Part 2:What Is Effect Of Memory Reactivation During Learning ?
Mar 30, 2022
Contact: Audrey Hu Whatsapp/hp: 0086 13880143964 Email: audrey.hu@wecistanche.com
Model definition The LBA model (Brown and Heathcote, 2008) assumes that, on each trial, the starting point k of each accumulator is drawn randomly from a uniform distribution on the interval [0, A]. Each accumulator then follows a line with a slope of d until it reaches the response threshold b. On each trial, the sloped of accumulator i is drawn from a normal distribution with mean vi and SD s (here, fixed at 1). The time for an accumulator to reach the threshold is (b k)/d. We modeled the three-alternative forced-choice inference tests using three accumulators with mean drift rates v1 (for the correct response) and v2 (for the other two responses). As derived in the initial description of the LBA model (Brown and Heathcote, 2008), the probability density function (PDF) for accumulator I at time t is as follows:
Where f and U are the probability density function and cumulative distribution functions, respectively, of the standard normal distribution. The cumulative distribution function (CDF) for accumulator I at time t is as follows:
The PDF for accumulator I hitting the threshold first, at time t, is the probability of accumulator I finishing at time t, conditional on the other accumulators not having finished yet as follows:
Because drift rate d is drawn from a normal distribution, there is some probability of no accumulators finishing. Following prior work
(Brown and Heathcote, 2008; Annis et al., 2017), we conditionalized on the probability of at least one accumulator having a positive drift rate as follows:
For each of the four chains, there was a tuning phase of 1000 iterations with a target acceptance rate of 0.99, followed by 5000 samples. Convergence was assessed using bulk effective sample size and rank-normalized split potential scale reduction statistic R^ (Vehtari et al., 2017). We assessed the fit of the model by calculating the mean posterior parameters for each trial as well as simulating responses and response times. We simulated 50 replications of each trial to obtain a robust estimate of model performance. Finally, we calculated the 95% high-density interval (HDI) for each of the group-level mean parameters (e.g., mCA1 for CA1) to determine whether they were different from zero, indicating a relationship between similarity change or reactivation and AC inference performance.
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Results
Behavioral performance
By the end of the initial pair (AB) learning phase, participants had formed strong memories of the face-shape and scene-shape pairs. All participants were above chance on the final test (mean proportion correct=0.91, SD=0.01) and were therefore included in subsequent analyses. Memory for the overlapping (BC) shape-object pairs was influenced by the visual similarity of the linking item across learn- ing (Fig. 2A, B). A repeated-measures ANOVA with the within-subjects factors
of overlapping pair block (1, 2, 3, 4) and visual similarity (exact match, high similarity, low similarity, new) revealed that visual similarity modulated memory accuracy (main effect of block, F(3,75) = 79.93,p,0.001, h2 = 0.762; block visual similarity interaction, F(9,225) = 2.88, p =0.003, h2 = 0.103) and response time (main effect of similarity on correct tri- als, F(3,72) = 5.14, p =0.003, h2 = 0.176). For the first learning block of overlapping pairs, performance was superior (Fig. 2A) when the linking item (B) was an exact match to the initially learned pairs (AB) relative to all other conditions. There was an effect of visual similarity in the first test block (effect of visual similarity in the first run, F(3,75) = 6.901, p,0.001, h2 = 0.216) but not in subsequent runs (F values 0.479, all p 0.698, all h2 0.019). In the first run, post hoc paired t tests revealed that accuracy was highest for pairs with an exact match relative to all other pairs (compared with high similarity: t(25) = 3.33,p =0.003, d =0.654; low similarity: t(25) = 4.52, p,0.001, d =0.894; new: t(25) = 2.74, p =0.011, d =0.539). Performance was greater for high similarity pairs than low similarity pairs (t(25) = 2.306, p =0.03, d=0.459). There was no difference in performance between the high similarity and new pairs (t(25) = 0.87, p =0.394, d=0.172) or the low similarity and new pairs (t(25) = 0.76, p =0.452, d=0.151). When collapsed across block, pairs with exact matches had the fastest response time (Fig. 2B) on correct trials (compared with all other conditions, t values 2.206, all p , 0.05, all d 0.445). Response time did not differ between the high similarity, low similarity, or new pairs (all t values 1.748, all p . 0.05, all d 0.348).
Visual similarity of the linking item also influenced a cross- episode inference accuracy (F(3,75) = 26.61, p ,0.001, h2 = 0.516). Participants were more likely to infer a relationship among indirectly related memory elements (AC) when the linking item (B) was an exact match or highly similar across overlapping pairs (Fig. 2C). Inference performance did not dif- fer between exact match and high similarity triads (t(25) = 1.20, p =0.24, d=0.230), but performance for exact match triads was superior to both low similarity triads (t(25) = 6.82, p,0.001, d=1.327) and new triads (t(25) = 6.61, p,0.001, d=1.286). Likewise, performance for high similarity triads exceeded low
similarity triads (t(25) = 5.05, p,0.001, d =0.987) and new triads (t(25) = 5.38, p,0.001, d =1.055). Follow with performance (Inference performance did not differ between the low similarity and new triads (t(25) = 1.17, p =0.254, d=0.224). However, per- formance for low similarity triads was reliably better than chance (t(25) = 2.22,p =0.04, d = 0.435), whereas performance for new tri- ads was not (t(25) = 0.47,p =0.64, d =0.093).
Inference decisions were also faster for the exact match and high similarity conditions relative to the new condition (F(3,72) = 11.79,p,0.001, h2 = 0.329), with inferences for the exact match condition being fastest overall (Fig. 2D). Response time was faster for exact match triads relative to high similarity conditions (t(25) = 3.41, p =0.002, d=0.669) and new triads (t(24) = 5.00, p,0.001, d =0.999), but no different from low similarity triads (t(25) =1.64, p =0.114, d =0.321). Response time was faster for high similarity triads compared with new triads (t(24) = 2.93, p =0.007, d =0.585), but did not differ from low similarity triads (t(25) = 1.11, p =0.28, d =0.217). Low similarity triads were faster than new triads (t(24) =3.86,p =0.001, d =0.773). Together, these findings show that associative memory and a cross-episode infer- ence, two processes that are thought to be supported by hippo- campal subfields (Schapiro et al., 2017), are influenced by the perceptual similarity of shared event elements, with facilitated performance with higher levels of a cross-episode similarity.
Reactivation of overlapping memories during learning To test how cortical memory reactivation during overlapping pair learning impacts hippocampal subfield representations, we first used a searchlight analysis to identify where information about the initial pairs was reactivated in the cortex during learning. Within each searchlight sphere, a pattern classifier was trained on data from a localizer phase and then applied to the overlapping pair study phase (Zeithamova et al., 2012). The searchlight identified regions in which classifier evidence for the target category of the related item (face or scene A items from the initial pairs) exceeded a baseline index of classifier evidence for the same category derived from the new (or nonoverlapping) trials. We found evidence that related memories were reactivated when learning the overlapping pairs in the posterior cingulate cortex, occipital cortex, and parietal cortex (Fig. 3A).
Importantly, there were no differences in reactivation strength as a function of A item category (face, scene) across regions identi- fied in the searchlight analysis (Fig. 3B). A repeated-measures ANOVA with the within-subjects factors of region (left parietal, right parietal, cingulate, superior occipital, inferior occipital) and stimulus category (face, scene) demonstrated that reactivation var- ied across regions (main effect of region, F(4,100) = 2.84, p =0.028, h2 = 0.102) but did not differ by stimulus category (main effect of category, F(1,25) = 0.002,p =0.967, h2 =0;category region inter- action, F(4,100) = 0.375, p =0.826, h2 = 0.015). Thus, our results were not driven by a single stimulus category and reflect memory reactivation rather than the engagement of category-specific proc- essing regions.
We further tested whether the visual similarity of the shared B item across learning influenced the strength of memory reactivation for the A items. We predicted that memory reactivation during learning would be stronger for pairs linked by a more visually similar item. Using a similar approach to the previous analysis, separate searchlight analyses identified regions where classifier evidence for the related A item was greater for the exact match condition than for the high and low similarity conditions. Consistent with our hypothesis, we found that the similarity of event components modulated the strength of memory reactivation in the left parietal cortex and occipital cortex (Fig. 3C).
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Memory reactivation impacts neural coding in hippocampal subfields
To test our hypothesis that reactivation of related memories during new encoding would lead to dissociable representation of overlapping memories in DG/CA2,3 and CA1, we quantified hippocampal subfield coding as a function of memory reactivation strength during learning. Both before and after learning the pairs, participants were scanned while viewing individual images of the A and C items from overlapping pairs in the high similarity condition (Fig. 1A). We indexed differentiation and integration by measuring learning-related changes in pattern similarity for indirectly related A and C items from the same triad (Schlichting et al., 2015). Similarity changes within the same triad were compared with a baseline of similarity changes between items in different triads. We measured differentiation by testing for a decrease in pattern similarity between A and C items after learning (Fig. 4A). In contrast, integration would be marked by increased pattern similarity among indirectly related A and C items, reflecting the formation of overlapping codes for related memories (Fig. 4A).
To assess the impact of memory reactivation during learning on neural coding of indirectly related memory elements, we calculated representational change for triads based on the strength of reactivation across overlapping learning trials. For each participant, we sorted overlapping pairs into those associated with stronger and weaker reactivation of the corresponding initial pair, based on a median split of averaged reactivation indices across all clusters identified in the reactivation searchlight (Fig. 3A). We then compared neural coding between indirectly related A and C items associated with different levels of reactivation. Critically, all analyses assessing representational change in hippocampal subfields were based on data from high similarity tri- ads only. This approach holds the visual similarity of the linking item constant, providing a critical test of whether memory reactivation mediates representational change above and beyond alterations in the physical environment.
We ran four searchlight analyses within individual hippocampal subfields to test for the effects of reactivation on learning-related representational change for indirectly related memory elements (Fig. 4B). First, we used two searchlight analyses to identify hippocampal regions that showed differentiation or integration of A and C items regardless of the degree of memory reactivation during overlapping pair learn- ing (DifferentiationOverall and IntegrationOverall, respectively) and observed no significant effects within the hippocampus.
Instead, we predicted that the representational similarity of indirectly related items in hippocampal subfields would depend on the strength of memory reactivation during learning- ing of the overlapping pairs. To test this hypothesis, we ran two additional searchlight analyses that looked for an interaction between learning-related representational change and memory reactivation; these searchlights isolated hippocampal regions showing either differentiation or integration on trials with stronger reactivation during overlapping pair learning (DifferentiationReactivation and IntegrationReactivation).
We found that stronger reactivation of initial pair memories during learning of the overlapping pairs had different consequences on the direction of representational change observed in hippocampal subfields. When initial (A) memories were strongly reactivated during overlapping (BC) pair learning, DG/CA2,3 pattern similarity decreased between A and C items from learning to post learning (Fig. 4C, D; DifferentiationReactivation). Subiculum exhibited the same pattern as DG/CA2,3, with stronger reactivation leading to decreased pattern similarity for A and C items. In contrast, CA1 showed an opposing pattern of representational change when memory reactivation was stronger, with increased similarity among A and C items after learning (Fig. 4C, D; IntegrationReactivation). These findings suggest that the representation of overlapping memories within hippocampal subfields is contingent on memory reactivation during learning, with the same conditions leading to dissociable representational codes within DG/CA2,3, CA1, and subiculum.
Finally, we performed a series of post hoc analyses on each hippocampal subfield identified in the searchlight analysis to further understand how reactivation modulated coding in each region. We first quantified whether there were any global shifts in neural similarity simply as a function of learning by calculating the across-triad D for unrelated A and C items (i.e., the across-triad baseline). Across-triad D was not significantly different from zero in CA1 (t(24) = 0.383, p =0.705, d =0.077) or subiculum (t(25) = 1.233, p =0.229, d =0.242), but was greater than zero for DG/CA2,3 (t(25) = 3.431,p =0.002, d =0.673). These results demonstrate the importance of comparing similarity change for related events to a baseline, as even unrelated items may change in similarity after learning.
Next, we compared the within-triad D for triads associated with strong reactivation to the across-triad D baseline as a validation of our searchlight results (Fig. 4D). As mentioned previously, a caveat to this analysis is that the results are potentially biased by selecting voxels identified in the neural coding searchlight analysis. Consistent with the predicted patterns of the searchlight contrasts (Fig. 4B), we found evidence for differentiation, whereby neural similarity change for triads associated with strong reactivation was less than the across-triad baseline in DG/ CA2,3 (t(25) = 2.298, p =0.030, d=0.451) and subiculum (t(25) = 3.158, p =0.004, d=0.619). Within CA1, we showed a trend for integration with greater similarity within triads associated with stronger reactivation after learning relative to the across-triad baseline (t(24) = 1.766, p =0.090, d=0.353). Together, these post hoc analyses support the outcome of the searchlight analysis and show that the representation of overlapping events in subfields is influenced by the reactivation of related memories during learning.
As an exploratory analysis, we also quantified within-triad D for triads associated with weaker reactivation during learning. We found evidence for integration in DG/CA2,3 (t(25) = 3.709, p =0.001, d=0.727) and a trend in subiculum (t(25) = 1.849, p =0.076, d =0.363), wherein D for triads associated with weaker reactivation was greater than that observed for the across-triad baseline. This result suggests that representational shifts in DG/ CA2,3 may vary as a function of the level of competition, which may be different when memories are strongly or weakly reactivated. No differences from baseline were observed for triads associated with weaker reactivation in CA1 (t(24) = 1.062, p =0.299, d =0.212).
Memory integration supports inference decisions
We used a Bayesian multilevel model to examine the relationship between similarity change after learning (i.e., integration or differentiation) and performance on the AC inference test. We also examined the relationship between reactivation of related memories during learning and inference performance. One participant was excluded from this analysis because of an insufficient number of voxels in CA1 (,10 voxels). We used an LBA model to simultaneously model inference accuracy and response times. We used Bayesian sampling with the model to estimate the slope of relationships between inference performance and triad-level variability in similarity change and memory reactivation. We first assessed whether the Bayesian sampling was converged. There were no divergences during sampling; for each parameter in the model, [Rhat] was,1.00102 and the effective sample size was at least 5225. These results indicate that the sampling successfully converged, and there were sufficient samples to estimate each parameter.
We used mean posterior parameters to simulate model responses and found that there was a good fit to the observed ac- curacy (Fig. 5A) and response times (Fig. 5B) on the inference test, with the exception of a small number of trials with very long response times. The mean slope parameters for learning-related change (Fig. 5C) were positive for subiculum (95% HDI = [0.043, 0.477], d =1.37) and memory reactivation (HDI = [0.005, 0.437], d=1.51). The slope parameters for CA1 (HDI = [ 0.189, 0.244], d=0.15) and DG/CA2,3 (HDI = [ 0.393, 0.102], d =0.50) were not different from zero. The 95% HDIs for the other model pa- rameters were as follows: A = [2.059, 5.601], t = [0.00,009, 0.515], m2 = [0.130, 0.812], s2 = [0.191, 0.831], sCA1 = [0.004, 0.458], sDG/CA2,3 = [0.010, 0.577], sSubiculum = [0.002, 0.408], and sReact = [0.0002, 0.327]. These results indicate that greater memory reacti- vation during learning and greater AC similarity after learning in subiculum predict faster and more accurate inference at the level of individual trials.
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Discussion
Our results indicate that reactivated memories guide how representations of related events are organized within the hippocampal circuit. Reactivation of prior memories during encoding of new, overlapping events predicted across-episode inference performance and had different consequences for representation in hippocampal subfields; strong reactivation led to differentiation of overlapping memories within DG/CA2,3 and subiculum, while simultaneously promoting the integration of those same memories in CA1. Prior work has focused on explaining hippocampal subfield coding in terms of a transfer function through which changes in environmental cues lead to differential neural output (Leutgeb et al., 2004, 2007; Lacy et al., 2011; Yassa and Stark, 2011). Here, we show that changes in perceptual input are not the only factor determining representation learning within hippocampal subfields. Rather, our data indicate that hippocampal subfield coding is further driven by the degree to which a new experience triggers the reactivation of related episodes. Our results thus extend prior findings to show, at a representational level, that cortical memory reactivation drives dissociations in hippocampal subfield coding in the face of competition between highly similar memories.
Prior work on hippocampal representation has primarily conceptualized subfield coding as an automatic process in response to environmental changes, wherein sensory inputs are assumed to be the main driver of hippocampal responses. For instance, early electrophysiological studies in rodents measured how to place field responses in hippocampal subfields remapped as animals navigated environments with gradually changing perceptual features (Guzowski et al., 2004; Lee et al., 2004; Leutgeb et al., 2004, 2007; Vazdarjanova and Guzowski, 2004). Such work revealed that small changes in environmental features led to dramatic changes in DG and CA3 responses, reflecting orthogonalization of input patterns. In contrast, CA1 responses changed gradually, scaling linearly with the amount of perceptual change between environments; for environments that were more perceptually similar, CA1 activity showed a greater overlap in responding. Prior work in humans took a similar approach, presenting participants with pairs of highly similar visual images (e.g., pictures of two different apples) and measuring the magnitude of hippocampal subfield responses to both images (Bakker et al., 2008; Lacy et al., 2011). In those studies, DG/CA2,3 showed a novelty response for both highly similar images, suggesting separate coding of the two images. CA1 and subiculum responses to the second, highly similar image from a pair, however, was suppressed relative to the presentation of the first pair member, suggesting a similar representation of the paired images.
While past animal and human work have revealed important dissociations between hippocampal subfield processing, our findings build on that work to show that hippocampal representation learning is not simply a passive process but instead is actively influenced by memory reactivation (Hulbert and Norman, 2015; Kim et al., 2017; Ritvo et al., 2019). We show that hippocampal subfield dissociations are most apparent when past memories are strongly reactivated, producing a competitive learning state that promotes differentiation in DG/CA2,3 and subiculum, simultaneously with integration in CA1. Our data thus indicate the need to quantify both the perceptual similarity among events and how overlapping perceptual features trigger memory reactivation to fully account for how dissociable representations emerge within the hippocampal circuit. One interesting aspect of the prior human work described above is that dissociations among sub-fields depended on the nature of the task being performed (Kirwan and Stark, 2007; Bakker et al., 2008; Lacy et al., 2011). When the critical experimental manipulation (i.e., the visual similarity among items) was incidental to the task participants performed, dissociations between subfields were observed (Bakker et al., 2008; Lacy et al., 2011). However, when the same stimuli and presentation procedures were combined with an intentional task focus, dissociations were less apparent (Kirwan and Stark, 2007). The mechanistic source of these divergent findings has yet to be revealed. By quantifying memory reactivation during tasks with an intentional or incidental focus, further insights might be gained into how task goals influence the dynamics of how memory competition impacts neural representation (Richter et al., 2016).
Our findings may be conceptualized in terms of supervised and unsupervised models of learning, which each focus on different learning targets. Whereas supervised learning is directed by matching representations to sensory cues observed directly in the environment, unsupervised learning adjusts representations to reduce competition between a current experience and reactivated memory representations triggered by the new event (Ritvo et al., 2019) through integration or differentiation. While learning likely reflects a balance between supervised and unsupervised mechanisms, our findings indicate that reactivated memories are an important facet of how dissociable coding strategies emerge across hippocampal subfields.
To date, only one other study in humans has used multivariate representational analyses to quantify a dissociation between hippocampal subfields, specifically when individuals retrieved information about shared or distinct spatial contexts (Dimsdale-Zucker et al., 2018). That study showed that items learned within the same spatial context elicited overlapping activation patterns in CA1 and differentiated patterns in DG/ CA2,3 during retrieval relative to items that did not share contextual information. The present findings differ from that study in several key ways. First, the prior study measured sub-field codes during memory retrieval, while our work reveals the active learning processes that drive the formation of dissociable subfield representations. Specifically, that prior study did not quantify how reactivation of similar memories, either during learning or retrieval, related to hippocampal subfield coding. Here, we show a dissociation in hippocampal subfield coding as a result of memory reactivation. Furthermore, we show that neural codes formed by hippocampal subregions not only support simple recognition (Dimsdale-Zucker et al., 2018) or spatial memory (Leutgeb et al., 2004, 2007), but also inference about the relationships among memories (see also Schlichting et al., 2014). Inference decisions were faster and more accurate with increasing similarity among indirect items after learning in the subiculum, indicating how to overlap- ping codes promote knowledge extraction beyond direct experience.
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Our finding that subiculum representations track inference decisions may reflect that subiculum is the output structure of the hippocampal circuit (O’Mara et al., 2001), which plays a key role in recollection (Viskontas et al., 2009; Lindberg et al., 2017). While the subiculum showed evidence of learning-related differentiation for overlapping pairs overall, our modeling data indicate that representational change in the subiculum reflects a continuum of responses. Increased integration (which can also be thought of as less differentiation) promoted faster and more accurate inference. Our results suggest that, when memories are more integrated (or less differentiated), the inference is facilitated by retrieving a stored connection between indirectly related items (Shohamy and Wagner, 2008; Schlichting et al., 2014); in contrast, differentiation might slow inference between two separate traces that would need to be retrieved and recombined at test (Koster et al., 2018).
Like subiculum, DG/CA2,3 exhibited learning-related differentiation of indirectly related memory elements when memory reactivation was stronger during encoding. However, it should be noted that DG/CA2,3 differentiation of overlapping memory elements was only observed relative to the unrelated, across-triad baseline; there was no change in similarity from learning to post learning for the indirectly related items on their own (Fig. 4D, inset). This finding is consistent with prior work showing hippocampal differentiation for related relative to unrelated events after learning (Favila et al., 2016; Dimsdale-Zucker et al., 2018), while also controlling for baseline changes in similarity that occur over time. Moreover, DG/CA2,3 showed evidence for memory integration when memory reactivation was weaker during learning, suggesting the potential for more nuanced representational dynamics in this region. For instance, memory competition elicited by reactivation may have a nonmonotonic relationship to representational change in DG/CA2,3 (Ritvo et al., 2019). Stronger reactivation may promote active differentiation; weaker or intermediate levels of reactivation may lead to integration, and no reactivation may produce non-overlapping representations that are separated via passive orthogonalization. This complex coding strategy could explain why DG/ CA2,3 shows evidence of differentiated (Kim et al., 2017) and integrated (Schapiro et al., 2012) representations under different circumstances. Alternatively, our results may reflect the use of a combined DG/CA2,3 region, the components of which are thought to exhibit different transfer functions between environmental cues and resulting memory representations (Yassa and Stark, 2011). The observed pattern of results indicates that quantifying memory reactivation along with representational change is necessary to fully understand how memory competition impacts representation learning in DG/ CA2,3.
In conclusion, our empirical findings support a recently proposed computational model of the hippocampal circuit (Schapiro et al., 2017); simulations from this model suggest that CA1 may represent relationships across events, whereas DG and CA3 representations may emphasize differences between similar episodes. Our findings align with these computational predictions, with CA1 forming integrated representations for similar memories, while DG/CA2,3 and subiculum differentiate those same experiences. Additionally, we show that hippocampal representations support novel inference, facilitating the discovery of unobserved relationships between distinct but related experiences. The present work further shows that hippocampal subfield dissociations are not a simple function of sensory input, but result from memory-based competition during learning. Together, the present study advances our understanding of how prior knowledge shapes how new events are represented within the hippocampal circuit, providing an empirical test of key predictions of computational models of hippocampal memory function.
