Projects

Cheating, dishonesty and deception

Dishonesty appears ubiquitous in everyday life. People misreport their taxes, inflate insurance claims and cheat on exams, and cases of corporate and scientific fraud show how large its financial and social costs can be (Speer et al., 2022a). In such situations, people face a conflict between the temptation to cheat for financial gain and the wish to maintain a positive image of themselves as a good person. Intuitively, dishonesty results from a failure of willpower to control selfish impulses. Our research suggests that the role of cognitive control in dishonesty is more complex.

To study cheating in the MRI scanner, we developed a task that measures spontaneous cheating inconspicuously and on a trial-by-trial basis (Speer et al., 2020). Using this task, we found that activity in the ‘reward network’ (Nucleus Accumbens) promotes cheating, particularly in individuals who cheat a lot, whereas activity in ‘self-referential network’ (comprising the posterior cingulate cortex, temporoparietal junction and medial prefrontal cortex), promotes honesty, particularly in individuals who are generally honest. Crucially, activity in regions associated with cognitive control (anterior cingulate cortex and inferior frontal gyrus) helped dishonest participants to be honest, but enabled honest participants to cheat.

Together, these findings suggest that cognitive control is not needed to be honest or dishonest per se, but serves to override what we call one’s moral default (Speer et al., 2021a, 2022a). Subsequent work supports this account: acute stress, presumably through its effects on cognitive control, increases dishonesty in relatively dishonest individuals but decreases it in relatively honest individuals (Speer, Martinovici, et al., 2023).

Individual differences in honesty are also visible in the brain at rest: functional connectivity between networks linked to self-referential thinking and reward processing predicts a person’s propensity to cheat in an independent task, and these neural measures were more important predictors than self-reported individual difference measures such as impulsivity (Speer et al., 2022b).

Collectively, these findings suggest that honesty and fairness are not simply a matter of more or less self-control, but of how control interacts with a person’s default inclinations. This may also explain why interventions that target willpower alone can have opposite effects in different people.

Publications

  • Speer, S. P. H., Martinovici, A., Smidts, A., & Boksem, M. A. S. (2023). The acute effects of stress on dishonesty are moderated by individual differences in moral default. Scientific Reports, 13(1), Article 3984. https://doi.org/10.1038/s41598-023-31056-2
  • Speer, S. P. H., Smidts, A., & Boksem, M. A. S. (2022a). Cognitive control and dishonesty. Trends in Cognitive Sciences, 26(9), 796–808. https://doi.org/10.1016/j.tics.2022.06.005
  • Speer, S. P. H., Smidts, A., & Boksem, M. A. S. (2022b). Individual differences in (dis)honesty are represented in the brain’s functional connectivity at rest. NeuroImage, 246, Article 118761. https://doi.org/10.1016/j.neuroimage.2021.118761
  • Speer, S. P. H., Smidts, A., & Boksem, M. A. S. (2021a). Cognitive control promotes either honesty or dishonesty, depending on one’s moral default. Journal of Neuroscience, 41(42), 8815–8825. https://doi.org/10.1523/JNEUROSCI.0666-21.2021
  • Speer, S. P. H., Smidts, A., & Boksem, M. A. S. (2021b). Different neural mechanisms underlie non-habitual honesty and non-habitual cheating. Frontiers in Neuroscience, 15, Article 610429. https://doi.org/10.3389/fnins.2021.610429
  • Speer, S. P. H., Smidts, A., & Boksem, M. A. S. (2020). Cognitive control increases honesty in cheaters but cheating in those who are honest. Proceedings of the National Academy of Sciences, 117(32), 19080–19091. https://doi.org/10.1073/pnas.2003480117
  • Speer, S. P. H., & Boksem, M. A. S. (2019). Decoding fairness motivations from multivariate brain activity patterns. Social Cognitive and Affective Neuroscience, 14(11), 1197–1207. https://doi.org/10.1093/scan/nsz097
  • Boksem, M. A. S., Mehta, P. H., Van den Bergh, B., van Son, V., Trautmann, S. T., Roelofs, K., Smidts, A., & Sanfey, A. G. (2013). Testosterone inhibits trust but promotes reciprocity. Psychological Science, 24(11), 2306–2314. https://doi.org/10.1177/0956797613495063
  • Boksem, M. A. S., & De Cremer, D. (2010). Fairness concerns predict medial frontal negativity amplitude in ultimatum bargaining. Social Neuroscience, 5(1), 118–128. https://doi.org/10.1080/17470910903202666
  • Boksem, M. A. S., & De Cremer, D. (2009). The neural basis of morality. In D. De Cremer (Ed.), Psychological perspectives on ethical behavior and decision making (pp. 153–166). Information Age Publishing.

Decoding mental states from (consumers’) brains

Much of what drives behavior remains hidden from direct observation. We can observe, and even manipulate, the input a person receives, and we can observe the decisions that follow, but not the feelings and cognitive processes that connect the two. Self-report offers only partial access to these processes, because many of them occur below the level of conscious experience and people may be unable or unwilling to report them accurately (Genevsky & Boksem, 2026). In this line of research, we decode such latent states from the brain by applying machine learning to distributed patterns of brain activity (multivariate pattern analysis, MVPA). Rather than asking which brain region is involved in a given process, this approach asks which mental process is present, a question that aligns well with the goals of consumer research.

A first set of studies focused on emotion. Using EEG, we classified experiences of happiness, sadness, fear and disgust elicited by audiovisual stimuli well above chance, and tracked these responses moment by moment (Eijlers et al., 2019). Using fMRI, we trained classifiers on responses to emotional pictures and showed that they generalize to naturalistic movie trailers, tracking the time course of arousal, and to a lesser extent valence, as reported by an independent sample of viewers (Chan et al., 2020). Applied to advertising, neural arousal decoded from EEG while participants viewed print ads was positively associated with how much the ads stood out in a large consumer panel, but negatively associated with attitude toward the ads (Eijlers et al., 2020).

We then extended this approach beyond emotion. Using a meta-analytic database of thousands of neuroimaging studies to decode multiple psychological processes simultaneously, we traced how these processes unfold over the course of video advertisements and when they predict ad liking (Chan et al., 2024). Early emotional responses, and engagement of social cognition (mentalizing) throughout the ad, were the strongest predictors, and these neural signals improved out-of-sample prediction of ad liking relative to self-report. The same logic applies to brands: by comparing neural responses during brand imagery with responses to images of social situations, we mapped brand images in consumers’ brains, and found that brands with similar neural profiles make more attractive partners for co-branding (Chan et al., 2018).

Decoding also serves more fundamental questions. We developed a multivariate brain signature for reward that generalizes across tasks and samples (Speer, Keysers, et al., 2023), decoded fairness motivations from multivariate activity patterns (Speer & Boksem, 2019), and showed that non-habitual honesty and non-habitual cheating are encoded differently (Speer et al., 2021). Finally, neural measures are only useful in practice if they are reliable. We therefore assessed six EEG metrics commonly used to evaluate video ads, found that their reliability varied markedly, from poor (alpha asymmetry) to excellent (inter-subject correlation), and derived recommendations for sample size (van Diepen et al., 2025).

Together, these studies suggest that decoding psychological processes from the brain can reveal the mechanisms underlying consumer responses, and can improve out-of-sample predictions of the success of marketing actions.

Publications

  • Genevsky, A. G., & Boksem, M. A. S. (2026). Applications in consumer neuroscience: Decoding and neuroforecasting. In D. V. Smith, P. L. Lockwood, & D. S. Fareri (Eds.), Neuroeconomics: Core topics and current directions. Springer. https://doi.org/10.1007/978-3-032-02925-6_34
  • van Diepen, R. M., Boksem, M. A. S., & Smidts, A. (2025). Reliability of EEG metrics for assessing video advertisements. Journal of Advertising, 54(4), 506–526. https://doi.org/10.1080/00913367.2024.2418109
  • Chan, H.-Y., Boksem, M. A. S., Venkatraman, V., Dietvorst, R. C., Scholz, C., Vo, K., Falk, E. B., & Smidts, A. (2024). Neural signals of video advertisement liking: Insights into psychological processes and their temporal dynamics. Journal of Marketing Research, 61(5), 891–913. https://doi.org/10.1177/00222437231194319
  • Speer, S. P. H., Keysers, C., Campdepadrós Barrios, J., Teurlings, C. J. S., Smidts, A., Boksem, M. A. S., Wager, T. D., & Gazzola, V. (2023). A multivariate brain signature for reward. NeuroImage, 271, Article 119990. https://doi.org/10.1016/j.neuroimage.2023.119990
  • Zhang, C., Beste, C., Prochazkova, L., Wang, K., Speer, S. P. H., Smidts, A., Boksem, M. A. S., & Hommel, B. (2022). Resting-state BOLD signal variability is associated with individual differences in metacontrol. Scientific Reports, 12(1), Article 18425. https://doi.org/10.1038/s41598-022-21703-5
  • Speer, S. P. H., Smidts, A., & Boksem, M. A. S. (2022). Individual differences in (dis)honesty are represented in the brain’s functional connectivity at rest. NeuroImage, 246, Article 118761. https://doi.org/10.1016/j.neuroimage.2021.118761
  • Speer, S. P. H., Smidts, A., & Boksem, M. A. S. (2021). Different neural mechanisms underlie non-habitual honesty and non-habitual cheating. Frontiers in Neuroscience, 15, Article 610429. https://doi.org/10.3389/fnins.2021.610429
  • Chan, H.-Y., Smidts, A., Schoots, V. C., Sanfey, A. G., & Boksem, M. A. S. (2020). Decoding dynamic affective responses to naturalistic videos with shared neural patterns. NeuroImage, 216, Article 116618. https://doi.org/10.1016/j.neuroimage.2020.116618
  • Eijlers, E., Boksem, M. A. S., & Smidts, A. (2020). Measuring neural arousal for advertisements and its relationship with advertising success. Frontiers in Neuroscience, 14, Article 736. https://doi.org/10.3389/fnins.2020.00736
  • Speer, S. P. H., & Boksem, M. A. S. (2019). Decoding fairness motivations from multivariate brain activity patterns. Social Cognitive and Affective Neuroscience, 14(11), 1197–1207. https://doi.org/10.1093/scan/nsz097
  • Eijlers, E., Smidts, A., & Boksem, M. A. S. (2019). Implicit measurement of emotional experience and its dynamics. PLOS ONE, 14(2), Article e0211496. https://doi.org/10.1371/journal.pone.0211496
  • Chan, H.-Y., Boksem, M. A. S., & Smidts, A. (2018). Neural profiling of brands: Mapping brand image in consumers’ brains with visual templates. Journal of Marketing Research, 55(4), 600–615. https://doi.org/10.1509/jmr.17.0019

(Neuro)forecasting consumer choices

Decoding psychological processes from the brain helps to explain why consumers respond as they do. In practice, however, the value of such insight rests largely on its ability to improve predictions and inform managerial decisions. In this line of research, we investigate whether neural measures obtained from a small sample of participants can forecast real-world, market-level outcomes, an approach that has become known as neuroforecasting (Genevsky & Boksem, 2026). The premise is that brain responses in a small group capture aspects of a product’s or message’s appeal that generalize to the wider population, including aspects that people do not report.

Movies offer an attractive test case, because their commercial success is publicly recorded and directly linked to the trailers through which they are advertised. In an early study, we recorded EEG while participants viewed movie trailers and found that brain responses predicted not only individual preferences for the movies, but also their population-wide box-office success, above and beyond participants’ stated preferences (Boksem & Smidts, 2015). Because single studies with small samples warrant caution, we subsequently reanalyzed EEG data from five independent datasets, collected in different labs and countries, and replicated this association (Boksem et al., 2025).

A second approach builds on neural similarity. When a video engages its audience, brain responses become more alike across viewers. Across three fMRI studies, the similarity of brain responses in the temporal lobe predicted out-of-sample preference for, and recall of, TV commercials and movie trailers, and added information beyond in-sample preference (Chan et al., 2019). Related work showed that functional and experiential ad appeals engage different brain regions, whose activation relates to advertising effectiveness (Couwenberg et al., 2017), and that decoded psychological processes, in particular social cognition, improve out-of-sample prediction of ad liking (Chan et al., 2024).

Neuroforecasting is not restricted to media. When professional investors evaluated real investment cases in the scanner, their explicit predictions did not forecast future stock performance. However, activity in the nucleus accumbens was higher for cases that later overperformed in the market, and predicted stock performance out of sample above chance (van Brussel et al., 2024).

A key question for the field is whether such findings generalize, because individual studies have often identified different brain regions as predictive. In a mega-analysis that pooled 16 fMRI datasets (572 participants, 739 messages), we found that activity in brain systems implicated in reward and social processing is associated with message effectiveness, both in individuals and at scale, across marketing and health domains, and that brain activity provides information beyond participants’ self-reports (Scholz et al., 2025).

Together, these findings suggest that brain responses in a small group of consumers can be a valuable, if modest, addition to traditional measures for forecasting market-level responses. Important questions remain, such as under which conditions neural measures add most to self-report, and through which mechanisms individual brain responses relate to aggregate behavior.

Publications

  • Genevsky, A. G., & Boksem, M. A. S. (2026). Applications in consumer neuroscience: Decoding and neuroforecasting. In D. V. Smith, P. L. Lockwood, & D. S. Fareri (Eds.), Neuroeconomics: Core topics and current directions. Springer. https://doi.org/10.1007/978-3-032-02925-6_34
  • Scholz, C., Chan, H.-Y., Ahn, J., Boksem, M. A. S., Cooper, N., Coronel, J. C., Doré, B. P., Genevsky, A., Huskey, R., Kang, Y., Knutson, B., Lieberman, M. D., Brook O’Donnell, M., Resnick, A., Smidts, A., Venkatraman, V., Vo, K., Weber, R., Yoon, C., & Falk, E. B. (2025). Brain activity explains message effectiveness: A mega-analysis of 16 neuroimaging studies. PNAS Nexus, 4(11), Article pgaf287. https://doi.org/10.1093/pnasnexus/pgaf287
  • Boksem, M. A. S., van Diepen, R. M., Eijlers, E., Boekel, W., & Smidts, A. (2025). Do EEG metrics derived from trailers predict the commercial success of movies? A systematic analysis of five independent datasets. Journal of Marketing Research, 62(4), 703–720. https://doi.org/10.1177/00222437241309875
  • Chan, H.-Y., Boksem, M. A. S., Venkatraman, V., Dietvorst, R. C., Scholz, C., Vo, K., Falk, E. B., & Smidts, A. (2024). Neural signals of video advertisement liking: Insights into psychological processes and their temporal dynamics. Journal of Marketing Research, 61(5), 891–913. https://doi.org/10.1177/00222437231194319
  • van Brussel, L. D., Boksem, M. A. S., Dietvorst, R. C., & Smidts, A. (2024). Brain activity of professional investors signals future stock performance. Proceedings of the National Academy of Sciences, 121(16), Article e2307982121. https://doi.org/10.1073/pnas.2307982121
  • Chan, H.-Y., Smidts, A., Schoots, V. C., Dietvorst, R. C., & Boksem, M. A. S. (2019). Neural similarity at temporal lobe and cerebellum predicts out-of-sample preference and recall for video stimuli. NeuroImage, 197, 391–401. https://doi.org/10.1016/j.neuroimage.2019.04.076
  • Couwenberg, L. E., Boksem, M. A. S., Dietvorst, R. C., Worm, L., Verbeke, W. J. M. I., & Smidts, A. (2017). Neural responses to functional and experiential ad appeals: Explaining ad effectiveness. International Journal of Research in Marketing, 34(2), 355–366. https://doi.org/10.1016/j.ijresmar.2016.10.005
  • Boksem, M. A. S., & Smidts, A. (2015). Brain responses to movie trailers predict individual preferences for movies and their population-wide commercial success. Journal of Marketing Research, 52(4), 482–492. https://doi.org/10.1509/jmr.13.0572