Bruce C. Hansen - US grants

With support from the Major Research Instrumentation Program, Dr. Bruce C Hansen and his collaborators will purchase a state-of-the-art electroencephalography (EEG) system from Electrical Geodesics Incorporated (GES 300 system) for shared use by faculty and undergraduate students in Colgate University's Department of Psychology and Neuroscience Program. The EEG technique itself involves placing surface electrodes on the scalp of a human participant and recording electrical signals generated by the brain in real-time, thereby allowing for a wide variety of analyses focused on the temporal localization of different brain signals. The system will enable this group to adopt an integrated model for understanding human behavior by blending traditional psychological methodology with functional neuroelectric activity in humans.

The scientists involved in this proposal are all active researchers from a broad range of disciplines including cognitive, perceptual, and social psychology. Five research projects (each consisting of several studies) are proposed. Project 1 uses machine learning for the classification of visual evoked potentials (VEPs) to investigate the time course of the brain's recognition and categorization of complex visual scenes in order to understand how such representations guide actions in different environments. Project 2 examines how biases in perception of novel social categories (e.g., multiracial groups), as well as individual differences in opposition to equality, contribute to the perpetuation of group-based social inequality (e.g., racial inequality). It will use event related potentials (ERPs) to explore how social motivations (e.g., anti-egalitarianism) and social contexts (e.g., economic progress for ethnic minorities) influence the way people react to multiracial individuals. Project 3 proposes to combine EEG frequency band power analyses with behavioral paradigms in order to establish a more direct and conclusive indicator of whether encoding or retrieval based memory processes determine the impact of changing task demands on development of expertise. Project 4 investigates how social power evokes self-deception and, as a consequence, enhances persuasive abilities. Specifically, the project combines traditional behavioral measures with ERP analysis to trace the timing of brain signals that selectively unleash changes in awareness. In essence, it aims to elucidate how lying to others may begin with lying to the self. Project 5 will utilize ERPs to explore whether embodied language instruction (i.e., speech, gesture, facial expression, eye gaze, etc.) is effective for inducing neural changes in second-language (L2) learning in two different contexts: face-to-face versus online instruction. The project will focus on components that reflect early perceptual and late semantic processes in the learning of novel speech sounds and new words.

A shared EEG system at Colgate will allow this group to directly engage their students in laboratory techniques that unite psychology and neuroscience into one cohesive field of study, thereby fostering non-traditional research connections that should spur fresh insights and creative new areas of study. Such an approach will no doubt yield students who are better prepared for graduate research labs at an early stage (most undergraduates at other schools will not have this sort of highly technical experience), thereby guaranteeing the rapid advancement of the broader field of science. Lastly, the majority of psychology and neuroscience concentrators at Colgate are female, and the enhanced training made possible by a shared EEG system will therefore increase the competitive representation of women pursuing advanced degrees in a STEM field.

A long-standing problem in cognitive neuroscience is understanding how we can categorize a novel scene in about the same amount of time that it takes to blink one's eyes. Categorization aids both identifying objects and locating them in cluttered scenes, and thus allows for intelligent action in the world. How do we derive semantically meaningful categories from the raw image pixels? Currently, there is experimental support for multiple mechanisms supporting scene categorization, such as through recognizing the scene's objects or other visual features such as spatial layout, color, or texture. Crucially, substantial correlations exist between all of these proposed features. This make it difficult to disentangle their relative contributions to categorization. For example, if two scenes share an object, they will often also share the texture features associated with that object. In this work, the PI (Dr. Bruce C Hansen, Colgate University) and co-PI (Dr. Michelle R Greene, Bates College) seek to disentangle the contribution of such features, and also to determine when these features become available for use, and how they combine to support scene categorization. By understanding the temporal dynamics of the brain activity related to scene categorization, it will be possible to obtain critical insights into how people rapidly but flexibly extract information from the environment. This work forms a bridge across several disciplines including psychology, cognitive neuroscience, computer vision, and machine learning. As such, the project will engage undergraduate students in truly interdisciplinary training that is at the cutting edge of multiple fields.

This project will make use of high-density EEG combined with machine learning, computational modeling behavioral measures, and advanced neuromodulation to determine how and when the behaviorally relevant features support scene categorization. First, the work will link the encoding of these features to visual event related potentials (vERPs) and also to category information using multivariate classification techniques from machine learning. Taken together, these techniques will allow the PIs to determine the unique contributions of each feature to category-related brain activity over time. A hallmark of intelligent action is flexibility. Therefore, the project will also investigate the flexibility of feature use by manipulating the diagnosticity of information available to observers. These studies will provide insights regarding feature space usage as a function of task demands, as well as the impact of such demands on the time course of feature space availability as indexed by vERPs. Lastly, the project will test for a potential causal role of vERPs to categorization through the use of advanced neuromodulation techniques.