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  • Title: ➤  Learning To Disregard: The Optimization Of Problem-Solving Strategies In The Presence Of Unhelpful Information
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  • Internet Archive ID: osf-registrations-6knbf-v1

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This project will investigate whether people primarily rely on factual information over other strategies when solving tasks and their capacity to disregard it when it proves unhelpful. In an online game, participants will estimate the needs (e.g., required food amount) of various seal species and adjust their guesses based on feedback (e.g., “too much,” “too little”). Each trial’s correct answer will serve as the prior clue for the next. We will study two conditions: “same need”, where all prior clues relate to sleep-time, and “different needs”, where the clues vary. We hypothesize that in the “same need” condition, participants will initially favor prior clues to estimate seals’ sleep-time needs but will gradually deviate as these clues prove unhelpful for quickly finding the correct response. However, in the “different needs” condition, participants are more likely to initially select a middle-range value on the slider, as using the middle point strategy halves the range of potential answers and prior information is clearly irrelevant to the current task. In a subsequent exploratory phase, we will examine the extent to which improved performance in the “same needs” condition is associated with participants’ ability to suppress interference from prior numeric clues and their conscious perception of these clues’ irrelevance. Unlike traditional anchoring effect studies focused on single responses, this project examines the influence of prior information in problem-solving contexts where participants can continuously evaluate its utility and explore alternative strategies.

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"Learning To Disregard: The Optimization Of Problem-Solving Strategies In The Presence Of Unhelpful Information" is available for download from The Internet Archive in "data" format, the size of the file-s is: 0.21 Mbs, and the file-s went public at Mon May 12 2025.

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  • Source: Internet Archive
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  • Number of Files: 5
  • Number of Available Files: 5
  • Added Date: 2025-05-12 10:44:52
  • Scanner: Internet Archive Python library 1.9.9

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