Biology subjects

Thompson-Schill, S. L.

Publications and source records attributed to Thompson-Schill, S. L..

4 recordsLinked to original sources

bioRxiv2018

Dynamic constraints on activity and connectivity during the learning of value

Human learning is a complex process in which future behavior is altered via the modulation of neural activity. Yet, the degree to which brain activity and functional connectivity during learning is constrained across subjects, for example by conserved anatomy and physiology or by the nature of the task, remains unknown. Here, we measured brain activity and functional connectivity in a longitudinal experiment in which healthy adult human participants learned the values of novel objects over the course of four days. We assessed the presence of constraints on activity and functional connectivity using an inter-subject correlation approach. Constraints on activity and connectivity were greater in magnitude than expected in a non-parametric permutation-based null model, particularly in primary sensory and motor systems, as well as in regions associated with the learning of value. Notably, inter-subject connectivity in activity and connectivity displayed marked temporal variations, with inter-subject correlations in activity exceeding those in connectivity during early learning and visa versa in later learning. Finally, individual differences in performance accuracy tracked the degree to which a subjects connectivity, but not activity, tracked subject-general patterns. Taken together, our results support the notion that brain activity and connectivity are constrained across subjects in early learning, with constraints on activity, but not connectivity, decreasing in later learning.

neuroscience↗

bioRxiv2018

Individual differences in response precision correlate with adaptation bias

The internal representation of stimuli is imperfect and subject to bias. Noise introduced at initial encoding and during maintenance degrades the precision of representation. Stimulus estimation is also biased away from recently encountered stimuli, a phenomenon known as adaptation. Within a Bayesian framework, greater biases are predicted to result from poor precision. We tested for this effect in individual difference measures. 202 subjects contributed data through an on-line experiment (https://cfn.upenn.edu/iadapt). During separate face and color blocks, subjects performed three different tasks: an immediate stimulus-match (15 trials), a 5 seconds delayed match (30 trials), and 5 seconds of adaptation followed by a delayed match (30 trials). The stimulus spaces were circular and subjects entered their responses using a color/face wheel. Bias and precision of responses were extracted by fitting a mixture of von Mises distributions to account for random guesses. Two blocks of each measure were obtained, allowing for tests of measure reliability. We found that reliable differences between individuals in precision were as great as those between tasks or materials. The adaptation manipulation induced the expected bias in responses (colors: 7.8{degrees}; faces: 5.0{degrees}), and the magnitude of this bias reliably and substantially varied between subjects. Across subjects, there was a negative correlation between mean precision and bias (color:{rho} = -0.26; faces:{rho} = -0.13). This relationship was replicated in a new experiment with 192 subjects (color:{rho} = -0.22; faces:{rho} = -0.19). This result is consistent with a Bayesian observer model, in which individual differences in the precision of perceptual representation influences the magnitude of adaptation bias.

neuroscience↗

bioRxiv2017

Subgraphs of functional brain networks identify dynamical constraints of cognitive control

Brain anatomy and physiology support the human ability to navigate a complex space of perceptions and actions. To maneuver across an ever-changing landscape of mental states, the brain invokes cognitive control - a set of dynamic processes that engage and disengage different sets of brain regions to modulate attention, switch between tasks, and inhibit prepotent responses. Current theory suggests that cooperative and competitive interactions between brain areas may mediate processes of network reorganization that support transitions between dynamical states. In this study, we used a quantitative approach to identify distinct topological states of functional interactions and examine how their expression relates to cognitive control processes and behavior. In particular, we acquired fMRI BOLD signal in twenty-eight healthy subjects as they performed two cognitive control tasks - a local-global perception switching task using Navon figures and a Stroop interference task - each with low cognitive control demand and high cognitive control demand conditions. Based on these data, we constructed dynamic functional brain networks and used a parts-based network decomposition technique called non-negative matrix factorization to identify putative cognitive control subgraphs whose temporal expression captured key dynamical states involved in control processes. Our results demonstrate that the temporal expression of these functional subgraphs reflect cognitive demands and are associated with individual differences in task-based performance. These findings offer insight into how coordinated changes in the cooperative and competitive roles of distributed brain networks map trajectories between cognitively demanding brain states.

neuroscience↗

bioRxiv2017

Process reveals structure: How a network is traversed mediates expectations about its architecture

Network science has emerged as a powerful tool through which we can study the higher-order architectural properties of the world around us. How human learners exploit this information remains an essential question. Here, we focus on the temporal constraints that govern such a process. Participants viewed a continuous sequence of images generated by three distinct walks on a modular network. Walks varied along two critical dimensions: their predictability and the density with which they sampled from communities of images. Learners exposed to walks that richly sampled from each community exhibited a sharp increase in processing time upon entry into a new community. This effect was eliminated in a highly regular walk that sampled exhaustively from images in short, successive cycles (i.e., that increasingly minimized uncertainty about the nature of upcoming stimuli). These results demonstrate that temporal organization plays an essential role in how robustly knowledge of network architecture is acquired.

neuroscience↗