The Fate Of Visual Long Term Memories For Images Across Weeks in Adults And Children Part 1

What is the content and the format of visual memories in Long Term Memory (LTM)? Is it similar in adults and children? To address these issues, we investigated, in both adults and 9-year-old children, how visual LTM is affected over time and whether visual vs semantic features are affected differently. In a learning phase, participants were exposed to hundreds of meaningless and meaningful images presented once or twice for either 120 ms or 1920 ms. Memory was assessed using a recognition task either immediately after learning or after a delay of three or six weeks. 

There is an inseparable relationship between visual memory and memory. Visual memory refers to memory acquired through visual sensations, including images, colors, shapes, etc., which can help us preserve specific details of what we have experienced. Memory refers to people's ability to save, process, and retrieve information.

The importance of visual memory to memory is self-evident. In learning, we need to acquire a large amount of knowledge, details, and information through visual memory. These knowledge and details need to be remembered to make better use of them. If we cannot save this information well, it will become very difficult to use it in the future.

Visual memory and memory can reinforce each other. Through multiple observations, memorization, and review, the ability of visual memory can be strengthened and improved. At the same time, good memory can also help people better record and preserve visual memories. Therefore, for people who want to improve their memory, it is also very important to strengthen their attention to visual details and continue to review and exercise.

In short, there is an inseparable relationship between visual memory and memory. We need to pay attention to and continuously improve and strengthen these two abilities so that they can better help our lives and learning. Strengthening visual memory can not only improve memory ability but also allow us to have a deeper understanding of the content of memory, thereby making our study and life more exciting. It can be seen that we need to improve our memory. Cistanche deserticola can significantly improve memory, because Cistanche deserticola can also regulate the balance of neurotransmitters, such as increasing the levels of acetylcholine and growth factors. These substances are very important for memory and learning. In addition, meat can also improve blood flow and promote oxygen delivery, which can ensure that the brain receives sufficient nutrients and energy, thus improving brain vitality and endurance.

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The results suggest that multiple and extended exposures are crucial for retaining an image for several weeks. Although a benefit was observed in the meaningful condition when memory was assessed immediately after learning, this benefit tended to disappear over weeks, especially when the images were presented twice for 1920 ms. This pattern was observed for both adults and children. Together, the results call into question the dominant models of LTM for images: although semantic information enhances the encoding & maintaining of images in LTM when assessed immediately, this seems not critical for LTM over weeks.

How are the landscapes of your last trip, the layout of the bedroom in which you grew up, and the face of your teacher when you were eight years old seared into your memory? How are images from unique visual episodes encoded, then consolidated to emerge as memories or recycled in the construction of new percepts? Studying the formation and the consolidation of sensory memories raises the problem of the content and format of such memories in Long Term Memory (LTM). In this respect, the present study aimed to investigate how visual LTM is affected by time and whether visual features vs semantic/conceptual information in visual LTM are affected differently over weeks. This question was examined in both adults and children.

In a closely related field, the literature on mental imagery has traditionally opposed two main classes of hypotheses to account for the coding of images in LTM. The first refers to the propositional position, which assumes that symbolic codes are used for LTM (for reviews1,2 ). These codes represent something conceptual and sometimes arbitrary as opposed to perceptual. In this view, coding in memory would be a sentence-like description of the image. By contrast, the functional-equivalency hypothesis supposes that the coding of images in memory has the same structure as the information being represented–5. In this view, symbolic codes are not required to account for LTM. At the interface, the dual-code theory assumes that both analog (or perceptual codes) and arbitrary symbols or verbal codes are used when retrieving representations of pictures from memory6,7.

Questions about the content and format of visual memories have also been addressed in the field of the perception of visual scenes through research aimed at assessing both the capacity of visual LTM and the fidelity of our representations of visual stimuli. In the 1960s and 70s, research using large-scale memory procedures revealed that people have an extraordinary capacity to remember thousands of images presented for only a few seconds each8,9. These studies concluded that the number of visual items that can be stored in LTM is potentially unlimited, that such memories last for at least several days, and that memory performance depends primarily on the distinctiveness between the target stimulus and the concurrent stimulus (foil stimulus) in the memory task (e.g., recognition)10. Nonetheless, because of the substantial visual and semantic heterogeneity between the used stimuli, those studies did not provide relevant information regarding the coding of visual memories into LTM.

Tree decades later, this issue received renewed interest following research reporting the phenomena of change blindness and inattentional blindness11. The dramatic inability to detect even massive changes in the visual input led many authors to claim that memory representations for real-world stimuli are impoverished, sparse, volatile, and lack visual details12–16. Influential theories in the early 2000s postulated that representations in visual LTM are gist-like and semantic (e.g.17). This position was later examined and undermined. The ability of participants to detect changes when they are tested with either forced-choice paradigms or with longer exposures provided strong evidence that visual episodes leave a more complete memory trace that includes "visual" (or perceptual) information and not just the gist18. Large-scale memory studies have subsequently strongly supported this conclusion, showing the massive capacity to store visual details from objects or scenes in visual LTM (for reviews19,20). 

For instance, participants initially exposed to 2500 objects for 3 seconds performed at 92 % in a two-forced choice recognition task when the target and the foil stimulus belonged to a different category, 88% when they belonged to the same basic-level category, and 87% when the same object was presented in a different state or pose21.

Recent research aimed at determining what makes an image memorable suggests, nonetheless, that high-level properties, such as distinctiveness, atypicality, emotional valence, and semantic attributes strongly contribute to its memorability. In contrast, low-level image properties, such as the salience, color, or other simple image features make relatively weak contributions22–24. While objects without semantics might not be effective at predicting memorability, the presence of semantic labels associated with objects or photographs could improve it. For example, the possibility to provide a single label for each image (i.e. a single gist) might explain most of what makes an image memorable22. Scene semantics would therefore be a primary substrate of memorability.

Thus far, most models and theories of VLTM give more weight to conceptual features than perceptual features in the coding used to retrieve visual representations in memory25–29. "Being perceptually rich and distinctive might be not sufficient to support VLTM. (…) VLTM representations are hierarchically structured, with conceptual or category-specific features at the top of the hierarchy and perceptual or more category-general features at lower levels of the hierarchy" (Brady et al., 2011, p1919). According to Mary Potter (2012a, p128), "although some specific visual information persists, the form and content of the perceptual and memory representations of pictures over time indicate that conceptual information is extracted early and determines most of what remains in LTM".

However, in most studies on visual LTM, the contents of memory were examined either immediately after learning or the next day. Thus, the question of how memories for images evolve remains unanswered. Yet this issue is crucial to determine how visual representations are transformed and consolidated into visual memories. In this framework, the goal of this study was to examine how visual and semantic features were affected by delays and to test whether the hypothesis according to which "conceptual information is extracted early and determines most of what remains in LTM" extends to memories that persist beyond several weeks. This hypothesis was examined in both adults and nine-year-old children.

The literature on memory development across the life span suggests large developmental differences in many aspects of memory, especially working memory30 and declarative memory31,32. Nonetheless, visual recognition memory is usually thought to be an early emerging form of memory, which can be measured from the first months of life33. Using an abbreviated version of the materials developed by Brady et al. (2008), Ferrara, Furlong, Park, and Landau34 reported impressive visual memory performance by four-year-old children, both in terms of the large number of items and the level of details required for recognition. Although the number of images was substantially less than in the experiments conducted in adults, the patterns of results were similar. However, to our knowledge very few studies, if any, have examined how memory for images evolved over weeks and whether this evolution differed across the development.

In this framework, we investigated, in both adults and nine-year-old children, how the recognition of images evolves, depending on whether they are meaningful or meaningless (Fig. 1). Te meaningful images were photographs of real-world scenes or objects. They were supposed to be easy to label (i.e. the gist was supposed to be automatically extracted). The meaningless images were abstract paintings, fractal images, or complex geometrical figures and were supposed to have no meaning a priori. This assumption has been validated in a pilot experiment. In this experiment, participants had to give a single label to those images presented once or twice during a learning phase. 

The results showed that for the meaningful images presented twice, the participants provided the same single label 85 % of the time. In reverse, they had much more difficulty in providing a label to the meaningless images and this label was consistent between the two exposures only 35% of the time. Moreover, this label was mostly related to the global colored pattern of the image, and the same label was used for many different images. Thus, in the framework of this study, we considered meaningful, images that could be designated with a single label (i.e. a single gist), and meaningless, images that were not derived from real-world, and for which the gist is not the given a priori, and not extracted automatically.

The experiment included two phases. In a learning phase, participants were exposed to hundreds of meaningless and meaningful images. Because most models on visual memory have been based on research using Rapid Serial Visual Presentation (RSVP) procedures or large-scale memory procedures (for example that combine both procedures, see 35,36), two exposure durations were examined. Indeed, based on this literature, exposure duration seems to have a different impact on memory performance and specifically on the extraction of visual vs. semantic features. Thus, change blindness might be due to a lack of encoding time or attention to each object instead of memory limitations for visual details37. 

Because we assumed a strong impact on the duration, the images were presented for either 120 ms or 1920 ms during the learning phase. We also examined the impact of another factor that potentially plays a critical role in memorization, that is, the repetition of the images. Indeed, we expected that a single exposure might not be sufficient to maintain an image for a very long term in memory. Thus, the images were presented either once or twice during the learning phase.

Immediately after the learning phase, or after a delay of three weeks or six weeks, the memory of the participants was assessed through a recognition task that included old and new meaningless and meaningful images. Among the new meaningful images, some belonged to a basic-level category not used during the learning phase (novel images), and some belonged to a basic-level category that had already been used during the learning phase (exemplar lures). This is illustrated in Fig. 2.

Participants were first asked to judge whether the image was old or new and then to indicate how confident they were in their response using a 4-point confidence scale ("Confidence? 1= just guessing, 2 = not sure, 3 = confident, 4 = very sure). Collecting those confidence ratings aimed at determining the most relevant measure to compare meaningful and meaningless conditions, given potentially different response biases in the meaningful and meaningless conditions 38. An examination of receiver operating characteristic curves (ROC), derived from signal detection theory (SDT) should help to provide the best model to apply to our data39.

The hypothesis that semantic information is extracted earlier and determines most of what remains in LTM28 leads to four predictions: (1) For very brief exposures, only meaningful images should be accessible to recognition; (2) Meaningless images should be more subject to forgetting over weeks than meaningful images; (3) False recognition for the exemplar lures should be more numerous than false recognition for novel images, and this effect should increase over time. Indeed, if only the gist is retained across weeks, more and more confusion between the old images and the exemplar lure images should be observed. (4) Concerning the developmental aspects, we expected lower performance in children. Nevertheless, given the literature on children's visual memory, similar patterns of results might be observed in nine-year-old children and adults 33,34. Given the weakness of the literature in the field, this question remains nevertheless very exploratory.

Results and discussion

The hits (i.e. when the image is old and the participant's response is old) and the false alarms (FA, i.e., when the image is new and the participant's response is old) observed in the recognition task depending on the type of images, the exposure duration (120 vs. 1920 ms), the number of exposures (1 vs. 2), the delay (immediate vs. 3-weeks vs. 6-weeks) and the age of participants (adults vs. children) are shown in Supplementary materials, Tables S1 & S2. The ROC curves in each condition derived from the confidence ratings are also shown in Supplementary materials, Figs. S1 & S2. 

Examination of the zROC (which corresponds to z scores of hits and FA plotted as coordinates) revealed a slope almost always different than 1, suggesting Gaussian distributions of unequal variance in the participants' responses. Therefore, recognition accuracy was calculated using the discriminability measure of da 38. Each day was computed separately from the false alarm and hit rates for each participant, for each type of image (meaningless vs. meaningful), and for exposure conditions (120 vs. 1920 ms and 1 vs. 2 exposures). Each day was also corrected by the slope of the zROC in each condition. Te da was calculated as follows:

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