I currently work in the labs of Greg Ashby and Miguel Eckstein studying the neural systems behind human category learning and vision. With better knowledge of these systems we can develop methods (whether human-focused such as better training paradigms or machine-focused such as computer assistance) to increase human performance.
Broadly I am interested in leveraging recent advances in machine learning, computational modeling, computing resources, and the increased accuracy of brain imaging/data collection schemes to pursue a deeper understanding of how the brain functions. In addition, I am fascinated by the interplay between AI and human intelligence and what we can learn from our quest to reproduce our intelligence in non-biological entities.
Every object we see in life requires categorization. From your morning cup of coffee (poison or edible?) to whether feed your dog (hungry or not?), we are continually categorizing objects and acting upon those categories. When this process goes well we often aren't even aware of the judgment we have made. But when objects are incorrectly categorized—for example, a tumor is categorized as normal tissue or a toy gun as a real gun—the results can be dire. By studying how people learn categories we can develop better training paradigms to improve accuracy and decrease the amount of training required to become an expert categorizer.
As perhaps our most prominent sense, vision plays a role in the majority of our daily lives. It underlies many of the tasks we perform and assists in some of the most dangerous activities we do on a daily basis (e.g. driving). As a sensory system, the output of our visual system is also the input to many other complex systems (such as category learning). Using eye tracking and VR I seek to understand how these higher level systems interact with vision and how reliance on visual information can impact performance.
To study Category Learning and Vision, I use a combination of mathematical models and behavioral data within the Computational Cognitive Neuroscience (CCN) framework. CCN is a field of neuroscience developed extensively by my advisor Greg Ashby that ties mathematical models to neuroscience data and human behavior to develop models that are both falsifiable and predictive. Briefly, the CCN approach follows four principles (from the paper cited below):
1. A CCN model should not make any assumptions that are known to contradict the current neuroscience literature.
2. No extra neuroscientific detail should be added to the model unless there are data to test this component of the model or the model cannot function without this detail.
3. Once set, the architecture of the network and the models of each individual unit should remain fixed throughout all applications.
4. A CCN model should provide good accounts of behavioral data and at least some neuroscience data.
For a great tutorial on CCN written by Greg see A tutorial on computational cognitive neuroscience: Modeling the neurodynamics of cognition.
Some tasks are more difficult than others, but why? And what can the difficulty of a task tell us about the system performing the task? These are the kind of questions I ask to as I seek to understand the brain by figuring out why it fails.
Along with difficulty, I am interested in what tasks require consciousness and to what extent. As our perception of the world is deeply rooted in consciousness, we tend to think it is required for everything. However, there is a growing body of work showing evidence for everything from color detection and facial recognition in "blindsight" patients (for an interesting review of blindsight research see Visual System: How Does Blindsight Arise?) to non-conscious working memory (though see Can working memory be non-conscious? for a critique of the claim that working memory can be non-conscious). I use Continuous Flash Suppression and Binocular Rivalry to target the role of consciousness in Category Learning, Visual Search, and more.
Working on something similar and interested in collaborating (or simply discussing an interesting idea)? Email me at firstname.lastname@example.org.