Processing fluency appears to influence recognition memory judgements, and the manipulation of fluency, if misattributed to an effect of prior exposure, can result in illusory memory. Although it is well established that fluency induced by masked repetition priming leads to increased familiarity, manipulations of conceptual fluency have produced conflicting results, variously affecting familiarity or recollection. Some recent studies have found that masked conceptual priming increases correct recollection (Taylor & Henson, 2012), and the magnitude of this behavioural effect correlates with analogous fMRI BOLD priming effects in brain regions associated with recollection (Taylor, Buratto, & Henson, 2013). However, the neural correlates and time-courses of masked repetition and conceptual priming were not compared directly in previous studies. The present study used event-related potentials (ERPs) to identify and compare the electrophysiological correlates of masked repetition and conceptual priming and investigate how they contribute to recognition memory. Behavioural results were consistent with previous studies: Repetition primes increased familiarity, whereas conceptual primes increased correct recollection. Masked repetition and conceptual priming also decreased the latency of late parietal component (LPC). Masked repetition priming was associated with an early P200 effect and a later parietal maximum N400 effect, whereas masked conceptual priming was only associated with a central-parietal maximum N400 effect. In addition, the topographic distributions of the N400 repetition priming and conceptual priming effects were different. These results suggest that fluency at different levels of processing is associated with different ERP components, and contributes differentially to subjective recognition memory experiences.

The present research manipulated the fluency of unstudied items using masked repetition priming procedures during an explicit recognition test. Based on fluency-attribution accounts, which posit that familiarity can be driven by multiple forms of fluency, the relationship between masked priming-induced fluency and familiarity was investigated. We classified pictographic characters into High-Meaningfulness (High-M) and Low-Meaningfulness (Low-M) categories on the basis of subjective meaningfulness ratings and identified the distinct electrophysiological correlates of perceptual and conceptual fluency. The two types of fluency differed in associated ERP effects: 150–250 ms effects for perceptual fluency and FN400 effects for conceptual fluency. The ERPs of Low-M MP-same (items that were preceded by matching masked items) false alarms were more positive than correct rejections during 150–250 ms, whereas the ERPs of High-M MP-same false alarms were more positive than correct rejections during 300–500 ms. The topographic patterns of FN400 effects between High-M MP-same false alarms and Low-M MP-same false alarms were not different from those of High-M hits and Low-M hits. These results indicate that both forms of fluency can contribute to familiarity, and the neural correlates of conceptual fluency are not different from those of conceptual priming induced by prior study-phase exposure. We conclude that multiple neural signals potentially contribute to recognition memory, such as numerous forms of fluency differing in terms of their time courses.

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Building an efficient, smart, and multifunctional power grid while maintaining high reliability and security is an extremely challenging task, particularly in the ever-evolving cyber threat landscape. The challenge is also compounded by the increasing complexity of power grids in both cyber and physical domains. In this article, we develop a stochastic Petri net based analytical model to assess and analyze the system reliability of smart grids, specifically against topology attacks under system countermeasures (i.e., intrusion detection systems and malfunction recovery techniques). Topology attacks, evolving from false data injection attacks, are growing security threats to smart grids. In our analytical model, we define and consider both conservative and aggressive topology attacks, and two types of unreliable consequences (i.e., system disturbances and failures). The IEEE 14-bus power system is employed as a case study to clearly explain the model construction and parameterization process. The benefit of having this analytical model is the capability to measure the system reliability from both transient and steady-state analysis. Finally, intensive simulation experiments are conducted to demonstrate the feasibility and effectiveness of our proposed model.