Mechanistic beliefs determine adherence to the Markov property in causal reasoning

Park, Juhwa; Sloman, Steven A.

doi:10.1016/j.cogpsych.2013.09.002

Cited by 50 publications

(73 citation statements)

References 51 publications

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“…Informally, if we know about the causes of some event X, then the descendants of X may give us information about X, but the non-descendants cannot give us any more information about X. Recently, various studies (Rottman & Hastie, 2016Park & Sloman, 2013;Rehder, 2014;Fernbach & Sloman, 2009;Waldmann, Cheng, Hagmayer, & Blaisdell, 2008;Hagmayer & Waldmann, 2002) have provided evidence that people often violate the Markov condition when making causal inferences.In another line of research, there has been an attempt to modify existing Bayesian models to explain away erroneous judgments (Costello, 2009;Costello & Watts, 2014). These models are interesting both from a philosophical point of view, because they may shed light on the principles underlying human reasoning, and also from a practical point of view, as an understanding of why we make judgment errors may inform strategies to improve decision making.…”

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confidence: 99%

A quantum probability framework for human probabilistic inference.

Trueblood

Yearsley

Pothos

2017

Journal of Experimental Psychology: General

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThere is considerable variety in human inference (e.g., a doctor inferring the presence of a disease, a juror inferring the guilt of a defendant, or someone inferring future weight loss based on diet and exercise). As such, people display a wide range of behaviors when making inference judgments.Sometimes, people's judgments appear Bayesian (i.e., normative), but in other cases, judgments deviate from the normative prescription of classical probability theory. How can we combine bothBayesian and non-Bayesian influences in a principled way? We propose a unified explanation of human inference using quantum probability theory. In our approach, we postulate a hierarchy of mental representations, from 'fully' quantum to 'fully' classical, which could be adopted in different situations. In our hierarchy of models, moving from the lowest level to the highest involves changing assumptions about compatibility (i.e., how joint events are represented). Using results from three experiments, we show that our modeling approach explains five key phenomena in human inference including order effects, reciprocity (i.e., the inverse fallacy), memorylessness, violations of the Markov condition, and anti-discounting. As far as we are aware, no existing theory or model can explain all five phenomena. We also explore transitions in our hierarchy, examining how representations change from more quantum to more classical. We show that classical representations provide a better account of data as individuals gain familiarity with a task.We also show that representations vary between individuals, in a way that relates to a simple measure of cognitive style, the Cognitive Reflection Test.Keywords: Human judgment, quantum probability theory, Bayes' rule, order effects, Markov condition A Quantum Framework for Probabilistic Inference 3 A Quantum Probability Framework for Human Probabilistic InferenceEveryday we face situations where we must make inferences about the world around us. For example, a doctor must determine the likelihood that a patient has a disease based on a set of symptoms. A juror must decide the probability that a defendant is guilty after hearing the cases made by the prosecution and defense. Or, maybe you want to judge the likelihood that you will weigh less next month if you start exercising more regularly and you improve your diet. In general, the inference problem involves judging the likelihood of some hypothesis (e.g., presence of a disease, guilt of a defendant, future weight loss) based on a series of evidence (e.g., medicalsymptoms, prosecution and defense cases, changes in your diet and exercise).Bayesian inference is widely accepted as the normative approach to inference. However, decades of research in human judgment and decision-making have suggested that people's judgments often violate the rules of Bayesian inference and classical probability theory (Tversky...

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confidence: 99%

A quantum probability framework for human probabilistic inference.

Trueblood

Yearsley

Pothos

2017

Journal of Experimental Psychology: General

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“…Accounts of violations of the Markov condition have focused more on associative, hidden cause, or mechanistic explanations [19, 51, 52, 53]. For example, for the CENC condition the sprinklers being on and rain may be negatively correlated by human intervention: there is no point in switching the sprinklers on when it is raining.…”

Section: Discussionmentioning

confidence: 99%

Discounting and Augmentation in Causal Conditional Reasoning: Causal Models or Shallow Encoding?

et al. 2016

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Recent research comparing mental models theory and causal Bayes nets for their ability to account for discounting and augmentation inferences in causal conditional reasoning had some limitations. One of the experiments used an ordinal scale and multiple items and analysed the data by subjects and items. This procedure can create a variety of problems that can be resolved by using an appropriate cumulative link function mixed models approach in which items are treated as random effects. Experiment 1 replicated this earlier experiment and analysed the results using appropriate data analytic techniques. Although successfully replicating earlier research, the pattern of results could be explained by a much simpler “shallow encoding” hypothesis. Experiment 2 introduced a manipulation to critically test this hypothesis. The results favoured the causal Bayes nets predictions and not shallow encoding and were not consistent with mental models theory. Experiment 1 provided qualified support for the causal Bayes net approach using appropriate statistics because it also replicated the failure to observe one of the predicted main effects. Experiment 2 discounted one plausible explanation for this failure. While within the limited goals that were set for these experiments they were successful, more research is required to account for the pattern of findings using this paradigm.

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“…Graphical causal models are an increasingly popular approach to thinking about causation in the philosophy and psychology literatures (see Burns and McCormack 2009;Danks THIS VOLUME;Fernbach and Sloman 2009;Glymour 2001;Glymour 2010;Gopnik et al 2004;Gopnik and Schulz 2007;Griffiths and Tenenbaum 2009;Lagnado and Sloman 2006;Lagnado et al 2007;Park and Sloman 2013;Park and Sloman THIS VOLUME;Pearl 2000;Reips and Waldmann 2008;Rottman and Keil 2012;Rottman et al 2014;Schulz et al 2007;Sloman 2005;Sobel and Kushnir 2003;and Spirtes et al 2000). In graphical causal modeling, we begin with a primitive relation of direct structural causation that takes variables as its relata.…”

Section: Graphical Causal Models and Causal Attributionsmentioning

confidence: 99%