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  1. Article ; Online: Face and context integration in emotion inference is limited and variable across categories and individuals.

    Goel, Srishti / Jara-Ettinger, Julian / Ong, Desmond C / Gendron, Maria

    Nature communications

    2024  Volume 15, Issue 1, Page(s) 2443

    Abstract: The ability to make nuanced inferences about other people's emotional states is central to social functioning. While emotion inferences can be sensitive to both facial movements and the situational context that they occur in, relatively little is ... ...

    Abstract The ability to make nuanced inferences about other people's emotional states is central to social functioning. While emotion inferences can be sensitive to both facial movements and the situational context that they occur in, relatively little is understood about when these two sources of information are integrated across emotion categories and individuals. In a series of studies, we use one archival and five empirical datasets to demonstrate that people could be integrating, but that emotion inferences are just as well (and sometimes better) captured by knowledge of the situation alone, while isolated facial cues are insufficient. Further, people integrate facial cues more for categories for which they most frequently encounter facial expressions in everyday life (e.g., happiness). People are also moderately stable over time in their reliance on situational cues and integration of cues and those who reliably utilize situation cues more also have better situated emotion knowledge. These findings underscore the importance of studying variability in reliance on and integration of cues.
    MeSH term(s) Humans ; Emotions ; Happiness ; Facial Expression ; Movement ; Cues
    Language English
    Publishing date 2024-03-19
    Publishing country England
    Document type Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-024-46670-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: An Ethical Framework for Guiding the Development of Affectively-Aware Artificial Intelligence

    Ong, Desmond C.

    2021  

    Abstract: The recent rapid advancements in artificial intelligence research and deployment have sparked more discussion about the potential ramifications of socially- and emotionally-intelligent AI. The question is not if research can produce such affectively- ... ...

    Abstract The recent rapid advancements in artificial intelligence research and deployment have sparked more discussion about the potential ramifications of socially- and emotionally-intelligent AI. The question is not if research can produce such affectively-aware AI, but when it will. What will it mean for society when machines -- and the corporations and governments they serve -- can "read" people's minds and emotions? What should developers and operators of such AI do, and what should they not do? The goal of this article is to pre-empt some of the potential implications of these developments, and propose a set of guidelines for evaluating the (moral and) ethical consequences of affectively-aware AI, in order to guide researchers, industry professionals, and policy-makers. We propose a multi-stakeholder analysis framework that separates the ethical responsibilities of AI Developers vis-\`a-vis the entities that deploy such AI -- which we term Operators. Our analysis produces two pillars that clarify the responsibilities of each of these stakeholders: Provable Beneficence, which rests on proving the effectiveness of the AI, and Responsible Stewardship, which governs responsible collection, use, and storage of data and the decisions made from such data. We end with recommendations for researchers, developers, operators, as well as regulators and law-makers.

    Comment: Accepted at IEEE Affective Computing and Intelligent Interaction 2021
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Computers and Society
    Subject code 170
    Publishing date 2021-07-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition

    Suresh, Varsha / Ong, Desmond C.

    2023  

    Abstract: Machine learning models automatically learn discriminative features from the data, and are therefore susceptible to learn strongly-correlated biases, such as using protected attributes like gender and race. Most existing bias mitigation approaches aim to ...

    Abstract Machine learning models automatically learn discriminative features from the data, and are therefore susceptible to learn strongly-correlated biases, such as using protected attributes like gender and race. Most existing bias mitigation approaches aim to explicitly reduce the model's focus on these protected features. In this work, we propose to mitigate bias by explicitly guiding the model's focus towards task-relevant features using domain knowledge, and we hypothesize that this can indirectly reduce the dependence of the model on spurious correlations it learns from the data. We explore bias mitigation in facial expression recognition systems using facial Action Units (AUs) as the task-relevant feature. To this end, we introduce Feature-based Positive Matching Contrastive Loss which learns the distances between the positives of a sample based on the similarity between their corresponding AU embeddings. We compare our approach with representative baselines and show that incorporating task-relevant features via our method can improve model fairness at minimal cost to classification performance.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
    Subject code 004
    Publishing date 2023-03-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: Management of a patient with a severely infraoccluded primary molar and hypodontia.

    Ng, Alaina / Ong, Desmond / Goh, Phillip

    Clinical case reports

    2022  Volume 10, Issue 11, Page(s) e6482

    Abstract: Infraocclusion of a primary molar is a relatively common occurrence which seldom leads to serious complications, particularly when the succedaneous permanent tooth is present. Despite the relative infrequency of unfavorable outcomes, an infraoccluded ... ...

    Abstract Infraocclusion of a primary molar is a relatively common occurrence which seldom leads to serious complications, particularly when the succedaneous permanent tooth is present. Despite the relative infrequency of unfavorable outcomes, an infraoccluded primary molar may be associated with other dental anomalies, including hypodontia. If diagnosis is delayed, infraocclusion of a primary molar can become severe, with associated negative effects on the adjacent teeth and alveolar bone. This emphasizes the need for routine and periodic orthodontic assessment of growing patients. Any deviation from the normal eruption sequence of teeth should be diagnosed and managed in a timely manner, to preserve dental arch integrity and reduce the potential for undesirable outcomes. This case report demonstrates how comprehensive assessment of the dentition, logical treatment planning and careful management of challenging orthodontic tooth movements can combine to provide a pleasing treatment outcome.
    Language English
    Publishing date 2022-11-15
    Publishing country England
    Document type Case Reports
    ZDB-ID 2740234-4
    ISSN 2050-0904
    ISSN 2050-0904
    DOI 10.1002/ccr3.6482
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Interdisciplinary diagnosis and treatment planning for Class III malocclusion and determining the appropriate anterior tooth positions for individual patients.

    Khan, Ambereen / Freer, Elissa / Ong, Desmond

    Clinical case reports

    2022  Volume 10, Issue 11, Page(s) e6468

    Abstract: Patients presenting with Class III malocclusion often present significant challenges for the orthodontist and restorative clinician. The characteristic anterior crossbite is commonly associated with a maxillo-mandibular skeletal base discrepancy leading ... ...

    Abstract Patients presenting with Class III malocclusion often present significant challenges for the orthodontist and restorative clinician. The characteristic anterior crossbite is commonly associated with a maxillo-mandibular skeletal base discrepancy leading to both functional and esthetic issues. Three potential incisal tooth positions are discussed using clinical examples including implants.
    Language English
    Publishing date 2022-11-16
    Publishing country England
    Document type Case Reports
    ZDB-ID 2740234-4
    ISSN 2050-0904
    ISSN 2050-0904
    DOI 10.1002/ccr3.6468
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  6. Article ; Online: Mu rhythm suppression over sensorimotor regions is associated with greater empathic accuracy.

    Genzer, Shir / Ong, Desmond C / Zaki, Jamil / Perry, Anat

    Social cognitive and affective neuroscience

    2022  Volume 17, Issue 9, Page(s) 788–801

    Abstract: When people encounter others' emotions, they engage multiple brain systems, including parts of the sensorimotor cortex associated with motor simulation. Simulation-related brain activity is commonly described as a 'low-level' component of empathy and ... ...

    Abstract When people encounter others' emotions, they engage multiple brain systems, including parts of the sensorimotor cortex associated with motor simulation. Simulation-related brain activity is commonly described as a 'low-level' component of empathy and social cognition. It remains unclear whether and how sensorimotor simulation contributes to complex empathic judgments. Here, we combine a naturalistic social paradigm with a reliable index of sensorimotor cortex-based simulation: electroencephalography suppression of oscillatory activity in the mu frequency band. We recruited participants to watch naturalistic video clips of people ('targets') describing emotional life events. In two experiments, participants viewed these clips (i) with video and sound, (ii) with only video or (iii) with only sound and provided continuous ratings of how they believed the target felt. We operationalized 'empathic accuracy' as the correlation between participants' inferences and targets' self-report. In Experiment 1 (US sample), across all conditions, right-lateralized mu suppression tracked empathic accuracy. In Experiment 2 (Israeli sample), this replicated only when using individualized frequency-bands and only for the visual stimuli. Our results provide novel evidence that sensorimotor representations-as measured through mu suppression-play a role not only in low-level motor simulation, but also in higher-level inferences about others' emotions, especially when visual cues are crucial for accuracy.
    MeSH term(s) Brain ; Electroencephalography ; Emotions ; Empathy ; Humans
    Language English
    Publishing date 2022-02-04
    Publishing country England
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2236933-8
    ISSN 1749-5024 ; 1749-5016
    ISSN (online) 1749-5024
    ISSN 1749-5016
    DOI 10.1093/scan/nsac011
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Interdisciplinary management of a partially dentate adult patient with a complex malocclusion.

    Low, Chui Yi Sarah / Ong, Desmond Cheer-Vern / Freer, Elissa

    Clinical case reports

    2021  Volume 9, Issue 8, Page(s) e04586

    Abstract: Adult patients may present with complex malocclusions requiring extensive oral rehabilitation. This case report demonstrates how carefully planned interdisciplinary management can provide pleasing outcomes despite the considerable initial clinical ... ...

    Abstract Adult patients may present with complex malocclusions requiring extensive oral rehabilitation. This case report demonstrates how carefully planned interdisciplinary management can provide pleasing outcomes despite the considerable initial clinical challenges.
    Language English
    Publishing date 2021-08-21
    Publishing country England
    Document type Case Reports
    ZDB-ID 2740234-4
    ISSN 2050-0904
    ISSN 2050-0904
    DOI 10.1002/ccr3.4586
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  8. Article: Neural signatures of emotional inference and experience align during social consensus.

    Reddan, Marianne / Ong, Desmond / Wager, Tor / Mattek, Sonny / Kahhale, Isabella / Zaki, Jamil

    Research square

    2023  

    Abstract: Humans seamlessly transform dynamic social signals into inferences about the internal states of the people around them. To understand the neural processes that sustain this transformation, we collected fMRI data from participants (N = 100) while they ... ...

    Abstract Humans seamlessly transform dynamic social signals into inferences about the internal states of the people around them. To understand the neural processes that sustain this transformation, we collected fMRI data from participants (N = 100) while they rated the emotional intensity of people (targets) describing significant life events. Targets rated themselves on the same scale to indicate the intended "ground truth" emotional intensity of their videos. Next, we developed two multivariate models of observer brain activity- the first predicted the "ground truth" (
    Language English
    Publishing date 2023-11-17
    Publishing country United States
    Document type Preprint
    DOI 10.21203/rs.3.rs-3487248/v1
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  9. Book ; Online: Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition

    Suresh, Varsha / Ong, Desmond C.

    2021  

    Abstract: Modern emotion recognition systems are trained to recognize only a small set of emotions, and hence fail to capture the broad spectrum of emotions people experience and express in daily life. In order to engage in more empathetic interactions, future AI ... ...

    Abstract Modern emotion recognition systems are trained to recognize only a small set of emotions, and hence fail to capture the broad spectrum of emotions people experience and express in daily life. In order to engage in more empathetic interactions, future AI has to perform \textit{fine-grained} emotion recognition, distinguishing between many more varied emotions. Here, we focus on improving fine-grained emotion recognition by introducing external knowledge into a pre-trained self-attention model. We propose Knowledge-Embedded Attention (KEA) to use knowledge from emotion lexicons to augment the contextual representations from pre-trained ELECTRA and BERT models. Our results and error analyses outperform previous models on several datasets, and is better able to differentiate closely-confusable emotions, such as afraid and terrified.

    Comment: Accepted at IEEE Affective Computing and Intelligent Interaction (ACII) 2021
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Publishing date 2021-07-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Not All Negatives are Equal

    Suresh, Varsha / Ong, Desmond C.

    Label-Aware Contrastive Loss for Fine-grained Text Classification

    2021  

    Abstract: Fine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them. Guiding the model to focus on differentiating dimensions between these commonly confusable classes is key to improving ... ...

    Abstract Fine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them. Guiding the model to focus on differentiating dimensions between these commonly confusable classes is key to improving performance on fine-grained tasks. In this work, we analyse the contrastive fine-tuning of pre-trained language models on two fine-grained text classification tasks, emotion classification and sentiment analysis. We adaptively embed class relationships into a contrastive objective function to help differently weigh the positives and negatives, and in particular, weighting closely confusable negatives more than less similar negative examples. We find that Label-aware Contrastive Loss outperforms previous contrastive methods, in the presence of larger number and/or more confusable classes, and helps models to produce output distributions that are more differentiated.

    Comment: Accepted at EMNLP 2021
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2021-09-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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