Adaptive Sampling of Information in Perceptual Decision-Making

In many perceptual and cognitive decision-making problems, humans sample multiple noisy information sources serially, and integrate the sampled information to make an overall decision. We derive the optimal decision procedure for two-alternative choice tasks in which the different options are sampled one at a time, sources vary in the quality of the information they provide, and the available time is fixed. To maximize accuracy, the optimal observer allocates time to sampling different information sources in proportion to their noise levels. We tested human observers in a corresponding perceptual decision-making task. Observers compared the direction of two random dot motion patterns that were triggered only when fixated. Observers allocated more time to the noisier pattern, in a manner that correlated with their sensory uncertainty about the direction of the patterns. There were several differences between the optimal observer predictions and human behaviour. These differences point to a number of other factors, beyond the quality of the currently available sources of information, that influences the sampling strategy.     

Rapid Decision-Making with Side-Specific Perceptual Discrimination in Ants

Background

Timely decision making is crucial for survival and reproduction. Organisms often face a speed-accuracy trade-off, as fully informed, accurate decisions require time-consuming gathering and treatment of information. Optimal strategies for decision-making should therefore vary depending on the context. In mammals, there is mounting evidence that multiple systems of perceptual discrimination based on different neural circuits emphasize either fast responses or accurate treatment of stimuli depending on the context.

Methodology/Principal Findings

We used the ant Camponotus aethiops to test the prediction that fast information processing achieved through direct neural pathways should be favored in situations where quick reactions are adaptive. Social insects discriminate readily between harmless group-members and dangerous strangers using easily accessible cuticular hydrocarbons as nestmate recognition cues. We show that i) tethered ants display rapid aggressive reactions upon presentation of non-nestmate odor (120 to 160 ms); ii) ants'' aggressiveness towards non-nestmates can be specifically reduced by exposure to non-nestmate odor only, showing that social interactions are not required to alter responses towards non-nestmates; iii) decision-making by ants does not require information transfer between brain hemispheres, but relies on side-specific decision rules.

Conclusions/Significance

Our results strongly suggest that first-order olfactory processing centers (up to the antennal lobes) are likely to play a key role in ant nestmate recognition. We hypothesize that the coarse level of discrimination achieved in the antennal lobes early in odor processing provides enough information to determine appropriate behavioral responses towards non-nestmates. This asks for a reappraisal of the mechanisms underlying social recognition in insects.     

Relating Pupil Dilation and Metacognitive Confidence during Auditory Decision-Making

The sources of evidence contributing to metacognitive assessments of confidence in decision-making remain unclear. Previous research has shown that pupil dilation is related to the signaling of uncertainty in a variety of decision tasks. Here we ask whether pupil dilation is also related to metacognitive estimates of confidence. Specifically, we measure the relationship between pupil dilation and confidence during an auditory decision task using a general linear model approach to take into account delays in the pupillary response. We found that pupil dilation responses track the inverse of confidence before but not after a decision is made, even when controlling for stimulus difficulty. In support of an additional post-decisional contribution to the accuracy of confidence judgments, we found that participants with better metacognitive ability – that is, more accurate appraisal of their own decisions – showed a tighter relationship between post-decisional pupil dilation and confidence. Together our findings show that a physiological index of uncertainty, pupil dilation, predicts both confidence and metacognitive accuracy for auditory decisions.     

A Bayesian Attractor Model for Perceptual Decision Making

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks.     

Metacognitive Confidence Increases with,but Does Not Determine,Visual Perceptual Learning

While perceptual learning increases objective sensitivity, the effects on the constant interaction of the process of perception and its metacognitive evaluation have been rarely investigated. Visual perception has been described as a process of probabilistic inference featuring metacognitive evaluations of choice certainty. For visual motion perception in healthy, naive human subjects here we show that perceptual sensitivity and confidence in it increased with training. The metacognitive sensitivity–estimated from certainty ratings by a bias-free signal detection theoretic approach–in contrast, did not. Concomitant 3Hz transcranial alternating current stimulation (tACS) was applied in compliance with previous findings on effective high-low cross-frequency coupling subserving signal detection. While perceptual accuracy and confidence in it improved with training, there were no statistically significant tACS effects. Neither metacognitive sensitivity in distinguishing between their own correct and incorrect stimulus classifications, nor decision confidence itself determined the subjects’ visual perceptual learning. Improvements of objective performance and the metacognitive confidence in it were rather determined by the perceptual sensitivity at the outset of the experiment. Post-decision certainty in visual perceptual learning was neither independent of objective performance, nor requisite for changes in sensitivity, but rather covaried with objective performance. The exact functional role of metacognitive confidence in human visual perception has yet to be determined.     

On the Reliability of Bayesian Confidence Limits for a Difference of Two Proportions

A method is presented of deriving confidence limits for a difference of two proportions using Bayesian Theory. The reliability of the method is assessed for a selection of small sample sizes. A limited table of confidence limits is displayed.     

Bayesian Confidence Intervals for Multiplexed Proteomics Integrate Ion-statistics with Peptide Quantification Concordance,

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Highlights

• •Bayesian Beta-Binomial model integrates ion statistics with peptide ratio agreement.
• •Model appropriately interprets information from low signal peptides.
• •Confidence can be assigned even without replicates.
• •Model adds sensitivity to detection of small changes.

     

Integration of Sensory and Reward Information during Perceptual Decision-Making in Lateral Intraparietal Cortex (LIP) of the Macaque Monkey

Single neurons in cortical area LIP are known to carry information relevant to both sensory and value-based decisions that are reported by eye movements. It is not known, however, how sensory and value information are combined in LIP when individual decisions must be based on a combination of these variables. To investigate this issue, we conducted behavioral and electrophysiological experiments in rhesus monkeys during performance of a two-alternative, forced-choice discrimination of motion direction (sensory component). Monkeys reported each decision by making an eye movement to one of two visual targets associated with the two possible directions of motion. We introduced choice biases to the monkeys'' decision process (value component) by randomly interleaving balanced reward conditions (equal reward value for the two choices) with unbalanced conditions (one alternative worth twice as much as the other). The monkeys'' behavior, as well as that of most LIP neurons, reflected the influence of all relevant variables: the strength of the sensory information, the value of the target in the neuron''s response field, and the value of the target outside the response field. Overall, detailed analysis and computer simulation reveal that our data are consistent with a two-stage drift diffusion model proposed by Diederich and Bussmeyer [1] for the effect of payoffs in the context of sensory discrimination tasks. Initial processing of payoff information strongly influences the starting point for the accumulation of sensory evidence, while exerting little if any effect on the rate of accumulation of sensory evidence.     

池蝶蚌线粒体基因组双单性遗传现象分析

部分双壳贝类的线粒体遗传方式不同于标准的母系遗传(SMI),被称为双单性遗传现象(DUI)。池蝶蚌(Hyriopsis schlegelii)是淡水双壳贝类,是否存在双单性遗传现象?本文采用普通PCR扩增、SHOT-GUN测序及软件拼接获得了雄性池蝶蚌线粒体基因组(以下简称Hs-mtDNA)全序列,并与本实验室已报道的雌性池蝶蚌线粒体基因组全序列进行差异性分析。结果表明,雄性和雌性Hs-mtDNA全长分别为15961 bp和15939 bp,雄性比雌性长22 bp,雌雄线粒体基因组成与排列顺序一致。各蛋白编码基因的碱基数目均一致,碱基转换率为1.01%～7.34%,颠换率为0.00%～0.62%,氨基酸差异率为0.00%～9.35%;其中,COX1基因变异率为2.72%;COX2基因碱基变异率最高,达7.50%,雄性COX2的3'末端没有出现编码延伸区。雄性12S rRNA基因发生5 bp的碱基转换,差异率为0.6%;16S rRNA基因比雌性长9 bp,碱基差异率仅为1.2%。雌雄tRNA-His均位于H链上,介于COX2与ND3之间,没有出现位置的差异性。雌雄Hs-mtDNA的非编码区共有28个1～393 bp的片段,但未见控制区。在tRNA-Glu与tRNA-Tyr间有一段长393 bp的非编码区存在蛋白质翻译功能,但非雄性特异性蛋白。以COX1基因建立系统进化树,池蝶蚌和三角帆蚌(H.cumingii)聚在一起,而含有双单性遗传现象的无齿蚌属的Pyganodon grandis、小方蚌亚科的Venustaconcha ellipsiformis及小方形蚌属的Quadrula quadrula三者雄性聚为一支,雌性聚为一支。因此,雌雄池蝶蚌线粒体存在一定的差异性,但其差异要比其他具有双单性遗传现象的淡水双壳类小得多,且池蝶蚌线粒体遗传可能不存在双单性遗传现象。     

Confidence limits of the probability of success in animal experiments and clinical studies: A Bayesian approach

PurposeTo determine from the number of trials, n, and the number of observed successes, k the most probable value, the variance and the confidence limits of the probability of success, p, in animal experiments and clinical studies subject to binomial statistics.MethodIn such experiments the probability of success is an unknown parameter. The Bayesian approach to the problem is advocated, based on constructed distribution of the probability of success.ResultsA simple Matlab code for the calculation of the confidence limits according to the proposed method is provided. The most probable, the mean, the variance and the confidence limits are calculated applying the usual definitions of these characteristics.ConclusionThe proposed method works for any number of trials – large and small and all possible values of the number of successes, including k = 0 and k = n, providing exact formulae for the calculation of the confidence limits in all cases.     

Scheffy's Confidence Intervals in the Models of Analysis of Covariance

Scheffe's confidence intervals for linear functions of some subvectors of a vector of parameters are presented. The considered subvectors are such that covariance matrices of their estimators are known non-negative definite matrices multiplied by unknown positive constants. This property is characteristic of the least squares estimators of vectors of main and interaction effects in the analysis of covariance models of the following experimental designs: split-block, split-plot, completely randomized two-factor design and randomized complete block design. The formulas for confidence intervals for linear functions of vectors of main or interaction effects in the designs mentioned above are given in the paper. The practical example is given as an illustration.     

Wide-Angle X-Ray Solution Scattering for Protein-Ligand Binding: Multivariate Curve Resolution with Bayesian Confidence Intervals

A new way to use wide-angle x-ray solution scattering to study protein-ligand binding is presented. First, scattering patterns are measured at different protein and ligand concentrations. Multivariate curve resolution based on singular value decomposition and global analysis is applied to estimate the binding affinities and reference patterns (i.e., the scattering patterns of individual components). As validated by simulation, Bayesian confidence intervals provide accurate uncertainty estimates for the binding free energies and reference patterns. Experimental results from several protein-ligand systems demonstrate the feasibility of the approach, which promises to expand the role of wide-angle x-ray scattering as a quantitative biophysical tool.     

Sensitivity Analysis in Multicriteria Spatial Decision-Making: A Review

The aim of this article is to review how sensitivity analysis has been applied to models based on Geographical Information Systems (GIS) and Multicriteria Evaluation techniques (MCE). This kind of analysis is conceived as a stage in the model evaluation that examines the extent of output variation of a model when parameters are systematically varied over a range of interest. Twenty-eight studies related to land planning processes, environmental management, and location of noxious facilities have been examined. This review reveals that sensitivity analysis is a) not a common practice, b) is more widely carried out in location of noxious facilities, and c) that the analysis most frequently used is based on the variation of the weights of the factors implied in the process to test whether it significantly modifies the results obtained.     

Bayesian Analysis of Composite Quantile Regression

This paper introduces a Bayesian approach for composite quantile regression employing the skewed Laplace distribution for the error distribution. We use a two-level hierarchical Bayesian model for coefficient estimation and future selection which assumes a prior distribution that favors sparseness. An efficient Gibbs sampling algorithm is developed to update the unknown quantities from the posteriors. The proposed approach is illustrated via simulation studies and two real datasets. Results indicate that the proposed approach performs quite good in comparison to the other approaches.     

Atypicalities in Perceptual Adaptation in Autism Do Not Extend to Perceptual Causality

A recent study showed that adaptation to causal events (collisions) in adults caused subsequent events to be less likely perceived as causal. In this study, we examined if a similar negative adaptation effect for perceptual causality occurs in children, both typically developing and with autism. Previous studies have reported diminished adaptation for face identity, facial configuration and gaze direction in children with autism. To test whether diminished adaptive coding extends beyond high-level social stimuli (such as faces) and could be a general property of autistic perception, we developed a child-friendly paradigm for adaptation of perceptual causality. We compared the performance of 22 children with autism with 22 typically developing children, individually matched on age and ability (IQ scores). We found significant and equally robust adaptation aftereffects for perceptual causality in both groups. There were also no differences between the two groups in their attention, as revealed by reaction times and accuracy in a change-detection task. These findings suggest that adaptation to perceptual causality in autism is largely similar to typical development and, further, that diminished adaptive coding might not be a general characteristic of autism at low levels of the perceptual hierarchy, constraining existing theories of adaptation in autism.     

Bayesian Pathway Analysis of Cancer Microarray Data

High Throughput Biological Data (HTBD) requires detailed analysis methods and from a life science perspective, these analysis results make most sense when interpreted within the context of biological pathways. Bayesian Networks (BNs) capture both linear and nonlinear interactions and handle stochastic events in a probabilistic framework accounting for noise making them viable candidates for HTBD analysis. We have recently proposed an approach, called Bayesian Pathway Analysis (BPA), for analyzing HTBD using BNs in which known biological pathways are modeled as BNs and pathways that best explain the given HTBD are found. BPA uses the fold change information to obtain an input matrix to score each pathway modeled as a BN. Scoring is achieved using the Bayesian-Dirichlet Equivalent method and significance is assessed by randomization via bootstrapping of the columns of the input matrix. In this study, we improve on the BPA system by optimizing the steps involved in “Data Preprocessing and Discretization”, “Scoring”, “Significance Assessment”, and “Software and Web Application”. We tested the improved system on synthetic data sets and achieved over 98% accuracy in identifying the active pathways. The overall approach was applied on real cancer microarray data sets in order to investigate the pathways that are commonly active in different cancer types. We compared our findings on the real data sets with a relevant approach called the Signaling Pathway Impact Analysis (SPIA).     

Cultural Differences in Donation Decision-Making

Decisions to help those in need are essential for human development and survival. Previous studies have demonstrated the “identified effect”, in which one identifiable individual typically invokes stronger feelings of compassion and receives greater aid than statistical victim. However, this preference might be influenced by cultural differences. In the current study, Chinese respondents’ ratings of distress and sympathy and their willingness to contribute are greater for a group of sick children than an individual. In the U.S., greater willingness to help and sympathy are elicited by an identified victim in comparison with an unidentified one. The different results may demonstrate the importance of cultural differences when trying to understand people’s prosocial behavior.     

Bayesian Analysis for Linearized Multi-Stage Models in Quantal Bioassay

Bayesian methods for estimating dose response curves from linearized multi-stage models in quantal bioassay are studied. A Gibbs sampling approach with data augmentation is employed to compute the Bayes estimates. In addition, estimation of the “relative additional risk” and the “risk specific dose” is studied. Model selection based on conditional predictive ordinates from cross-validated data is developed. Model adequacy is addressed by means of a posterior predictive tail-area test.