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1. Affective Priming in Emotional Annotations and its Effect on Speech Emotion Recognition

   https://doi.org/10.1109/TAFFC.2025.3597034  

   Martinez-Lucas, Luz; Salman, Ali; Leem, Seong-Gyun; Chien, Woan-Shiuan; Upadhyay, Shreya G; Lee, Chi-Chun; Busso, Carlos (January 2025, IEEE Transactions on Affective Computing)

   Emotional annotation of data is important in affective computing for the analysis, recognition, and synthesis of emotions. As raters perceive emotion, they make relative comparisons with what they previously experienced, creating “anchors” that influence the annotations. This unconscious influence of the emotional content of previous stimuli in the perception of emotions is referred to as the affective priming effect. This phenomenon is also expected in annotations conducted with out-of-order segments, a common approach for annotating emotional databases. Can the affective priming effect introduce bias in the labels? If yes, how does this bias affect emotion recognition systems trained with these labels? This study presents a detailed analysis of the affective priming effect and its influence on speech emotion recognition (SER). The analysis shows that the affective priming effect affects emotional attributes and categorical emotion annotations. We observe that if annotators assign an extreme score to previous sentences for an emotional attribute (valence, arousal, or dominance), they will tend to annotate the next sentence closer to that extreme. We conduct SER experiments using the most biased sentences. We observe that models trained on the biased sentences perform the best and have the lowest prediction uncertainty. 

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2. Analyzing the Effect of Affective Priming on Emotional Annotations

   https://doi.org/10.1109/ACII59096.2023.10388184  

   Martinez-Lucas, Luz; Salman, Ali; Leem, Seong-Gyun; Upadhyay, Shreya G; Lee, Chi-Chun; Busso, Carlos (September 2023, IEEE)

   na (Ed.)

   In the field of affective computing, emotional annotations are highly important for both the recognition and synthesis of human emotions. Researchers must ensure that these emotional labels are adequate for modeling general human perception. An unavoidable part of obtaining such labels is that human annotators are exposed to known and unknown stimuli before and during the annotation process that can affect their perception. Emotional stimuli cause an affective priming effect, which is a pre-conscious phenomenon in which previous emotional stimuli affect the emotional perception of a current target stimulus. In this paper, we use sequences of emotional annotations during a perceptual evaluation to study the effect of affective priming on emotional ratings of speech. We observe that previous emotional sentences with extreme emotional content push annotations of current samples to the same extreme. We create a sentence-level bias metric to study the effect of affective priming on speech emotion recognition (SER) modeling. The metric is used to identify subsets in the database with more affective priming bias intentionally creating biased datasets. We train and test SER models using the full and biased datasets. Our results show that although the biased datasets have low inter-evaluator agreements, SER models for arousal and dominance trained with those datasets perform the best. For valence, the models trained with the less-biased datasets perform the best. 

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3. Why Am I Supposed to Love Math?: Digital Mathematics Storytelling in Asian American Communities

   Chao, Theodore; Hidayat, Angga; Oliwe, Ruth N (October 2023, Proceedings of the Forty-Fifth Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   Lamberg, T; Moss, D (Ed.)

   In this research study, we detail how Digital Mathematics Storytelling, in which youth create video stories detailing the mathematics knowledge existing within their families and communities, can actively create counter-stories to the model minority myth. Through intergenerational video storytelling in historic Asian American communities, the research team and participants used a community participatory action research and narrative inquiry framework to engage elementary and middle-school aged youth in mathematics-based storytelling that not only detailed the painful effects of the model minority myth but also showcased that mathematics identities within Asian American communities can be rich and joyful. 

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4. An Emotion Translator: Speculative Design By Neurodiverse Dyads

   https://doi.org/10.1145/3613904.3642210  

   Zolyomi, Annuska; Snyder, Jaime (May 2024, ACM Digital Library)

   For autistic individuals, navigating social and emotional interactions can be complex, often involving disproportionately high cognitive labor in contrast to neurotypical conversation partners. Through a novel approach to speculative co-design, autistic adults explored affective imaginaries — imagined futuristic technology interventions — to probe a provocative question: What if technology could translate emotions like it can translate spoken language? The resulting speculative prototype for an image-enabled emotion translator chat application included: (1) a visual system for representing personalized emotion taxonomies, and (2) a Wizard of Oz implementation of these taxonomies in a low-fidelity chat application. Although wary of technology that purports to understand emotions, autistic participants saw value in being able to deploy visual emotion taxonomies during chats with neurotypical conversation partners. This work shows that affective technology should enable users to: (1) curate encodings of emotions used in system artifacts, (2) enhance interactive emotional understanding, and (3) have agency over how and when to use emotion features. 

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5. Hearing Pronouns Primes Speakers to Use Pronouns

   https://doi.org/10.1162/opmi_a_00178  

   Arnold, Jennifer E (January 2025, Open Mind)

   Abstract Speaking requires frequent decisions about how to refer, for example whether to use a pronoun (she) or a name (Ana). It is well known that this choice is guided by the discourse context, but little is known about the representations that are activated. We use priming to test whether this choice can be facilitated through recent exposure, and if so, what representations are activated. In a storytelling task, participants take turns with experimenters telling a story that is illustrated in 2-panel cartoons. The first sentence is given, and participants describe the second panel in their own words. We manipulate whether the experimenter used a pronoun or name in the prior story. Experiment 1 provides the first evidence in the literature that reference form choice can be primed, and that it is not dependent on the syntactic position of the antecedent. However, the effect is not finely tuned to the preceding prime. Instead, exposure at the start of the experiment persists throughout, even when the prime changes. Experiments 2 and 3 further show that exposure to pronoun primes result in greater pronoun use than at baseline, but that there is no sensitivity to the prime on the most recent trial. Results argue against a role for production facilitation in pronoun use, which suggests that reference production is not impacted by production efficiency. 

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