Remembering is something we do countless times each day. The detail and vividness with which we can remember is part of what makes memories so precious. Given the significance and complexity of memories, it is perhaps unsurprising that several parts of the brain are needed for us to experience them. Indeed, the brain regions involved in memory all work so closely together that it is a challenge to identify what role each region plays.

Richter, Cooper et al. aimed to design a memory task that could separate key characteristics of remembering, which would allow them to study links between each aspect and the different brain regions involved in memory. The resulting test involved showing people images of different objects whilst they were in an MRI medical imaging scanner. The people taking the test were asked to remember several objects that could vary in color, position and orientation. Participants were asked to rate how vividly they remembered the objects and then tried to precisely recreate their color, orientation and position.

The test allowed Richter, Cooper et al. to link specific parts of the brain to certain aspects of remembering. The hippocampus, an area known to be important in memory processing, indicated whether or not information had been remembered. More vivid memories were linked to greater activity in a region called the precuneus, which plays a role in imagination. Lastly, activity in a third region – the angular gyrus – indicated the precision of each memory.

Being able to study different aspects of memory using tests like this that collect detailed measurements could be important in identifying memory problems, for example, in people with brain diseases or head injuries, or after a stroke. Specifically, the methods developed by Richter, Cooper et al. could provide sensitive tools for detecting memory difficulties at an early stage. This may help more people to get treated sooner, potentially minimizing lasting complications.

Remembering previous events is one of the hallmarks of human cognition, with memory retrieval contributing to, and depending on, many other cognitive abilities. Memory retrieval involves a complex set of processes that activate a large network of brain regions (Rugg and Vilberg, 2013; Spaniol et al., 2009; Kim, 2010). Recently, it has been suggested that the memory retrieval network can be divided into two sub-systems (Ranganath and Ritchey, 2012), both anchored on the hippocampus: an anterior temporal system, comprising the amygdala, temporopolar cortex, orbitofrontal cortex and perirhinal cortex, and a posterior medial system, including regions such as the posterior cingulate, precuneus, angular gyrus (AnG), medial prefrontal cortex, and parahippocampal cortex. According to this framework, the anterior temporal system is more related to semantic memory, familiarity judgments, and event salience, whereas the posterior medial system contributes to episodic memory and recollection. Despite such attempts to characterize sub-systems within the retrieval network, and suggestions about the roles of some of its constituent regions, functional specializations within the memory retrieval network remain poorly understood, in part due to common co-activation during most retrieval tasks.

It is likely that memory retrieval tasks typically activate such a wide network of regions because the components of successful retrieval can reflect the contribution of a number of different properties of memory representations, such as their subjective vividness, objective accuracy, and the objective level of detail, specificity, or precision with which they are retrieved. To better understand how memory judgments are derived, it is important to consider that the outcome of a retrieval attempt is typically not ‘all-or-none’, but that the amount and quality of information we retrieve can vary considerably (Parks and Yonelinas, 2007). In order to distinguish memory retrieval components and the role of regions within the retrieval network, it is necessary to use sensitive measures that can detect such variations in retrieval performance. Consistent with this approach, some research has studied the specificity with which memories can be retrieved as an alternative to simple 'old/new' decisions or other categorical judgments such as ‘remember/know’. For example, source memory tasks, requiring participants to remember the context in which an item was studied, have revealed that participants sometimes remember 'partial source' information (such as the gender of a speaker), but not necessarily all specific details (Simons et al., 2004). The idea that memory representations can vary in quality has prompted researchers to measure recollection objectively using more graded measures, exploring how many details can be remembered (Horner and Burgess, 2014; Qin et al., 2011; Vilberg and Rugg, 2009). Although such measures are important advances in capturing differences in retrieval outcome, they remain essentially categorical. Memory is not only defined by the number of details that are remembered, but also by the fidelity with which those details can be recalled (Brady et al., 2013).

Recent developments in the study of short-term memory have demonstrated that memory representations vary along a continuous scale of memory precision (Bays and Husain, 2008; Zhang and Luck, 2008; Fougnie et al., 2012; van den Berg et al., 2012; Bays, 2014), highlighting the flexibility of memory representations in terms of both quantity and quality (Ma et al., 2014). These influential methods from the working memory literature have started to be applied to behavioral studies of long-term memory (Brady et al., 2013). Importantly, it is possible to separate behaviorally the probability of recollection success from the precision with which information is retrieved (Harlow and Donaldson, 2013; Harlow and Yonelinas, 2016), which can be differentially influenced by attention and retrieval practice (Fan and Turk-Browne, 2013). Furthermore, a recent EEG study supports the idea that variations in long-term memory precision may be associated with distinct neural signatures, finding the well-established left parietal old/new effect to be graded according to recollection precision (Murray et al., 2015). The observation that retrieval success and retrieval precision can be manipulated independently suggests that using continuous measures to differentiate such components will lead to a more detailed understanding of episodic memory retrieval at both behavioral and neural levels.

Retrieval success and retrieval precision both constitute objective measures of memory performance. However, increasing research has additionally considered subjective or metacognitive measures of memory performance, such as confidence (Yonelinas, 1994) or the vividness with which retrieved memories are experienced (Kuhl and Chun, 2014; St-Laurent et al., 2015). Research has found that subjective and objective measures of memory performance can diverge (Chua et al., 2012; Harlow and Yonelinas, 2016). Most notably, patients with parietal cortex lesions exhibit impairment in subjective memory reports even though their objective performance appears to be intact (Simons et al., 2010), and lateral parietal transcranial magnetic stimulation has been found to selectively reduce memory confidence but not retrieval success (Yazar et al., 2014). One possibility is that the subjective and objective memory components that may drive mnemonic decisions are separable, with subjective aspects of retrieval, such as vividness, and objective aspects of retrieval, such as success and precision, relying on largely independent processes. Alternatively, it is possible that performance on subjective measures of memory retrieval used to date can be fully explained by subtle variations in objective memory, such as the precision with which a memory is recalled, which previous objective measures may not have been sensitive enough to detect.

To summarize, memory retrieval involves a wide network of brain regions and may reflect the contribution of both objective and subjective memory components, with objective memory success being distinguishable from objective memory precision and both being distinguishable from subjective memory vividness. To understand the roles of brain regions within the retrieval network and to dissociate memory retrieval components, more sensitive, continuous measures of memory performance are needed. In the current study, we combined continuous long-term memory measures in an fMRI paradigm with model-based approaches derived from the working memory literature. For each retrieval trial we obtained a binary measure of retrieval success (successful versus unsuccessful), a continuous measure of the precision with which color, orientation, and location features of visual objects were remembered (based on the error between a target value and the participant’s response), as well as a subjective vividness rating, on a continuous scale. These measures allowed us to characterize behavioral and neural mechanisms that distinguish between recollection success, precision, and the vividness with which information is retrieved.

Many cognitive components contribute to successful recollection, supported by a complex network of brain regions; however, cognitive and neural dissociations within the episodic retrieval network remain underspecified. Here, we sought to distinguish the neurocognitive mechanisms underlying retrieval success, retrieval precision, and retrieval vividness using model-based analyses of continuous episodic retrieval measures. Retrieval success (successful vs. unsuccessful) was associated with BOLD signal increases in hippocampus, whereas retrieval precision scaled with brain activity in AnG. Both these objective measures of retrieval performance were dissociable from a subjective continuous measure of retrieval vividness, which was tracked by activity in the precuneus. The apparently separable contribution of these three nodes of the episodic retrieval network was confirmed by significant statistical interactions between region and component, and by the lack of overlap in activity revealed by conjunction analyses. Together, the observed behavioral and neural dissociations, revealed by the use of continuous memory measures, shed new light on the distinct contributions made by components of the episodic retrieval network.

Using continuous measures of retrieval performance in addition to the traditional categorical distinction of retrieval success, we were able to dissociate the functions of regions that are often co-activated during memory retrieval. The hippocampus has received most attention in the literature as a 'memory region', because of the profound memory deficits associated with hippocampal damage (Scoville and Milner, 1957), and the frequent reports of hippocampal activity during neuroimaging studies of memory retrieval (Schacter and Wagner, 1999). One proposal in the cognitive literature has been that hippocampal activity might relate to a threshold retrieval signal (Yonelinas, 2002), such that items only elicit a hippocampal recollection response once a specific qualitative information threshold is reached. Consistent with this view, computational models of hippocampal function have produced thresholded output, such that some items produce a memory signal strong enough to elicit an 'old' response, whereas items that remain below the required threshold are indistinguishable to the model from new items (Norman, 2010). fMRI results have supported the view that hippocampal activity during retrieval is characterized by distinct states, such that it responds to recollection responses, for example in source memory tasks (Ranganath et al., 2004), but not graded familiarity judgments (Montaldi et al., 2006). Our observation of a categorical retrieval success effect in the hippocampus is thus consistent with these prior results, but our findings provide novel constraints on understanding of the specificity of the hippocampal role in episodic memory by demonstrating a disproportionately greater role in retrieval success than graded precision or subjective vividness.

In contrast to successful versus unsuccessful retrieval, the precision with which recollected information is retrieved was tracked by activity in AnG, consistent with recent EEG evidence that the late lateral parietal ERP effect is graded according to memory precision (Murray et al., 2015). This dissociation between hippocampus and AnG is consistent with the notion of a strongly interconnected cortico-hippocampal network in which hippocampus initiates retrieval, which is then further supported by cortical regions (McClelland et al., 1995), with increasing connectivity between different nodes in the network being related to superior mnemonic performance (Wang et al., 2014). The specific dissociation between hippocampus and AnG observed here is also consistent with recent reports that hippocampal signals are transient during memory retrieval, as might be expected of a threshold signal, whereas AnG responses are more sustained over the retrieval period, possibly consistent with a role in the active representation of information (Vilberg and Rugg, 2012). The various roles attributed to inferior parietal regions such as AnG during memory retrieval include attentional capture by retrieved information (Cabeza et al., 2008), the accumulation of evidence in memory (Wagner et al., 2005), or a 'buffer' that stores information for further evaluation during long-term memory retrieval (Vilberg and Rugg, 2008). Consistent with the latter account, recent work supports a role for AnG in carrying specific details of memory representations, finding that episodic memories are represented individually and can be decoded by multivariate classifier analyses in this area (Bonnici et al., 2016; Kuhl and Chun, 2014). These findings indicate that AnG may represent the information on which a mnemonic decision is based, with properties of the representation such as its level of detail or the fidelity of the information retrieved driving decisions about a memory’s origin and other associated features. This representation of detailed memory features might uniquely qualify AnG for demanding mnemonic computations like precision judgments or the integration of complex retrieved features, which hippocampus might instead only support indirectly by initiating and orchestrating other mnemonic processes (Vilberg and Rugg, 2012). Additionally, the observation of trial-to-trial variation in AnG activity, correlated with retrieval precision, is consistent with proposals from the working memory literature that memory fidelity may vary from one retrieval instance to the next (Fougnie et al., 2012; van den Berg et al., 2012), and specifically that memory precision may be determined by the gain of neural activity encoding a stimulus (Bays, 2014).

In addition to the dissociation between objective measures of memory retrieval involving the hippocampus and AnG, our subjective measure of memory performance, retrieval vividness, correlated with precuneus activity, consistent with recent findings linking activity in dorsomedial brain areas with how vividly video clips were recalled (St-Laurent et al., 2015). In line with a proposed involvement in vividness judgments, previous evidence has found greater connectivity between prefrontal cortex and precuneus during metacognitive decisions concerning retrieved memories (Baird et al., 2013), supporting a role for medial parietal regions in assessments of subjective memory quality. Furthermore, a correlation between precuneus structural volume and recollection from a first-person perspective (Freton et al., 2014) is consistent with a contribution to reinstating and maintaining a vivid subjective memory representation. The involvement of the precuneus in facilitating subjective memory vividness could explain its common activation during memory retrieval tasks, such as tests of source memory, that often require retrieval of rich and vivid episodic information (Lundstrom et al., 2005). It has been proposed that activity in the precuneus reflects the requirement for mental imagery during retrieval (Fletcher et al., 1995). The function of the precuneus might thus be complementary to that of the hippocampus, which may be involved in the initial aspects of a proposed ‘scene construction’ process that occurs when a memory is retrieved, but not in the maintenance of the scene in working memory following construction (Zeidman et al., 2015). In accordance with such a distinction, our current data are consistent with the idea that the hippocampus might play an initial role in driving the reconstruction process, but that further translation into an imageable, first person perspective event occurs via precuneus (Burgess et al., 2001).

It is worth noting that previous studies have also linked activity in AnG with rated vividness (Bonnici et al., 2016; Kuhl and Chun, 2014) and confidence (Qin et al., 2011), and that hippocampal activity, too, has been reported to be associated with vividness ratings (Ford and Kensinger, 2016; Gilboa et al., 2004). Moreover, patients with lesions that include the lateral parietal lobe often report reduced memory detail and diminished recollection vividness (Berryhill et al., 2007; Simons et al., 2010). At first glance, our results might appear to diverge from these prior reports. However, we believe our findings help to distinguish the contributions of precuneus, hippocampus, and AnG to subjective aspects of recollection, by capitalizing on two specific advantages of our study. In previous work, vividness ratings were often recorded after the mnemonic decision, meaning that judgments could reflect the participants’ perception of how they had performed on that trial; moreover, vividness and precision or retrieval success effects were not distinguished, such that vividness is likely to have been confounded with precision and/or retrieval success. In the current study, making the vividness rating before any decision on the objects ensured that it reflected participants’ assessment of their subjective ability to bring to mind the appearance of the objects on the display, rather than the influences of perceived difficulty or post-decision processing (Siedlecka et al., 2016). Second, we modeled vividness, retrieval success and precision within the same general linear model, meaning that they accounted for independent sources of variance. While our three memory measures were clearly dissociable, they were not completely independent from each other (and in fact were correlated at r = ~0.5 across subjects in the current study). This moderate correlation would imply that trials which were more precisely and/or successfully remembered would be, on average, associated with higher vividness ratings, possibly explaining previous reports of a link between vividness ratings and brain activity in AnG (cf. Bonnici et al., 2016; Kuhl and Chun, 2014), or hippocampus (Ford and Kensinger, 2016; Gilboa et al., 2004). Regarding reports of diminished vividness following parietal cortex lesions, such lesions are frequently not selective to lateral parietal cortex (Berryhill et al., 2007), often including medial parietal areas. Moreover, lateral and medial areas are heavily interconnected (Zhang and Li, 2012), such that lateral parietal lesions could to some extent affect medial parietal function, by resulting in diminished input into medial parietal regions. This diminished input to medial parietal areas could affect judgments of memory vividness, because the information necessary for the maintenance of a vivid mental scene might be less readily available. Thus, our current data suggest that medial parietal regions play a role in evaluating the perceived vividness of participants’ memories, a judgment that may be based on the level of detail or precision of the memory represented in AnG, although further research is still required to tease apart the relative contributions of these regions and elucidate the processes underlying judgments of memory vividness and confidence.

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Author details

1. Franziska R Richter

   Department of Psychology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   FRR, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Rose A Cooper

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-1917-0435

2. Rose A Cooper

   Department of Psychology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   RAC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Franziska R Richter

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-1521-8371

3. Paul M Bays

   Department of Psychology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   PMB, Conception and design, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-4684-4893

4. Jon S Simons

   Department of Psychology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   JSS, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   jss30@cam.ac.uk

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-7508-9084

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