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首页> 外文期刊>Journal of Cognitive Neuroscience >Not-so-working Memory: Drift in Functional Magnetic Resonance Imaging Pattern Representations during Maintenance Predicts Errors in a Visual Working Memory Task
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Not-so-working Memory: Drift in Functional Magnetic Resonance Imaging Pattern Representations during Maintenance Predicts Errors in a Visual Working Memory Task

机译：工作记忆不佳：维护期间功能性磁共振成像模式表示的漂移预测视觉工作记忆任务中的错误

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Working memory (WM) is critical to many aspects of cognition, but it frequently fails. Much WM research has focused on capacity limits, but even for single, simple features, the fidelity of individual representations is limited. Why is this? One possibility is that, because of neural noise and interference, neural representations do not remain stable across a WM delay, nor do they simply decay, but instead, they may "drift" over time to a new, less accurate state. We tested this hypothesis in a functional magnetic resonance imaging study of a matchonmatch WM recognition task for a single item with a single critical feature: orientation. We developed a novel pattern-based index of "representational drift" to characterize ongoing changes in brain activity patterns throughout the WM maintenance period, and we were successfully able to predict performance on the matchonmatch recognition task using this representational drift index. Specifically, in trials where the target and probe stimuli matched, participants incorrectly reported more nonmatches when their activity patterns drifted away from the target. In trials where the target and probe did not match, participants incorrectly reported more matches when their activity patterns drifted toward the probe. On the basis of these results, we contend that neural noise does not cause WM errors merely by degrading representations and increasing random guessing; instead, one means by which noise introduces errors is by pushing WM representations away from the target and toward other meaningful (yet incorrect) configurations. Thus, we demonstrate that behaviorally meaningful drift within representation space can be indexed by neuroimaging.
机译：工作记忆（WM）对于认知的许多方面都至关重要，但是它经常失败。 WM的许多研究都集中在容量限制上，但是即使对于单个简单的功能，单个表示的保真度也是有限的。为什么是这样？一种可能性是，由于神经噪声和干扰，神经表示在WM延迟中不会保持稳定，也不会简单衰减，而是随着时间的流逝会漂移到新的，精度较低的状态。我们在功能磁共振成像研究中对具有单个关键特征的单个项目的匹配/不匹配WM识别任务进行了检验，从而验证了这一假设。我们开发了一种新颖的基于模式的“代表性漂移”指数，以表征整个WM维护期间大脑活动模式的持续变化，并且我们能够成功地使用此代表性漂移指数预测匹配/不匹配识别任务的表现。具体而言，在目标和探针刺激相匹配的试验中，参与者的活动模式偏离目标时，错误地报告了更多的不匹配。在目标和探针不匹配的试验中，参与者的活动模式向探针漂移时，错误地报告了更多匹配。根据这些结果，我们认为仅通过降低表示质量和增加随机猜测，神经噪声就不会导致WM错误。相反，噪声引入错误的一种方法是通过将WM表示推离目标，并推向其他有意义的（但不正确的）配置。因此，我们证明了在表征空间内行为上有意义的漂移可以由神经影像索引。

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