Thinking About Decisions: How Human Variability Influences Decision-Making

McElroy, Todd; Salapska-Gelleri, Joanna; Schuller, Kelly L.; Bourgeois, Martin J.

doi:10.1007/978-3-031-15959-6_23

Cited by 2 publications

(2 citation statements)

References 63 publications

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“…In other words, participants had a self-enhancing memory bias for the negative words. This self-enhancement bias likely accounts for a good deal of the variability in human decision-making and regret (McElroy et al, 2023). In fact, self-enhancing attributions are pervasive across human interactions and are often referred to as the self-serving bias (Bradley, 1978;Miller, & Ross, 1975).…”

Section: Failure But Who's To Blame?mentioning

confidence: 99%

Romantic Relationships and the Actions (or Inactions) That End Them: Blaming Self or Other Influences Feelings of Regret

McElroy¹,

Salapska-Gelleri²

2023

ACP

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Section: Failure But Who's To Blame?mentioning

confidence: 99%

Romantic Relationships and the Actions (or Inactions) That End Them: Blaming Self or Other Influences Feelings of Regret

McElroy¹,

Salapska-Gelleri²

2023

ACP

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“…To convey our internal states effectively, we employ a range of tools, including facial expressions, body language, and speech [3]. Our communication skills, decision-making abilities, and social interactions are profoundly influenced by our emotional states [4]. The individual's internal emotional state becomes more discernible through personal experiences, physiological conditions, and environmental stimuli.…”

Section: A Backgroundmentioning

confidence: 99%

FF-BTP Model for Novel Sound-Based Community Emotion Detection

Yildiz,

Tanabe,

Kobayashi

et al. 2023

IEEE Access

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Background and purpose: Emotion classification models have focused on subject-but not crowd-level signal inputs. We proposed sound-based community emotion recognition (SCED) as a new machine learning challenge and have developed a novel deep learning-inspired forward-forward binary ternary pattern (FF-BTP)-based feature engineering model for crowd sentiment classification. Materials and Methods: From 187 YouTube videos, we curated a new dataset comprising 919, 905 and 909 (total 2733) 3-second recordings (sampling frequency 44.1 KHz) of crowd sounds (overlapping speech, environmental noise, etc.) belonging to verified dominant negative, neutral, and positive emotion classes. Our model architecture combined BTP-based textural feature extractor, which generated three feature vectors using signum, lower and upper ternary functions, with a novel Hinton's FF algorithm-inspired handcrafted feature vector selection function that identified the most distinctive feature vector based on maximum calculated mean square error. Our FF-BTP-based model also incorporated upstream multilevel discrete wavelet transform, which enabled multilevel feature generation in the spatial and frequency domains, and downstream iterative neighborhood component analysis-based feature selection and support vector machine classifier. Results: The FF-BTP-based model attained excellent 97.22% overall three-class classification accuracy on the SCED dataset. Conclusions: Our handcrafted feature engineering model emulated the deep feature characterization of deep learning models, and attained excellent classification results at low computation cost. It can be implemented for emerging SCED-related applications. INDEX TERMS FF-BTP; sound community emotion classification; sound processing; textural feature extraction.

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Thinking About Decisions: How Human Variability Influences Decision-Making

Cited by 2 publications

References 63 publications

Romantic Relationships and the Actions (or Inactions) That End Them: Blaming Self or Other Influences Feelings of Regret

Romantic Relationships and the Actions (or Inactions) That End Them: Blaming Self or Other Influences Feelings of Regret

FF-BTP Model for Novel Sound-Based Community Emotion Detection

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