Subliminally Enhancing Self-esteem: Impact on Learner Performance and Affective State

Jraidi, Imène; Frasson, Claude

doi:10.1007/978-3-642-13437-1_2

Cited by 22 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Grumm, Nestler, and Collani (2009) reported similar effects in a larger sample, but no effect was found on explicit state self-esteem. A nearly identical SEC procedure was used by Jraidi and Frasson (2010) and resulted in higher implicit self-esteem, learning performance, positive emotions, and delta-low-theta activity, which is indicative of higher concentration. Furthermore, Svaldi, Zimmermann, and Naumann (2012) showed that SEC using slightly longer presentation times for stimuli and more trials resulted in higher implicit self-esteem.…”

Section: Subliminal Evaluative Conditioningmentioning

confidence: 99%

Converging evidence that subliminal evaluative conditioning does not affect self‐esteem or cardiovascular activity

2017

View full text Add to dashboard Cite

Self‐esteem moderates the relationship between stress and (cardiovascular) health, with low self‐esteem potentially exacerbating the impact of stressors. Boosting self‐esteem may therefore help to buffer against stress. Subliminal evaluative conditioning (SEC), which subliminally couples self‐words with positive words, has previously been successfully used to boost self‐esteem, but the existing studies are in need of replication. In this article, we aimed to replicate and extend previous SEC studies. The first 2 experiments simultaneously examined whether SEC increased self‐esteem (Experiment 1, n = 84) and reduced cardiovascular reactivity to a stressor in high worriers (Experiment 2, n = 77). On the basis of these results, the 3rd experiment was set up to examine whether an adjusted personalized SEC task increased self‐esteem and reduced cardiac activity in high worriers (n = 81). Across the 3 experiments, no effects were found of SEC on implicit or explicit self‐esteem or affect or on cardiovascular (re)activity compared to a control condition in which the self was coupled with neutral words. The results do not support the use of the subliminal intervention in its current format. As stress is highly prevalent, future studies should focus on developing other cost‐effective and evidence‐based interventions.

show abstract

Section: Subliminal Evaluative Conditioningmentioning

confidence: 99%

Converging evidence that subliminal evaluative conditioning does not affect self‐esteem or cardiovascular activity

2017

View full text Add to dashboard Cite

show abstract

“…Indeed, the potential integration of EEG into the ITS community can greatly enhance mental state detection and adaptation. Indeed, recent work in the ITS field combined neuroscience, psychology and pedagogy by demonstrating that using neurological properties of unconscious cognition can have a positive impact on learner's intuition as well as his self-esteem in problem solving tasks Jraidi and Frasson 2010). Finally, Chaouachi and al.…”

Section: Future Directionsmentioning

confidence: 99%

Managing Learner’s Affective States in Intelligent Tutoring Systems

Frasson

Chalfoun

2010

Studies in Computational Intelligence

Self Cite

View full text Add to dashboard Cite

Abstract. Recent works in Computer Science, Neurosciences, Education, and Psychology have shown that emotions play an important role in learning. Learner's cognitive ability depends on his emotions. We will point out the role of emotions in learning, distinguishing the different types and models of emotions which have been considered until now. We will address an important issue concerning the different means to detect emotions and introduce recent approaches to measure brain activity using Electroencephalograms (EEG). Knowing the influence of emotional events on learning it becomes important to induce specific emotions so that the learner can be in a more adequate state for better learning or memorization. To this end, we will introduce the main components of an emotionally intelligent tutoring system able to recognize, interpret and influence learner's emotions. We will talk about specific virtual agents that can influence learner's emotions to motivate and encourage him and involve a more cooperative work, particularly in narrative learning environments. Pushing further this paradigm, we will present the advantages and perspectives of subliminal learning which intervenes without conscious perception. Finally, we conclude with new directions to emotional learning. Emotions and LearningLearning involves mainly two processes: reasoning and memorizing. Reasoning is developed during cognitive tasks in which a learner tries to solve a problem using deductions or inductions. If a new knowledge (fact, rule) is obtained then the knowledge will be memorized in long term memory. Memorizing is also triggered when a learner tries to remember a previously acquired element using several means such as similarity, imagery, case-based analysis, etc. The two processes work alternatively and can be considered as a "Cartesian" approach of brain functions. Recent researches in neurosciences, education, and psychology have shown that emotions play an important role in learning. People often separate emotions and reason, believing that emotions are an obstacle in rational decision making or

show abstract

“…Study Participants [50] and [89] 50 tutoring sessions [41] 39 participants [45] 25 students [25] 6 course authors [36] 4 classrooms [24] 2 classrooms, 58 students [56] 18 learners [2] 106 students [47] 105 students In order to reflect on the value of our research, we begin with a comparison to related work conducted in the area of e-learning and intelligent tutoring systems.…”

Section: In Contrast To Studies With Human Studentsmentioning

confidence: 99%

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

Champaign

2010

Advances in Artificial Intelligence

View full text Add to dashboard Cite

In this thesis, we present an artificial intelligence approach for tutoring students in environments where there is a large repository of possible learning objects (e.g. texts, videos). In particular, we advocate that students learn on the basis of past experiences of peers. This aligns with McCalla's proposed ecological approach for intelligent tutoring, where a learning object's history-of-use is retained and leveraged to instruct future students. We offer three distinct models that serve to deliver the required intelligent tutoring: (i) a curriculum sequencing algorithm selecting which learning objects to present to students based on benefits to knowledge obtained by similar peers (ii) a framework for peers to provide commentary on the learning objects they've experienced (annotations) together with an algorithm for reasoning about which annotations to present to students that incorporates modeling trust in annotators (i.e. their reputation) and ratings provided by students (votes for and against) for the annotations they have been shown (iii) an opportunity for peers to guide the growth of the corpus by proposing divisions of current objects, together with an algorithm for reasoning about which of these new objects should be offered to students in order to enhance their learning. All three components are validated as beneficial in improving the learning of students. This is first of all achieved through a novel approach of simulated student learning, designed to enable the tracking of the experiences of a very large number of peers with an extensive repository of objects, through the effective modeling of knowledge gains. This is also coupled with a preliminary study with human participants that confirms the value of our framework. In all, we offer a rich and varied role for peers in guiding the learning of students in intelligent tutoring environments, made possible by careful modeling of the students who are being taught and of the potential benefits to learning that would be derived with the selection of appropriate tutorial content.iii Acknowledgements First and foremost I would like to thank my supervisor, Professor Robin Cohen, for guiding me through the completion of this dissertation and my PhD studies. Robin overwhelmed me with the amount of time and effort she put in to helping me complete my doctoral work. My committee members, Professor Edward Lank and Professor Peter van Beek, have provided detailed and helpful feedback throughout the process, which I greatly appreciate. Thanks as well to Professor Gord McCalla for his helpful advice. My appreciation goes out to the anonymous reviewers who considered earlier versions of this work and provided feedback during conference and journal submissions over the last 4 years.I would like to acknowledge Ahmed Ataullah, Simina Brânzei, John Doucette and Lachlan Dufton for being wonderful officemates: always willing to talk through issues I was working on, go for coffee and share a laugh. I greatly enjoyed working in the AI Group at the University of ...

show abstract

Subliminally Enhancing Self-esteem: Impact on Learner Performance and Affective State

Cited by 22 publications

References 8 publications

Converging evidence that subliminal evaluative conditioning does not affect self‐esteem or cardiovascular activity

Converging evidence that subliminal evaluative conditioning does not affect self‐esteem or cardiovascular activity

Managing Learner’s Affective States in Intelligent Tutoring Systems

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

Contact Info

Product

Resources

About