Our research in the spotlight!
How do you train a perceptual expert? Drexel's Ask Magazine recently featured our research on applying computational modeling to understanding perceptual expertise in STEM disciplines. Link to the article.
Rate-distortion theory and human perception
A new paper is out in print: 'Rate-distortion theory and human perception'. This paper provides a tutorial introduction to a theoretical framework for understanding perception and perceptual memory based on a branch of information theory. Link to publications page.
The Cost of Misremembering
A new paper is out in print: 'The cost of misremembering: Inferring the loss funciton in visual working memory'. This paper applies information theory and decision theory to understand visual working memory in an entirely new light. Head on over to the publications page to download a copy.
The Adaptive Nature of Visual Working Memory
A new paper is out in print: 'The Adaptive Nature of Visual Working Memory'. This is an accessible overview of how statistical learning plays a key role in visual memory, and highlights the crucial role of computational theory in psychology. Head on over to the publications page to download a copy.
Drexel Laboratory for Adaptive Cognition
Our laboratory is interested in answering the following question: How is the brain able to efficiently accomplish complex goals in an uncertain world?
This fundamental question spans multiple areas of cognitive science: decision-making, learning from feedback, perception, and motor control. Even a seemingly simple task like reaching to pick up a glass of water involves solving tremendous challenges: the brain must compensate for time-delayed and uncertain sensory signals, and process this information extremely rapidly to control a noisy and error-prone motor system. Similarly, when we look at a photograph and try to remember some particular detail, we face a complex challenge as the capacity of visual memory is severely limited. Therefore the brain faces a decision regarding what visual information to attend to, what to ignore, and how best to store it in memory in order to achieve our goals. These examples illustrate that low-level perception, memory, and basic motor control all involve complex forms of decision-making under uncertainty. Our laboratory is interested in understanding these processes.
In everyday life, the incredible complexity of the problems solved by the brain is hidden from our awareness. Oftentimes, it is only when we try to replicate the breadth and robustness of human performance in a computational model is the extent of the mind's complexity apparent. Computational models of human cognition serve an important role in all of the research carried out in our laboratory. They serve as an explicit statement of a scientific theory, but they can also generate novel predictions that can be tested experimentally. In many cases, human performance exceeds that of any existing algorithm (for example, in categorizing images or recognizing faces). By studying human behavior and building computational models of cognition, we can also advance the state of the art in engineering and applied sciences.
Recent and ongoing research projects in the lab focus on:
To address these questions, our research relies on a combination of behavioral and psychophysical experiments, computational modeling, along with data provided from eye tracking (SR Research Eyelink II, shown above) and motion capture technology (an array of 8 Optitrack Flex13 cameras).
If you are interested in becoming involved in our research, please feel free to contact us.