The conditions of everyday life are such that people often hear speech that has been degraded (e.g., by background noise or electronic transmission) or when they are distracted by other tasks. However, it remains unclear what role attention plays in processing speech that is difficult to understand. In the current study, we used functional magnetic resonance imaging to assess the degree to which spoken sentences were processed under distraction, and whether this depended on the acoustic quality (intelligibility) of the speech. On every trial, adult human participants attended to one of three simultaneously presented stimuli: a sentence (at one of four acoustic clarity levels), an auditory distracter, or a visual distracter. A postscan recognition test showed that clear speech was processed even when not attended, but that attention greatly enhanced the processing of degraded speech. Furthermore, speech-sensitive cortex could be parcellated according to how speech-evoked responses were modulated by attention. Responses in auditory cortex and areas along the superior temporal sulcus (STS) took the same form regardless of attention, although responses to distorted speech in portions of both posterior and anterior STS were enhanced under directed attention. In contrast, frontal regions, including left inferior frontal gyrus, were only engaged when listeners were attending to speech and these regions exhibited elevated responses to degraded, compared with clear, speech. We suggest this response is a neural marker of effortful listening. Together, our results suggest that attention enhances the processing of degraded speech by engaging higher-order mechanisms that modulate perceptual auditory processing.

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Recent years have seen neuroimaging data sets becoming richer, with larger cohorts of participants, a greater variety of acquisition techniques, and increasingly complex analyses. These advances have made data analysis pipelines complicated to set up and run (increasing the risk of human error) and time consuming to execute (restricting what analyses are attempted). Here we present an open-source framework, automatic analysis (aa), to address these concerns. Human efficiency is increased by making code modular and reusable, and managing its execution with a processing engine that tracks what has been completed and what needs to be (re)done. Analysis is accelerated by optional parallel processing of independent tasks on cluster or cloud computing resources. A pipeline comprises a series of modules that each perform a specific task. The processing engine keeps track of the data, calculating a map of upstream and downstream dependencies for each module. Existing modules are available for many analysis tasks, such as SPM-based fMRI preprocessing, individual and group level statistics, voxel-based morphometry, tractography, and multi-voxel pattern analyses (MVPA). However, aa also allows for full customization, and encourages efficient management of code: new modules may be written with only a small code overhead. aa has been used by more than 50 researchers in hundreds of neuroimaging studies comprising thousands of subjects. It has been found to be robust, fast, and efficient, for simple-single subject studies up to multimodal pipelines on hundreds of subjects. It is attractive to both novice and experienced users. aa can reduce the amount of time neuroimaging laboratories spend performing analyses and reduce errors, expanding the range of scientific questions it is practical to address.

Most people will at some point experience not getting enough sleep over a period of days, weeks, or months. However, the effects of this kind of everyday sleep restriction on high-level cognitive abilities—such as the ability to store and recall information in memory, solve problems, and communicate—remain poorly understood. In a global sample of over 10000 people, we demonstrated that cognitive performance, measured using a set of 12 well-established tests, is impaired in people who reported typically sleeping less, or more, than 7–8 hours per night—which was roughly half the sample. Crucially, performance was not impaired evenly across all cognitive domains. Typical sleep duration had no bearing on short-term memory performance, unlike reasoning and verbal skills, which were impaired by too little, or too much, sleep. In terms of overall cognition, a self-reported typical sleep duration of 4 hours per night was equivalent to aging 8 years. Also, sleeping more than usual the night before testing (closer to the optimal amount) was associated with better performance, suggesting that a single night’s sleep can benefit cognition. The relationship between sleep and cognition was invariant with respect to age, suggesting that the optimal amount of sleep is similar for all adult age groups, and that sleep-related impairments in cognition affect all ages equally. These findings have significant real-world implications, because many people, including those in positions of responsibility, operate on very little sleep and may suffer from impaired reasoning, problem-solving, and communications skills on a daily basis.

Whether acquiring a second language affords any general advantages to executive function has been a matter of fierce scientific debate for decades. If being bilingual does have benefits over and above the broader social, employment, and lifestyle gains that are available to speakers of a second language, then it should manifest as a cognitive advantage in the general population of bilinguals. We assessed 11,041 participants on a broad battery of 12 executive tasks whose functional and neural properties have been well described. Bilinguals showed an advantage over monolinguals on only one test (whereas monolinguals performed better on four tests), and these effects all disappeared when the groups were matched to remove potentially confounding factors. In any case, the size of the positive bilingual effect in the unmatched groups was so small that it would likely have a negligible impact on the cognitive performance of any individual.