Intraobject And Extraobject Memory Binding Across Early Development Part 3

Results and Discussion

Three 5-year-olds were excluded because of computer failures, and an additional 5-year-old was excluded for not completing the task. Because children and adults made responses in different ways (a touch screen and keyboard, respectively), we could not analyze response times, but we excluded individual trials with very fast (i.e., < 200 ms) responses from all analyses because they were likely unintentional. The percentage of excluded trials was 1.3% in 5-year-olds, .2% in 8-year-olds, and 0% in adults. 

Children's reactions and memory are closely related. A good reaction speed can help a child process information and learn better, while a strong memory can help a child better recall and apply what they have learned.

A child who can easily judge things and react quickly will be more successful in learning. These children can process large amounts of information efficiently and react quickly when needed. Children develop a confident attitude and perform better when they are asked to respond quickly in all aspects of school and life. In addition, their quick reactions will also give them better hand-eye coordination and more motor skills to meet the needs of their healthy growth.

On the other hand, a strong memory is also very important. The amount of information children need to remember is overwhelming, including tasks such as lessons and homework in school. If a child does not have a good memory, they are likely to have problems with school tasks and homework. If children forget what they should do, or forget what the teacher taught in class, they will encounter blind spots and omissions in their learning.

Fortunately, we can help children improve their reflexes and memory through exercises and training. For example, we can engage children in challenging activities such as memory card games, painting, music, etc. to help them optimize their memory and reaction speed. We can also encourage children to engage in more outdoor activities and sports to improve their physical fitness.

In the process of parenting, we need to encourage children to constantly try new things and actively participate in various activities in school and family life so that they can get more opportunities to practice. We need to give children positive encouragement and support so that they can confidently face various challenges in life so that their reaction speed and memory ability will be quickly improved. It can be seen that we need to improve our memory. Cistanche deserticola can significantly improve memory because Cistanche deserticola is a traditional Chinese medicinal material with many unique effects, one of which is to improve memory. The efficacy of minced meat comes from the various active ingredients it contains, including acid, polysaccharides, flavonoids, etc. These ingredients can promote brain health in various ways.

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After excluding individual trials, we excluded all data from participants who did not perform well in both the Learning and Learning test phases, because it would be difficult to make inferences about interference or memory binding in participants who did not understand the task or were not paying attention. Specifically, for every participant we performed two one-tailed binomial tests. The first test combined all trials from Blocks 2–4 of the Set A Learning phase with Unique trials from Blocks 2–4 of the Set B Learning phase. We excluded the first Block of these phases because we anticipated accuracy near chance when the contingencies were first presented, and we excluded Overlapping trials from Set B because we reasoned that accuracy might be lower in those trials due to proactive interference. 

The second statistical test combined all trials from the first Learning test of Set A with Unique trials from the Learning test of Set B. The reason for excluding Overlapping trials for Set B was again the possibility of proactive interference. We excluded 12 five-year-olds, five 8-year-olds, and zero adults whose accuracy was not above chance on both binomial tests.1 The final sample included 32 five-year-olds (Mage = 5.08 years, SDage = .20, language = 4.74-5.51; 13 females, 19 males), 30 eight-year-olds (Mage = 8.54 years, SDage = .28, language = 8.05–8.99; 15 females, 15 males), and 30 adults (15 females, 15 males). 

To examine whether these final sample sizes were appropriate, we conducted a power analysis based on the overall accuracy in the Binding test phase for 5-year-olds and 8-year-olds. The power to detect a difference between five-year-olds and eight-year-olds was 1.00 in this experiment, indicating these sample sizes were more than sufficient to detect an age effect in memory binding accuracy.

The data for this experiment are presented in Figure 2 and Table 1. To analyze the data, we conducted a series of hierarchical Bayesian linear regressions predicting mean accuracy in different task phases. These analyses are presented in full in the Hierarchical Bayesian Regression Models and Regression Model Results sections of the online supplemental materials. 

Overall, the results suggest strong overlap-specific interference effects in all groups. Specifically, although we found little evidence of proactive interference when learning or being tested on Set B contingencies, we found strong evidence of retroactive interference in the second Learning test for Set A (i.e., in Learning test 2A, following learning and testing of Set B), as accuracy was substantially lower for Overlapping compared with Unique contingencies. Interestingly, accuracy also tended to be lower in Learning Test 2A overall. There are multiple reasons why this may have been the case: memories may have decayed across time, participants may have been fatigued at this later stage of the experiment, or there may have been some interference from other contingencies that were not specific to stimulus overlap. 

We also found strong evidence of interference specifically attributable to stimulus overlap in the Binding test phase, as accuracy was substantially lower for Overlapping compared with Unique contingencies. We also found evidence of lower accuracy overall for 5-year-olds than the older age groups in the Binding test phase. The results of strong interference and developmental differences were somewhat surprising given that the shape and color features were presented within objects and we expected intraobject binding to be relatively strong in all age groups.

However, it is not clear from these results what associations were formed or how these binding structures may have differed between the age groups: participants may have formed simple binding structures between the individual shape or color features and the character, between the shapes and colors together within objects, and potentially between both features and the characters, which would be considered by a complex binding structure. It is difficult to estimate the extent to which these binding structures were formed with standard statistical models such as regression. Instead, we need a model that predicts both correct responses and various types of error responses based on the underlying representation (i.e., the binding structure). To formally estimate the formation of different binding structures and developmental differences in these processes, we created a generative computational model.

Computational Model Description—To formally characterize the binding processes that we hypothesized to underlie performance in this experiment, we developed a new computational model (for a full description of the model, see the Generative Computational Model section of the online supplemental materials). The model assumes that participants’ responses were supported by the strength of associations learned during the experiment.

In the model, matrices MFF, MFC, and MFFC store associations between the shape and color features (i.e., feature-feature bindings, or FF), between each separate object feature (shape or color) and a character (i.e., feature-character bindings, or FC), and between a conjunction of both features together within the object and a character (i.e., feature-feature-character binding, or FFC), respectively. These matrices are updated on a trial level to simulate binding processes and probed by memory cues to simulate retrieval.

Each binding structure is bidirectional: for example, as a shape is associated with a character, the character is also associated with the shape (see Figure 3). Associations between different elements are increased in the three matrices on a trial level, scaled by learning rate parameters specific to each type of binding: αFF, αFC, and αFFC. The different binding structures affect the model’s performance in different ways. FF associations do not affect performance in the Learning or Learning test phases, but in the Binding test contribute to Correct responses for Unique contingencies and to both the Correct and Overlapping response options for Overlapping contingencies. 

In Experiment 1, intraobject FF binding was encouraged by presenting shapes and colors within the same object, whereas in Experiment 2 the features were spatially separated to encourage extraobject binding (Figure 3, upper panel). FC associations contribute to accurate responses in the Learning and Learning test phases but can also contribute to the inaccurate response for Overlapping contingencies after Set B is introduced; these associations may also be used in the Binding test to increase the probability of choosing response options that have been associated with the given character. Finally, complex FFC associations contribute only to the Correct response for both Overlapping and Unique contingencies in all phases of the task.

In addition to strengthening these types of associations, the model can “forget” by weakening previously formed associations that conflict with current learning. For example, when learning Overlapping contingencies in the Learning phase for Set B, each object feature has been previously associated with the other character through FC binding, and the model can weaken those competing associations to facilitate greater accuracy, and hence reduce proactive interference, while at the same time increasing retroactive interference later when Set A is revisited in Learning test A2. 

The effect of forgetting, then, is similar to an inhibition process as discussed in the Introduction, although we did not include a mechanism by which forgetting could be reversed other than new learning, whereas inhibition is often considered a temporary phenomenon (Geiselman & Bagheri, 1985). As a result, the forgetting mechanism in the model could be considered an unlearning process. The extent of this forgetting process affecting all binding structures was controlled by one additional parameter, β.

These learning and forgetting processes are defined mathematically in the model. Although the full model equations are provided in the online supplemental materials, we now provide simplified equations to summarize the components of the model. Each matrix is updated on every trial:

where M is one of the three associative matrices and α is the corresponding learning rate (recall that different learning rates are estimated for FC, FF, and FFC binding: αFC, αFF αFFC). The vector representations of two elements (e.g., a shape and color) are denoted as f1 and f2. These elements are associated using an outer product, denoted by the symbol ⊗. Forgetting in the model is controlled by the parameter β, which is the same for all types of binding (FC, FF, and FFC). Forgetting occurs by downweighting the association between each element and the other elements that have been previously associated with it but are not presented in the current trial, denoted fx. 

For example, if in Set A a blue circle and yellow star were presented, when a blue star is presented in the Learning phase for Set B, the blue star association would be strengthened by new learning, whereas the blue circle association would be weakened by forgetting. Note that in the full model, learning occurs bidirectionally (e.g., as blue is associated with a star, the star is associated with blue; see the online supplemental materials for the full model equations).

Finally, r is a single scalar value representing a trial-level novelty signal that changes from trial to trial, tracking how strongly the elements f1 and f2 have already been associated in the past: r = e −f1·(M·f2)+f2·(M·f1)). 

In this equation, f2 is used as a cue to retrieve an array of previously associated elements from M via a dot product, and then a second dot product “reads out” how strongly f1 in particular has been retrieved. This scalar strength value is then added to a corresponding strength value for the association in the opposite direction (i.e., how strongly f2 has been retrieved by f1). The exponential function is applied to this total strength value. If no learning has occurred for these associations previously, the strengths will be zero, resulting in r = 1, and full learning. 

However, as the associations become stronger, strength values become larger, and r approaches zero. The result of this mechanism is that learning is reduced for associations that have already been well-learned, which is needed to keep association strengths from growing without bounds, as learning takes place over multiple trials.

To simulate decision-making, the model calculates the strength of association between the provided memory cues and possible targets on each trial. Strengths are determined by probing associative matrices with cues: s = (M · fcue ) · target. This equation retrieves an array of elements that have been associated with the cue (cue) and reads out a single strength value (s) for a specific target (target). These strength estimates provide the basis for calculating the probability of each possible response while allowing for competition from other possible responses that could result in interference. 

This competitive retrieval rule is implemented as a softmax function: Pcℎoice = e s cℎoice ∑e s. If the strengths supporting all possible choices are the same, the model will predict chance-level performance, but to the extent that a particular choice is supported by greater strength values than other choices, the model will be more likely to make the corresponding choice.

We fit the model to the observed data with four free parameters: αFC, αFF, αFFC, and β. Importantly, the model was not fit to summarize statistics, such as the proportion of correct responses in a particular phase, but took into account participants’ choice for each trial in every phase of the experiment. We applied hierarchical Bayesian techniques to fit the model, allowing us to assess age differences with posterior parameter distributions. See the online supplemental materials for additional details on the model and how it was fit to data.

Computational Model Results—To assess model fit, we generated trial-level task performance given each participant’s best-fitting parameter estimates (see Figure 2 to compare observed and model-predicted performance). Despite overestimating proactive interference in the first block of Set B learning in all age groups, the model fit most patterns of performance well across all experimental phases, suggesting that it was able to capture at least some of the processes underlying task performance and how they differed between age groups.

The posterior distributions of hyper-parameters for each age group are shown in Figure 4A. We assess age differences for each parameter by calculating η, a measure of distributional overlap described above in the Analyses section. There was no substantial evidence of any age differences in FC binding, estimated with parameter αFC, or forgetting, estimated with β (ηs > .17). By contrast, we found very strong evidence of weaker FF binding, estimated with parameter αFF, in 5-year-olds compared with both 8-year-olds (η = .005) and adults (η = .001). There was little evidence of a difference between the two older age groups (η= .642). These novel findings suggest strong developmental changes in intraobject binding between 5 and 8 years of age, although this ability may be adult-like by 8 years of age.

There was also strong evidence of lower values of the complex binding parameter, αFFC, in 5-year-olds compared with both 8-year-olds (η = .010) and adults (η = .002). Although the estimated parameter values tended to be higher in adults compared with eight-year-olds, this difference was not very robust (η = .429). This latter finding was somewhat surprising because prior work using a recall task has suggested protracted development of complex binding after 7 years of age (Yim et al., 2013). Perhaps, the emerging ability to form complex bindings is more detectable in recognition tasks than in more difficult recall tasks; we return to this issue in the General Discussion. 

To examine whether all of the model’s mechanisms were necessary to fit the data, we performed a model comparison study in which each parameter was eliminated from the model (i.e., set to zero) while fitting the others to the data, and we found that the full model including all four parameters best fit the data even when accounting for model complexity (see the model Comparison Study section of the online supplemental materials and see Figure S5 in the same section for how these different models predict different patterns of performance).

Overall, in this experiment, we found strong evidence of memory interference effects in all age groups based on hierarchical regression models, along with developmental differences in performance in the Binding Test. Perhaps more importantly, with a novel computational model, we found evidence of substantial developmental differences in intraobject binding and complex binding after 5 years of age, but not after 8 years of age. It is not clear from these results, however, how memory binding and interference were affected by presenting features within the same object. Prior work in adults suggests that extraobject binding is more attentionally demanding and is associated with less accurate associative memory performance compared with intraobject binding (Asch et al., 1960; Ecker et al., 2007, 2013; van Geldorp et al., 2015). We hypothesized, then, that spatially separating object features would disrupt binding, especially for young children, which could increase interference effects. We investigated these possibilities in Experiment 2.

Experiment 2: The Development of Extraobject Binding Method

Participants—Forty-five 5-year-olds (Mage = 5.16 years, SDage = .23, language = 4.80 −5.74; 17 females, 28 males), 43 eight-year-olds (Mage = 8.49 years, SDage = .38, language = 7.74 – 8.99; 23 females, 20 males), and 34 adults (19 females, 15 males) participated in Experiment 2. See the Results and Discussion section, below, for a power analysis. Assignment to this experiment or Experiment 1 was randomized.

Stimuli and Procedure—In this experiment, the shape and color features were not presented together within the same object but were spatially separated (see Figure 5). On each trial, a transparent shape and a blob of color were positioned in vertical alignment, and the relative spatial position (top or bottom) of these features was counterbalanced for each color-shape pairing within each block of every phase. The procedure was identical to that of Experiment 1 except that the stimuli were referred to as “pairs of shapes and colors” instead of objects during instructions and performance feedback.

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