Biology subjects

Schuck, N. W.

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

24 records · Page 2Linked to original sources

bioRxiv2020

Similarities and differences in spatial and non-spatial cognitive maps

Learning and generalization in spatial domains is often thought to rely on a "cognitive map", representing relationships between spatial locations. Recent research suggests that this same neural machinery is also recruited for reasoning about more abstract, conceptual forms of knowledge. Yet, to what extent do spatial and conceptual reasoning share common computational principles, and what are the implications for behavior? Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks. Participant behavior indicated sensitivity to both spatial and conceptual distance, and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel. The same Gaussian Process model best captured human search decisions and judgments in both domains, and could simulate realistic learning curves, where we found equivalent levels of generalization in spatial and conceptual tasks. At the same time, we also find characteristic differences between domains. Relative to the spatial domain, participants showed reduced levels of uncertainty-directed exploration and increased levels of random exploration in the conceptual domain. Participants also displayed a one-directional transfer effect, where experience in the spatial task boosted performance in the conceptual task, but not vice versa. While confidence judgments indicated that participants were sensitive to the uncertainty of their knowledge in both tasks, they did not or could not leverage their estimates of uncertainty to guide exploration in the conceptual task. These results support the notion that value-guided learning and generalization recruit cognitive-map dependent computational mechanisms in spatial and conceptual domains. Yet both behavioral and model-based analyses suggest domain specific differences in how these representations map onto actions. Author summaryThere is a resurgence of interest in "cognitive maps" based on recent evidence that the hippocampal-entorhinal system encodes both spatial and non-spatial relational information, with far-reaching implications for human behavior. Yet little is known about the commonalities and differences in the computational principles underlying human learning and decision making in spatial and non-spatial domains. We use a within-subject design to examine how humans search for either spatially or conceptually correlated rewards. Using a Bayesian learning model, we find evidence for the same computational mechanisms of generalization across domains. While participants were sensitive to expected rewards and uncertainty in both tasks, how they leveraged this knowledge to guide exploration was different: participants displayed less uncertainty-directed and more random exploration in the conceptual domain. Moreover, experience with the spatial task improved conceptual performance, but not vice versa. These results provide important insights about the degree of overlap between spatial and conceptual cognition.

animal behavior and cognition↗

bioRxiv2019

Effects of Aging on the Encoding of Spatial Direction in the Human Brain

Human aging is characterized by impaired spatial cognition and reductions in the distinctiveness of category-specific fMRI activation patterns. Yet, little is know about age-related decline in neural distinctiveness of spatial information. Here, we asked whether neural tuning functions of walking direction are broadened in older versus younger adults. To test this idea, we developed a novel method that allowed us to investigate changes in fMRI-measured pattern similarity while participants navigated in different directions in a virtual spatial navigation task. We expected that directional tuning functions would be broader in older adults, and thus activation patterns that reflect neighboring directions would be less distinct as compared to non-adjacent directions. Because loss of distinctiveness leads to more confusions when information is read out by downstream areas, we analyzed predictions of a decoder trained on these representations and asked (1) whether decoder confusions between two directions increase proportionally to their angular similarity, (2) and how this effect may differ between age groups. Evidence for tuning-function-like signals was found in the retrosplenial complex and primary visual cortex. Significant age differences in tuning width, however, were only found in the primary visual cortex, suggesting that less precise visual information could lead to worse directional signals in older adults. Yet, age differences in visual tuning were not related to behavior. Instead, directional information encoded in RSC correlated with memory on task. These results shed new light on neural mechanisms underling age-related spatial navigation impairments and introduce a novel approach to measure tuning specificity using fMRI.

neuroscience↗

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↗