Contrasting cue-density effects in causal and prediction judgments (original) (raw)
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Several studies have shown that predictive and causal judgments vary depending on whether the question used to assess the relationship between events is presented after each piece of information or only after all the available information has been observed. This effect could be understood by assuming that in the two cases people perceive that the test question requires that different sets of evidence be taken into account. This hypothesis is tested in the present experiments through contextual manipulations that take place at the time of training and at the time of test. Our results show that people use this contextual information to infer which set of events should be considered when making their subjective assessments. The results are at odds with current theoretical approaches, but it is possible to develop mechanisms that would allow these models to account for the observed evidence.
Judgement frequency, belief revision, and serial processing of causal information
The Quarterly Journal of Experimental Psychology: Section B, 2002
The main aim of this research was to study the cognitive architecture underlying causal/covariation learning by investigating the frequency of judgement effect. Previous research has shown that decreasing the number of trials between opportunities to make a judgement in a covariation learning task led to a higher score after an a or d type of trial (positive cases) than after b and c trials (negative cases). Experiment 1 replicated this effect using a trial-by-tria l procedure and examined the conditions under which it occurs. Experiment 2 demonstrated a similar frequency of judgement effect when the information was presented in the form of contingency tables. Associative or statistical single-mechanism accounts of causal and covariation learning do not provide a satisfactory explanation for these findings. An alternative belief revision model is presented.
Magnitude and valence of outcomes as determinants of causal judgments
Revista Latinoamericana de Psicolgía, 2013
This study examines if the blocking effect paradigm predicts causal judgments when consequences of events vary in valence and magnitude. The procedure consists on presenting participants with reports describing the positive or negative effects produced by different substances, when these are consumed either separately or simultaneously with others. Two groups of participants were exposed to high and low magnitude consequences, respectively. The extent to which behavior with respect to causal judgments is consistent with the predictions of the blocking effect was evaluated in in both groups using two types of questions. One of them asked whether or not substance X produced the effect, while the other one asked about the probability of substance X producing the effect. Differences in causal judgments as a product of logical or intuitive reasoning were examined. Even though the blocking effect was not observed, a significant interaction was obtained between the factors valence and experimental condition (blocking and control). Findings are discussed in terms of the differences between associative learning in humans and in non-human animals, and in terms of the theoretical differences between evaluative conditioning and predictive or causal conditioning.
Although normatively irrelevant to the relationship between a cue and an outcome, outcome density (i.e. its base-rate probability) affects people's estimation of causality. By what process causality is incorrectly estimated is of importance to an integrative theory of causal learning. A potential explanation may be that this happens because outcome density induces a judgement bias. An alternative explanation is explored here, following which the incorrect estimation of causality is grounded in the processing of cue–outcome information during learning. A first neural network simulation shows that, in the absence of a deep processing of cue information, cue–outcome relationships are acquired but causality is correctly estimated. The second simulation shows how an incorrect estimation of causality may emerge from the active processing of both cue and outcome information. In an experiment inspired by the simulations, the role of a deep processing of cue information was put to test. In addition to an outcome density manipulation, a shallow cue manipulation was introduced: cue information was either still displayed (concurrent) or no longer displayed (delayed) when outcome information was given. Behavioural and simulation results agree: the outcome-density effect was maximal in the concurrent condition. The results are discussed with respect to the extant explanations of the outcome-density effect within the causal learning framework.
Contingency is used to prepare for outcomes: implications for a functional analysis of learning
It is generally assumed that the function of contingency learning is to predict the occurrence of important events in order to prepare for them. This assumption, however, has scarcely been tested. Moreover, the little evidence that is available suggests just the opposite result. People do not use contingency to prepare for outcomes, nor to predict their occurrence, although they do use it to infer the causal and predictive value of cues. By using both judgmental and behavioral data, we designed the present experiments as a further test for this assumption. The results show that-at least under certain conditions-people do use contingency to prepare for outcomes, even though they would still not use it to predict their occurrence. The functional and adaptive aspects of these results are discussed in the present article
Cue interaction and judgments of causality: Contributions of causal and associative processes
Memory & Cognition, 2004
In four experiments, the predictions made by causal model theory and the Rescorla-Wagner model were tested by using a cue interaction paradigm that measures the relative response to a given event based on the influence or salience of an alternative event. Experiments 1 and 2 uncorrelated two variables that have typically been confounded in the literature (causal order and the number of cues and outcomes) and demonstrated that overall contingency judgments are influenced by the causal structure of the events. Experiment 3 showed that trial-by-trial prediction responses, a second measure of causal assessment, were not influenced by the causal structure of the described events. Experiment 4 revealed that participants became less sensitive to the influence of the causal structure in both their ratings and their predictions as trials progressed. Thus, two experiments provided evidence for highlevel (causal reasoning) processes, and two experiments provided evidence for low-level (associative) processes. We argue that both factors influence causal assessment, depending on what is being asked about the events and participants’ experience with those events.
The criterion-calibration model of cue interaction in contingency judgments
Learning & Behavior, 2011
demonstrated that cue interaction effects in human contingency judgments reflect processing that occurs after the acquisition of information. This finding is in conflict with a broad class of theories. We present a new postacquisition model, the criterion-calibration model, that describes cue interaction effects as involving shifts in a report criterion. The model accounts for the Siegel et al. data and outperforms the only other postacquisition model of cue interaction, Stout and Miller's (2007) SOCR model. We present new data from an experiment designed to evaluate a prediction of the two models regarding reciprocal cue interaction effects. The new data provide further support for the criterion-calibration model.