Biology subjects

Gold, J. I.

Publications and source records attributed to Gold, J. I..

27 records · Page 2Linked to original sources

bioRxiv2020

Context-Dependent Relationships between Locus Coeruleus Firing Patterns and Coordinated Neural Activity in the Anterior Cingulate Cortex

Ascending neuromodulatory projections from the locus coeruleus (LC) affect cortical neural networks via the release of norepinephrine (NE). However, the exact nature of these neuromodulatory effects on neural activity patterns in vivo is not well understood. Here we show that in awake monkeys, LC activation is associated with changes in coordinated activity patterns in the anterior cingulate cortex (ACC). These relationships, which are largely independent of changes in firing rates of individual ACC neurons, depend on the type of LC activation: ACC pairwise correlations tend to be reduced when ongoing (baseline) LC activity increases but enhanced when external events evoke transient LC responses. Both relationships covary with pupil changes that reflect LC activation and arousal. These results suggest that modulations of information processing that reflect changes in coordinated activity patterns in cortical networks can result partly from ongoing, context-dependent, arousal-related changes in activation of the LC-NE system.

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bioRxiv2020

Distinct neural encoding of context-dependent error signals and context-dependent changes in behavior during adaptive learning

Effective learning requires using errors in a task-dependent manner, for example adjusting to errors that result from unpredicted environmental changes but ignoring errors that result from environmental stochasticity. Where and how the brain represents errors in a task-dependent manner and uses them to guide behavior are not well understood. We imaged the brains of human participants performing a predictive-inference task with two conditions that had different sources of errors. Their performance was sensitive to this difference, including more choice switches after fundamental changes versus stochastic fluctuations in reward contingencies. Using multi-voxel pattern classification, we identified task-dependent representations of error magnitude and past errors in posterior parietal cortex. These representations were distinct from representations of the resulting behavioral adjustments in dorsomedial frontal, anterior cingulate, and orbitofrontal cortex. The results provide new insights into how the human brain represents errors in a task-dependent manner and guides subsequent adaptive behavior.

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bioRxiv2020

The complexity of model-free and model-based learning strategies

Different learning strategies are thought to fall along a continuum that ranges from simple, inflexible, and fast "model-free" strategies, to more complex, flexible, and deliberative "model-based strategies". Here we show that, contrary to this proposal, strategies at both ends of this continuum can be equally flexible, effective, and time-intensive. We analyzed behavior of adult human subjects performing a canonical learning task used to distinguish between model-free and model-based strategies. Subjects using either strategy showed similarly high information complexity, a measure of strategic flexibility, and comparable accuracy and response times. This similarity was apparent despite the generally higher computational complexity of model-based algorithms and fundamental differences in how each strategy learned: model-free learning was driven primarily by observed past responses, whereas model-based learning was driven primarily by inferences about latent task features. Thus, model-free and model-based learning differ in the information they use to learn but can support comparably flexible behavior. Statement of RelevanceThe distinction between model-free and model-based learning is an influential framework that has been used extensively to understand individual- and task-dependent differences in learning by both healthy and clinical populations. A common interpretation of this distinction that model-based strategies are more complex and therefore more flexible than model-free strategies. However, this interpretation conflates computational complexity, which relates to processing resources and generally higher for model-based algorithms, with information complexity, which reflects flexibility but has rarely been measured. Here we use a metric of information complexity to demonstrate that, contrary to this interpretation, model-free and model-based strategies can be equally flexible, effective, and time-intensive and are better distinguished by the nature of the information from which they learn. Our results counter common interpretations of model-free versus model-based learning and demonstrate the general usefulness of information complexity for assessing different forms of strategic flexibility.

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bioRxiv2019

Functional brain network reconfiguration during learning in a dynamic environment

When learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.

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bioRxiv2019

Pupil diameter encodes the idiosyncratic, cognitive complexity of belief updating

Pupils tend to dilate in response to surprising events, but it is not known whether these responses are primarily stimulus driven or instead reflect a more nuanced relationship between pupil-linked arousal systems and cognitive expectations. Using an auditory adaptive decision-making task, we show that evoked pupil diameter is more parsimoniously described as signaling violations of learned, top-down expectations than changes in low-level stimulus properties. We further show that both baseline and evoked pupil diameter is modulated by the degree to which individual subjects use these violations to update their subsequent expectations, as reflected in the complexity of their updating strategy. Together these results demonstrate a central role for idiosyncratic cognitive processing in how arousal systems respond to new inputs and, via our complexity-based analyses, provide a unified framework for understanding these effects in terms of both inference processes aimed to reduce belief uncertainty and more traditional notions of mental effort.

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bioRxiv2019

The caudate nucleus contributes causally to decisions that combine noisy visual evidence and reward context

Our decisions often need to balance what we observe and what we desire. However, our understanding of how and where in the brain such decisions are made remains limited. A prime candidate for integrating sensory observations and desired rewards, and a focus of many modeling studies, is the basal ganglia pathway, which is known to make separate contributions to perceptual decisions that require the interpretation of uncertain sensory evidence and value-based decisions that select among outcome options 1-16. Here we report direct evidence for a causal role for a major input station of the basal ganglia, the caudate nucleus, in incorporating reward context and uncertain visual evidence to guide adaptive decision-making. In monkeys making saccadic decisions based on visual motion evidence and asymmetric reward-choice associations 17, single caudate neurons encoded information about both the visual evidence and the asymmetric rewards. Electrical microstimulation at caudate sites with task-modulated activity during motion viewing affected how the visual and reward information was used to form the decision. The microstimulation effects included coordinated changes in multiple computational components of the decision process, mimicking the monkeys voluntary adjustments in response to the asymmetric reward contexts. These results imply that the caudate nucleus plays key roles in coordinating the deliberative decision process that balances external evidence and internal preferences to guide adaptive behavior.

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bioRxiv2019

The complexity dividend: when sophisticated inference matters

Animals continuously infer latent properties of the world from noisy and changing observations. Complex approaches to this challenge such as Bayesian inference are accurate but cognitively demanding, requiring extensive working memory and adaptive learning. Simple strategies such as always using a prior bias or following the last observation are easy to implement but may be less accurate. What is the appropriate balance between complexity and accuracy? We construct a hierarchy of strategies that vary in complexity between these limits and find a power law of diminishing returns: increasing complexity gives progressively smaller gains in accuracy. Moreover, the rate at which the gain decrements depends systematically on the statistical uncertainty in the world, such that complex strategies do not provide substantial benefits over simple ones when uncertainty is too high or too low. In between, when the world is neither too predictable nor too unpredictable, there is a complexity dividend.

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bioRxiv2018

Ongoing, rational calibration of reward-driven perceptual biases

Decision-making is often interpreted in terms of normative computations that maximize a particular reward function for stable, average behaviors. Aberrations from the reward-maximizing solutions, either across subjects or across different sessions for the same subject, are often interpreted as reflecting poor learning or physical limitations. Here we show that such aberrations may instead reflect the involvement of additional satisficing and heuristic principles. For an asymmetric-reward perceptual decision-making task, three monkeys produced adaptive biases in response to changes in reward asymmetries and perceptual sensitivity. Their choices and response times were consistent with a normative accumulate-to-bound process. However, their context-dependent adjustments to this process deviated slightly but systematically from the reward-maximizing solutions. These adjustments were instead consistent with a rational process to find satisficing solutions based on the gradient of each monkeys reward-rate function. These results suggest new dimensions for assessing the rational and idiosyncratic aspects of flexible decision-making.

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bioRxiv2016

Adaptive, arousal-related adjustments of perceptual biases optimize perception in a dynamic environment

Prior expectations can be used to improve perceptual judgments about ambiguous stimuli. However, little is known about if and how these improvements are maintained in dynamic environments in which the quality of appropriate priors changes from one stimulus to the next. Using a novel sound-localization task, we show that changes in stimulus predictability lead to arousal-mediated adjustments in the magnitude of prior-driven biases that optimize perceptual judgments about each stimulus. These adjustments depend on task-dependent changes in the relevance and reliability of prior expectations, which subjects update using both normative and idiosyncratic principles. The resulting variations in biases across task conditions and individuals are reflected in modulations of pupil diameter, such that larger stimulus-evoked pupil responses correspond to smaller biases. These results suggest a critical role for the arousal system in adjusting the strength of perceptual biases with respect to inferred environmental dynamics to optimize perceptual judgements.

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