Contingency and causality: From cognitive theories to clinical reasoning, social stereotypes, pseudoscience, and legal issues.
Friday, September 30th, 2011 [08:30 - 10:30]
SY_02. Contingency and causality: From cognitive theories to clinical reasoning, social stereotypes, pseudoscience, and legal issues
Matute, H. 1 & Cobos, P. 2
1 Deusto University
2 Malaga University
From time to time a concept that has been largely studied in the cognitive laboratory reaches a level of maturity where it can be profitably extrapolated to one or several areas of applied research. This is what is happening right now with the areas of contingency learning and judgments, and causal learning and reasoning. The presenters in this symposium are all experimental cognitive psychologists that are currently trying to extrapolate their expertise on how causal and contingent relationships are acquired and used to several areas of social interest. Simultaneously but independently of each other, they have focused their efforts on applied settings that are as diverse as clinical reasoning, social stereotypes, legal issues, and the reduction of superstitious and pseudoscientific beliefs in knowledge-based societies. This symposium brings them together and shows how common (and sometimes discrepant) cognitive theories of contingency learning and causal learning and reasoning can transfer to the outside world and have an impact in our society.
SY_02.1 - The temporal sequence of symptoms also matters: Further evidence of the use of causal theories in clinical psychologists
Cobos, P. L. , Flores, A. , López, F. J. , Godoy, A. & González-Martín, E.
University of Malaga
Previous studies have shown that clinical psychologists’ causal theories about DSM-IV disorders determine the weight of diagnostic criteria in the diagnosis of such disorders. Specifically, the presence or absence of causally central symptoms responsible for the appearance of many other symptoms have a greater impact on the diagnosis of DSM-IV disorders than causally peripheral symptoms that are the ultimate effects of other symp-toms or are not connected at all. This has been shown to occur even with symptoms that, according to DSM-IV, should be equally considered for diagnosis purposes. Our study provides further evidence of clinicians’ use of causal theories of DSM-IV disorders in several ways. First, we show that what matters is not only which symptoms are present or absent, but also the temporal order in which symptoms appear in a client. When the temporal order is consistent with a well known causal theory of a disorder, clinicians spend less time reading sentences reporting the symptoms than when the temporal order is inconsistent with the causal theory. This result shows that subtle causal reasoning processes are also at work when clinicians face a reading comprehension task. Second, we show that the temporal order of symptoms together with more explicit information about causal links between them affects clinicians’ diagnostic judgements as well as judgements on treatment efficacy and treatment selection. Finally, when inconsistent clinical reports were read, clinicians took longer to make all these judgements than when consistent clinical reports were given. Altogether, our results provide strong and converging evidence that clinical psychologists use causal theories of DSM-IV disorders when processing the information given through clinical reports.
SY_02.2 - Causal reasoning in repeated judgment and choice
University of Goettingen
Most theories of repeated decision making do not take causal considerations into account. The two most prominent theories assume that people either learn about exemplars or abstract linear rules from the observations made (e.g., Juslin et al., 2003). Causal model theories, by contrast, claim that people acquire and use knowledge about the causal structure underlying a decision problem to decide on interventions (Sloman & Hagmayer, 2006). An experiment was conducted combining paradigms on multiple cue and causal learning. Participants were confronted with a fictitious medical system consisting of four factors contributing to an outcome. Participants’ assumptions about the causal relations between the four factors were manipulated by instruction in three groups. In a learning phase participants received identical learning input about the state of the four factors and the value of the outcome before and after an intervention on one of the factors. The learning input consisted of 110 trials and was equally compatible with all causal assumptions. In two test phases (before and after the learning phase) participants were requested to estimate the value of the outcome based on the observed factors, choose one of two interventions, and predict the value of the resulting outcome. Learning input and test cases were constructed to allow differentiating the three theoretical accounts. The results showed that participants considered causal structure even after extensive learning experience. Depending on their causal assumptions, participants preferred different interventions. To further analyze the data, an exemplar model, a linear model and a causal-model model were fitted to participants’ answers. The model assuming that participants induce a causal model and update its parameters based on the learning input predicted participants’ judgments and choices best. These findings indicate that people may also use causal reasoning as a decision making strategy.
SY_02.3 - An algorithm and its implementation for minority group stereotype formation
Murphy, R. A.
University of Oxford
The Illusory Correlation in psychology refers to a class of phenomenon in which people judge relationships to exist where some normative measures suggest none does. This effect has been suggested to reflect a bias in information processing. I will discuss several experiments with specific relevance to how we develop our beliefs about minority social groups. According to some theories our dislike of minority groups is a natural by-product of an attentional processes biased towards unusual events (Hamilton & Gifford, 1974). A cognitive associative account originally developed to account for Pavlovian conditioning (Rescorla and Wagner, 1972), that also predicts a range of correlation and causal learning phenomenon, predicts that any bias is temporary and pre-asymptotic. We report two sets of experiments one designed to test the effect of more experience, and a second designed to assess the effect of changes in event rates. A related prediction of the associative account is that this learning is the result of an error correction principle guided by a negative evaluative response which, according to several theories of localized brain function, should result in certain neural signatures. We discuss an experiment with fMRI responses to explore this prediction.
SY_02.4 - Speculating from absent evidence: A Bayesian network approach
Lagnado, D. , Harris, A. & Cullen, V.
University College London
The extent to which people speculate from absent evidence is an important issue for legal theory and practice. It also presents challenges to psychological theories of causal reasoning. This paper proposes a Bayesian Network (BN) analysis of inference from the absence of evidence. We claim that the inferences people draw depend on their causal models of the case, and their explanations for the absence. Thus the same information about absence can be treated as incriminating, exonerating or neutral depending on which factors are considered as most likely explanations for that absence. An empirical study supported this analysis. Sixty participants were given an identical murder case, and saw the same incriminating evidence. They were all informed of potential eyewitnesses to the crime who were not presented at court. The reasons for this absence were manipulated in three between-subject conditions: participants received ‘incriminating’, ‘exonerating’ or neutral explanations. As predicted, judgments of guilt were modulated by the explanations given for the absence of eyewitnesses: judgments of guilt increased with incriminating reasons and decreased with exonerating reasons. Moreover, BN analyses based on participants’ verbal explanations matched their probability of guilt judgments. These findings have implications for psychological models of causal reasoning, and for legal decision making.
SY_02.5 - Illusion of control when the participant is a mere observer
Matute, H. , Yarritu Corrales, I. & Vadillo, M. A.
The illusion of control is at the heart of superstition and pseudoscience. It consists of overestimating the degree of control that we have over desired outcomes that are actually occurring independently of our behavior. This illusion is stronger when the outcome occurs frequently and the participant is personally involved in trying to obtain it. The traditional social psychology explanation assumes that this is due to a need to protect self-esteem. In consequence, it predicts that there should be no illusion when there is no threat to self-esteem, a prediction that is contrary to many instances of superstition and pseudoscience in real life. By contrast, a cognitive explanation assumes that the illusion of control is caused by the response-outcome coincidences that take place when the participant is involved in trying to obtain the outcome. We conducted two experiments in which another participant or a fictitious patient played the role of the agent, with the actual participants being mere observers. The agent could administer or not a given medical treatment. The outcome (healing) occurred frequently though independently of the treatment, that is, it followed a pre-programmed sequence. Participants observed the sequence of events and developed the illusion that there was a causal relationship between the behavior and the outcome. This illusion was strongest when the agent was responding at a higher rate. That is, personal involvement (of the agent) increased the illusion in the observer. Self-esteem of the observer was not at risk, so this cannot explain the results. We conclude that personal involvement increases the illusion because it increases responding, which biases exposure to the contingency information that is needed in order to accurately estimate the degree of causal relationship between any two events.