1 Hippocampal-cortical encoding activity predicts the precision of episodic memory Saana M. Korkki1, Franziska R. Richter2, Jon S. Simons1 1Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom 2Cognitive Psychology Unit, Institute of Psychology, University of Leiden, Leiden 2333 AK, Netherlands Corresponding authors: Saana M. Korkki, smk62@cam.ac.uk; Jon S. Simons, jss30@cam.ac.uk In Press, Journal of Cognitive Neuroscience Acknowledgements This study was funded by BBSRC grant BB/L02263X/1 and James S. McDonnell Foundation Scholar Award #220020333, and was carried out within the University of Cambridge Behavioural and Clinical Neuroscience Institute, funded by a joint award from the Medical Research Council and the Wellcome Trust. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. We are grateful to Paul Bays for valuable advice and to the staff of the MRC Cognition and Brain Sciences Unit MRI facility for scanning assistance. 2 Abstract Our recollections of past experiences can vary both in the number of specific event details accessible from memory and the precision with which such details are reconstructed. Prior neuroimaging evidence suggests the success and precision of episodic recollection to rely on distinct neural substrates during memory retrieval. In contrast, the specific encoding mechanisms supporting later memory precision, and whether they differ from those underlying successful memory formation in general, are currently unknown. Here, we combined continuous measures of memory retrieval with model-based analyses of behavioural and neuroimaging data to tease apart the encoding correlates of successful memory formation and mnemonic precision. In the MRI scanner, participants encoded object-scene displays, and later reconstructed features of studied objects using a continuous scale. We observed overlapping encoding activity in inferior prefrontal and posterior perceptual regions to predict both which object features were later remembered versus forgotten, and the precision with which they were reconstructed from memory. In contrast, hippocampal encoding activity significantly predicted the precision, but not overall success, of subsequent memory retrieval. The current results align with theoretical accounts proposing the hippocampus to be critical for representation of high-fidelity associative information, and suggest a contribution of shared cortical encoding mechanisms to the formation of both accessible and precise memory representations. 3 Introduction Our memories are not an exact reproduction of the past, but can range from high- fidelity, precise, reconstructions of previous experiences to less precise, lower- resolution, representations. Behavioural evidence suggests such variation in mnemonic precision to be distinguishable from the general success of memory retrieval (Brady et al., 2013; Harlow & Donaldson, 2013; Harlow & Yonelinas, 2016; Richter, Cooper, Bays, & Simons, 2016; but see Schurgin, Wixted, & Brady, 2020). While the likelihood of successful retrieval of information from memory and the precision of the retrieved information correlate across individuals, the majority of variance in each measure is nevertheless unrelated to the other (Richter et al., 2016). Moreover, these two aspects of objective memory performance appear to be associated with distinct subjective characteristics (Harlow & Yonelinas, 2016), are differentially sensitive to various experimental manipulations (e.g., Berens, Richards, & Horner, 2020; Sun et al., 2017; Sutterer & Awh, 2016; Xie & Zhang, 2017), as well as to memory impairments in distinct populations (Cooper et al., 2017; Korkki, Richter, Jeyarathnarajah, & Simons, 2020; Nilakantan, Bridge, VanHaerents, & Voss, 2018), eliciting proposals that they may at least to some degree reflect a dissociable neurocognitive basis. Indeed, prior neuroimaging evidence indicates the success and precision of episodic recollection to recruit distinct regions of the posterior-medial network during memory retrieval (Richter et al., 2016). While retrieval activity in the hippocampus has been observed to increase for successful in comparison to unsuccessful retrieval, trial- wise variation memory precision appears to correlate with retrieval-related activity in the lateral parietal cortex (Richter et al., 2016), although others highlight a role for medial temporal regions also (Montchal, Reagh, & Yassa, 2019; Stevenson et al., 4 2018). However, despite increased interest in the neural basis of mnemonic precision, the focus of prior studies has been on retrieval mechanisms (e.g., Cooper & Ritchey, 2019; Montchal et al., 2019; Richter et al., 2016; Stevenson et al., 2018), whereas the encoding substrates supporting the formation of precise memory representations, and whether they differ from those supporting successful encoding in general, remain unresolved. Successful episodic memory formation is typically associated with activity increases in a network of medial temporal, lateral prefrontal, and posterior perceptual regions (Kim, 2011; Spaniol et al., 2009). The hippocampus receives input from content- specific perceptual regions, and is thought to bind disparate event features into a coherent memory representation (Cooper & Ritchey, 2020; Davachi, 2006; Paller & Wagner, 2002; Ranganath, 2010), and allow for the storage of similar experiences in an orthogonalised, or non-overlapping, manner (Norman & O’Reilly, 2003; O’Reilly & McClelland, 1994). Lateral prefrontal regions, on the other hand, are involved in the strategic and controlled encoding of information into memory via processes such as attentional selection, elaboration and integration of information relevant for current task goals (Blumenfeld & Ranganath, 2007; Simons & Spiers, 2003). The specific neural substrates supporting successful memory formation have been found to exhibit process-specificity, varying for instance according to the depth of stimulus processing engaged in at encoding (Fletcher et al., 2003; Otten et al., 2001), and the type of retrieval process later recruited (Ranganath et al., 2004; Staresina & Davachi, 2006). Moreover, encoding correlates appear sensitive to more subtle differences in the quality of retained representations, including their objective amount of detail (Cooper & Ritchey, 2020; Qin et al., 2011), and subjective ratings of memory vividness or confidence (Kensinger, Addis, & Atapattu, 2011; Qin et al., 5 2011). However, while beginning to elucidate the encoding mechanisms underlying variation in more qualitative aspects of later retrieval, prior studies have typically been limited by the use of categorical measures of the quantity of details remembered, or participants’ subjective reports, which may not directly map onto more graded variations in objective memory precision. It is possible that, in addition to relying on distinct brain regions during retrieval (Richter et al., 2016), the success and precision of episodic recollection may be supported by at least partly separable neural mechanisms during memory encoding. For instance, the successful retrieval of information from memory may depend on the strength of an association between a retrieval cue and the target memory, thus drawing in particular on associative encoding processes supported by the hippocampus and the prefrontal cortex (Blumenfeld & Ranganath, 2007; Davachi, 2006). In contrast, the precision with which specific mnemonic features can be reconstructed from memory may closely relate to the fidelity of stimulus encoding in posterior perceptual regions (Emrich et al., 2013), and/or to hippocampal function supporting the formation of distinct and detailed memory traces that can be later reconstructed with high precision (Moscovitch, Cabeza, Winocur, & Nadel, 2016). Indeed, an association between hippocampal encoding activity and subsequent mnemonic precision would align with prior accounts suggesting the hippocampus to be critical for representation of high-fidelity relational information across perception and memory (Aly, Ranganath, & Yonelinas, 2013; Ekstrom & Yonelinas, 2020; Kolarik et al., 2016; Yonelinas, 2013). Alternatively, it is possible that, contrary to dissociable neural substrates observed during retrieval (Richter et al., 2016), the successful and precise encoding of information into memory may rely on shared 6 neural mechanisms that perhaps act to increase the strength of the memory more generally, rendering it both accessible and precise at retrieval. In the current study, we employed continuous measures of memory retrieval and model-based analyses of behavioural and neuroimaging data to elucidate the encoding substrates of mnemonic precision. In the MRI scanner, participants encoded visual stimulus displays depicting an object overlaid on a scene background. The location and colour of the objects were drawn from circular spaces, and at retrieval, participants recreated these attributes of the studied items using a continuous response dial. This approach allowed us to segregate encoding activity supporting later successful memory retrieval from that supporting subsequent mnemonic precision in a manner not afforded by more typical categorical measures of retrieval performance (e.g., old/new, remember/know), thus providing novel insight into the encoding mechanisms supporting the acquisition of precise episodic memories. Methods Participants Twenty-one young adults (18-29 years old) participated in the current experiment. All participants were right-handed, native English-speakers, had normal or corrected-to- normal vision, no colour blindness, and no current or historical diagnosis of any neurological, psychiatric, or developmental disorder, or learning difficulty. Participants indicated no current use of any psychoactive medication, and no medical or other contradictions to MRI scanning. One participant was excluded from all analyses due to excessive movement (> 4mm) in the scanner, leaving 20 7 participants to contribute to the present analyses (8 male, 12 female; mean age 22.15 years, SD: 3.10). The participants were recruited via the University of Cambridge Psychology Department Sona volunteer recruitment system (Sona Systems, Ltd) and community advertisements, and were reimbursed with £30 for their participation. All participants gave written informed consent in a manner approved by the Cambridge Psychology Research Ethics Committee. Materials Stimuli for the memory task comprised 180 images of outdoor scenes and 180 images of distinct everyday objects. The images were obtained from existing stimuli sets (scenes: Richter et al., 2016; objects: Brady et al., 2013) and Google image search. Each object image was randomly paired with a scene image to generate a total of 180 trial-unique study displays (size 750 x 750 pixels). Across the study displays, we varied the appearance of two object features: colour and location. For each display, object colour and location were randomly selected from circular parameter spaces (0-360°) (cf. Cooper et al., 2017; Richter et al., 2016) (see Figure 1). All participants viewed the same study displays in a randomized order. Design and procedure Prior to the scan, participants read the instructions and undertook practice trials of the memory task. The task was modified from Richter et al. (2016) by reducing the number of objects presented at study and the number of features later tested, in order to result in one feature retrieval trial per study display. In total, participants completed 9 study-test blocks over 9 functional runs (one study and one test phase 8 per run). At study, participants sequentially viewed 20 object-background displays (stimulus duration: 5s), and were instructed to try and memorise the appearance of each display, including the location and colour of the object. The study phase was followed by a 10s delay during which a “Get Ready” message was presented on a black screen. Following this delay, participants were asked to reconstruct either the location or the colour of each object viewed in the preceding study phase (one feature question per each encoding trial, total of 20 retrieval trials per block). At retrieval, the test object reappeared on its associated background with a central cue word “Location” or “Colour” indicating the type of feature tested on that trial. The initial appearance of the tested feature was randomly selected from a circular parameter space (0-360°), while the appearance of the untested feature remained unchanged from study to test. In other words, for location trials, the test object reappeared in its original colour, but in a randomly selected location, whereas for colour trials the test object reappeared in its original location but in a randomly chosen colour. Participants were asked to recreate the object’s original features as accurately as they could by moving a slider around a 360-degree response dial using their middle and index finger on a button box, and were able to confirm their answer by pressing a third key with their thumb. The retrieval phase was self-paced with the constraint of a minimum trial length of 7s and a maximum response time of 11s. Participants on average produced response times that were well under this limit (M: 5.64s, SD: 0.68s), and the percentage of trials where response selection was not confirmed in time was very low (M: 1.36%, SD: 1.77%). Note that if a participant failed to confirm their answer within 11s, their last position on the response wheel was recorded as their answer for that trial. 9 Participants completed 90 location and 90 colour trials in total (10 trials of each type per task block). The type of feature tested for each object was randomised across displays, but constant across participants so that all participants answered the same feature question for each study display. To ensure that memory was tested for feature values spanning the entire circular space, the randomisation was conducted with a constraint of roughly equal number (i.e., 20-25) of target feature values sampled from each quadrant around the circular space for both the location and colour condition. The order of study and test displays was then randomised across participants with the constraint of no more than four encoding or retrieval trials in a row for which the same type of feature was tested. Study and test trials were separated by a fixation cross with jittered duration between 0.4s and 2.4s (mean ISI duration: 1s) following an approximate Poisson distribution. After the first five of the nine task blocks, participants were given a 10-minute break from the memory task in the scanner, during which a diffusion-weighted structural scan was acquired (analysis of diffusion-weighted data not reported here). 10 Figure 1. Example study and test trials of the memory task. At study, participants viewed stimuli displays consisting of one object overlaid on a scene background (stimulus duration: 5s). The location and colour of the objects at study were randomly chosen from circular parameter spaces (0-360°). At test, participants recreated either the location or the colour of each studied object using a 360-degree continuous response dial, allowing for a fine-grained assessment of memory fidelity. Behavioural analysis For each trial, we calculated participants’ retrieval error as the angular difference between their response value and the target feature value (0 ± 180°). To distinguish the likelihood of successful memory retrieval from the precision of the retrieved information, we fitted a two-component mixture model (Bays et al., 2009; Zhang & Luck, 2008) to each participant’s retrieval error data using maximum likelihood 11 estimation (code available at: https://www.paulbays.com/code/JV10/index.php). This mixture model has previously been shown to characterize long-term memory performance in similar tasks (e.g., Brady et al., 2013; Korkki et al., 2020; Richter et al., 2016), and has been employed to gain insights about the neural basis of the precision of episodic recollection (Cooper & Ritchey, 2019; Richter et al., 2016; Stevenson et al., 2018). The model assumes that two distinct sources of error contribute to participants’ retrieval performance across trials: variability, that is, noise, in successful retrieval of target features, and the presence of random guess responses where memory retrieval has failed to bring any diagnostic information about the target to mind. These two sources of error are modelled by a von Mises distribution (circular equivalent of a Gaussian distribution) centred at a mean error of zero degrees from the target value, with a concentration, K, and a circular uniform distribution with a probability, pU, respectively. Precision of memory retrieval can be estimated as the concentration parameter (K, higher values reflect higher precision) of the target von Mises distribution, and the likelihood of successful memory retrieval (pT) as the probability of responses stemming from the target von Mises distribution (pT = 1 – pU). Consistent with prior studies (Korkki et al., 2020; Richter et al., 2016), this two-component model was found to fit the current data better than an alternative one-component model where participants’ responses were assumed to stem from a von Mises distribution around the target feature value only (mean Bayesian Information Criterion (BIC) for the one-component model: 386.10; mean BIC for the two-component model: 317.62; models fitted to individual participants’ data across the feature conditions). 12 MRI acquisition MRI scanning took place at the University of Cambridge Medical Research Council Cognition and Brain Sciences Unit using a 3T Siemens Tim Trio scanner (Siemens, Germany) with a 32-channel head coil. For each participant, a whole brain structural image was acquired using a T1-weighted 3D magnetization prepared rapid gradient echo (MPRAGE) sequence (repetition time (TR): 2.25s, echo time (TE): 3ms, flip angle = 9°, field of view (FOV): 256 x 256 x 192mm, resolution: 1mm isotropic, GRAPPA acceleration factor 2). Functional data were acquired over 9 runs each comprising one task block (one encoding and one retrieval phase), using a single- shot echoplanar imaging (EPI) sequence (TR: 2s, TE: 30ms, flip angle° = 78, FOV: 192 x 192mm, resolution: 3mm isotropic). Each volume consisted of 32 sequential oblique-axial slices (interslice gap: 0.75mm) acquired parallel to the anterior commissure – posterior commissure transverse plane. Across the participants, the mean number of volumes acquired per functional run was 161.60 (SD: 9.63). The scanning protocol further included a diffusion-weighted structural scan that was acquired after the first five functional runs (not analysed here). fMRI preprocessing Data preprocessing and analysis was performed with Statistical Parametric Mapping (SPM) 12 (https://www.fil.ion.ucl.ac.uk/spm/) implemented in MATLAB R2016a. The first five volumes of each functional run were discarded to allow for T1 equilibration. Furthermore, any additional volumes collected after each task block had finished were discarded for each participant so that the last volume of each run corresponded to a time point of ~2s after the last fixation cross. The functional images were 13 spatially realigned to the mean image to correct for head motion and temporally interpolated to the middle slice to correct for differences in slice acquisition time. The anatomical image was coregistered to the mean EPI image, bias-corrected and segmented into different tissue classes (grey matter, white matter, cerebrospinal fluid). These segmentations were used to create a study-specific structural template image using the DARTEL (Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra) toolbox (Ashburner, 2007). The functional data was normalized to MNI space using DARTEL and spatially smoothed with an isotropic 8mm full-width at half-maximum (FWHF) Gaussian kernel. Main fMRI analysis In order to obtain trial-specific estimates of the success and precision of memory retrieval for the fMRI analyses, we fitted the two-component mixture model (von Mises + uniform distribution) to retrieval error data across all participants and feature conditions (3600 trials in total). Using the best fitting model probability density function, we then calculated the probability of each error belonging to the target von Mises distribution over the uniform distribution, and classified errors with at least 0.05 probability of stemming from the von Mises distribution as ‘successful’ and errors with less than 0.05 probability of belonging to the von Mises distribution as ‘unsuccessful’ (cf., Cooper et al., 2017; Richter et al., 2016). In terms of degrees, this corresponded to a subsequent retrieval success cut-off of ± 51°, where trials with an absolute error ≤ 51° (range: 0 – 51°) were classified as ‘successful’ and trials with an absolute error > 51° as ‘unsuccessful’ (range: 52 – 180°). As done in prior studies (Cooper et al., 2017; Cooper & Ritchey, 2019, 2020; Richter et al., 2016), we used 14 the across-participant model-derived cut-off to ensure that responses of the same error magnitude were consistently classified as successful or unsuccessful across individuals, as well as to avoid any bias in the error cut-offs due to differences in individual model fits. We further note that using feature-specific cut-offs, rather than the threshold estimated across all retrieval trials, did not change the significance of our main results. For trials classified as successfully encoded, a trial-specific measure of memory precision was further calculated as 180° – participant’s absolute retrieval error on that trial so that higher values (smaller error) reflected higher precision (range: 129 – 180°) (cf., Cooper et al., 2017; Richter et al., 2016). For each participant, a first level General Linear Model (GLM) was constructed containing three regressors corresponding to each event of interest (successful location encoding, successful colour encoding, unsuccessful encoding), and a fourth regressor modelling the retrieval trials. For the successful encoding trials, the trial- specific estimates of memory precision were included as parametric modulators comprising two additional regressors in the model. The precision parametric modulators were rescaled to range between 0 and 1 to facilitate the direct comparison of success and precision-related effects, and mean centred for each participant. Neural activity was modelled with a boxcar function convolved with the canonical hemodynamic response function (HRF), with a duration of 5s for the encoding trials and a variable duration (7s-11s) for the retrieval trials, capturing the duration of the study and test displays, respectively. Six participant-specific movement parameters estimated during realignment (3 rigid-body translations, 3 rotations) were further included as covariates in the first level model to capture any residual movement-related artefacts. Due to the small number of guessing trials in each functional run, data from all functional runs were concatenated for each 15 participant, and 9 constant block regressors included as additional covariates. Autocorrelation in the data was estimated with an AR(1) model and a temporal high pass filter with a 1/128 Hz cut-off was used to eliminate low frequency noise. First level subject-specific parameter estimates were submitted to second level random effects analyses. Contrasts The contrasts for the fMRI analyses focused on identifying regions where encoding activity positively predicted the subsequent success and/or precision of episodic memory retrieval (i.e., increases in BOLD signal for successful encoding, or higher memory precision). To examine encoding activity associated with the subsequent success of memory retrieval, we contrasted encoding trials for which memory retrieval subsequently succeeded against trials for which memory retrieval subsequently failed (subsequent retrieval success effects). To identify encoding activity predicting the later precision of memory retrieval, positive correlations between BOLD signal and the precision parametric modulator were examined (i.e., linear relationship between BOLD signal and precision parametric modulator; subsequent precision effects). We further assessed the overlap between subsequent success and subsequent precision effects using conjunction analyses. Conjunction analyses were conducted testing against the conjunction null hypothesis to ensure that regions identified in this analysis displayed reliable encoding activity associated with each individual contrast, i.e., both subsequent success and subsequent precision of memory retrieval (see Nichols et al., 2005). Moreover, we assessed the specificity of the subsequent success and subsequent precision effects by 16 conducting exclusive masking of each subsequent memory contrast by the other (i.e., subsequent retrieval success masked by subsequent precision contrast and vice versa). For this analysis, the mask image was thresholded at p < .050 uncorrected (cf., Smith, Henson, Dolan, & Rugg, 2004; Uncapher, Otten, & Rugg, 2006). Due to a relatively low number of guess trials per feature condition for some individuals, it was not possible to investigate feature-specific subsequent success effects. Furthermore, analysis of feature-specific subsequent precision effects did not yield any significant differences across the ROIs (ps > .208), or the whole brain (ps > .303). Thus, our analyses focused on examining BOLD activity predicting the subsequent success and precision of memory retrieval across the features conditions, consistent with the approach taken in previous studies employing a similar paradigm (Richter et al., 2016; Cooper et al., 2017). Regions of interest The main analyses focused on a small number of a priori regions of interest (ROIs) implicated by meta-analytic evidence in supporting the successful formation of episodic memories for visual information (Kim, 2011; Spaniol et al., 2009). Specifically, the ROIs included the hippocampus (HC), the inferior frontal gyrus (IFG) and the fusiform gyrus (FFG). Given evidence for greater consistency of subsequent memory effects in the left hemisphere (Spaniol et al., 2009), left-lateralized ROIs were used, each comprising the left anatomical region as defined by the Automated Anatomical Labelling (AAL) atlas. Statistical significance within each anatomical ROI was assessed using small-volume correction with a peak-level familywise error 17 (FWE) corrected (based on random field theory) threshold of p < .05, correcting for the number of voxels in each ROI. In addition to the ROI analyses, we sought to identify any additional brain regions displaying a relationship between encoding activity and the subsequent success and/or precision of memory retrieval in exploratory whole brain analyses conducted at a whole brain FWE-corrected threshold of p < .05, minimum extent of 5 contiguous voxels. Additional control analyses In addition to the main fMRI analyses described above, we conducted two additional analyses to assess whether BOLD signal in any of the regions of interest was associated with trial-wise variation in participants’ memory error for trials classified as ‘unsuccessful’ based on the model-derived cut-off (absolute error > 51°), i.e., when variation in memory error was assumed to be driven by guessing, or when collapsing across all encoding trials without assuming a model-based separation between successful and unsuccessful retrieval. For the first analysis, the first-level GLM was identical to what described above, but with the addition of a parametric modulator for unsuccessful trials also that reflected trial-wise variation in participants’ subsequent memory error (180° – absolute error; range: 0 – 128°). For the second, model-free, analysis all encoding trials in the location and colour condition were modelled with one regressor each, and a parametric modulator reflecting trial-wise variation in subsequent memory error (180° – absolute error; range: 0-180°) was added for each condition. Parametric modulators were mean-centered, and the contrast of interest investigated linear increases in BOLD signal with decreasing memory error. 18 Results Behavioural results For each trial, we calculated participants’ retrieval error as the angular difference between their response value and the target feature value (0±180°) (see Figure 2A). Across participants and feature conditions, overall task performance, as measured by the mean absolute retrieval error, was 30.43° (SD: 15.04°), with significantly higher mean absolute error in the colour (M: 34.48°, SD: 15.90°) in comparison to the location condition (M: 26.37°, SD: 15.80°), t(19) = 3.63, p = .002, d = 0.81. To further decompose the specific sources of error contributing to participants’ overall performance, we fitted the two component mixture model (von Mises + uniform distribution) to each individual participant’s retrieval error data using maximum likelihood estimation (Bays et al., 2009). The mean model-estimated probability of successful memory retrieval, defined as the probability of responses stemming from a von Mises distribution centred at the target feature value (pT), was 0.73 (SD: 0.18) across participants and feature conditions (see Figure 2B). The mean model- estimated precision of memory retrieval, estimated as the concentration parameter, Κ, of the target von Mises distribution, was 16.79 (SD: 7.92) across participants and feature conditions (see Figure 2B) (note that this value of Κ is comparable to an SD of approximately 14.20°). Mean memory precision (K) was significantly higher in the location (M: 34.65, SD: 27.24) in comparison to the colour condition (M: 10.94, SD: 7.15), t(19) = 4.04, p = 001, d = 0.90, whereas mean probability of successful memory retrieval (pT) did not significantly differ between the two feature conditions (location M: 0.75, SD: 0.18; colour M: 0.73, SD: 0.20), t(19) = 0.65, p = .524. Consistent with previous results (Richter et al., 2016), we also observed a moderate 19 positive correlation between estimates of the probability of successful memory retrieval and memory precision across participants, rs = .54, p = .014. Figure 2. A) Distribution of retrieval errors (response – target) across all trials and participants. Black line illustrates response probabilities predicted by the two- component mixture model (von Mises + uniform distribution; model fitted to data across all participants for visualization). B) Mean model-estimated probability of successful memory retrieval (pT) and memory precision (Κ) across participants. Error bars display 95% confidence interval of the mean and data points individual participant parameter estimates. fMRI results Encoding activity predicting subsequent retrieval success and memory precision in a priori ROIs Our ROI analyses focused on examining whether encoding activity in three regions typically displaying subsequent memory effects for visual information; the hippocampus, the inferior frontal gyrus and the fusiform gyrus, differentially 20 contributes to the later success and precision of episodic memory retrieval. We first examined increases in encoding activity for trials that were subsequently successfully retrieved (absolute retrieval error ≤ 51°) in contrast to trials that were subsequently forgotten (absolute retrieval error > 51°; note that only one object feature, i.e., location or colour, was reconstructed for each encoding display). Within our anatomical ROIs, we observed increased encoding activity in the inferior frontal gyrus, t(19) = 6.75, p = .001, peak: -36, 27, 18, and the fusiform gyrus, t(19) = 8.88, p < .001, peak: -30, -63, -9, to predict whether object features were later successfully retrieved from memory or forgotten (peak-level FWE-corrected within each ROI) (see Figure 3A and 4A). In contrast, no significant subsequent retrieval success effects were detected in the hippocampus (ps > .151). We next examined whether encoding activity in these regions predicted the graded precision with which object features were later successfully retrieved from memory (linear relationship between BOLD signal and precision parametric modulator). In addition to predicting which trials were successfully remembered, encoding activity in the inferior frontal gyrus, t(19) = 5.63, p = .011, peak: -57, 15, 15, and the fusiform gyrus, t(19) = 6.27, p = .001, peak: -33, -75, -18, positively correlated with the precision of later memory retrieval (see Figure 3B and 4B). Furthermore, increased encoding activity in the hippocampus, t(19) = 4.20, p = .029, peak: -33, -30, -9, was associated with greater mnemonic precision for object features (see Figure 3B and 3C). As a control analysis, we further investigated whether BOLD signal in any of the ROIs predicted trial-wise variation in memory error across trials classified as unsuccessful (i.e., when variation in memory error was assumed to be driven by guessing). No significant associations between BOLD signal and subsequent 21 memory error were detected for trials classified as unsuccessful in any of the ROIs (ps > .238). Figure 3. Mean parameter estimates for A) subsequent success (successful > unsuccessful) and B) subsequent precision (positive association between BOLD signal and precision parametric modulator) effects in the left inferior frontal gyrus (IFG), hippocampus (HC) and fusiform gyrus (FFG). Error bars display +/- 1 SEM. C) Encoding activity correlating with the subsequent precision of memory retrieval in the hippocampal ROI (visualised at an uncorrected threshold of p < .001). Thus, results from the ROI analyses suggest encoding activity in the inferior frontal and fusiform cortex to support both the later success and precision of memory retrieval, while significant increases in BOLD signal in the hippocampus were 22 observed for subsequent memory precision only. We next sought to assess whether encoding activity predicting these two aspects of later retrieval performance overlapped in any of the ROIs. Conjunction analyses indicated significant overlap between subsequent success and subsequent precision effects in both the inferior frontal t(19) = 4.86, p = .007, peak: -42, 3, 27, and the fusiform gyrus, t(19) = 6.12, p < .001, peak: -42, -57, -12, whereas no significant overlap was detected in the hippocampus (ps > .778). Furthermore, hippocampal encoding activity still predicted the subsequent precision of memory retrieval after exclusive masking with the subsequent retrieval success contrast (mask thresholded at p > .050 uncorrected), t(19) = 4.20, p = .029, peak: -33, -30, -9. On the contrary, significant subsequent retrieval success effects were detected in the inferior frontal gyrus, t(19) = 5.76, p = .006, peak = -36, 27, 15, and the fusiform gyrus, t(19) = 4.35, p = .039, peak = -18, - 45, -12, after exclusive masking with the subsequent precision contrast, consistent with the observation of more widespread subsequent retrieval success than subsequent precision effects in these two regions (see Figure 4). No significant subsequent precision effects were observed in these two regions (ps > .052) after exclusive masking with the subsequent retrieval success contrast. Encoding activity predicting variation in memory error across all encoding trials in a priori ROIs In addition to the model-based analyses described above, we investigated whether trial-by-trial variation in BOLD signal in any of the ROIs was associated with trial-by- trial variation in subsequent memory error across all encoding trials, without assuming a categorical distinction between successful and unsuccessful retrieval. 23 Consistent with the pattern of results observed in the model-based analyses, which indicated encoding activity in the inferior frontal and fusiform gyrus to be sensitive to both the subsequent success and subsequent precision of memory retrieval, we observed encoding activity in these two regions to also predict the magnitude of subsequent memory error when collapsing across all encoding trials (IFG: t(19) = 7.03, p = .001, peak: -51, 9, 27; FFG: t(19) = 12.18, p < .001, peak: -42, -57, -12). In contrast, trial-wise variation in memory error was not significantly associated with encoding activity in the hippocampus when examining all encoding trials (ps > .323). Encoding activity predicting subsequent retrieval success and memory precision across the whole brain To identify any additional brain regions, beyond our a priori ROIs, where encoding activity predicted the later success and/or precision of memory retrieval, we further performed complementary whole brain analyses. Activity in several regions of the dorsal and ventral visual stream, including the middle occipital gyrus, inferior parietal gyrus, fusiform gyrus, and inferior temporal gyrus, was found to predict which object features were later successfully remembered vs. forgotten (see Table 1 and Figure 4A). For subsequent memory precision, no significant voxels survived a whole brain peak-level corrected significance threshold (p < .050 FWE-corrected, k > 5), although we note that three clusters spanning the left inferior temporal gyrus, middle occipital gyrus and cerebellum, t(19) = 7.08, p < .001, the right middle occipital gyrus and fusiform gyrus, t(19) = 5.84, p < .001, and the left inferior frontal gyrus, t(19) = 5.63, p < .001, survived FWE-correction at the cluster level (cluster forming threshold p < .001 uncorrected; see Figure 4B for whole brain results visualized at an 24 uncorrected threshold). Whole brain conjunction analyses further indicated significant overlap between subsequent success and subsequent precision effects in the middle occipital and fusiform gyri (see Table 1 and Figure 4C). Exclusive masking of each subsequent memory contrast by the other did not reveal any further regions where encoding activity significantly predicted only the subsequent success or the subsequent precision of memory retrieval. Table 1. Encoding activity associated with the subsequent success (successful > unsuccessful) and precision (positive relationship between BOLD activity and precision parametric modulator) of memory retrieval in the whole brain analyses, p < .050 FWE-corrected at peak-level, k > 5. Region Voxels x y z t p Subsequent retrieval success L middle occipital gyrus 355 -36 -87 12 10.50 < .001 L inferior parietal gyrus 87 -33 -45 39 9.41 < .001 R middle occipital gyrus 63 33 -75 30 8.83 .001 R inferior temporal gyrus 15 45 -54 -12 8.27 .003 R middle occipital gyrus 21 42 -81 9 7.95 .005 R fusiform gyrus 8 30 -27 -21 7.33 .016 Subsequent precision No significant voxels Conjoint activity L middle occipital gyrus 30 -24 -75 30 6.59 .002 R middle occipital gyrus 41 33 -78 24 6.39 .004 L fusiform gyrus 30 -42 -57 -12 6.12 .008 L middle occipital gyrus 22 -39 -84 6 6.01 .010 Note. L = left, R = right. 25 Figure 4. Encoding activity predicting the A) subsequent success (successful > unsuccessful) and B) subsequent precision (positive association between BOLD activity and precision parametric modulator) of memory retrieval, and C) overlap between encoding activity predicting both later success and precision of memory retrieval. Visualized at an uncorrected threshold of p < .001, minimum cluster of 10 voxels. Discussion Amid growing interest in the neural substrates underlying the precision of episodic memory, prior studies have predominantly focused on retrieval processes (Cooper & Ritchey, 2019; Montchal et al., 2019; Richter et al., 2016; Stevenson et al., 2018), 26 leaving the specific encoding mechanisms supporting the acquisition of high-fidelity memories largely uncharacterised. Here, we employed continuous measures of memory retrieval in combination with model-based analyses of fMRI data to segregate the encoding activity supporting the later success and precision of episodic retrieval. We observed encoding activity in overlapping cortical regions, including the inferior frontal, fusiform and middle occipital gyri, to predict both which object features were later successfully retrieved from memory versus forgotten, and the precision with which they were reconstructed. In contrast, encoding activity in the hippocampus significantly predicted the precision of later memory retrieval only. Together, these findings highlight a hippocampal-cortical basis for the formation of precise memories of perceptual information, and provide novel insight into the encoding substrates supporting the accessibility and precision of episodic memory. The current findings demonstrating a relationship between trial-by-trial variation in hippocampal encoding activity and later memory precision are consistent with previous accounts emphasizing a critical role for this region in supporting detailed episodic memories (Moscovitch et al., 2016; Robin & Moscovitch, 2017). Related to our current findings, prior neuroimaging studies have found hippocampal encoding activity to correlate with subjective measures of later retrieval quality, such as participants’ ratings of the confidence (Qin et al., 2011) or vividness (Kensinger et al., 2011) of their memory retrieval. Moreover, trial-wise variation in hippocampal encoding activity has been found to predict the specificity of subsequent neural reinstatement of mnemonic content (Danker et al., 2016; Wing et al., 2015), providing support for the idea that hippocampal function at encoding may in part determine the fidelity with which information can be later recalled. Emerging evidence suggests the posterior hippocampus to be particularly important for 27 supporting the fine-grained representation of perceptual details (Brunec et al., 2018; Poppenk et al., 2013), consistent with our current finding of the peak of the subsequent precision effect being located in the posterior part of the hippocampus. In contrast, we did not observe significant subsequent retrieval success effects in the hippocampus in our current paradigm. Furthermore, hippocampal encoding activity still predicted the precision of later memory retrieval after exclusive masking with the subsequent retrieval success contrast, suggesting specificity of this effect to memory precision. The lack of significant retrieval success effects in the hippocampus may seem surprising given previous evidence for hippocampal encoding increases for successful versus unsuccessful encoding of associative information (Davachi, 2006; Staresina & Davachi, 2008), however, we note that prior studies have not attempted to distinguish memory precision related activity from that related to successful encoding in general, both of which are likely associated with accurate performance in a categorical memory task. Similar to the pattern of results observed here, others have further observed hippocampal encoding activity to predict graded variation in participants’ subjective ratings of memory confidence only for responses above a certain threshold, while not categorically distinguishing between remembered and forgotten items (Shrager, Kirwan, & Squire, 2008). Theoretical accounts postulate that hippocampal involvement across cognitive domains may be explained by requirement for representation of high-fidelity (i.e., highly precise) and high- dimensional (i.e., comprising of multiple associations) information (Ekstrom & Yonelinas, 2020; Yonelinas, 2013). This account aligns with our current finding of greater hippocampal encoding activity with greater precision of object feature bindings, although we note that, while requiring binding of multiple event attributes (i.e., object identity to colour and spatial location), event complexity was not explicitly 28 manipulated here and participants reconstructed only one feature of each studied object while the untested feature remained unchanged from study to test. Future studies manipulating the number and type of object attributes encoded, and testing memory for multiple features, can more directly evaluate the relationship of hippocampal encoding activity to remembered event complexity. Our findings are further in line with patient evidence demonstrating medial temporal lesions to disproportionately impair both short and long term memory for high-fidelity associations (Koen et al., 2017; Nilakantan et al., 2018), and suggest a potential role for a deficient hippocampal encoding mechanism in such impairments. A prior study employing a similar paradigm to the one used here found hippocampal retrieval activity to be associated with the success, but not precision, of episodic memory retrieval (Richter et al., 2016). While likely not directly mapping onto prior distinctions made in the literature, it is possible that this apparent difference between encoding and retrieval effects in the hippocampus could reflect differential demands on hippocampal function during memory encoding and retrieval. More specifically, hippocampal pattern separation during memory encoding may be critical for the storage of differentiated memory representations that can be later retrieved with high precision, in particular when feature overlap is high (i.e., when multiple objects encoded in similar colours, or spatial locations) (Bakker, Kirwan, Miller, & Stark, 2008; Moscovitch et al., 2016; Norman & O’Reilly, 2003; Xie, Park, Zaghloul, & Zhang, 2020; Yassa & Stark, 2011). At retrieval, hippocampal pattern completion is thought to enable access to stored memory representations when presented with a noisy or partial cue, resulting in a thresholded memory signal where only items above a certain criteria elicit successful retrieval (Norman, 2010; Norman & O’Reilly, 2003). Interestingly, some evidence suggests that hippocampal response during 29 perception may be more graded, supporting fine-grained perceptual discrimination (Aly et al., 2013; Elfman, Aly, & Yonelinas, 2014), a proposal consistent the pattern of memory-related activity observed here. Beyond the hippocampus, we observed activity in overlapping cortical regions, including the inferior frontal, fusiform, and middle occipital gyrus, to predict both the later success and precision of episodic memory retrieval. Our finding of left inferior frontal involvement in subsequent retrieval success and precision is consistent with previous evidence implicating this region in cognitive control of memory encoding, supporting successful memory formation across a range of encoding tasks and mnemonic content (Blumenfeld et al., 2011; Blumenfeld & Ranganath, 2006; Murray & Ranganath, 2007; Park & Rugg, 2008, 2011). Specifically, ventrolateral regions of the prefrontal cortex have been proposed to support the attentional selection and elaborative encoding of goal-relevant information, leading to formation of strong and distinctive memory traces for specific item features (Blumenfeld et al., 2014; Blumenfeld & Ranganath, 2007; Simons & Spiers, 2003). Such selective encoding processes supported by this region may act to enhance the representation of goal- relevant features in posterior perceptual regions (Chun & Turk-Browne, 2007; Gilbert & Li, 2013; Sprague et al., 2015; Xue et al., 2013), and/or modulate hippocampal encoding more directly (Aly & Turk-Browne, 2017; Carr et al., 2013), aiding the formation of durable and precise memory representations. The current results further emphasize the role of perceptual regions in supporting the formation of accessible and precise memory traces. Specifically, we observed encoding activity in the fusiform gyrus, a region typically associated with object perception and memory (Bar et al., 2001; Haxby et al., 2001; Vaidya et al., 2002), to predict both the later success and precision of object feature retrieval. This finding is 30 consistent with previous studies that have observed memory-related activity increases in the fusiform gyrus during episodic encoding (reviewed in Kim, 2011; Spaniol et al., 2009), potentially playing an important role in formation of detailed object representations (Garoff et al., 2005; Kensinger et al., 2007), and with evidence suggesting representational specificity in the occipitotemporal cortex during encoding to predict subsequent memory performance (Gordon et al., 2014; Ward et al., 2013; Xue et al., 2010). Beyond our regions of interest, we further observed that encoding activity in a wider network of ventral and dorsal visual regions predicted the subsequent success of memory retrieval. Of these regions, conjoint subsequent retrieval success and precision effects were observed in the middle occipital gyrus. The involvement of a broad set of ventral and dorsal visual regions aligns with demands of the current task for processing various visual attributes of the study displays. We further note that the interpretation of the mixture model parameters as reflecting two distinct sources of memory error in the context of long-term retrieval has recently been challenged (Schurgin et al., 2020). Specifically, Schurgin et al. (2020) suggest errors in visual working memory, and at least under specific constraints also in long- term memory, to be explained by a single parameter signal detection model when taking the non-linear relationship between physical and psychological stimulus spaces into account (Schurgin et al., 2020). While the ability of this model to account for selective changes in retrieval success or precision observed in previous studies of long-term memory (e.g., Cooper et al., 2017; Nilakantan et al., 2017, 2018; Sutterer & Awh, 2016), as well as to generalize to other stimulus spaces, such as spatial locations employed here, remains unclear, we note that our current findings regarding encoding activity in the inferior frontal and ventral visual cortex are not 31 inconsistent with a single parameter conceptualization. It is possible that the common subsequent success and subsequent precision effects observed in these regions could reflect a single dimension of memory strength or quality. Indeed, encoding activity in these two regions was also found to predict trial-wise variation in memory error when collapsing across all encoding trials, although we note that no such effects were still observed if examining trials classified as unsuccessful only. However, we did not observe hippocampal encoding activity to predict trial-wise variation in memory error when collapsing across all encoding trials (or for trials classified as unsuccessful), suggesting a benefit of the mixture modelling approach for characterizing memory-related activity in the hippocampus. We further note that while our current approach of using model-derived retrieval success thresholds estimated at the group level ensured consistent classification of trials to conditions across participants, this nevertheless means that our threshold estimate was not sensitive to individual differences in memory precision. In summary, the current study aimed to elucidate the encoding mechanisms supporting the formation of accessible and precise memory traces. 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