European Journal of Social Psychology
Eur. J. Soc. Psychol. 32, 667–684 (2002)
Published online 3 July 2002 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/ejsp.115
Causal attribution from covariation information:
the evidential evaluation model
PETER A. WHITE*
School of Psychology, Cardiff University, UK
Abstract
It is hypothesized that causal attributions are made by transforming covariation information into
evidence according to notions of evidential value, and that causal judgement is a function of the
proportion of instances that are evaluated as confirmatory for the causal hypothesis under test: this is
called the evidential evaluation model. An experiment was designed to test the judgemental rule in this
model by setting up problems presenting consensus, distinctiveness, and consistency information in
which the proportion of confirmatory instances varied but the objective contingency did not. It was
found that judgements tended to vary with the proportion of confirmatory instances. Several other
current models of causal judgement or causal attribution fail to account for this result. Similar findings
have been obtained in studies of causal judgement from contingency information, so the present
findings support an argument that the evidential evaluation model provides a unified account of
judgement in both domains. Copyright # 2002 John Wiley & Sons, Ltd.
For more than forty years the idea that people make causal attributions by assessing covariation
between candidate causes and effects has been a central theme of attribution research. Heider (1958)
defined the covariation principle as follows: ‘that condition will be held responsible for an effect which
is present when the effect is present and absent when the effect is absent’ (p. 152). He derived this
principle from the work of Cohen and Nagel (1934) who propounded a general view of a causal
relation as a kind of invariant relation in which the cause is invariably connected with the effect
(White, 2000a). The principle reflects Heider’s view that people make causal attributions in the
manner of naive scientists concerned primarily with the goals of understanding, prediction, and
control. In accord with this he suggested that causal analysis is ‘in a way analogous to experimental
methods’ (p. 297).
Kelley (1967, 1972, 1973) developed Heider’s suggestion in a systematic way, using the covariation
principle as the core of a model of causal attribution based on an analogy with the orthogonal
manipulation of independent variables in analysis of variance designs. The aim of the model was to
distinguish between three possible causal candidates. For example, where the effect to be explained is,
‘John laughs at the comedian’, the three candidates are the actor (John, the person who carries out the
*Correspondence to: Peter A. White, School of Psychology, Cardiff University, PO Box 901, Cardiff CF10 3YG, Wales, UK.
E-mail: whitepa@cardiff.ac.uk
Copyright # 2002 John Wiley & Sons, Ltd.
Received 14 August 2001
Accepted 22 January 2002
668
Peter A. White
behaviour in question), the target or object (the thing to which the behaviour is done, in this case the
comedian), and the occasion or circumstances under which the behaviour is carried out. Causal attribution can, in principle, also be made to interactions between any two or all three of the candidates.
To make a causal attribution people are presumed to seek information on three orthogonal
dimensions. These are consensus, information about the behaviour of different actors with the same
target under the same circumstances, distinctiveness, information about the behaviour of the same
actor with different targets under the same circumstances, and consistency, information about the
behaviour of the same actor with the same target under different circumstances. Covariation is
assessed for each causal candidate across a sample of instances drawn from these three dimensions in a
manner computationally equivalent to analysis of variance. The candidate that covaries with the effect
is identified as the cause. If more than one candidate covaries with the effect then the cause is identified
as an interaction between the covariates.
Subsequently several other models of causal attribution from covariation information were
proposed. Some of these were proposals about the kind of information that is sampled for purposes
of causal attribution (Orvis, Cunningham, & Kelley, 1975; Pruitt & Insko, 1980; Forsterling, 1989;
Cheng & Novick, 1990). Others have proposed different inductive rules in an attempt to provide a
better account of the evidence from causal attribution experiments (Jaspars, 1983; Hilton & Slugoski,
1986; Hewstone & Jaspars, 1987; Cheng & Novick, 1990, 1992; Hilton, Smith, & Kim, 1995; Van
Overwalle & Heylighen, 1995). The inductive rule that best describes the way in which people make
causal attributions is the main concern of the present paper. Specifically, this research was designed to
test the hypothesis that a model recently developed to account for the phenomena of causal judgement
from contingency information accounts for phenomena of causal attribution as well, and that the two
kinds of judgement are therefore made in the same way.
Contingency information is information about the occurrence or nonoccurrence of a certain effect
in the presence or absence of a certain causal candidate. The conventional identification of cells in a
2 2 contingency table is shown in Table 1: those conventions will be adopted herein. Studies of
causal judgement from contingency information do not employ the verbal summary statements
commonly used in causal attribution research. They present quantitative information, either summarised in a contingency table (e.g. Wasserman, 1990) or, more commonly, as a list of individual
instances, either all available for scrutiny at once (e.g. White, 2000c, ‘Making causal judgements from
contingency information: the Evidential Evaluation model’, submitted 2001) or in a trial-by-trial
procedure in which each instance is removed and no longer available for scrutiny before the next one is
presented (e.g. Shanks, 1987, White, 2000b, submitted 2001).
The generally accepted objective measure of contingency is the P (delta P) rule (Jenkins & Ward,
1965; McKenzie, 1994; Ward & Jenkins, 1965). In terms of Table 1, P ¼ ða=a þ bÞ ðc=c þ dÞ.
More commonly it is represented as
P ¼ pðe=cÞ pðe=cÞ
ð1Þ
Table 1. Conventional identification of cells in the 2 2
contingency table
Effect occurs
Candidate cause
Present
Absent
Copyright # 2002 John Wiley & Sons, Ltd.
Yes
No
a
c
b
d
Eur. J. Soc. Psychol. 32, 667–684 (2002)
Causal attribution
669
where p(e/c) is the probability that the effect occurs when the cause is present and p(e/c) is the
probability that the effect occurs when the cause is absent. Values of P fall in the range 1 (perfect
negative contingency) to þ1 (perfect positive contingency).
THE EVIDENTIAL EVALUATION MODEL
The central proposition of the evidential evaluation model, which has been developed in a series of
recent papers (White, 1998, 2000b, 2000c, 2002, in press, submitted 2001), is that instances
of contingency information are treated as evidence with respect to a causal interpretation. In the
case of causal attribution this means that instances of covariation information are treated as evidence
with respect to whichever locus is being judged. When people are exposed to an instance of
information they evaluate that instance in terms of its evidential value for the causal locus being
judged. Judgement of a particular causal interpretation is determined by the proportion of instances in
a sample that are evaluated as confirmatory for that interpretation. This is called the pCI rule, where
pCI ¼ proportion of confirming instances.
Normatively cells a and d are confirmatory, meaning that such instances increase the likelihood that
the candidate being judged is the cause of the effect, and cells b and c are disconfirmatory. By this
criterion the proportion of confirming instances would be the proportion of instances that fall into cells
a and d. Most individuals conform to these normative evaluations for most cells but there are also
frequent individual differences (Anderson & Sheu, 1995; White, 2000b). This implies the general
model of the pCI rule applied to the 2 2 contingency table format that is shown in equation (2)
(White, submitted 2001, in press):
JI ¼
Wa a þ Wd d
Wa a þ Wb b þ Wc c þ Wd d
ð2Þ
JI is the judgement of interpretation I and Wa, Wb, Wc, and Wd are weights assigned to cells a, b, c, and
d respectively. Equation (2) is intended to accommodate individual differences in notions of evidential
value by expressing them as cell weights. The general model therefore accommodates judges who
assign equal weight to cells a and d, those who assign more weight to cell a than to cell d, and those
who assign no weight to cell d at all. It can even accommodate those who assign counter-normative,
disconfirmatory value to cell d if Wd can take negative values.
The model can accommodate a considerable range of individual differences, but White (2002,
submitted 2001) observed that at the level of sample means it is over-inclusive, in other words capable
of predicting tendencies that happen not to obtain as well as those that do. White (2002, submitted
2001) therefore proposed a more specific and predictively powerful version of the model that takes its
form from two observations about causal judgement: most uses of contingency information conform to
normative evidential evaluations, and individual differences in notions of evidential value are nonrandomly distributed (Anderson & Sheu, 1995; White, 2000b).
The Pad Rule
The finding that most uses of contingency information are normative implies that the unweighted
version of the model provides a good approximation for predictive purposes at the level of sample
means and should outperform other inductive rules. The unweighted rule is shown in
JI ¼
Copyright # 2002 John Wiley & Sons, Ltd.
aþd
T
ð3Þ
Eur. J. Soc. Psychol. 32, 667–684 (2002)
670
Peter A. White
where T ¼ the total number of instances under consideration. This is called the Pad rule, referring to
the Proportion of instances in cells a and d. Several experiments have found that Pad is a better
predictor of causal judgements than P is. White (2002, submitted 2001) found that when Pad was
manipulated and P was held constant causal judgements tended to vary with Pad, but when P was
manipulated and Pad held constant, no significant differences were found.
When p(c), the proportion of instances on which the candidate is present overall, ¼ 0.5, then values
of P and Pad are isomorphic and generate identical ordinal predictions for causal judgement (White,
submitted 2001). This is an important feature of the Pad rule. Many studies have shown that causal
judgements conform closely to objective differences in P (Allan, 1993; Cheng, 1997; Shanks, 1995;
Vallée-Tourangeau, Hollingsworth, & Murphy, 1998; Wasserman, Elek, Chatlosh, & Baker, 1993;
White, 2000b). But in all these studies p(c) ¼ 0.5, and so the Pad rule fits that evidence as well as any
other rule could. The only way to distinguish between Pad and P (and models based on P) is by
using stimulus materials in which p(c) 6¼ 0.5. That was the approach taken by White (2002, submitted
2001), and the same strategy is used here.
The Cause-present Effect
The Pad rule is the main focus of the present research. However, the finding that individual differences
in notions of evidential value are non-randomly distributed implies an important qualification to the
Pad rule. White (2000b) found that cause-present information was consistently used according to
normative notions of evidential value, but the evaluation of cause-absent information varied
considerably between individuals, with some conforming to normative notions, others assigning it
no evidential value, and others evaluating it in other idiosyncratic ways. This implies that causepresent information has a greater effect on mean tendencies in causal judgement than cause-absent
information does, other things being equal. This will be called the cause-present effect. The causepresent effect will not be observed in every individual: it is an emergent property of the aggregation of
individual tendencies in the evaluation of contingency information.
To see how the cause-present effect works, suppose a set of instances in which P ¼ þ0.5. If p(e),
the proportion of instances under consideration in which the effect occurs, ¼ 0.5, then p(e/c) ¼ 0.75
and p(e/c) ¼ 0.25. However if p(e) ¼ 0.75, then p(e/c) ¼ 1.0 and p(e/c) ¼ 0.5. No other values are
possible without P changing. Both p(e/c) and p(e/c) have increased by 0.25. The increase in p(e/c)
tends to raise causal judgement and the increase in p(e/ c) tends to lower it. However, under the
cause-present effect the increase in p(e/c) raises judgement more than the increase in p(e/c) lowers it,
and so mean judgement tends to be higher at the higher value of p(e). Numerous studies have found
effects of p(e) of this sort (Allan, 1993) and the explanation of this tendency in terms of the causepresent effect is directly supported by the findings of Wasserman et al. (1993) and White (2000b,
submitted 2001).
TESTING THE PAD RULE IN APPLICATION TO CAUSAL ATTRIBUTION
The present experiment was designed to test the hypothesis that the Pad rule and the cause-present
effect apply to causal attribution as they do to causal judgement from contingency information. In
other words, the effect to be explained will be behavioural, as in other causal attribution studies, and
the stimulus information concerns the three dimensions of consensus, distinctiveness, and consistency.
The experiment has the same kind of design as previous tests of the evidential evaluation model
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
Causal attribution
Table 2.
671
Stimulus materials for candidate C1, test of Pad rule
Cell frequencies
Problem
1a
1b
2a
2b
a
b
c
d
Pad
P
p(e/c)
p(e/c)
p(e)
p(c)
4
6
12
2
0
6
0
2
6
0
2
0
6
4
2
12
0.625
0.625
0.875
0.875
þ0.5
þ0.5
þ0.5
þ0.5
1.0
0.5
1.0
0.5
0.5
0.0
0.5
0.0
0.625
0.375
0.875
0.125
0.25
0.75
0.75
0.25
0.625
0.875
þ0.5
þ0.5
0.75
0.75
0.25
0.25
0.50
0.50
0.50
0.50
Mean of 1a þ 1b
Mean of 2a þ 2b
(White, 2000c, 2002, in press, submitted 2001). There is one test of the Pad rule which holds P
values constant and one test of the P rule that holds Pad values constant. These will be described
separately.
The Test of the Pad Rule
Control is a basic principle of experimental design, and in order to obtain an unambiguous test of the
Pad rule it is necessary to control all other variables that might influence causal judgement. This
control is achieved by comparing pairs of contingency tables, and the test of the Pad rule compares two
such pairs. Basic information about these is shown in Table 2. The table shows cell frequencies and
values of relevant variables for each of four problems. The critical information about the design is
contained in the bottom two rows, which show mean values of each of the variables for each pair of
problems: 1a þ 1b, and 2a þ 2b. This shows that the mean value of Pad differs between the two pairs
but that the mean values of all other variables are held constant. Under the Pad rule it is predicted that
the mean of the mean judgements for problems 2a and 2b will be higher than the mean of the mean
judgements for problems 1a and 1b. If this difference is found, none of the other variables can account
for it because the design controls them all.
In effect the four problems constitute a 2 2 ANOVA design in which Pad value is one variable,
and the main prediction is of a main effect of Pad. The other variable can be regarded as a same size
manipulation of p(e/c) and p(e/c): both variables change by the same amount, which they must if P
is to be held constant. The fact that it is a same size manipulation warrants a second prediction for
these four problems. Under the cause-present effect a change in value of p(e/c) has a greater influence
on causal judgement than the same change in value of p(e/ c). Thus, if we compare problems 1a and
1b, p(e/c) and p(e/c) are both greater by 0.5 in 1a than in 1b. The increase in value of p(e/c) tends to
raise causal judgement and the increase in value of p(e/c) tends to lower it, but under the causepresent effect the change in p(e/c) raises judgement more than the change in p(e/c) lowers it, so the
net effect should be higher judgement for 1a than for 1b. The same applies to the comparison between
2a and 2b. The general prediction is therefore a main effect of this manipulation with higher causal
judgements at the higher combined values of p(e/c) and p(e/c).
The Test of the P Rule
This test conforms to the same general principles: P is manipulated and Pad held constant across two
pairs of problems. Basic information about these is shown in Table 3. The table shows cell frequencies
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
672
Peter A. White
Table 3. Stimulus materials for candidate C1, test of P rule
Cell frequencies
Problem
3a
3b
4a
4b
a
b
c
d
Pad
P
p(e/c)
p(e/ c)
p(e)
p(c)
10
2
4
8
0
4
0
4
4
0
4
0
2
10
8
4
0.75
0.75
0.75
0.75
þ0.33
þ0.33
þ0.67
þ0.67
1.00
0.33
1.00
0.67
0.67
0.00
0.33
0.00
0.875
0.125
0.500
0.500
0.625
0.375
0.250
0.750
0.75
0.75
þ0.33
þ0.67
0.67
0.83
0.33
0.17
0.50
0.50
0.50
0.50
Mean of 3a þ 3b
Mean of 4a þ 4b
and values of relevant variables for each of four problems. The critical information about the design is
contained in the bottom two rows, which show mean values of each of the variables for each pair of
problems: 3a þ 3b, and 4a þ 4b. This shows that the mean value of P differs between the two pairs
but the mean values of Pad, p(e), and p(c) are held constant.
Values of p(e/c) and p(e/c) cannot be held constant because they are the determinants of P: if
P varies, one or both of p(e/c) and p(e/c) must vary too. This has two implications. One is that this
design is different from that of the test of the Pad rule: one manipulated variable is P but the other
has no clear identity. It will be termed a manipulation of p(e/c) and p(e/c) because values of both
variables are substantially higher in 3a and 4a than in 3b and 4b. The other implication is that
differences between problems can be predicted from the cause-present effect. In particular, the mean
value of p(e/c) is higher in 4b than in 3b, but there is no difference between 3a and 4a. On this basis it is
predicted that causal judgements will be higher in 4b than in 3b and that there will be no significant
difference between 3a and 4a, even though they differ in P value. The value of p(e/c) is higher in 3a
than in 4a and this might support a prediction of higher judgement in 4a than in 3a, but under the
cause-present effect cause-absent information has only weak influence on causal judgement, so little
or no difference is predicted. Thus, under the P rule both 4a > 3a and 4b > 3b are predicted, but
under the cause-present effect only 4b > 3b is predicted.
METHOD
Participants
The participants were 49 first-year undergraduate students at Cardiff University, participating in return
for course credit. Fifty participants were originally run but one was excluded prior to data analysis for
noncompliance with the instructions.
Stimulus Materials
Instructions to Participants
Initial written instructions read as follows
On the following pages you will see a series of judgemental problems all arranged in the same
way. Each one starts with a sentence of the form: ‘Somebody made a good score on such-and-such
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
Causal attribution
673
golf course under such-and-such circumstances’, followed by the question ‘why?’. There are three
possible reasons for the good score: something about the person (e.g. he might be a good player),
something about the course (e.g. it might be an easy course), or something about the
circumstances (e.g. they might have made it easy to get a good score). Each of these is listed under
the question ‘why?’ as follows:
1. Something about the person.
2. Something about the course.
3. Something about the circumstances.
Any or all of these things might have been responsible for the good score: in the absence of any
further information, each of them is equally likely to have been. Obviously, to work out why the
good score occurred, you need additional information. So under the three possibilities you will see
a lot more information about various people getting good or bad scores on various courses under
various circumstances. Each line identifies a person, a course, the circumstances, and the score
(good or bad). You should study this information and use it to help you make your three
judgements.
To make each judgement you should put a number from 0 (zero) to 100 by each of the three
possibilities. 0 (zero) means that the thing in question was not at all responsible for the good score,
and 100 means that the thing in question was totally responsible for the good score. The more you
think a particular thing was responsible for the good score, the higher the number you should give
that thing. Important: please make sure you put a number beside each of the three possibilities and
don’t miss any out.
Problem Content
There were eight problems identifed as 1a to 4b in Tables 2, 3, and 4. Each problem had a common
format, illustrated by the example in the Appendix. As the example shows, each problem began with a
description of an outcome to be explained. The outcome in all problems was ‘got a good score’ and the
description specified an actor, a target (a golf course), and circumstantial information. Under this the
three causal candidates were listed.
The crucial manipulations concerned just one candidate, which in any given problem could be
either the actor or the target. The identity of this candidate was manipulated in a way described below:
here, the candidate will be identified as candidate C1 (described by Tables 2 and 3), the other two
being identified as candidates C2 and C3, respectively (described by Table 4).
Table 4.
Stimulus materials for candidates C2 and C3
Cell frequencies
Problem
a
b
c
d
Pad
P
1a
1b
2a
2b
3a
3b
4a
4b
8
3
8
1
9
1
6
4
6
7
2
13
2
12
8
6
2
3
6
1
5
1
2
4
0
3
0
1
0
2
0
2
0.5
0.375
0.5
0.125
0.56
0.19
0.375
0.375
0.43
0.2
0.2
0.43
0.18
0.26
0.57
0.27
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
674
Peter A. White
Under this was a list of information about other instances. This is the information about consensus,
distinctiveness, and consistency. Each of the consensus instances identified a different actor with the
same target and circumstances. Each of the distinctiveness instances identified a different target with
the same actor and circumstances. Each of the consistency instances identified a different circumstance with the same actor and target. In the case of circumstance information, the nature of the
circumstance varied across problems and included four time-related circumstances, months of the year
(winter), months of the year (summer), days of the week, and times of day, and four object-related
circumstances, make of golf club (fictitious), make of golf ball (fictitious), drink consumed prior to
playing, and food consumed prior to playing.
Each instance specified an actor, a target, a circumstance, and an outcome, either good or bad score.
In each problem there were 16 randomly ordered instances. The content of these 16 instances was
dictated by the cell frequencies in Tables 2, 3, and 4. To illustrate, the Appendix shows what the
participants saw in the case of problem 1b. In this problem there were 6 instances in which a good
score was obtained (sum of cells a and c in the row for problem 1b in Table 2 and Table 4) and 10 in
which a bad score was obtained (sum of cells b and d ). In all of the 6 instances where a good score was
obtained candidate C1 (in this case the actor) was present (cell a in Table 2), in 3 of them candidate C2
was present, and in 3 of them candidate C3 was present (cell a in Table 4). The contingency
information stipulates how many instances of each kind are included in each problem: thus, in problem
1b there were four consensus instances (because Table 2 stipulates only four instances in which the
actor is absent for this problem, the sum of cells c and d ), six distinctiveness instances and six
consistency instances.
A decision was taken to use identical matrices for candidates C2 and C3. With this decision and the
orthogonal manipulation of causal candidates that is part of the logic of Kelleyan causal attribution,
setting cell frequencies for candidate C1 fully determines the cell frequencies for the other two
candidates. These cell frequencies and other relevant properties are described in Table 4. The stimuli
have some interesting features. In particular, Pad and P values vary across matrices, but not in the
same way. This yields a further possible discriminative test of the two models: judgements of
candidates C2 and C3 should vary in accordance with Pad if the evidential evaluation model is correct
and in accordance with P if models incorporating P are correct.
P values for candidate C1 are in all cases positive and those for candidates C2 and C3 are in all
cases negative. For this reason it was deemed unwise to make candidate C1 the same causal locus
(actor, target, or circumstances) in all eight problems. Half of the participants received a set of
materials in which candidate C1 was the actor for problems 1a to 2b and the target for problems 3a to
4b, and the other half received the opposite arrangement. Thus, the identity of the causal candidate was
constant within each set of four problems, to rule out the possibility of contamination from scenariospecific effects.
Procedure
Participants took part in groups of two or three in a large and comfortably furnished office. They were
presented with a booklet containing the instructions for the task on the first page followed by the eight
problems in random order. A different random order was generated for each participant. Participants
were invited to ask questions if anything in the instructions was not clear. None did so. When they had
completed the questionnaire they were thanked and given course credit. They were not debriefed at
this stage for fear of contaminating future participants, but at the conclusion of the research
information about the study was made available.
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
Causal attribution
Table 5.
675
Means, Pad manipulation
Problem
Candidate
C1
C2
C3
Table 6.
1a
1b
2a
2b
69.77
43.75
26.14
41.26
33.35
25.62
74.31
45.82
30.59
50.45
27.18
18.26
Means, P manipulation
Problem
Candidate
C1
C2
C3
3a
3b
4a
4b
69.92
55.10
40.33
41.36
24.06
18.90
71.31
34.04
25.88
52.93
40.53
25.96
Results
Mean judgements for the Pad manipulation are shown in Table 5 and those for the P manipulation
in Table 6. Each causal candidate was analysed separately.
Candidate C1
Taking the Pad manipulation first, data were analysed with a 2 (levels of Pad, 0.625 versus 0.875) 2
(high versus low p(e/c) and p(e/c)) analysis of variance (ANOVA) with repeated measures on both
factors. There was a significant effect of Pad, F(1, 48) ¼ 11.34, p < 0.01, with higher ratings at the
higher level of Pad. This is the predicted effect of Pad.
There was also a significant effect of the manipulation of p(e/c) and p(e/c), F(1, 48) ¼ 60.33,
p < 0.001, with higher ratings when p(e/c) and p(e/c) were high. This was the tendency predicted
under the hypothesis of the cause-present effect. The interaction between the two variables was not
statistically significant, F(1, 48) ¼ 0.73, ns.
Data from the P manipulation were analysed with a 2 (levels of P, þ0.33 versus þ0.67) 2
(high versus low p(e/c) and p(e/c)) ANOVA with repeated measures on both factors. There was a
significant effect of P, F(1, 48) ¼ 10.20, p < 0.01, with higher ratings at the higher level of P. The
hypothesis of the cause-present effect led to a specific prediction of a difference between problems 3b
and 4b but no difference between problems 3a and 4a. Simple effects analysis confirmed this
prediction. For problems 3a and 4a there was no significant effect of P, F(1, 48) ¼ 0.27, ns; but for
problems 3b and 4b there was a significant effect of P, F(1, 48) ¼ 6.28, p < 0.05. The conclusion is
that the main effect is not a true effect of P but is instead attributable to the cause-present effect,
because the difference was specific to problems 3b and 4b.
There was also a significant effect of the manipulation of p(e/c) and p(e/c), F(1, 48) ¼ 46.97,
p < 0.001, with higher ratings when p(e/c) and p(e/c) were high. The interaction between the two
variables was not statistically significant, F(1, 48) ¼ 2.52, ns.
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
676
Peter A. White
Candidates C2 and C3
As Table 4 shows, the Pad and P manipulations on candidate C1 entailed a pattern of contingencies
for candidates C2 and C3 that does not fall into a clear experimental design. Accordingly, it was
decided to assess the effects of Pad and P on judgement by paired comparisons between means using
the t-test for related means. For each candidate eight problems entail a total of 28 paired comparisons.
For candidate C2 19 of the 28 t-tests produced a difference significant at the 0.05 level. All 19 were
in the direction predicted by the Pad rule: in other words, the mean was higher for the problem with the
higher value of Pad. Of the nine non-significant results, four were predicted on the grounds that the
Pad values were identical. Another four showed non-significant trends in the predicted direction and
one showed a non-significant trend in the other direction. 12 of the 19 significant results were in the
direction predicted by P, five were in the opposite direction, and two were significant differences
between problems with identical P values. Of the non-significant results, four were in the direction
predicted by P and five were in the opposite direction.
For candidate C3 13 of the 28 t-tests produced a difference significant at the 0.05 level. All 13 were
in the direction predicted by the Pad rule. Of the 15 non-significant results, 10 showed trends in the
direction predicted by the Pad rule, one was in the opposite direction, and four were predicted on the
grounds that the Pad values were identical. Twelve of the 13 significant results were in the direction
predicted by P and one was a significant difference between a pair of matrices with identical P
values. Of the non-significant results, six were in the direction predicted by P, eight were in the
opposite direction, and one was predicted on the grounds that the P values were identical.
DISCUSSION
The findings support the basic contention of the evidential evaluation model that causal attributions
are predicted by the Pad rule and the cause-present effect. When Pad was manipulated and all
other variables were controlled a significant effect of Pad was found. The manipulation of p(e/c) and
p(e/c) also yielded a significant difference in the direction predicted under the hypothesis of the
cause-present effect. When P was manipulated and Pad and other variables were controlled a
significant effect was found: however, the effect was specific to a pair of problems in which p(e/c)
varied, and the result was therefore interpreted as evidence for the cause-present effect, not the P
rule. The pattern of the results for the other two causal candidates also fitted closely with the pattern of
differences in Pad values and did not fit so well with the pattern of differences in P values.
Other Models
The key finding, obtained in several experiments on causal judgement from contingency information
(White, 2000c, 2002, in press, submitted 2001) and now in an experiment on causal attribution, is that
causal judgements vary with Pad, or more generally pCI, when P is controlled and do not vary
significantly with P when Pad or pCI is controlled. Any model of causal judgement or causal
attribution must be capable of accounting for these results if it is to be considered viable.
There are two main classes of competing models in the contingency judgement literature, namely
inductive rule models such as the probabilistic contrast model (Cheng & Novick, 1990, 1992), the
power PC theory (Cheng, 1997), and a weighted version of the P rule (Lober & Shanks, 2000) and
associative learning models, which model causal judgement according to basic learning mechanisms
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
Causal attribution
677
(Allan, 1993; Shanks, 1993, 1995; Shanks & Dickinson, 1987; Van Hamme & Wasserman, 1994;
Wasserman, Kao, Van Hamme, Katagiri, & Young, 1996). White (submitted 2001) has shown that
none of these can account for the key finding. The inductive rule models uniformly fail to predict the
tendency of causal judgements to vary with Pad when P is controlled. Associative learning models
also fail to predict this tendency, though the extent of the failure depends on assumptions about
learning rates (White, submitted 2001).
Since the logic of the design of the present experiment was the same as in White (submitted 2001),
the inductive rule and associative learning models have the same failings. This can be illustrated with
two of the inductive rule models, the power PC theory (Cheng, 1997) and the weighted P model
(Lober & Shanks, 2000). A full account of these models can be found in the respective cited
publications: for the sake of brevity, this discussion concentrates on the rules used to predict
judgements. In the power PC theory the inductive rule for a single causal candidate is P modified
by information about the rate of occurrence of the effect when the causal candidate is absent. The
version of this rule that applies when 0 is shown in
p ¼ P=1pðe=cÞ
ð4Þ
In equation (4), p stands for causal power. As for the weighted P model, Lober and Shanks (2000)
proposed that the best fit to the data could be found if P was modified such that p(e/c) was weighted
W1 ¼ 1.0 and p(e/c) was weighted W2 ¼ 0.6.
Values of p and weighted P, PW , for the eight problems in the present experiment are shown in
Table 7. Both p and PW predict some features of the means quite well, particularly the tendency for
problems 1a, 2a, 3a, and 4a to receive higher judgements than the other four, the finding interpreted
here as showing the cause-present effect. However the key finding is the main effect of Pad and both
models fail to predict this. The mean values of p and PW for the two pairs 1a þ 1b and 2a þ 2b are the
same, þ0.75 and þ0.60 respectively, so neither model predicts any difference between the different
values of Pad. The same inadequacy was found by White (submitted 2001).
Both P and p have the further drawback that they require both p(e/c) and p(e/c) to be computed.
This means that they require samples of both cause-present and cause-absent information, and that
they can be unreliable if one or other sample is of small size. For example if there is a large sample of
cause-present information but only three instances of cause-absent information p(e/c) ¼ 0.33 if two
of the three are cell d and p(e/c) ¼ 0.67 if two of the three are cell c. Changing just one instance
therefore makes a substantial difference to P. Pad can be computed even if there is no cause-absent
Table 7.
Test of power PC theory and weighted P rule against mean judgements of candidate C1
Cell frequencies
Problem
a
b
c
d
Pad
p
Pw
Mean
1a
1b
2a
2b
3a
3b
4a
4b
4
6
12
2
10
2
4
8
0
6
0
2
0
4
0
4
6
0
2
0
4
0
4
0
6
4
2
12
2
10
8
4
0.625
0.625
0.875
0.875
0.75
0.75
0.75
0.75
þ1.00
þ0.50
þ1.00
þ0.50
þ1.00
þ0.33
þ1.00
þ0.67
0.70
0.50
0.70
0.50
0.60
0.33
0.80
0.50
69.77
41.26
74.31
50.45
69.92
41.36
71.31
52.93
Note: p ¼ causal power (equation (4)); Pw ¼ weighted P.
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
678
Peter A. White
information, when it effectively becomes a/(a þ b). It is not significantly affected by small sample size
of one kind of information because it aggregates across all four cells rather than just two at a time: the
three cause-absent instances in the above example are combined with the cause-present instances
when Pad is computed, so changing just one of the cause-absent instances has little effect on the value
of Pad.
Other models of causal judgement have been proposed, including simpler judgemental rules (Allan,
1993; Kao & Wasserman, 1993; Smedslund, 1963), linear combination models (Schustack &
Sternberg, 1981), and information integration models (Anderson, 1981, 1982; Busemeyer, 1991).
Numerous published studies have shown that these do not adequately account for the phenomena of
causal judgement (Allan, 1993; Cheng, 1997; Wasserman et al., 1996). To illustrate, Busemeyer’s
(1991) information integration model was tested by Wasserman et al. (1996). Wasserman et al. (1996)
obtained causal judgements from participants after each instance of information. They determined cell
weights empirically from participants’ final judgements and then entered the obtained weights into the
information integration model to generate predictions for judgements after each trial. These
predictions proved less accurate than those of an associative learning model. In particular, on several
problems predictions for the early trials were too extreme compared to observed means, and
only converged on observed mean judgements towards the end of the series (Wasserman et al.,
1996, Figure 3).
Existing models of causal attribution are similarly unable to account for the present results. In most
cases this is because the models are not equipped to deal with quantitative information. Most of them
offer a definition of what will be identified as a cause, and a causal attribution is made only to a locus
that conforms to the definition. Models of this kind are not equipped to predict differences between
causal judgements of different objective contingencies of the sort obtained here. This applies to
models based on the covariation principle such as Kelley’s original ANOVA model (Kelley, 1967), the
template-matching model (Orvis et al., 1975), and the true ANOVA model (Forsterling, 1989), to the
inductive logic model (Jaspars, 1983; Hewstone & Jaspars, 1987), the abnormal conditions focus
model (Hilton & Slugoski, 1986), and models based on Mill’s Methods of Experimental Inquiry (Mill,
1843/1973; Hilton et al., 1995; Van Overwalle & Heylighen, 1995). In fact the only model of causal
attribution that is equipped to deal with quantitative information is the probabilistic contrast model
(Cheng & Novick, 1990). In this model the basic rule of judgement is P computed for a defined focal
set of instances. As we have already seen, this model fails to predict the key finding of judgements
varying with Pad when P is controlled.
One class of models that may be able to account for the present findings is that based on
connectionist principles (Read & Marcus-Newhall, 1993; Read & Montoya, 1999; Van Overwalle,
1998; Van Overwalle & Van Rooy, 2001). The feedforward connectionist model proposed by Van
Overwalle (1998) and Van Overwalle and Van Rooy (2001) has two layers of nodes: input nodes,
which represent possible causes, and output nodes, which represent effects. In a feedforward network
activation spreads only from input to output nodes. When a cause is present its input node is activated
and that activation spreads to the output nodes in proportion to the weight of the connections. The
weight of an input–output connection is adjusted in accordance with the delta algorithm (McClelland
& Rumelhart, 1988). The weight is adjusted upward when the effect is underestimated and downward
when it is overestimated. Research by Van Overwalle and Van Rooy (2001) shows that this model
successfully predicts incremental changes in causal judgement over a series of trials.
By adjusting learning rates for individual causes and context factors it is possible for a simulation of
the feedforward model to produce output that is highly correlated with the mean judgements observed
in the present research.1 The feedforward connectionist model can therefore be viewed as a competitor
1
I am indebted to Frank Van Overwalle for this information.
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
Causal attribution
679
to the evidential evaluation model. This is a surprising result, however. As Van Overwalle and Van
Rooy (2001) observed, weight adjustment with the delta algorithm is formally identical to acquisition
of associative bonds in the Rescorla–Wagner model of animal learning (Rescorla & Wagner, 1972). It
is a well-established property of this model that its asymptotic output matches P (Allan, 1993), and it
must therefore be assumed that the same is true of a feedforward connectionist model that utilises the
delta algorithm. As we have seen, however, the P rule fails to predict the tendencies observed in the
present research.
Asymptotic output of the Rescorla–Wagner model does deviate from P when the learning rate for
reinforced trials (i.e. those on which the effect occurs) is different from that for nonreinforced trials (on
which the effect does not occur). However several simulations run by White (submitted 2001) have
shown that no combination of learning rates yields output that matches the observed effects of Pad
manipulations. This led White (submitted 2001) to conclude that the Rescorla–Wagner model failed to
account for the findings. The same would be the case here because the same kind of manipulation was
employed. The feedforward connectionist model may be superior to the Rescorla–Wagner model in
this respect, but the explanation for its superiority remains to be elucidated. Furthermore there are
reasons for thinking that a feedforward model may not be the most appropriate connectionist model of
causal judgement (Read & Montoya, 1999). On the other hand, one advantage of connectionist
models, which they share with associative learning models such as the Rescorla–Wagner model, is that
they can model the development of causal judgement over a series of trials (Van Overwalle &
Van Rooy, 2001).
The Psychological Meaning of the Evidential Evaluation Model
This research has concentrated on the capacity of a simple mathematical formulation, the Pad rule, to
account for observed tendencies in causal judgement. However the Pad rule is not just a mathematical
model of judgement. The rule is a mathematical instantiation of notions of evidential value. In essence,
people seek a causal interpretation of an event or set of events. Different possible causal interpretations
function as hypotheses about the event or events to be explained. Hypotheses are tested by gathering
evidence. The status of a given interpretation is assessed by the proportion of relevant evidence that is
deemed to be confirmatory for that interpretation. The Pad rule expresses this idea.
This point deserves emphasis because research on causal judgement from contingency information
can appear to be abstract and far removed from the problems of coping with information about
causality in the natural environment, in its utilisation of the formal structure of the 2 2 contingency
table. In the power PC theory a natural substrate of competence at causal judgement is assumed
(Cheng, 1997), which effectively includes the conceptual framework of the 2 2 contingency table.
This framework is not assumed in the Pad rule: all one has to assume is that judges encode individual
instances of information as confirmatory or disconfirmatory for the causal candidate or hypothesis
under consideration. The Pad rule is a specific version of the general pCI principle of judging from the
proportion of confirmatory instances, specific to contingency information about a single causal
candidate. But the pCI rule is capable of predicting causal judgement from any kind of information,
given only that instances of that kind are encoded as confirmatory or disconfirmatory (or irrelevant) by
the judge. This makes it, in principle, both flexible and applicable to naturally occurring forms of
causal judgement.
This approach differs from those of its main competitors in the contingency judgement literature at
the most fundamental level. Those models treat causal judgement as an outcome of basic learning
mechanisms (Shanks, 1993, 1995; Shanks & Dickinson, 1987; Wasserman et al., 1996) or innate
inductive rules (Cheng, 1997). In both cases causal judgement is a matter of passively registering
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
680
Peter A. White
contingencies in an automatic process. Under the evidential evaluation model, although the possibility
of an innate substrate of competence is not repudiated, causal judgement is a relatively sophisticated
activity drawing on acquired notions of evidential value, and assessing causal interpretations that gain
much of their meaning from a framework of acquired causal concepts (White, 1995, 2000c). White
(2000b) has shown that notions of the evidential value of different types of contingency information
vary between individuals, and that individuals’ reports of their notions of evidential value are better
predictors of their causal judgements than normative models such as P.
This approach is consistent with the consensus of developmental research, that competence at
causal judgement from contingency information is a relatively late development (Kuhn, 1989; White,
1988, 1995). It is also consistent with previous research showing that people adopt a hypothesis testing
approach to causal attribution (Hansen, 1980; Lalljee, Lamb, Furnham, & Jaspars, 1984; Shaklee &
Fischhoff, 1982). The mathematical aspect of the model links the fundamental constituents of causal
understanding, causal concepts, to the manifest phenomena of the world by means of notions of evidential value. To understand causal judgement and attribution it is necessary to understand the meaning that contingency and covariation information have for judges: that meaning is evidential value.
Scope of the Model
Kelley’s original model specified three dimensions of information for causal attribution, consensus,
distinctiveness, and consistency. Those dimensions were retained in the present research for the
specific purpose of demonstrating the conformity of judgements to Pad with the traditional format.
However the pCI rule is not confined in application to those dimensions. In principle the rule can be
applied to any kind of information so long as there is a clear proposition about the evidential value of
that information, either for an individual judge or for a mean tendency across a sample of judges. It can
therefore generate predictions for the extra dimensions postulated by Pruitt and Insko (1980) and
Cheng and Novick (1990). Indeed, it does not even require covariation information. Because cell a
information is consensually treated as confirmatory and cell b information as disconfirmatory, the
model can generate predictions for sets of information in which there is no cause-absent information.
White (2000c) found that, under some circumstances, people gave higher causal judgements to factors
present in all instances in a set (i.e. factors about which no cause-absent information was presented)
than to factors that correlated moderately with the effect. The Pad rule can generate a causal
judgement for any dimension or format of information, so long as the evidential value of each kind of
information is known.
Having said that, the Pad rule is a model of asymptotic judgement only. It does not model the
acquisition of causal judgement over a series of individual instances or trials. Because the rule is
proportional, it predicts essentially the same judgement at all stages of acquisition so long as
the proportion of confirmatory evidence does not vary significantly over the course of a sequential
presentation. Many other rules, such as the P rule and p, have the same property. In fact it is well
known that causal judgement does change over trials until at some point asymptote is reached (Shanks,
1987; White, 2000b). Several classes of models can explain many of the findings on acquisition,
including associative learning models (Shanks, 1987, 1995), connectionist models (Read & MarcusNewhall, 1993; Read & Montoya, 1999; Van Overwalle, 1998; Van Overwalle & Van Rooy, 2001), and
belief revision models (Catena, Maldonado, & Cándido, 1998; Hogarth & Einhorn, 1992). One
possibility is that the evidential evaluation model can be integrated with a belief revision model to
form a comprehensive account of causal judgement, because these two kinds of models both
conceptualise causal judgement as involving the treatment of evidence in a cognitive process.
However the viability of such an integration remains to be ascertained.
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
Causal attribution
681
CONCLUSION
Heider (1958) propounded a view of ordinary people as naive scientists, primarily concerned to
understand the world and the events that take place in it as best they can. For Heider this implied a
methodological orientation aimed at extracting invariant (dispositional) properties from the flow of
events, and in later models of causal attribution it came to mean an equally methodological concern
with sampling and rules of inference. Whether people should be regarded as naive scientists or not,
they share with science a concern with hypotheses and the evaluation of evidence with respect to
hypotheses. A large body of developmental research has been interpreted along these lines (Kuhn,
1989), and this view is consistent with the founding principles of the evidential evaluation model. One
cannot afford to be too particular about methodology in a world where controlled experimental designs
are rarely practicable, so it is unlikely that a model that stipulates the gathering of evidence on
particular orthogonal dimensions could account for everyday causal attribution. But an attitude of
concern with hypotheses and evidence that yields an inductive rule flexible enough to cope with the
vagaries of information availability in the real world can perhaps be regarded as both scientific and
practical. The pCI rule does not specify dimensions from which information must be sampled: all it
requires is that people assess the evidential value of any piece of information they come across. It is
therefore both practical, in its applicability under real world conditions, and scientific, in its concern
with hypothesis and evidence.
It has been shown that the evidential evaluation model predicts judgemental tendencies equally
well in both causal judgement from contingency information (White, 2000c, 2002, in press, submitted
2001) and causal attribution (the present experiment). This supports a contention that these two areas
of research can be unified under a single explanatory account. The possibility of such unification may
have been partly obscured by differences in methodology, particularly the traditional use of verbal
summaries as stimulus information in the causal attribution literature. The future use of information
about single instances as stimulus information, which has been common practice in the contingency
judgement literature, should be a valuable adjunct to the further theoretical and experimental
integration of these two important bodies of research.
REFERENCES
Allan, L. G. (1993). Human contingency judgements: rule based or associative? Psychological Bulletin, 114,
435–448.
Anderson, J. R., & Sheu, C.-F. (1995). Causal inferences as perceptual judgments. Memory and Cognition, 23,
510–524.
Anderson, N. H. (1981). Foundations of information integration theory. New York: Academic Press.
Anderson, N. H. (1982). Methods of information integration theory. New York: Academic Press.
Busemeyer, J. R. (1991). Intuitive statistical estimation. In N. H. Anderson (Ed.), Contributions to information
integration theory (Vol. 1, pp. 187–215). Hillsdale, NJ: Erlbaum.
Catena, A., Maldonaldo, A., & Cándido, A. (1998). The effect of the frequency of judgment and the type of
trials on covariation learning. Journal of Experimental Psychology: Human Perception and Performance, 24,
481–495.
Cheng, P. W. (1997). From covariation to causation: a causal power theory. Psychological Review, 104, 367–405.
Cheng, P. W., & Novick, L. R. (1990). A probabilistic contrast model of causal induction. Journal of Personality
and Social Psychology, 58, 545–567.
Cheng, P. W., & Novick, L. R. (1992). Covariation in natural causal induction. Psychological Review, 99,
365–382.
Cohen, M. R., & Nagel, E. (1934). An introduction to logic and scientific method. New York: Harcourt, Brace.
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
682
Peter A. White
Forsterling, F. (1989). Models of covariation and attribution: how do they relate to the analogy of analysis of
variance? Journal of Personality and Social Psychology, 57, 615–625.
Hansen, R. D. (1980). Common sense attribution. Journal of Personality and Social Psychology, 39, 996–1009.
Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley.
Hewstone, M., & Jaspars, J. (1987). Covariation and causal attribution: a logical model of the intuitive analysis of
variance. Journal of Personality and Social Psychology, 53, 663–672.
Hilton, D. J., & Slugoski, B. (1986). Knowledge-based causal attribution: the abnormal conditions focus model.
Psychological Review, 93, 75–88.
Hilton, D. J., Smith, R. H., & Kim, S. H. (1995). Processes of causal explanation and dispositional attribution.
Journal of Personality and Social Psychology, 68, 377–387.
Hogarth, R. M., & Einhorn. (1992). Order effects in belief updating: the belief-adjustment model. Cognitive
Psychology, 24, 1–55.
Jaspars, J. (1983). The process of causal attribution in common sense. In M. Hewstone (Ed.), Attribution theory:
social and functional extensions (pp. 28–44). Oxford: Blackwell.
Jenkins, H. M., & Ward, W. C. (1965). Judgement of contingency between responses and outcomes.
Psychological Monographs, 79, whole number 10.
Kao, S.-F., & Wasserman, E. A. (1993). Assessment of an information integration account of contingency
judgment with examination of subjective cell importance and method of information presentation. Journal of
Experimental Psychology: Learning, Memory, and Cognition, 19, 1363–1386.
Kelley, H. H. (1967). Attribution in social psychology. In D. Levine (Ed.), Nebraska symposium on motivation
(Vol. 15, pp. 192–238). Lincoln: University of Nebraska Press.
Kelley, H. H. (1972). Attribution in social interaction. In E. E. Jones, D. Kanouse, H. H. Kelley, R. E. Nisbett,
S. Valins, & B. Weiner (Eds.), Attribution: perceiving the causes of behaviour (pp. 1–26). Morristown, NJ:
General Learning Press.
Kelley, H. H. (1973). The processes of causal attribution. American Psychologist, 28, 107–128.
Kuhn, D. (1989). Children and adults as intuitive scientists. Psychological Review, 96, 674–689.
Lalljee, M., Lamb, R., Furnham, A., & Jaspars, J. (1984). Explanations and information search: inductive
and hypothesis-testing approaches to arriving at an explanation. British Journal of Social Psychology, 23,
201–212.
Lober, K., & Shanks, D. R. (2000). Is causal induction based on causal power? Critique of Cheng (1997).
Psychological Review, 107, 195–212.
McClelland, J. M., & Rumelhart, D. E. (1988). Explorations in parallel distributed processing: a handbook of
models, programs, and exercises. Cambridge, MA: Bradford Books.
McKenzie, C. R. M. (1994). The accuracy of intuitive judgment strategies: covariation assessment and Bayesian
inference. Cognitive Psychology, 26, 209–239.
Mill, J. S. (1973). A system of logic ratiocinative and inductive. London: Longman. (Originally published 1843.)
Orvis, B. R., Cunningham, J. D., & Kelley, H. H. (1975). A closer examination of causal inference: the roles of
consensus, distinctiveness, and consistency information. Journal of Personality and Social Psychology, 32,
605–616.
Pruitt, D. G., & Insko, C. (1980). Extension of the Kelley attribution model: the role of comparison-object
consensus, target-object consensus, distinctiveness, and consistency. Journal of Personality and Social
Psychology, 39, 39–58.
Read, S. J., & Marcus-Newhall, A. (1993). Explanatory coherence in social explanations: a parallel distributed
processing account. Journal of Personality and Social Psychology, 65, 429–447.
Read, S. J., & Montoya, J. A. (1999). An autoassociative model of causal reasoning and causal learning: reply to
Van Overwalle’s (1998) critique of Read and Marcus-Newhall (1993). Journal of Personality and Social
Psychology, 76, 728–742.
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: variations on the effectiveness of
reinforcement and non-reinforcement. In A. H. Black, & W. F. Prokasy (Eds.), Classical conditioning II:
current research and theory (pp. 64–99). New York: Appleton-Century-Crofts.
Schustack, M. W., & Sternberg, R. J. (1981). Evaluation of evidence in causal inference. Journal of Experimental
Psychology: General, 110, 101–120.
Shaklee, H., & Fischhoff, B. (1982). Strategies of information search in causal analysis. Memory and Cognition,
10, 520–530.
Shanks, D. R. (1987). Acquisition functions in causality judgement. Learning and Motivation, 18, 147–166.
Shanks, D. R. (1993). Human instrumental learning: a critical review of data and theory. British Journal of
Psychology, 84, 319–354.
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
Causal attribution
683
Shanks, D. R. (1995). The psychology of associative learning. Cambridge: Cambridge University Press.
Shanks, D. R., & Dickinson, A. (1987). Associative accounts of causality judgement. In G. H. Bower (Ed.), The
psychology of learning and motivation (Vol. 21, pp. 229–261). New York: Academic Press.
Smedslund, J. (1963). The concept of correlation in adults. Scandinavian Journal of Psychology, 4, 165–173.
Vallée-Tourangeau, F., Hollingsworth, L., & Murphy, R. A. (1998). ‘Attentional bias’ in correlation judgments?
Smedslund (1963) revisited. Scandinavian Journal of Psychology, 39, 221–233.
Van Hamme, L. J., & Wasserman, E. A. (1994). Cue competition in causality judgments: the role of
nonrepresentation of compound stimulus elements. Learning and Motivation, 25, 127–151.
Van Overwalle, F. (1998). Causal explanation as constraint satisfaction: a critique and a feedforward connectionist
alternative. Journal of Personality and Social Psychology, 74, 312–328.
Van Overwalle, F. J., & Heylighen, F. P. (1995). Relating covariation information to causal dimensions through
principles of contrast and invariance. European Journal of Social Psychology, 25, 435–455.
Van Overwalle, F., & Van Rooy, D. (2001). When more observations are better than less: a connectionist account
of the acquisition of causal strength. European Journal of Social Psychology, 31, 155–175.
Ward, W. C., & Jenkins, H. M. (1965). The display of information and the judgment of contingency. Canadian
Journal of Psychology, 19, 231–241.
Wasserman, E. A. (1990). Attribution of causality to common and distinctive elements of compound stimuli.
Psychological Science, 1, 298–302.
Wasserman, E. A., Elek, S. M., Chatlosh, D. L., & Baker, A. G. (1993). Rating causal relations: the role of
probability in judgments of response-outcome contingency. Journal of Experimental Psychology: Learning,
Memory, and Cognition, 19, 174–188.
Wasserman, E. A., Kao, S.-F., Van Hamme, L. J., Katagiri, M., & Young, M. E. (1996). Causation and association.
In D. R. Shanks, K. J. Holyoak, & D. L. Medin (Eds.), The psychology of learning and motivation: Vol. 34.
Causal learning (pp. 207–264). San Diego, CA: Academic Press.
White, P. A. (1988). Causal processing: origins and development. Psychological Bulletin, 104, 36–52.
White, P. A. (1995). The understanding of causation and the production of action: from infancy to adulthood.
Hove: Erlbaum.
White, P. A. (1998). Causal judgement from contingency information: subjective notions of confirmatory and
disconfirmatory information. European Journal of Cognitive Psychology, 10, 131–170.
White, P. A. (2000a). Causal attribution and Mill’s Methods of Experimental Inquiry: past, present, and prospect.
British Journal of Social Psychology, 39, 429–447.
White, P. A. (2000b). Causal judgement from contingency information: relation between subjective reports and
individual tendencies in judgement. Memory and Cognition, 28, 415–426.
White, P. A. (2000c). Causal judgement from contingency information: the interpretation of factors common to all
instances. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 1083–1102.
White, P. A. (2002). Perceiving a strong causal relation in a weak contingency: further investigation of the
evidential evaluation model of causal judgement. Quarterly Journal of Experimental Psychology, 55A, 97–114.
White, P. A. (in press). Causal judgement from contingency information: judging interactions between two causal
candidates. Quarterly Journal of Experimental Psychology.
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)
684
Peter A. White
APPENDIX: EXAMPLE CAUSAL ATTRIBUTION PROBLEM (PROBLEM 1B)
George got a good score on Muirfield course in June. Why?
1. Something about George.
2. Something about Muirfield course.
3. Something about the circumstances (time of year).
Information About Other Scores
Person
Course
Circumstances
Score
George
George
George
George
Rick
Seth
George
George
George
Mitch
George
George
George
George
Vic
George
Troon
St. Andrews
Nairn
Muirfield
Muirfield
Muirfield
Ross
Muirfield
Prestwick
Muirfield
Muirfield
Muirfield
Muirfield
Aberdeen
Muirfield
Muirfield
June
June
June
March
June
June
June
August
June
June
September
April
May
June
June
July
Bad
Good
Bad
Good
Bad
Bad
Bad
Bad
Good
Bad
Bad
Good
Bad
Good
Bad
Good
Copyright # 2002 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 32, 667–684 (2002)