| Autor: |
Evan F. Risko, Derek J. Koehler, Timothy L. Dunn, Daev McLean, Connor Gaspar |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Technology, Mind, and Behavior. 2 |
| ISSN: |
2689-0208 |
| DOI: |
10.1037/tmb0000039 |
| Popis: |
Our metacognitive ability to monitor and evaluate our cognitive performance is central to efficient and adaptive behaviors. Research investigating this ability has focused largely on tasks that rely exclusively on internal processes (e.g., memory). However, our dayto-day cognitive activities often consist of the mixes of internal and external processes. In the present investigation, we expand research on metacognition to this distributed domain. We examined participants’ ability to accurately monitor their performance in a knowledge retrieval task when they were required to rely on only their internal knowledge and when required to rely on both internal knowledge and utilizing the internet. One hundred and ninety-four participants completed an online study consisting of answering general knowledge questions. Individuals were also randomly assigned to provide accuracy judgments either prospectively or retrospectively. Results revealed metacognitive bias (i.e., overconfidence) increased when using the internet and when making retrospective judgments. Metacognitive sensitivity was also worse when using the internet, especially when individuals made prospective judgments about what their performance would be. Furthermore, metacognitive bias was positively related across the internal knowledge and internet conditions. These results provide the beginnings of an understanding of metacognition and behavior in distributed cognitive contexts involving the internet. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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