Bridging the Gap Between Self-Report and Behavioral Laboratory Measures: A Real-Time Driving Task With Inverse Reinforcement Learning.

A major challenge in assessing psychological constructs such as impulsivity is the weak correlation between self-report and behavioral task measures that are supposed to assess the same construct. To address this issue, we developed a real-time driving task called the "highway task," in which participants often exhibit impulsive behaviors mirroring real-life impulsive traits captured by self-report questionnaires. Here, we show that a self-report measure of impulsivity is highly correlated with performance in the highway task but not with traditional behavioral task measures of impulsivity (47 adults aged 18-33 years). By integrating deep neural networks with an inverse reinforcement learning (IRL) algorithm, we inferred dynamic changes of subjective rewards during the highway task. The results indicated that impulsive participants attribute high subjective rewards to irrational or risky situations. Overall, our results suggest that using real-time tasks combined with IRL can help reconcile the discrepancy between self-report and behavioral task measures of psychological constructs.

Multimodal-based machine learning approach to classify features of internet gaming disorder and alcohol use disorder: A sensor-level and source-level resting-state electroencephalography activity and neuropsychological study.

OBJECTIVES: Addictions have recently been classified as substance use disorder (SUD) and behavioral addiction (BA), but the concept of BA is still debatable. Therefore, it is necessary to conduct further neuroscientific research to understand the mechanisms of BA to the same extent as SUD. The present study used machine learning (ML) algorithms to investigate the neuropsychological and neurophysiological aspects of addictions in individuals with internet gaming disorder (IGD) and alcohol use disorder (AUD). METHODS: We developed three models for distinguishing individuals with IGD from those with AUD, individuals with IGD from healthy controls (HCs), and individuals with AUD from HCs using ML algorithms, including L1-norm support vector machine, random forest, and L1-norm logistic regression (LR). Three distinct feature sets were used for model training: a unimodal-electroencephalography (EEG) feature set combined with sensor- and source-level feature; a unimodal-neuropsychological feature (NF) set included sex, age, depression, anxiety, impulsivity, and general cognitive function, and a multimodal (EEG + NF) feature set. RESULTS: The LR model with the multimodal feature set used for the classification of IGD and AUD outperformed the other models (accuracy: 0.712). The important features selected by the model highlighted that the IGD group had differential delta and beta source connectivity between right intrahemispheric regions and distinct sensor-level EEG activities. Among the NFs, sex and age were the important features for good model performance. CONCLUSIONS: Using ML techniques, we demonstrated the neurophysiological and neuropsychological similarities and differences between IGD (a BA) and AUD (a SUD).

Deterministic Orientation Control of Ferroelectric HfO2 Thin Film Growth by a Topotactic Phase Transition of an Oxide Electrode.

The scale-free ferroelectricity with superior Si compatibility of HfO2 has reawakened the feasibility of scaled-down nonvolatile devices and beyond the complementary metal-oxide-semiconductor (CMOS) architecture based on ferroelectric materials. However, despite the rapid development, fundamental understanding, and control of the metastable ferroelectric phase in terms of oxygen ion movement of HfO2 remain ambiguous. In this study, we have deterministically controlled the orientation of a single-crystalline ferroelectric phase HfO2 thin film via oxygen ion movement. We induced a topotactic phase transition of the metal electrode accompanied by the stabilization of the differently oriented ferroelectric phase HfO2 through the migration of oxygen ions between the oxygen-reactive metal electrode and the HfO2 layer. By stabilizing different polarization directions of HfO2 through oxygen ion migration, we can gain a profound understanding of the oxygen ion-relevant unclear phenomena of ferroelectric HfO2.

Enhanced ferroelectric switching speed of Si-doped HfO2 thin film tailored by oxygen deficiency.

Investigations concerning oxygen deficiency will increase our understanding of those factors that govern the overall material properties. Various studies have examined the relationship between oxygen deficiency and the phase transformation from a nonpolar phase to a polar phase in HfO2 thin films. However, there are few reports on the effects of oxygen deficiencies on the switching dynamics of the ferroelectric phase itself. Herein, we report the oxygen- deficiency induced enhancement of ferroelectric switching properties of Si-doped HfO2 thin films. By controlling the annealing conditions, we controlled the oxygen deficiency concentration in the ferroelectric orthorhombic HfO2 phase. Rapid high-temperature (800 °C) annealing of the HfO2 film accelerated the characteristic switching speed compared to low-temperature (600 °C) annealing. Scanning transmission electron microscopy and electron energy-loss spectroscopy (EELS) revealed that thermal annealing increased oxygen deficiencies, and first-principles calculations demonstrated a reduction of the energy barrier of the polarization flip with increased oxygen deficiency. A Monte Carlo simulation for the variation in the energy barrier of the polarization flipping confirmed the increase of characteristic switching speed.

Ferroelectric Polarization-Switching Dynamics and Wake-Up Effect in Si-Doped HfO2.

The ferroelectricity in ultrathin HfO2 offers a viable alternative to ferroelectric memory. A reliable switching behavior is required for commercial applications; however, many intriguing features of this material have not been resolved. Herein, we report an increase in the remnant polarization after electric field cycling, known as the "wake-up" effect, in terms of the change in the polarization-switching dynamics of a Si-doped HfO2 thin film. Compared with a pristine specimen, the Si-doped HfO2 thin film exhibited a partial increase in polarization after a finite number of ferroelectric switching behaviors. The polarization-switching behavior was analyzed using the nucleation-limited switching model characterized by a Lorentzian distribution of logarithmic domain-switching times. The polarization switching was simulated using the Monte Carlo method with respect to the effect of defects. Comparing the experimental results with the simulations revealed that the wake-up effect in the HfO2 thin film is accompanied by the suppression of disorder.

Stable Subloop Behavior in Ferroelectric Si-Doped HfO2.

The recent demand for analogue devices for neuromorphic applications requires modulation of multiple nonvolatile states. Ferroelectricity with multiple polarization states enables neuromorphic applications with various architectures. However, deterministic control of ferroelectric polarization states with conventional ferroelectric materials has been met with accessibility issues. Here, we report unprecedented stable accessibility with robust stability of multiple polarization states in ferroelectric HfO2. Through the combination of conventional voltage measurements, hysteresis temperature dependence analysis, piezoelectric force microscopy, first-principles calculations, and Monte Carlo simulations, we suggest that the unprecedented stability of intermediate states in ferroelectric HfO2 is due to the small critical volume size for nucleation and the large activation energy for ferroelectric dipole flipping. This work demonstrates the potential of ferroelectric HfO2 for analogue device applications enabling neuromorphic computing.