Humans actively sample evidence to support prior beliefs.
Ontology highlight
ABSTRACT:
SUBMITTER: Kaanders P
PROVIDER: S-EPMC9038198 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Recent work in psychology and neuroscience has revealed differences in impression updating across social distance and group membership. Observers tend to maintain prior impressions of close (vs. distant) and ingroup (vs. outgroup) others in light of new information, and this belief maintenance is at times accompanied by increased activity in Theory of Mind regions. It remains an open question whether differences in the strength of prior beliefs, in a context absent social motivation, contribute to neural differences during belief updating. We devised a functional magnetic resonance imaging study to isolate the impact of experimentally induced prior beliefs on mentalizing activity. Participants learned about targets who performed 2 or 4 same-valenced behaviors (leading to the formation of weak or strong priors), before performing 2 counter-valenced behaviors. We found a greater change in activity in dorsomedial prefrontal cortex (DMPFC) and right temporo-parietal junction following the violation of strong versus weak priors, and a greater change in activity in DMPFC and left temporo-parietal junction following the violation of positive versus negative priors. These results indicate that differences in neural responses to unexpected behaviors from close versus distant others, and ingroup versus outgroup members, may be driven in part by differences in the strength of prior beliefs.
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Project description:Apathy is a debilitating syndrome that is associated with reduced goal-directed behavior. Although apathy is common and detrimental to prognosis in many neuropsychiatric diseases, its underlying mechanisms remain controversial. We propose a new model of apathy, in the context of Bayesian theories of brain function, whereby actions require predictions of their outcomes to be held with sufficient precision for "explaining away" differences in sensory inputs. In the active inference model, apathy results from reduced precision of prior beliefs about action outcomes. We tested this hypothesis using a visuomotor task in healthy adults (N = 47), with experimental manipulation of physical effort and financial reward. Bayesian modeling of performance and participants' perception of their performance was used to infer the precision of their priors. We confirmed that the perception of performance was biased toward the target, which was accounted for by relatively precise prior beliefs about action outcomes. These priors were consistently more precise than the corresponding performance distribution, and were scaled to effort and reward. Crucially, prior precision was negatively associated with trait apathy, suggesting that apathetic individuals had less precise prior beliefs about action outcomes. The results support a Bayesian account of apathy that could inform future studies of clinical populations. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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Project description:We present a definition for the effective sample size of a parametric prior distribution in a Bayesian model, and propose methods for computing the effective sample size in a variety of settings. Our approach first constructs a prior chosen to be vague in a suitable sense, and updates this prior to obtain a sequence of posteriors corresponding to each of a range of sample sizes. We then compute a distance between each posterior and the parametric prior, defined in terms of the curvature of the logarithm of each distribution, and the posterior minimizing the distance defines the effective sample size of the prior. For cases where the distance cannot be computed analytically, we provide a numerical approximation based on Monte Carlo simulation. We provide general guidelines for application, illustrate the method in several standard cases where the answer seems obvious, and then apply it to some nonstandard settings.
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Project description:Prior effective sample size (ESS) of a Bayesian parametric model was defined by Morita, et al. (2008, Biometrics,64, 595-602). Starting with an ?-information prior defined to have the same means and correlations as the prior but to be vague in a suitable sense, the ESS is the required sample size to obtain a hypothetical posterior very close to the prior. In this paper, we present two alternative definitions for the prior ESS that are suitable for a conditionally independent hierarchical model. The two definitions focus on either the first level prior or second level prior. The proposed methods are applied to important examples to verify that each of the two types of prior ESS matches the intuitively obvious answer where it exists. We illustrate the method with applications to several motivating examples, including a single-arm clinical trial to evaluate treatment response probabilities across different disease subtypes, a dose-finding trial based on toxicity in this setting, and a multicenter randomized trial of treatments for affective disorders.
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Project description:Increased glucose consumption is a hallmark of cancer cells. The increased consumption and subsequent metabolism of glucose during proliferation creates the need for a constant supply of NAD, a co-factor in glycolysis. Regeneration of the NAD required to support enhanced glycolysis has been attributed to the terminal glycolytic enzyme, lactate dehydrogenase (LDH). However, loss of glucose carbons to biosynthetic pathways early in glycolysis reduces the carbon supply to LDH. Thus, alternative routes for NAD regeneration must exist to support the increased glycolytic rate while allowing for the diversion of glucose to generate biomass and support proliferation. Here we demonstrate, using a variety of cancer cell lines as well as activated primary T cells, that cytosolic malate dehydrogenase 1 (MDH1) is an alternative to LDH as a supplier of NAD. Moreover, our results indicate that MDH1 generates malate with carbons derived from glutamine, thus enabling utilization of glucose carbons for glycolysis and for biomass. Amplification of MDH1 occurs at an impressive frequency in human tumors and correlates with poor prognosis. Together, our findings suggest that proliferating cells rely on both MDH1 and LDH to replenish cytosolic NAD, and that therapies designed at targeting glycolysis must consider both dehydrogenases.
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Project description:BACKGROUND:Inadvertent sample swaps are a real threat to data quality in any medium to large scale omics studies. While matches between samples from the same individual can in principle be identified from a few well characterized single nucleotide polymorphisms (SNPs), omics data types often only provide low to moderate coverage, thus requiring integration of evidence from a large number of SNPs to determine if two samples derive from the same individual or not. METHODS:We select about six thousand SNPs in the human genome and develop a Bayesian framework that is able to robustly identify sample matches between next generation sequencing data sets. RESULTS:We validate our approach on a variety of data sets. Most importantly, we show that our approach can establish identity between different omics data types such as Exome, RNA-Seq, and MethylCap-Seq. We demonstrate how identity detection degrades with sample quality and read coverage, but show that twenty million reads of a fairly low quality RNA-Seq sample are still sufficient for reliable sample identification. CONCLUSION:Our tool, SMASH, is able to identify sample mismatches in next generation sequencing data sets between different sequencing modalities and for low quality sequencing data.
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Project description:ObjectivesGreater patient activation, defined as having the knowledge, skills, and confidence to manage one's health, is associated with cancer control behaviors. Cancer risk beliefs may be associated with patient activation, and delineating this relationship could inform cancer control interventions across diverse patient subgroups. This study examines associations between cancer risk beliefs, language preference, and patient activation within a multilingual urban primary care setting.DesignPatients 18 years and older within a New York City public hospital serving a large proportion of non-native-born Americans were surveyed regarding their cancer risk beliefs and patient activation in Haitian Creole, Spanish, or English based on language preference during a health care visit.ResultsThe sample (N = 460) included 150 Haitian Creole speakers, 159 Spanish speakers, and 151 English speakers and was primarily non-White (92%). Most participants (84%) had not been born in the United States. Cancer risk beliefs differed across language preference. Beliefs that cancer could be avoided by minimizing thoughts about cancer risk were significantly higher in Haitian Creole speakers than in others; reported negative emotion when thinking about cancer risk was higher in Spanish and English than in Haitian Creole speakers. These cancer risk beliefs were positively related to patient activation, even when controlling for language preference.ConclusionCancer risk beliefs differ across language preference and are related to patient activation, making them potentially important in cancer control. Consideration of language represents important demographic stratification for understanding the frequency and relevance of different beliefs about cancer and patient activation.
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