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Response inhibition refers to the suppression of prepared or initiated actions. Typically, the go/no-go task (GNGT) or the stop signal task (SST) are used interchangeably to capture individual differences in response inhibition. On the one hand, factor analytic and conjunction neuroimaging studies support the association of both tasks with a single inhibition construct. On the other hand, studies that directly compare the two tasks indicate distinct mechanisms, corresponding to action restraint and cancellation in the GNGT and SST, respectively. We addressed these contradictory findings with the aim to identify the core differences in the temporal dynamics of the functional networks that are recruited in both tasks. We extracted the time-courses of sensory, motor, attentional, and cognitive control networks by group independent component (G-ICA) analysis of electroencephalography (EEG) data from both tasks. Additionally, electromyography (EMG) from the responding effector muscles was recorded to detect the timing of response inhibition. The results indicated that inhibitory performance in the GNGT may be comparable to response selection mechanisms, reaching peripheral muscles at around 316 ​ms. In contrast, inhibitory performance in the SST is achieved via biasing of the sensorimotor system in preparation for stopping, followed by fast sensory, motor and frontal integration during outright stopping. Inhibition can be detected at the peripheral level at 140 ​ms after stop stimulus presentation. The GNGT and the SST therefore seem to recruit widely different neural dynamics, implying that the interchangeable use of superficially similar inhibition tasks in both basic and clinical research is unwarranted.

Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver et al. developed a program called AlphaZero, which taught itself to play Go, chess, and shogi (a Japanese version of chess) (see the Editorial, and the Perspective by Campbell). AlphaZero managed to beat state-of-the-art programs specializing in these three games. The ability of AlphaZero to adapt to various game rules is a notable step toward achieving a general game-playing system. Science , this issue p. 1140 ; see also pp. 1087 and 1118 AlphaZero teaches itself to play three different board games and beats state-of-the-art programs in each. The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.

Alzheimer's disease is a severe irreversible syndrome, characterized by a slow and progressive cognitive decline that interferes with the standard instrumental and essential functions of daily life. Promptly identifying the impairment of particular cognitive functions could be a fundamental condition to limit, through preventive or therapeutic interventions, the functional damages found in this degenerative dementia. This study aims to analyse, through a systematic review of the studies, the sensitivity of four experimental paradigms (Wisconsin Card Sorting Test, Stroop Task, Go/No-Go Task, and Flanker Task) considered as golden standard instruments for executive functions assessment in elderly subjects affected by Alzheimer dementia. This review was carried out according to the PRISMA method. Forty-five studies comparing the executive performance of patients with Alzheimer's dementia (diagnosed according to different classification criteria for dementia) and healthy elderly patients both over the age of sixty, were selected. For the research, PubMed, PsycINFO, PsycArticles databases were used. The study highlighted the importance of using standard protocols to evaluate executive dysfunction in Alzheimer's disease. The Stroop task allows discriminating better between healthy and pathological aging.

Childhood maltreatment is a potent interpersonal trauma associated with dysregulation of emotional processes relevant to the development of psychopathology. The current study identified prospective links between patterns of maltreatment exposures and dimensions of emotion regulation in emerging adulthood. Participants included 427 individuals (48% Male; 75.9% Black, 10.8% White, 7.5% Hispanic, 6% Other) assessed at two waves. At Wave 1, children (10–12 years) from families eligible for public assistance with and without involvement with Child Protective Services took part in a research summer camp. Patterns of child maltreatment subtype and chronicity (based on coded CPS record data) were used to predict Wave 2 (age 18–24 years) profiles of emotion regulation based on self-report, and affective processing assessed via the Affective Go/No-Go task. Results identified associations between task-based affective processing and self-reported emotion regulation profiles. Further, chronic, multi-subtype childhood maltreatment exposure predicted difficulties with aggregated emotion dysregulation. Exposure to neglect with and without other maltreatment subtypes predicted lower sensitivity to affective words. Nuanced results distinguish multiple patterns of emotion regulation in a sample of emerging adults with high exposure to trauma and socioeconomic stress and suggest that maltreatment disrupts emotional development, resulting in difficulties identifying emotions and coping with emotional distress.

Information from multiple sensory modalities interacts. Using functional magnetic resonance imaging (fMRI), we aimed to identify the neural structures correlated with how cooccurring sound modulates the visual motor response execution. The reaction time (RT) to audiovisual stimuli was significantly faster than the RT to visual stimuli. Signal detection analyses showed no significant difference in the perceptual sensitivity (d’) between audiovisual and visual stimuli, while the response criteria (β or c) of the audiovisual stimuli was decreased compared to the visual stimuli. The functional connectivity between the left inferior parietal lobule (IPL) and bilateral superior temporal gyrus (STG) was enhanced in Go processing compared with No-go processing of audiovisual stimuli. Furthermore, the left precentral gyrus (PreCG) showed enhanced functional connectivity with the bilateral STG and other areas of the ventral stream in Go processing compared with No-go processing of audiovisual stimuli. These results revealed that the neuronal network correlated with modulations of the motor response execution after the presentation of both visual stimuli along with cooccurring sound in a multisensory Go/Nogo task, including the left IPL, left PreCG, bilateral STG and some areas of the ventral stream. The role of the interaction between the IPL and STG in transforming audiovisual information into motor behavior is discussed. The current study provides a new perspective for exploring potential brain mechanisms underlying how humans execute appropriate behaviors on the basis of multisensory information.

•Heartbeat-Evoked Potential (HEP) amplitudes were significantly higher during motor preparation in the High Proactive Inhibition (HPI) context.•Heartbeat-Evoked Potential did not show context-driven effects during the inter-stimulus phase.•Proactive motor inhibition modulates cortical processing of cardiac signals, reflected in increased Heartbeat-Evoked Potential (HEP) amplitude during motor preparation in high-inhibition contexts.•HEP responses dynamically change throughout a motor trial, with specific enhancement during the motor preparation phase.•Cardiac-brain interactions contribute to motor control, with enhanced cortical cardiac signal encoding occurring independently of cardiac deceleration. The brain and heart work together to optimize responses to environmental demands, with cardiac signals influencing perception and action. Cardiac deceleration, often associated with response inhibition, enhances the brain’s ability to encode stimuli and prepare for action, minimizing interference from internal signals. While this adaptive mechanism facilitates motor preparation, the reciprocal influence of cardiac signals and motor inhibition at the cortical level remains unclear. This study investigated whether context-driven motor inhibition modulates cortical cardiac signal processing, as measured by the Heartbeat-Evoked Potential (HEP). Participants completed a Go/No-Go task with two contexts: low proactive inhibition (LPI, infrequent No-Go stimuli) and high proactive inhibition (HPI, frequent No-Go stimuli). EEG and ECG analyses focused on HEPs during the pre-stimulus phase, with the hypothesis that HPI would enhance HEP amplitude, particularly during motor preparation (HEP2). Consistent with proactive inhibition strategies, participants displayed slower reaction times and reduced readiness potential (BP) amplitudes in the HPI condition, indicating reduced motor engagement during preparation compared to LPI. Crucially, HEP2 amplitudes were significantly higher during motor preparation in the HPI context, while no context-driven effects were observed for HEP1. Importantly, this modulation was independent of broader physiological adjustments, including cardiac deceleration. These findings suggest that proactive inhibition contexts specifically enhance cardiac signal processing during motor preparation. This modulation highlights a functional link between cardiac processing and motor inhibition, likely serving an adaptive role in optimizing physiological and cognitive readiness for scenarios requiring restraint and control.

Recent research shows that emotional facial expressions impact behavioral responses only when their valence is relevant to the task. Under such conditions, threatening faces delay attentional disengagement, resulting in slower reaction times and increased omission errors compared to happy faces. To investigate the neural underpinnings of this phenomenon, we used functional magnetic resonance imaging to record the brain activity of 23 healthy participants while they completed two versions of the go/no-go task. In the emotion task (ET), participants responded to emotional expressions (fearful or happy faces) and refrained from responding to neutral faces. In the gender task (GT), the same images were displayed, but participants had to respond based on the posers’ gender. Our results confirmed previous behavioral findings and revealed a network of brain regions (including the angular gyrus, the ventral precuneus, the left posterior cingulate cortex, the right anterior superior frontal gyrus, and two face-responsive regions) displaying distinct activation patterns for the same facial emotional expressions in the ET compared to the GT. We propose that this network integrates internal representations of task rules with sensory characteristics of facial expressions to evaluate emotional stimuli and exert top-down control, guiding goal-directed actions according to the context.

Infant faces are prioritized by the attentional system in parents, resulting in a greater cognitive engagement in terms of response time. However, many biological, contextual and environmental factors relating to this cognitive mechanism have been left unexplored. To fill this gap, this study aims to (i) confirm that infant faces engage more attention compared to adult faces; (ii) investigate whether the attention to infant faces is affected early care experiences of parents; (iii) explore the effect of parents’ sex by taking the amount of involvement with early childcare into consideration. 51 mothers and 46 fathers completed a modified Go/no-Go task, a brief sociodemographic questionnaire, the short version of the Adult Parental Acceptance–Rejection scale, and an ad-hoc question relating to the amount of parental involvement with early childcare. Parents’ response times were slowed in the presence of infant versus adult faces. Parents whose mother was perceived as more sensitively accepting were more engaged by infant cues. By considering the amount of early parental involvement, the sex of parents did not significantly interact with the type of face. These findings provide new insights on the attention process in response to infant cues in parents and suggest that the investigation of experience-based factors may shed further light on this topic.

Building a large-scale quantum computer requires effective strategies to correct errors that inevitably arise in physical quantum systems 1 . Quantum error-correction codes 2 present a way to reach this goal by encoding logical information redundantly into many physical qubits. A key challenge in implementing such codes is accurately decoding noisy syndrome information extracted from redundancy checks to obtain the correct encoded logical information. Here we develop a recurrent, transformer-based neural network that learns to decode the surface code, the leading quantum error-correction code 3 . Our decoder outperforms other state-of-the-art decoders on real-world data from Google’s Sycamore quantum processor for distance-3 and distance-5 surface codes 4 . On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk and leakage, utilizing soft readouts and leakage information. After training on approximate synthetic data, the decoder adapts to the more complex, but unknown, underlying error distribution by training on a limited budget of experimental samples. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers. A recurrent, transformer-based neural network, called AlphaQubit, learns high-accuracy error decoding to suppress the errors that occur in quantum systems, opening the prospect of using neural-network decoders for real quantum hardware.

Inhibitory control (IC) deficits have been associated with psychiatric symptoms in all ages. However, longitudinal studies testing the direction of the associations in childhood are scarce. We used a sample of 2,874 children (7 to 9 years old) to test the following three hypotheses: (a) IC deficits are an underlying risk factor with a potentially causal role for psychopathology, (b) IC deficits are a complication of psychopathology, and (c) IC deficits and psychopathology are associated at the trait level but not necessarily causally related. We used the go/no-go task to assess IC, the parent-rated Strengths and Difficulties Questionnaire to evaluate externalizing/internalizing symptoms, and the random intercepts cross-lagged panel model to test the hypotheses. The results showed no support for the underlying risk factor hypothesis, suggesting that IC unlikely has a causal role in this age group's psychopathology. The complication hypothesis received support for externalizing symptoms, suggesting that externalizing symptoms may hamper the normal development of IC. IC deficits and both externalizing and internalizing symptoms were correlated at the trait level, indicating a possible common origin. We suggest that it may be useful to support children with externalizing symptoms to promote and protect their IC development.