Their popularity makes celebrities easy to spot. However, strangers can also be mistaken for celebrities, leading to cases of false “celebrity sightings”. In an attempt to explain the contradiction, a study by the University of California, Riverside, reported that celebrity faces were remembered more accurately, but less accurately.
Precision in this context refers to how similar memories of a particular person are across repeated memory retrievals, which can be likened to the clustering of arrows on a target in archery. Accuracy measures how well remembered faces resemble newly encountered faces—or off-target in archery.
“What our findings say is that people can accept mistakes by misidentifying someone as a celebrity in the interest of providing a ‘celebrity look,'” said Weiwei Zhang, an associate professor of psychology who led the study, which appears in the journal Psychonomic Bulletin & Review. “Our study explains why people are good and bad at spotting celebrities and highlights the importance of assessing both memory inaccuracy and working memory biases.”
The study tested the memory of 52 college students for transformed faces that looked like celebrities Anne Hathaway, Brad Pitt, Zendaya Coleman or George Clooney. The goal was to assess whether and how prior knowledge of famous people affects participants’ memory.
The researchers collected a total of eight face stimuli: those of Hathaway, Pitt, Coleman, and Clooney, and four non-celebrity faces. Participants were first briefly presented with a picture of a celebrity or an unknown. After a short interval, they were presented with a test face and asked if it was the same face as the study face (the test faces were the same half of the time and changed the rest of the time). For example, if the first photo is 100% celebrity, the test face can be changed to 78% celebrity 50% of the time, Zhang said. The same procedure was followed when participants were first shown pictures of unknown individuals.
“We found that knowing celebrities leads to sharpened and more accurate memories of celebrities compared to non-celebrities,” he said. “But it also led to impaired memory accuracy, where celebrity lookalikes or transformed faces were mistakenly remembered as celebrities.”
According to Zhang, the findings may help explain a trade-off in human behavior.
“The familiarity of the celebrities in our study is key to the trade-off between variation and bias in face recognition for celebrities,” he said. “We don’t seem to do this for anyone else.”
Bias and variance are forecast errors. The total error is the sum of these two error terms, resulting in a trade-off between the two. In machine learning, the bias is the difference between the average prediction and the correct value. Variance is a measure of the spread of data points. The variance-bias trade-off, as its name suggests, is the trade-off between variance and bias. Finding a good balance between these prediction errors helps minimize the overall error.
Zhang explained that human cognition seems to work like machine learning; as far as cognition is concerned, variance, which is the opposite of precision, and bias, which is the opposite of accuracy, will have to trade off to maximize the ability to process and represent information.
“The conventional wisdom is that we want our memory to be super accurate and precise,” he said. “But such a rigid memory would not be able to accommodate the variations seen in natural stimuli. For example, under different lighting conditions, make-up, dresses and hairstyle, a person’s appearance can vary greatly. Our memories must be noisy and fuzzy enough—high variance—to support recognizing faces with all the variation we find in appearance. However, when our memory is fuzzy, face recognition can occasionally fail, which is not optimal in celebrity sightings, given that we don’t want to miss celebrity encounters. So as a solution we introduce recognition biases into the mix. We begin to identify strangers or celebrity lookalikes as celebrities as an overcorrection of vague memories.
Zhang isn’t sure if the findings have applications beyond faces—for example, to objects and places.
“At least theoretically, it is possible to extend the trade-off between variation and bias to objects and places that are important to people,” he said. “We think our findings may be related to déjà vu experiences, as we may have inaccurate but subjectively strong memories.”
Next, the research team plans to conduct research to assess how memory accuracy and precision interact with each other and how these two aspects of memories are encoded in the brain.
Jan was joined on the research by Bo-Yong Won and Hyung-Bum Park. Won is now an assistant professor of psychology at California State University, Chico. Park is now a postdoctoral fellow at the University of Chicago.
The study was funded by the National Institute of Mental Health, the lead federal agency for research on mental disorders.
The research paper is titled “Familiarity Improves Mnemonic Accuracy But Impairs Mnemonic Accuracy in Visual Working Memory.”
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