scholarly journals Automatic Detection of a Student’s Affective States for Intelligent Teaching Systems

2021 ◽  
Vol 11 (3) ◽  
pp. 331
Author(s):  
Mark H. Myers
Keyword(s):  
Emotional State ◽  
A Priori ◽  
Affective State ◽  
Affective States ◽  
Extraction Techniques ◽  
Frequent Item ◽  
Using Data ◽  
Affect States ◽  
Frequent Item Sets

AutoTutor is an automated computer tutor that simulates human tutors and holds conversations with students in natural language. Using data collected from AutoTutor, the following determinations were sought: Can we automatically classify affect states from intelligent teaching systems to aid in the detection of a learner’s emotional state? Using frequency patterns of AutoTutor feedback and assigned user emotion in a series of pairs, can the next pair of feedback/emotion series be predicted? Through a priori data mining approaches, we found dominant frequent item sets that predict the next set of responses. Thirty-four participants provided 200 turns between the student and the AutoTutor. Two series of attributes and emotions were concatenated into one row to create a record of previous and next set of emotions. Feature extraction techniques, such as multilayer-perceptron and naive Bayes, were performed on the dataset to perform classification for affective state labeling. The emotions ‘Flow’ and ‘Frustration’ had the highest classification of all the other emotions when measured against other emotions and their respective attributes. The most common frequent item sets were ‘Flow’ and ‘Confusion’.

scholarly journals Pharmacological manipulations of judgement bias: a systematic review and meta-analysis

2019 ◽  
Author(s):  
Vikki Neville ◽  
Shinichi Nakagawa ◽  
Josefina Zidar ◽  
Elizabeth S. Paul ◽  
Malgorzata Lagisz ◽  
...  
Keyword(s):  
Systematic Review ◽  
Adverse Effects ◽  
Meta Analysis ◽  
Affective State ◽  
Affective States ◽  
Moderating Factors ◽  
Judgement Bias ◽  
Animal Welfare Science ◽  
Using Data ◽  
Pharmacological Manipulations

AbstractValidated measures of animal affect are crucial to research spanning a number of disciplines including neuroscience, psychopharmacology, and animal welfare science. Judgement bias, which assesses decision-making under ambiguity, is a promising measure of animal affect. One way of validating this measure is to induce affective states using pharmacological manipulations and determine whether the predicted judgement biases are observed. Here, we conducted a systematic review and meta-analysis using data from 19 published research articles that use this approach from which 440 effect sizes were extracted. The results of the meta-analysis suggest that pharmacological manipulations overall altered judgement bias as predicted. However, there were several moderating factors including the neurobiological target of the drug, whether the drug was hypothesised to induce a relatively positive or negative affective state, dosage, and the presented cue. This may partially reflect interference from adverse effects of the drug, such as sedation. Thus, while judgement bias can be used to measure pharmacologically-induced affective states, potential adverse effects of the drug should be considered when interpreting results.

scholarly journals Are These Cats Playing? A Closer Look at Social Play in Cats and Proposal for a Psychobiological Approach and Standard Terminology

2021 ◽  
Vol 8 ◽  
Author(s):  
Noema Gajdoš Kmecová ◽  
Barbara Pet'ková ◽  
Jana Kottferová ◽  
Lenka Skurková ◽  
Daniel S. Mills
Keyword(s):  
Social Relationships ◽  
Physical Environment ◽  
Emotional State ◽  
Affective State ◽  
Social Play ◽  
Future Research ◽  
Domestic Cats ◽  
Standard Terminology ◽  
Psychobiological Approach

Play in domestic cats has been largely studied using a contextual approach, i.e., with a focus on what the cat is playing with, such as an object, itself or another cat. Such classification may be superficially attractive scientifically but it limits the ability to investigate function. We propose consideration of a psychobiological approach, which increases attention on hypotheses about the motivational and emotional state of the actors, may be more valuable. This may be particularly important in the case of intercat exchanges that might involve play, for example when one cat may chase another which does not want to be chased, the general interaction should not be considered playful. Key to improving the scientific study of such interactions is the need to adopt a common terminology, thus we synthesise a common ethogram from the published literature. Secondly at the heart of a psychobiological approach is a consideration of both the affective state and motivational goal of each actor in an interaction, since they may not be congruent, and recognition of the hypothetical nature of any such functional classification. However, this bottom up approach provides valuable insights that can be tested. We argue that when one cat treats another as an object or prey, such activity relates to the former cat seeking to learn about its own skills in relation to manipulating its physical environment (prey are not considered part of the complex social relationships and thus social environment of an individual). However, when interaction between cats is reciprocal it may function to facilitate social learning and may be best described as mutual social play. It needs to be recognised that interactions are dynamic and thus our classification of a situation needs to be flexible. So mutual social play may turn into a form of non-reciprocal interaction. We conclude by outlining priorities for future research to help us improve our ability to answer the question “Are these cats playing?” in a wider range of contexts.

Associations Between Self-Report of Emotional State and the EEG Patterns in Affective Disorders Patients

2010 ◽  
Vol 24 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Miroslaw Wyczesany ◽  
Jan Kaiser ◽  
Anton M. L. Coenen
Keyword(s):  
State Energy ◽  
Emotional State ◽  
Spectral Power ◽  
Bipolar Affective Disorder ◽  
Affective State ◽  
Manic Episode ◽  
High Energy ◽  
Self Report ◽  
Major Depressive ◽  
High Tension

The study determines the associations between self-report of ongoing emotional state and EEG patterns. A group of 31 hospitalized patients were enrolled with three types of diagnosis: major depressive disorder, manic episode of bipolar affective disorder, and nonaffective patients. The Thayer ADACL checklist, which yields two subjective dimensions, was used for the assessment of affective state: Energy Tiredness (ET) and Tension Calmness (TC). Quantitative analysis of EEG was based on EEG spectral power and laterality coefficient (LC). Only the ET scale showed relationships with the laterality coefficient. The high-energy group showed right shift of activity in frontocentral and posterior areas visible in alpha and beta range, respectively. No effect of ET estimation on prefrontal asymmetry was observed. For the TC scale, an estimation of high tension was related to right prefrontal dominance and right posterior activation in beta1 band. Also, decrease of alpha2 power together with increase of beta2 power was observed over the entire scalp.

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

scholarly journals Towards estimating affective states in Virtual Reality based on behavioral data

2021 ◽  
Author(s):  
Valentin Holzwarth ◽  
Johannes Schneider ◽  
Joshua Handali ◽  
Joy Gisler ◽  
Christian Hirt ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Adaptive Systems ◽  
User Study ◽  
Affective State ◽  
Head Movements ◽  
Affective States ◽  
Physical Demand ◽  
Perceived Performance ◽  
Sequential Administration ◽  
Better Than

AbstractInferring users’ perceptions of Virtual Environments (VEs) is essential for Virtual Reality (VR) research. Traditionally, this is achieved through assessing users’ affective states before and after being exposed to a VE, based on standardized, self-assessment questionnaires. The main disadvantage of questionnaires is their sequential administration, i.e., a user’s affective state is measured asynchronously to its generation within the VE. A synchronous measurement of users’ affective states would be highly favorable, e.g., in the context of adaptive systems. Drawing from nonverbal behavior research, we argue that behavioral measures could be a powerful approach to assess users’ affective states in VR. In this paper, we contribute by providing methods and measures evaluated in a user study involving 42 participants to assess a users’ affective states by measuring head movements during VR exposure. We show that head yaw significantly correlates with presence, mental and physical demand, perceived performance, and system usability. We also exploit the identified relationships for two practical tasks that are based on head yaw: (1) predicting a user’s affective state, and (2) detecting manipulated questionnaire answers, i.e., answers that are possibly non-truthful. We found that affective states can be predicted significantly better than a naive estimate for mental demand, physical demand, perceived performance, and usability. Further, manipulated or non-truthful answers can also be estimated significantly better than by a naive approach. These findings mark an initial step in the development of novel methods to assess user perception of VEs.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

scholarly journals The Interpretation of E-Motions in Faces and Bodies Derived from Static Artworks by Individuals with High Functioning Autistic Spectrum

Vision ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 17
Author(s):  
Maria Elisa Della-Torre ◽  
Daniele Zavagno ◽  
Rossana Actis-Grosso
Keyword(s):  
Positive Emotions ◽  
Emotional Experience ◽  
Affective State ◽  
Likert Scale ◽  
Autism Spectrum ◽  
Emotional Expressions ◽  
Affective States ◽  
Dynamic Component ◽  
Specific Emotion ◽  
Similar Range

E-motions are defined as those affective states the expressions of which—conveyed either by static faces or body posture—embody a dynamic component and, consequently, convey a higher sense of dynamicity than other emotional expressions. An experiment is presented, aimed at testing whether e-motions are perceived as such also by individuals with autism spectrum disorders (ASDs), which have been associated with impairments in emotion recognition and in motion perception. To this aim we replicate with ASD individuals a study, originally conducted with typically developed individuals (TDs), in which we showed to both ASD and TD participants 14 bodiless heads and 14 headless bodies taken from eleven static artworks and four drawings. The Experiment was divided into two sessions. In Session 1 participants were asked to freely associate each stimulus to an emotion or an affective state (Task 1, option A); if they were unable to find a specific emotion, the experimenter showed them a list of eight possible emotions (words) and asked them to choose one from such list, that best described the affective state portrayed in the image (Task 1, option B). After their choice, they were asked to rate the intensity of the perceived emotion on a seven point Likert scale (Task 2). In Session 2 participants were requested to evaluate the degree of dynamicity conveyed by each stimulus on a 7 point Likert scale. Results showed that ASDs and TDs shared a similar range of verbal expressions defining emotions; however, ASDs (i) showed an impairment in the ability to spontaneously assign an emotion to a headless body, and (ii) they more frequently used terms denoting negative emotions (for both faces and bodies) as compared to neutral emotions, which in turn were more frequently used by TDs. No difference emerged between the two groups for positive emotions, with happiness being the emotion better recognized in both faces and in bodies. Although overall there are no significant differences between the two groups with respect to the emotions assigned to the images and the degree of perceived dynamicity, the interaction Artwork x Group showed that for some images ASDs assigned a different value than TDs to perceived dynamicity. Moreover, two images were interpreted by ASDs as conveying completely different emotions than those perceived by TDs. Results are discussed in light of the ability of ASDs to resolve ambiguity, and of possible different cognitive styles characterizing the aesthetical/emotional experience.

scholarly journals A Tree Projection Algorithm for Generation of Frequent Item Sets

2001 ◽  
Vol 61 (3) ◽  
pp. 350-371 ◽  
Author(s):  
Ramesh C. Agarwal ◽  
Charu C. Aggarwal ◽  
V.V.V. Prasad
Keyword(s):  
Projection Algorithm ◽  
Frequent Item ◽  
Frequent Item Sets
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