A neural network for information seeking.
Ontology highlight
ABSTRACT:
SUBMITTER: White JK
PROVIDER: S-EPMC6856099 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In a dynamic world, accurate beliefs about the environment are vital for survival, and individuals should therefore regularly seek out new information with which to update their beliefs. This aspect of behaviour is not well captured by standard theories of decision making, and the neural mechanisms of information seeking remain unclear. One recent theory posits that valuation of information results from representation of informative stimuli within canonical neural reward-processing circuits, even if that information lacks instrumental use. We investigated this question by recording EEG from twenty-three human participants performing a non-instrumental information-seeking task. In this task, participants could pay a monetary cost to receive advance information about the likelihood of receiving reward in a lottery at the end of each trial. Behavioural results showed that participants were willing to incur considerable monetary costs to acquire early but non-instrumental information. Analysis of the event-related potential elicited by informative cues revealed that the feedback-related negativity independently encoded both an information prediction error and a reward prediction error. These findings are consistent with the hypothesis that information seeking results from processing of information within neural reward circuits, and suggests that information may represent a distinct dimension of valuation in decision making under uncertainty.
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Project description:To make adaptive decisions, we must actively demand information, but relatively little is known about the mechanisms of active information gathering. An open question is how the brain estimates expected information gains (EIG) when comparing the current decision uncertainty with the uncertainty that is expected after gathering information. We examined this question using fMRI in a task in which people placed bids to obtain information in conditions that varied independently by prior decision uncertainty, information diagnosticity, and the penalty for an erroneous choice. Consistent with value of information theory, bids were sensitive to EIG and its components of prior certainty and expected posterior certainty. Expected posterior certainty was decoded above chance from multivoxel activation patterns in the posterior parietal and extrastriate cortices. This representation was independent of instrumental rewards and overlapped with distinct representations of EIG and prior certainty. Thus, posterior parietal and extrastriate cortices are candidates for mediating the prospection of posterior probabilities as a key step to estimate EIG during active information gathering.
| S-EPMC10705234 | biostudies-literature
Project description:BackgroundPeople engage in health information-seeking behavior to support health outcomes, and being able to predict such behavior can inform the development of interventions to guide effective health information seeking. Obtaining a comprehensive list of the predictors of health information-seeking behavior through a systematic search of the literature and exploring the interrelationship of these predictors are critical first steps in this process.ObjectiveThis study aims to identify significant predictors of health information-seeking behavior in the primary literature, develop a common taxonomy for these predictors, and identify the evolution of the concerned research field.MethodsA systematic search of PsycINFO, Scopus, and PubMed was conducted for all years up to and including December 10, 2019. Quantitative studies identifying significant predictors of health information-seeking behavior were included. Information seeking was broadly defined and not restricted to any source of health information. Data extraction of significant predictors was performed by 2 authors, and network analysis was conducted to observe the relationships between predictors with time.ResultsA total of 9549 articles were retrieved, and after the screening, 344 studies were retained for analysis. A total of 1595 significant predictors were identified. These predictors were categorized into 67 predictor categories, with the most central predictors being age, education, gender, health condition, and financial income. With time, the interrelationship of predictors in the network became denser, with the growth of new predictor grouping reaching saturation (1 new predictor identified) in the past 7 years, despite increasing publication rates.ConclusionsA common taxonomy was developed to classify 67 significant predictors of health information-seeking behavior. A time-aggregated network method was developed to track the evolution of the research field, showing the maturation of new predictor terms and an increase in primary studies reporting multiple significant predictors of health information-seeking behavior. The literature has evolved with a decreased characterization of novel predictors of health information-seeking behavior. In contrast, we identified a parallel increase in the complexity of predicting health information-seeking behavior, with an increase in the literature describing multiple significant predictors.
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Project description:We investigated a mathematical model composed of a spiking neural network (SNN) interacting with astrocytes. We analysed how information content in the form of two-dimensional images can be represented by an SNN in the form of a spatiotemporal spiking pattern. The SNN includes excitatory and inhibitory neurons in some proportion, sustaining the excitation-inhibition balance of autonomous firing. The astrocytes accompanying each excitatory synapse provide a slow modulation of synaptic transmission strength. An information image was uploaded to the network in the form of excitatory stimulation pulses distributed in time reproducing the shape of the image. We found that astrocytic modulation prevented stimulation-induced SNN hyperexcitation and non-periodic bursting activity. Such homeostatic astrocytic regulation of neuronal activity makes it possible to restore the image supplied during stimulation and lost in the raster diagram of neuronal activity due to non-periodic neuronal firing. At a biological point, our model shows that astrocytes can act as an additional adaptive mechanism for regulating neural activity, which is crucial for sensory cortical representations.
| S-EPMC10217185 | biostudies-literature
Project description:Vast amounts of personalized information are now available to individuals. A vital research challenge is to establish how people decide what information they wish to obtain. Here, over five studies examining information-seeking in different domains we show that information-seeking is associated with three diverse motives. Specifically, we find that participants assess whether information is useful in directing action, how it will make them feel, and whether it relates to concepts they think of often. We demonstrate that participants integrate these assessments into a calculation of the value of information that explains information seeking or its avoidance. Different individuals assign different weights to these three factors when seeking information. Using a longitudinal approach, we find that the relative weights assigned to these information-seeking motives within an individual show stability over time, and are related to mental health as assessed using a battery of psychopathology questionnaires.
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Project description: Not available
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Project description:All-optical diffractive neural networks, as analog artificial intelligence accelerators, leverage parallelism and analog computation for complex data processing. However, their low space transmission efficiency or large spatial dimensions hinder miniaturization and broader application. Here, we propose a terahertz spoof plasmonic neural network on a planar diffractive platform for direct multi-target recognition. Our approach employs a spoof surface plasmon polariton coupler array to construct a diffractive network layer, resulting in a compact, efficient, and easily integrable architecture. We designed three schemes: basis vector classification, multi-user recognition, and MNIST handwritten digit classification. Experimental results reveal that the terahertz spoof plasmonic neural network successfully classifies basis vectors, recognizes multi-user orientation information, and directly processes handwritten digits using a designed input framework comprising a metal grating array, transmitters, and receivers. This work broadens the application of terahertz plasmonic metamaterials, paving the way for terahertz on-chip integration, intelligent communication, and advanced computing systems.
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Project description:Interventions: None
Primary outcome(s): The primary outcome of the study is the accuracy of the CAD-CNN system for predicting histology of diminutive colorectal polyps (1-5mm) compared with the accuracy of the prediction of the endoscopist. Both the CAD-CNN system and the endoscopist will use NBI for their predictions.
Accuracy is defined as the percentage of correctly predicted optical diagnoses of the CAD-CNN system and/or endoscopist compared to the gold standard pathology. For the calculation of the accuracy, adenomas and SSLs will be dichotomised as neoplastic polyps, while HPs and other non-neoplastic histology are considered non-neoplastic.
Study Design: N/A: single arm study, Open (masking not used), N/A , unknown, Other
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Project description:From an early age, children need to gather information to learn about their environment. Deciding which knowledge to pursue can be difficult because information can serve several, sometimes competing, purposes. Here, we examine the developmental trajectories of such diverse information-seeking motives. Over five experiments involving 521 children (aged 4-12), we find that school-age children integrate three key factors into their information-seeking choices: whether information reduces uncertainty, is useful in directing action, and is likely to be positive. Choices that likely reveal positive information and are useful for action emerge as early as age 4, followed by choices that reduce uncertainty (at ~age 5). Our results suggest that motives related to usefulness and uncertainty reduction become stronger with age, while the tendency to seek positive news does not show a statistically significant change throughout development. This study reveals how the relative importance of diverging, sometimes conflicting, information-seeking motives emerges throughout development.
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Project description:We present a robotic chemical discovery system capable of navigating a chemical space based on a learned general association between molecular structures and reactivity, while incorporating a neural network model that can process data from online analytics and assess reactivity without knowing the identity of the reagents. Working in conjunction with this learned knowledge, our robotic platform is able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown chemical spaces, regardless of the identity of the starting materials. Through the system, we identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, and some unpredictable reactions that yielded new molecules. The validation of the system was done within a budget of 15 inputs combined in 1018 reactions, further analysis of which allowed us to discover not only a new photochemical reaction but also a new reactivity mode for a well-known reagent (p-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of 6 equiv of TosMIC in a "multistep, single-substrate" cascade reaction yielding a trimeric product in high yield (47% unoptimized) with the formation of five new C-C bonds involving sp-sp2 and sp-sp3 carbon centers. An analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of a reactivity-first robotic discovery of unknown reaction methodologies without requiring human input.
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