Personalization algorithms create an illusion of competence, study finds
Psychology
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Selected highlights:
The researchers divided the participants into different groups to test the specific effects of algorithmic personalization. One group served as a control and viewed a random assortment of items with all features available to inspect. Another group engaged in active learning, where they freely chose which categories to study without algorithmic interference.
the study measured the participants’ confidence in their decisions using a rating scale from zero to ten. The analysis showed that participants in the personalized groups frequently reported high confidence levels even when their answers were wrong. This effect was particularly distinct when they encountered items from categories they had rarely or never seen during the learning phase.
This indicates a disconnection between actual competence and perceived competence caused by the filtered learning environment. The participants were unaware that the algorithm had hidden significant portions of the information landscape from them. They assumed the limited sample they viewed was representative of the whole.
The findings provide evidence that the structure of information delivery systems plays a significant role in shaping human cognition. By optimizing for engagement, current algorithms may inadvertently sacrifice the accuracy of user knowledge. This trade-off suggests that online platforms can shape not just what people see, but how they reason about the world.
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Selected highlights:
The researchers divided the participants into different groups to test the specific effects of algorithmic personalization. One group served as a control and viewed a random assortment of items with all features available to inspect. Another group engaged in active learning, where they freely chose which categories to study without algorithmic interference.
the study measured the participants’ confidence in their decisions using a rating scale from zero to ten. The analysis showed that participants in the personalized groups frequently reported high confidence levels even when their answers were wrong. This effect was particularly distinct when they encountered items from categories they had rarely or never seen during the learning phase.
This indicates a disconnection between actual competence and perceived competence caused by the filtered learning environment. The participants were unaware that the algorithm had hidden significant portions of the information landscape from them. They assumed the limited sample they viewed was representative of the whole.
The findings provide evidence that the structure of information delivery systems plays a significant role in shaping human cognition. By optimizing for engagement, current algorithms may inadvertently sacrifice the accuracy of user knowledge. This trade-off suggests that online platforms can shape not just what people see, but how they reason about the world.
