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Welcome to LINCD!
Welcome to LINCD!
A process cannot be understood by stopping it. Understanding must move with the flow of the process, must join it and flow with it.
-The First Law of Mentat, Dune, Frank Herbert
The Laboratory for Integrative Neuro-Cognitive Dynamics (LINCD) is supervised by Assistant Professor Greg Cox and is generously housed in the Department of Psychology at the University at Albany, State University of New York.
At LINCD, we take a dynamic approach to the study of cognition. We investigate the dynamics of processes involved in attention, perception, memory retrieval, and decision making, and how these processes interact and contribute to adaptive behavior. These are the processes engaged when we find and recognize a familiar face in a crowd, when we retrieve that person's name, and when we make the decisions involved in plotting a route to walk up to that person. While these processes largely occur quickly and without notice, like the instruments of an orchestra, they combine to form our experiences and memories of the moment which, in turn, are the foundation for long-term learning and understanding.
Our dynamic approach is both integrative and cumulative. It is integrative in that, rather than trying to understand cognitive processes in isolation, we attempt to understand how these processes mutually unfold and interact over time. It is cumulative in that, rather than focusing on the identification of "effects", we aim to explain how both novel and prior results are manifestations of the same set of underlying causes. Ultimately, the dynamic approach helps us build theories, formalized as computational models, that explain how the myriad instruments of the cognitive orchestra combine to create a symphony of mind. These models help us design more effective clinical assessments and interventions, help tailor instruction to the needs and understanding of individual students, and develop new generations of artificial intelligence applications.
What kinds of questions do we ask?
What kinds of questions do we ask?
Memory
Memory
How do people recognize when they have experienced something before?
How do people search through memory for specific desired information?
How do people use information from memory to guide decision making?
How do people integrate different aspects of an experience into a coherent memory representation?
How do people's knowledge and memory interact over time as they learn?
Attention
Attention
How do primates integrate multiple streams of visual information to select a target from among distractor objects?
How do neurons in the visual system implement the computations involved in localizing and identifying objects?
How do people choose what to store in memory, based on their goals?
How do people direct their attention "inward" when searching for information in memory?
Models and methods
Models and methods
How can we determine whether someone processes multiple sources of information in series or in parallel?
How can we characterize people's capacity for processing multiple sources of information?
How can we determine whether multiple factors are necessary to explain a set of phenomena?
How can we ensure that statistical methods serve the goals of science?
How do we address those questions?
How do we address those questions?
Computational modeling
Computational modeling
Computational modeling is at the core of our work. Minds and brains are extremely complex systems comprised of a many ongoing, interacting processes. To have any hope of understanding how and why those processes operate, we need to build models of our theories of those processes. Just like engineers use physical models and wind tunnels to explore different aircraft designs, computational models enable us to simulate the kinds of behavior that different arrangements of cognitive processes would produce. By comparing simulated to observed behavior, we can reverse-engineer which theories are more likely to be good descriptions of the cognitive processes that convert experiences into actions.
Behavioral experiments
Behavioral experiments
At LINCD, we conduct behavioral experiments both in-person and online. Such experiments typically involve making a judgment or taking an action based on information encountered during the experiment. Our dynamic approach means that we are interested not just in what decisions people make, but in the time it takes to make those decisions and the mental steps they take along the way to their final choices. As a result, our experiments often focus on dynamic measures like response times, as well as technology like eye-tracking which enables us to track where people direct their attention as they think and decide.
Single-neuron functional neurophysiology
Single-neuron functional neurophysiology
LINCD is fortunate to work with great collaborators like Jeffrey Schall at York University who study the individual neurons involved in visual attention and decision making. This gives us a unique opportunity to bridge the gap to jointly understand the dynamics of cognitive processes as well as the dynamics of the neurons that implement those processes. In this way, a dynamic approach helps link "how" with "why" in the brain.
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