Abstract: Judgments of learning (JOLs) refer to predictions about whether learned items can be recalled in a future test. Previous studies have shown that JOLs are not always accurate and often exhibit systematic biases. When JOLs are higher than the recall performance, this discrepancy manifests as an overestimation bias; conversely, when JOLs are lower than the recall performance, it manifests as an underestimation bias. These biases may distort individuals’ selection of learning strategies and allocation of learning time, thereby reducing learning efficiency.
Findings regarding whether feedback reduces metamemory bias remain inconsistent. Some studies have reported no effect, whereas others have observed partial calibration. Furthermore, the mechanism underlying these effects remains unclear. According to the anchoring-and-adjustment heuristic, individuals make JOLs based on a psychological anchor, which is partially derived from prior learning experiences or mnemonic beliefs. This anchor may shift across the learning of different lists. Therefore, feedback may shape JOLs for subsequent lists by updating individuals’ anchor associated with each list. Based on this heuristic, the present study employed two types of feedback in a multi-list learning context: high-anchor (performance +10%) and low-anchor (performance -10%) feedback.
In two experiments, participants studied four lists. Before each learning phase, they made a global judgment of learning (GJOL) to predict the number of items they would recall on a subsequent memory test; this measure served as an indicator of participants’ psychological anchor prior to learning each list. Participants then studied each item and made item-level JOLs. After each list, they completed a 2-minute distractor task, followed by a memory test, and received list-level feedback. Experiment 1 used 40 word pairs as study materials and adopted a cued-recall task to induce overestimation bias, while Experiment 2 used 30 word pairs and employed a two-alternative forced-choice recognition task to induce underestimation bias.
Experiment 1 used a 3 (Feedback Type: high-anchor, correct, low-anchor) × 4 (List Order: Lists 1-4) mixed experimental design with 102 participants (34 in high-anchor group, 33 in correct group, and 35 in low-anchor group). The results showed that neither high-anchor nor correct feedback exerted a calibration effect on overestimation bias across all four lists, whereas low-anchor feedback fully calibrated this bias for List 4. Meanwhile, GJOL (i.e., the anchor) mediated the effect of prior-list feedback on subsequent JOLs across the first two feedback-learning cycles, but showed no mediating effect in the final cycle. Experiment 2 adopted an identical design and recruited 99 participants (33 per group). The results showed that neither low-anchor nor correct feedback calibrated underestimation bias across all four lists, whereas high-anchor feedback fully calibrated this bias for List 3. Also, GJOL mediated the effect of feedback on subsequent JOLs in the first feedback-learning cycle, but not in the final two cycles. In both experiments, adjustment of JOLs in the final cycle was primarily driven by the transfer of anchors (i.e., GJOL) across adjacent lists, which ultimately led to calibration of the metamemory confidence bias.
These findings indicate that low-anchor and high-anchor feedback can specifically calibrate overestimation and underestimation biases in metamemory, respectively. Practically, this study provides targeted calibration strategies for distinct types of metamemory confidence bias. Theoretically, it extends the typology and functional boundaries of external cues within the cue-utilization framework, and demonstrates that calibration of metamemory confidence bias must follow the principle that cue types should match bias types. In addition, the study expands the applicability of the anchoring-and-adjustment heuristic and reveals the divergent effects of feedback and anchors on JOLs across different stages of multi-list learning.

Key words: metamemory confidence bias, judgments of learning, psychological anchor, low-anchor feedback, high-anchor feedback