Let me tell you a story
Our world unfolds in stories. There are stories we experience personally, stories we tell each other, and stories we tell ourselves. The magic of our mind is intertwined in how we generate countless such stories and how we glean useful information from every new story we encounter.
Our lab's mission is to explore and explain how we construct, remember, and communicate our stories when we experience the world naturally, such as when we watch a captivating movie or when we talk to our friends about our adventures.
Because stories are brilliant and always moving, we study them using human experiments involving dynamic, complex naturalistic stimuli (e.g., movies, theatre productions, podcasts).
Because stories affect all avenues of our cognition, we study them anywhere they could be instantiated, including in human behavior, in the human brain, and in artificial neural networks.
Because stories are a whirlwind of incredibly complex patterns of information, we study their underpinnings using a diverse array of methods and experimental techniques, including machine learning, psychophysics, neuroimaging (fMRI), neural network models, and real-time neurofeedback (neural sculpting).
Some of the questions our lab is currently focusing on include:
♦ neural + behavioral mechanisms of information summarization in complex narratives (Sun & Iordan, 2024)
♦ how anticipation of future events influences perception of naturalistic narratives
♦ how story immersion and sequential decision making affect event perception and comprehension
♦ the roles that prior experience and expertise play in story encoding and recall
Our lab's mission is to explore and explain how we construct, remember, and communicate our stories when we experience the world naturally, such as when we watch a captivating movie or when we talk to our friends about our adventures.
Because stories are brilliant and always moving, we study them using human experiments involving dynamic, complex naturalistic stimuli (e.g., movies, theatre productions, podcasts).
Because stories affect all avenues of our cognition, we study them anywhere they could be instantiated, including in human behavior, in the human brain, and in artificial neural networks.
Because stories are a whirlwind of incredibly complex patterns of information, we study their underpinnings using a diverse array of methods and experimental techniques, including machine learning, psychophysics, neuroimaging (fMRI), neural network models, and real-time neurofeedback (neural sculpting).
Some of the questions our lab is currently focusing on include:
♦ neural + behavioral mechanisms of information summarization in complex narratives (Sun & Iordan, 2024)
♦ how anticipation of future events influences perception of naturalistic narratives
♦ how story immersion and sequential decision making affect event perception and comprehension
♦ the roles that prior experience and expertise play in story encoding and recall
Additional Research Directions
We're also broadly interested in how visual and semantic knowledge (e.g., objects, scenes, concepts, categories, events) is learned, organized, and modulated by attention in human behavior, in the human brain, and in artificial neural networks. Three research directions we pursue in this space are:
(1) investigating ecologically-relevant aspects of visual and semantic cognition (e.g., categorization, learning, efficient perception) in the brain and in behavior using neuroimaging (fMRI) and psychophysics (Iordan et al., 2015, 2016, & in prep).
(2) improving automatic prediction of human behavioral judgments and neural responses from large-scale human-centric data, e.g., by using deep neural networks applied to vast corpora of text, images, and empirical human judgments (Iordan et al., 2018 & 2022).
(3) probing the causal links between human neural representations and behavior and potentially improving human cognitive processes via neurofeedback and neural sculpting (Iordan et al., 2024 & Peng et al., 2024).
(1) investigating ecologically-relevant aspects of visual and semantic cognition (e.g., categorization, learning, efficient perception) in the brain and in behavior using neuroimaging (fMRI) and psychophysics (Iordan et al., 2015, 2016, & in prep).
(2) improving automatic prediction of human behavioral judgments and neural responses from large-scale human-centric data, e.g., by using deep neural networks applied to vast corpora of text, images, and empirical human judgments (Iordan et al., 2018 & 2022).
(3) probing the causal links between human neural representations and behavior and potentially improving human cognitive processes via neurofeedback and neural sculpting (Iordan et al., 2024 & Peng et al., 2024).