Salient experiences enhance mundane memories through graded prioritization

Abstract

Salient experiences open temporal windows that boost otherwise mundane memories encoded before and after pivotal events. A proposed feature of this phenomenon is its selectivity: Salient stimuli preferentially strengthen weak memories that share semantic connections. However, evidence in humans remains inconclusive, and a key question persists: Which factors determine the presence and magnitude of memory enhancement within current neurobiological and behavioral frameworks? We present results from 10 independent studies with a total of 648 participants and provide clear evidence of both retroactive and proactive enhancements in weak memories, directly addressing ongoing debates about the existence of these effects. Notably, stronger salience learning facilitates proactive, but not retroactive, memory enhancement, challenging prevailing theories about salience's role in these processes. Instead, retroactive enhancement depends on the proximity between incidentally encoded and conditioned stimuli in a high-level feature embedding extracted from a convolutional neural network, revealing a graded prioritization mechanism. These findings offer insights into the mechanisms driving the consolidation of everyday experiences.

Fig. 1.. Behavioral tagging hypothesis.

Imagine yourself walking through the Yellowstone National Park. Then, you suddenly notice a herd of bison grazing in the nearby plains. In your excitement, you take a quick picture, eager to share the experience with your friends back home. When you reflect on this moment, the sight of those magnificent bison serves as a salient event, leaving a strong impression on your memory. Furthermore, the strong event can enhance weak memories that were encoded before and after it, as illustrated metaphorically by the water leaking from the “strong event” river into the “weak event” streams. The salient event creates a temporal penumbra, lasting several hours both before and after the event. According to the behavioral tagging hypothesis, this process involves weak memory “tags” attaching to synapses of neurons. If neighboring neurons are stimulated by a salient event, they release plasticity-related proteins that can be captured by these synaptic tags, stabilizing the memory into long-term storage. However, if there is no stimulation of neighboring neurons, these synaptic tags fade over time. The hypothesis suggests that both spatial and temporal components in neural activity are essential for strengthening initially weak memories that have occurred or are about to occur. PRPs, plasticity-related proteins.

Fig. 2.. Category specificity of memory enhancement.

(A) Task in experiments 1 to 3. During the encoding session, participants either categorized images (animals or tools) or performed a match-to-sample task. In each trial, participants performed either task after the images had disappeared. During conditioning, successful task performance for one image category was associated with high reward, whereas the other category received low reward. A surprise recognition memory test occurred 24 hours later. Participants observed a mix of images from the previous day along with new images and reported whether each image was “definitely new,” “maybe new,” “maybe old,” or “definitely old.” Task, image categorization (experiments 1 and 2) or match-to-sample task (experiment 3); ITI, intertrial interval. (B to E) Results of experiments 1 to 3 (Dataset 1). HR, high reward; LR, low reward. (B) Presence of selective RME and PME in experiment 1. (C) Significant RME and PME were observed for animal images. †P = 0.062. (D) No significant RME or PME for tool images. (E) In Dataset 1, recognition performance for animals was significantly lower than that for tools. (F to I) Mega-dataset results. (F) Presence of selective RME and PME in the mega-dataset. (G) Salience conditioning improved the recognition performance for animals across phases. (H) The recognition performance for tools was enhanced by salience only during the conditioning and postconditioning phases. (I) Recognition performance for animals was significantly lower than that for tools in the mega-dataset. In the box-violin plots, the boxes represent interquartile ranges. Horizontal lines within boxes indicate medians, whereas diamonds represent means. The error bars show the 95% CIs. A, animal category; T, tool category; HS, high salience; LS, low salience. *P < 0.05; **P < 0.01; ***P < 0.001; n.s., not significant.

Fig. 3.. Dissociable influence of memory tag strength and salience learning in RME and PME.

(A) Two scenarios of category-specific enhancement in the mega-dataset. In Scenario I, pronounced enhancement within the weak-memory category drives the overall selective memory enhancement, despite no enhancement in the other category (top). In Scenario II, both weak and strong categories show moderate memory enhancements, jointly contributing to the overall selective memory enhancement (bottom). The dashed line marks a theoretical significance threshold. Weak: (relatively) weak memory category, animal in the present study; Strong: (relatively) strong memory category, tool in the present study. Note that animals showed weaker memories than tools in the mega-dataset, and this pattern did not differ between RME/PME present and absent studies (all P > 0.555). (B and E) Studies with a significant overall RME (B) conform to Scenario I: Only the weak memory category shows retroactive enhancement. Studies with significant PME (E) fit Scenario II: Both categories exhibit proactive enhancement. Violin plots (right y axis) display the distribution of recognition performance for participants receiving high versus low rewards for animals and tools; bar plots (left y axis) depict the difference between high-reward and low-reward conditions, with error bars representing 95% CI. (C and F) Combining RME absent studies (C), neither an overall RME nor RME in either image category was observed. However, when combining PME absent studies (F), the weak memory category continues to contribute to a significant overall PME, coherent with Scenario II. (D and G) The salience learning strength, measured as the average recognition performance for the conditioning phase stimuli, correlates with PME but not with RME. The shaded areas in the correlation plots represent the 95% CI.

Fig. 4.. Retroactive graded memory prioritization based on high-level image features.

(A) Left: Architecture of the ResNet-18 CNN used to extract image embeddings (see Materials and Methods). Features were extracted from the global average pooling layer (Pool 5), which aggregated spatial information from preceding layers into a compact representation. Right: Illustration of computing Euclidean distances between image vectors in a simplified three-dimensional space (note that the actual feature embedding space is 512-dimensional). (B) Distribution of MAR for preconditioning stimuli across experiments 1 to 3 (A, animal images; T, tool images). (C) Left: MAR for preconditioning animal images, but not tool images, correlated with each image’s distance from the centroid of high-salience images in the same category during conditioning. The MAR for each image was calculated by dividing the percentage of participants who correctly identified the image under the high-reward condition by the corresponding percentage of the same image under the low-reward condition. Middle: No analogous graded pattern emerges for PME. Right: Graded prioritization shows up only in Pool 5 (high-level) features, not in Pool 1 (low-level) features (see Materials and Methods). T1, tool images, output from the Pool 1 layer; T5, tool images, output from the Pool 5 layer; A1, animal images, output from the Pool 1 layer; A5, animal images, output from the Pool 5 layer. (D) Distribution of MAR for incidentally encoded images during the preconditioning phase in a t-distributed stochastic neighbor embedding (t-SNE) two-dimensional space, showing individual preconditioning images (white dots) and the centroid for all high-salience images during conditioning (stars). **P = 0.008 (OR P < 0.01).