The brain is able to adapt rapidly and continually to the surrounding environment, becoming increasingly sensitive to important and frequently encountered stimuli. It is often claimed that this adaptive learning is highly task-specific, that is, we become more sensitive to the critical signals in the tasks we attend to. Here, we show a new type of perceptual learning, which occurs without attention, without awareness and without any task relevance. Subjects were repeatedly presented with a background motion signal so weak that its direction was not visible; the invisible motion was an irrelevant background to the central task that engaged the subject's attention. Despite being below the threshold of visibility and being irrelevant to the central task, the repetitive exposure improved performance specifically for the direction of the exposed motion when tested in a subsequent suprathreshold test. These results suggest that a frequently presented feature sensitizes the visual system merely owing to its frequency, not its relevance or salience.

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.

Visual perceptual learning (VPL) is defined as a long-term improvement in performance on a visual task. In recent years, the idea that conscious effort is necessary for VPL to occur has been challenged by research suggesting the involvement of more implicit processing mechanisms, such as reinforcement-driven processing and consolidation. In addition, we have learnt much about the neural substrates of VPL and it has become evident that changes in visual areas and regions beyond the visual cortex can take place during VPL.

Simple exposure is sufficient to sensitize the human visual system to a particular direction of motion, but the underlying mechanisms of this process are unclear. Here, in a passive perceptual learning task, we found that exposure to task-irrelevant motion improved sensitivity to the local motion directions within the stimulus, which are processed at low levels of the visual system. In contrast, task-irrelevant motion had no effect on sensitivity to the global motion direction, which is processed at higher levels. The improvement persisted for at least several months. These results indicate that when attentional influence is limited, lower-level motion processing is more receptive to long-term modification than higher-level motion processing in the visual cortex.

SUMMARY Visual perceptual learning is defined as performance enhancement on a sensory task and is distinguished from other types of learning and memory in that it is highly specific for location of the trained stimulus. The location specificity has been shown to be paralleled by changes in neural activity in V1 or V4 of monkeys [1, 2] and enhancement in functional magnetic resonance imaging (fMRI) signal in the trained region of the primary visual cortex (V1) [3–5] after visual training. Although recently the role of sleep in strengthening visual perceptual learning has attracted much attention, its underlying neural mechanism has yet to be clarified. Here, for the first time, fMRI activation of early visual cortex was measured and compared during sleep with and without preceding visual perceptual learning training. The fMRI measurement was conducted concurrently with polysomnogram, which indicates a subject’s sleep/wake status. As a result of predetermined region-of-interest (ROI) analysis of the human primary cortex (V1), activation enhancement during non rapid eye movement sleep after training was observed specifically in the trained region of V1. Furthermore, improvement of task-performance measured subsequently to the post-training sleep session was significantly correlated with the amount of the trained-region-specific fMRI activation in V1 during sleep. These results suggest that as far as V1 is concerned, only the trained region is involved in improving task performance after sleep.