Positive correlations between mean decision time and second-thought probability.

According to two-stage models, mean DT—as a proxy for the proportion of slow decisions—should increase with the probability of using the second stage. Indeed, mean DT and second-thought probability were positively correlated, separately for each cost condition (the first three panels) and when aggregated across all cost conditions (the last panel), thus providing additional support for the two-stage decision process. Each dot is for one participant in one specific cost condition. Lines and shaded areas respectively represent regression lines and standard errors. The rS refers to Spearman’s correlation coefficient.</p

The bead-sampling task.

(a) Time course of one trial. “Preview” informed the participant of the pink-to-blue ratios of the two jars (80%:20% vs. 20%:80% in this example, corresponding to the high-evidence condition). Then the participant could sample beads from the unknown pre-selected jar one at a time up to 20 beads (“sampling”) or quit sampling at any time. Afterward, the participant judged which jar had been selected (“judgment”). Feedback followed, showing the correctness of judgment and winning of the current trial. Feedback was presented for 1 s, whereas preview, sampling, and judgment were self-paced. During sampling, the remaining bonus points (green bar), as well as the array of bead samples, were visualized and updated after each additional sample. (b) Optimal sampling strategy vs. participants’ performance for each of the six cost-by-evidence conditions. On a specific trial, the expected probability of correctness (dashed lines) and the remaining bonus points (dotted lines) are respectively increasing and decreasing functions of the number of bead samples. The expected gain (solid lines), as their multiplication product, first increases and then decreases with the number of samples. Note that the sample size that maximizes expected gain varies across different cost and evidence conditions. Each circle represents a participant with the color indicating their AQ score.</p

Effects of autistic traits on decision process and how it relates to sampling optimality.

(a) Correlation between AQ and cost-evidence strategy index (AICccost→evidence−AICcevidence→cost). More negative cost-evidence strategy index indicates stronger preference for cost-first over evidence-first decision processes, while more positive cost-evidence strategy index indicates the reverse. Each dot is for one participant. The blue line and the shaded area respectively represent regression line and standard error. (b) Correlation coefficients between cost-evidence strategy index and efficiency for each cost and evidence condition. C:0 = zero-cost, C:0.1 = low-cost, C:0.4 = high-cost, E:0.6 = low-evidence, E:0.8 = high-evidence. Error bars represent FDR-corrected 95% confidence intervals. All these correlations were consistent with what we would expect if AQ influences sampling efficiency through its influence on the use of cost-first vs. evidence-first decision processes. For example, given that AQ was negatively correlated with cost-evidence strategy index, and cost-evidence strategy index was negatively correlated with the efficiency in the zero-cost, low-evidence condition, we would expect AQ to be positively correlated with the efficiency in the zero-cost, low-evidence condition, and indeed it was. (c) Correlation between AQ and cost-evidence strategy index varied with the value of cost-evidence strategy index. We ranked all participants by cost-evidence strategy index in ascending order, that is, from the strongest preference for cost-first to the strongest preference for evidence-first, and plot the Spearman’s correlation coefficient between cost-evidence strategy index and AQ as a function of the number of participants included in the correlation analysis. The observed overall negative correlation and the stronger correlation given only the cost-first-dominated participants were included supports the cost-first vs. balanced-strategy hypothesis (see text): Participants with higher AQ tended to always consider cost first, while those with lower autistic traits considered cost or evidence first in a more balanced way. Statistical significance marked on the plot was based on cluster-based permutation tests (see Methods).</p

Decision time (DT) for each sampling.

(a) The distributions of DTs aggregated over all participants (main plot) and for each cost and evidence condition (insets). In the main plot, the distribution of DTs (histogram) was clearly bimodal, well fitted by a Gaussian mixture (gray curve) with two Gaussian components (black curves). Such bimodality was also visible in most inset plots, though the relative weights of the two components varied with experiment conditions. (b) Mean DTs varied with cost (abscissa) and evidence (different colors) conditions. Error bars represent between-subject standard errors. (c) Effects of AQ levels on participants’ DTs in different cost (different colors) and evidence (abscissa) conditions. ΒAQ is the unstandardized coefficient of AQ indicating how much the mean DT in a condition would change when AQ increases by one unit. Error bars represent standard errors of the coefficients.</p

Optimality of sampling performance and the effects of autistic traits.

(a) Sampling efficiency varied with cost (abscissa) and evidence (different colors) conditions. Participants’ efficiency was on average 94% (i.e. close to optimality) but decreased with increasing cost or decreasing evidence, and decreased more dramatically when high cost and low evidence co-occurred. (b) The mean number of bead samples participants drew in a condition (solid lines) decreased with increasing cost or increasing evidence. Compared to the optimal number of samples (dashed lines), participants undersampled in the zero- or low-cost conditions while oversampled in the high-cost conditions. (c) Sampling variability (standard deviation of the numbers of samples drawn across trials) varied with cost and evidence conditions. Error bars in (a)–(c) denote between-subject standard errors. (d)–(f) Effects of AQ levels on participants’ sampling performance in different cost (different colors) and evidence (abscissa) conditions. ΒAQ is the unstandardized coefficient of AQ indicating how much the efficiency (d), number of samples (e), and sampling variability (f) would change when AQ increases by one unit. Error bars represent standard errors of the coefficients. Orange asterisk: p p < .1.</p

Computational modeling of sampling choices and decision times.

(a) Schematic of one-stage and two-stage models. One-stage models only consist of the steps on the left-hand side: Each time a participant decides whether to stop or continue sampling, the probability of stopping is a sigmoid function of a linear combination of multiple decision variables. Two-stage models assume that participants may probabilistically have a second thought to reconsider the choice (the coral dashed arrow). The second stage (on the right-hand side) works in the same way as the first stage but the two stages are controlled by different sets of decision variables. (b) Results of model comparison based on the joint fitting of choice and DT. The ΔAICc for a specific model was calculated for each participant with respect to the participant’s best-fitting model (i.e. lowest-AICc) and then summed across participants. Both fixed-effects (summed ΔAICc: lower is better) and random-effects (estimated model frequency: higher is better) comparisons revealed that the best-fitting model was a two-stage model with cost-related variables considered in the first stage and evidence-related variables in the second stage (i.e. ). The best one-stage model was the model involving only cost-related decision variables (i.e. Cost only). See Methods (or S1 Table) for the description of each model. Estimated model frequency (color coded) is a random effects measure of the proportion of participants best fit by the model. (c) Distribution of sample sizes (i.e. number of bead samples) for each condition: data vs. model predictions. (d) Distribution of DTs for each condition: data vs. model predictions. The best-fitted two-stage model (red curves) well predicted the observed distributions (histograms) of sample sizes and DTs for each cost and evidence condition, including the bimodality of the observed DT distributions, while the best-fitted one-stage model (blue curves) failed to do so. Both data and model predictions were aggregated across participants.</p

Data_Sheet_1_Vigilance or avoidance: How do autistic traits and social anxiety modulate attention to the eyes?.docx

IntroductionSocial anxiety disorder (SAD) and autism spectrum disorder (ASD) are highly overlapping in symptoms and have a high rate of comorbidity, posing challenges in diagnosis and intervention for both disorders. Both disorders are linked to abnormal attention to the eyes, yet how they interactively modulate the attentional process to the eyes remains unclear.MethodsIn this study, we explored how autistic traits and social anxiety in college students separately and together affected different temporal stages of attention to the eyes. Participants were instructed to view virtual faces for 10 s and make an emotional judgment, while their eye movements were recorded.ResultsWe found that social anxiety and autistic traits affected different temporal stages of eye-looking. Social anxiety only affected the first fixation duration on the eyes, while autistic traits were associated with eye avoidance at several time points in the later stage. More importantly, we found an interactive effect of autistic traits and social anxiety on the initial attention to the eyes: Among people scoring high on autistic traits, social anxiety was related to an early avoidance of the eyes as well as attention maintenance once fixated on the eyes.DiscussionOur study suggests the separate and interactive roles of social anxiety and autistic traits in attention to the eyes. It contributes to a deeper understanding of the mechanisms of social attention in both SAD and ASD and highlights the application of psychiatric diagnoses using eye-tracking techniques.</p

sj-docx-1-aut-10.1177_13623613211064373 – Supplemental material for Investigating intra-individual variability of face scanning in autistic children

Supplemental material, sj-docx-1-aut-10.1177_13623613211064373 for Investigating intra-individual variability of face scanning in autistic children by Qiandong Wang, Haoyang Lu, Shuyuan Feng, Ci Song, Yixiao Hu and Li Yi in Autism</p

Data_Sheet_1_Association of the P441L KCNQ1 variant with severity of long QT syndrome and risk of cardiac events.PDF

Dysfunction of potassium voltage-gated channel subfamily Q member 1 (KCNQ1) is a primary cause of long QT syndrome type 1 (LQT1). Here, we report a missense mutation P441L in KCNQ1 C-terminus of a 37-year-old woman with severe LQT1 phenotype. Variant P441L transporting to the plasma membrane and interacting with KCNE1 were both markedly decreased, leading to potassium efflux disorder and eventually LQT1. Mutations between the C-terminal helix A and helix B of KCNQ1 have linked with low cardiac event risk, however, we firstly find variant P441L causing a severe LQT1 phenotype with a high risk of cardiac events.</p

Image1_Proteomics and weighted gene correlated network analysis reveal glutamatergic synapse signaling in diazepam treatment of alcohol withdrawal.JPEG

Background: Alcohol use disorder (AUD) is characterized by chronic excessive alcohol consumption, often alternating with periods of abstinence known as alcohol withdrawal syndrome (AWS). Diazepam is the preferred benzodiazepine for treatment of alcohol withdrawal syndrome under most circumstances, but the specific mechanism underlying the treatment needs further research.Methods: We constructed an animal model of two-bottle choices and chronic intermittent ethanol exposure. LC-MS/MS proteomic analysis based on the label-free and intensity-based quantification approach was used to detect the protein profile of the whole brain. Weighted gene correlated network analysis was applied for scale-free network topology analysis. We established a protein–protein interaction network based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software and identified hub proteins by CytoHubba and MCODE plugins of Cytoscape. The online tool Targetscan identified miRNA–mRNA pair interactions.Results: Seven hub proteins (Dlg3, Dlg4, Shank3, Grin2b, Camk2b, Camk2a and Syngap1) were implicated in alcohol withdrawal syndrome or diazepam treatment. In enrichment analysis, glutamatergic synapses were considered the most important pathway related to alcohol use disorder. Decreased glutamatergic synapses were observed in the late stage of withdrawal, as a protective mechanism that attenuated withdrawal-induced excitotoxicity. Diazepam treatment during withdrawal increased glutamatergic synapses, alleviating withdrawal-induced synapse inhibition.Conclusion: Glutamatergic synapses are considered the most important pathway related to alcohol use disorder that may be a potential molecular target for new interventional strategies.</p