Intraobject And Extraobject Memory Binding Across Early Development Part 2

Memory Interference and Binding

Memory binding processes may be related to interference effects between similar memories (Darby & Sloutsky, 2015; Hedden & Park, 2003; McClelland et al., 1995; Yim et al., 2013). Before considering this hypothesis, we provide an overview of interference effects and how they may differ across development.

Memory binding refers to connecting the things to be remembered with existing experience, knowledge, emotions, and other information for better memory and understanding. This memory method can help us better remember and understand new information, and improve memory efficiency.

When we learn new knowledge, it is easier to remember and understand it if we can connect it to existing knowledge and experiences. For example, when you learn a new word, you can remember it faster if you can associate it with a scene or picture. Likewise, if you learn a new skill, you'll get better at it if you can relate it to similar skills and practice it over and over again.

Memory binding can also help us better leverage our emotional experiences to remember and understand information. For example, when we see a pleasing painting, we can remember it more quickly. Similarly, when we have some kind of emotional experience, such as joy, surprise, sadness, etc., it can better help us remember related information.

To sum up, memory binding can help us better remember and understand new information and improve memory efficiency. Therefore, we should try to connect new information with existing knowledge, experience, emotions, etc. in learning and daily life to better use our memory. It can be seen that we need to improve memory, and 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, thereby improving brain vitality and endurance.

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A long history of research suggests that memories are often more difficult to retrieve due to interference from other memories (see Anderson & Neely, 1996; Wixted, 2004; for reviews). Proactive interference occurs when new learning is more difficult as a result of memory for previously learned information, and retroactive interference occurs when retaining what was learned in the past is more difficult due to subsequent learning. 

Interference effects are often studied with paradigms in which participants learn to associate pairs of items (e.g., words or images) in one phase, and then learn to associate different combinations of the same items in a second phase. Less robust learning of the new combinations in the second phase reflects proactive interference, and reduced memory accuracy for the original combinations of items after the second phase reflects retroactive interference.

Although most work on interference has been performed with adults, developmental work has found evidence that not only are 4- to 7-year-old children susceptible to interference (Benear et al., 2021), but they may be more susceptible than adults to both proactive (Yim et al., 2013) and retroactive (Darby & Sloutsky, 2015) interference. Different mechanisms could modulate interference and explain this pattern of developmental change.

Some research has suggested that interference may be modulated by inhibiting competitors at retrieval (Anderson, 2003; Anderson et al., 1994; Hulbert & Anderson, 2020). For example, proactive interference could be resolved by temporarily inhibiting previously learned information that competes for retrieval of the newly learned information. Inhibiting this information would presumably make it more difficult to retrieve, producing retroactive interference until an additional process releases the inhibition. 

Children could be more susceptible to retroactive interference owing to stronger inhibition, although this would imply that children should be less susceptible to proactive interference, which has not been reported in the literature (see Yim et al., 2013, for evidence of proactive interference in children). Relatedly, initially learned information could be permanently unlearned, which would facilitate new learning of similar information but would necessarily impair memory for the first-learned information. Unlearning has sometimes been rejected in theories of memory (Slamecka, 1966), although some recent computational work has explored the possibility of unlearning processes in the context of memory aging (Darby & Sederberg, 2022).

Another possibility is that interference is modulated by memory-binding processes. Specifically, complex binding may help reduce interference effects by decreasing the similarity, and hence competition, between memories. This idea may be illustrated by the recognition memory interference paradigm of Darby and Sloutsky (2015). In this paradigm, participants learned to associate combinations of objects with cartoon characters across three phases. In Phase 1, participants learned that AB → X (i.e., objects A and B were associated with character X), CD → X, EF → Y, and GH → Y. In Phase 2, participants learned that AC → Y, BD → Y, IJ → X, and KL → X. 

Two of the associative triplets in each phase were overlapping, in that the same objects (i.e., A, B, C, and D) were recombined and associated with different characters across phases, whereas the other triplets were unique in that the objects were different across phases. In Phase 3, participants were again presented with the initial set of overlapping triplets learned in Phase 1 and faced a conflict: A, B, C, and D had each been associated with both X and Y. Simply binding individual objects to a character in each phase, then, would be expected to produce interference, as each item would be bound to both characters. By contrast, specific pairs of objects were only associated with a single character across phases of the task, such that complex binding of the two objects within each pair along with the character could allow for high performance without interference.

With this paradigm, Darby and Sloutsky (2015) found that 5-year-olds exhibited substantially more retroactive interference than adults in Phase 3, as measured by a greater drop in accuracy for overlapping relative to unique triplets. Because children experienced greater interference, the authors inferred that children likely formed simple binding structures, whereas adults likely formed more complex binding structures. However, this work did not formally characterize the formation of memory-binding structures, or developmental differences therein.

Importantly, participants in the Darby and Sloutsky (2015) paradigm learned to predict a character from pairs of objects, and memory for these pairings presumably relied to a large extent on extra object binding. As discussed above, however, intraobject binding may typically be less attention-demanding and more accurate (Asch et al., 1960; Ecker et al., 2007, 2013; van Geldorp et al., 2015; Walker & Cuthbert, 1998), suggesting that features within the same object may be bound more easily with each other and with other elements in a complex binding structure, potentially reducing interference effects. Prior work has demonstrated that retroactive interference can affect intraobject binding in working memory paradigms (Allen et al., 2006; Logie et al., 2009; Ueno et al., 2011), but we are unaware of work that has directly addressed potential differences in interference between intraobject and extraobject memory binding.

The Present Work

In the present work, we examine intraobject, extraobject, and complex memory binding with a variant of the Darby and Sloutsky (2015) interference paradigm. In this variant, characters are associated not with pairs of objects, but with pairs of shapes and colors. In Experiment 1, the shapes and colors are presented within the same object, requiring intraobject binding, whereas in Experiment 2 the features are spatially separated, requiring extraobject binding. In both experiments, the shapes, colors, and characters are recombined across different phases, creating the potential for interference.

We tested 5-year-olds, 8-year-olds, and adults in both experiments to examine developmental differences in binding and interference. These age groups were chosen because prior work has found evidence of decreases in interference effects between 4- to 5-year-old children and adults (Darby & Sloutsky, 2015; Yim et al., 2013), as well as continued development of complex binding beyond 7 years of age (Yim et al., 2013).

To formally hypothesize how information was learned and retrieved in this task, we developed a novel computational model. The model parameters estimated three kinds of binding: (a) simple binding between shape and color features, (b) simple binding between each separate feature and a cartoon character, and (c) more complex binding between a conjunction of the shape and color features and the character. In addition, the model included a mechanism by which previously learned associations could be forgotten if they conflicted with current learning.

We hypothesized that extraobject binding of spatially separated shapes and colors would be weaker than intraobject binding of these features, and, as a result, memory interference would be greater when extraobject binding is required. Additionally, we expected to find greater developmental differences in extraobject compared with intraobject binding. We begin by examining the development of intraobject binding in experiment 1.

Experiment 1: The Development of Intraobject Binding

The purpose of this experiment was to assess the development of intraobject memory binding about interference effects. Given evidence from prior work that intraobject binding may be less attention-demanding and more accurate compared with extraobject binding (Ecker et al., 2007, 2013; van Geldorp et al., 2015), we expected that developmental differences in binding of intraobject features would be minor. We also expected that recombining the shapes and colors in different phases of the experiment would produce relatively small interference effects.

Method

Participants—Forty-eight 5-year-olds (Mage = 5.08 years, SDage = .19, language = 4.74 – 5.51; 23 females, 25 males), 35 eight-year-olds (Mage = 8.50 years, SDage = .28, language = 8.01–8.99; 18 females, 17 males), and 30 adults (15 females, 15 males) participated in this experiment. The approximate sample sizes were chosen to be comparable to those of a previous study demonstrating developmental differences in interference effects using a similar paradigm (Darby & Sloutsky, 2015). See the Results and Discussion section, below, for a power analysis. 

Children were tested in local preschools and elementary schools located primarily in middle-class neighborhoods of Columbus, Ohio. They were recruited based on returned permission slips and received stickers for participating. Adults were recruited from introductory psychology classes and received partial course credit. This project was approved by The Ohio State University Institutional Review Board Protocol # 2004B042, “Comprehensive protocol for cognitive development research.”

Stimuli—Participants were presented with objects that varied by shape and color. There were eight shapes (e.g., square, circle, triangle) and eight colors (e.g., red, green, blue) that were combined in different ways into objects throughout the experiment. The combinations of shapes and colors were randomized for every participant. In addition to objects, participants were presented with two cartoon characters (Winnie the Pooh and Mickey Mouse), which were associated with the objects as described below.

Procedure—The experiment was presented with OpenSesame software (Mathôt et al., 2012), on computer screens with a resolution of 1920 × 1080. Children were tested individually in a quiet room in their preschool or elementary school and made responses on a touchscreen. Adults were tested in groups of up to four participants in the lab with standard screens and made responses on a keyboard.

Participants first completed a Learning phase for one set of contingencies (i.e., Set A), in which they learned, with feedback, to associate four objects with one of two cartoon characters across multiple trials (each object was always associated with a single character). Next, participants completed a Learning test phase for Set A without feedback. After this phase, a one-minute break was provided, during which children received a sticker and adults were asked to sit quietly. These Learning and Learning test phase procedures were then repeated for Set B, which included four new contingencies, including two Overlapping contingencies, in which shapes and colors previously seen in Set A were recombined to make new objects that were associated with a different character, as well as two Unique contingencies, involving objects with new shapes and colors (see Figure 1C). 

Learning and testing of Set B associations may be assessed to measure proactive interference, which we infer from reduced accuracy in Set B compared with Set A, particularly for Overlapping contingencies. After these phases, a second break was given. Set A was then revisited with a second Learning test to measure retroactive interference, or reduced memory for overlapping Set A contingencies. Finally, a Binding test phase was administered, which further tested memory for the contingencies learned in both Sets A and B in an interleaved manner. See Figure 1A for an illustration of the sequence of task phases. Details on the procedure of each phase type are provided below.

Learning Phases.: Before the first Learning phase, participants were instructed that they would be shown different objects along with their friends Winnie the Pooh and Mickey Mouse, that each object belonged to one of these friends, and that their job was to figure out whether each object belonged to “Pooh Bear” or Mickey. Therefore, while participants were informed that they needed to learn object-character associations, they were not given instructions on what specific kinds of associations they should form. For example, participants were not informed that they should try to remember the specific shape-color combinations within objects, and they were not told that some of the shapes and colors would be recombined later in the task.

On each trial of the two Learning phases, a single object was shown to the participant, centered near the bottom of the screen, along with two cartoon characters (Winnie the Pooh and Mickey Mouse), which were situated in the top two corners of the screen (see Figure 1B). The position (left or right) of the characters was randomized for each participant but remained consistent throughout the experiment. 

The task of each Learning trial was to determine whether the presented object belonged to “Pooh Bear” or Mickey. Child participants made responses by touching one of the two characters on the touchscreen monitor; adults responded by pressing the left or right arrow key. After each response was made, feedback was given in the following ways: (a) the correct character appeared above the object, regardless of response accuracy, (b) text appeared giving explicit feedback (e.g., “Awesome, that object belongs to Mickey!”), which was read aloud to children but not adults, (c) a smiling or frowning face was shown for correct and incorrect responses, respectively, and (d) an auditory tone was presented (a high tone for correct responses or a low tone for incorrect responses). Each Learning phase included four blocks of eight trials, for a total of 32 trials; each object was presented twice per block, for a total of eight presentations across the phase.

Learning Test Phases.: The Learning test procedure was identical to that of the Learning phases except for the following changes. First, no feedback was provided on any trial; instead, the characters remained on the screen, but no object was present for an intertrial interval of 200 ms. Additionally, only two blocks of eight trials were presented in each Learning test phase. As in the Learning phases, only one set of objects was tested in each Learning test phase.

Binding Test Phase.: The purpose of the Binding test phase was to further probe the memory binding structures learned in the experiment. In each trial, participants were shown a character (either Winnie the Pooh or Mickey Mouse) in the center of the screen, and five objects positioned along an invisible horizontal line near the bottom of the screen (see Figure 1B). Participants were told that one of the presented objects had belonged to the given character earlier in the task and that their job was to identify that exact object. Children responded by touching the chosen object on the screen; adults responded by pressing a corresponding number key on the keyboard.

Importantly, one feature (i.e., the shape or color) was given to the participants as a cue, that every answer choice had the same feature value (e.g., all answer choices were blue). The second feature differed among the choices, requiring memory for an association with the given feature and character; we refer to this as the tested feature. Each object from both sets was tested twice per block; shape was the tested feature on one of these trials, and color was the tested feature on the other. The order of trials within each block was randomized for each participant. A total of 32 trials were presented in the phase.

On every trial, one of the object choices was correct (i.e., had been associated with the given character earlier in the task) and the remaining four were incorrect. Three of the incorrect foils always had values of the tested feature that had been part of (a) an Overlapping object (which had been presented during the task but was associated with the other character), (b) a Unique object from Set A, or (c) a Unique object from Set B. For each of these categories, the particular feature value was randomly chosen from the available options for each trial. The fourth foil always had a new feature, randomly selected on each trial from two shapes or two colors that were not shown during the Learning phases. The spatial position of the five response options (referred to as Correct, Overlapping, Unique A, Unique B, and New) was randomized for every trial. See Figure 1D for examples of answer choice arrays.

We designed the Binding test so that participants could make use of different binding structures to narrow their response options. For example, we reasoned that if participants had bound the character to values of a single feature (i.e., shapes or colors), they could exclude response options with a tested feature that had not been associated with the character (e.g., the cross and diamond shapes at the top of Figure 1D). Similarly, intraobject binding of the shape and color would allow the participant to exclude objects with shape-color combinations that had not been seen in the Learning phases (e.g., the blue cross, blue square, and blue diamond in Figure 1D). 

Notably, shape-color binding would allow perfect accuracy for Unique objects, but not for Overlapping objects, as the Overlapping object foil had been seen previously (e.g., the blue star in Figure 1D), but was associated with the other character. Complex binding of the shape and color within the object as well as the character, however, could be used to correctly identify the correct answer choice in all trials. To quantitatively estimate the extent to which these binding structures were formed by each participant we constructed a computational model, which is summarized in the Results section and presented in detail in the online supplemental materials.

Analyses—We conducted all analyses with hierarchical Bayesian methods, including conventional regression models and our novel computational model. Hierarchical Bayesian approaches allow estimation of model parameters for each age group, while properly accounting for variability between participants. In addition, these methods allow for the estimation of posterior distributions of parameter values, which inherently provide information about uncertainty in parameter estimates, such that broad distributions indicate greater uncertainty in the estimates.

In addition to examining posterior distributions of parameter values for each age group, it is possible to compare these distributions between age groups. To do so, we applied a technique to calculate the overlap between two distributions based on kernel density estimates (Pastore & Calcagnì, 2019). Briefly, the overlap metric, η, calculates the area shared by two distributions, compared with the total area. For two completely nonoverlapping distributions, η = 0, whereas for two identical distributions, η = 1. This is a continuous measure between 0 and 1, but for ease of exposition, we consider η<.05 values to be very strong evidence of a difference between groups (Darby & Sederberg, 2022).

Transparency and Openness—We have reported above our sample sizes and how they were determined, as well as the data exclusion procedure. We also reported all experimental manipulations and measures. All analyses were conducted in Python (Van Rossum & Drake, 2011), and all models were implemented with the Python library RunDEMC (https:// github.com/compmem/RunDEMC). The study’s design and analyses were not preregistered. The data and model code associated with this study are publicly available at https://osf.io/ x9m83/.

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