Abstract
People’s prior beliefs often lead them to make biased responses that violate logical rules in reasoning tasks. Conflict detection studies have found that biased reasoners can detect the conflict between heuristic beliefs and logical rules despite their ultimately biased responses, leading to the proposal of logical intuition. However, these studies have mainly used simple single-model reasoning tasks, and the generalization of conflict detection research and the boundary conditions of logical intuition still need to be clarified. The current study explores this issue directly by manipulating logical complexity through the number of mental models. Both response time and response confidence data found that reasoners took more time to respond and had lower confidence in their responses when they incorrectly solved conflict problems compared to correctly solving no-conflict problems. Furthermore, none of these differences were influenced by the number of mental models. The results suggest that the biased reasoners were not blind heuristic performers in complex three-model problems and that they at least detected that their heuristic beliefs were problematic. Moreover, there is no difference in conflict detection between single-model and three-model problems, indicating that logical complexity does not affect the conflict detection process. Overall, the current study indicates that successful conflict detection is not specific to simple tasks, extending the scope of conflict detection and logical intuition.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
References
Bago, B., & De Neys, W. (2017). Fast logic?: Examining the time course assumption of dual process theory. Cognition, 158, 90–109.
Bago, B., & De Neys, W. (2020). Advancing the specification of dual process models of higher cognition: A critical test of the hybrid model view. Thinking & Reasoning, 26(1), 1–30.
Ball, L. J., Phillips, P., Wade, C. N., & Quayle, J. D. (2006). Effects of belief and logic on syllogistic reasoning - Eye-movement evidence for selective processing models. Experimental Psychology, 53(1), 77–86.
Bonner, C., & Newell, B. R. (2010). In conflict with ourselves? An investigation of Heuristic and Analytic processes in decision making. Memory & Cognition, 38, 186–196.
Brisson, J., Schaeken, W., Markovits, H., & De Neys, W. (2018). Conflict detection and logical complexity. Psychologica Belgica, 58(1), 318–332.
De Neys, W. (2014). Conflict detection, dual processes, and logical intuitions: Some clarifications. Thinking & Reasoning, 20(2), 169–187.
De Neys, W. (2015). Heuristic bias and conflict detection during thinking. In B. H. Ross (Ed.), Psychology of learning and motivation (Vol. 62, pp. 1–32). Academic Press.
De Neys, W., Cromheeke, S., & Osman, M. (2011). Biased but in doubt: conflict and decision confidence. PLoS One, 6(1), e15954.
De Neys, W., & Glumicic, T. (2008). Conflict monitoring in dual process theories of thinking. Cognition, 106(3), 1248–1299.
Epstein, S. (1994). Integration of the cognitive and the psychodynamic unconscious. The American Psychologist, 49(8), 709–724.
Evans, J. S. B. T. (2011). Dual-process theories of reasoning: Contemporary issues and developmental applications. Developmental Review, 31(2–3), 86–102.
Evans, J. S. B. T. (2017). Dual process theories: Perspectives and problems. In W. De Neys (Ed.), Dual process theory 2.0 (pp. 137–155). Routledge.
Evans, J. S. B. T., Barston, J. L., & Pollard, P. (1983). On the conflict between logic and belief in syllogistic reasoning. Memory & Cognition, 11(3), 295–306.
Evans, J. S. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8(3), 223–241.
Franssens, S., & De Neys, W. (2009). The effortless nature of conflict detection during thinking. Thinking & Reasoning, 15(2), 105–128.
Frey, D., Johnson, E. D., & De Neys, W. (2018). Individual differences in conflict detection during reasoning. Quarterly Journal of Experimental Psychology, 71(5), 1188–1208.
Ghasemi, O., Handley, S., & Howarth, S. (2022). The bright homunculus in our head: Individual differences in intuitive sensitivity to logical validity. Quarterly Journal of Experimental Psychology, 75(3), 508–535.
Handley, S. J., Newstead, S. E., & Trippas, D. (2011). Logic, beliefs, and instruction: A test of the default interventionist account of Belief Bias. Journal of Experimental Psychology-Learning Memory and Cognition, 37(1), 28–43.
Handley, S. J., & Trippas, D. (2015). Dual processes and the interplay between knowledge and structure: a new parallel processing model. In B. H. Ross (Ed.), Psychology of learning and motivation (Vol. 62, pp. 33–58). Academic Press.
Howarth, S., Handley, S., & Polito, V. (2022). Uncontrolled logic: Intuitive sensitivity to logical structure in random responding. Thinking & Reasoning, 28(1), 61–96.
Janssen, E. M., Velinga, S. B., De Neys, W., & van Gog, T. (2021). Recognizing biased reasoning: Conflict detection during decision-making and decision-evaluation. Acta Psychologica, 217, 103322.
JASP Team (2022). JASP (Version 0.16.4) [Computer software].
Johnson-Laird, P. N. (2001). Mental models and deduction. Trends in Cognitive Sciences, 5(10), 434–442.
Johnson-Laird, P. N., & Bara, B. G. (1984). Syllogistic inference. Cognition, 16, 1–62.
Johnson, E. D., Tubau, E., & De Neys, W. (2016). The Doubting System 1: Evidence for automatic substitution sensitivity. Acta Psychologica, 164, 56–64.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Strauss, & Giroux.
Morsanyi, K., & Handley, S. J. (2012). Logic feels so Good-I like it! Evidence for intuitive detection of Logicality in Syllogistic reasoning. Journal of Experimental Psychology-Learning Memory and Cognition, 38(3), 596–616.
Mevel, K., Poirel, N., Rossi, S., Cassotti, M., Simon, G., Houde, O., & De Neys, W. (2015). Bias detection: Response confidence evidence for conflict sensitivity in the ratio bias task. Journal of Cognitive Psychology, 27(2), 227–237.
Pennycook, G., Fugelsang, J. A., & Koehler, D. J. (2012). Are we good at detecting conflict during reasoning? Cognition, 124(1), 101–106.
Pennycook, G., Fugelsang, J. A., & Koehler, D. J. (2015). What makes us think? A three-stage dual-process model of analytic engagement. Cognitive Psychology, 80, 34–72.
Pennycook, G., Trippas, D., Handley, S. J., & Thompson, V. A. (2014). Base Rates: Both neglected and intuitive. Journal of Experimental Psychology-Learning Memory and Cognition, 40(2), 544–554.
Raoelison, M., Boissin, E., Borst, G., & De Neys, W. (2021). From slow to fast logic: The development of logical intuitions. Thinking & Reasoning, 27(4), 599.
Schroyens, W., Schaeken, W., Fias, W., & d’Ydewalle, G. (2000). Heuristic and analytic processes in propositional reasoning with negatives. Journal of Experimental Psychology Learning Memory and Cognition, 26(6), 1713–1734.
Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3–22.
Srol, J., & De Neys, W. (2021). Predicting individual differences in conflict detection and bias susceptibility during reasoning. Thinking & Reasoning, 27(1), 38–68.
Stanovich, K. E. (2011). Rationality and the reflective mind. Oxford University Press.
Stupple, E. J. N., & Ball, L. J. (2008). Belief-logic conflict resolution in syllogistic reasoning: Inspection-time evidence for a parallel-process model. Thinking & Reasoning, 14(2), 168–181.
Travers, E., Rolison, J. J., & Feeney, A. (2016). The time course of conflict on the cognitive reflection test. Cognition, 150, 109–118.
Trippas, D., Handley, S. J., Verde, M. F., & Morsanyi, K. (2016). Logic brightens my day: Evidence for implicit sensitivity to logical validity. Journal of Experimental Psychology-Learning Memory and Cognition, 42(9), 1448–1457.
Trippas, D., Thompson, V. A., & Handley, S. J. (2017). When fast logic meets slow belief: Evidence for a parallel-processing model of belief bias. Memory & Cognition, 45(4), 539–552.
Tversky, A., & Kahneman, D. (1974). Judgement under uncertainty: Heuristics and biases. Science, 185, 1124–1131.
Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E. J. (2011). Statistical evidence in experimental psychology: An empirical comparison using 855 t tests. Perspectives on Psychological Science, 6(3), 291–298.
Woodworth, R. S., & Sells, S. B. (1935). An atmosphere effect in formal syllogistic reasoning. Journal of Experimental Psychology, 18(4), 451–460.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics approval
This study was performed in line with the principles of the Declaration of Helsinki. The study was approved by the Ethics Committee of the School of Psychology, Jiangxi Normal University.
Consent to participate
Informed consent was obtained from all individual participants included in the study.
Conflict of interest
No conflict of interest was reported by the authors.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix 1 Overview of the problem content
Model number | Conflict | Premise 1 | Premise 2 | Conclusion |
|---|---|---|---|---|
Single (Valid form: B-C, A-B premises with A-C conclusion) | No-conflict (Believable-valid) | All X are creatures | All dolphins are X | Therefore, all dolphins are creatures |
All X are balls | All basketballs are X | Therefore, all basketballs are balls | ||
Conflict (Unbelievable-valid) | All X are red | All colors are X | Therefore, all colors are red | |
All X are females | All human are X | Therefore, all human are females | ||
Single (Invalid form: B-C, A-B premises with C-A conclusion) | Conflict (Believable-invalid) | All X are pork | All meat is X | Therefore, all pork is meat |
All X are colas | All drinks are X | Therefore, all colas are drinks | ||
No-conflict (Unbelievable-invalid) | All X are eggs | All chicken eggs are X | Therefore, all eggs are chicken eggs | |
All X are food | All breads are X | Therefore, all foods are breads | ||
Three (Valid form: B-C, A-B premises with A-C conclusion) | No-conflict (Believable-valid) | No X are apples | Some fruits are X | Therefore, some fruits are not apples |
No X are poplars | Some trees are X | Therefore, some trees are not poplars | ||
Conflict (Unbelievable-valid) | No X are animals | Some tigers are X | Therefore, some tigers are not animals | |
No X are fish | Some carp are X | Therefore, some carp are not fish | ||
Three (Invalid form: B-C, A-B premises with C-A conclusion) | Conflict (Believable-invalid) | No X are flowers | Some osmanthus are X | Therefore, some flowers are not osmanthus |
No X are insects | Some ants are X | Therefore, some insects are not ants | ||
No-conflict (Unbelievable-invalid) | No X are pigeons | Some birds are X | Therefore, some pigeons are not birds | |
No X are cabbages | Some vegetables are X | Therefore, some cabbages are not vegetables |
Appendix 2 The accuracy analysis of the reasoners included in the conflict detection analysis
As Appendix Fig. 3 shows, there was a main effect of Conflict, with higher accuracy for no-conflict (M = 0.91, SE = 0.02) than conflict (M = 0.41, SE = 0.03) problems, F (1,38) = 179.64, p < 0.001, η2p = 0.83. While we found no significant effect of Model, F (1,38) = 1.59, p = 0.215, η2p = 0.04, and, critically, no significant interaction between Conflict and Model, F (1,38) < 1.
The accuracy of the reasoners included in the conflict detection analysis. Error bars are standard errors
In contrast to the overall accuracy analysis, the accuracy results of the reasoners included in the conflict detection analysis did not reveal a main effect of Model. To verify whether our logical complexity manipulation was successful in these reasoners, we also performed a supplementary analysis of the response time (s) of these reasoners. As Appendix Fig. 4 shows, there was a main effect of Conflict, with more response time for conflict (M = 18.49, SE = 1.53) than no-conflict (M = 14.18, SE = 0.91) problems, F (1,38) = 18.06, p < 0.001, η2p = 0.32. Moreover, a main effect of Model showed that participants took more time to solve three-model problems (M = 18.90, SE = 1.59) than single-model problems (M = 13.78, SE = 0.97), F (1,38) = 16.51, p < 0.001, η2p = 0.30. While no significant interaction between Conflict and Model was found, F (1,38) < 1. Thus, the accuracy and response time results suggest that the logical complexity manipulation remains successful in these reasoners.
The response time of the reasoners included in the conflict detection analysis. Error bars are standard errors
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yang, J., Hu, Z., Zhu, D. et al. Belief bias, conflict detection, and logical complexity. Curr Psychol 43, 2641–2649 (2024). https://doi.org/10.1007/s12144-023-04562-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12144-023-04562-9