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# (p.353) Index

# (p.353) Index

accidental properties, 190

adjacency consistent-adjacency inconsistent inclusion, 134–36

adjacency consistent error, 134–35

adjacency-integrity, 134–35

alternative causes, unobserved, 284–87

alternatives, contrasting, 106

ambiguous stimuli, dynamic interpretations of, 288–92

Anderson’s rational analysis model, 183

associative challenge, 141–44

associative learning account of causal learning, 10

associative learning models, 281

assumptions, 107–8

auditory observation, 45

Bayes net assumption connecting causation and probabilities, 23

Bayes’ rule, 315–18

Bayesian framework for causal inference, hierarchical, 315, 320, 340

basic Bayes, 315–16

inferring causal networks, 317–18

inferring causal principles, 318–19

inferring causes and predicting effects, 316–17

belief-desire psychology, 261–62

belief-updating model, 283

biologically caused actions, 267

boundary intensification, 195–97

categorical

*vs*. projective inferences, 253category labels, 204–5

causal Bayes nets (CBNs), 3–4, 94, 95, 97, 119, 263.

*See also*Bayes net approachescausal structure and conditional probabilities, 4–5

deriving predictions from, 87–92

interventions and, 5–6

learning and, 6–8

representational theory of, 129

representing causal systems, 121–23

causal chain, 74

causal conditional, 93

causal direction, 105

causal grammar(s), 82, 323, 342–44.

*See also*grammar(s); graph grammarsin hierarchical Bayesian framework, 324–25

intuitive theories as, 307–15

toward, 312–14

causal induction, primacy

*vs*. recency effects in, 281–84causal knowledge.

*See also specific topics*development, 262–63

domain-specific, 9–10

how statistical regularity can translate to, 140–41

causal laws, 337

causal maps, 274–75

causal models, evolving set of, 205

causal pathways, 107–8

causal power, 330

causal predicates, 336

causal relations, 103–4.

*See also specific topics*role of interventions in infants’ developing sensitivity to, 50–55

role of interventions in infants’ understanding of psychological, 51–53.

*See also*causal understandingcausal structure(s), 73

learning a wide range of, 73–76

statistical structure and, 249

causal taxonomy, 108–9

causal understanding.

*See also under*causal relationscomponents, 48

levels, 45–46

mechanisms underlying the development of, 50

chain graphs, 181

Cheng, Patricia, 298–300

childhood, emotional importance and frequency of explanations in, 265–68

children, determinism in causal inferences of, 213–22

class graph, 326

classical conditioning, 26

classical learning, 10–12

classification.

*See also*categorization; essentialism; generative theory of classificationas diagnosis, 193–95

as prospective

*vs*. diagnostic reasoning, 197–200clique potentials, 179

cognitive cuing explanations, 267

computational models of learning, 169–70

conditional intervention principle, 75

confounded interventions, 80–82

connectionist account of causal learning, 10

connectionist models, 176–77

context-specific independence (CSI), 184–86

contrasting alternatives, 106

control variables, 60–61

counterfactual reasoning, 87

counterfactual theories, 19–20

covariation, statistical, 157–59, 293.

*See also*alternative causes; ambiguous stimuli; causal inductioncovariation account of causal learning, 10

cuing explanations, cognitive, 267

decision making, causal, 97–99

decision theory, 111

depression, stressful life events and, 64–65

deterministic detector theory, 336–43

deterministic systems, learning the structure of, 231–40

diagnostic learning, 164–65

discovery, logic of, 3

domain-specific causal knowledge, 9–10

dominance (decision-making principle), 98

double effect, doctrine of, 111

effect, law of, 10

efficiency, 255–57

egocentric conception, 32–33

egocentrism, infantile, 38–39

error rate, 336

errors, commission

*vs*. omission, 133–34essences, real

*vs*. nominal, 190essential properties, 190

exemplar-based category structures/models, 175–82

expected utility, maximizing, 98

expected utility theory, 110–11

experimental design, theory of optimal, 128

explanations

of actions, 266–67

in childhood, emotional importance and frequency of, 265–68

foster knowledge acquisition in other domains, 271

of humans

*vs*. objects, 266implications for theory development and causal learning, 274–76

of mental states, 267–68

false beliefs, 268–70

Fisher, Ronald, 121

focal event, 248

generative theory of classification, 191–93, 202–4.

*See also*essentialismempirical support for, 193–204

generative transmission models, 68–69

generative transmission rule, 257

Goldvarg, E., 295

Gopnik, Alison, 263

grammar(s), 320.

*See also*causal grammar(s)bird’s eye view of generative, 308–12

theories as logical, 333–42

graph schemas, 324, 326–27, 352 nn.1,2

examples of, in different domains, 327–29

extensions and limitations, 332–33

learning, 331–32

role of learning causal structure, 329–31

graphical clique, 179

Humean rule, 256–57

Humeanism, 259

identification procedures (categories), 191

infant imitation, 38, 46.

*See also*imitation and imitative learninginnate mapping between observation and execution, 39

goal-directedness, 39–40

innate representation of human action, 40

theories, 38

infants.

*See also under*causal relations; explanationsinfer interventions for agents, 42

inferring an intervention based on unsuccessful action patterns, 41–42

agents and goals, 42

inventing new means to achieve an inferred intervention, 42

learning actions

*vs*. outcomes, 41learning interventions from observation, 40

privileged role for manipulations performed by self, 40–41

learning to use a tool, 43–45

primacy of people in their notion of interventions

agent transformations, 43

involvement of people results in different interpretations of same scene, 42–43

understanding of interventions by self and other, 37–39

understanding of physical and psychological causation, 49–50

instrumental learning, 10

intended actions, 267

intervention assumption, 6

interventional reasoning, 87

intervention(s), 21, 37, 161–63, 227.

*See also under*causal relations; infants; observation(s)aids learning, 162

Bayes nets and, 5–6

distinguishing evidence from, 77–79

distinguishing good from confounded, 80–82

learning direct causes from, 228–29

learning indirect causes from, 228

making novel, 70–73

modeling, 89–91

social effects, invariance, and, 294–300

*vs*. temporal order, 163

voluntary actions and, 29–30

intuitive theories, 301–4, 319–20, 323, 343–44.

*See also*Bayesian framework for causal inferenceas causal grammars, 307–15

as causal networks, 303–5

framework theories and specific theories, 305–7

as hypothesis space generators, 303

three questions about, 301–2

“invisible imitation,” 39

Johnson-Laird, P. N., 295

knowledge-driven approach to causal structure, 317–18

Lagnado, Dave, 294–96

language comprehension, 309

Laplace, Pierre Simon, 208–25

learning order and causal order, 163–64

logical conditional, 93

logical theories, learning, 341–42

logical

*vs*. causal reasoning, 92–94magic, 43

magnetism theory, 328

manipulability, 250–51

Markov equivalence, 157–58

Markov random field, 179–82

means-ends relationships, 34

Meno, 208–25

mental model theory, contemporary, 295

metacognition, 80–81

mistaken actions, 267

“mixed causes,” 107

modular view of causal reasoning, 9

*modus tollens*, 93

moral deliberation, 111

morality, 110

multimodal integration, problem of, 149

nativist view of causal reasoning, 9

net-promoting-cause-on-average, 109–10

Newcomb’s paradox, 98

node classes, 326

nominal essences, 190

normative and descriptive theories, 26

novel interventions, making, 70–73

observation(s), 39–41.

*See also*unobservablesauditory, 45

distinguishing evidence from, 77–79

modeling, 88–89

predicting outcomes of hypothetical interventions from, 94–96

one-cause trials, 140–41

ontological problem, 121

operant learning, 10

parameterization, 4

parent events, 248

parsing, 309

“path diagrams,” 121

path-specific effect, 108

PC algorithm, 233–38

PCD algorithm, 238–40

philosophy and psychology, 25–26

physical causation, infants’ understanding of, 49–50

physically caused actions, 267

Piaget, Jean, 68

Piagetian theory of infant imitation, 38–39

power theory of probabilistic contrast (PC), 12–13

practical intelligence, 38

predicates, causal and structural, 336

prediction.

*See also under*Bayesian framework for causal inference; causal Bayes nets; explanationsasymmetry and, 247–50

manipulability and, 250

“prevent,” 107

primacy

*vs*. recency effects in causal induction, 281–84primate causal cognition, 30–35

prior probability distribution, 315

probabilistic contrast (PC), 12–13

probabilistic theories, 19, 23, 192, 203, 333–37.

*See also*Bayesian framework for causal inference; conditional probabilities; determinism; graphical models, probabilistic; statistical dataprocessing constraints, 166

projection, 252–55

promoting

*vs*. preventing/inhibiting causes, 105propositional logic

*vs*. causal reasoning, 92–94prudential rationality, 110–11

psychological

*vs*. physical causality, 8race as cause, 300

“radical conceptual change,” 331

rational agent theory, 328

rational analysis model, Anderson’s, 183

reaching events, 272

real essences, 190

recency

*vs*. primacy effects in causal induction, 281–84Reichenbach asymmetry, 247–50

Reichenbach dyads, 248–50

representativeness judgments, stability of, 183

Rescorla-Wagner (RW) model, 281–83

retrospective inferences made by younger children, 144

“screening off,” 5

screening-off reasoning, 141

search strategies, 257–58

second-order features, 176

self and other, differentiation between, 40

setting priors, problem of, 150

sex as cause, 300

Shultz, T. R., 256–57

similarity functions, three, 174–80

Skinnerian theory of infant imitation, 38

Sloman, Steve, 294–96

social effects, interventions, and invariance, 294–300

soft intervention, 64

spatial cognitive maps, 274–75

spatial relations, 71

specific theories, 305–7

spurious

*vs*. causal relationships, 104stability across background conditions, 106–7

statistical and causal structure, 249

statistical jokes, 295

statistical learning, 139, 144–46, 150

across modalities, 146–47

how it informs our understanding of causal learning, 147–49

new directions for integrating causal learning with, 149–50

problem of constraining, 149–50

“stickball machine,” 76–79

stressful life events and depression, 64–65

structural predicates, 336

“supramodal” framework, 40

syntactic comprehension, 309

task complexity, 283–84

tertiary relationship, 32

theoretical coherence, 201–2

thought insertion, 62

tools, learning to use, 43–45

total cause (TC), 20–25

total effect, 108

total evidence, principle of, 249

two-cause trials, 141

undoing, 91–92

unexplained effects, 285

unobservables, 31

unobserved causes, inferring the existence of, 76–77

vision science, 7–8

“wrong variables” analysis, 32

zero sum competitions, 298–300