This article is reprinted from the publicaccount: Beijing Normal University Brain and Cognitive Science
Every day, we face various choices, and sometimes we get confused because of them. For example, should we buy a small toy with the money we have now, or wait until we save enough money to buy a better one? Research shows that delayed gratification is a very important cognitive ability that can effectively predict future academic achievement and even quality of life. The ability to delay gratification in children begins to develop around the age of 3-4. In addition to the child’s own physiological maturity, social interaction with adults, especially parents or other primary caregivers, is the main way for children to learn the ability to delay gratification.
Figure 1 Observational Learning: Children Love to Wear Adults’ Shoes.
In addition to direct interaction, children can also learn by observing the behaviors of adults, a process known as observational learning. The famous “Bobo doll” experiment by psychologist Albert Bandura in the 1960s is important evidence of observational learning. During the process of observational learning, people’s learning is not merely a simple stimulus-response but undergoes implicit cognitive processing. The implicit cognitive processing cannot be discerned through explicit behavioral responses. Studies on adults have found that when the behavior of a model is highly uncertain, people are more likely to adopt an intention reasoning strategy, inferring the intentions behind the model’s behavior through observation to decide whether to learn. But how do young children learn? Do they also adopt the intention reasoning strategy, or are they unable to reason and can only use simple imitation strategies? What are the neural mechanisms behind this? Currently, this question remains unclear.
In November 2022, the research team led by Lu Chunming from the Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University, in collaboration with the research team led by Wang Zhengyan from the School of Psychology at Capital Normal University, published a research paper titled “Neurocomputational mechanisms of young children’s observational learning of delayed gratification” online in the journal Cerebral Cortex. By combining computational modeling and functional near-infrared spectroscopy (fNIRS) technology, the study reveals the cognitive strategies and neural mechanisms through which young children learn delayed gratification by observing adult behavior.
The study recruited 62 pairs of mothers and children and non-mothers and children, using a 2 (model type: mother, non-mother) x 2 (reward level: high, low) x 2 (learning method: observational learning, self-directed learning) factorial design. Stickers featuring popular characters like “Peppa Pig” and “Hello Kitty” were used as rewards to complete a classic delay choice task. During the experiment, the experimenter simultaneously presented two choices to the model/child: receive 1 sticker now and 2 (low reward) or 6 (high reward) stickers after the experiment. The model and child took turns making choices. The results showed that observing the behavior of adult models significantly increased the proportion of children choosing the second option, thereby enhancing their ability to delay gratification.
To understand the implicit cognitive strategies behind children’s observational learning, we constructed two computational models. One model assumes that children reason about the intentions behind the model’s behavior when observing it, thereby deciding whether to learn; the other model assumes that children may not be able to or do not engage in intention reasoning and simply learn through imitation. The results indicate that the first model better explains children’s behavioral patterns, with the model-predicted data highly consistent with children’s actual behavioral data. These results suggest that even 3-year-old children can engage in observational learning by reasoning about the intentions behind behaviors, similar to adults.
Figure 1 Model Results. a-b) Model fitting results. The lower the BIC, the better the result. c-f) The model predicted results are highly consistent with actual results. g-h) The model validation effect is good.
To further understand the neural mechanisms behind the cognitive strategies of observational learning, we simultaneously collected brain functional signals from both the model and the child using portable functional near-infrared spectroscopy. The results showed that when children faced different choices, their prefrontal cortex would reason by “replaying” the brain activity patterns of the model during the choice, as evidenced by significant similarities between the brain activity of the child during thinking and that of the model during choice (i.e., neural synchrony). Moreover, this neural synchrony was significantly higher in conditions where children engaged in observational learning compared to conditions without observational learning; it was also significantly higher when learning delayed gratification options than when learning immediate gratification options. More importantly, this neural synchrony had a significant positive correlation with the implicit value representation level of the options for the children.
Additionally, we also analyzed the brain activation of the children themselves. However, the children’s single-brain activation could not effectively distinguish whether observational learning occurred or what was learned. This indicates that, unlike individual learning, observational learning requires interaction between people. Therefore, inter-brain neural synchrony is a more effective neural marker in the process of observational learning compared to single-brain activation. The “replay” of the model’s brain activity during selection in the observer’s prefrontal cortex may be an important neural mechanism of observational learning.
Figure 2 Inter-brain Neural Synchrony Results. a-f) Analysis ideas and main results. The brain function of children during the thinking phase is significantly synchronized with the model’s execution during the choice phase. g-f) Random validation and multiple comparison corrections. i-k) Significantly synchronized brain regions and patterns.
Figure 3 Children’s Single-Brain Activation Results. a-b) Significant activation in the prefrontal cortex when children make choices. c) Activation patterns.
In conclusion, this study demonstrates that even young children around the age of 3 can engage in observational learning through implicit cognitive reasoning, suggesting that the complexity of children’s cognitive abilities may far exceed our common estimates. Furthermore, unlike individual independent learning, the delayed “replay” of the learner’s brain activity regarding the model may be a unique neural mechanism for various social learning processes, including observational learning.
This research is part of a series of studies on teaching processes and cognitive mechanisms by Lu Chunming’s research team and is the second collaborative research result with Professor Wang Zhengyan’s team from Capital Normal University (the first paper can be found at the link: https://mp.weixin.qq.com/s/6OnBSwTdW5K-rSmCTP1s1g). Doctoral student Zhao Hui, master’s student Zhang Tengfei, and doctoral student Cheng Tong from the university are the first authors of the paper (where Zhao Hui and Cheng Tong have graduated and successfully obtained their doctoral degrees), and Professors Lu Chunming and Wang Zhengyan are the corresponding authors of the paper.
This research was funded by major projects from the National Natural Science Foundation, general projects, and the China Postdoctoral Science Foundation, among others. We thank all families participating in this research, especially the babies involved!
Paper: Zhao H†, Zhang T†, Cheng T†, Chen C, Zhai Y, Liang X, Cheng N, Long Y, Wang Z* and Lu C*.(In press). Neurocomputational mechanisms of young children’s observational learning of delayed gratification. Cerebral Cortex.
Paper URL: http://dx.doi.org/10.1093/cercor/bhac484
Contact: Lu Chunming, [email protected]; Wang Zhengyan, [email protected]
Lu Chunming’s research team website: http://sc.bnu.edu.cn/
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