Participants more averse to thinking are more susceptible to the speedup illusion; AI familiarity measures do not predict calibration error¶

Claim · All claims
Evidence · 1 study · causal r3 · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? Negative Need for Cognition item coefficients indicate that participants who dislike thinking believe AI saves more time; AI-use frequency and AIAS did not predict calibration error magnitude. → Sunny Yu 2026

Evidence¶

Sunny Yu 2026¶

Sunny Yu, Myra Cheng, Ahmad Jabbar, Ilia Sucholutsky, Katherine M. Collins, Dan Jurafsky, Robert D. Hawkins. (2026). Cognitive offloading and the speedup illusion in human-AI interaction. arXiv. https://arxiv.org/abs/2605.23177 doi:10.48550/arxiv.2605.23177

q3 · i? · causal · r3

In the prediction sample, Need for Cognition items 3 and 4 showed negative coefficients (β=−6.55 and β=−6.66, both p<0.05), meaning "the more participants dislike having to think, the more susceptible they are to the speedup illusion". AI-use frequency and the AI Assessment scale did not predict calibration error.

"we focus on the prediction sample and identify negative coefficients for the difference between the predicted independent versus assisted completion times for item 3 ("thinking is not my idea of fun";β=−6.55,SE=3.14,t=−2.11,p<0.05)"

Discussion¶

Related Claims¶

• Access to AI advice roughly doubled reported confidence in answers in one experiment — the general claim this finding bears on, with the studies behind it
• People are well-calibrated about their own independent completion times — related
• People significantly underestimate AI-assisted completion times on simple cognitive tasks, even though AI assistance saves little or no actual time (the speedup illusion) — related
• People expect AI assistance to save far more time than assistance from another highly intelligent human participant — related
• Prompting is a major source of AI-assisted cognitive effort: copying prompts reduces effort but not time, and verbose model responses can make AI-assisted completion slower than predicted — related
• Slight control over an algorithm's output reduces algorithm aversion, with process control producing the strongest calibration effects — related
• Prior AI use carries over: brief AI exposure increases subsequent AI adoption and exacerbates the speedup illusion, while independent exposure does not reduce AI use — related
• People overestimate how much mental effort AI assistance alleviates (offloading illusion), driven by overestimating the effort of independent completion while AI-assisted effort is accurately estimated — related