Learning & memory最新文献

The mnemonic discrimination task (MDT) is a widely used cognitive assessment tool. Performance in this task is believed to indicate an age-related deficit in episodic memory stemming from a decreased ability to pattern-separate among similar experiences. However, cognitive processes other than memory ability might impact task performance. In this study, we investigated whether nonmnemonic decision-making processes contribute to the age-related deficit in the MDT. We applied a hierarchical Bayesian version of the Ratcliff diffusion model to the MDT performance of 26 younger and 31 cognitively normal older adults. It allowed us to decompose decision behavior in the MDT into different underlying cognitive processes, represented by specific model parameters. Model parameters were compared between groups, and differences were evaluated using the Bayes factor. Our results suggest that the age-related decline in MDT performance indicates a predominantly mnemonic deficit rather than differences in nonmnemonic decision-making processes. In addition, this mnemonic deficit might also involve a slowing in processes related to encoding and retrieval strategies, which are relevant for successful memory as well. These findings help to better understand what cognitive processes contribute to the age-related decline in MDT performance and may help to improve the diagnostic value of this popular task.

This study investigated how humans process probabilistic-associated information when encountering varying levels of uncertainty during implicit visual statistical learning. A novel probabilistic cueing validation paradigm was developed to probe the representation of cues with high (75% probability), medium (50%), low (25%), or zero levels of predictiveness in response to preceding targets that appeared with high (75%), medium (50%), or low (25%) transitional probabilities (TPs). Experiments 1 and 2 demonstrated a significant negative association between cue probe identification accuracy and cue predictiveness when these cues appeared after high-TP but not medium-TP or low-TP targets, establishing exploration-like cue processing triggered by lower-uncertainty rather than high-uncertainty inputs. Experiment 3 ruled out the confounding factor of probe repetition and extended this finding by demonstrating (1) enhanced representation of low-predictive and zero-predictive but not high-predictive cues across blocks after high-TP targets and (2) enhanced representation of high-predictive but not low-predictive and zero-predictive cues across blocks after low-TP targets for learners who exhibited above-chance awareness of cue-target transition. These results suggest that during implicit statistical learning, input characteristics alter cue-processing mechanisms, such that exploration-like and exploitation-like mechanisms are triggered by lower-uncertainty and higher-uncertainty cue-target sequences, respectively.

An in vitro analog of learning that a food is inedible provided insight into mechanisms underlying the learning. Aplysia learn to stop responding to a food when they attempt but fail to swallow it. Pairing a cholinergic agonist with an NO donor or histamine in the Aplysia cerebral ganglion produced significant decreases in fictive feeding in response to the cholinergic agonist alone. Acetylcholine (ACh) is the transmitter of chemoreceptors sensing food touching the lips. Nitric oxide (NO) and histamine (HA) signal failed attempts to swallow food. Reduced responses to the cholinergic agonist after pairing with NO or HA indicate that learning partially arises via a decreased response to ACh in the cerebral ganglion.

Historically, the development of valid and reliable methods for assessing higher-order cognitive abilities (e.g., rule learning and transfer) has been difficult in rodent models. To date, limited evidence supports the existence of higher cognitive abilities such as rule generation and complex decision-making in mice, rats, and rabbits. To this end, we sought to develop a task that would require mice to learn and transfer a rule. We trained mice to visually discriminate a series of images (image set, six total) of increasing complexity following three stages: (1) learn a visual target, (2) learn a rule (ignore any new images around the target), and finally (3) apply this rule in abstract form to a comparable but new image set. To evaluate learning for each stage, we measured (1) days (and performance by day) to discriminate the original target at criterion, (2) days (and performance by day) to get back to criterion when images in the set were altered by the introduction of distractors (rule learning), and (3) overall days (and performance by day) to criterion when experienced versus naïve cohorts of mice were tested on the same image set (rule transfer). Twenty-seven wild-type male C57 mice were tested using Bussey-Saksida touchscreen operant conditioning boxes (Lafayette Instruments). Two comparable black-white image sets were delivered sequentially (counterbalanced for order) to two identical cohorts of mice. Results showed that all mice were able to effectively learn their initial target image and could recall it >80 d later. We also found that mice were able to quickly learn and apply a "rule" : Ignore new distractors and continue to identify their visual target embedded in more complex images. The presence of rule learning was supported because performance criterion thresholds were regained much faster than initial learning when distractors were introduced. On the other hand, mice appeared unable to transfer this rule to a new set of stimuli. This is supported because visual discrimination curves for a new image set were no better than an initial (naïve) learning by a matched cohort of mice. Overall results have important implications for phenotyping research and particularly for the modeling of complex disorders in mice.

To date, there is insufficient evidence to explain the role of adenosinergic receptors in the reconsolidation of long-term spatial memory. In this work, the role of the adenosinergic receptor family (A1, A2A, A2B, and A3) in this process has been elucidated. It was demonstrated that when infused bilaterally into the hippocampal CA1 region immediately after an early nonreinforced test session performed 24 h posttraining in the Morris water maze task, adenosine can cause anterograde amnesia for recent and late long-term spatial memory. This effect on spatial memory reconsolidation was blocked by A1 or A3 receptor antagonists and mimicked by A1 plus A3 receptor agonists, showing that this effect occurs through A1 and A3 receptors simultaneously. The A3 receptor alone participates only in the reconsolidation of late long-term spatial memory. When the memory to be reconsolidated was delayed (reactivation 5 d posttraining), the amnesic effect of adenosine became transient and did not occur in a test performed 5 d after the reactivation of the mnemonic trace. Finally, it has been shown that the amnesic effect of adenosine on spatial memory reconsolidation depends on the occurrence of protein degradation and that the amnesic effect of inhibition of protein synthesis on spatial memory reconsolidation is dependent on the activation of A3 receptors.

Fear memory formation and recall are highly regulated processes, with the central amygdala (CeA) contributing to fear memory-related behaviors. We recently reported that a remote fear memory engram is resident in the anterior basolateral amygdala (aBLA). However, the extent to which downstream neurons in the CeA participate in this engram is unknown. We tested the hypothesis that CeA neurons activated during fear memory formation are reactivated during remote memory retrieval such that a CeA engram participates in remote fear memory recall and its associated behavior. Using contextual fear conditioning in TRAP2;Ai14 mice, we identified, by persistent Cre-dependent tdTomato expression (i.e., "TRAPing"), CeA neurons that were c-fos-activated during memory formation. Twenty-one days later, we quantified neurons activated during remote memory recall using Fos immunohistochemistry. Dual labeling was used to identify the subpopulation of CeA neurons that was both activated during memory formation and reactivated during recall. Compared with their context-conditioned (no shock) controls, fear-conditioned (electric shock) mice (n = 5/group) exhibited more robust fear memory-related behavior (freezing) as well as larger populations of activated (tdTomato⁺) and reactivated (dual-labeled) CeA neurons. Most neurons in both groups were mainly located in the capsular CeA subdivision (CeAC). Notably, however, only the size of the TRAPed population distributed throughout the CeA was significantly correlated with time spent freezing during remote fear memory recall. Our findings indicate that fear memory formation robustly activates CeA neurons and that a subset located mainly in the CeAC may contribute to both remote fear memory storage/retrieval and the resulting fear-like behavior.

Emotional memories are processed during sleep; however, the specific mechanisms are unclear. Understanding such mechanisms may provide critical insight into preventing and treating mood disorders. Consolidation of neutral memories is associated with the coupling of NREM sleep slow oscillations (SOs) and sleep spindles (SPs). Whether SO-SP coupling is likewise involved in emotional memory processing is unknown. Furthermore, there is an age-related emotional valence bias such that sleep consolidates and preserves reactivity to negative but not positive emotional memories in young adults and positive but not negative emotional memories in older adults. If SO-SP coupling contributes to the effect of sleep on emotional memory, then it may selectively support negative memory in young adults and positive memory in older adults. To address these questions, we examined whether emotional memory recognition and overnight change in emotional reactivity were associated with the strength of SO-SP coupling in young (n = 22) and older (n = 32) adults. In younger adults, coupling strength predicted negative but not positive emotional memory performance after sleep. In contrast, coupling strength predicted positive but not negative emotional memory performance after sleep in older adults. Coupling strength was not associated with emotional reactivity in either age group. Our findings suggest that SO-SP coupling may play a mechanistic role in sleep-dependent consolidation of emotional memories.

While the benefits of sleep for associative memory are well established, it is unclear whether single-item memories profit from overnight consolidation to the same extent. We addressed this question in a preregistered, online study and also investigated how the temporal proximity between learning and sleep influences overnight retention. Sleep relative to wakefulness improved retention of item and associative memories to similar extents irrespective of whether sleep occurred soon after learning or following a prolonged waking interval. Our findings highlight the far-reaching influences of sleep on memory that can arise even after substantial periods of wakefulness.

Episodic memories are thought to be stabilized through the coordination of cortico-hippocampal activity during sleep. However, the timing and mechanism of this coordination remain unknown. To investigate this, we studied the relationship between hippocampal reactivation and slow-wave sleep up and down states of the retrosplenial cortex (RTC) and prefrontal cortex (PFC). We found that hippocampal reactivations are strongly correlated with specific cortical states. Reactivation occurred during sustained cortical Up states or during the transition from up to down state. Interestingly, the most prevalent interaction with memory reactivation in the hippocampus occurred during sustained up states of the PFC and RTC, while hippocampal reactivation and cortical up-to-down state transition in the RTC showed the strongest coordination. Reactivation usually occurred within 150-200 msec of a cortical Up state onset, indicating that a buildup of excitation during cortical Up state activity influences the probability of memory reactivation in CA1. Conversely, CA1 reactivation occurred 30-50 msec before the onset of a cortical down state, suggesting that memory reactivation affects down state initiation in the RTC and PFC, but the effect in the RTC was more robust. Our findings provide evidence that supports and highlights the complexity of bidirectional communication between cortical regions and the hippocampus during sleep.

Sleep supports memory consolidation, and slow-wave sleep (SWS) in particular is assumed to benefit the consolidation of verbal learning material. Re-exposure to previously learned words during SWS with a technique known as targeted memory reactivation (TMR) consistently benefits memory. However, TMR has also been successfully applied during sleep stage N2, though a direct comparison between words selectively reactivated during SWS versus N2 is still missing. Here, we directly compared the effects of N2 TMR and SWS TMR on memory performance in a vocabulary learning task in a within-subject design. Thirty-four healthy young participants (21 in the main sample and 13 in an additional sample) learned 120 Dutch-German word pairs before sleep. Participants in the main sample slept for ∼8 h during the night, while participants in the additional sample slept ∼3 h. We reactivated the Dutch words selectively during N2 and SWS in one single night. Forty words were not cued. Participants in the main sample recalled the German translations of the Dutch words after sleep in the morning, while those in the additional sample did so at 2:00 a.m. As expected, we observed no differences in recall performance between words reactivated during N2 and SWS. However, we failed to find an overall memory benefit of reactivated over nonreactivated words. Detailed time-frequency analyses showed that words played during N2 elicited stronger characteristic oscillatory responses in several frequency bands, including spindle and theta frequencies, compared with SWS. These oscillatory responses did not vary with the memory strengths of individual words. Our results question the robustness and replicability of the TMR benefit on memory using our Dutch vocabulary learning task. We discuss potential boundary conditions for vocabulary reactivation paradigms and, most importantly, see the need for further replication studies, ideally including multiple laboratories and larger sample sizes.