Biology subjects

Rich, E. L.

Publications and source records attributed to Rich, E. L..

6 recordsLinked to original sources

bioRxiv2024

Attention-dependent attribute comparisons underlie multi-attribute decision-making in orbitofrontal cortex

Economic decisions often require weighing multiple dimensions, or attributes. The orbitofrontal cortex FC) is thought to be important for computing the integrated value of an option from its attributes and comparing lues to make a choice. Although OFC neurons are known to encode integrated values, evidence for value mparison has been limited. Here, we used a multi-attribute choice task for monkeys to investigate how OFC eurons integrate and compare multi-attribute options. Attributes were represented separately and eye tracking as used to measure attention. We found that OFC neurons encode the value of attended attributes, dependent of other attributes in the same option. Encoding was negatively weighted by the value of the same tribute in the other option, consistent with a comparison between the two like attributes. These results indicate at OFC computes comparisons among attributes rather than integrated values, and does so dynamically, ifting with the focus of attention.

neuroscience↗

bioRxiv2024

Pulvinar perturbation causes functional reorganization of visual attentional information in posterior parietal cortex

The pulvinar nucleus of the thalamus (PUL) is a higher-order thalamic relay and the main visual extrageniculate thalamic nucleus. Evidence suggests the PUL coordinates information processing across cortical areas involved in visual perception and attention. Other findings suggest the PUL may also influence higher-order cognitive processes, such as attentional control, through interactions with connected neocortical areas like the posterior parietal cortex (PPC). We hypothesized that PUL input to the dorsal and caudal PPC (DPPC and CPPC) enhances visuospatial processing and attention. To test this hypothesis, we recorded neuronal activity in the PUL, DPPC, and CPPC of freely behaving rats performing the visuospatial attention (VSA) task while optogenetically suppressing PUL neurons on some trials. We found that PUL manipulation did not affect behavioral performance, but reorganized neural codes in DPPC and CPPC as well as PUL itself.

animal behavior and cognition↗

bioRxiv2023

Anterior cingulate cortex neurons in macaques encode social image identities

The anterior cingulate cortex gyrus (ACCg) has been implicated in prosocial behaviors involving complex reasoning about social cues. While this indicates that the ACCg is involved in social behavior, it remains unclear whether ACCg neurons also encode social information during goal-directed actions without social consequences. To address this, we assessed how social information is processed by ACCg neurons in a reward localization task. Two rhesus monkeys performed the task using either social or nonsocial visual guides to locate rewarding targets. We found that monkeys can use both sets of guides, and many neurons in the ACCg distinguished social from nonsocial trials. Yet, this encoding was no more common in ACCg than in the prearcuate cortex (PAC), which has not been strongly linked to social behavior. However, unlike PAC, ACCg neurons were more likely to encode the unique identity of social visual guides compared to nonsocial, even though identity was irrelevant to the reward localization task. This suggests that ACCg neurons are uniquely sensitive to social information that differentiates individuals, which may underlie its role in complex social reasoning.

neuroscience↗

bioRxiv2023

Multi-attribute decision-making in macaques relies on direct attribute comparisons

In value-based decisions, there are frequently multiple attributes, such as cost, quality, or quantity, that contribute to the overall goodness of an option. Since one option may not be better in all attributes at once, the decision process should include a means of weighing relevant attributes. Most decision-making models solve this problem by computing an integrated value, or utility, for each option from a weighted combination of attributes. However, behavioral anomalies in decision-making, such as context effects, indicate that other attribute-specific computations might be taking place. Here, we tested whether rhesus macaques show evidence of attribute-specific processing in a value-based decision-making task. Monkeys made a series of decisions involving choice options comprising a sweetness and probability attribute. Each attribute was represented by a separate bar with one of two mappings between bar size and the magnitude of the attribute (i.e., bigger=better or bigger=worse). We found that translating across different mappings produced selective impairments in decision-making. When like attributes differed, monkeys were prevented from easily making direct attribute comparisons, and choices were less accurate and preferences were more variable. This was not the case when mappings of unalike attributes within the same option were different. Likewise, gaze patterns favored transitions between like attributes over transitions between unalike attributes of the same option, so that like attributes were sampled sequentially to support within-attribute comparisons. Together, these data demonstrate that value-based decisions rely, at least in part, on directly comparing like attributes of multi-attribute options. Significance StatementValue-based decision-making is a cognitive function impacted by a number of clinical conditions, including substance use disorder and mood disorders. Understanding the neural mechanisms, including online processing steps involved in decision formation, will provide critical insights into decision-making deficits characteristic of human psychiatric disorders. Using rhesus monkeys as a model species capable of complex decision-making, this study shows that decisions involve a process of comparing like features, or attributes, of multi-attribute options. This is contrary to popular models of decision-making in which attributes are first combined into an overall value, or utility, to make a choice. Therefore, these results serve as an important foundation for establishing a more complete understanding of the neural mechanisms involved in forming complex decisions.

animal behavior and cognition↗

bioRxiv2022

Abstraction of reward context facilitates relative reward coding in dorsal and ventral anterior cingulate cortex

The anterior cingulate cortex (ACC) is believed to be involved in many cognitive processes, including linking goals to actions and tracking decision-relevant contextual information. ACC neurons robustly encode expected outcomes, but how this relates to putative functions of ACC remains unknown. Here, we approach this question from the perspective of population codes by analyzing neural spiking data in the ventral and dorsal banks of the ACC in monkeys trained to perform a stimulus-motor mapping task. We found that neural populations favor a representational geometry that emphasizes contextual information, while facilitating the independent, abstract representation of multiple task-relevant variables. In addition, trial outcomes were primarily encoded relative to task context, suggesting that the population structures we observe could be a mechanism allowing feedback to be interpreted in a context-dependent manner. Together, our results point to a prominent role for ACC in context-setting and relative interpretation of outcomes, facilitated by abstract, or "untangled," representations of task variables. Author SummaryThe ability to interpret events in light of the current context is a critical facet of higher-order cognition. The anterior cingulate cortex is suggested to be important for tracking information about current contexts, while alternate views hold that its function is more related to the motor system and linking goals to appropriate motor responses. Here, we evaluated these two possibilities by recording anterior cingulate neurons from monkeys performing a stimulus-motor mapping task in which compound cues both defined the current reward context and instructed appropriate motor responses. By analyzing geometric properties of neural population activity, we found that the ACC prioritized context information, representing it as a dominant, abstract concept. Ensuing trial outcomes were then coded relative to these contexts, suggesting an important role for these representations in context-dependent evaluation. Such mechanisms may be critical for the abstract reasoning and generalization characteristic of biological intelligence.

neuroscience↗

bioRxiv2021

TRAKR - A reservoir-based tool for fast and accurate classification of neural time-series patterns

Distinguishing between complex nonlinear neural time-series patterns is a challenging problem in neuroscience. Accurately classifying different patterns could be useful for a wide variety of applications, e.g. detecting seizures in epilepsy and optimizing control spaces for brain-machine interfaces. It remains challenging to correctly distinguish nonlinear time-series patterns because of the high intrinsic dimensionality of such data, making accurate inference of state changes (for intervention or control) difficult. On the one hand, simple distance metrics, which can be computed quickly, often do not yield accurate classifications; on the other hand, ensembles or deep supervised approaches offer high accuracy but are training data intensive. We introduce a reservoir-based tool, state tracker (TRAKR), which provides the high accuracy of ensembles or deep supervised methods while preserving the benefits of simple distance metrics in being applicable to single examples of training data (one-shot classification). We show that TRAKR instantaneously detects deviations in dynamics as they occur through time, and can distinguish between up to 40 patterns from different chaotic data recurrent neural networks (RNNs) with above-chance accuracy. We apply TRAKR to a benchmark time-series dataset - permuted sequential MNIST - and show that it achieves high accuracy, performing on par with deep supervised networks and outperforming other distance-metric based approaches. We also apply TRAKR to electrocorticography (ECoG) data from the macaque orbitofrontal cortex (OFC) and, similarly, find that TRAKR performs on par with deep supervised networks, and more accurately than commonly used approaches such as Dynamic Time Warping (DTW). Altogether, TRAKR allows for high accuracy classification of time-series patterns from a range of different biological and non-biological datasets based on single training examples. These results demonstrate that TRAKR could be a viable alternative in the analysis of time-series data, offering the potential to generate new insights into the information encoded in neural circuits from single-trial data.

neuroscience↗