Adam G. Turnbull

This paper replicates and extends Puccetti et al. (2021) by examining age-related differences in affect and amygdala connectivity using fMRI and diary data.

This study examines brain-thought relationships during movie-watching, finding that sensory-related neural signatures are consistent across films, while memory-related processes vary with context and modeling approaches.

The paper uses fMRI and AI models to show that the medial temporal default mode network encodes visual imagery from autobiographical memories.

The paper uses fMRI to show that imagined social and solitary scenarios activate different patterns of the frontoparietal and default mode networks, revealing a gradient that reflects semantic differences in internal cognitive states.

The paper develops and validates a Facial Signature Score (FSS) from facial dynamics during cognitive interventions to predict executive function response in older adults.

The paper examines macro-scale brain activity patterns during wakeful rest, linking them to personality traits and ongoing thoughts by analyzing interactions between brain networks.

The paper shows that personality traits are more strongly associated with brain activity in specific tasks than in others, highlighting the need for context-specific analyses.

The paper develops a method to map thought patterns onto brain activity during movie-watching and finds sensory and association cortex play key roles in engagement and distraction.

This study uses experience sampling to examine covert goal-relevant states during task performance, finding that fronto-parietal network activity is linked to better task performance.

This study uses real-world experience sampling to analyze how daily activities influence thought patterns, finding that socializing and environmental factors shape ongoing thought.

The paper examines how ongoing thought patterns vary across tasks and individual affective styles, identifying four patterns and their contextual and personal influences.

This study identifies two distinct neural states during wakeful rest using machine learning and links them to specific psychological features like problem-solving and negative rumination.

This paper compares functional connectivity variability during movies and rest, finding movies enhance individual differences detection.