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Distinct neural mechanisms underlie subjective and objective recollection and guide memory-based decision making

  1. Yana Fandakova  Is a corresponding author
  2. Elliott G Johnson
  3. Simona Ghetti  Is a corresponding author
  1. Center for Lifespan Psychology, Max Planck Institute for Human Development, Germany
  2. Human Development Graduate Group & Center for Mind and Brain, University of California at Davis, United States
  3. Department of Psychology & Center for Mind and Brain, University of California at Davis, United States
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Cite this article as: eLife 2021;10:e62520 doi: 10.7554/eLife.62520

Abstract

Accurate memories are often associated with vivid experiences of recollection. However, the neural mechanisms underlying subjective recollection and their unique role in decision making beyond accuracy have received limited attention. We dissociated subjective recollection from accuracy during a forced-choice task. Distractors corresponded either to non-studied exemplars of the targets (A-A’ condition) or to non-studied exemplars of different studied items (A-B’ condition). The A-A’ condition resulted in higher accuracy and greater activation in the superior parietal lobe, whereas the A-B’ condition resulted in higher subjective recollection and greater activation in the precuneus and retrosplenial regions, indicating a dissociation between objective and subjective memory. Activation in insular, cingulate, and lateral prefrontal regions was also associated with subjective recollection; however, during a subsequent decision phase, activation in these same regions was greater for discarded than for selected responses in anticipation of a social reward, underscoring their role in evaluating memory evidence flexibly based on current goals.

Introduction

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.

Experimental paradigm.

(A) During encoding, participants incidentally encoded pictures of objects while making indoor-outdoor judgments with a jittered interstimulus interval (500–6500 ms). (B) During retrieval, participants completed a two alternative forced choice (2AFC) recognition test in which a studied target was presented either along with a distractor that was perceptually similar to the target (i.e., A-A’ condition) or along with a distractor that was perceptually similar to another studied item (i.e., A-B’ condition). Participants chose between a remember (R), very familiar (VF), or somewhat familiar (SF) judgment with the hand corresponding to the position of the image they recognized as the target. Target and distractor positions were randomized across trials. After a jittered fixation period (500–6500 ms) following the memory phase, the two alternatives were presented again and participants were asked to select their memory to be counted toward their final score by pressing the button corresponding to the treasure chest or to discard the item by pressing the button corresponding to the trash can (i.e., decision phase). The position of the treasure chest and trash can buttons were randomized across trials.

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.

Results

Behavioral results

Dissociation between memory accuracy and subjective recollection

We examined differences in memory accuracy and subjective recollection as a function of experimental condition. First, we used mixed-effects logistic regressions to predict accuracy as a function of experimental condition. Consistent with previous studies, memory accuracy was higher in the A-A’ than in the A-B’ condition, estimated difference (Est.) = 0.38, SE = 0.05, p<0.0001 (Figure 2A). Next, we examined whether the probability for a remember judgment (as opposed to a familiar judgment) differed as a function of experimental condition, accuracy, and their interaction. As predicted, there was an effect of experimental condition, Est. = 1.35, SE = 0.11, p<0.0001, with a higher rate of remember responses in the A-B’ than in the A-A’ condition. The interaction between experimental condition and accuracy was also significant, Est. = −0.64, SE = 0.13, p<0.0001, suggesting that although remember judgments were overall more likely in the A-B’ condition, the difference as compared with the A-A’ condition was more pronounced for incorrect responses (Figure 2B). This interaction was found above and beyond the expected main effect of accuracy showing that remember responses were overall more likely for accurate than for inaccurate responses, Est. = 0.96, SE = 0.10, p<0.0001 (Figure 2B).

Figure 2 with 1 supplement see all
Behavioral results.

(A) Accuracy across experimental conditions. There were more correct responses in the A-A’ condition. (B) Proportion of remember judgments out of all correct and incorrect responses across experimental conditions. Participants more often claimed subjective recollection in the A-B’ condition both for accurate and inaccurate responses. (C) Proportion of memory responses that participants selected to count toward their final score split as a function of experimental condition, accuracy, and subjective judgment. For correct responses, there were no condition differences in selection rates for remember responses, but participants’ tendency to select familiar responses increased in the A-B’ condition. Error bars around condition means represent standard error of the mean. For plots of estimated logistic regression functions, see Figure 2—figure supplement 1.

Figure 2—source data 1

Accuracy across experimental conditions.

https://cdn.elifesciences.org/articles/62520/elife-62520-fig2-data1-v1.csv
Figure 2—source data 2

Proportion of remember responses by experimental condition and accuracy.

https://cdn.elifesciences.org/articles/62520/elife-62520-fig2-data2-v1.csv
Figure 2—source data 3

Proportion of select responses by experimental condition, accuracy, and subjective judgment.

https://cdn.elifesciences.org/articles/62520/elife-62520-fig2-data3-v1.csv

Together, these results indicate that memory accuracy and subjective recollection were behaviorally dissociated across experimental conditions: Accuracy was higher in the A-A’ condition, but subjective recollection was more frequent in the A-B’ condition.

Decision making aligns with subjective recollection

Next, we sought to confirm that subjective recollection drives the decision to select responses toward participants’ final score. Mixed-effect logistic regressions were used to predict the probability of selecting a memory as a function of experimental condition (A-A’ vs. A-B’), subjective rating (remember vs. familiar), memory accuracy, and their interactions. Participants were more likely to select an answer to count toward their final score in the A-B’ than in the A-A’ condition, Est. = 0.72, SE = 0.12, p<0.0001, above and beyond the greater tendency to select memories associated with remember relative to familiar judgments, Est. = 1.64, SE = 0.20, p<0.0001, and with accurate compared to inaccurate responses, Est. = 1.02, SE = 0.09, p<0.0001. Critically, we also observed a three-way interaction between condition, rating, and accuracy, Est. = −0.78, SE = 0.30, p=0.02 (Figure 2C).

For correct responses, selecting a response was more likely in the A-B’ than in the A-A’ condition, Est. = 0.45, SE = 0.08, p<0.0001, and more frequent for remember than for familiar judgments, Est. = 2.69, SE = 0.14, p<0.0001. There was also a significant condition by subjective judgment interaction, Est. = −0.64, SE = 0.18, p=0.0004, such that selection rates were similar for remember ratings between conditions, but participants extended their tendency to select A-B’ trials even to some familiar judgments.

For incorrect responses, selection rates were again higher in the A-B’ than in the A-A’ condition, Est. = 0.74, SE = 0.12, p<0.0001 and more frequent for remember than for familiar judgments, Est. = 1.56, SE = 0.20, p<0.0001; the interaction between subjective judgment and experimental condition was not significant, Est. = 0.08, SE = 0.24, p=0.73 (Figure 1C). Together, these results indicate that participants were more likely to select an answer toward their final score in the A-B’ than in the A-A’ condition, particularly in association with remember responses. For accurate responses, participants also showed a higher likelihood to select familiar responses in the A-B’ condition.

Faster evidence accumulation occurs in the A-A’ condition

If highly similar lure items in the A-A’ condition induce participants to engage in examination of specific diagnostic features, then the A-A’ condition should be associated with a higher rate of evidence accumulation compared to the A-B’ condition. We thus used drift diffusion models (Ratcliff, 1978) to compare drift rates and threshold separation between conditions. An ANOVA with condition (A-A’ vs. A-B’) as a within-subject factor revealed significant differences in drift rate, F(1,28) = 15.48, p=0.002, ηp2 = 0.36, with higher drift rate estimates in the A-A’ condition, M = 2.06, SD = 0.62, than in the A-B’ condition, M = 1.22, SD = 0.61. Threshold separation, reflecting how cautious participants were in their memory choice, did not differ between conditions, F(1,28) = 3.59, p=0.07, ηp2 = 0.11 (A-A’: M = 2.42, SD = 0.25; A-B’: M = 2.51, SD = 0.21). Together, evidence accumulation followed the pattern found for accuracy differences between conditions, such that participants showed faster evidence accumulation in the A-A’ than in the A-B’ condition, consistent with the idea that the A-A’ condition promotes specific retrieval of more diagnostic features.

Neuroimaging results: memory phase

Activations in parietal regions dissociate subjective and objective recollection

We performed whole-brain analyses to examine differences between the A-A’ and A-B’ conditions during the memory phase. Here, we were particularly interested in PPC regions, in which we expected a dissociation aligned to condition with stronger engagement of dorsal PPC in the A-A’ condition, but stronger engagement of medial/ventral parietal regions in the A-B’ condition. Consistent with our expectations, the contrast A-A’ > A-B’ revealed greater activation in a cluster encompassing the right superior parietal lobe (SPL) and the supramarginal gyrus (Figure 3A). Instead, the contrast A-B’ > A-A’ revealed greater activation in the bilateral retrosplenial cortex, PCC, and the precuneus (Figure 3A).

Figure 3 with 1 supplement see all
Neuroimaging results for the memory phase.

(A) Results of a whole-brain comparison between experimental conditions. (B) Differences between remember and familiar judgments across conditions in parietal areas identified in A. (C) Differences between remember and familiar judgments across conditions for regions identified in A that have been implicated in metacognitive monitoring and appraisal. Error bars around condition means represent standard error of the mean. R SPL = right superior parietal lobe; PCC = posterior cingulate cortex, dACC = dorsal anterior cingulate; R DLPFC = right dorsolateral prefrontal cortex.

Figure 3—source data 1

Signal change estimates plotted in Figure 3 by region of interest (ROI) and contrast.

https://cdn.elifesciences.org/articles/62520/elife-62520-fig3-data1-v1.csv

Given that behavioral rates of subjective recollection differed between experimental conditions, we next sought to evaluate whether activation in these parietal clusters varied as a function of subjective assessments, namely remember vs. familiar judgments. To this end, we conducted a mixed ANOVA on the parameter estimates with condition (A-A’ vs. A-B’) and subjective judgment (remember vs. familiar) as within-subject factors. Given that the regions of interest (ROIs) were identified as showing a main effect of condition, we focused only on the main effect of subjective judgment and the interaction between condition and subjective judgment here. In the SPL, which showed higher activation in the A-A’ condition, there were neither differences between remember and familiar judgments, F(1,28) = 0.03, p=0.86, ηp2 = 0.001, nor an interaction between subjective judgment and experimental condition, F(1,28) = 0.60, p=0.45, ηp2 = 0.021 (Figure 3B). In contrast, in the PCC/precuneus cluster, which showed higher activation in the A-B’ condition, activation was enhanced for remember relative to familiar judgments across both experimental conditions, F(1,28) = 30.54, p<0.001, padj < 0.001, ηp2 = 0.52 (Figure 3B). We observed no significant interaction between subjective judgment and experimental condition in this region, F(1,28) = 0.02, p=0.90, padj = 0.90, ηp2 = 0.001. Additional analyses comparing remember to familiar judgments in each experimental condition demonstrated that the left angular gyrus showed greater activation for remember than for familiar judgments across both experimental conditions (see Figure 3—figure supplement 1).

Taken together, there was a clear dissociation between experimental conditions in the parietal cortex. The SPL showed greater engagement in the A-A’ condition, presumably reflecting the increased scrutiny promoted by this condition. In contrast, the PCC/precuneus was more strongly engaged in the A-B’ condition, and consistent with behavioral trends, showed greater activation for subjective recollection.

Frontal and cingulo-opercular regions track dimensions that are relevant for decision making

The contrast between A-B’ and A-A’ also revealed increased activation in the bilateral anterior insula, the dACC, and the right dorsolateral PFC (dlPFC) (Figure 3A), regions that have been implicated in retrospective metacognitive assessments. To examine the role of these regions in subjective judgments, we again conducted a mixed ANOVA on the parameter estimates with condition (A-A’ vs. A-B’) and subjective judgment (remember vs. familiar) as within-subject factors. The bilateral anterior insula showed enhanced activation for remember relative to familiar judgments across conditions, F(1,28) = 73.43, p<0.001, ηp2 = 0.72. This main effect was accompanied by an interaction with experimental condition, F(1,28) = 6.19, p=0.02, padj = 0.057, ηp2 = 0.18 (Figure 3C). Post hoc tests indicated that although anterior insula activation was similar across conditions for remember judgments, t(28) = 0.12, p=0.91, it was enhanced in the A-B’ condition for familiar judgments, t(28) = 3.18, p=0.004. In addition, the cluster encompassing the dACC and dlPFC showed more pronounced engagement in remember than in familiar judgments across both experimental conditions, F(1,28) = 29.49, p<0.001, padj <0.001, ηp2 = 0.51, but no interaction with condition, F(1,28) = 0.900, p=0.35, padj = 0.53, ηp2 = 0.03 (Figure 3C).

Taken together, these results suggest that anterior insular, cingulate, and dlPFC regions track subjective recollection, in line with their role in metacognitive assessments, and in line with the tendency to select memories to submit for performance ranking among remembered answers. At the same time, these regions were enhanced in the A-B’ condition suggesting that these areas may be ideally suited to carry over metacognitive information about subjective assessments associated with different contexts to the decision phase.

Hippocampal activations respond to memory accuracy

We tested whether activation in an anatomical mask of the bilateral hippocampus varied by experimental condition (A-A’ vs. A-B’) and accuracy (correct vs. incorrect). The results revealed a main effect of accuracy, F(1,28) = 6.39, p=0.02, ηp2 = 0.19, with enhanced activation for correct responses. There was also a main effect of condition, F(1,28) = 9.17, p=0.01, ηp2 = 0.25 with enhanced activation in the A-B’ condition. We found no evidence for an accuracy-by-experimental condition interaction, F(1,28) = 0.04, p=0.85, ηp2 = 0.001, suggesting similar retrieval success effects across experimental conditions in the hippocampus.

Neuroimaging results: decision phase

Frontal and cingulo-opercular regions track betting decisions

To assess whether patterns of activation during memory retrieval persisted during decision making, we took several complementary approaches. First, we examined decision-related activation in the parietal regions that differentiated between conditions during the preceding memory phase. Thus, we performed mixed ANOVAs with condition (A-A’ vs. A-B’), decision (select vs. discard), and their interaction. In the SPL, which showed enhanced activation in the A-A’ condition during the memory phase, we again observed enhanced activation for the A-A’ compared to the A-B’ condition during the decision phase, F(1,28) = 9.29, p=0.01, ηp2 = 0.25. SPL activation was not modulated by the decision to select or discard the memory, F(1,28) = 0.99, p=0.33, ηp2 = 0.03, and the interaction between condition and betting decision was not significant, F(1,28) = 0.30, p=0.59, ηp2 = 0.01 (Figure 4A). In the PCC/precuneus cluster, which showed enhanced activation in the A-B’ condition during the memory phase, we observed a trend for a main effect of condition with greater engagement in the A-A’ condition, F(1,28) = 5.190, p=0.031, padj = 0.09, ηp2 = 0.156, no significant main effect of decision to select or discard a response, F(1,28) = 1.62, p=0.21, padj = 0.21, ηp2 = 0.06, and no decision by experimental condition interaction, F(1,28) = 0.011, p=0.92, padj = 0.92, ηp2 = 0.00 (Figure 4A). Thus, these analyses did not reveal any decision-based modulation in the parietal regions identified during the memory phase.

Neuroimaging results for the decision phase.

(A) Differences between select and discard decisions across conditions in the PPC areas identified during the memory phase. (B) Differences between select and discard decisions across conditions for the regions identified during the memory phase that have been implicated in metacognitive monitoring and appraisal. (C) Results of a whole-brain comparison comparing select and discard decisions. Error bars around condition means represent standard error of the mean. R SPL = right superior parietal lobe; PCC = posterior cingulate cortex, dACC = dorsal anterior cingulate; R DLPFC = right dorsolateral prefrontal cortex.

Figure 4—source data 1

Signal change estimates plotted in Figure 4 by region of interest (ROI) and contrast.

https://cdn.elifesciences.org/articles/62520/elife-62520-fig4-data1-v1.csv

Second, data analysis during the memory phase also revealed increased activation in the bilateral anterior insula and a cluster including dACC and dlPFC regions, which are consistently implicated in monitoring and control of memory retrieval. These regions showed enhanced activation in the A-B’ condition relative to the A-A’ condition with varying engagement as a function of subjective assessment. They are therefore excellent candidates to carry over metacognitive information about subjective assessments from the memory to the decision phase. Thus, we expected activation in these regions to be modulated by decisions to select or discard responses. In line with this expectation, in the bilateral anterior insula, there was a main effect of decision, F(1,28) = 4.81, p=0.04, padj = 0.06, ηp2 = 0.15, along with a decision-by-confidence interaction, F(1,28) = 5.88, p=0.02, padj = 0.03, ηp2 = 0.17 (Figure 4B). There was no main effect of experimental condition, F(1,28) = 0.02, p=0.90, padj = 0.90, ηp2 = 0.001. A similar pattern emerged in the cluster encompassing right dACC and dlPFC, with a main effect of decision to select, F(1,28) = 8.65, p=0.01, padj = 0.03, ηp2 = 0.236, as well as an experimental condition by decision interaction, F(1,28) = 6.09, p=0.02, padj = 0.03, ηp2 = 0.18 (Figure 4B). Again, there was no main effect of experimental condition, F(1,28) = 0.07, p=0.79, padj = 0.90, ηp2 = 0.003. Across all areas, activation was enhanced for decisions to discard a response, especially in the A-B’ condition (Figure 4B). Thus, although these regions were identified through our experimental manipulation in the memory phase, the effect of the experimental manipulation was no longer dominant during the decision phase, whereas the effect of decision to select or discard emerged.

Finally, we sought to identify regions that were sensitive to memory decisions, by comparing activation for decisions to select or discard a memory using whole-brain analysis. In analogy to the ROI results above, frontal regions showed enhanced activation for the decision to discard a memory, including bilateral middle frontal gyrus extending into the bilateral frontal pole, and the right angular gyrus (Figure 4C). In contrast, the decision to select a response was associated with increased activation in ventral temporal areas and posterior hippocampus (Figure 4C).

Taken together, parietal regions that dissociated between subjective and objective recollection as a function of our experimental manipulation during the memory phase demonstrated overall condition differences during decision making. In contrast, the anterior insula, dACC, and dlPFC showed distinct activation profiles during the memory phase and the decision phase, such that their activation was enhanced when participants discarded a response, especially in the A-B’ condition.

Discussion

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.

Materials and methods

Participants

Thirty healthy right-handed volunteers (18–25 years, M = 21.0, SD = 1.7, 15 females) with no history of or current neurological or psychiatric illness participated in the study after signing informed consent approved by the Institutional Review Board of the University of California, Davis (protocol #217322). One additional participant took part in the experiment but was excluded from further analyses due to an incidental finding. The sample size was determined based on the moderate effect sizes for the dissociation between objective and subjective memory measures observed in each of the four behavioral experiments included in Hembacher and Ghetti, 2017 and corresponds to the largest sample employed in those behavioral experiments. One participant failed to comply with instructions, resulting in a final sample of 29 participants included in the present analyses.

Stimuli

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Stimuli included pairs of 342 color images of familiar everyday objects. These stimuli depicted very similar but distinct versions of the same object selected from Yassa et al., 2011 and from the nternet.

Experimental design

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During the encoding task, participants were presented with 342 unique items across three runs (114 items per run). The selection of the individual item from each pair for encoding, allocation of the item to a specific block, and the order of block presentation were randomized across participants. Participants viewed each object for 1500 ms while performing an indoor–outdoor judgment. The placement of the response options (left and right index fingers) was randomized across participants. A jittered fixation cross (500–6500 ms) was presented after every image. Encoding runs were performed in the scanner but were not analyzed for the present research questions.

The retrieval task included a total of 228 2AFC recognition trials distributed across three runs with 76 trials per run. In the 2AFC recognition task, two images, a target and a lure, were presented to the left and right of the center of the screen for 3000 ms. The placement of the target was randomized across trials so that it was equally likely to occur on the left or the right for each participant. In each run, there were a total of 38 A-A’ trials in which the lure depicted a different version of the same object (Figure 1B) and 38 A-B’ trials in which the lure depicted a different version of a studied object that was not concurrently presented on the screen (and was not used as a target in other trials). Thus, all lures resembled studied items, but the target was sometimes presented beside a perceptually similar and familiar lure in the A-A’ condition, and it was sometimes presented next to a perceptually dissimilar but familiar lure in the A-B’ condition. The studied versions of the stimuli used as lures in the A-B’ condition were not included as targets, resulting in two-thirds of all studied items, or 228 trials, that were tested as targets. Targets and lure assignments to conditions were randomized across participants.

The retrieval task included a memory phase and a decision phase (Figure 1B). During the memory phase, participants were instructed to select the target image by using their left hand if they thought it was the image on the left and their right hand if they thought it was the image on the right. On each side, they could select ‘remember’ (R) if they vividly recollected the image from the encoding phase with specific details (index finger), ‘very familiar’ (VF) if they knew they had previously seen the item but could not recall specific details about its presentation (middle finger), or ‘somewhat familiar’ (SF) if the image was familiar but to a lesser degree (ring finger). Participants were asked to use all response options (see Appendix for exact instructions to participants). After the retrieval phase, a jittered period of 500–6500 ms (in 2000 ms steps) preceded the decision phase. For longer jitters of 2500, 4500, and 6500 ms duration (ca. 50% of all trials), a number was presented on the screen every 2000 ms and participants had to press the corresponding number. This active baseline was included to prevent participants from actively thinking about their previously submitted memory response during jittering, while being undemanding enough to avoid forgetting of the preceding answer or interference with the subsequent decision phase.

During the decision phase, the two images appeared again at the same locations as during retrieval in the memory phase. Participants were asked to decide whether they wanted to select their response and count it toward their final score. A picture of a treasure chest and a trash can were placed on the left and right sides in the lower half of the screen. If they decided to select their memory, participants were asked to press the button corresponding to the treasure chest, and if they chose to discard their answer they were to press the button corresponding to the trash can (see Appendix for exact instructions to participants). The position of the treasure chest and the trash can on the screen was counterbalanced across participants. The duration of the decision phase was 1500 ms, followed by a jittered fixation period of 500–6500 ms. Participants used their pinkies to respond, thereby preventing any motor response overlap across all responses and phases of the experiment. Participants were instructed to select about 50% of the trials. To motivate participants, they were told that their final score would be ranked in comparison to all other students who had participated in the study, and their ranking would be displayed on the screen at the end of the task in the presence of the experimenter. We did not mention anything about subjective judgments or make any connections between participants’ decision to select a response and their subjective judgment during the memory phase. Thus, participants believed that their score was calculated based purely on the objective memory accuracy of the items selected to be placed in the treasure chest. At the end of the task, participants were debriefed that the ranking did not exist.

Behavioral analyses

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All analyses were conducted in R (R Development Core Team, 2020) using RStudio (Team R Studio, 2020). ANOVAs were performed using the ezANOVA function in the package ez (Lawrence, 2016). We used mixed-effects models to examine the effects of experimental condition on accuracy, subjective recollection, and decision making. Models were implemented using the lme4 package (Bates et al., 2014). Mixed-effects logistic regressions were fit to single-trial data, with intercepts varying by participant. P-values were derived using lmerTest via Satterthwaite’s degrees of freedom (DOF) method (Kuznetsova et al., 2017). Data were visualized using raincloud plots (Allen et al., 2019) implemented in the ggplot package (Wickham, 2016). To examine condition differences in evidence accumulation, we utilized drift diffusion models. The software fast-dm (Voss et al., 2015) was used to estimate the three parameters: drift rate (v), threshold separation (a), and non-decision time parameter (t0). The starting point bias was set to 0.5 (i.e., in the middle between the two thresholds); all other parameters were set to 0. We estimated v and a separately for each individual's A-A’ and A-B’ conditions, and t0 was estimated to be equal across conditions.

fMRI acquisition

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Images were acquired on Siemens magnetom Skyra 3T scanner (Siemens Medical AG, Erlangen, Germany) using a 32-channel head coil. Each block of the 2AFC recognition test was scanned using a gradient EPI sequence (TR = 1500 ms; TE = 24.2 ms; FOV = 216×216×138; voxel size = 3 mm isotropic). A high-resolution MPRAGE scan was obtained for co-registration of the functional images (TR = 2500 ms; TE = 3.23 ms; TI = 1100 ms; flip angle = 7 deg; FOV = 226×226×180; voxel size = 0.7 mm isotropic).

fMRI data processing

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Preprocessing was performed using FEAT in FSL 6.0.1 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki, Woolrich et al., 2004). The first four scans of each run were discarded to account for field inhomogeneities. Preprocessing included non-brain tissue removal, slice time and motion correction, and spatial smoothing using an 8 mm full-width half-maximum Gaussian filter. A prewhitening technique was used to account for the intrinsic temporal autocorrelation of BOLD imaging. Low-frequency artifacts were removed by applying a high-pass temporal filter (Gaussian-weighted straight-line fitting, sigma = 50 s). Registration to each participant’s structural image using a boundary-based registration algorithm (Greve and Fischl, 2009) and to the MNI template (12 DOF) was carried out using FLIRT (Jenkinson and Smith, 2001).

First, to examine differences associated with the A-A’ and the A-B’ conditions, individual time series were modeled separately for the memory and decision phases of each trial with regressors for each condition (GLM1). Second, to examine differences between conditions specifically associated with remember and familiar responses, we modeled individual time series with separate regressors for remember and familiar responses (collapsing across VF and SF responses) in each condition (i.e., A-A’ remember, A-A’ familiar, A-B’ remember, A-B’ familiar) and during each of the memory and decision phases (GLM2). Finally, to investigate the neural signatures associated with decision making based on states of subjective recollection, in GLM3 we modeled individual time series with separate regressors for choosing to select an answer or to discard it (A-A’ select, A-A’ discard, A-B’ select, A-B’ discard) during each of the memory and decision phases.

Regressors in each model were generated by convolving the impulse function related to the onsets of events of interest with a double-gamma hemodynamic response function and were modeled with the response time of each individual trial. Motion correction parameters estimated from the realignment procedure were entered as covariates of no interest in all GLMs. In each GLM, contrast images were computed for each run per subject, spatially normalized, transformed into MNI standard space and submitted to a within-subject fixed-effects analysis across runs. Higher-level analysis across subjects was carried out using a mixed-effects model in FSL (FLAME, Woolrich et al., 2004). Whole-brain images were thresholded at Z > 3.1, cluster-corrected at p<0.05. ROI analyses were corrected for multiple comparisons within a contrast using a false discovery rate correction (labeled padj).

Data availability

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.

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Decision letter

  1. Muireann Irish
    Reviewing Editor; University of Sydney, Australia
  2. Timothy E Behrens
    Senior Editor; University of Oxford, United Kingdom

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This is an interesting and timely study exploring the relationship between objective and subjective indices of recollection to provide novel insights into the mechanisms underlying memory-guided decision-making. Using an innovative experimental paradigm comprising a memory phase and decision phase, the authors provide an elegant behavioural dissociation between two conditions; A-A' condition in which the diagnostic features of stimuli are prioritised thus promoting higher levels of objective accuracy, versus the A-B' condition in which a global appraisal of the target stimulus instantiates a stronger sense of subjective recollection. Results suggest that participants' behaviour is derived from subjective global appraisal, rather than fine-grained consideration of objective features.

Decision letter after peer review:

Thank you for submitting your article "Distinct Neural Mechanisms Underlie Subjective and Objective Recollection and Guide Memory-based Decision Making" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors and the evaluation has been overseen by Timothy Behrens as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

Summary:

This fMRI study of young healthy adults explores potential dissociations between subjective recollection and objective episodic memory accuracy within the posterior parietal cortex and PFC. Using a 2AFC object-memory task, the authors reveal clear behavioural dissociations between two conditions; A-A' condition in which the diagnostic features of stimuli are prioritised thus promoting higher levels of objective accuracy, versus the A-B' condition in which a global appraisal of the target stimulus instantiates a stronger sense of subjective recollection. The study further explores metacognitive appraisals of this process, by having participants make judgements about keeping or discarding recollection trials towards an overall score. Taken together, the behavioural and neural dissociations are interesting and timely, and this study should make a useful contribution to the literature.

Essential revisions:

1) The authors need to provide a much more thorough overview of the extant literature. For example, several studies exist that have already examined this dissociation, some cited here (Richter et al., 2016), others not cited or discussed in this way (Duarte, Henson, Graham, Cerebral Cortex 2008; Mark and Rugg, 1998) as well as PPC lesion studies (Davidson et al., Neuropsychologia 2008; Hower et al., Neuropsychologia 2014; Simons et al., 2010; Ciaramelli et al., Cortex 2017). The authors need to clearly articulate how their study builds on and extends previous work, as well as delineating precisely the novel contribution that their study makes.

2) Related to the above point, a substantial body of work exploring the contribution of posterior parietal regions to episodic recollection has not been discussed (e.g. Kuhl and Chun, J.Neuroscience 2014; Favila et al., J. Neuroscience 2018; Frithsen and Miller., Neuropsychologia 2014; Ramanan S. et al., The Neuroscientist, 2018). It would be important to integrate this prior work to appreciate how the current results fit within the broader memory literature.

3) It is not clear how the distinction between subjective and objective recollection maps on to the distinction made between "local" and "global" processing. The authors should clearly explain what they mean by "global assessment" (provide a definition) and why exactly this process might give rise to a stronger sense of subjective recollection (e.g. as proposed in the Introduction). Similarly, the authors should clarify why this "global assessment" would also come alongside a situation where "salient retrieval output will be more likely to serve as a basis for response".

4) While it is acceptable to have an active baseline to reduce the probability of negative BOLD memory effects, due to greater activity at rest, it seems highly unlikely that the baseline task ("press a number") would prevent participants from remembering their previous memory decision just a few seconds earlier. Given that the decision phase task asks participants to keep or trash their prior memory decision, it is not clear how this would be an effective distractor task, as subjects knew they needed to make this decision based on their first memory choice. Furthermore, it is stated in the decision phase results that the authors wanted to determine if patterns of activity in the memory phase persisted in the decision phase, which of course, many did. While the decision phase may provide some behavioural support for the authors' predictions about the two task conditions, it is not clear what the decision vs. memory phase neural comparison is intended to show. Presumably, the authors could compare BOLD responses during the memory phase for select and discard trials and see similar results, though peaks and significance values may vary somewhat.

5) The precise instructions and conditions of the decision phase of the experiment warrant further explanation. Under what circumstances would a participant “trash” their responses? What were the specific instructions provided to participants? Were participants instructed explicitly on how their score was calculated? Did they think their score simply reflected the correct versus incorrect retrievals? It is not clear from the manuscript whether participants thought their score was calculated based purely on objective retrieval accuracy or whether it also related to their "remember vs. very familiar vs. somewhat familiar" judgements. This is a subtle point but one with serious implications for the interpretation of the behavioural and neural data.

6) Related to the neural predictions on the decision phase, there is no discussion of the differences between retrospective and prospective metacognitive monitoring within the PFC (e.g. Fleming and Dolan, Phil. Trans. Royal Society. 2012). Metacognitive monitoring is treated as a unitary construct here.

7) Please detail how the sample size was determined – was a power analysis conducted? Was this sample a convenience sample or determined based on available resources? What was the stopping rule for data collection? Additionally, was the study pre-registered? Which hypotheses and tests were a-priori, and which were conducted after the data had been examined? Ideally, this should be specified throughout.

8) Further details are required regarding the data analysis strategy. For example, for the mixed-effects modelling approach, was there a reason why only intercepts were allowed to vary by participant but not random slopes? There are good theoretical and empirical reasons to expect that the coefficients for the key effects would also vary by participant, not only the intercepts. Some justification of the modelling decisions is warranted here.

9) Similarly, the reader would benefit from more detail about where the reported results are drawn from – in the mixed effects framework, for instance, where are the p values derived? Did you use a package like "lmerTest" with Satterthwaite's method, or did you do some kind of model selection (comparing between models with/ without focal variables)? As far as I know, lme4 does not, by default, provide p-values.

10) The plots lack the overlaid raw data, making inferences difficult. The authors should overlay the raw datapoints on top of the bar charts so that readers can see for themselves what the distributions look like. Similarly, there are no plotted logistic regression functions, meaning the plots for the behavioural data do not match up with the analytical tools used for the inferential statistics and reported in text – perhaps this needs at least to be explained in the figure legend?

11) A point worthy of discussion is the possibility that the motivation to do well on this task may not necessarily be financial, but could have been socially driven, in that participants' scores were displayed. Thus, motivation to perform well may not reflect "reward" as per previous incentive-compatible confidence studies, but potentially both social reward and punishment (e.g. embarrassment at poor performance). Aspects of the Abstract and Discussion should be re-phrased because the decision task was not incentivized with tangible rewards – instead, it could also contain the motivation to avoid a penalty like embarrassment.

12) I would like to see some consideration of the laterality of the parietal regions that were recruited during the memory phase, given that it was the right supramarginal gyrus that emerged in the analyses. For example, I was surprised that the angular gyrus did not emerge as a key region during the memory phase and I wonder if the authors could comment on the lack of AG involvement in the current study (e.g. see work by Preston Thakral; Siddharth Ramanan, Heidi Bonnici). It would help the reader to place some of these findings in context and comment on how the paradigms etc. potentially give rise to these differences across studies.

https://doi.org/10.7554/eLife.62520.sa1

Author response

Essential revisions:

1) The authors need to provide a much more thorough overview of the extant literature. For example, several studies exist that have already examined this dissociation, some cited here (Richter et al., 2016), others not cited or discussed in this way (Duarte, Henson, Graham, Cerebral Cortex 2008; Mark and Rugg, 1998) as well as PPC lesion studies (Davidson et al., Neuropsychologia 2008; Hower et al., Neuropsychologia 2014; Simons et al., 2010; Ciaramelli et al., Cortex 2017). The authors need to clearly articulate how their study builds on and extends previous work, as well as delineating precisely the novel contribution that their study makes.

We thank the reviewer for encouraging us to consider these papers. We have now included them in the Introduction and Discussion of the revised manuscript, and we have clarified the novel contribution of our work. Specifically, we emphasize that previous evidence regarding the neural dissociation between subjective and objective recollection comes primarily from populations in which either one or the other aspect of recollection is altered (e.g., older adults or patients with PPC damage; Duarte et al., 2008; Simons et al., 2010). In contrast, dissociating these processes in typically-developing adults has proved quite difficult because objective and subjective recollection measures are typically highly correlated. Indeed, even in studies where task instructions are designed to trigger these different types of recollection, the two aspects continue to be highly correlated (i.e., a successful source judgment will typically be associated with reports of subjective recollection). This limits our ability to isolate correlates of one aspect of recollection even when the other is statistically controlled for. Overall, it has been difficult to fully evaluate the distinction between objective and subjective recollection in young, healthy brains. Our experiment was designed to dissociate subjective and objective recollection allowing us to directly examine the underlying neural correlates.

2) Related to the above point, a substantial body of work exploring the contribution of posterior parietal regions to episodic recollection has not been discussed (e.g. Kuhl and Chun, J.Neuroscience 2014; Favila et al., J. Neuroscience 2018; Frithsen and Miller., Neuropsychologia 2014; Ramanan S. et al., The Neuroscientist, 2018). It would be important to integrate this prior work to appreciate how the current results fit within the broader memory literature.

We have now added a discussion of these papers (Introduction and Discussion). We think these papers have helped us to integrate the present results in the broader literature and thank the reviewer for the helpful suggestions.

3) It is not clear how the distinction between subjective and objective recollection maps on to the distinction made between "local" and "global" processing. The authors should clearly explain what they mean by "global assessment" (provide a definition) and why exactly this process might give rise to a stronger sense of subjective recollection (e.g. as proposed in the Introduction). Similarly, the authors should clarify why this "global assessment" would also come alongside a situation where "salient retrieval output will be more likely to serve as a basis for response".

We thank the reviewer for making us aware that this discussion lacked clarity. We have now revised the Introduction to define more clearly what we mean by local vs. global processing. In addition, to be more precise we now use describe the processing as specific rather than local:

“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. […] 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.“

Thus, in line with previous research using this paradigm, we expect a retrieval context promoting more specific retrieval to be associated with a greater likelihood for objective recollection at the cost of reduced subjective recollection. In contrast, a retrieval context promoting global retrieval is expected to increase subjective recollection and potentially reduce objective performance, especially when the to-be-remembered information/feature is highly specific.

We also clarified the link between more global processing favoring subjective recollection, and saliency:

“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.”

4) While it is acceptable to have an active baseline to reduce the probability of negative BOLD memory effects, due to greater activity at rest, it seems highly unlikely that the baseline task ("press a number") would prevent participants from remembering their previous memory decision just a few seconds earlier. Given that the decision phase task asks participants to keep or trash their prior memory decision, it is not clear how this would be an effective distractor task, as subjects knew they needed to make this decision based on their first memory choice. Furthermore, it is stated in the decision phase results that the authors wanted to determine if patterns of activity in the memory phase persisted in the decision phase, which of course, many did. While the decision phase may provide some behavioural support for the authors' predictions about the two task conditions, it is not clear what the decision vs. memory phase neural comparison is intended to show. Presumably, the authors could compare BOLD responses during the memory phase for select and discard trials and see similar results, though peaks and significance values may vary somewhat.

We were interested in attempting to isolate the neural substrates associated with behavior during the memory phase from those associated with behavior during the decision phase. Thus, we were confronted with the question of how to best fill the jittering time between the memory and decision probes in a way that maximized our chances to address our question. Leaving the time empty would probably have introduced confounds as different participants may have engaged in continued retrieval attempts, rehearsal or preparation for decision making at different times. Including a demanding task would have risked eliminating any of such potential sources of variability, but also interfering with participants’ ability to keep their memory decision in mind, thereby introducing a new demand for memory retrieval during the decision phase.

Our choice of an active baseline task was thus motivated by our attempt to systematically limit participants’ further engagement in retrieval processes or preparation for the upcoming decision, while avoiding an overly taxing task that could result in forgetting the answers submitted during the memory phase. With the active baseline, we thus sought to increase comparability to the previous behavioral studies with this paradigm (Hembacher and Ghetti, 2016), in which delays between memory and selection were shorter than the jittered fixation periods necessary in the context of the present event-related fMRI design. While we agree with the reviewer that it would be surprising to find completely non-overlapping activation for the contrast of select vs. discard responses performed at the memory vs. decision phase, we assumed that activation during the memory phase would primarily reflect recollection-based processes. Thus, we expected that analyzing the decision to select or discard a response during the decision phase would allow us to better isolate the neural substrates associated with these decisions.

We performed the contrast of select vs. discard responses at the memory phase as well. As can be seen in Author response image 1, the activations are largely overlapping, especially for the discard decisions. However, for select responses, activation during the memory phase is much more widespread and includes clusters observed in association with remember responses, reflecting the fact that decisions were tightly coupled with subjective recollection.

Author response image 1
Select > discard (in yellow/red) and discard > select (in blue) during the memory phase (A) and during the decision phase (B; reported in main manuscript).

C. Overlay of select > discard from memory phase (in dark red) and select > discard from the decision phase (in yellow/red). D. Overlay of discard > select from memory phase (in dark blue) and discard > select from the decision phase (in light blue).

5) The precise instructions and conditions of the decision phase of the experiment warrant further explanation. Under what circumstances would a participant “trash” their responses? What were the specific instructions provided to participants? Were participants instructed explicitly on how their score was calculated? Did they think their score simply reflected the correct versus incorrect retrievals? It is not clear from the manuscript whether participants thought their score was calculated based purely on objective retrieval accuracy or whether it also related to their "remember vs. very familiar vs. somewhat familiar" judgements. This is a subtle point but one with serious implications for the interpretation of the behavioural and neural data.

We thank the reviewer for the opportunity to clarify our procedure. Participants were informed that at the end of the experiment their performance would be compared to that of their peers who completed the experiment. Specifically, we informed participants that to compute the score used for the comparison we would only use the answers selected for the treasure chest, thereby discarding the answers in the trash bin. Thus, if a participant did not want their answer to be counted towards their final score, they had the opportunity to discard it by using the “trash” option. Of note, we did not mention anything about subjective states and/or the relation between their subjective judgment of “remember,” “very familiar,” or “somewhat familiar,” and their upcoming decision. Thus, participants believed that their score was calculated based purely on the objective retrieval accuracy for the treasured items. We have now clarified this point in the manuscript. After completing the task, participants were debriefed and we informed them that we would not be comparing their performance to others’. We have now provided the exact instructions participants received in the Appendix.

6) Related to the neural predictions on the decision phase, there is no discussion of the differences between retrospective and prospective metacognitive monitoring within the PFC (e.g. Fleming and Dolan, Phil. Trans. Royal Society. 2012). Metacognitive monitoring is treated as a unitary construct here.

We thank the reviewer for alerting us to a lack of clarity on our part. We have now clarified that we focus on retrospective metacognitive monitoring in the present study in the Introduction and the Discussion. Additionally, we have now noted in the Discussion that future research is needed to examine whether and how prospective metacognitive judgments, such as judgment of learning, guide future decisions:

“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.”

7) Please detail how the sample size was determined – was a power analysis conducted? Was this sample a convenience sample or determined based on available resources? What was the stopping rule for data collection? Additionally, was the study pre-registered? Which hypotheses and tests were a-priori, and which were conducted after the data had been examined? Ideally, this should be specified throughout.

We did not conduct a formal power analysis to determine the sample for the present experiment. We selected the sample size for this experiment (N = 30) based on the moderate effect sizes for the dissociation between objective and subjective memory measures observed across each of four behavioral experiments included in Hembacher and Ghetti, 2016. The current sample size corresponds to the largest sample employed in one of those behavioral experiments. The stopping rule for data collection was to achieve the sample size of N = 30. Due to an incidental finding in one participant, their data were replaced (and not used for analyses), resulting in N = 31 participating adults. In the process of data analyses, it became clear that one participant had not followed the instructions and was excluded from analyses. However, their data were not replaced, resulting in an effective sample of N = 29. We have now included this information in the revised manuscript.

The present study was not pre-registered. We had a-priori expectations regarding the dissociation between subjective and objective recollection (based on previous work with this paradigm, Hembacher and Ghetti, 2016; see also Selmeczy, Kazemi and Ghetti, 2021), the condition dissociation in the parietal cortex (based on close alignment of the experimental conditions with bottom-up vs. top-down attention to memory, e.g., Cabeza et al., 2008) as well as PFC, insula and dACC involvement in the decision to select or discard responses (based on our own developmental metamemory work, e.g., Fandakova et al., 2017, 2018).

8) Further details are required regarding the data analysis strategy. For example, for the mixed-effects modelling approach, was there a reason why only intercepts were allowed to vary by participant but not random slopes? There are good theoretical and empirical reasons to expect that the coefficients for the key effects would also vary by participant, not only the intercepts. Some justification of the modelling decisions is warranted here.

We included a random factor for the intercept to account for overall individual variability in performance in our analysis. We did not include random slope effects in the estimated logistic regressions because we did not have strong expectations about individual variability in the effect of the experimental manipulation and our previous studies had shown showed a moderate-to large effect sizes for our manipulation. In our sample, 83% (24 out of 29 participants) showed the expected effect of the manipulation (i.e., higher accuracy in the AA’ condition accompanied by higher subjective recollection in the A-B’ condition). However, we agree with the reviewer that one might expect individual variability in the extent of the manipulation effects. Our results are fully replicated if we also include random slopes for the condition effects. We elected to keep the current analysis because it is simpler and more accessible, but we would be happy to include additional analyses if the reviewers and Editors deem this helpful or necessary.

9) Similarly, the reader would benefit from more detail about where the reported results are drawn from – in the mixed effects framework, for instance, where are the p values derived? Did you use a package like "lmerTest" with Satterthwaite's method, or did you do some kind of model selection (comparing between models with/ without focal variables)? As far as I know, lme4 does not, by default, provide p-values.

We are grateful to the reviewer for pointing out that we left out this important detail in our manuscript. As anticipated by the reviewer, we used the package lmerTest with Satterthwaite’s degrees-of-freedom method. We have now added this information to the Materials and methods section of the manuscript.

10) The plots lack the overlaid raw data, making inferences difficult. The authors should overlay the raw datapoints on top of the bar charts so that readers can see for themselves what the distributions look like. Similarly, there are no plotted logistic regression functions, meaning the plots for the behavioural data do not match up with the analytical tools used for the inferential statistics and reported in text – perhaps this needs at least to be explained in the figure legend?

We have now revised all of the figures and instead of bars showing mean effects we now present raincloud plots with raw data, probability density, and key summary statistics for all effects reported in the manuscript. We decided to include the raw data instead of the estimated logistic regression functions in order to facilitate the correspondence between the regression results and the actual experimental data. Following the reviewer’s suggestion, we have now clarified this fact in the legend of Figure 2 and also provide the plotted logistic regression functions in Figure 2—figure supplement 1.

11) A point worthy of discussion is the possibility that the motivation to do well on this task may not necessarily be financial, but could have been socially driven, in that participants' scores were displayed. Thus, motivation to perform well may not reflect "reward" as per previous incentive-compatible confidence studies, but potentially both social reward and punishment (e.g. embarrassment at poor performance). Aspects of the Abstract and Discussion should be re-phrased because the decision task was not incentivized with tangible rewards – instead, it could also contain the motivation to avoid a penalty like embarrassment.

We have now clarified that the reward in the present study was not financial, but social – the incentive was to do well in comparison to peers. We have also indicated that future research is needed to confirm that subjective recollection similarly guides decision making when financial gains or losses are expected (Discussion). Indeed, there are hints in the literature suggesting that the neural networks supporting social decision making are similar to the neural circuits identified in non-social situations (e.g., Izuma et al., 2008; Lin et al., 2012; Ruff and Fehr, 2014).

12) I would like to see some consideration of the laterality of the parietal regions that were recruited during the memory phase, given that it was the right supramarginal gyrus that emerged in the analyses. For example, I was surprised that the angular gyrus did not emerge as a key region during the memory phase and I wonder if the authors could comment on the lack of AG involvement in the current study (e.g. see work by Preston Thakral; Siddharth Ramanan, Heidi Bonnici). It would help the reader to place some of these findings in context and comment on how the paradigms etc. potentially give rise to these differences across studies.

We thank the reviewer for encouraging us to position our results better and provide additional clarification. We now report that the present paradigm did produce reliable activity in left AG during the memory phase, as expected based on previous research. As depicted in Figure 3—figure supplement 1, the contrast generated by subjective judgments (Remember > Familiar) produced reliable activation in left angular gyrus in both the A-A’ (red) and the A-B’ (blue) conditions, and there were no differences between conditions in this region. Thus, the results involving this general contrast during the memory phase, irrespective of condition, are fully consistent with the literature. Despite this general effect in the angular gyrus, the condition differences found in the present study involve other parietal subregions. The results regarding the SPL are consistent with previous studies showing that memory goals bias feature representations towards relevant information in dorsal PPC, but not in ventral PPC (Favila et al., 2018). Based on previous studies, one might expect to find the increased subjective recollection effects in the angular gyrus, but instead we found them in the precuneus. While these results corroborate the findings of Richter et al., 2016, they also help to clarify the role of the angular gyrus – previous research implicating this area in subjective recollection has not accounted for the fact that the precision of retrieved memories and the subjective experience of recollection are highly correlated (see Ramanan et al., 2018). By dissociating accuracy from recollection in the present study, we showed that precuneus activity varied with the subjective experience of recollection. We have now included these results in Figure 3—figure supplement 1 and refer to them in the Results section.

https://doi.org/10.7554/eLife.62520.sa2

Article and author information

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

Funding

James S. McDonnell Foundation (Scholar Award)

  • Simona Ghetti

German Research Foundation (FA 1196/1-1)

  • Yana Fandakova

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: This research was approved by the Institutional Review Board at the University of California, Davis after signing informed consent received.(protocol #217322).

Senior Editor

  1. Timothy E Behrens, University of Oxford, United Kingdom

Reviewing Editor

  1. Muireann Irish, University of Sydney, Australia

Publication history

  1. Received: August 27, 2020
  2. Accepted: February 18, 2021
  3. Version of Record published: March 9, 2021 (version 1)

Copyright

© 2021, Fandakova et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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