Publications by authors named "Lauren Dunlap"
Article Synopsis
- A study aimed to analyze the acoustic features of infant cries using machine learning to determine their underlying behavioral states, such as pain, fussiness, or hunger.
- The ChatterBaby app evaluated over 1,000 cries, achieving a 90.7% accuracy in identifying pain cries and highlighting that cries associated with colic were more often linked to pain than fussiness or hunger.
- The findings indicate that there are consistent acoustic characteristics in infant cries that can serve as objective indicators of different states, suggesting a possible shared neuronal pathway for colic and pain-related cries.
Dysfunctional attitudes can foreshadow depressive relapse/recurrence. Priming mood, through induction paradigms, is hypothesized to activate dysfunctional attitudes. Cognitive reactivity (CR) refers to mood-linked increases in dysfunctional attitudes after priming.
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