预期错误与急性应激对不同强度恐惧记忆提取消退的影响

在条件性恐惧记忆再巩固模型下, 预期错误被证明是引发记忆不稳定的必要条件, 但其在不同强度恐惧记忆下的作用尚不明确。对于高强度可能导致的提取无效, 缺乏相应的探索以寻找解决办法, 而应激(stress)在其中发挥的作用值得探索。本研究考察人类被试中, 预期错误在不同强度恐惧记忆下的作用, 以及提取之后施加外源性应激对于消退进程的影响。结果发现, 对于较弱的恐惧记忆, 单个预期错误提取后消退可显著抑制恐惧自发恢复; 而对于较强恐惧记忆, 单个预期错误不能提取恐惧记忆进入再巩固, 已消退的记忆还会复发; 且在该种情况下, 如果在提取后施加外源性急性应激, 会进一步增大恐惧恢复。     

预期错误在复合恐惧记忆提取消退中的作用

基于记忆再巩固理论的提取消退范式被证明是一种有效和颇有前景的消除不良记忆的方法。本研究将预期错误(Prediction Error, PE)应用于提取消退范式中, 采用多感官复合刺激模型(声音 + 图片)作为条件刺激, 以皮电反应作为恐惧反应指标, 考察在提取阶段不同的预期错误设置(无PE、单个负性PE、单个正性PE和多重PE)对条件性恐惧记忆提取消退效果有何差异。结果表明：无PE组和多重PE组出现了恐惧的自发恢复和重建效应, 而负性PE组和正性PE组均没有出现恐惧的自发恢复和重建效应。说明了在对复合恐惧记忆进行提取消退时, 提取阶段适当的PE才能使记忆进入再巩固过程, 随后传统消退达到抑制恐惧返回效果, 提取阶段没有PE或PE量过多都不能达到恐惧消退效果。     

远期恐惧记忆再巩固更新机制的线索选择性特点

已有动物和人类研究均表明, 通过记忆的再巩固更新机制能有效削弱新形成的条件性恐惧记忆(1天), 并且存在线索选择性特点。然而创伤后应激障碍(PTSD)往往在形成相当一段时间后才能得到治疗, 且现实生活中人们通常一次习得对多个线索的恐惧。因此找到针对多线索创伤记忆的有效治疗方法显得尤为重要。目前未有人研究远期恐惧记忆的再巩固更新机制是否存在线索选择性特点。为探究远期恐惧记忆(>7天)的再巩固更新机制是否同样存在线索选择性特点, 本研究采用被试内实验设计, 以皮肤电作为恐惧反应指标, 多个线索作为条件刺激进行恐惧习得, 习得14天后给被试单独呈现一个线索进行恐惧记忆提取, 10分钟后进行消退训练, 24小时后对不同线索进行自发恢复测试。结果显示：未提取线索的自发恢复程度显著高于提取线索。说明远期记忆(14天)的再巩固更新机制同样存在线索选择性特点, 并确认了提取消退作为一种行为手段对远期恐惧记忆再巩固进行干预的有效性, 对临床干预具有一定指导意义。     

时间感知在跨期决策中的作用——时间决策模型的新探索

“时间”是跨期决策的“必需品”, 人们感知到的延迟时间决定跨期决策的结果。近年来, 研究者发现“时间长度感知”、“时间资源感知”和“时间框架感知”是时间感知作用于跨期决策的主要方式。时间感知的神经作用机制包含微观层面和宏观层面两种。“对数/指数时间折扣模型”、“感知时间基础模型”及“多模态漂移扩散模型”解释了时间感知的作用方式。然而, 现有理论模型还存在诸多局限, 主要包括“长短时距预测偏差”和“实际决策与预期决策偏差”两个方面。因此, 深入探讨时间感知影响跨期决策的基本方式, 分析现有理论模型的局限性并提出整合的机制框架具有十分重要的意义。未来研究亟需进一步整合时间感知的理论模型, 开展脑机制与应用方面的研究, 从深层揭露时间感知的作用本质, 帮助个人与社会更理性地决策。     

错误记忆产生的认知与神经机制：信息加工视角

采用信息加工视角, 在划分不同信息来源的基础上分析编码、存储(巩固)、再激活/再巩固和提取的一系列加工过程如何导致错误记忆形成, 由此总结出错误记忆产生的三个可能原因：(1)因缺乏针对目标事物特异性细节的记忆表征而侧重于编码和提取目标和非目标事物共享的抽象记忆表征, 使被试更倾向于依赖抽象表征对缺失的目标细节进行重构, 引发错误记忆; (2)目标事物启动了对应图式, 导致与图式相关的非目标事物记忆表征得到增强, 引发错误记忆; (3)误导信息干扰了再度激活状态下目标事物的记忆表征, 妨碍其进行准确的记忆再巩固, 从而引发错误记忆。未来研究可进一步探讨目标事物特异性细节的表征区域、不同类型的图式表征促进非目标事物记忆表征的具体机制以及提取阶段的图式复现对错误记忆形成的影响等问题。     

催产素影响条件化恐惧情绪加工的认知机制及神经基础

恐惧是一种与进化密切相关的情绪,对人类的生存与适应具有重要价值。过度的恐惧可能会导致恐惧症、焦虑症和创伤后应激障碍等病理性恐惧的产生。而催产素对各种病理性恐惧的干预和治疗具有重要的价值。因此,研究拟采用行为和fMRI技术,系统探究催产素影响条件化恐惧情绪加工的认知神经机制。重点关注:(1)催产素影响恐惧习得的认知神经机制;(2)催产素影响恐惧记忆巩固的认知神经机制;(3)催产素影响恐惧记忆再巩固的认知神经机制;(4)催产素影响恐惧消退的认知神经机制。这项研究的开展对于探究催产素影响条件化恐惧情绪加工的认知神经机制具有很高的科学价值,对于各种病理性恐惧的干预与治疗也具有重要的应用价值。     

条件性恐惧记忆消退的提取干预范式及其作用的神经机制

基于记忆再巩固理论的恐惧记忆提取干预范式被证明可以有效消退恐惧记忆, 能克服传统消退容易复发的缺点。该范式通过单独呈现条件刺激激活原有恐惧记忆, 使记忆重返不稳定状态, 随后在再巩固时间窗内实施干预则能改写原有记忆。目前该范式起作用的神经机制尚不明确, 本文在现有的人类研究和动物研究基础上, 总结了杏仁核、前额叶和海马三个脑区在提取干预过程中的作用, 以及该领域研究的争议点, 为之后的研究提供思路。     

如何克服边界条件：来自记忆强度影响记忆去稳定的分子机制的启示

长时记忆在激活后首先会变得不稳定(去稳定过程), 继而会经历一个再巩固过程重新稳定下来以维持记忆的关联性。在再巩固期间给予电击、药理或行为训练以干预记忆再巩固, 可以更改原有记忆的强度或内容。这有望成为临床上治疗病理性记忆的一种方法。然而, 一些边界条件(记忆痕迹强、时间久远等)导致记忆在简单激活后不能去稳定, 不会经历再巩固过程, 使干预再巩固的方法无法发挥作用。动物实验表明, 通过药理学地调控参与记忆去稳定的分子的活动以促进记忆去稳定, 可以成功克服边界条件。可见, 边界条件不是绝对的。未来研究可进一步探索更多、更优的促进记忆去稳定并克服边界条件的方法, 提升干预记忆再巩固疗法的临床应用潜能。     

骨源性因子ucOCN在运动抗抑郁中的作用机制

羧化不全骨钙素(ucOCN)是骨中成骨细胞分泌的特异性蛋白, 因其在调控神经发育、神经可塑性等中的重要角色而受到神经科学领域关注。“骨-脑”串联“对话”是骨内分泌-神经介导的应答系统, ucOCN透过血脑屏障后介导单胺类神经递质、神经内分泌、神经免疫、神经再生及基因表达等机制, 进而作用于海马CA3区、扣带回等脑区功能发挥来调节抑郁发生及改善。而ucOCN作为骨源性力学刺激敏感基因, 运动上调其表达后进入血液循环, 通过介导5-HT/GABA分泌、HPA轴功能、炎症反应、神经营养因子(BDNF等)表达或信号途径(如GSK3β/β-catenin、TLR4/miR-223/NLRP3等)激活等来实现“骨串联脑”, 发挥运动抗抑郁作用。通过对骨源性因子ucOCN介导脑区功能变化从而实现运动抗抑郁的作用机制进行探讨、梳理, 一方面有助于更深入了解骨内分泌功能, 另一方面为抑郁发生、改善和运动抗抑郁研究提供新的理论基础和研究思路。     

行为干预情绪记忆再巩固：从实验室到临床转化

重新激活已经储存的记忆会重返暂时不稳定的状态, 需要经历重新稳定, 称为记忆再巩固。在这期间可以通过多种行为手段破坏、强化或更新原始记忆痕迹, 这为开发一种革命性的情绪记忆障碍治疗方法开辟了道路。然而, 即使在简单的实验模型中引发记忆再巩固的条件也是复杂的, 这给临床转化带来了困难与挑战。未来研究可以设置更具生态效应的基础模型, 寻求引发以及干预再巩固的最佳方式, 从神经生理、细胞分子层面进一步深化其内在机理研究。     

Computational heterogeneity in the human mesencephalic dopamine system

Recent evidence in animals has indicated that the mesencephalic dopamine system is heterogeneous anatomically, molecularly, and functionally, and it has been suggested that the dopamine system comprises distinct functional systems. Identifying and characterizing these systems in humans will have widespread ramifications for understanding drug addiction and mental health disorders. Model-based studies in humans have suggested an analogous computational heterogeneity, in which dopaminergic targets in striatum encode both experience-based learning signals and counterfactual learning signals that are based on hypothetical information. We used brainstem-tailored fMRI to identify mesencephalic sources of experiential and counterfactual learning signals. Participants completed a decision-making task based on investing in markets. This sequential investment task generated experience-based learning signals, in the form of temporal difference (TD) reward prediction errors, and counterfactual learning signals, in the form of “fictive errors.” Fictive errors are reinforcement learning signals based on hypothetical information about “what could have been.” An additional learning signal was constructed to be relatable to a motivational salience signal. Blood oxygenation level dependent responses in regions of substantia nigra (SN) and ventral tegmental area (VTA), where dopamine neurons are located, coded for TD and fictive errors, and additionally were related to the motivational salience signal. These results are highly consistent with animal electrophysiology and provide direct evidence that human SN and VTA heterogeneously handle important reward-harvesting computations.     

Repetition priming of words, pseudowords, and nonwords

In 5 experiments, the authors assessed repetition priming for words, pseudowords, and nonwords using a task that combines an implicit perceptual fluency measure and a recognition memory assessment for each list item. Words and pseudowords generated a consistently strong repetition effect even when there was a failure to recognize the stimulus. In 2 of the experiments, the repetition effect for nonwords was reliably above chance even when there was a failure to recognize the stimulus. The authors propose a parallel distributed processing (PDP) model based on the work of J. McClelland and D. Rumelhart (1985) as a way to understand the mechanisms potentially responsible for the pattern of findings. Although the error-driven nature of learning in the model results in a poor fit to the nonword priming data, this is not endemic to all PDP models. Using a model based on Hebbian learning, the authors instantiate a property that they believe is characteristic of implicit memory--that learning is primarily based on the strengthening of connections between units that become active during the processing of a stimulus. This model provides a far more satisfactory account of the data than does the error-driven model.     

When is an error not a prediction error? An electrophysiological investigation

A recent theory holds that the anterior cingulate cortex (ACC) uses reinforcement learning signals conveyed by the midbrain dopamine system to facilitate flexible action selection. According to this position, the impact of reward prediction error signals on ACC modulates the amplitude of a component of the event-related brain potential called the error-related negativity (ERN). The theory predicts that ERN amplitude is monotonically related to the expectedness of the event: It is larger for unexpected outcomes than for expected outcomes. However, a recent failure to confirm this prediction has called the theory into question. In the present article, we investigated this discrepancy in three trial-and-error learning experiments. All three experiments provided support for the theory, but the effect sizes were largest when an optimal response strategy could actually be learned. This observation suggests that ACC utilizes dopamine reward prediction error signals for adaptive decision making when the optimal behavior is, in fact, learnable.     

ALCOVE: an exemplar-based connectionist model of category learning.

ALCOVE (attention learning covering map) is a connectionist model of category learning that incorporates an exemplar-based representation (Medin & Schaffer, 1978; Nosofsky, 1986) with error-driven learning (Gluck & Bower, 1988; Rumelhart, Hinton, & Williams, 1986). Alcove selectively attends to relevant stimulus dimensions, is sensitive to correlated dimensions, can account for a form of base-rate neglect, does not suffer catastrophic forgetting, and can exhibit 3-stage (U-shaped) learning of high-frequency exceptions to rules, whereas such effects are not easily accounted for by models using other combinations of representation and learning method.     

Postdecision evaluation of choice alternatives as a function of valence of alternatives,choice, and expected delay of choice consequences

The effects of valence of alternatives, choice, and expected delay of choice consequences on postdecision evaluations of choice alternatives were studied by either giving subjects a choice between two experimental tasks (both either pleasant or unpleasant) or assigning subjects their initially preferred task. Crosscutting valence and choice, subjects expected either no delay, 10-min delay, or 30-min delay before engaging in the task. All subjects evaluated both tasks immediately after the delay instructions but before experiencing the actual delay. Results confirmed the expected dissonance effect; that is, an upward evaluation of the chosen alternative and a downward evaluation of the rejected alternative, relative to no-choice conditions. Moreover, the relative upward evaluation of the chosen alternative increased as the expected delay decreased. Valence was not found to interact with other variables. The results are discussed in terms of differential salience of choice alternatives.     

Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gambling

Because learned associations are quickly renewed following extinction, the extinction process must include processes other than unlearning. However, reinforcement learning models, such as the temporal difference reinforcement learning (TDRL) model, treat extinction as an unlearning of associated value and are thus unable to capture renewal. TDRL models are based on the hypothesis that dopamine carries a reward prediction error signal; these models predict reward by driving that reward error to zero. The authors construct a TDRL model that can accommodate extinction and renewal through two simple processes: (a) a TDRL process that learns the value of situation-action pairs and (b) a situation recognition process that categorizes the observed cues into situations. This model has implications for dysfunctional states, including relapse after addiction and problem gambling.     

Blocking in category learning

Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. The authors tested this hypothesis by conducting three category-learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effects were difficult to obtain. Conversely, when participants learned to predict an outcome in a task with the same formal structure and materials, blocking effects were robust and followed the predictions of error-driven learning. The authors discuss their findings in relation to models of category learning and the usefulness of category knowledge in the environment.     

Stroop and Garner effects in and out of Posner's beam: reconciling two conceptions of selective attention

Space- or object-based models, on the one hand, and structural-informational models, on the other hand, reflect conceptually distinct approaches to visual selective attention. In 3 studies, the authors contrasted these approaches by jointly applying prototypical routines prescribed in each approach. Following a space-based paradigm developed by M. I. Posner, participants were cued to attend to a certain spatial location, and performance at expected and unexpected locations was compared. Following a structural paradigm developed by W. R. Garner, the targets were color words printed in various colors, and the participants responded to either the color or the word component of the stimulus. Performance was poorer at unexpected than at expected locations. However, comparable amounts of Stroop and Garner interference affected performance at both expected and unexpected locations. It is suggested that the processes that govern (a) input selection from the visual field and (b) dimensional selection from the stimulus reflect fundamentally different systems of attention.     

Choice reaction performance following an error

Some data are presented showing the changes in choice-reaction time (CRT) and the probability of error (PE) on the five trials following an error in a two-choice experiment. The magnitudes of the increase in RT and the decrease in PE are different according as the error-trial stimulus is re-presented or the alternative stimulus is preservec. In addition, RT is quicker to recover its equilibrium value than is PE.This pattern of changes implicates two distinct adjustments to the underlying decision process following an error: (i) a selective outward adjustment of the boundary at which the error was uttered, and (ii) a delay, with respect to presentation of the reaction stimulus, of the epoch at which the subject begins sampling information from the stimulus display. When these ideas are concatenated with the sequential probability-ratio-test model for CRT, a theoretical relation can be derived between the difference in mean RT for an error and for the same response given correctly, on the one hand, and the decrease in PE on the trial following an error, given that the alternate stimulus is presented, on the other. This relation is satisfied by the data.     

Feedback-related negativity codes outcome valence, but not outcome expectancy, during reversal learning

Optimal behavior depends on the ability to assess the predictive value of events and to adjust behavior accordingly. Outcome processing can be studied by using its electrophysiological signatures—that is, the feedback-related negativity (FRN) and the P300. A prominent reinforcement-learning model predicts an FRN on negative prediction errors, as well as implying a role for the FRN in learning and the adaptation of behavior. However, these predictions have recently been challenged. Notably, studies so far have used tasks in which the outcomes have been contingent on the response. In these paradigms, the need to adapt behavioral responses is present only for negative, not for positive feedback. The goal of the present study was to investigate the effects of positive as well as negative violations of expectancy on FRN amplitudes, without the usual confound of behavioral adjustments. A reversal-learning task was employed in which outcome value and outcome expectancy were orthogonalized; that is, both positive and negative outcomes were equally unexpected. The results revealed a double dissociation, with effects of valence but not expectancy on the FRN and, conversely, effects of expectancy but not valence on the P300. While FRN amplitudes were largest for negative-outcome trials, irrespective of outcome expectancy, P300 amplitudes were largest for unexpected-outcome trials, irrespective of outcome valence. These FRN effects were interpreted to reflect an evaluation along a good–bad dimension, rather than reflecting a negative prediction error or a role in behavioral adaptation. By contrast, the P300 reflects the updating of information relevant for behavior in a changing context.