§ IIIRelated Psychological Research

The literature it stands on.

The framework draws on established findings. It does not claim their authority as its own.

01Metacognitive self-assessment

Kruger, J., & Dunning, D. (1999).Unskilled and unaware of it: How difficulties in recognizing one's own incompetence lead to inflated self-assessments.Journal of Personality and Social Psychology, 77(6), 1121–1134.doi:10.1037/0022-3514.77.6.1121

Reported finding

Participants scoring in the lowest quartile on tests of humor, grammar, and logic substantially overestimated their performance; the authors attribute this partly to a metacognitive deficit.

Relation to the framework

Suggests that the skills needed to produce a judgment overlap with those needed to evaluate it — a gap AI assistance may widen rather than close.

02Illusion of explanatory depth

Rozenblit, L., & Keil, F. (2002).The misunderstood limits of folk science: An illusion of explanatory depth.Cognitive Science, 26(5), 521–562.doi:10.1207/s15516709cog2605_1

Reported finding

People rated their understanding of everyday devices highly, then lowered those ratings after attempting to produce step-by-step explanations.

Relation to the framework

Reading a fluent AI explanation may resemble the pre-explanation state: a felt understanding that has not been tested by generation.

Fisher, M., Goddu, M. K., & Keil, F. C. (2015).Searching for explanations: How the Internet inflates estimates of internal knowledge.Journal of Experimental Psychology: General, 144(3), 674–687.doi:10.1037/xge0000070

Reported finding

Searching the Internet for explanations led participants to rate their own knowledge higher, even in unrelated domains.

Relation to the framework

The closest precedent for Borrowed Intelligence: externally retrieved knowledge being misattributed to the self.

03Cognitive offloading

Sparrow, B., Liu, J., & Wegner, D. M. (2011).Google effects on memory: Cognitive consequences of having information at our fingertips.Science, 333(6043), 776–778.doi:10.1126/science.1207745

Reported finding

When people expected future access to information, they showed lower recall of the information itself and better recall of where to find it.

Relation to the framework

Note: some findings in this literature have had mixed replication results; it is cited here for its conceptual framing.

Risko, E. F., & Gilbert, S. J. (2016).Cognitive offloading.Trends in Cognitive Sciences, 20(9), 676–688.doi:10.1016/j.tics.2016.07.002

Reported finding

A review framing offloading as the use of physical action or external tools to reduce cognitive demand, shaped by metacognitive evaluations of one's own abilities.

Relation to the framework

Offloading is often rational. The framework concerns cases where the offloaded work is then claimed as internal capability.

04Automation bias

Parasuraman, R., & Riley, V. (1997).Humans and automation: Use, misuse, disuse, abuse.Human Factors, 39(2), 230–253.doi:10.1518/001872097778543886

Reported finding

Distinguishes appropriate use of automation from misuse (overreliance) and disuse (neglect), and discusses factors that drive each.

Relation to the framework

Provides vocabulary for overreliance; the Lisa Latte Effect adds the self-perception dimension.

Goddard, K., Roudsari, A., & Wyatt, J. C. (2012).Automation bias: A systematic review of frequency, effect mediators, and mitigators.Journal of the American Medical Informatics Association, 19(1), 121–127.doi:10.1136/amiajnl-2011-000089

Reported finding

Systematic review documenting automation bias in clinical decision support and identifying mediators such as trust, workload, and task complexity.

Relation to the framework

Demonstrates that deference to automated advice occurs even among trained professionals.

05Calibrated trust

Lee, J. D., & See, K. A. (2004).Trust in automation: Designing for appropriate reliance.Human Factors, 46(1), 50–80.doi:10.1518/hfes.46.1.50_30392

Reported finding

Argues that trust should be calibrated to an automation's actual capabilities, and describes how mis-calibration leads to misuse or disuse.

Relation to the framework

The framework can be read as a calibration failure directed inward: trust in one's own AI-assisted competence.

06AI-assisted decision-making

Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021).To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making.Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), Article 188.doi:10.1145/3449287

Reported finding

Interventions that required people to engage analytically before or while seeing AI recommendations reduced overreliance compared with simple explanations, though participants rated them less favorably.

Relation to the framework

Supports the model's claim that understanding responds to effortful evaluation, not to exposure.