There is considerable lack of evidence concerning the linguistic and cognitive skills underpinning abstract vocabulary acquisition. The present study considers the role of emotional valence in providing an embodied learning experience in which to anchor abstract meanings. First, analyses of adult ratings of age-of-acquisition, concreteness and valence demonstrate that abstract words acquired early tend to be emotionally valenced. Second, auditory Lexical Decision accuracies of children aged 6-7, 8-9, and 10-11 years (n = 20 per group) complement these analyses, demonstrating that emotional valence facilitates processing of abstract words, but not concrete. These findings provide the first evidence that young, school-aged children are sensitive to emotional valence and that this facilitates acquisition of abstract words.
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PLoS One
January 2025
Department of Political Science, Middlebury College, Middlebury, Vermont, United States of America.
Assessing whether texts are positive or negative-sentiment analysis-has wide-ranging applications across many disciplines. Automated approaches make it possible to code near unlimited quantities of texts rapidly, replicably, and with high accuracy. Compared to machine learning and large language model (LLM) approaches, lexicon-based methods may sacrifice some in performance, but in exchange they provide generalizability and domain independence, while crucially offering the possibility of identifying gradations in sentiment.
View Article and Find Full Text PDFElife
January 2025
Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, United States.
The central amygdala (CeA) has emerged as an important brain region for regulating both negative (fear and anxiety) and positive (reward) affective behaviors. The CeA has been proposed to encode affective information in the form of valence (whether the stimulus is good or bad) or salience (how significant is the stimulus), but the extent to which these two types of stimulus representation occur in the CeA is not known. Here, we used single cell calcium imaging in mice during appetitive and aversive conditioning and found that majority of CeA neurons (~65%) encode the valence of the unconditioned stimulus (US) with a smaller subset of cells (~15%) encoding the salience of the US.
View Article and Find Full Text PDFCell Rep
January 2025
School of Neuroscience, Virginia Tech, Blacksburg, VA 24060, USA. Electronic address:
Words represent a uniquely human information channel-humans use words to express thoughts and feelings and to assign emotional valence to experience. Work from model organisms suggests that valence assignments are carried out in part by the neuromodulators dopamine, serotonin, and norepinephrine. Here, we ask whether valence signaling by these neuromodulators extends to word semantics in humans by measuring sub-second neuromodulator dynamics in the thalamus (N = 13) and anterior cingulate cortex (N = 6) of individuals evaluating positive, negative, and neutrally valenced words.
View Article and Find Full Text PDFJ Neurosci Methods
January 2025
College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China.
Background: Recognition of emotion changes is of great significance to a person's physical and mental health. At present, EEG-based emotion recognition methods are mainly focused on time or frequency domains, but rarely on spatial information. Therefore, the goal of this study is to improve the performance of emotion recognition by integrating frequency and spatial domain information under multi-frequency bands.
View Article and Find Full Text PDFFront Psychol
December 2024
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
Introduction: While the fact that visual stimuli synthesized by Artificial Neural Networks (ANN) may evoke emotional reactions is documented, the precise mechanisms that connect the strength and type of such reactions with the ways of how ANNs are used to synthesize visual stimuli are yet to be discovered. Understanding these mechanisms allows for designing methods that synthesize images attenuating or enhancing selected emotional states, which may provide unobtrusive and widely-applicable treatment of mental dysfunctions and disorders.
Methods: The Convolutional Neural Network (CNN), a type of ANN used in computer vision tasks which models the ways humans solve visual tasks, was applied to synthesize ("dream" or "hallucinate") images with no semantic content to maximize activations of neurons in precisely-selected layers in the CNN.
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