Critical flicker fusion thresholds (CFFTs) describe when quick amplitude modulations of a light source become undetectable as the frequency of the modulation increases and are thought to underlie a number of visual processing skills, including reading. Here, we compare the impact of two vision-training approaches, one involving contrast sensitivity training and the other directional dot-motion training, compared to an active control group trained on Sudoku. The three training paradigms were compared on their effectiveness for altering CFFT. Directional dot-motion and contrast sensitivity training resulted in significant improvement in CFFT, while the Sudoku group did not yield significant improvement. This finding indicates that dot-motion and contrast sensitivity training similarly transfer to effect changes in CFFT. The results, combined with prior research linking CFFT to high-order cognitive processes such as reading ability, and studies showing positive impact of both dot-motion and contrast sensitivity training in reading, provide a possible mechanistic link of how these different training approaches impact reading abilities.

Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.

Misconceptions about mental health can have negative effects on therapy, education, and social interactions. Misconceptions about mental health can be formed through misinformation being spread online from a variety of sources. The current study manipulates and examines the effects of social media users’ justification for knowing on participants’ perceived credibility and knowledge revision. Justification for evidence was manipulated within subjects. There were 3 types of justifications: personal experience, professional experience, or no justification. To test the effects of evidence justification, we used two dependent variables: perceived credibility and knowledge revision. MTurk participants (n = 111) completed pretest assessments regarding mental health and general science knowledge. They then read 11 experimenter-derived Twitter threads, each containing a misconception, two tweets with a refutation, and a statement of justification for the refutation. After each Twitter thread, participants were asked to rate the perceived credibility of the refutation texts. Participants were later given a posttest to measure knowledge revision as well as a series of questions that measured epistemic belief systems. We hypothesized that participants would be more likely to revise their misconceptions when the justification was personal expertise compared to when the justification was professional expertise or no justification is given. The findings did not support these hypotheses, instead indicating that the highest perceived credibility rankings came from professional expertise while knowledge revision occurred in all conditions.