Abstract
Promoting a better understanding of statistical data is becoming increasingly important for improving risk comprehension and decision-making. In this regard, previous studies on Bayesian problem solving have shown that iconic representations help infer frequencies in sets and subsets. Nevertheless, the mechanisms by which icons enhance performance remain unclear. Here, we tested the hypothesis that the benefit offered by icon arrays lies in a better alignment between presented and requested relationships, which should facilitate the comprehension of the requested ratio beyond the represented quantities. To this end, we analyzed individual risk estimates based on data presented either in standard verbal presentations (percentages and natural frequency formats) or as icon arrays. Compared to the other formats, icons led to estimates that were more accurate, and importantly, promoted the use of equivalent expressions for the requested probability. Furthermore, whereas the accuracy of the estimates based on verbal formats depended on their alignment with the text, all the estimates based on icons were equally accurate. Therefore, these results support the proposal that icons enhance the comprehension of the ratio and its mapping onto the requested probability and point to relational misalignment as potential interference for text-based Bayesian reasoning. The present findings also argue against an intrinsic difficulty with understanding single-event probabilities.
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Notes
We are aware that the response “30%” also coincides with the literal representation of the false alarm rate for the PE group. Nevertheless, given the extremely low percent accuracy for this group (0% according to the strict criterion; 5% considering the rounded responses), the reported effects are independent of the interpretation of this ambiguity.
We considered the use of 100 or 10 (only observed in IA responses) in the denominator as an attempt to normalize the response. Nevertheless, as discussed below, most of the NF responses using the 100 might not be “true” normalization attempts, but rather a consequence of misleading associations.
In Experiment 2, most of the Bayesian (misaligned) ratios used 10 or 100 as denominator (66 and 80% for IA and NF formats, respectively). Therefore, although simplifications were fewer than in Experiment 1, the overall percentage of correct responses to IA problems expressed as equivalent ratios was indeed higher (49 vs 67% for Experiments 1 vs 2).
In the pilot experiment, different groups received the same IA and NF problems of Experiment 1 (see “Appendix”), but were asked for frequencies (e.g., “of the women who test positive, how many have breast cancer?”), in the IA (N = 20) and NF (N = 22) formats. For the IA group, the mean number of correct responses was similar as in the present experiments (1.42). For the NF group, it was higher than in present experiments (0.82), but still lower than for the IA group (p = .02).
Although a default frequency-based representation is defended by these authors, it is also claimed that a frequentist mechanism might produce subjective confidence for single event probabilities: “even though it might initially output a frequency, and perhaps even store the information as such, other mechanisms may make that frequentist output consciously accessible in the form of a subjective degree of confidence” (Cosmides & Tooby, 1996, p. 66, note 19).
For NF problems, the mean number of correct posterior probability estimates, among participants who correctly estimated both probabilities of the datum, was 0.5. For IA problems, this mean was 1.6.
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Funding
ET was funded by Secretaría de Estado de Investigación, Desarrollo e Innovación of Spanish Ministerio de Economía y Competividad (PSI2013-41568-P). AC and ET were also funded by the Catalan Government (2014-SGR-79).
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Experimental procedure was in accordance with the ethical standards of the University of Barcelona’s Bioethics Commission, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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Appendix
Appendix
Problems presented in each format in each experiment (original in Spanish).
Mammogram problem (Experiment 1) | |||
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Icon array (IA) The following Figure1 shows the prevalence of breast cancer among women over 50 who participate in routine screening, as well as the results of the mammogram for women who have and do not have breast cancer | Natural frequencies (NF) Among 100 women over 50 who participate in routine screening, four have breast cancer. Three of the four women with breast cancer and 12 of the 96 women without breast cancer receive a positive mammogram | Percentages (PE) Among the women over 50 who participate in routine screening, 4% have breast cancer. 75% of the women with breast cancer and 12% of the women without breast cancer receive a positive mammogram | |
Posterior probability question Imagine a friend at that age receives a positive mammogram. Based on the above information, what is the probability of her having breast cancer? (NF and IA versions prompted a “X of Y” response; PE version prompted a “%” response) | |||
Hypertension problem (Experiment 1) | |||
IA The following Figure1 shows the prevalence of hypertension among women over 40 who participate in routine screening, as well as the type of diet followed by women who have and do not have hypertension | NF Among 100 women over 40 who participate in routine screening, 20 have hypertension. 12 of the 20 women with hypertension and 24 of the 80 women without hypertension follow a sodium-rich diet | PE Among the women over 40 who participate in routine screening, 20% have hypertension. 60% of the women with hypertension and 30% of the women without hypertension follow a sodium-rich diet | |
Posterior probability question Imagine a friend at that age follows a sodium-rich diet. Based on the above information, what is the probability of her having hypertension? (NF and IA versions prompted a “X of Y” response; PE version prompted a “%” response) | |||
Mammogram problem (Experiment 2) | |||
IA The following Figure1 shows the prevalence of breast cancer among women over 50 who participate in routine screening, as well as the results of the mammogram for women who have and do not have breast cancer | NF Among 100 women over 50 who participate in routine screening, four have breast cancer and 96 have not breast cancer. Three of the women with breast cancer and 12 of the women without breast cancer receive a positive mammogram | ||
Aligned question2(probability of the datum) Based on the above data, what is the probability of a woman at that age receiving a positive mammogram? (X of Y) Misaligned question (posterior probability) (the same as in Experiment 1) | |||
Hypertension problem (Experiment 2) | |||
IA The following Figure1 shows the prevalence of hypertension among women over 40 who participate in routine screening, as well as the type of diet followed by women who have and do not have hypertension | NF Among 100 women over 40 who participate in routine screening, tem have hypertension and 90 have not hypertension. Eight of the women with hypertension and 16 of the women without hypertension follow a sodium-rich diet | ||
Aligned question2(probability of the datum) Based on the above data, what is the probability of a woman at that age following a rich-sodium diet? (X of Y) Misaligned question (posterior probability) (the same as in Experiment 1) |
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Tubau, E., Rodríguez-Ferreiro, J., Barberia, I. et al. From reading numbers to seeing ratios: a benefit of icons for risk comprehension. Psychological Research 83, 1808–1816 (2019). https://doi.org/10.1007/s00426-018-1041-4
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DOI: https://doi.org/10.1007/s00426-018-1041-4