Biology subjects

Schuck, N. W.

Publications and source records attributed to Schuck, N. W..

4 recordsLinked to original sources

bioRxiv2018

Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias

The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degree of similarity between these neural activity patterns in response to different events is used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patterns, which can be further used for fMRI decoding. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).\n\nAuthor summaryWe show the severity of the bias introduced when performing representational similarity analysis (RSA) based on neural activity pattern estimated within imaging runs. Our Bayesian RSA method significantly reduces the bias and can learn a shared representational structure across multiple participants. We also demonstrate its extension as a new multi-class decoding tool.

neuroscience↗

bioRxiv2018

Sequential replay of non-spatial task states in the human hippocampus

Neurophysiological research has found that previously experienced sequences of spatial events are reactivated in the hippocampus of rodents during wakeful rest. This phenomenon has become a cornerstone of modern theories of memory and decision making. Yet, whether hippocampal sequence reactivation at rest is of general importance also for humans and non-spatial tasks has remained unclear. Here, we investigated sequences of fMRI BOLD activation patterns in humans during wakeful rest following a sequential non-spatial decision-making task. We found that pattern reactivations within the human hippocampus reflected the order of previous task state sequences, and that the extent of this offline reactivation was related to the on-task representation of task states in the orbitofrontal cortex. Permutation analyses and fMRI signal simulations confirmed that these results reflected underlying BOLD activity, and showed that our novel statistical analyses are, in principle, sensitive to sequential neural events occurring as fast as one hundred milliseconds apart. Our results support the importance of sequential reactivation in the human hippocampus for decision making, and establish the feasibility of investigating such rapid signals with fMRI, despite its substantial temporal limitations.\n\nHighlightsO_LIWe provide fMRI evidence for sequential pattern reactivation in the human hippocampus\nC_LIO_LISequences of patterns reflect states from a sequential, non-spatial decision-making task\nC_LIO_LISimulations show that our novel fMRI analysis is sensitive to fast sequences of sub-second neural events\nC_LIO_LIResults support the importance of sequential reactivation in the human hippocampus for decision making\nC_LI

neuroscience↗

bioRxiv2018

Connecting conceptual and spatial search via a model of generalization

The idea of a \"cognitive map\" was originally developed to explain planning and generalization in spatial domains through a representation of inferred relationships between experiences. Recently, new research has suggested similar principles may also govern the representation of more abstract, conceptual knowledge in the brain. We test whether the search for rewards in conceptual spaces follows similar computational principles as in spatial environments. Using a within-subject design, participants searched for both spatially and conceptually correlated rewards in multi-armed bandit tasks. We use a Gaussian Process model combining generalization with an optimistic sampling strategy to capture human search decisions and judgments in both domains, and to simulate human-level performance when specified with participant parameter estimates. In line with the notion of a domain-general generalization mechanism, parameter estimates correlate across spatial and conceptual search, yet some differences also emerged, with participants generalizing less and exploiting more in the conceptual domain.

animal behavior and cognition↗

bioRxiv2017

A state representation for reinforcement learning and decision-making in the orbitofrontal cortex

Despite decades of research, the exact ways in which the orbitofrontal cortex (OFC) influences cognitive function have remained mysterious. Anatomically, the OFC is characterized by remarkably broad connectivity to sensory, limbic and subcortical areas, and functional studies have implicated the OFC in a plethora of functions ranging from facial processing to value-guided choice. Notwithstanding such diversity of findings, much research suggests that one important function of the OFC is to support decision making and reinforcement learning. Here, we describe a novel theory that posits that OFCs specific role in decision-making is to provide an up-to-date representation of task-related information, called a state representation. This representation reflects a mapping between distinct task states and sensory as well as unobservable information. We summarize evidence supporting the existence of such state representations in rodent and human OFC and argue that forming these state representations provides a crucial scaffold that allows animals to efficiently perform decision making and reinforcement learning in high-dimensional and partially observable environments. Finally, we argue that our theory offers an integrating framework for linking the diversity of functions ascribed to OFC and is in line with its wide ranging connectivity.

neuroscience↗