental rotation strategies. Participants performed the Shepard and Metzler (1971) mental rotation task. On half of the trials, the 3-D figures were manipulated to create “fragmented” figures, with some cubes missing. Good imagers were less accurate and had longer response times on fragmented figures than on complete figures. Poor imagers performed similarly on fragmented and complete figures. These results suggest that good imagers use holistic mental rotation strategies by default, but switch to alternative strategies depending on task demands, whereas poor imagers are less flexible and use piecemeal strategies regardless of the task demands.

Current Task Activation Predicts General Effects of Advance Preparation in Task Switching

2006 ◽  
Vol 53 (4) ◽  
pp. 260-267 ◽  
Author(s):  
Edita Poljac ◽  
Ab de Haan ◽  
Gerard P. van Galen
Keyword(s):  
Task Performance ◽  
Task Switching ◽  
Shape Matching ◽  
Response Times ◽  
Error Rates ◽  
Matching Task ◽  
Task Switch ◽  
Current Task ◽  
Within Subjects ◽  
And Task

Two experiments investigated the way that beforehand preparation influences general task execution in reaction-time matching tasks. Response times (RTs) and error rates were measured for switching and nonswitching conditions in a color- and shape-matching task. The task blocks could repeat (task repetition) or alternate (task switch), and the preparation interval (PI) was manipulated within-subjects (Experiment 1) and between-subjects (Experiment 2). The study illustrated a comparable general task performance after a long PI for both experiments, within and between PI manipulations. After a short PI, however, the general task performance increased significantly for the between-subjects manipulation of the PI. Furthermore, both experiments demonstrated an analogous preparation effect for both task switching and task repetitions. Next, a consistent switch cost throughout the whole run of trials and a within-run slowing effect were observed in both experiments. Altogether, the present study implies that the effects of the advance preparation go beyond the first trials and confirms different points of the activation approach ( Altmann, 2002) to task switching.

Quality of Life Change and Individual Differences in Stimulus Screening in Relation to Incidence of Illness

1977 ◽  
Vol 41 (1) ◽  
pp. 267-278 ◽  
Author(s):  
Albert Mehrabian ◽  
Marion Ross
Keyword(s):  
Quality Of Life ◽  
Individual Differences ◽  
Well Being ◽  
High Rate ◽  
Life Change ◽  
Emotional States ◽  
Life Changes ◽  
Comprehensive Description ◽  
High Arousal

A considerable amount of evidence indicates that a high rate of life changes—a source of continued and unavoidable arousal—is detrimental to health and psychological well-being. The present study hypothesized that sustained high-arousal states are unpreferred and that the persistence of unpreferred emotional states is harmful. Using a conceptual framework for a comprehensive description of emotional states and the differential preferences for these, it is possible to make more precise predictions on the illness consequences of emotionally unpreferred life changes. Particular hypotheses which received support were that more arousing life changes are more conducive to illness; that among the more arousing life changes, unpleasant changes are associated with more illness than pleasant ones; that unpleasant life changes are more detrimental to health when combined with dominance-inducing life changes; and that arousing life changes are particularly harmful to more arousable (non-screening) individuals.

scholarly journals A Measure of the Interference Effect Distribution

2019 ◽  
Author(s):  
thibault gajdos ◽  
Mathieu Servant ◽  
Thierry Hasbroucq ◽  
Karen Davranche
Keyword(s):  
Experimental Data ◽  
Interference Effect ◽  
Graphical Representation ◽  
Response Times ◽  
Cognitive Model ◽  
Individual Data ◽  
Interference Effects ◽  
Distribution Index

We elaborated an index, the Interference Distribution Index, that allows to quantify the relation between response times and the size of the interference effect. This index is associated to an intuitive graphical representation, the Lorenz-interference plot. We show that this index has some convenient properties in terms of sensitivity to changes in the distribution of the interference effect and to aggregation of individual data. Moreover, it turns out that this index is the only one (up to an arbitrary increasing transformation) possessing these properties. The relevance of this index is illustrated through simulations of a cognitive model of interference effects and reanalysis of experimental data.

scholarly journals Personalised Prediction of Self-Reported Emotion Responses to Music Stimuli

2021 ◽  
Author(s):  
◽  
Kameron Christopher
Keyword(s):  
Deep Learning ◽  
Individual Differences ◽  
Latent Variable ◽  
Emotional Experience ◽  
Emotional Responses ◽  
Stimulus Selection ◽  
Emotion Induction ◽  
Musical Background ◽  
Musical Tempo ◽  
The Many

<p>In this thesis I develop a robust system and method for predicting individuals’ emotional responses to musical stimuli. Music has a powerful effect on human emotion, however the factors that create this emotional experience are poorly understood. Some of these factors are characteristics of the music itself, for example musical tempo, mode, harmony, and timbre are known to affect people's emotional responses. However, the same piece of music can produce different emotional responses in different people, so the ability to use music to induce emotion also depends on predicting the effect of individual differences. These individual differences might include factors such as people's moods, personalities, culture, and musical background amongst others. While many of the factors that contribute to emotional experience have been examined, it is understood that the research in this domain is far from both a) identifying and understanding the many factors that affect an individual’s emotional response to music, and b) using this understanding of factors to inform the selection of stimuli for emotion induction. This unfortunately results in wide variance in emotion induction results, inability to replicate emotional studies, and the inability to control for variables in research.  The approach of this thesis is to therefore model the latent variable contributions to an individual’s emotional experience of music through the application of deep learning and modern recommender system techniques. With each study in this work, I iteratively develop a more reliable and effective system for predicting personalised emotion responses to music, while simultaneously adopting and developing strong and standardised methodology for stimulus selection. The work sees the introduction and validation of a) electronic and loop-based music as reliable stimuli for inducing emotional responses, b) modern recommender systems and deep learning as methods of more reliably predicting individuals' emotion responses, and c) novel understandings of how musical features map to individuals' emotional responses.  The culmination of this research is the development of a personalised emotion prediction system that can better predict individuals emotional responses to music, and can select musical stimuli that are better catered to individual difference. This will allow researchers and practitioners to both more reliably and effectively a) select music stimuli for emotion induction, and b) induce and manipulate target emotional responses in individuals.</p>

scholarly journals Emotional processing of ironic vs. literal criticism in autistic and non-autistic adults: Evidence from eye-tracking

2019 ◽  
Author(s):  
Mahsa Barzy ◽  
Ruth Filik ◽  
David Williams ◽  
Heather Jane Ferguson
Keyword(s):  
Emotional Processing ◽  
Real Life ◽  
Emotional Responses ◽  
Target Sentence ◽  
Emotional States ◽  
Typically Developing ◽  
Two Stage ◽  
Autistic People ◽  
Autistic Adults ◽  
Specific Difficulty

Typically developing (TD) adults are able to keep track of story characters’ emotional states online while reading. Filik et al. (2017) showed that initially, participants expected the victim to be more hurt by ironic comments than literal, but later considered them less hurtful; ironic comments were regarded as more amusing. We examined these processes in autistic adults, since previous research has demonstrated socio-emotional difficulties among autistic people, which may lead to problems processing irony and its related emotional processes despite an intact ability to integrate language in context. We recorded eye movements from autistic and non-autistic adults while they read narratives in which a character (the victim) was either criticised in an ironic or a literal manner by another character (the protagonist). A target sentence then either described the victim as feeling hurt/amused by the comment, or the protagonist as having intended to hurt/amused the victim by making the comment. Results from the non-autistic adults broadly replicated the key findings from Filik et al. (2017), supporting the two-stage account. Importantly, the autistic adults did not show comparable two-stage processing of ironic language; they did not differentiate between the emotional responses for victims or protagonists following ironic vs. literal criticism. These findings suggest that autistic people experience a specific difficulty taking into account other peoples’ communicative intentions (i.e. infer their mental state) to appropriately anticipate emotional responses to an ironic comment. We discuss how these difficulties might link to atypical socio-emotional processing in autism, and the ability to maintain successful real-life social interactions.

Individual Differences in Processing Strategy for a Bargraph Display

1993 ◽  
Vol 37 (19) ◽  
pp. 1315-1319 ◽  
Author(s):  
Julie R. Nowicki ◽  
Bruce G. Coury
Keyword(s):  
Individual Differences ◽  
Response Times ◽  
Reaction Times ◽  
Classification Problem ◽  
Processing Strategy ◽  
Verbal Reports ◽  
Processing Strategies ◽  
Emergent Feature ◽  
Emergent Features ◽  
Individual Strategies

The bargraph has been described in several ways: as a separable display, as an integral display, and as a configural display with emergent features. The versatility of the bargraph may be in part due to the support it provides for different individual processing strategies. This research identifies two general types of strategies - holistic and analytic - which are developed by individuals to solve a classification problem on the bargraph. Multidimensional scaling (MDS), response times, and verbal reports are used to analyze individual strategies. Individuals who developed holistic strategies produced significantly faster reaction times, and reported simple, efficient strategies, with the emergent feature of bargraph shape as an important dimension. The results indicate that the bargraph provides perceptual features which can support several general types of processing strategy.

scholarly journals Individual Differences in Vicarious Pain Perception Linked to Heightened Socially Elicited Emotional States

2018 ◽  
Vol 9 ◽  
Author(s):  
Vanessa Botan ◽  
Natalie C. Bowling ◽  
Michael J. Banissy ◽  
Hugo Critchley ◽  
Jamie Ward
Keyword(s):  
Individual Differences ◽  
Pain Perception ◽  
Emotional States

scholarly journals Large-scale analysis of test–retest reliabilities of self-regulation measures

2019 ◽  
Vol 116 (12) ◽  
pp. 5472-5477 ◽  
Author(s):  
A. Zeynep Enkavi ◽  
Ian W. Eisenberg ◽  
Patrick G. Bissett ◽  
Gina L. Mazza ◽  
David P. MacKinnon ◽  
...  
Keyword(s):  
Individual Differences ◽  
Large Scale ◽  
Response Times ◽  
Self Regulation ◽  
Self Report ◽  
Model Parameters ◽  
Retest Reliability ◽  
Large Scale Analysis ◽  
Behavioral Tasks ◽  
Test Retest Reliability

The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Self-regulation is generally viewed as a trait, and individual differences are quantified using a diverse set of measures, including self-report surveys and behavioral tasks. Accurate characterization of individual differences requires measurement reliability, a property frequently characterized in self-report surveys, but rarely assessed in behavioral tasks. We remedy this gap by (i) providing a comprehensive literature review on an extensive set of self-regulation measures and (ii) empirically evaluating test–retest reliability of this battery in a new sample. We find that dependent variables (DVs) from self-report surveys of self-regulation have high test–retest reliability, while DVs derived from behavioral tasks do not. This holds both in the literature and in our sample, although the test–retest reliability estimates in the literature are highly variable. We confirm that this is due to differences in between-subject variability. We also compare different types of task DVs (e.g., model parameters vs. raw response times) in their suitability as individual difference DVs, finding that certain model parameters are as stable as raw DVs. Our results provide greater psychometric footing for the study of self-regulation and provide guidance for future studies of individual differences in this domain.

Adaptive Personalized Multiple Machine Learning Architecture for Estimating Human Emotional States

2020 ◽  
Vol 24 (5) ◽  
pp. 668-675
Author(s):  
Akihiro Matsufuji ◽  
◽  
Eri Sato-Shimokawara ◽  
Toru Yamaguchi
Keyword(s):  
Machine Learning ◽  
Individual Differences ◽  
Internal State ◽  
Weighted Average ◽  
Learning System ◽  
Emotional States ◽  
Characteristic Model ◽  
Ensemble Machine Learning ◽  
Model Training ◽  
Future Education

Robots have the potential to facilitate the future education of all generations, particularly children. However, existing robots are limited in their ability to automatically perceive and respond to a human emotional states. We hypothesize that these sophisticated models suffer from individual differences in human personality. Therefore, we proposed a multi-characteristic model architecture that combines personalized machine learning models and utilizes the prediction score of each model. This architecture is formed with reference to an ensemble machine learning architecture. In this study, we presented a method for calculating the weighted average in a multi-characteristic architecture by using the similarities between a new sample and the trained characteristics. We estimated the degree of confidence during a communication as a human internal state. Empirical results demonstrate that using the multi-model training of each person’s information to account for individual differences provides improvements over a traditional machine learning system and insight into dealing with various individual differences.

Sign in / Sign up

Export Citation Format

Share Document