Measures of metacognition on signal-detection theoretic models

Analysing metacognition, specifically knowledge of accuracy of internal perceptual, memorial or other knowledge states, is vital for many strands of psychology, including determining the accuracy of feelings of knowing, and discriminating conscious from unconscious cognition. Quantifying metacognitive sensitivity is however more challenging than quantifying basic stimulus sensitivity. Under popular signal detection theory (SDT) models for stimulus classification tasks, approaches based on type II receiver-operator characteristic (ROC) curves or type II d-prime risk confounding metacognition with response biases in either the type I (classification) or type II (metacognitive) tasks. A new approach introduces meta-d′: the type I d-prime that would have led to the observed type II data had the subject used all the type I information. Here we (i) further establish the inconsistency of the type II d-prime and ROC approaches with new explicit analyses of the standard SDT model, and (ii) analyse, for the first time, the behaviour of meta-d′ under non-trivial scenarios, such as when metacognitive judgments utilize enhanced or degraded versions of the type I evidence. Analytically, meta-d′ values typically reflect the underlying model well, and are stable under changes in decision criteria; however, in relatively extreme cases meta-d′ can become unstable. We explore bias and variance of in-sample measurements of meta-d′ and supply MATLAB code for estimation in general cases. Our results support meta-d′ as a useful measure of metacognition, and provide rigorous methodology for its application. Our recommendations are useful for any researchers interested in assessing metacognitive accuracy

Granger causality analysis in neuroscience and neuroimaging

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Linear Hamilton Jacobi Bellman Equations in High Dimensions

The Hamilton Jacobi Bellman Equation (HJB) provides the globally optimal solution to large classes of control problems. Unfortunately, this generality comes at a price, the calculation of such solutions is typically intractible for systems with more than moderate state space size due to the curse of dimensionality. This work combines recent results in the structure of the HJB, and its reduction to a linear Partial Differential Equation (PDE), with methods based on low rank tensor representations, known as a separated representations, to address the curse of dimensionality. The result is an algorithm to solve optimal control problems which scales linearly with the number of states in a system, and is applicable to systems that are nonlinear with stochastic forcing in finite-horizon, average cost, and first-exit settings. The method is demonstrated on inverted pendulum, VTOL aircraft, and quadcopter models, with system dimension two, six, and twelve respectively.Comment: 8 pages. Accepted to CDC 201

Co-citation Analysis: An Overview

This article gives an overview of co-citation analysis and its applications in tracking the linkages among the intellectual works and mapping the evolutionary structure of scientific disciplines. It also focuses on the features, interface, terminology used, merits and demerits of co-citation based online database applications

The Determinants of Child Weight and Height in Sri Lanka: A Quantile Regression Approach

Reducing child malnutrition is a key goal of most developing countries. To combat child malnutrition with the right set of interventions, policymakers need to have a better understanding of its economic, social and policy determinants. While there is a large literature that investigates the determinants of child malnutrition, it focuses almost exclusively on mean effects of these determinants. However, socioeconomic background variables and policy interventions may affect child nutrition differently at different points of the conditional nutritional distribution. Using quantile regressions, this paper explores the effects of variables such as a child?s age, sex and birth order; household expenditure per capita; parental schooling; and infrastructure on child weight and height at different points of the conditional distributions of weight and height using data from Sri Lanka?s Demographic and Health Survey. Results indicate that OLS estimates can be misleading in predicting the effects of determinants at the lower end of the distributions of weight and height. For example, even though on average Sri Lankan girls are not nutritionally-disadvantaged relative to boys, among children at the highest risk of malnutrition girls are disadvantaged relative to boys. Likewise, although expenditure per capita is associated with strong nutritional improvement on average, it is not a significant determinant of child height or weight at the lower end of the distribution. Similarly, parental education, electricity access, and the availability of piped water have larger effects on child weight and height at the upper quantiles than at the lower quantiles. The policy implication is that general interventions?parental schooling, infrastructure and income growth?are not as effective for children in the lower tail of the conditional weight and height distributions. These children, who are at the highest risk of malnutrition, are likely to need specialized nutritional interventions.child health, child nutrition, malnutrition, child weight, child height, quantile regression, Sri Lanka

Blind insight: metacognitive discrimination despite chance task performance

Blindsight and other examples of unconscious knowledge and perception demonstrate dissociations between judgment accuracy and metacognition: Studies reveal that participants’ judgment accuracy can be above chance while their confidence ratings fail to discriminate right from wrong answers. Here, we demonstrated the opposite dissociation: a reliable relationship between confidence and judgment accuracy (demonstrating metacognition) despite judgment accuracy being no better than chance. We evaluated the judgments of 450 participants who completed an AGL task. For each trial, participants decided whether a stimulus conformed to a given set of rules and rated their confidence in that judgment. We identified participants who performed at chance on the discrimination task, utilizing a subset of their responses, and then assessed the accuracy and the confidence-accuracy relationship of their remaining responses. Analyses revealed above-chance metacognition among participants who did not exhibit decision accuracy. This important new phenomenon, which we term blind insight, poses critical challenges to prevailing models of metacognition grounded in signal detection theory