Публикации

Items presented in large font are rated with higher judgments of learning (JOLs) than those presented in small font. According to current explanations of this phenomenon in terms of processing fluency or implicit beliefs, this effect should be present no matter the type of material under study. However, we hypothesized that the linguistic cues present in sentences may prevent using font size as a cue for JOLs. Experiment 1, with short sentences, showed the standard font-size effect on JOLs, and Experiment 2, with pairs of longer sentences, showed a reduced effect. These results suggest that linguistic factors do not prevent font size from being used for JOLs. However, Experiment 3, with both short and long sentences, showed an effect of font size only for the former and not the latter condition, suggesting that the greater amount of to-be-remembered information eliminated the font-size effect. In Experiment 4, we tested a mechanism to explain this result and manipulated cognitive load using the dot-memory task. The short sentences from Experiments 1 and 3 were used, and the results replicated the font-size effect only in the low-cognitive load condition. Our results are consistent with the idea that perceptual information is used to make JOLs only with materials such as words, word pairs, or short sentences, and that the increased cognitive load required to process longer sentences prevents using font size as a cue for JOLs.

Metacognitive monitoring is a powerful tool that supports our ongoing cognitive processes (Flavell, 1976). In applied settings, such as when we are trying to learn a new language, monitoring the learning progress may determine the difference between success and failure. One way to measure metacognitive monitoring in relation to learning new material is the so-called Judgments of Learning (JOLs). JOLs are estimations of future success in recalling recently learned information. Depending on the confidence that we have in remembering the new information later, we may decide to keep rehearsing it or just move on. Existing research shows that several variables can mislead our JOLs in relation to the subsequent recall accuracy; at the same time, other variables that influence the recall itself do not affect JOLs. Perceptual fluency, manipulated in different sensory modalities by e.g. font size or presentation volume, leads to differences in JOLs (e.g., higher JOLs for bigger font size), although recall accuracy remains the same regardless of the manipulation. On the other and, the animacy manipulation (e.g., dog vs. table) does not affect JOLs but animate words are remembered better. Our main aim was to study JOL brain correlates for variables that differently affect JOLs and memory. Participants were presented with words in an easy- or difficult to-read font that referred to animate or inanimate objects while EEG was recorded. For each word, participants had to choose on a 0-100% scale the confidence they had in remembering it in near future. We found a higher P2 response for high- (70–100%) than to medium- JOLs (40–60%) ratings, which may reflect attentional recruitment resulting in modulation of perceptual processing. Furthermore, we found a  greater P600 response for medium- than high-JOLs, suggesting a deeper reanalysis of these type of “less confident” answers. When animacy and perceptual fluency are split between medium and high-JOLs, we found LPC (late positive component) only for animacy, being showing a higher amplitude for the high- than medium-JOLs.. This might indicate a higher involvement of memory processes during the processing of animacy-related information. Finally, when comparing difficult type font words rated with medium and high-JOLs, we obtained larger P3b for high-JOLs rated words, which may attributed to their deeper evaluation. This is the first evidence of differential brain signatures for JOLs depending on their ratings level and different experimental manipulations. Our results highlight the relevance of metacognitive evaluations in the cognitive processing.

The work was supported by the Russian Science Foundation (project №19-18-00534).

Human memory is not a literal record of our experiences but a fallible and malleable cognitive process. Because of the reconstructive nature of memory, we are often prone to accept false events and recall them as truthful (Bartlett, 1932). One easy and reliable method to create and study false memories in the laboratory is the misinformation paradigm. In this paradigm participants are presented with a story (original information). After some time, parts of this story are presented again but now including some modifications (misinformation). Finally, the memory is measured for the original information, the misinformation, and, as control, some other incorrect information never presented before. The misinformation effect occurs when the percentage of misinformation accepted is higher than the acceptance of control incorrect information. This effect has been largely studied in relation to its applied relevance in eyewitness testimony research. Yet, the neural substrates and temporal dynamics of processing correct and false information remain scarcely studied. In this study the neural activity was recorded using EEG while participants performed a memory recognition test which comprised misinformation, true, and simply incorrect items. The only previous EEG study on neural correlates focused on misinformation pointed to the P3b and LPC (late positive component) ERPs components as the key to distinguishing between memories for correct and false memories. High P3b is linked with a strong match between the expectation and the stimuli presented. LPC is a late component around 400 to 800 ms after the stimulus presentation, associated with the recollection of accurate information. Our results show that for the contrasts of misinformation accepted vs rejected, and false information accepted vs rejected (correct rejections), P3b was significantly more positive when the inaccurate information was accepted. These differences suggest a larger cognitive workload on accepting this type of information than when it is correctly rejected. Furthermore, in both contrasts we found differences in P600 which is linked to reprocessing of detected anomalies in the input. Here, we found a more expressed P600 for accepted than for rejected misinformation. P600 was also stronger for correct rejections than false alarms. In this latter case, the higher P600 amplitude may reflect the detection and reanalysis of the rejection of this false information. Interestingly, in the case of acceptance of misinformation, the higher P600 amplitude suggest that participants are not totally blind to the inaccuracy of the misinformation, though still they accept it.

The work was supported by the Russian Science Foundation (project №19-18-00534).