Toward a synthesis of cognitive biases: How noisy information processing can bias human decision making.
Open accessAbstract
A single coherent framework is proposed to synthesize long-standing research on 8 seemingly unrelated cognitive decision-making biases. During the past 6 decades, hundreds of empirical studies have resulted in a variety of rules of thumb that specify how humans systematically deviate from what is normatively expected from their decisions. Several complementary generative mechanisms have been proposed to explain those cognitive biases. Here it is suggested that (at least) 8 of these empirically detected decision-making biases can be produced by simply assuming noisy deviations in the memory-based information processes that convert objective evidence (observations) into subjective estimates (decisions). An integrative framework is presented to show how similar noise-based mechanisms can lead to conservatism, the Bayesian likelihood bias, illusory correlations, biased self-other placement, subadditivity, exaggerated expectation, the confidence bias, and the hard-easy effect. Analytical tools from information theory are used to explore the nature and limitations that characterize such information processes for binary and multiary decision-making exercises. The ensuing synthesis offers formal mathematical definitions of the biases and their underlying generative mechanism, which permits a consolidated analysis of how they are related. This synthesis contributes to the larger goal of creating a coherent picture that explains the relations among the myriad of seemingly unrelated bia…
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Measures in accordance with (b) seem out of place because they introduce a concrete psychological aspect that sticks out from more technical and operational aspects, especially since automation bias is just one of many relevant biases, heuristics, and psychological phenomena.
“Measures in accordance with (b), which are intended to maintain an awareness of the automation bias, seem out of place: it introduces a concrete psychological aspect that sticks out from the more technical and operational aspects outlined in the other provisions, especially considering that automation bias is just one of many relevant biases (e.g., hindsight bias, confidence bias [36, 66]), heuris”
From On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives (2024) · cites this paper as [36]
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“While algorithms have been shown to be attributing factors, users themselves seem to process information differently based on their pre-existing notions and beliefs [42, 61].”
BackgroundInvariants in probabilistic reasoning.
Costello, F., & Watts, P. (2018) · Cognitive Psychology · doi:10.1016/j.cogpsych.2017.11.003 · Cited by 30 · see citing works
“We begin by considering some recent results from Yearsley and Trueblood (2017), who investigate the simultaneous occurrence of both sequential order effects and the conjunction fallacy in judgements about real-world events: the probabilities of various different Republican and Democratic candidates winning various U.”
MethodResultCraig, A. B., Phillips, M. E., Zaldivar, A., Bhattacharyya, R., & Krichmar, J. L. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00017 · Cited by 30 · see citing works
“The most conservative explanation is that this effect was caused by noise in the data (Erev et al., 1994; Hilbert, 2012; Costello and Watts, 2014), perhaps due to feature choice error or the random sampling of features in lieu of a more concrete strategy.”
BackgroundResultCognitive reflection test and behavioral biases in Malaysia
Albaity, M., Rahman, M., & Shahidul, I. (2014) · Judgment and Decision Making · doi:10.1017/s1930297500005507 · Cited by 30 · see citing works
“The results also confirm the results by Oechssler et al. (2009) and Frederick (2005).”
BackgroundResultInfluentialHow Do HCI Researchers Study Cognitive Biases? A Scoping Review
Boonprakong, N., Tag, B., Gonçalves, J., & Dingler, T. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713450 · Cited by 22 · see citing works
“Hilbert [86] proposes that cognitive biases can be statistically modelled as a result of humans’ noisy memory and information processing.”
Toward a General Framework of Biased Reasoning: Coherence-Based Reasoning
Simon, D., & Read, S. J. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231204579 · Cited by 44 · see citing works
“…et al., 1999; Edwards & Smith, 1996; Forgas, 2008; Kunda, 1990), noisy deviations in memory-based information processes (Dougherty et al., 1999; Hilbert, 2012; Usher & McClelland, 2001), erroneous assessments of memory strength (Tidwell et al., 2016), and limited capacity in working memory (De…”
Presti, S. L., Mattavelli, G. C., Canessa, N., & Gianelli, C. (2022) · PLoS ONE · doi:10.1371/journal.pone.0262319 · Cited by 33 · see citing works
“In the acute lockdown stage, the behavioural effects of the well-known human biases in judgment and decision-making [24, 25] might thus be heightened by the need to filter an unprecedented amount of incoming information from multiple, often contradictory, sources [26].”
BackgroundFalling for fake news: the role of political bias and cognitive ability
Tandoc, E. C., Lee, J., Chew, M., Tan, F. X., & Goh, Z. H. (2021) · Asian Journal of Communication · doi:10.1080/01292986.2021.1941149 · Cited by 34 · see citing works
“Scholarship on heuristic processing has also led scholars to study cognitive biases, which reduce cognitive load but also potentially interfere with cognitive ability and critical thinking (Hilbert, 2012; Jeffrey, 2015).”
BackgroundConservation decision making: integrating the precautionary principle with uncertainty
Falcy, M. (2016) · Frontiers in Ecology and the Environment · doi:10.1002/fee.1423 · Cited by 26 · see citing works
Paradiso, A., Kumar, S., & Margani, P. (2014) · Journal of Economic Psychology · doi:10.1016/j.joep.2014.04.006 · Cited by 26 · see citing works
“For a brief survey on cognitive biases in decision making process, see Hilbert (2012).”
BackgroundWhither Now, Opinion Modelers?
Sobkowicz, P. (2020) · Frontiers in Physics · doi:10.3389/fphy.2020.587009 · Cited by 25 · see citing works
Wyrich, M., Preikschat, A., Graziotin, D., & Wagner, S. (2021) · 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) · doi:10.1109/icse43902.2021.00055 · Cited by 24 · see citing works
“Many of these factors are cognitive in nature [10].”
BackgroundChaturvedi, A., Yadav, N., & Dasgupta, M. (2025) · International journal of human computer interactions · doi:10.1080/10447318.2025.2456534 · Cited by 24 · see citing works
“Cognitive biases, such as framing, overconfidence, and anchoring, continue to influence managerial decisions (Hilbert, 2012; Meissner & Wulf, 2016).”
On the generality and cognitive basis of base-rate neglect
Stengård, E., Juslin, P., Hahn, U., & van den Berg, R. (2022) · bioRxiv · doi:10.1101/2021.03.11.434913 · Cited by 24
“, Drugowitsch, Wyart, Devauchelle, & Koechlin, 2016; Stengard & 262 Van Den Berg, 2019), but may potentially also explain, at least in part, many of the biases that 263 have been found in cognition (Erev et al., 1994; Hilbert, 2012).”
InfluentialApplying artificial intelligence to determination of legal age of majority from radiographic.
Murray, J., Heng, D., Lygate, A., Porto, L., Abade, A., Mânica, S., & Franco, A. (2023) · Morphologie : bulletin de l'Association des anatomistes · doi:10.1016/j.morpho.2023.100723 · Cited by 23 · see citing works
Oxman, A. D., & García, L. M. (2020) · F1000Research · doi:10.12688/f1000research.21858.1 · Cited by 26 · see citing works
Developing a smart operational research with hybrid practice theories
Bürger, K., White, L., & Yearworth, M. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.03.027 · Cited by 28 · see citing works
“As such, while automated analytics applications may facilitate the identification of patterns, we still need to make sense of these patterns in order to transform them into actionable insights ( Hilbert, 2012 ).”
BackgroundZhu, J.-Q., Sundh, J., Spicer, J., Nick Chater, A., & Sanborn, A. N. (2023) · Psychological Review · doi:10.1037/rev0000427 · Cited by 25 · see citing works
“A more promising alternative is to consider corruptive noise in memory or evidence accumulation, which can produce stronger biases (e.g., Costello & Watts, 2014; Erev et al., 1994; Hilbert, 2012).”
BackgroundUndergraduate Students’ Critical Online Reasoning—Process Mining Analysis
Schmidt, S., Zlatkin‐Troitschanskaia, O., Roeper, J., Klose, V., Weber, M., Bültmann, A.-K., & Brückner, S. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.576273 · Cited by 31 · see citing works
“…and scientific reasoning (e.g., Fischer et al., 2014; Stanovich, 2016) and (corresponding) reasoning biases and heuristics (Kahneman et al., 1982; Gigerenzer, 2008; Hilbert, 2012) of information processing and decision making (for an overview, see, e.g., Stanovich, 2003; Toplak et al., 2007).”
BackgroundRelevance insensitivity: A new look at some old biases
Hsee, C. K., Yang, Y., & Li, X.-L. (2019) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2019.05.002 · Cited by 22 · see citing works
“…also a form of noise or regressiveness in response (Ayton, Hunt, & Wright, 1991; Burson, Larrick, & Klayman, 2006; Costello & Watts, 2014; Erev, Wallsten, & Budescu, 1994; Faro & Rottenstreich, 2006; Fiedler & Unkelbach, 2014; Gino et al., 2011; Hilbert, 2012; Juslin et al., 2000; Kahneman, 2011).”
Linking the Ethics and Management Control Literatures
Merchant, K., & White, L. (2017) · Emerald Group Publishing Limited eBooks · doi:10.1108/s1474-787120170000028001 · Cited by 22 · see citing works
Chen, Q. Z., & Zhang, A. X. (2023) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3610074 · Cited by 21 · see citing works
“Problems like demographic skew [21] or cognitive biases [38] cannot be “patched out” in the same way as errors.”
BackgroundSurprising rationality in probability judgment: Assessing two competing models.
Costello, F., Watts, P., & Fisher, C. (2018) · Cognition · doi:10.1016/j.cognition.2017.08.012 · Cited by 21 · see citing works
“This approach follows a line of research leading back at least to Thurstone (1927) and continued by various more recent researchers (see, e.g. Dougherty, Gettys, & Ogden, 1999; Erev, Wallsten, & Budescu, 1994; Hilbert, 2012).”
El-Assady, M., & Moruzzi, C. (2022) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2022.3200328 · Cited by 20 · see citing works
“Overconfidence Effect: Tendency of having greater confidence in one’s own judgments than the objective one.(7) What can go wrong: the sender provides information through an inappropriate strategy with respect to the explanation intention, based on the confidence of the correctness of their role as an explainer.”
BackgroundIntegrating Non-Psychiatric Models of Delusion-Like Beliefs into Forensic Psychiatric Assessment
Pierre, J. M. (2019) · The journal of the American Academy of Psychiatry and the Law · doi:10.29158/jaapl.003833-19 · Cited by 20 · see citing works
“According to error-management theory, the systematic cognitive biases that govern information processing may result in misbeliefs that are at odds with reality but remain adaptive overall.(49,51,55,56) Still, it is hard to deny that cognitive biases and their resulting misbeliefs can sometimes have significant negativ…”
BackgroundComputational method for corrective mechanism of cognitive decision-making biases
Foldesi, P., & Botzheim, J. (2012) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2012.6421982 · Cited by 20
“That is because of the imperfect information processing system the decision is imperfect as well, and very often some distortion appears in the solution [3].”
BackgroundThe tendency to stop collecting information is linked to illusions of causality
Moreno-Fernández, M. M., Blanco, F., & Matute, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-82075-w · Cited by 15 · see citing works
Performance Evaluation: Subjectivity, Bias and Judgment Style in Sport
Osório, A. (2020) · Group Decision and Negotiation · doi:10.1007/s10726-020-09672-4 · Cited by 19 · see citing works
“…that are difficult to separate from each other—for instance, heuristics (Shah and Oppenheimer 2008; Tversky and Kahneman 1974), noise and limited information processing capacity (Hilbert 2012; Simon 1955), emotional and moral motivations (Pfister and Böhm 2008), and social influence (Wang et al.”
BackgroundChoice Architecture as a Strategy to Encourage Elegant Infrastructure Outcomes
Shealy, T., & Klotz, L. E. (2017) · Journal of Infrastructure Systems · doi:10.1061/(asce)is.1943-555x.0000311 · Cited by 18 · see citing works
“This is when decision makers deviate from predicted outcomes and make seemingly irrational choices that are not in their best interest (e.g., Ariely 2008; Hilbert 2012).”
BackgroundExploring the Design of Technology-Mediated Nudges for Online Misinformation
Konstantinou, L., Panos, D., & Karapanos, E. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2301265 · Cited by 16 · see citing works
“…assumption is that individuals are cognitive misers; reluctant to engage in deliberative, effortful, time-consuming and rule-based cognition (Early et al., 2020; Fiske & Taylor, 1991) but more likely to engage in intuitive and automatic information processing (Hilbert, 2012; Maynes, 2015).”
Framing Subjective Emotion Reports as Dynamic Affective Decisions
Teoh, Y. Y., Cunningham, W. A., & Hutcherson, C. A. C. (2023) · Affective Science · doi:10.1007/s42761-023-00197-y · Cited by 21 · see citing works
“…common across these choice specifications is that the evidence construction process is psychologically and biophysically (e.g., neurally) constrained and thus noisy, due to random variation in the processing environment (both external and internal to the human body: Hilbert, 2012; Rat-cliff, 2001).”
Background
- Authors
- Hilbert, M.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Hilbert, Martin
- ORCID
- Hilbert, Martin
- Venue
- Psychological Bulletin
- Published
- 2012-03
- Volume
- 138
- Issue
- 2
- Pages
- 211-237
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- OpenAlex
- W1990584714
- Cited by
- 391
- References
- 130
- Citations by year
- 2026: 19 · 2025: 30 · 2024: 38 · 2023: 36 · 2022: 30 · 2021: 29 · 2020: 41 · 2019: 32 · 2018: 27 · 2017: 25 · 2016: 29 · 2015: 12 · 2014: 21 · 2013: 14 · 2012: 8
- FWCI
- 7.58
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Licence
- cc-by-nc-sa
- Locations
- Psychological Bulletin (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-sa)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Keywords
- cognitive biases, decision-making, conservatism, illusory correlation, subadditivity, confidence bias, information theory, integrative framework
- MeSH terms
- Decision Making, Female, Humans, Information Theory, Judgment, Male, Memory, Models, Psychological, Probability, Set, Psychology, Stochastic Processes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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