Sleep after practice reduces the attentional blink

Cellini, Nicola; Goodbourn, Patrick T.; McDevitt, Elizabeth A.; Martini, Paolo; Holcombe, Alex O.; Mednick, Sara C.

doi:10.3758/s13414-015-0912-7

Cited by 19 publications

(18 citation statements)

References 53 publications

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“…Measuring the AB effect and its training-related modulation requires comparing the accuracy at a short lag versus accuracy at a long lag 49 . Previous studies using similar RSVP stimulation as in our study showed a most pronounced AB effect at the lag of two or three (180 to 270 ms) 35,36,50 and specific effects of training were found for the target at a lag of two [51][52][53] . Therefore, we observed the T2 | T1 accuracy at a lag of two and compared this measure with the accuracy in the control condition, assuming that the accuracy in the control condition reflects the asymptotic T2 | T1 accuracy for a long lag.…”

Section: Meg Recordingssupporting

confidence: 80%

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40-Hz Binaural beats enhance training to mitigate the attentional blink

Roß

Lopez

2020

Sci Rep

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This study investigated whether binaural beat stimulation could accelerate the training outcome in an attentional blink (AB) task. The AB refers to the lapse in detecting a target T2 in rapid serial visual presentation (RSVP) after the identification of a preceding target T1. Binaural beats (BB) are assumed to entrain neural oscillations and support cognitive function. Participants were assigned into two groups and presented with BB sounds while performing the AB task on three subsequent days in a cross-over design. Group A was presented with 40-Hz BB during the first day and 16 Hz during the second day, while the order of beat frequencies was reversed in Group B. No sound was presented on the third day. MEG recordings confirmed a strong entrainment of gamma oscillations during 40-Hz BB stimulation and smaller gamma entrainment with 16-Hz BB. The rhythm of the visual stimulation elicited 10-Hz oscillations in occipital MEG sensors which were of similar magnitude for both BB frequencies. The AB performance did not increase within a session. However, participants improved between sessions, with overall improvement equal in both groups. Group A improved more after the first day than the second day. In contrast, group B gained more from the 40 Hz stimulation on the second day than from 16-Hz stimulation on the first day. Taken together, 40-Hz BB stimulation during training accelerates the training outcome. The improvement becomes evident not immediately, but after consolidation during sleep. Therefore, auditory beats stimulation is a promising method of non-invasive brain stimulation for enhancing training and learning which is well-suited to rehabilitation training. Perceptual and motor skills can improve to a certain degree through training. However, successful training requires time and effort. Notably, the outcome of rehabilitation training could benefit from new methods for improving the efficacy of learning. One proposed method to accelerate learning employs non-invasive brain stimulation, paired with a training task 1. The current study used the attentional blink paradigm to investigate whether auditory stimulation with binaural beats during the training improves the performance in detecting a target within a rapidly presented sequence of visual stimuli. Non-invasive brain stimulation using transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) has been shown to improve learning and training outcomes 2,3. However, the underlying mechanisms are not well understood. It has been suggested that the electric or magnetic stimulation modulates the neural membrane potential 4 and causes inhibitory or excitatory effects depending on stimulus parameters 5. Also, the stimulation may increase synaptic efficacy through stimulus-induced long-term potentiation 6 and thus facilitates neural plasticity underlying learning. A recent approach to non-invasive brain stimulation employs the concept that rhythmic magnetic or electric stimulation could entrain neural oscillations in a specific freq...

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Section: Meg Recordingssupporting

confidence: 80%

“…Such delayed effect is characteristic of perceptual learning requiring consolidation during sleep 67 . That sleep after training improves the AB performance has been shown previously 51 . Thus, we assume that the BB stimulation interacted with a neural network underlying learning and training rather than immediately with perception.…”

Section: Discussionsupporting

confidence: 67%

40-Hz Binaural beats enhance training to mitigate the attentional blink

Roß

Lopez

2020

Sci Rep

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“…Indeed, when controlling for total sleep time, which was significantly associated with consolidation of unpleasant pictures presented before sleep, the correlation between memory discrimination and number of N2 spindles became non-significant. Therefore we can only speculate that sleep spindles, which are a hallmark feature of N2 sleep and were previously associated with memory , attention (Cellini, Goodbourn, McDevitt, Martini, Holcombe, & Mednick, 2015), and intelligence abilities (Fogel & Smith, 2011), may facilitate the consolidation of negative information. This result may be consistent with two recent studies reporting an association between emotional memory consolidation and sleep spindles (Cairney, et al, 2014a;Kaestner, et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Sleep before and after learning promotes the consolidation of both neutral and emotional information regardless of REM presence

Cellini

Torre

Stegagno

et al. 2016

Neurobiology of Learning and Memory

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“…It is now well established that distinct cognitive domains benefit from specific sleep stages and features. For example, our lab and others have shown that activity of sleep spindles (Mednick et al, 2013) and slow oscillations (SO: .5-1Hz, Rihms et al, 2014), as well spindle/SO coincidence (Niknazar et al, 2015) in a post-training sleep period correlate with the magnitude of declarative memory improvement (e.g., conscious, episodic memories), and spindles are also associated with improved attention (Cellini et al, 2015). On the other hand, minutes in REM sleep and REM theta activity (4-8Hz) correlate with improvement in non-declarative memories (e.g., unconscious, perceptual or sensorimotor skills) (Mednick, Nakayama, & Stickgold, 2003) and emotional salient memories (Groch, Wilhelm, Diekelmann, & Born, 2013;Hutchison & Rathore, 2015).…”

Section: An Introduction To Sleepmentioning

confidence: 75%

P(Sleep) 2.0: Capturing the dynamics of nighttime sleep using probabilistic models and big data

Yetton¹

2019

Preprint

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Sleep, a critical necessity for health, cognition, and well-being, consumes 25 years of the average human’s life. Alterations in sleep’s macro and microstate have been observed in multiple illnesses, including depression and Alzheimer’s, and sleep may represent a biomarker of these difficult to diagnose illnesses. Despite this, adequate models of the dynamics of sleep able to capture the fine-grained minutia of sleep’s many states and processes are lacking. The current work leverages over 1000 nights of healthy sleep polysomnography recordings from multiple open-source datasets. Data were automatically analyzed through a machine learning and data engineering pipeline to extract important features of the electroencephalogram, such as sleep spindles, slow oscillations (SO), and Rapid Eye Movement (REM) events. The counts of these sleep features and transition probabilities of sleep stages were modeled across the night in 30-second epoch intervals, using a flexible yet principled Bayesian cognitive modeling approach. The model, christened P(Sleep) 2.0, defines “typical” patterns of healthy sleep, and how they are affected by time, circadian processes and individual differences (e.g., age, sex). Transition probability dynamics were found to best be predicted by the previous 2 stages, time spent in stage, time across the night, age and sex, but not an age*sex interaction. Spindles, SO and REM events were also affected by the same features, but an age*sex interaction was additionally present. N2 spindles and both N2 and N3 slow oscillations were found to increase with time spent in a stage, whereas N3 spindles and REM decreased. Spindles and SO were more present in the evening compared to the morning; in contrast, REM increased over the night. REM counts and N3 spindles increased with age, while N2 spindles and SO reduced. Females had more N2 spindles, and SO in both N2 and N3 than men. The current work replicates a body of previous literature based on small sample sizes, and the increased power in this study provides firm conclusions to arguments on the patterns of healthy sleep across the night, both at the macroscale (stages) and microscale (feature events). The proposed work has broader impacts on the detection of abnormal (and potentially pathologic) sleep patterns.

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Sleep after practice reduces the attentional blink

Cited by 19 publications

References 53 publications

40-Hz Binaural beats enhance training to mitigate the attentional blink

40-Hz Binaural beats enhance training to mitigate the attentional blink

Sleep before and after learning promotes the consolidation of both neutral and emotional information regardless of REM presence

P(Sleep) 2.0: Capturing the dynamics of nighttime sleep using probabilistic models and big data

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