Examining the robustness of the relationship between metacognitive efficiency and metacognitive bias.

We recently found a positive relationship between estimates of metacognitive efficiency and metacognitive bias. However, this relationship was only examined on a within-subject level and required binarizing the confidence scale, a technique that introduces methodological difficulties. Here we examined the robustness of the positive relationship between estimates of metacognitive efficiency and metacognitive bias by conducting two different types of analyses. First, we developed a new within-subject analysis technique where the original n-point confidence scale is transformed into two different (n-1)-point scales in a way that mimics a naturalistic change in confidence. Second, we examined the across-subject correlation between metacognitive efficiency and metacognitive bias. Importantly, for both types of analyses, we not only established the direction of the effect but also computed effect sizes. We applied both techniques to the data from three tasks from the Confidence Database (N > 400 in each). We found that both approaches revealed a small to medium positive relationship between metacognitive efficiency and metacognitive bias. These results demonstrate that the positive relationship between metacognitive efficiency and metacognitive bias is robust across several analysis techniques and datasets, and have important implications for future research.

Effect of community-initiated kangaroo mother care on survival of infants with low birthweight: a randomised controlled trial.

BACKGROUND: Coverage of kangaroo mother care remains very low despite WHO recommendations for its use for babies with low birthweight in health facilities for over a decade. Initiating kangaroo mother care at the community level is a promising strategy to increase coverage. However, knowledge of the efficacy of community-initiated kangaroo mother care is still lacking. We aimed to assess the effect of community-initiated kangaroo mother care provided to babies weighing 1500-2250 g on neonatal and infant survival. METHODS: In this randomised controlled, superiority trial, undertaken in Haryana, India, we enrolled babies weighing 1500-2250 g at home within 72 h of birth, if not already initiated in kangaroo mother care, irrespective of place of birth (ie, home or health facility) and who were stable and feeding. The first eligible infants in households were randomly assigned (1:1) to the intervention (community-initiated kangaroo mother care) or control group by block randomisation using permuted blocks of variable size. Twins were allocated to the same group. For second eligible infants in the same household as an enrolled infant, if the first infant was assigned to the intervention group the second infant was also assigned to this group, whereas if the first infant was assigned to the control group the second infant was randomly assigned (1:1) to the intervention or control group. Mothers and infants in the intervention group were visited at home (days 1-3, 5, 7, 10, 14, 21, and 28) to support kangaroo mother care (ie, skin-to-skin contact and exclusive breastfeeding). The control group received routine care. The two primary outcomes were mortality between enrolment and 28 days and between enrolment and 180 days. Analysis was by intention to treat and adjusted for clustering within households. The effect of the intervention on mortality was assessed with person-time in the denominator using Cox proportional hazards model. This study is registered with ClinicalTrials.gov, NCT02653534 and NCT02631343, and is now closed to new participants. FINDINGS: Between July 30, 2015, and Oct 31, 2018, 8402 babies were enrolled, of whom 4480 were assigned to the intervention group and 3922 to the control group. Most births (6837 [81·4%]) occurred at a health facility, 36·2% (n=3045) had initiated breastfeeding within 1 h of birth, and infants were enrolled at an average of about 30 h (SD 17) of age. Vital status was known for 4470 infants in the intervention group and 3914 in the control group at age 28 days, and for 3653 in the intervention group and 3331 in the control group at age 180 days. Between enrolment and 28 days, 73 infants died in 4423 periods of 28 days in the intervention group and 90 deaths in 3859 periods of 28 days in the control group (hazard ratio [HR] 0·70, 95% CI 0·51-0·96; p=0·027). Between enrolment and 180 days, 158 infants died in 3965 periods of 180 days in the intervention group and 184 infants died in 3514 periods of 180 days in the control group (HR 0·75, 0·60-0·93; p=0·010). The risk ratios for death were almost the same as the HRs (28-day mortality 0·71, 95% CI 0·52- 0·97; p=0·032; 180-day mortality 0·76, 0·60-0·95; p=0·017). INTERPRETATION: Community-initiated kangaroo mother care substantially improves newborn baby and infant survival. In low-income and middle-income countries, incorporation of kangaroo mother care for all infants with low birthweight, irrespective of place of birth, could substantially reduce neonatal and infant mortality. FUNDING: Research Council of Norway and University of Bergen.

Distinguishing the Roles of Dorsolateral and Anterior PFC in Visual Metacognition.

Visual metacognition depends on regions within the prefrontal cortex (PFC). Two areas in particular have been implicated repeatedly: the dorsolateral PFC (DLPFC) and the anterior PFC (aPFC). However, it is still unclear what the function of each of these areas is and how they differ from each other. To establish the specific roles of DLPFC and aPFC in metacognition, we used online transcranial magnetic stimulation (TMS) to interfere causally with their functioning during confidence generation. Human subjects from both sexes performed a perceptual decision making and provided confidence ratings. We found a clear dissociation between the two areas: DLPFC TMS lowered confidence ratings, whereas aPFC TMS increased metacognitive ability, but only for the second half of the experimental blocks. These results support a functional architecture in which DLPFC reads out the strength of the sensory evidence and relays it to aPFC, which makes the confidence judgment by potentially incorporating additional, nonperceptual information. Indeed, simulations from a model that incorporates these putative DLPFC and aPFC functions reproduced our behavioral results. These findings establish DLPFC and aPFC as distinct nodes in a metacognitive network and suggest specific contributions from each of these regions to confidence generation.SIGNIFICANCE STATEMENT The prefrontal cortex (PFC) is known to be critical for metacognition. Two of its subregions, the dorsolateral PFC (DLPFC) and the anterior PFC (aPFC), have been specifically implicated in confidence generation. However, it is unclear whether these regions have distinct functions related to the underlying metacognitive computation. Using a causal intervention with transcranial magnetic stimulation, we demonstrate that DLPFC and aPFC have dissociable contributions: targeting DLPFC decreased average confidence ratings, whereas targeting aPFC affected metacognitive scores specifically. Based on these results, we postulated specific functions for DLPFC and aPFC in metacognitive computation and corroborated them using a computational model that reproduced our results. Our causal results reveal the existence of a specialized modular organization in PFC for confidence generation.

Kangaroo mother care: using formative research to design an acceptable community intervention.

BACKGROUND: Low and middle income countries (LMICs), including India, contribute to a major proportion of low birth weight (LBW) infants globally. These infants require special care. Kangaroo Mother Care (KMC) in hospitals is a cost effective and efficacious intervention. In institutional deliveries, the duration of facility stay is often short. In LMICs, a substantial proportion of deliveries still occur at home and access to health care services is limited. In these circumstances, a pragmatic choice may be to initiate KMC at home for LBW babies. However, evidence is lacking on benefits of community-initiated KMC (cKMC). Promoting KMC at home without an understanding of its acceptability may lead to limited success. METHODS: We conducted formative research to assess the feasibility, acceptability and adoption of cKMC with the aim of designing an intervention package for a randomised controlled trial in LBW infants in Haryana, India. Qualitative methods included 40 in-depth interviews with recently delivered women and 6 focus group discussions, two each with fathers and grandfathers, grandmothers, and community health workers. A prototype intervention package to promote cKMC was developed and tested in 28 mother-infant pairs (of them, one mother had twins), using Household (HH) trials. RESULTS: We found that most mothers in the community recognized that babies born small required special care. In spite of not being aware of the practice of KMC, respondents felt that creating awareness of KMC benefits will promote practice. They expressed concerns about doing KMC for long periods because mothers needed rest after delivery. However, the cultural practice of recently delivered women not expected to be doing household chores and availability of other family members were identified as enablers. HH trials provided an opportunity to test the intervention package and showed high acceptability for KMC. Most mothers perceived benefits such as weight gain and increased activity in the infant. CONCLUSIONS: Community-initiated KMC is acceptable by mothers and adoption rates are high. Formative research is essential for developing a strategy for delivery of an intervention. TRIAL REGISTRATION: Trial registration number CTRI/2015/10/006267 . Name of Registry: Clinical Trials Registry - India. URL of Registry: http://ctri.nic.in/Clinicaltrials/login.php Date of Registration: 15/10/2015. Date of enrolment of the first participant to the trial: 18/04/2015.

How do humans give confidence? A comprehensive comparison of process models of perceptual metacognition.

Humans have the metacognitive ability to assess the accuracy of their decisions via confidence judgments. Several computational models of confidence have been developed but not enough has been done to compare these models, making it difficult to adjudicate between them. Here, we compare 14 popular models of confidence that make various assumptions, such as confidence being derived from postdecisional evidence, from positive (decision-congruent) evidence, from posterior probability computations, or from a separate decision-making system for metacognitive judgments. We fit all models to three large experiments in which subjects completed a basic perceptual task with confidence ratings. In Experiments 1 and 2, the best-fitting model was the lognormal meta noise (LogN) model, which postulates that confidence is selectively corrupted by signal-dependent noise. However, in Experiment 3, the positive evidence (PE) model provided the best fits. We evaluated a new model combining the two consistently best-performing models-LogN and the weighted evidence and visibility (WEV). The resulting model, which we call logWEV, outperformed its individual counterparts and the PE model across all data sets, offering a better, more generalizable explanation for these data. Parameter and model recovery analyses showed mostly good recoverability but with important exceptions carrying implications for our ability to discriminate between models. Finally, we evaluated each model's ability to explain different patterns in the data, which led to additional insight into their performances. These results comprehensively characterize the relative adequacy of current confidence models to fit data from basic perceptual tasks and highlight the most plausible mechanisms underlying confidence generation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Human-like dissociations between confidence and accuracy in convolutional neural networks.

Prior research has shown that manipulating stimulus energy by changing both stimulus contrast and variability results in confidence-accuracy dissociations in humans. Specifically, even when performance is matched, higher stimulus energy leads to higher confidence. The most common explanation for this effect is the positive evidence heuristic where confidence neglects evidence that disconfirms the choice. However, an alternative explanation is the signal-and-variance-increase hypothesis, according to which these dissociations arise from low-level changes in the separation and variance of perceptual representations. Because artificial neural networks lack built-in confidence heuristics, they can serve as a test for the necessity of confidence heuristics in explaining confidence-accuracy dissociations. Therefore, we tested whether confidence-accuracy dissociations induced by stimulus energy manipulations emerge naturally in convolutional neural networks (CNNs). We found that, across three different energy manipulations, CNNs produced confidence-accuracy dissociations similar to those found in humans. This effect was present for a range of CNN architectures from shallow 4-layer networks to very deep ones, such as VGG-19 and ResNet -50 pretrained on ImageNet. Further, we traced back the reason for the confidence-accuracy dissociations in all CNNs to the same signal-and-variance increase that has been proposed for humans: higher stimulus energy increased the separation and variance of the CNNs' internal representations leading to higher confidence even for matched accuracy. These findings cast doubt on the necessity of the positive evidence heuristic to explain human confidence and establish CNNs as promising models for adjudicating between low-level, stimulus-driven and high-level, cognitive explanations of human behavior.

The neural network RTNet exhibits the signatures of human perceptual decision-making.

Convolutional neural networks show promise as models of biological vision. However, their decision behaviour, including the facts that they are deterministic and use equal numbers of computations for easy and difficult stimuli, differs markedly from human decision-making, thus limiting their applicability as models of human perceptual behaviour. Here we develop a new neural network, RTNet, that generates stochastic decisions and human-like response time (RT) distributions. We further performed comprehensive tests that showed RTNet reproduces all foundational features of human accuracy, RT and confidence and does so better than all current alternatives. To test RTNet's ability to predict human behaviour on novel images, we collected accuracy, RT and confidence data from 60 human participants performing a digit discrimination task. We found that the accuracy, RT and confidence produced by RTNet for individual novel images correlated with the same quantities produced by human participants. Critically, human participants who were more similar to the average human performance were also found to be closer to RTNet's predictions, suggesting that RTNet successfully captured average human behaviour. Overall, RTNet is a promising model of human RTs that exhibits the critical signatures of perceptual decision-making.

The nature of metacognitive inefficiency in perceptual decision making.

Humans have the metacognitive ability to judge the accuracy of their own decisions via confidence ratings. A substantial body of research has demonstrated that human metacognition is fallible but it remains unclear how metacognitive inefficiency should be incorporated into a mechanistic model of confidence generation. Here we show that, contrary to what is typically assumed, metacognitive inefficiency depends on the level of confidence. We found that, across 5 different data sets and 4 different measures of metacognition, metacognitive ability decreased with higher confidence ratings. To understand the nature of this effect, we collected a large dataset of 20 subjects completing 2,800 trials each and providing confidence ratings on a continuous scale. The results demonstrated a robustly nonlinear zROC curve with downward curvature, despite a decades-old assumption of linearity. This pattern of results was reproduced by a new mechanistic model of confidence generation, which assumes the existence of lognormally distributed metacognitive noise. The model outperformed competing models either lacking metacognitive noise altogether or featuring Gaussian metacognitive noise. Further, the model could generate a measure of metacognitive ability which was independent of confidence levels. These findings establish an empirically validated model of confidence generation, have significant implications about measures of metacognitive ability, and begin to reveal the underlying nature of metacognitive inefficiency. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Sources of Metacognitive Inefficiency.

Confidence judgments are typically less informative about one's accuracy than they could be; a phenomenon we call metacognitive inefficiency. We review the existence of different sources of metacognitive inefficiency and classify them into four categories based on whether the corruption is due to: (i) systematic or nonsystematic influences, and (ii) the input to or the computation of the metacognitive system. Critically, the existence of different sources of metacognitive inefficiency provides an alternative explanation for behavioral findings typically interpreted as evidence for domain-specific (and against domain-general) metacognitive systems. We argue that, contrary to the dominant assumption in the field, metacognitive failures are not monolithic and suggest that understanding the sources of metacognitive inefficiency should be a primary goal of the science of metacognition.

Overlapping and unique neural circuits are activated during perceptual decision making and confidence.

The period of making a perceptual decision is often followed by a period of rating confidence where one evaluates the likely accuracy of the initial decision. However, it remains unclear whether the same or different neural circuits are engaged during periods of perceptual decision making and confidence report. To address this question, we conducted two functional MRI experiments in which we dissociated the periods related to perceptual decision making and confidence report by either separating their respective regressors or asking for confidence ratings only in the second half of the experiment. We found that perceptual decision making and confidence reports gave rise to activations in large and mostly overlapping brain circuits including frontal, parietal, posterior, and cingulate regions with the results being remarkably consistent across the two experiments. Further, the confidence report period activated a number of unique regions, whereas only early sensory areas were activated for the decision period across the two experiments. We discuss the possible reasons for this overlap and explore their implications about theories of perceptual decision making and visual metacognition.

Sensory noise increases metacognitive efficiency.

Metacognitive efficiency quantifies people's ability to introspect into their own decision making relative to their ability to perform the primary task. Despite years of research, it is still unclear how visual metacognitive efficiency can be manipulated. Here, we show that a hierarchical model of confidence generation makes a counterintuitive prediction: Higher sensory noise should increase metacognitive efficiency. The reason for this is that hierarchical models assume that although the primary decision is corrupted only by sensory noise, the confidence judgment is corrupted by both sensory and metacognitive noise. Therefore, increasing sensory noise has a smaller negative influence on the confidence judgment than on the perceptual decision, resulting in increased metacognitive efficiency. To test this prediction, we used a perceptual learning paradigm to decrease sensory noise. In Experiment 1, 7 days of training led to a significant decrease in sensory noise and a corresponding decrease in metacognitive efficiency. Experiment 2 showed the same effect in a brief 97-trial learning for each of 2 different tasks. Finally, in Experiment 3, we combined increasingly dissimilar stimulus contrasts to create conditions with higher sensory noise and observed a corresponding increase in metacognitive efficiency. Our findings demonstrate the existence of a robust positive relationship between across-trial sensory noise and metacognitive efficiency. These results could not be captured by a standard model in which decision and confidence judgments are made based on the same underlying information. Thus, our study provides direct evidence for the existence of metacognitive noise that corrupts confidence but not the perceptual decision. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Costing of three feeding regimens for home-based management of children with uncomplicated severe acute malnutrition from a randomised trial in India.

TRIAL DESIGN: Three feeding regimens-centrally produced ready-to-use therapeutic food, locally produced ready-to-use therapeutic food, and augmented, energy-dense, home-prepared food-were provided in a community setting for children with severe acute malnutrition (SAM) in the age group of 6-59 months in an individually randomised multicentre trial that enrolled 906 children. Foods, counselling, feeding support and treatment for mild illnesses were provided until recovery or 16 weeks. METHODS: Costs were estimated for 371 children enrolled in Delhi in a semiurban location after active survey and identification, enrolment, diagnosis and treatment for mild illnesses, and finally treatment with one of the three regimens, both under the research and government setting. Direct costs were estimated for human resources using a price times quantity approach, based on their salaries and average time taken for each activity. The cost per week per child for food, medicines and other consumables was estimated based on the total expenditure over the period and children covered. Indirect costs for programme management including training, transport, non-consumables, infrastructure and equipment were estimated per week per child based on total expenditures for research study and making suitable adjustments for estimations under government setting. RESULTS: No significant difference in costs was found across the three regimens per covered or per treated child. The average cost per treated child in the government setting was estimated at US$56 (<3500 rupees). CONCLUSION: Home-based management of SAM with a locally produced ready-to-use therapeutic food is feasible, acceptable, affordable and very cost-effective in terms of the disability-adjusted life years saved and gross national income per capita of the country. The treatment of SAM at home needs serious attention and integration into the existing health system, along with actions to prevent SAM. TRIAL REGISTRATION NUMBER: NCT01705769; Pre-results.