The ability to remember past events in vivid detail is central to our experience owing to our proclivity to reflect or reminisce about our personal pasts. Recollection refers to the retrieval process yielding memories that capture the richness of our past, including such aspects as where, when, or with whom we experienced an event (Yonelinas, 2002). This process is often accompanied by the subjective feeling of vivid remembering, a sense of mental reliving the event (Tulving, 1985). At the neural level, recollection is associated with a network of brain regions including the medial temporal lobes, particularly the hippocampus and the parahippocampal gyrus, the retrosplenial/posterior cingulate cortex (PCC) and the precuneus, and the medial prefrontal cortex (PFC) and the angular gyrus (Rugg and Vilberg, 2013).

The subjective sense of vivid recollection is typically positively correlated with the accuracy of recollected details (Moscovitch et al., 2016). This subjective experience, therefore, represents a reliable cue for the degree to which a memory can be trusted to guide decision making. However, the investigation of the neural mechanisms supporting the subjective experience of recollection has received relatively little attention. This may be due to the noted strong correlation between accuracy and subjective experience, resulting in a convergence of these aspects within the dominant theories of recollection (e.g., Bastin et al., 2019; Yonelinas, 2002). Indeed, the neural network underlying recollection has been reliably identified across studies that require participants either to reflect on their subjective experience (Daselaar et al., 2006; Vilberg and Rugg, 2007) or to retrieve specific memory details (Dobbins et al., 2002; Mitchell and Johnson, 2009). However, accurate detail retrieval, or objective recollection, does not necessarily align with the subjective experience of recollection.

Behavioral dissociations between accuracy and subjective recollection suggest that subjective assessments carry unique information that signal the diagnostic value of current mnemonic evidence. For example, after studying objects, faces, or parts of scenes, adults are more likely to correctly identify a studied item in a two alternative forced-choice (2AFC) task if the distractor corresponds to a non-studied exemplar of the target or a different section of the same scene (i.e., A-A’ condition) than if the distractor corresponds to a non-studied exemplar of a different studied item (i.e., A-B’ condition) (Dobbins et al., 1998; Heathcote et al., 2009; Hembacher and Ghetti, 2017; Tulving, 1981). However, participants are consistently less likely to claim subjective recollection in the A-A’ condition, likely because it forces them to identify the most diagnostic feature from the comparison between the two perceptually similar probes. Despite gaining in memory accuracy, individuals may therefore recognize the challenge of identifying a diagnostic feature or may base their subjective recollection judgment only on that feature rather than on the entire experience (Dobbins et al., 1998; Hembacher and Ghetti, 2017). We refer to this reliance on specific details as ‘specific retrieval’ because it encourages participants to claim recollection only when they remember specific, highly diagnostic details. In contrast, the A-B’ condition encourages a more global assessment of the identity of the presented probes. That is, in this condition, the presentation of the two dissimilar probes discourages identification of the most diagnostic features. Instead, it encourages participants to assess their retrieval of the probe as a whole, resulting in more errors, but also in a stronger sense of subjective recollection.

In the present study, we used this type of paradigm (Figure 1) to probe differences in the neural substrates of subjective recollection not confounded by memory accuracy. To confirm that the A-A’ condition was associated with greater evidence accumulation as to be expected if recollection is based on remembering specific, highly diagnostic details, we modeled responses in the two experimental conditions using drift diffusion models (Ratcliff, 1978). We hypothesized that greater emphasis on diagnostic features in the A-A’ condition would be manifested in greater evidence accumulation relative to the A-B’ condition.

Figure 1

Download asset Open asset

As for neural correlates, previous research offers a starting point for distinguishing subjective and objective recollection. Regions of the posterior parietal cortex (PPC) have been associated with retrieval of accurate details (Wagner et al., 2005) as well as with subjective recollection (Chua et al., 2006; Simons et al., 2010). Previous studies also hint at the possibility that subjective and objective recollection associated with the PPC are not always aligned. For example, patients with PPC damage have been shown to display equivalent source memory to matched controls, but considerably lower levels of subjective recollection (Davidson et al., 2008Hower et al., 2014). A separate line of research in aging populations has also identified dissociations between subjective and objective recollection. During a recognition memory task, Duarte and colleagues (Duarte et al., 2008) asked younger and older participants to indicate their subjective recollection, followed by a judgment about the temporal or spatial context in which items were studied. High-performing older adults showed similar levels of subjective recollection to younger adults but lower levels of objective recollection. This may be due to older adults retrieving comparable amounts of contextual details that were not diagnostic of the required source judgment, but that may have sufficed to induce a subjective feeling of recollection, similar to procedures encouraging retrieval of the target probe as a whole. Similarly, Mark and Rugg, 1998 tested both subjective (remember-know procedure) and objective (source memory test) recollection in younger and older adults. While older adults displayed significantly lower objective memory, there were no age differences in subjective recollection. Even though these behavioral dissociations have been identified in some populations, mapping subjective and objective recollection to the corresponding neural circuits has been difficult due to the typically high correlation between the two. Corroborating previous research, we expected the functional dissociations between objective and subjective recollection to map onto regional differences in the PPC.

Functional dissociations between the ventral/medial and dorsal/lateral PPC have proved informative to distinguish objective and subjective aspects of retrieval (Cabeza et al., 2008; Vilberg and Rugg, 2007; Wagner et al., 2005). In a direct comparison of subjective (remember-know procedure) and objective recollection (source memory test), Frithsen and Miller, 2014 showed enhanced activation in the angular gyrus in association with subjective recollection. Further dissociating the precision of memory retrieval from memory vividness, Richter et al., 2016 reported that activation in the angular gyrus was associated with objective memory precision, whereas activation in the precuneus was associated with subjective memory vividness. Of particular relevance, Favila and colleagues (Favila et al., 2018) examined differences between ventral and dorsal PPC with respect to how the representation of object features in these regions is influenced by retrieval goals. Participants were presented with stimuli that varied across color and object category, but these features were tested separately, allowing evaluation of the degree to which PPC regions carried information about goal-relevant vs. goal-irrelevant feature information. The results demonstrated that memory goals biased feature representation toward relevant information in dorsal PPC but not in ventral PPC. Based on these reported dissociations, we expected a dissociation between dorsal PPC and precuneus activation. We predicted enhanced dorsal PPC activation in the A-A’ relative to the A-B’ condition because the former encourages identification of more precise, diagnostic features. This would corroborate previous research showing that this region is under stronger influence from top-down control and retrieval goals (Cabeza et al., 2008). In contrast, we predicted enhanced ventral PPC activation, including the angular gyrus and/or the precuneus, in the A-B’ condition because this condition triggers a more global assessment of the test probes, resulting in greater subjective recollection (Hembacher and Ghetti, 2017).

Subjective assessments of recollection are metacognitive acts (Koriat and Goldsmith, 1996) and as such can be expected to be recapitulated in regions associated with metacognitive monitoring and evaluation (Vaccaro and Fleming, 2018). Metacognitive assessments of decision confidence, which represent retrospective metacognitive monitoring, have been associated with activation in dorsal anterior cingulate cortex (dACC) and anterior insula supporting performance or error monitoring (Fandakova et al., 2018), and with activation in lateral PFC to support the evaluation of retrieved content in the context of current task demands (Bang and Fleming, 2018). Interestingly, a behavioral study found that participants were more likely to bet on their responses in the A-B’ condition in anticipation of a social reward (Hembacher and Ghetti, 2017), suggesting that cues utilized to support subjective recollection are more heavily factored in subsequent decision making than cues associated with accurate response selection. Thus, we set to examine the neural mechanisms that connect subjective recollection with decision making. We hypothesized that when participants are asked to select a memory, this decision would reflect their assessment of how well they had performed on a given trial in the context of the specific task goal, such as obtaining a higher performance score (Koriat and Goldsmith, 1996). Thus, we expected neural activation in regions associated with metacognitive monitoring (i.e., anterior insula and dACC) and evaluation (i.e., lateral PFC) to be aligned to participants’ selection decisions at the time of making those decisions. Taken together, we expected that our task would engage fronto-parietal regions, with parietal regions tracking the dissociation between objective and subjective recollection during the memory phase, and insular, cingulate, and lateral PFC regions tracking goal-relevant dimensions of the task associated with subsequent decision making.

The goal of the present study was to examine the neural basis underlying subjective recollection and subsequent decision making. To this end, we used an experimental paradigm that allowed us to dissociate subjective recollection from memory accuracy. When participants encountered a target studied item along with a perceptually similar (and semantically identical) lure, their memory accuracy was higher, but their subjective recollection was lower (A-A’ condition) compared to when participants encountered a target studied item along with a lure that was similar to another studied item (A-B’ condition). At the neural level, we observed a dissociation within the PPC during the memory phase of the experiment. Specifically, the A-A’ condition was associated with enhanced activation in the right SPL across both remember and familiar responses. In contrast, the A-B’ condition was associated with enhanced activation in the bilateral precuneus and retrosplenial cortex, which also showed overall greater engagement during remember than during familiar responses in both experimental conditions. Together, these findings suggest that when the retrieval context favors identifying more precise diagnostic features, the resulting higher accuracy is associated with dorsal PPC at the expense of subjective recollection. In contrast, activation in the precuneus and retrosplenial cortex tracks the experience of subjective recollection, which is promoted in a retrieval context that favors global retrieval.

The PPC has a long-established role in memory retrieval (Wagner et al., 2005), but its precise role is a matter of ongoing debate (Cabeza et al., 2008; Gilmore et al., 2015). It has been implicated as one of the core recollection regions (Gilmore et al., 2015; Rugg and Vilberg, 2013) as well as in the subjective experience of recollection (Simons et al., 2010). However, subjective recollection and the retrieval of accurate details usually go hand in hand, making it difficult to isolate the unique neural underpinnings of the phenomenological experience that lies at the heart of episodic memory. At the same time, there is evidence that subjective and objective memory measures can be dissociated (e.g., Harlow and Yonelinas, 2016). For example, older adults give similar or even higher judgments of subjective recollection compared to younger adults, yet they show considerable deficits in measures of objective recollection (Addis et al., 2011; Duarte et al., 2008; Folville et al., 2020; Mark and Rugg, 1998). Dissociations between subjective and objective memory measures are also observed in neuropsychiatric disorders, such as schizophrenia (Huron et al., 1995). At the neural level, a meta-analysis comparing regions implicated in subjective vs. objective recollection Spaniol et al., 2009 found that prefrontal areas showed greater engagement in objective recollection, whereas parietal, hippocampal, and parahippocampal areas were more strongly associated with subjective recollection. However, objective and subjective recollections were highly correlated in most of these studies, precluding clear conclusions regarding the neural underpinnings of subjective recollection.

The experimental dissociation between SPL and precuneus/retrosplenial cortex during the memory phase is in line with the attention-to-memory framework (Cabeza et al., 2008) and with recent evidence implicating precuneus in memory vividness (Richter et al., 2016). The A-A’ condition is more likely to encourage the identification of the most diagnostic features from the comparison between the two similar probes (Dobbins et al., 1998). Consistent with this, we found that the A-A’ condition was associated with higher rates of evidence accumulation. Together, our behavioral and neural results converge to suggest that the SPL plays an important role in supporting diagnostic processing during retrieval. These findings are in line with the established role of the SPL in the dorsal attention network (Corbetta and Shulman, 2002) and corroborate a recent suggestion that this region supports higher memory accuracy via its role in perceptual search and attention to probes during retrieval (Sestieri et al., 2017).

The precuneus showed a different activation profile in our task. In line with previous evidence of the involvement of this area in subjective recollection (Richter et al., 2016), it showed greater activation for remember relative to familiar responses across conditions. Of note, the present results additionally showed overall greater activation in the precuneus in the A-B’ condition, suggesting that this region is modulated by the current retrieval context and can be promoted when a more global assessment of the probe is encouraged. The precuneus has been implicated consistently in autobiographical retrieval (McDermott et al., 2009) and may be one of the distinguishing features of individuals with superior memory (Mazzoni et al., 2019). A recent study compared retrieval of recently studied events and autobiographical events (Chen et al., 2017) and found that while memory for recently learned scenes was associated with activation of the posterior middle intraparietal sulcus, autobiographical memories involving scenes engaged the retrosplenial cortex and the precuneus. Autobiographical retrieval was also more likely to evoke subjective recollection, similar to our A-B’ condition. This idea is in accordance with the suggestion that the diagnostic value of the recollective experience varies depending on retrieval context (Dobbins et al., 1998). In line with our behavioral findings, our neural findings reveal a dissociation across parietal regions, with SPL activation tracking the retrieval of diagnostic features in the A-A’ condition, and precuneus activation tracking the subjective experience of recollection. Our results are consistent with clinical studies implicating ventral PPC in subjective recollection. One study showed that two patients with inferior parietal lesions (extending into the precuneus in one of the two) exhibited similar levels of recognition memory as did controls but were less likely to report high confidence for correctly recognized items (Hower et al., 2014). Similarly, Simons et al., 2010 showed that patients with bilateral parietal lesions performed as well as matched controls on source memory but exhibited reduced confidence in their source judgments, again linking PPC lesions to impairments in the experience of rich and vivid episodic details typically associated with recollection judgments. Finally, Ciaramelli and colleagues (2017) showed that subjective recollection experiences in healthy controls were marked by multi-featural context retrieval, that is, participants remembered both the position and the color of items that they recognized as old. In contrast, subjective recollections in PPC patients were less likely to involve multi-featural retrieval, indicating a misalignment between subjective and objective recollection following PPC damage. These results may be interpreted in a number of ways. On the one hand, patients may truly experience reduced richness and vividness of remembered details, due perhaps to problems with mental imagery. On the other hand, it is also possible that patients’ deficits are related to difficulties with metacognitive evaluation of memory evidence. By dissociating subjective and objective recollection experimentally and requiring memory selections in anticipation of a social reward, the present study reveals the close interplay between the subjective experience of recollection and metacognitive monitoring, and their role for subsequent decision making.

The present results also contribute to a growing body of literature examining the contributions of PPC to different aspects of episodic recollection. Using multivoxel pattern analysis, Kuhl and Chun, 2014 showed that the angular gyrus is not only involved in vivid recollections but also carries information about the stimulus category and event-specific information. Notably, these effects were reduced or absent in the SPL, in line with a dissociation between these regions. Angular gyrus activation has also been reported during false memory, that is, when participants experience novel items as studied (McDermott et al., 2017), reinforcing the idea that this area is associated with the processing of the subjective feeling of remembering. Accordingly, a recent account of the role of the angular gyrus in episodic memory suggested that it is involved in contextual integration (Ramanan et al., 2018). More specifically, this account suggests that sensory-perceptual and emotionally salient features are integrated in the angular gyrus and form the basis for the subjective experience of vivid recollection. In line with this account and previous studies, the left angular gyrus showed enhanced activation for remember judgments across both experimental conditions. However, we found that increased subjective recollection in the A-B’ condition was associated with the precuneus instead. Previous studies in which accurate detail retrieval and subjective recollection were examined separately also found these two aspects of episodic memory to be associated with the angular gyrus and the precuneus, respectively (Richter et al., 2016). Of note, in our study, the precuneus showed an overall higher activation in the A-B’ condition and also differentiated remember from familiar responses in the A-A’ condition. Overall, the present results suggest that the precuneus may be involved in enhancing the salience of retrieved memories in line with its role in mental imagery (Cavanna and Trimble, 2006).

As compared to the A-A’ condition, the A-B’ condition also triggered the overall greater activation in the bilateral anterior insula, dACC, and right dlPFC regions during the memory phase. In addition, these regions were more active for remember judgments across both experimental conditions. These results fit well with previous meta-analyses that have implicated the anterior insula in association with the salience of the retrieved output (Kim, 2010). Specifically, the anterior insula may signal salient information during retrieval in light of concurrent goals (Craig, 2009; Fandakova et al., 2018). From this perspective, we speculate that the enhanced anterior insular activation in the A-B’ condition (and in other tasks emphasizing judgments of subjective recollection) reflects the fact that the most goal-salient aspect of the task is to identify which items are subjectively recollected.

In separate literature on metacognition, the anterior insula as well as the dACC have been implicated in retrospective metacognitive monitoring (Fleck et al., 2006; Morales et al., 2018). Furthermore, lateral PFC regions have been implicated in the evaluation of retrieval outputs in context of current goals and demands (Chua et al., 2006; Fandakova et al., 2014; Kim and Cabeza, 2009), and in making decisions based on retrieved output (Fandakova et al., 2018). For example, Fandakova et al., 2018 showed that the anterior insula and dACC were activated both when participants inaccurately remembered the context in which an object was encountered as well as when they decided to withhold their reports of memory for item–context associations, whereas the anterior PFC was uniquely engaged when participants withheld an answer. The increased engagement of these regions during retrieval in the A-B’ condition is consistent with the idea that this condition poses greater demands on retrospective metacognitive monitoring and control due to a greater tendency to claim recollection despite less mnemonic evidence.

Furthermore, according to metacognitive frameworks, decisions and actions based on memory result from a metacognitive appraisal process (Koriat and Goldsmith, 1996). Thus, the activation profiles of these brain regions, which varied both as function of experimental condition and as function of subjective judgment, are particularly well suited to carry over information about retrieval output from the memory to the decision phase. Consistent with the idea that retrospective metacognitive monitoring processes mediate the effects of subjective recollection on subsequent decision making, the ROIs in the bilateral anterior insula, dACC, and dlPFC found during the memory phase showed enhanced activation when participants decided to discard a response, especially in the A-B’ condition. Thus, it is clear that these regions do not respond to simple variations in memory strength, be it objective or subjective. If this were the case, we would have expected increased activation for remember responses during the memory phase and increased activation of selected memories during the decision phase. Instead, during the memory phase, these regions tracked whether memories were claimed to be recollected, given the relevance of subjective recollection for the upcoming decision. During decisions, however, they were engaged more in eliminating the memories that should not be submitted to count toward participants' final score, the socially motivated reward used in the present study. These results were further supported by the direct comparison of select and discard responses during decision making, which confirmed that lateral PFC is involved in decisions to discard a memory.

It should be noted that while participants were instructed to select about half of their responses, they placed more than 50% of their answers into the treasure chest, possibly leading to greater demands on monitoring and control to overcome this tendency and to discard a response. Together, these results suggest that when subjective recollection is salient and important for current goals, activation in the anterior insula will signal this during memory retrieval and, together with dACC and lateral PFC, will recapitulate this signal when the output of memory retrieval is evaluated as the basis for decision making.

This study is limited in that it does not include a direct manipulation of decision demands or relevance of specific task features for decision making. Recent research has underscored contributions of mechanisms signaling decision value (e.g., Vaidya and Badre, 2020) or supporting adaptive changes in decision criteria as the task progresses (e.g., Scimeca et al., 2016), but these factors are not accounted for in the present research. Future studies manipulating incentive structures are needed to better understand the modulatory effects of metacognitive processes for decision making based on memory. We additionally note that the order of the memory and decision phases was not counterbalanced in the present experiment to preserve a naturalistic course of events, such that people render memory responses first and then make decisions about them. However, the manipulation of the order in future research would elucidate the potential effect of committing to a memory decision on subjective evaluations given demonstrated effects of choice on subsequent retention (e.g., Murty et al., 2019). It should also be noted that decision making in the present study was supported via a social motivation, namely the motivation to do well and/or avoid embarrassment due to poor performance. More research is needed to extend the effects of subjective recollection to decision making with financial incentives or other forms of decisions. However, the literature suggests that social and monetary rewards recruit similar neural circuits (Ruff and Fehr, 2014). Moreover, as the present study focused on retrospective metacognitive judgments, future research should also elucidate the degree to which prospective metacognitive judgments, such as judgments of learning, play a similar role for future decision making.

Finally, we found comparable retrieval success effects in the hippocampus between the A-A’ and the A-B’ conditions. These results are consistent with a previous study by Richter et al., 2016 who found that hippocampus activation distinguished between successful vs. unsuccessful retrieval but did not vary with memory precision or subjective vividness. These results tentatively suggest that while hippocampus plays a key role for episodic memory, it is not the main driver of the subjective experience of recollection and is less dependent on the specific context in which retrieval takes place.

Taken together, the present study investigated the neural basis of subjective and objective recollection using an experimental paradigm to dissociate these naturally intertwined aspects of episodic memory. Our results highlight the critical role of distinct PPC regions in supporting memory accuracy via the identification of diagnostic feature information associated with the SPL. In contrast, activation in the retrosplenia cortex and the precuneus was associated with subjective recollection when participants retrieved item-relevant information and they did not have to compare opposing mnemonic signals regarding their diagnostic value. In line with the nature of subjective recollection judgments as metacognitive assessments that guide upcoming decision making, insular, cingulate, and lateral frontal regions signal saliency of the retrieved information at retrieval. This will then be carried over to later decision stages where these task-relevant signals are recapitulated and evaluated in the context of concurrent goals. These results provide insights into the neural mechanisms supporting our phenomenological experience of remembering and its assessment in context of our goals. Our findings also open up vistas toward a better understanding of how these mechanisms develop across the life span.

Data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 2, 3 and 4.

1. 1. Addis DR
   2. Roberts RP
   3. Schacter DL

   (2011) Age-related neural changes in autobiographical remembering and imagining

   Neuropsychologia 49:3656–3669.

   https://doi.org/10.1016/j.neuropsychologia.2011.09.021

   • PubMed
   • Google Scholar
2. 1. Allen M
   2. Poggiali D
   3. Whitaker K
   4. Marshall TR
   5. Kievit RA

   (2019) Raincloud plots: a multi-platform tool for robust data visualization

   Wellcome Open Research 4:63.

   https://doi.org/10.12688/wellcomeopenres.15191.1

   • PubMed
   • Google Scholar
3. 1. Bang D
   2. Fleming SM

   (2018) Distinct encoding of decision confidence in human medial prefrontal cortex

   PNAS 115:6082–6087.

   https://doi.org/10.1073/pnas.1800795115

   • PubMed
   • Google Scholar
4. 1. Bastin C
   2. Besson G
   3. Simon J
   4. Delhaye E
   5. Geurten M
   6. Willems S
   7. Salmon E

   (2019) An integrative memory model of recollection and familiarity to understand memory deficits

   Behavioral and Brain Sciences 42:e281.

   https://doi.org/10.1017/S0140525X19000621

   • Google Scholar
5. Software

   1. Bates D
   2. Maechler M
   3. Bolker B
   4. Walker S

   (2014)

   Lme4: Linear Mixed-Effects Models Using Eigen and S4

   Lme4: Linear Mixed-Effects Models Using Eigen and S4.
6. 1. Cabeza R
   2. Ciaramelli E
   3. Olson IR
   4. Moscovitch M

   (2008) The parietal cortex and episodic memory: an attentional account

   Nature Reviews Neuroscience 9:613–625.

   https://doi.org/10.1038/nrn2459

   • PubMed
   • Google Scholar
7. 1. Cavanna AE
   2. Trimble MR

   (2006) The precuneus: a review of its functional anatomy and behavioural correlates

   Brain 129:564–583.

   https://doi.org/10.1093/brain/awl004

   • PubMed
   • Google Scholar
8. 1. Chen HY
   2. Gilmore AW
   3. Nelson SM
   4. McDermott KB

   (2017) Are there multiple kinds of episodic memory? an fMRI investigation comparing autobiographical and recognition memory tasks

   The Journal of Neuroscience 37:2764–2775.

   https://doi.org/10.1523/JNEUROSCI.1534-16.2017

   • PubMed
   • Google Scholar
9. 1. Chua EF
   2. Schacter DL
   3. Rand-Giovannetti E
   4. Sperling RA

   (2006) Understanding metamemory: neural correlates of the cognitive process and subjective level of confidence in recognition memory

   NeuroImage 29:1150–1160.

   https://doi.org/10.1016/j.neuroimage.2005.09.058

   • PubMed
   • Google Scholar
10. 1. Corbetta M
    2. Shulman GL

    (2002) Control of goal-directed and stimulus-driven attention in the brain

    Nature Reviews Neuroscience 3:201–215.

    https://doi.org/10.1038/nrn755

    • PubMed
    • Google Scholar
11. 1. Craig AD

    (2009) How do you feel--now? the anterior insula and human awareness

    Nature Reviews Neuroscience 10:59–70.

    https://doi.org/10.1038/nrn2555

    • PubMed
    • Google Scholar
12. 1. Daselaar SM
    2. Fleck MS
    3. Cabeza R

    (2006) Triple dissociation in the medial temporal lobes: recollection, familiarity, and novelty

    Journal of Neurophysiology 96:1902–1911.

    https://doi.org/10.1152/jn.01029.2005

    • PubMed
    • Google Scholar
13. 1. Davidson PS
    2. Anaki D
    3. Ciaramelli E
    4. Cohn M
    5. Kim AS
    6. Murphy KJ
    7. Troyer AK
    8. Moscovitch M
    9. Levine B

    (2008) Does lateral parietal cortex support episodic memory? evidence from focal lesion patients

    Neuropsychologia 46:.

    https://doi.org/10.1016/j.neuropsychologia.2008.01.011

    • PubMed
    • Google Scholar
14. 1. Dobbins IG
    2. Kroll NE
    3. Liu Q

    (1998) Confidence-accuracy inversions in scene recognition: a remember-know analysis

    Journal of Experimental Psychology: Learning, Memory, and Cognition 24:1306–1315.

    https://doi.org/10.1037/0278-7393.24.5.1306

    • PubMed
    • Google Scholar
15. 1. Dobbins IG
    2. Foley H
    3. Schacter DL
    4. Wagner AD

    (2002) Executive control during episodic retrieval

    Neuron 35:989–996.

    https://doi.org/10.1016/S0896-6273(02)00858-9

    • Google Scholar
16. 1. Duarte A
    2. Henson RN
    3. Graham KS

    (2008) The effects of aging on the neural correlates of subjective and objective recollection

    Cerebral Cortex 18:2169–2180.

    https://doi.org/10.1093/cercor/bhm243

    • PubMed
    • Google Scholar
17. 1. Fandakova Y
    2. Lindenberger U
    3. Shing YL

    (2014) Deficits in process-specific prefrontal and hippocampal activations contribute to adult age differences in episodic memory interference

    Cerebral Cortex 24:1832–1844.

    https://doi.org/10.1093/cercor/bht034

    • PubMed
    • Google Scholar
18. 1. Fandakova Y
    2. Bunge SA
    3. Wendelken C
    4. Desautels P
    5. Hunter L
    6. Lee JK
    7. Ghetti S

    (2018) The importance of knowing when you Don't remember: neural signaling of retrieval failure predicts memory improvement over time

    Cerebral Cortex 28:90–102.

    https://doi.org/10.1093/cercor/bhw352

    • PubMed
    • Google Scholar
19. 1. Favila SE
    2. Samide R
    3. Sweigart SC
    4. Kuhl BA

    (2018) Parietal representations of stimulus features are amplified during memory retrieval and flexibly aligned with Top-Down goals

    The Journal of Neuroscience 38:7809–7821.

    https://doi.org/10.1523/JNEUROSCI.0564-18.2018

    • PubMed
    • Google Scholar
20. 1. Fleck MS
    2. Daselaar SM
    3. Dobbins IG
    4. Cabeza R

    (2006) Role of prefrontal and anterior cingulate regions in decision-making processes shared by memory and nonmemory tasks

    Cerebral Cortex 16:1623–1630.

    https://doi.org/10.1093/cercor/bhj097

    • PubMed
    • Google Scholar
21. 1. Folville A
    2. Bahri MA
    3. Delhaye E
    4. Salmon E
    5. D'Argembeau A
    6. Bastin C

    (2020) Age-related differences in the neural correlates of vivid remembering

    NeuroImage 206:116336.

    https://doi.org/10.1016/j.neuroimage.2019.116336

    • PubMed
    • Google Scholar
22. 1. Frithsen A
    2. Miller MB

    (2014) The posterior parietal cortex: comparing remember/know and source memory tests of recollection and familiarity

    Neuropsychologia 61:31–44.

    https://doi.org/10.1016/j.neuropsychologia.2014.06.011

    • PubMed
    • Google Scholar
23. 1. Gilmore AW
    2. Nelson SM
    3. McDermott KB

    (2015) A parietal memory network revealed by multiple MRI methods

    Trends in Cognitive Sciences 19:534–543.

    https://doi.org/10.1016/j.tics.2015.07.004

    • Google Scholar
24. 1. Greve DN
    2. Fischl B

    (2009) Accurate and robust brain image alignment using boundary-based registration

    NeuroImage 48:63–72.

    https://doi.org/10.1016/j.neuroimage.2009.06.060

    • PubMed
    • Google Scholar
25. 1. Harlow IM
    2. Yonelinas AP

    (2016) Distinguishing between the success and precision of recollection

    Memory 24:114–127.

    https://doi.org/10.1080/09658211.2014.988162

    • PubMed
    • Google Scholar
26. 1. Heathcote A
    2. Freeman E
    3. Etherington J
    4. Tonkin J
    5. Bora B

    (2009) A dissociation between similarity effects in episodic face recognition

    Psychonomic Bulletin & Review 16:824–831.

    https://doi.org/10.3758/PBR.16.5.824

    • PubMed
    • Google Scholar
27. 1. Hembacher E
    2. Ghetti S

    (2017) Subjective experience guides betting decisions beyond accuracy: evidence from a metamemory illusion

    Memory 25:575–585.

    https://doi.org/10.1080/09658211.2016.1197946

    • PubMed
    • Google Scholar
28. 1. Hower KH
    2. Wixted J
    3. Berryhill ME
    4. Olson IR

    (2014) Impaired perception of mnemonic oldness, but not mnemonic newness, after parietal lobe damage

    Neuropsychologia 56:409–417.

    https://doi.org/10.1016/j.neuropsychologia.2014.02.014

    • PubMed
    • Google Scholar
29. 1. Huron C
    2. Danion JM
    3. Giacomoni F
    4. Grangé D
    5. Robert P
    6. Rizzo L

    (1995) Impairment of recognition memory with, but not without, conscious recollection in schizophrenia

    The American Journal of Psychiatry 152:1737–1742.

    https://doi.org/10.1176/ajp.152.12.1737

    • PubMed
    • Google Scholar
30. 1. Jenkinson M
    2. Smith S

    (2001) A global optimisation method for robust affine registration of brain images

    Medical Image Analysis 5:143–156.

    https://doi.org/10.1016/S1361-8415(01)00036-6

    • PubMed
    • Google Scholar
31. 1. Kim H

    (2010) Dissociating the roles of the default-mode, dorsal, and ventral networks in episodic memory retrieval

    NeuroImage 50:1648–1657.

    https://doi.org/10.1016/j.neuroimage.2010.01.051

    • PubMed
    • Google Scholar
32. 1. Kim H
    2. Cabeza R

    (2009) Common and specific brain regions in high- versus low-confidence recognition memory

    Brain Research 1282:103–113.

    https://doi.org/10.1016/j.brainres.2009.05.080

    • PubMed
    • Google Scholar
33. 1. Koriat A
    2. Goldsmith M

    (1996) Monitoring and control processes in the strategic regulation of memory accuracy

    Psychological Review 103:490–517.

    https://doi.org/10.1037/0033-295X.103.3.490

    • PubMed
    • Google Scholar
34. 1. Kuhl BA
    2. Chun MM

    (2014) Successful remembering elicits event-specific activity patterns in lateral parietal cortex

    Journal of Neuroscience 34:8051–8060.

    https://doi.org/10.1523/JNEUROSCI.4328-13.2014

    • PubMed
    • Google Scholar
35. 1. Kuznetsova A
    2. Brockhoff PB
    3. Christensen RHB

    (2017) lmerTest Package: Tests in Linear Mixed Effects Models

    Journal of Statistical Software 82:i13.

    https://doi.org/10.18637/jss.v082.i13

    • Google Scholar
36. Software

    1. Lawrence MA

    (2016) Ez: Easy Analysis and Visualization of Factorial Experiments

    Ez: Easy Analysis and Visualization of Factorial Experiments.

    https://CRAN.R-project.org/package=ez
37. 1. Mark RE
    2. Rugg MD

    (1998) Age effects on brain activity associated with episodic memory retrieval. an electrophysiological study

    Brain 121 ( Pt 5:861–873.

    https://doi.org/10.1093/brain/121.5.861

    • PubMed
    • Google Scholar
38. 1. Mazzoni G
    2. Clark A
    3. De Bartolo A
    4. Guerrini C
    5. Nahouli Z
    6. Duzzi D
    7. De Marco M
    8. McGeown W
    9. Venneri A

    (2019) Brain activation in highly superior autobiographical memory: the role of the precuneus in the autobiographical memory retrieval network

    Cortex 120:588–602.

    https://doi.org/10.1016/j.cortex.2019.02.020

    • PubMed
    • Google Scholar
39. 1. McDermott KB
    2. Szpunar KK
    3. Christ SE

    (2009) Laboratory-based and autobiographical retrieval tasks differ substantially in their neural substrates

    Neuropsychologia 47:2290–2298.

    https://doi.org/10.1016/j.neuropsychologia.2008.12.025

    • PubMed
    • Google Scholar
40. 1. McDermott KB
    2. Gilmore AW
    3. Nelson SM
    4. Watson JM
    5. Ojemann JG

    (2017) The parietal memory network activates similarly for true and associative false recognition elicited via the DRM procedure

    Cortex 87:96–107.

    https://doi.org/10.1016/j.cortex.2016.09.008

    • PubMed
    • Google Scholar
41. 1. Mitchell KJ
    2. Johnson MK

    (2009) Source monitoring 15 years later: what have we learned from fMRI about the neural mechanisms of source memory?

    Psychological Bulletin 135:638–677.

    https://doi.org/10.1037/a0015849

    • PubMed
    • Google Scholar
42. 1. Morales J
    2. Lau H
    3. Fleming SM

    (2018) Domain-General and Domain-Specific patterns of activity supporting metacognition in human prefrontal cortex

    The Journal of Neuroscience 38:3534–3546.

    https://doi.org/10.1523/JNEUROSCI.2360-17.2018

    • PubMed
    • Google Scholar
43. 1. Moscovitch M
    2. Cabeza R
    3. Winocur G
    4. Nadel L

    (2016) Episodic memory and beyond: the Hippocampus and neocortex in transformation

    Annual Review of Psychology 67:105–134.

    https://doi.org/10.1146/annurev-psych-113011-143733

    • PubMed
    • Google Scholar
44. 1. Murty VP
    2. DuBrow S
    3. Davachi L

    (2019) Decision-making increases episodic memory via postencoding consolidation

    Journal of Cognitive Neuroscience 31:1308–1317.

    https://doi.org/10.1162/jocn_a_01321

    • PubMed
    • Google Scholar
45. Software

    1. R Development Core Team

    (2020) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org/
46. 1. Ramanan S
    2. Piguet O
    3. Irish M

    (2018) Rethinking the role of the angular gyrus in remembering the past and imagining the future: the contextual integration model

    The Neuroscientist 24:342–352.

    https://doi.org/10.1177/1073858417735514

    • PubMed
    • Google Scholar
47. 1. Ratcliff R

    (1978) A theory of memory retrieval

    Psychological Review 85:59–108.

    https://doi.org/10.1037/0033-295X.85.2.59

    • Google Scholar
48. 1. Richter FR
    2. Cooper RA
    3. Bays PM
    4. Simons JS

    (2016) Distinct neural mechanisms underlie the success, precision, and vividness of episodic memory

    eLife 5:e18260.

    https://doi.org/10.7554/eLife.18260

    • PubMed
    • Google Scholar
49. 1. Ruff CC
    2. Fehr E

    (2014) The neurobiology of rewards and values in social decision making

    Nature Reviews Neuroscience 15:549–562.

    https://doi.org/10.1038/nrn3776

    • PubMed
    • Google Scholar
50. 1. Rugg MD
    2. Vilberg KL

    (2013) Brain networks underlying episodic memory retrieval

    Current Opinion in Neurobiology 23:255–260.

    https://doi.org/10.1016/j.conb.2012.11.005

    • PubMed
    • Google Scholar
51. 1. Scimeca JM
    2. Katzman PL
    3. Badre D

    (2016) Striatal prediction errors support dynamic control of declarative memory decisions

    Nature Communications 7:13061.

    https://doi.org/10.1038/ncomms13061

    • PubMed
    • Google Scholar
52. 1. Sestieri C
    2. Shulman GL
    3. Corbetta M

    (2017) The contribution of the human posterior parietal cortex to episodic memory

    Nature Reviews Neuroscience 18:183–192.

    https://doi.org/10.1038/nrn.2017.6

    • PubMed
    • Google Scholar
53. 1. Simons JS
    2. Peers PV
    3. Mazuz YS
    4. Berryhill ME
    5. Olson IR

    (2010) Dissociation between memory accuracy and memory confidence following bilateral parietal lesions

    Cerebral Cortex 20:479–485.

    https://doi.org/10.1093/cercor/bhp116

    • PubMed
    • Google Scholar
54. 1. Spaniol J
    2. Davidson PS
    3. Kim AS
    4. Han H
    5. Moscovitch M
    6. Grady CL

    (2009) Event-related fMRI studies of episodic encoding and retrieval: meta-analyses using activation likelihood estimation

    Neuropsychologia 47:1765–1779.

    https://doi.org/10.1016/j.neuropsychologia.2009.02.028

    • PubMed
    • Google Scholar
55. Software

    1. Team R Studio

    (2020) RStudio: Integrated Development for R. RStudio, PBC

    RStudio: Integrated Development for R. RStudio, PBC.

    http://www.rstudio.com/
56. 1. Tulving E

    (1981) Similarity relations in recognition

    Journal of Verbal Learning and Verbal Behavior 20:479–496.

    https://doi.org/10.1016/S0022-5371(81)90129-8

    • Google Scholar
57. 1. Tulving E

    (1985) Memory and consciousness

    Canadian Psychology/Psychologie Canadienne 26:1–12.

    https://doi.org/10.1037/h0080017

    • Google Scholar
58. 1. Vaccaro AG
    2. Fleming SM

    (2018) Thinking about thinking: a coordinate-based meta-analysis of neuroimaging studies of metacognitive judgements

    Brain and Neuroscience Advances 2:239821281881059.

    https://doi.org/10.1177/2398212818810591

    • PubMed
    • Google Scholar
59. 1. Vaidya AR
    2. Badre D

    (2020) Neural systems for Memory-based value judgment and Decision-making

    Journal of Cognitive Neuroscience 32:1896–1923.

    https://doi.org/10.1162/jocn_a_01595

    • PubMed
    • Google Scholar
60. 1. Vilberg KL
    2. Rugg MD

    (2007) Dissociation of the neural correlates of recognition memory according to familiarity, recollection, and amount of recollected information

    Neuropsychologia 45:2216–2225.

    https://doi.org/10.1016/j.neuropsychologia.2007.02.027

    • Google Scholar
61. 1. Voss A
    2. Voss J
    3. Lerche V

    (2015) Assessing cognitive processes with diffusion model analyses: a tutorial based on fast-dm-30

    Frontiers in Psychology 6:336.

    https://doi.org/10.3389/fpsyg.2015.00336

    • PubMed
    • Google Scholar
62. 1. Wagner AD
    2. Shannon BJ
    3. Kahn I
    4. Buckner RL

    (2005) Parietal lobe contributions to episodic memory retrieval

    Trends in Cognitive Sciences 9:445–453.

    https://doi.org/10.1016/j.tics.2005.07.001

    • PubMed
    • Google Scholar
63. Book

    1. Wickham H

    (2016) Ggplot2: Elegant Graphics for Data Analysis

    Springer.

    https://doi.org/10.1007/978-0-387-98141-3

    • Google Scholar
64. 1. Woolrich MW
    2. Behrens TE
    3. Beckmann CF
    4. Jenkinson M
    5. Smith SM

    (2004) Multilevel linear modelling for FMRI group analysis using bayesian inference

    NeuroImage 21:1732–1747.

    https://doi.org/10.1016/j.neuroimage.2003.12.023

    • PubMed
    • Google Scholar
65. 1. Yassa MA
    2. Lacy JW
    3. Stark SM
    4. Albert MS
    5. Gallagher M
    6. Stark CE

    (2011) Pattern separation deficits associated with increased hippocampal CA3 and dentate gyrus activity in nondemented older adults

    Hippocampus 21:968–979.

    https://doi.org/10.1002/hipo.20808

    • PubMed
    • Google Scholar
66. 1. Yonelinas AP

    (2002) The nature of recollection and familiarity: a review of 30 years of research

    Journal of Memory and Language 46:441–517.

    https://doi.org/10.1006/jmla.2002.2864

    • Google Scholar

Author details

1. Yana Fandakova

   Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Writing - original draft, Writing - review and editing

   For correspondence

   fandakova@mpib-berlin.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3747-0359

2. Elliott G Johnson

   Human Development Graduate Group & Center for Mind and Brain, University of California at Davis, Davis, United States

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

3. Simona Ghetti

   Department of Psychology & Center for Mind and Brain, University of California at Davis, Davis, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   sghetti@ucdavis.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8282-0616

• 2,650

  views

• 282

  downloads

• 8

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.