Agents and Causes : Reconciling Competing Theories of Causal Reasoning Michael R. Waldmann

1. Agents and Causes: Reconciling Competing Theories of Causal Reasoning Michael R. Waldmann Cognitive and Decision Sciences Department of Psychology University of Göttingen With: Ralf Mayrhofer

2. Overview • CausalReasoning: Two Frameworks • CausalBayesNetsas Psychological Models • Overviewofempiricalevidence • Markovviolations in causalreasoning • DispositionalTheories • Force dynamics • Agentsandpatients 2. HowDispositionalIntuitions Guide theStructuringofCausalBayesNets • Experiments: Markov Violation • Error attribution in an extendedcausalBayesnet

3. CausalReasoning: TwoFrameworks

4. Causal Models: Psychological Evidence • People are sensitive tothedirectionalityofthecausalarrow (Waldmann & Holyoak, 1992; Waldmann, 2000, 2001) • People estimatecausal power based on covariationinformation, and controlforco-factors(Waldmann & Hagmayer, 2001) • CausalBayesnetsasmodelsofcausallearning(Waldmann & Martignon, 1998) • People (and rats) differentiatebetweenobservational and interventionalpredictions(Waldmann & Hagmayer, 2005; Blaisdell, Sawa, Leising, & Waldmann, 2006) • Counterfactualcausalreasoning(Meder, Hagmayer, & Waldmann, 2008, 2009) • Categoriesandconcepts: The neglecteddirection(Waldmann & Hagmayer, 2006) • A computationalBayesianmodelofdiagnosticreasoning(Meder, Mayrhofer, & Waldmann, 2009) • Abstract knowledgeaboutmechanismsinfluencestheparameterizationofcausalmodels(Waldmann, 2007)

5. CausalBayes Net Research: Summary „The BayesianProbabilisticCausal Networks frameworkhasstimulated a productiveresearchprogram on human inferences on causalnetworks. Such inferenceshaveclearanalogues in everydayjudgmentsaboutsocialattributions, medicaldiagnosisandtreatment, legal reasoning, and in manyotherdomainsinvolvingcausalcognition. So far,researchsuggeststwo persistent deviationsfromthe normative model. People‘sinferencesaboutoneeventareofteninappropiatelyinfluencedbyothereventsthatarenormatively irrelevant; theyareunconditionallyindependentorare „screened off“ byinterveningnodes. Atthe same time, people‘sinferencestendtobeweakerthanarewarrantedbythe normative framework.“ Rottman, B., & Hastie, R. (2013). Reasoningaboutcausalrelationships: Inferenceson causalnetworks. Psychological Bulletin.

6. MarkovViolations in CausalReasoning

7. The Causal Markov Condition • Definition: Conditional upon its parents (“direct causes”) each variable X is independent of all other variables that are not causal descendants of X (i.e., a cause “screens off” each of its effects from the rest of the network) E1 E2 C E3 6

8. But recent research shows… • Recent research shows that human reasoners do consider the states of other effects of a target effect’s cause when inferring from the cause to a single effect(see Rehder & Burnett, 2005; Walsh & Sloman, 2007) vs. 0 1 0 1 1 1 ? ?

9. An Augmented Causal Bayes Net? Rehder & Burnett, 2005

10. The Causal Markov Condition: Psychological Evidence • Subjects typically translate causal model instructions into representation that on the surface violate the Markov condition. • Humans seem to add assumptions about hidden mechanisms that lead to violations of screening-off, even when the cover stories are abstract. • It is unclear where the assumptions about hidden structure come from. People typicallyhaveonlysparseknowledgeaboutmechanisms(Rozenblit & Keil, 2002).

11. DispositionalTheories

12. Abstract Dispositions, Force Dynamics, andtheDistinctionbetweenAgentsandPatients • Causationastheproductof an interactionbetweencausalparticipants (agents, patients) whichareendowedwithdispositions, powers, orcapacities. • e.g., Aspirin hasthecapacitytorelieveheadaches. Brains havethecapacitytobeinfluencedby Aspirin. • Agents (whodon‘thavetobehumans) aretheactiveentitiesemittingforces. Patientsaretheentitiesacted upon bytheagents. Patientsmoreorlessresisttheinfluenceoftheagents. • Intuitionsaboutabstractpropertiesofagentsandpatientsmayguidecausalreasoning in theabsenceoffurthermechanismknowledge.

13. Wolff’s Theory of Force Dynamics(Wolff, 2007) Examples „Winds causedtheboattoheel“ (cause) „Vitamin B allowedthebodytodigest“ (allow) „Winds preventedtheboatfromreachingtheharbor“ (prevent)

14. Problems • Wheredoestheknowledgeabouttendenciescomefromifcovariationinformationisexcluded? • Howcanpredictive and diagnosticinferenceswithincomplexcausalmodelsbeexplained? • How do weknowwhether a causalparticipantplaystheroleof an agentorpatient?

15. HowDispositionalIntuitions Guide theStructuringofCausalBayesNets

16. Hypotheses EE.g., • Bothagentsandpatientsarerepresentedascapacityplaceholdersforhiddeninternalmechanisms. • Thereis a tendencytoblametheagentto a large extentforbothsuccessfulandunsuccessfulcausaltransmissions. • These intuitionscanberepresentedbyelaboratingorre-parameterizingthecausalBayesnet.

17. Experiments: Markov Violation

18. An Unfamiliar Domain: Mind Reading Aliens (see also Steyvers et al., 2003) POR POR=food(in alien language) POR POR

19. DissociatingCausesandAgents EE.g., I. Cause Effect Agent Patient II. Cause Effect Patient Agent

20. Manipulatingthe Agent Role Sender Condition (Cause Object as Agent)„Gonz is capable of sending out his thoughts,and hence transmit them into the heads of Murks, Brrrx, andZoohng.“ Reader Condition (Effect Objects as Agents) „Murks, Brrrx, and Zoohng are capable of reading the thoughts ofGonz. “ Murks Gonz Brrrx Zoohng

21. Experiment 1a: Which Alien is the Cause?(Intervention Question) Imagine „POR“ was implantedin headofcause/effectalien. How probable isitthattheotheralienthinksof „POR“.

22. Experiment 1b: Blame Attributions Who ismoreresponsible, ifcauseispresent and effect absent, thecausealienortheeffectalien?

23. MarkovViolations: Experiment 2 • Instruction: 4 aliens either think of POR or not; thoughts of pink top alien (cause) covary with thoughts of bottom aliens (effects); aliens think of POR in 70% of their time. Test Question: “Imagine 10 situations with this configuration. In how many instances does the right alien think of POR?” ? ? ?

24. Predictions Gonz Murks Sender Condition: The pattern seems to indicate that something is wrong with Gonz‘s capacity to send. Hence, the probability of Murks having Gonz‘s thought should be low (i.e., strong Markov violation). Reader Condition: The pattern seems to indicate that something is wrong with Brrrx‘s and Zoohng‘s capacity to read. Hence, the probability of Murks having Gonz‘s thought should be relatively intact (i.e., weak Markov violation). ?

25. Results: Experiment 2 ?

26. Error attributionin an extendedcausalBayesnet

27. Error attribution in causal Bayes nets C Stan Standard Model: E wC 15

28. Distinguishing between two types of error sources C C Differentiating between Cause (FC)- and Effect (FE)-Based Preventers: FC FE FC E wC E Simplified Version: 15

29. Error Attribution in a Common Cause-Model C FC En E1 E2 … wC • FCis an unobserved common preventer, and must be inferred from the states of C and its effects • When C involves the agent, the strength of FCis high (i.e., error is mainly attributed to C), when E involves the agent, the strength of FC is low (hence errors are primarily attributed to the individual effects (i.e., FE, that is, wC). 15

30. FC C E1 E2 E3 Model Predictions The strength of the FC (red – green – blue) influences the size of the Markov violation (i.e., slope). wF_C 17

31. Further Predictions (1): A/B Case • In the basic experiments an asymmetry between the two states of the cause was found • In the absent case the cause is not active, thus mechanism assumptions cannot have an influence ? • Prediction: When both states of the cause are described as active, the differential assumptions about error attribution should matter in both cases 18

32. Markov-Experiment A/B: Results ? N=56 19

33. FC FC FC IC1 DC IC2 E Further Predictions (2): Causal Chains ? • If each C comes with its own FC, the difference between reading and sending conditions should completely disappear in a causal chain situation 20

34. Chain Experiment: Results ? N=50 21

35. Summary • Causalmodelinstructionsaretypicallyaugmentedwithhiddenstructure. • In theabsenceofspecificmechanismknowledgeintuitionsaboutabstractdispositionalpropertiesofcausalparticipantsguidethestructuringofthemodels.