Magic Curiosity Arousing Tricks (MagicCATs): A novel stimulus collection to induce epistemic emotions

Recent years have seen considerable interest in empirical research on epistemic emotions, i.e. emotions related to knowledge-generating qualities of cognitive tasks and activities such as curiosity, interest, and surprise. One big challenge when studying epistemic emotions is systematically inducting these emotions in restricted experimental settings. The current study created a novel stimulus set called Magic Curiosity Arousing Tricks (MagicCATs): a collection of 166 short magic trick video clips that aim to induce a variety of epistemic emotions. MagicCATs are available for research, and can be used in a variety of ways to examine epistemic emotions. Rating data also supports that the magic tricks elicit a variety of epistemic emotions with sufficient inter-stimulus variability, demonstrating good psychometric properties for their use in psychological experiments.

Predicting the partition of behavioral variability in speed perception with naturalistic stimuli

A core goal of visual neuroscience is to predict human perceptual performance from natural signals. Performance in any natural task can be impacted by at least three sources of uncertainty: stimulus variability, internal noise, and sub-optimal computations. Determining the relative importance of these factors has been a focus of interest for decades, but most successes have been achieved with simple tasks and simple stimuli. Drawing quantitative links directly from natural signals to perceptual performance has proven a substantial challenge. Here, we develop an image-computable (pixels in, estimates out) Bayesian ideal observer that makes optimal use of the statistics relating image movies to speed. The optimal computations bear striking resemblance to descriptive models proposed to account for neural activity in area MT. We develop a model based on the ideal, stimulate it with naturalistic signals, predict the behavioral signatures of each performance-limiting factor, and test the predictions in an interlocking series of speed discrimination experiments. The critical experiment collects human responses to repeated presentations of each unique image movie. The model, highly constrained by the earlier experiments, tightly predicts human response consistency without free parameters. This result implies that human observers use near-optimal computations to estimate speed, and that human performance is near-exclusively limited by natural stimulus variability and internal noise. The results demonstrate that human performance can be predicted from a task-specific statistical analysis of naturalistic stimuli, show that image-computable ideal observer analysis can be generalized from simple to natural stimuli, and encourage similar analyses in other domains.

How experimental procedures influence estimates of metacognitive ability

It is becoming widely appreciated that higher stimulus sensitivity trivially increases estimates of metacognitive sensitivity. Therefore, meaningful comparisons of metacognitive ability across conditions and observers necessitates equating stimulus sensitivity. To achieve this, one common approach is to use a continuous staircase that runs throughout the duration of the experiment under the assumption that this procedure has no influence on the estimated metacognitive ability. Here we critically examine this assumption. Using previously published data, we find that, compared to using a single level of stimulus contrast, staircase techniques lead to inflated estimates of metacognitive ability across a wide variety of measures including area under the type 2 ROC curve, the confidence-accuracy correlation phi, meta-d’, meta-d’/d’, and meta-d’–d’. Further, this metacognitive inflation correlates with the degree of stimulus variability experienced by each subject. Finally, we show that the degree of stimulus variability in a staircase procedure may itself be driven by individual differences in metacognitive ability. These results suggest that studies using a staircase approach are likely to report inflated estimates of metacognitive ability. Further, we argue that similar inflation likely occurs in the presence of variability in task difficulty caused by other factors such as fluctuations in alertness or gradual improvement on the task. We offer practical solutions to these issues, both in the design and analysis of metacognition experiments.

Tendon Tapping Stimulus Characterization Through Contact Modeling

Tendon tapping is a common procedure with both diagnostic and therapeutic applications. Significant variability is observed in the reflex response both between and within even healthy subjects, which is likely partially caused by imprecise control or measurement of the mechanical stimulus. Reducing or measuring stimulus variability is critical to future novel applications of tendon tapping for rehabilitative neuromodulation. This work presents a contact mechanics based method for characterization of tendon mechanical stimulus. This work utilizes easily observable dynamic information about hammer impacts to estimate the properties of impacted tissue.