Are children's judgments of another's accuracy linked to their metacognitive confidence judgments?

The world can be a confusing place, which leads to a significant challenge: how do we figure out what is true? To accomplish this, children possess two relevant skills: reasoning about the likelihood of their own accuracy (metacognitive confidence) and reasoning about the likelihood of others' accuracy (mindreading). Guided by Signal Detection Theory and Simulation Theory, we examine whether these two self- and other-oriented skills are one in the same, relying on a single cognitive process. Specifically, Signal Detection Theory proposes that confidence in a decision is purely derived from the imprecision of that decision, predicting a tight correlation between decision accuracy and confidence. Simulation Theory further proposes that children attribute their own cognitive experience to others when reasoning socially. Together, these theories predict that children's self and other reasoning should be highly correlated and dependent on decision accuracy. In four studies (N = 374), children aged 4-7 completed a confidence reasoning task and selective social learning task each designed to eliminate confounding language and response biases, enabling us to isolate the unique correlation between self and other reasoning. However, in three of the four studies, we did not find that individual differences on the two tasks correlated, nor that decision accuracy explained performance. These findings suggest self and other reasoning are either independent in childhood, or the result of a single process that operates differently for self and others. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11409-021-09263-x.

An Assessment of Six Muscle Spindle Models for Predicting Sensory Information during Human Wrist Movements.

BACKGROUND: The muscle spindle is an important sensory organ for proprioceptive information, yet there have been few attempts to use Shannon information theory to quantify the capacity of human muscle spindles to encode sensory input. METHODS: Computer simulations linked kinematics, to biomechanics, to six muscle spindle models that generated predictions of firing rate. The predicted firing rates were compared to firing rates of human muscle spindles recorded during a step-tracking (center-out) task to validate their use. The models were then used to predict firing rates during random movements with statistical properties matched to the ergonomics of human wrist movements. The data were analyzed for entropy and mutual information. RESULTS: Three of the six models produced predictions that approximated the firing rate of human spindles during the step-tracking task. For simulated random movements these models predicted mean rates of 16.0 ± 4.1 imp/s (mean ± SD), peak firing rates <50 imp/s and zero firing rate during an average of 25% of the movement. The average entropy of the neural response was 4.1 ± 0.3 bits and is an estimate of the maximum information that could be carried by muscles spindles during ecologically valid movements. The information about tendon displacement preserved in the neural response was 0.10 ± 0.05 bits per symbol; whereas 1.25 ± 0.30 bits per symbol of velocity input were preserved in the neural response of the spindle models. CONCLUSIONS: Muscle spindle models, originally based on cat experiments, have predictive value for modeling responses of human muscle spindles with minimal parameter optimization. These models predict more than 10-fold more velocity over length information encoding during ecologically valid movements. These results establish theoretical parameters for developing neuroprostheses for proprioceptive function.

Automatic method to assess local CT-MR imaging registration accuracy on images of the head.

BACKGROUND AND PURPOSE: Precise registration of CT and MR images is crucial in many clinical cases for proper diagnosis, decision making or navigation in surgical interventions. Various algorithms can be used to register CT and MR datasets, but prior to clinical use the result must be validated. To evaluate the registration result by visual inspection is tiring and time-consuming. We propose a new automatic registration assessment method, which provides the user a color-coded fused representation of the CT and MR images, and indicates the location and extent of poor registration accuracy. METHODS: The method for local assessment of CT-MR registration is based on segmentation of bone structures in the CT and MR images, followed by a voxel correspondence analysis. The result is represented as a color-coded overlay. The algorithm was tested on simulated and real datasets with different levels of noise and intensity non-uniformity. RESULTS: Based on tests on simulated MR imaging data, it was found that the algorithm was robust for noise levels up to 7% and intensity non-uniformities up to 20% of the full intensity scale. Due to the inability to distinguish clearly between bone and cerebro-spinal fluids in the MR image (T1-weighted), the algorithm was found to be optimistic in the sense that a number of voxels are classified as well-registered although they should not. However, nearly all voxels classified as misregistered are correctly classified. CONCLUSION: The proposed algorithm offers a new way to automatically assess the CT-MR image registration accuracy locally in all the areas of the volume that contain bone and to represent the result with a user-friendly, intuitive color-coded overlay on the fused dataset.