[Attention] [Learning-Memory] [Decision-making]
Hello, my name is Sirawaj Itthipuripat or Sean. I am a cognitive neuroscientist. My work involves examining brain mechanisms that support selective attention, learning, and memory in both healthy and clinical populations (e.g., schizophrenia). To solve how these cognitive functions operate in the human brain, I use combinations of neuroscience methods including EEG, fMRI, brain stimulation, psychophysics, eye-tracking, and computational models. I graduated from Duke University, where I worked with Prof. Marty Woldorff and received undergraduate degrees in Neuroscience (BS) and Psychology (BS). After Duke, I went to University of California, San Diego (UCSD) and received master and doctoral degrees in Neurosciences (MS and PhD). At UCSD, I work with Prof. John Serences and Prof. Adam Aron. Currently, I am a postdoctoral researcher working with Prof. Geoffrey Woodman and Prof. Sohee Park at Vanderbilt University, while holding an adjunct research position at King's Mongkut University of Technology Thonburi in Thailand. I am also collaborating with Dr. Thomas Sprague at University of California, Santa Barbara and Dr. Yixuan Ku at New York University in Shanghai.
Different attentional mechanisms during different phases of training
In the neuroscience community, we employ vast variety of techniques and model systems to understand how the brain works. Comparing results obtained from monkey and human subjects could be problematic, since monkeys have to be trained up to a year but humans require only a few minutes to understand the task. My goal is to understand the impact of extended training on higher cognitive functions such as selective attention and to understand if/how learning could alter the linking hypothesis between neural and behavioral modulations. Another goal related to this topic is to examine whether/how training in selective attention tasks (within and across sensory modalities) could shape the allocation of priority maps in the brain and change the appearance of visual stimuli that appear at the trained versus untrained visual spaces.
[ Relevant paper and press releases]
Sirawaj Itthipuripat [email@example.com]
Neural mechanisms and computational models of selective visual attention
For many decades, different attentional gain patterns measured in visual cortex of humans and monkeys have been observed. Also, many research groups have proposed different neural mechanisms to explain how attention may operate in the brain (e.g., sensory gain, noise modulation, and efficient read-out mechanisms). My research goal is to study the different factors (e.g., properties of stimulus displays, task strategies, learning duration, imaging methods) that contribute to different patterns of attentional modulations and different linking hypotheses between neural and behavioral data.
How is attention shaped by reward learning
In many real world situations, selective attention helps guide decision-making. The majority of studies in the past have employed simple 2-choice tasks. My goal is to study the role of attentional gain control in more complex tasks with multiple alternatives. I'm also interested in studying attentional capture induced by reward learning and how it interferes with the optimality of value-based decision-making. I'm also investigating how choice selection and reward history shape neural representations of relevant and distracting information in early visual cortex and parietal cortex.