Memory consolidation is a critical process in learning, with new information and skills strengthened 'offline' during periods of rest. Research has typically investigated consolidation over hours-days, but recent work has identified rapid consolidation across a scale of seconds, termed 'micro-consolidation'. Behaviourally, micro-consolidation has been interrogated through manipulations of rest/practice duration, sequence task paradigms, and the addition of interference or dual tasking. The relative online and offline contributions to learning depend on the nature/demands of the task and may be influenced by attention, 'reactive inhibition', and fatigue effects. Evidence of hippocampal neural replay and offline beta oscillatory activity in frontoparietal networks provides strong support for a micro-consolidation mechanism and is incompatible with alternative interpretations of the observed behaviour.

Visual working memory (VWM) enables the temporary storage and manipulation of visual information, yet its limited capacity makes it sensitive to the amount and structure of the information it must retain. The vast majority of previous VWM research has used two-dimensional stimuli, while real-world visual perception incorporates depth-related spatial cues which may affect performance. Prior work suggests that depth could enhance VWM performance by enabling perceptual enrichment and better individuation, but may also introduce additional cognitive processing costs and impair performance. Here, we directly tested how dimensionality influences VWM under different memory loads using a virtual reality adaptation of the change detection task, enabling the presentation of ecologically valid, real-world 2D and 3D objects. While accuracy was comparable across stimulus dimensionality, response times for 3D stimuli showed larger increases at higher memory loads. These results suggest that even though 3D stimuli may enrich perceptual input, they introduce processing costs that become more apparent under high memory load, possibly demanding additional neural resources associated with depth processing.

Perceptual learning can significantly improve visual sensitivity even in fully matured adults. However, the ability to generalize learning to untrained conditions is often limited. While traditionally, perceptual learning is attributed to practice-dependent plasticity mechanisms, recent studies suggest that brief memory reactivations can efficiently improve visual perception, recruiting higher-level brain regions. Here we provide evidence that similar memory reactivation mechanisms promote generalization of offline learning mechanisms. Human participants encoded a visual discrimination task with the target stimulus at retinotopic location A. Then, brief memory reactivations of only five trials each were performed on separate days at location A. Generalization was tested at retinotopic location B. Results indicate remarkable enhancement of location B performance following memory reactivations, pointing to efficient offline generalization mechanisms. A control experiment with no reactivations showed minimal generalization. These findings suggest that reactivation-induced learning further enhances learning efficiency by promoting offline generalization mechanisms to untrained conditions, and can be further tested in additional learning domains, with potential future clinical implications.

Previous studies using a visual texture discrimination task (TDT) have demonstrated that performance enhancements resulting from extensive daily training ( full training condition) remained intact after replacing all training, except for the first and last session, with a few daily reminder trials ( reminder condition). Omitting reminders ( control condition) yielded only limited learning, supporting their crucial contribution. We first confirmed these findings and excluded gaze position differences among conditions as a contributing factor. Next, we tested whether the reminders’ effectiveness is specific to a context of limited attention to the peripheral target caused by simultaneously performing a demanding fixation task. Removing the fixation task yielded performance levels in the first session matching those normally reached after lengthy daily training, suggesting that learning in the standard TDT involves the redeployment of attention. After changing texture parameters to increase the difficulty of the task, performing the TDT without a fixation task yielded learning in all three conditions. This indicates that in a dual-task, reminders can produce learning outcomes comparable to full training . In contrast, when the TDT is performed with full attention to the target, consolidation of the initial session alone can yield improvements equivalent to those observed in reminder and full training conditions. ### Competing Interest Statement The authors have declared no competing interest. NWO open competition, 406.21.GO.044

Negative visual memories are persistent distressing mental images and a hallmark symptom of post-traumatic stress disorder (PTSD). However, ongoing attempts to directly modulate negative memories are often ineffective. Here, we show that targeting the memory with embedded positive verbal content can indirectly modulate negative visual memory. Participants encoded sequences of positive words embedded within negative pictures. Following encoding, participants were instructed to remember or to forget the embedded words. Results show that verbal memory was weaker when participants were directly instructed to forget relative to remember the words. Importantly, intentional forgetting of positive verbal memory indirectly reduced negative visual memory for pictures that followed the words. An additional experiment revealed that in the absence of verbal memory retrieval, instructions to remember the words indirectly reduced negative visual memory. These findings emphasize the potential of positive experiences to downregulate negative memories, possibly uncovering future strategies for treating psychopathologies such as PTSD.

While visual perceptual learning improves during non-REM sleep and stabilizes during REM sleep via excitatory-inhibitory neurotransmitter (E/I) balance in early visual areas (EVA), the role of prefrontal regions remains unclear. Here, we show that contributions of the dorsolateral prefrontal cortex (DLPFC) and medial prefrontal cortex (mPFC) differ by sleep stage in human adults. During non-REM sleep, plasticity increased in DLPFC, indexed by elevated E/I balance measured with magnetic resonance spectroscopy and polysomnography, in correlation with performance gains. During REM sleep, stability increased in mPFC, indexed by reduced E/I balance, in correlation with resilience to retrograde interference from new learning. E/I balance changes and their effects on learning paralleled those in EVA. Connectivity weights between EVA and DLPFC, and between EVA and mPFC, switched with sleep stage. These findings suggest the presence of dorsal and medial pathways that unconsciously alternate between non-REM sleep and REM sleep to improve and stabilize learning.

Neutral memories can be modulated via intentional memory control paradigms such as directed forgetting. In addition, previous studies have shown that neutral visual memories can be modulated indirectly, via remember and forget instructions towards competing verbal memories. Here we show that direct modulation of neutral verbal memory strength is impaired by negative visual context, and that negative visual context is resistant to indirect memory modulation. Participants were directly instructed to intentionally remember or forget newly encoded neutral verbal information. Importantly, this verbal information was interleaved with embedded negative visual context. Results showed that negative visual context eliminated the well-documented effect of direct instructions to intentionally remember verbal content. Furthermore, negative visual memory was highly persistent, overcoming its sensitivity to indirect modulation shown in previous studies. Finally, these memory effects persisted to the following day. These results demonstrate the dominance of negative visual context over neutral content, highlighting the challenges associated with memory modulation in psychopathologies involving maladaptive processing of negative visual memories.

The ability to accurately retrieve visual details of past events is a fundamental cognitive function relevant for daily life. While a visual stimulus contains an abundance of information, only some of it is later encoded into long-term memory representations. However, an ongoing challenge has been to isolate memory representations that integrate various visual features and uncover their dynamics over time. To address this question, we leveraged a novel combination of empirical and computational frameworks based on the hierarchal structure of convolutional neural networks and their correspondence to human visual processing. This enabled to reveal the contribution of different levels of visual representations to memory strength and their dynamics overtime. Visual memory strength was measured with distractors selected based on their shared similarity to the target memory along low or high layers of the convolutional neural network hierarchy. The results show that visual working memory relies similarly on low and high-level visual representations. However, already after a few minutes and on to the next day, visual memory relies more strongly on high-level visual representations. These findings suggest that visual representations transform from a distributed to a stronger high-level conceptual representation, providing novel insights into the dynamics of visual memory over time.

The developed human brain shows remarkable plasticity following perceptual learning, resulting in improved visual sensitivity. However, such improvements commonly require extensive stimuli exposure. Here we show that efficiently enhancing visual perception with minimal stimuli exposure recruits distinct neural mechanisms relative to standard repetition-based learning. Participants (n = 20, 12 women, 8 men) encoded a visual discrimination task, followed by brief memory reactivations of only five trials each performed on separate days, demonstrating improvements comparable with standard repetition-based learning (n = 20, 12 women, 8 men). Reactivation-induced learning engaged increased bilateral intraparietal sulcus (IPS) activity relative to repetition-based learning. Complementary evidence for differential learning processes was further provided by temporal-parietal resting functional connectivity changes, which correlated with behavioral improvements. The results suggest that efficiently enhancing visual perception with minimal stimuli exposure recruits distinct neural processes, engaging higher-order control and attentional resources while leading to similar perceptual gains. These unique brain mechanisms underlying improved perceptual learning efficiency may have important implications for daily life and in clinical conditions requiring relearning following brain damage.

Perceptual learning is the process by which experience alters how incoming sensory information is processed by the brain to give rise to behavior—it is critical for how humans educate children, train experts, treat diseases, and promote health and well-being throughout the lifespan. Knowledge of perceptual learning requires basic and applied research in humans and nonhuman animal models, which informs strategic targets for advancing applications. Commercial products to induce perceptual learning are proliferating rapidly with limited regulation (e.g., for rehabilitation), while at the same time basic science is increasingly restricted by changing regulations (such as new granting-agency definitions of clinical trials). Realizing the full potential of perceptual learning requires balancing basic and translational science to advance new knowledge, while serving and protecting consumers. Reforms can promote open, accessible, and representative research, and the translation of this research to applications across different sectors of society.

People show vast variability in skill performance and learning. What determines a person's individual performance and learning ability? In this study we explored the possibility to predict participants’ future performance and learning, based on their behavior during initial skill acquisition. We recruited a large online multi-session sample of participants performing a sequential tapping skill learning task. We used machine learning to predict future performance and learning from raw data acquired during initial skill acquisition, and from engineered features calculated from the raw data. Strong correlations were observed between initial and final performance, and individual learning was not predicted. While canonical experimental tasks developed and selected to detect average effects may constrain insights regarding individual variability, development of novel tasks may shed light on the underlying mechanism of individual skill learning, relevant for real-life scenarios.

Human visual perception can be improved through perceptual learning. However, such learning is often specific to stimulus and learning conditions. Here, we explored how temporal dynamics of performance across conditions impact learning generalization. Participants performed a visual task, with the target at retinotopic location A. Then, the target was presented at location B either immediately after location A (same-session performance) or following a 48h consolidation period (different-session performance). Long-term generalization was measured the following week. Following initial training, both groups demonstrated generalization, consistent with previous accounts of fast learning. However, long-term generalization was enhanced in the same-session performance group. Consistently, improvements at locations A and B were correlated only following same-session performance, implying an integrated learning process across locations. The results support a new account of perceptual learning and generalization dynamics, suggesting that the temporal proximity of learning and consolidation of different conditions may integrate correlated learning processes, facilitating generalized learning.

Distress tolerance (DT), the capability to persist under negative circumstances, underlies a range of psychopathologies. It has been proposed that DT may originate from the activity and connectivity in diverse neural networks integrated by the reward system. To test this hypothesis, we examined the link between DT and integration and segregation in the reward network as derived from resting-state functional connectivity data. DT was measured in 147 participants from a large community sample using the Behavioral Indicator of Resiliency to Distress task. Prior to DT evaluation, participants underwent a resting-state functional magnetic resonance imaging scan. For each participant, we constructed a whole-brain functional connectivity network and calculated the degree of reward network integration and segregation based on the extent to which reward network nodes showed functional connections within and outside their network. We found that distress-intolerant participants demonstrated heightened reward network integration relative to the distress-tolerant participants. In addition, these differences in integration were higher relative to the rest of the brain and, more specifically, the somatomotor network, which has been implicated in impulsive behavior. These findings support the notion that increased integration in large-scale brain networks may constitute a risk for distress intolerance and its psychopathological correlates.