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Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, that provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine: 1) decoding the direction of grating stimuli from spike recordings in primary visual cortex in monkeys, 2) decoding movement direction from recordings in primary motor cortex in monkeys, 3) decoding natural images from multi-region recordings in mice, and 4) decoding position from hippocampal recordings in rats. For each setting we characterize the overconfidence, and we describe a possible method to correct miscalibration post-hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain machine interfaces that more accurately reflect confidence levels when identifying external variables.

Significance Statement Bayesian decoding is a statistical technique for making probabilistic predictions about external stimuli or movements based on recordings of neural activity. These predictions may be useful for robust brain machine interfaces or for understanding perceptual or behavioral confidence. However, the probabilities produced by these models do not always match the observed outcomes. Just as a weather forecast predicting a 50% chance of rain may not accurately correspond to an outcome of rain 50% of the time, Bayesian decoders of neural activity can be miscalibrated as well. Here we identify and measure miscalibration of Bayesian decoders for neural spiking activity in a range of experimental settings. We compare multiple statistical models and demonstrate how overconfidence can be corrected.

 

Computerized assessment provides rich multidimensional data including trial-by-trial accuracy and response time (RT) measures. A key question in modeling this type of data is how to incorporate RT data, for example, in aid of ability estimation in item response theory (IRT) models. To address this, we propose a joint model consisting of a two-parameter IRT model for the dichotomous item response data, a log-normal model for the continuous RT data, and a normal model for corresponding paper-and-pencil scores. Then, we reformulate and reparameterize the model to capture the relationship between the model parameters, to facilitate the prior specification, and to make the Bayesian computation more efficient. Further, we propose several new model assessment criteria based on the decomposition of deviance information criterion (DIC) the logarithm of the pseudo-marginal likelihood (LPML). The proposed criteria can quantify the improvement in the fit of one part of the multidimensional data given the other parts. Finally, we have conducted several simulation studies to examine the empirical performance of the proposed model assessment criteria and have illustrated the application of these criteria using a real dataset from a computerized educational assessment program. 

Abstract While nanoscale quantum emitters are effective tags for measuring biomolecular interactions, their utilities for applications that demand single-unit observations are limited by the requirements for large numerical aperture (NA) objectives, fluorescence intermittency, and poor photon collection efficiency resulted from omnidirectional emission. Here, we report a nearly 3000-fold signal enhancement achieved through multiplicative effects of enhanced excitation, highly directional extraction, quantum efficiency improvement, and blinking suppression through a photonic crystal (PC) surface. The approach achieves single quantum dot (QD) sensitivity with high signal-to-noise ratio, even when using a low-NA lens and an inexpensive optical setup. The blinking suppression capability of the PC improves the QDs on-time from 15% to 85% ameliorating signal intermittency. We developed an assay for cancer-associated miRNA biomarkers with single-molecule resolution, single-base mutation selectivity, and 10-attomolar detection limit. Additionally, we observed differential surface motion trajectories of QDs when their surface attachment stringency is altered by changing a single base in a cancer-specific miRNA sequence. 

In the current literature on latent variable models, much effort has been put on the development of dichotomous and polytomous cognitive diagnostic models (CDMs) for assessments. Recently, the possibility of using continuous responses in CDMs has been brought to discussion. But no Bayesian approach has been developed yet for the analysis of CDMs when responses are continuous. Our work is the first Bayesian framework for the continuous deterministic inputs, noisy, and gate (DINA) model. We also propose new interpretations for item parameters in this DINA model, which makes the analysis more interpretable than before. In addition, we have conducted several simulations to evaluate the performance of the continuous DINA model through our Bayesian approach. Then, we have applied the proposed DINA model to a real data example of risk perceptions for individuals over a range of health‐related activities. The application results exemplify the high potential of the use of the proposed continuous DINA model to classify individuals in the study.

 

Parametric methods, such as autoregressive models or latent growth modeling, are usually inflexible to model the dependence and nonlinear effects among the changes of latent traits whenever the time gap is irregular and the recorded time points are individually varying. Often in practice, the growth trend of latent traits is subject to certain monotone and smooth conditions. To incorporate such conditions and to alleviate the strong parametric assumption on regressing latent trajectories, a flexible nonparametric prior has been introduced to model the dynamic changes of latent traits for item response theory models over the study period. Suitable Bayesian computation schemes are developed for such analysis of the longitudinal and dichotomous item responses. Simulation studies and a real data example from educational testing have been used to illustrate our proposed methods. 

We report here that dissolution and regrowth of resorcinol formaldehyde (RF) colloidal particles can occur spontaneously when they are subjected to etching in solvents such as ethanol and tetrahydrofuran, resulting in the formation of hollow nanostructures with controllable shell thickness. The hollowing process of the RF particles is attributed to their structural inhomogeneity, which results from the successive deposition of oligomers with different chain lengths during their initial growth. As the near-surface layer of RF colloids mainly consists of long-chain oligomers while the inner part consists of short-chain oligomers, selective etching removes the latter and produces the hollow structures. By revealing the important effects of the condensation degree of RF, the etching time and temperature, and the composition of solvents, we demonstrate that the morphology and structure of the resulting RF nanostructures can be conveniently and precisely controlled. This study not only improves our understanding of the structural heterogeneity of colloidal polymer particles, but also provides a practical and universal self-templated approach for the synthesis of hollow nanostructures.