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The more, the merrier: Treatment frequency influences effectiveness perception and further treatment choice


Causal illusions have been postulated as cognitive mediators of pseudoscientific beliefs, which, in turn, might lead to the use of pseudomedicines. However, while the laboratory tasks aimed to explore causal illusions typically present participants with information regarding the consequences of administering a fictitious treatment versus not administering any treatment, real-life decisions frequently involve choosing between several alternative treatments. In order to mimic these realistic conditions, participants in two experiments received information regarding the rate of recovery when each of two different fictitious remedies were administered. The fictitious remedy that was more frequently administered was given higher effectiveness ratings than the low-frequency one, independent of the absence or presence of information about the spontaneous recovery rate. Crucially, we also introduced a novel dependent variable that involved imagining new occasions in which the ailment was present and asking participants to decide which treatment they would opt for. The inclusion of information about the base rate of recovery significantly influenced participants’ choices. These results imply that the mere prevalence of popular treatments might make them seem particularly effective. It also suggests that effectiveness ratings should be interpreted with caution as they might not accurately reflect real treatment choices. Materials and datasets are available at the Open Science Framework [].

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    For exploratory reasons, and only in Experiment 1, after the effectiveness and action questions were completed, participants were also asked to report retrospectively the number of patients who recovered during training, among both those who took the high- and those who took the low-frequency remedies (frequency questions, see Appendix). The responses to these questions (available at the OSF []) were consistent with causal impressions as measured by the effectiveness and action questions (i.e., higher proportion of recovery reported for the high- vs. the low-frequency remedy) but, for the sake of brevity, they are not further discussed in the paper.


  1. Allan, L. G., & Jenkins, H. M. (1983). The effect of representations of binary variables on judgment of influence. Learning and Motivation, 14, 381–405.

    Article  Google Scholar 

  2. Astin, J. A. (1998). Why patients use alternative medicine. JAMA, 279(19), 1548.

    Article  PubMed  Google Scholar 

  3. Barberia, I., Vadillo, M. A., & Rodríguez-Ferreiro, J. (2019). Persistence of causal illusions after extensive training. Frontiers in Psychology, 10, 24.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Blanco, F., Barberia, I., & Matute, H. (2014). The lack of side effects of an ineffective treatment facilitates the development of a belief in its effectiveness. PLoS ONE, 9(1), e84084.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Blanco, F., Barberia, I., & Matute, H. (2015). Individuals who believe in the paranormal expose themselves to biased information and develop more causal illusions than nonbelievers in the laboratory. PLoS ONE, 10(7), e0131378.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Blanco, F., Matute, H., & Vadillo, M. A. (2010). Contingency is used to prepare for outcomes: Implications for a functional analysis of learning. Psychonomic Bulletin and Review, 17(1), 117–121.

    Article  Google Scholar 

  7. Blanco, F., Matute, H., & Vadillo, M. A. (2011). Making the uncontrollable seem controllable: The role of action in the illusion of control. Quarterly Journal of Experimental Psychology, 64(7), 1290–1304.

    Article  Google Scholar 

  8. Blanco, F., Matute, H., & Vadillo, M. A. (2013). Interactive effects of the probability of the cue and the probability of the outcome on the overestimation of null contingency. Learning & Behavior, 41(4), 333–340.

    Article  Google Scholar 

  9. Buehner, M. J., Cheng, P. W., & Clifford, D. (2003). From covariation to causation: A test of the assumption of causal power. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 1119–1140.

    PubMed  Google Scholar 

  10. Curley, S. P., Yates, J. F., & Abrams, R. A. (1986). Psychological sources of ambiguity avoidance. Organizational Behavior and Human Decision Processes, 38(2), 230–256.

    Article  Google Scholar 

  11. Don, H. J., Otto, A. R., Cornwall, A. C., Davis, T., & Worthy, D. A. (2019). Learning reward frequency over reward probability: A tale of two learning rules. Cognition, 193, 104042.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Erdfelder, E., Faul, F., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.

    Article  PubMed  Google Scholar 

  13. Fiedler, K., Russer, S., & Gramm, K. (1993). Illusory correlations and memory performance. Journal of Experimental Social Psychology, 29(2), 111–136.

    Article  Google Scholar 

  14. Griffiths, O., Shehabi, N., Murphy, R. A., & Le Pelley, M. E. (2018). Superstition predicts perception of illusory control. British Journal of Psychology, 110(3), 499–518.

    Article  PubMed  Google Scholar 

  15. Hamilton, D. L., & Gifford, R. K. (1976). Illusory correlation in interpersonal perception: A cognitive basis of stereotypic judgments. Journal of Experimental Social Psychology, 12, 392–407.

    Article  Google Scholar 

  16. Kutzner, F. L., & Fiedler, K. (2015). No correlation, no evidence for attention shift in category learning: Different mechanisms behind illusory correlations and the inverse base-rate effect. Journal of Experimental Psychology: General, 144(1), 58–75.

    Article  Google Scholar 

  17. Liljeholm, M., & Cheng, P. W. (2009). The influence of virtual sample size on confidence and causal-strength judgments. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(1), 157–172.

    Article  PubMed  Google Scholar 

  18. Matute, H., Blanco, F., & Díaz-Lago, M. (2019). Learning mechanisms underlying accurate and biased contingency judgments. Journal of Experimental Psychology: Animal Learning and Cognition, 45(4), 373–389.

    Google Scholar 

  19. Matute, H., Blanco, F., Yarritu, I., Díaz-Lago, M., Vadillo, M. A. & Barberia, I. (2015). Illusions of causality: How they bias our everyday thinking and how they could be reduced. Frontiers in Psychology, 6, 888.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Matute, H., Yarritu, I., & Vadillo, M. A. (2011). Illusions of causality at the heart of pseudoscience. British Journal of Psychology, 102(3), 392–405.

    Article  PubMed  Google Scholar 

  21. Murphy, R. A., Schmeer, S., Vallée-Tourangeau, F., Mondragón, E., & Hilton, D. (2011). Making the illusory correlation effect appear and then disappear: The effects of increased learning. Quarterly Journal of Experimental Psychology, 64(1), 24–40.

    Article  Google Scholar 

  22. Peer, E., Brandimarte, L., Samat, S., & Acquisti, A. (2017). Beyond the Turk: Alternative platforms for crowdsourcing behavioral research. Journal of Experimental Social Psychology, 70, 153–163.

    Article  Google Scholar 

  23. Perales, J. C., Catena, A., Cándido, A., & Maldonado, A. (2017). Rules of causal judgment: Mapping statistical information onto causal beliefs. In M. R. Waldmann (Ed.), The Oxford handbook of causal reasoning. New York, NY: Oxford University Press.

    Google Scholar 

  24. Perales, J. C., & Shanks, D. R. (2008). Driven by power? Probe question and presentation format effects on causal judgment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34(6), 1482–1494.

    Article  PubMed  Google Scholar 

  25. Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current research and theory (pp. 64–99). New York, NY: Appleton-Century Crofts.

    Google Scholar 

  26. Rodríguez-Ferreiro, J., & Barberia, I. (2017). The moral foundations of illusory correlation. PLoS ONE, 12(10), e0185758.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Torres, M. N., Barberia, I., & Rodríguez-Ferreiro, J. (2020). Causal illusion as a cognitive basis of pseudoscientific beliefs. British Journal of Psychology.

  28. Yarritu, I., Matute, H., & Vadillo, M. A. (2014). Illusion of control: The role of personal involvement. Experimental Psychology, 61(1), 38–47.

    Article  PubMed  Google Scholar 

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Author note

This study was supported by grant PSI2016-75776-R (AEI/FEDER, UE) to IB and grant PSI2017-83196-R (AEI/FEDER, UE) to FB, both from the Agencia Estatal de Investigación of the Spanish government and the European Regional Development Fund. We thank Marta N. Torres for testing the computer task used in Experiment 1 and for running that experiment.

Open practices statements

The data and materials for the two experiments are available at the Open Science Framework repository ( None of the experiments was preregistered.

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Corresponding author

Correspondence to Javier Rodríguez-Ferreiro.

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Experiment 1

Task instructions (English translation)

“Imagine you are a researcher exploring to what extent two herbal infusions brought from the Amazon work as a treatment for headaches (to simplify, we will call them infusions A and B).

To do this, you have a series of medical records corresponding to patients suffering from headaches. In each record you will observe a patient during one of the headache episodes. We will tell you if the patient has taken infusion A or B during the episode. You will have to decide whether you think the patient's headache will disappear within the next two hours or not. Then, we will tell you if the pain disappeared or not, and you will go ahead to observe the next patient.

After observing several patients, we will ask you some questions. Remember your goal is to determine to what extent each of the infusions is effective.

You can click on the CONTINUE button to start.”

Final questions (English translation)

Causal questions: “To what extent do you think the herbal infusion A [B] is effective against headaches? Please use the slider to respond and then click OK. You can select any value between 0 and 100. A value of 0 means that the herbal infusion A [B] is not effective at all and a value of 100 means that is totally effective.”

Action question: “If you had a headache episode, which of the two infusions would you prefer to take? Click on the image of the infusion of your choice.”

Frequency questions: “You have observed 36 [12] medical records in which the patients had a headache and took the A [B] herbal infusion. Could you remember in how many of these patients the headache disappeared within two hours? Please use the slider to respond and then click OK. You can select any value between 0 and 36 [12].”

Task instructions (original in Spanish)

“Imagina que eres un/a investigador/a que está explorando hasta qué punto dos infusiones de hierbas traídas del Amazonas funcionan como tratamiento contra el dolor de cabeza (las llamaremos infusiones A y B para simplificar).

Para ello, dispones de una serie de fichas médicas correspondientes a pacientes que sufren dolores de cabeza. En cada ficha podrás observar a un paciente durante uno de sus episodios de dolor de cabeza. Te indicaremos qué infusión, A o B, ha tomado el paciente durante el episodio. Tú deberás indicar si piensas que el dolor de cabeza del paciente desaparecerá en las siguientes dos horas o no. A continuación te diremos si el dolor desapareció o no, y pasarás a observar el siguiente paciente.

Después de observar varios pacientes te haremos algunas preguntas. Recuerda que tu objetivo es determinar hasta qué punto cada una de las infusiones es efectiva.

Puedes hacer clic en el botón de CONTINUAR para comenzar.”

Final questions (original in Spanish)

Causal questions: “¿Hasta qué punto crees que la infusión de hierbas A [B] es efectiva contra el dolor de cabeza? Por favor, utiliza la escala móvil para responder y luego haz clic en OK. Puedes seleccionar cualquier valor entre 0 y 100. Un valor de 0 significa que la infusión de hierbas A [B] no es nada efectiva y un valor de 100 que es totalmente efectiva.”

Action question: “Si tuvieses un episodio de dolor de cabeza, ¿cuál de las dos infusiones preferirías tomar? Haz clic en la imagen de la infusión que escojas.”

Frequency questions: “Has observado 36 [12] fichas médicas en las que los pacientes tenían dolor de cabeza y tomaban la infusión de hierbas A [B]. ¿Podrías recordar en cuántos de estos pacientes el dolor de cabeza desapareció en las siguientes dos horas? Por favor, utiliza la escala móvil para responder y luego haz clic en OK. Puedes seleccionar cualquier valor entre 0 y 36 [12]”.

Experiment 2

Task instructions

Imagine that you are a medical researcher who is interested in how two drugs, Batatrim and Dugetil, are able to reduce headaches. In this study, you will observe a series of medical records corresponding to patients who suffer from intense headaches. On each record, you will know which treatment the patient took: either Batatrim, or Dugetil (or nothing). Then, you will have to predict whether you think that the headache stopped within the next two hours. Immediately after, you will be informed about whether the headache stopped. This will proceed to the next patient. Once you see all the information available, you will be asked about how effective you think the two treatments are. Remember that in this study you cannot jot down notes: rather, you should rely on your memory and intuition abilities. Good luck!

Final questions

Causal questions: How effective is Dugetil? / How effective is Batatrim?

Answer on the scale. Any number between 0 and 100 is valid:

  • 0: Not effective at all

  • 50: Moderately effective

  • 100: Completely effective

Action question: What would you DO in case you have a headache?

  1. a)

    I would take Dugetil

  2. b)

    I would take Batatrim

  3. c)

    I would take nothing

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Barberia, I., Blanco, F. & Rodríguez-Ferreiro, J. The more, the merrier: Treatment frequency influences effectiveness perception and further treatment choice. Psychon Bull Rev 28, 665–675 (2021).

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  • Contingency learning
  • Causal illusion
  • Illusory correlation
  • Illusion of causality
  • Cue-density effect
  • Pseudomedicines
  • Pseudoscience