A New Spin on Spatial Cognition in ADHD: A Diffusion Model Decomposition of Mental Rotation

Objectives: Multiple studies have found evidence of task non-specific slow drift rate in ADHD, and slow drift rate has rapidly become one of the most visible cognitive hallmarks of the disorder. In this study, we use the diffusion model to determine whether atypicalities in visuospatial cognitive processing exist independently of slow drift rate. Methods: Eight- to twelve-year-old children with (n = 207) and without ADHD (n = 99) completed a 144-trial mental rotation task. Results: Performance of children with ADHD was less accurate and more variable than non-ADHD controls, but there were no group differences in mean response time. Drift rate was slower, but nondecision time was faster for children with ADHD. A Rotation × ADHD interaction for boundary separation was also found in which children with ADHD did not strategically adjust their response thresholds to the same degree as non-ADHD controls. However, the Rotation × ADHD interaction was not significant for nondecision time, which would have been the primary indicator of a specific deficit in mental rotation per se. Conclusions: Poorer performance on the mental rotation task was due to slow rate of evidence accumulation, as well as relative inflexibility in adjusting boundary separation, but not to impaired visuospatial processing specifically. We discuss the implications of these findings for future cognitive research in ADHD.

Generation Mechanism of Diffuser Stall in a Centrifugal Compressor with Vaneless Diffuser

The generation mechanism of a diffuser stall in a centrifugal compressor with a vaneless diffuser was investigated by experimental and computational analyses. The diffuser stall generated as the mass flow rate decreased. The diffuser stall cell rotated at 25-30 % of the impeller rotational speed, with diffuser stall fluctuations observed at 180° from the cutoff. The diffuser stall fluctuation magnitude gradually increased near the cutoff. According to the CFD analysis, the mass flow fluctuations at the diffuser exit showed a low mass flow region, rotating at approximately 25% of the impeller rotational speed. They began at 180° from the cutoff and developed as this region approached the cutoff. Therefore, the diffuser stall could be simulated by CFD analysis. First, the diffuser stall cell originated at 180° from the cutoff by interaction with boundary separation and impeller discharge vortex. Then, the diffuser stall cell further developed by boundary separation accumulation and the induced low velocity area The low velocity region formed a blockage across the diffuser passage span. The diffuser stall cell expanded due to boundary separation caused by a positive flow angle. Finally, the diffuser stall cell vanished when it passed the cutoff, because mass flow recovery occurred.

The Unsteady Behavior of Diffuser Stall in a Centrifugal Compressor With Vaneless Diffuser

The unsteady diffuser stall behavior in a centrifugal compressor with a vaneless diffuser was investigated by experimental and computational analyses. The diffuser stall generated as the mass flow rate decreased. The diffuser stall cell rotated at 25–30% of the impeller rotational speed, with diffuser stall fluctuations observed at 180° from the cutoff. The diffuser stall fluctuation magnitude gradually increased near the cutoff. Based on diffuser inlet velocity measurements, the diffuser stall fluctuations generated near both the shroud and hub sides, and the diffuser stall appeared at 180° and 240° from the cutoff. According to the CFD analysis, the mass flow fluctuations at the diffuser exit showed a low mass flow region, rotating at approximately 25% of the impeller rotational speed. They began at 180° from the cutoff and developed as this region approached the cutoff. Therefore, the diffuser stall could be simulated by CFD analysis. First, the diffuser stall cell originated at 180° from the cutoff by interaction with boundary separation and impeller discharge vortex. Then, the diffuser stall cell further developed by boundary separation accumulation and the induced low velocity area, located at the stall cell center. The low velocity region formed a blockage across the diffuser passage span. The diffuser stall cell expanded in the impeller rotational direction due to boundary separation caused by a positive flow angle. Finally, the diffuser stall cell vanished when it passed the cutoff, because mass flow recovery occurred.

Diffusion modeling reveals reinforcement learning impairments in gambling disorder that are linked to attenuated ventromedial prefrontal cortex value representations

Gambling disorder is associated with deficits in classical feedback-based learning tasks, but the computational mechanisms underlying such learning impairments are still poorly understood. Here, we examined this question using a combination of computational modeling and functional resonance imaging (fMRI) in gambling disorder participants (n=23) and matched controls (n=19). Participants performed a simple reinforcement learning task with two pairs of stimuli (80% vs. 20% reinforcement rates per pair). As predicted, gamblers made significantly fewer selections of the optimal stimulus, while overall response times (RTs) were not significantly different between groups. We then used comprehensive modeling using reinforcement learning drift diffusion models (RLDDMs) in combination with hierarchical Bayesian parameter estimation to shed light on the computational underpinnings of this performance impairment. In both groups, an RLDDM in which both non-decision time and response threshold (boundary separation) changed over the course of the experiment accounted for the data best. The model showed good parameter recovery, and posterior predictive checks revealed that in both groups, the model reproduced the evolution of both accuracy and RTs over time. Examination of the group-wise posterior distributions revealed that the learning impairment in gamblers was attributable to both reduced learning rates and a more rapid reduction in boundary separation over time, compared to controls. Furthermore, gamblers also showed substantially shorter non-decision times. Model-based imaging analyses then revealed that value representations in gamblers in the ventromedial prefrontal cortex were attenuated compared to controls, and these effects were partly associated with model-based learning rates. Exploratory analyses revealed that a more anterior ventromedial prefrontal cortex cluster showed attenuations in value representations in proportion to gambling disorder severity in gamblers. Taken together, our findings reveal computational mechanisms underlying reinforcement learning impairments in gambling disorder, and confirm the ventromedial prefrontal cortex and as a critical neural hub in this disorder.

Perceptual decision-making in children: Age-related differences and EEG correlates

Children make faster and more accurate decisions about perceptual information as they get older, but it is unclear how different aspects of the decision-making process change with age. Here, we used hierarchical Bayesian diffusion models to decompose performance in a perceptual task into separate processing components, testing age-related differences in model parameters and links to neural data. We collected behavioural and EEG data from 96 six- to twelve-year-olds and 20 adults completing a motion discrimination task. We used a component decomposition technique to identify two response-locked EEG components with ramping activity preceding the response in children and adults: one with activity that was maximal over centro-parietal electrodes and one that was maximal over occipital electrodes. Younger children had lower drift rates (reduced sensitivity), wider boundary separation (increased response caution) and longer non-decision times than older children and adults. Yet model comparisons suggested that the best model of children’s data included age effects only on drift rate and boundary separation (not non-decision time). Next we extracted the slope of ramping activity in our EEG components and covaried these with drift rate. The slopes of both EEG components related positively to drift rate, but the best model with EEG covariates included only the centro-parietal component. By decomposing performance into distinct components and relating them to neural markers, diffusion models have the potential to identify the reasons why children with developmental conditions perform differently to typically developing children - and to uncover processing differences inapparent in the response time and accuracy data alone.