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THE QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2003, 56B (4), 345–357
Secondary task difficulty modulates forward
blocking in human contingency learning
Jan De Houwer
Ghent University, Ghent, Belgium
Tom Beckers
University of Leuven, Leuven, Belgium
The influence of a secondary task on forward blocking of human contingency ratings was examined. A smaller blocking effect was found when participants performed a highly demanding
secondary task than when they performed a less demanding secondary task. The modulatory
effect of secondary task difficulty was significant only when the secondary task was administered
during both the learning and the test phase of the contingency judgement task. The results
suggest that forward blocking in human contingency learning cannot be fully accounted for by
associative processes. Instead, forward blocking seems to depend at least partially on deliberate
deductive reasoning processes.
During the past two decades, the ability of humans to learn about contingencies between
events has resurfaced as an important topic in experimental psychology (see De Houwer &
Beckers, 2002, and Dickinson, 2001, for reviews). This revival is largely due to the proposal
that associative models of Pavlovian conditioning in animals might also provide an accurate
account of human contingency learning (e.g., Dickinson, Shanks, & Evenden, 1984). For
instance, the well-known Rescorla–Wagner (Rescorla & Wagner, 1972) and SOP (Wagner,
1981) models of Pavlovian conditioning have been explicitly put forward as models of human
contingency learning (e.g., Dickinson & Burke, 1996; Dickinson et al., 1984).
Historically, associative models gained much credibility from the fact that blocking can be
found in human contingency learning (e.g., Dickinson et al., 1984). When trials on which A
and the outcome (+) are paired precede trials on which a compound consisting of cues A and T
is followed by that outcome (A + trials followed by AT+ trials), judgements about the strength
of the relation between T and the outcome will be lower than those when the A+ trials are
omitted. This blocking effect is predicted on the basis of the Rescorla–Wagner model, but
Requests for reprints should be sent to Jan De Houwer, Department of Psychology, Ghent University, Henri
Dunantlaan 2, B-9000 Ghent, Belgium. Email: Jan.DeHouwer@rug.ac.be
Tom Beckers is a postdoctoral researcher for the Fund for Scientific Research (FWO–Flanders, Belgium). We
thank Tom Randell and Isabel Muyllaert for their help in collecting the data.
 2003 The Experimental Psychology Society
http://www.tandf.co.uk/journals/pp/02724995.html
DOI:10.1080/02724990244000296
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DE HOUWER AND BECKERS
could not be explained on the basis of other models of human contingency learning that were
available at the time.
More recent findings, however, suggest that associative models do not provide a complete
and accurate account of forward blocking and thus human contingency learning in general.
For instance, Waldmann (2000; Waldmann & Holyoak, 1992; also see De Houwer, Beckers, &
Glautier, 2002) demonstrated that blocking is more likely to occur when participants regard
the presented cues as potential causes of the outcome (e.g., the cues are substances in the blood
that can produce a certain disease) than when the cues are regarded as potential effects of the
outcome (e.g., the cues are substances in the blood that can be produced by a certain disease).
Moreover, blocking seems to be much stronger when participants are informed that the causal
effectiveness of cues A and T in producing the outcome is additive and when participants can
verify whether the outcome has a different intensity on the AT+ trials than on the A+ trials
(e.g., De Houwer et al., 2002; Lovibond, Been, Mitchell, Bouton, & Frohardt, 2002). These
results are inconsistent with associative models of human contingency learning and together
with other findings (e.g., Shanks & Darby, 1998) suggest that, like other forms of human inferences (see Evans & Over, 1996; Sloman, 1996), human contingency judgements might rely on
more than one mechanism (e.g., De Houwer & Beckers, 2002; Dickinson, 2001; Lovibond et
al., 2002; Mackintosh, 1995; McLaren, Green, & Mackintosh, 1994; Shanks & Darby, 1998).
Even though associative mechanisms might be important, contingency judgements presumably also depend on rule-based or deductive reasoning processes similar to those that people
use in reasoning tasks (e.g., Braine, 1990; Johnson-Laird & Byrne, 1991; see Lovibond et al.,
2002).
The suggestion that participants use deliberate deductive processes to arrive at contingency judgements is consistent with the observation that blocking depends on the causal
model that people adopt (e.g., Waldmann, 2000) and instructions about the additivity and the
intensity of the outcomes (e.g., De Houwer et al., 2002; Lovibond et al., 2002). Blocking might
for, instance, result from the fact that participants apply the following rule:1
If cue A on its own causes the outcome to occur with a certain intensity and probability, and if cue
A and T together cause the outcome to occur with the same intensity and probability, this implies
that cue T is not a cause of the outcome.
1
When applied to binary outcomes (i.e., outcomes that are either present or absent but do not vary in intensity
when present), this rule can be formalized by probabilistic contrasts similar to those that form the core of probabilistic
contrast models (e.g., Cheng, 1997; Cheng & Holyoak, 1995). However, probabilistic contrast models are normative
models in that they only make predictions about the value of contingency judgements but do not incorporate any
assumptions about the processes and representations that participants use to arrive at such judgements (but see Cheng
& Holyoak, 1995). The deductive reasoning account that we tested is different from probabilistic contrast models in
the assumption that participants engage in reasoning in order to determine their judgements and in that it can also be
applied to situations with continuous outcomes. Waldmann (2000; Waldmann & Hagmayer, 2001; Waldmann &
Holyoak, 1992) proposed a theory that also postulates that judgements reflect the outcome of appropriate probabilistic contrasts. His model is more similar to a deductive reasoning account in that it emphasizes the fact that people
know when and why probabilistic contrasts provide a good basis for contingency judgements and that they intentionally act in ways consistent with this knowledge.
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347
This reasoning yields a conclusion only if participants can actually verify the premise that cue
T does not lead to an increase in the intensity or probability of the outcome. If, for instance,
cue A on its own always causes the outcome to occur with a maximal intensity, it is impossible
to verify whether T further increases the intensity of the outcome on the AT+ trials and thus
whether T is a cause of the outcome (e.g., Cheng, 1997). Moreover, if the effects of A and T are
thought to interact rather than to be additive, then the above rule is not valid and will not be
applied. Finally, the above rule will be applied only to situations in which the cues are effectively considered to be potential causes of the outcome. Assume, for instance, that A and T are
potential effects of the outcome. The fact that the outcome is sometimes accompanied by both
A and T (AT+ trials), together with the observation that T does not appear without the
outcome (no T– trials), allows for the conclusion that T is caused by the outcome even when
there are other trials on which the outcome is accompanied by A only (A+ trials; see
Waldmann, 2000).
In sum, the findings and arguments described above suggest that blocking may result not
only from associative processes, but also from rule-based deductive reasoning processes.
Unlike associative processes, which are assumed to operate in an automatic manner, rulebased deductive reasoning is typically regarded as an effortful, controlled process (Sloman,
1996), operating only to the extent that participants have the motivation and opportunity to
engage in such reasoning. Therefore, if rule-based deductive reasoning (co-)determines
blocking, one should be able to modulate blocking in human contingency learning by manipulating the opportunity or motivation to engage in such reasoning. That is, blocking should
be less pronounced when participants have little motivation or opportunity to engage in
deductive reasoning than when they do have ample opportunity and motivation to do so (see
De Houwer & Beckers, 2002).
In the present experiments, we manipulated the opportunity for deductive reasoning by
varying the cognitive load imposed by a secondary task that participants performed during the
contingency learning task. In the easy secondary task, the same tone was presented every 1200
ms, and participants were asked to press a key each time a tone was presented. In the difficult
secondary task, either a low- or high-pitched tone could be presented, and participants
pressed one key when the low tone was presented and another key when the high tone was
presented. Moreover, the interval between the tones was 900 ms on some trials and 1500 ms on
other trials. Previous research has demonstrated that the difficult secondary task imposes a
higher cognitive load than the easy secondary task (Szmalec, Vandierendonck, & Kemps,
2002).
The contingency learning task was identical to the task that we used in earlier studies (e.g.,
De Houwer, 2002; De Houwer et al., 2002). Participants saw a pictorial representation of an
army tank that moved across the computer screen. At the bottom of the screen, there were five
squares that were said to represent five weapons. During a first phase, the tank was destroyed
whenever weapon A fired on its own (A+) but never when weapon Z fired on its own (Z–).
During a second phase, weapon A always fired together with weapon T, weapon K always
fired together with weapon L, and weapon Z always fired alone. The tank was destroyed when
A and T fired (AT+) and when K and L fired (KL+), but not when Z fired (Z–). Tank explosions were always accompanied by a message that stated that the weapon(s) had an impact of 10
out of a maximal impact of 20. When the tank did not explode, an impact of 0 out of 20 was
reported. At the end of the experiment, participants rated the extent to which each weapon
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DE HOUWER AND BECKERS
was effective in destroying tanks. In our previous studies (e.g., De Houwer, 2002; De Houwer
et al., 2002), we found strong blocking effects (lower ratings for T than for K and L) in this task
mainly because of the causal nature of the cues and the submaximal intensity of the outcome on
the outcome-present trials (De Houwer et al., 2002). If this blocking effect is (partially) due to
deductive reasoning, then one would expect it to be less pronounced if participants perform a
difficult secondary task during the contingency learning task than if they perform an easy
secondary task.
One could argue that secondary task difficulty might also influence the operation of associative processes. For instance, when the secondary task is difficult, less attention might be
given to the learning task, which could result in less learning at the associative level (see
Dawson & Shell, 1987, for a review of the studies that show that associative learning is
hampered when attention is drawn away from the contingencies). Less learning on the A+
trials would result in less blocking on the AT+ trials (e.g., Rescorla & Wagner, 1972). Note
that according to such an account, one would expect an impact of secondary task difficulty on
the effectiveness ratings of all cues (e.g., less learning, and thus lower ratings for A, K, and L,
under difficult secondary task conditions). In contrast, on the basis of a deductive reasoning
account, one would predict secondary task difficulty to affect primarily the rating for T
because only this rating crucially depends on the application of the complex rule described
above. Therefore, we not only analysed blocking effects and the ratings for T but also looked at
the effect of secondary task difficulty on the ratings of all other cues.
Apart from collecting effectiveness ratings for each cue, we also asked participants to
express their confidence in each effectiveness rating. Although confidence ratings lie outside
of the scope of associative models, one can make predictions about such ratings on the basis of a
deductive reasoning account. According to such an account, confidence should be high whenever participants can apply a rule in order to make a rational inference about the relation
between a cue and the outcome. Given that a difficult secondary task reduces the opportunity
to apply the complex rule that can be used to infer the effectiveness of T (see above), participants should be less confident in their rating for T when the secondary task is difficult than
when the secondary task is easy. Secondary task difficulty should have less impact on confidence in the ratings for the other cues, because these ratings can be based on less complex rules
or information (as is the case for cues A and Z), or because no rule is available to make a definite
inference (as is the case for cues K and L). We therefore examined the impact of secondary task
difficulty on the confidence ratings for each cue. We also calculated a blocking score for confidence ratings. As explained above, when participants can make a rational inference about the
effectiveness of T, they should be confident in their rating for T. However, participants can
never be confident in their ratings for K and L, because a definite inference regarding these
cues is simply impossible. Therefore, the difference between the confidence ratings for T and
the mean confidence rating for K and L should be larger when the secondary task is easy (and a
rational inference for T is possible) than when the secondary task is difficult (and a rational
inference for T is less likely).
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EXPERIMENT 1
Method
Participants
Forty-one first-year psychology students at the Universities of Leuven and Ghent participated for
partial fulfilment of course requirements. Of these, 21 were randomly assigned to the easy secondary task
condition, and the other were assigned to the difficult secondary task condition.
Stimuli and materials
The contingency learning task and the secondary task were presented on separate IBM-compatible
486 PCs with 15″ SVGA screens. Both tasks were implemented using a custom-made Turbo Pascal 5.0
program. Participants faced the computer on which the contingency learning task was presented and
held one hand on the keyboard of the computer that was used to present the secondary task. The drawings of the tanks that were presented during the contingency learning task were 4 cm long and 2 cm wide.
A tank moved in a continuous manner from the left to the right side of the computer screen on a straight
line was situated 10 cm from the top of the screen. It took approximately 6 s for a tank to get from the left
to the right side of the screen. When a tank exploded, this always occurred 2 s after the tank appeared, at a
point 12 cm from the left side of the screen. During an explosion, the tank disappeared from the screen
and was replaced by 10 lines that gradually increased in length from 1 cm to 7 cm and then decreased in
length until they disappeared. The lines diverged as they became longer, thus forming a fan-like shape.
The explosion took about 1 s. At the same time the message IMPACT 10/20 appeared on the screen for
3 s. When the tank did not explode, the message IMPACT 0/20 appeared on the screen until the tank,
which drove on, had reached the right side of the screen. Five rectangles of 2.5 cm wide and 1.7 cm high
were situated at the bottom of the screen at equal distances from each other. The rectangles were
numbered 1 to 5, 1 being the rectangle on the far left side of the screen, and 5 being the rectangle on the far
right side. A cue was said to be on when a solid white rectangle measuring 2.1 cm × 1.3 cm appeared in the
rectangle that represented the cue. The solid square was presented for 300 ms, during which time the
tank kept on moving at the same speed as previously. When a tank explosion occurred, it came immediately after the solid white square disappeared. Participants entered their ratings using the keyboard of the
PC that was used to present the tanks. All stimuli were white and were presented on a black background.
During the easy secondary task, all tones had a frequency of 750 Hz. During the difficult secondary
task, tones had a frequency of 500 Hz or 1000 Hz. A feedback tone of 100 Hz was used in both conditions.
All tones were presented through the internal speaker of the second PC, and responses to the tones were
given on a keyboard connected to this PC.
Procedure
At the beginning of the experiment, participants received written instructions that provided the
following information: Participants were told that they would perform two tasks, a learning task and a
reaction time task. The instructions for the reaction time task were given first. Participants who were
assigned to the easy secondary task condition were told that a tone would be presented at certain
moments. Their task was to press the key 1 as quickly as possible after hearing a tone. In the difficult
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secondary task condition, participants were informed that a high or a low tone would be presented at
certain moments. They were asked to press the key 1 as quickly as possible after hearing the high tone and
to press the key 3 as quickly as possible after hearing the low tone. All participants were told that they
would hear a very low feedback tone if they pressed too early, too late, or if they pressed an incorrect key.
They were asked not to react to the feedback tone.
After indicating that they understood these instructions, participants completed 30 secondary task
practice trials. In the easy task, a 750-Hz tone was presented for 60 ms every 1200 ms. In the difficult task,
the computer also presented 30 tones for 60 ms each. However, whether this tone had a pitch of 500 Hz or
1000 Hz was determined randomly on each trial. Moreover, the interval between the tones could be
either 900 ms or 1500 ms, again determined randomly on each trial. However, the pitch of the tone and
the interval between tones could not be the same on more than two consecutive trials. In both the easy and
the difficult tasks a response was considered to be too early if given more than 100 ms before the onset of
the tone and too late if given more than 700 ms after the end of the tone. If the response was too early or
too late, or if participants pressed an incorrect key, a feedback tone was presented for 100 ms and the
interval between the end of the previous tone and the start of the next tone was lengthened by 100 ms.
After completing the secondary task practice trials, participants read the instructions for the contingency learning task. These instructions stated that drawings of army tanks would ride across the
computer screen and that five weapons were represented by five squares at the bottom of the screen.
Participants were told that the firing of a weapon would be indicated by a white light appearing in the
square representing that weapon. They were asked to determine the effectiveness of each weapon in
destroying tanks. Their task would be complicated by the fact that sometimes two weapons would fire
together. On each trial, information about the combined impact of all fired weapons would be displayed.
Finally, it was stressed that the learning task and the reaction time task were equally important. Participants were asked to keep looking attentively at the screen and to keep listening attentively to the tones at
all times.
When participants had read the instructions, a screen appeared on which the five squares were
visible, as was the horizontal line on which the tank would ride. The experimenter briefly repeated the
instructions while pointing at the relevant sections of the screen. When the participant indicated that he
or she had fully understood the instructions, the experimenter started the secondary task and, after a few
seconds, the contingency learning task.
The secondary task proceeded in the same way as during the 30 practice trials. The contingency
learning task consisted of the following events. First, 10 A+ and 10 Z- trials were presented. Then 10
AT+, 10 KL+, and 10 Z– trials were presented. There was no break between the two phases. The order
of the trials within each phase was determined randomly for each participant. Which square functioned
as which cue was counterbalanced across participants. The square on the left (Square 1) always functioned as cue Z. For one group of participants, cue A was Square 2, cue T Square 4, cue K Square 3, and
cue L Square 5. For a second group, cue A was Square 4, cue T Square 2, cue K Square 3, and cue L
Square 5. For the third group, cue A was Square 3, cue T Square 5, cue K Square 2, and cue L Square 4.
For the fourth group, cue A was Square 5, cue T Square 3, cue K Square 2, and cue L Square 4. As such,
cues that were presented in compound never appeared next to each other, and cues A and T were
assigned to each of the four possible positions equally often. There were an equal number of participants
from both conditions in each counterbalanced subgroup. An extra participant was run in the easy
secondary task condition and was assigned to the second subgroup.
When all 50 contingency learning trials had been presented, the instructions for the rating phase
appeared on the screen. At that time, the experimenter stopped the secondary task. Participants were
asked to indicate for each weapon separately how effective it was in destroying tanks. They could do so by
entering a score between 0 (very ineffective; never causes the destruction of a tank) and 100 (very effective; always leads to the destruction of a tank). After entering an effectiveness rating, participants
expressed how certain they were that their effectiveness rating was accurate. They did so by entering a
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351
score between 0 (very unsure) and 100 (very sure). All participants first rated the cue that was represented by Square 1 (the square on the far left side), then the cue represented by Square 2, and so on.
During the rating phase, the rectangles and horizontal line were presented on the screen in the same way
as before. A 10-cm rating scale ranging from 0 to 100 was also present on the screen, together with the
question “How effective is weapon x?”. Once participants had entered their rating, a second 10-cm
rating scale (0–100) appeared, accompanied by the question “How sure are you?”. After all cues had been
rated in this way, participants were asked to indicate how difficult they found the secondary task and the
learning task by entering scores between 0 (very easy) and 100 (very difficult) for each task separately.
Results
The mean effectiveness and confidence ratings for each cue are listed in Table 1. The table also
contains mean blocking scores and mean confidence blocking scores. The blocking score was
calculated by subtracting the effectiveness rating for T from the mean of the effectiveness
ratings for K and L. A high blocking score thus indicates that T was given a lower effectiveness
rating than K and L, which shows that blocking has occurred. The confidence blocking score
corresponds to the difference between the confidence rating for T and the mean of the confidence ratings for K and L. A high confidence blocking score indicates that participants had
more confidence in their rating for T than in their rating for K and L.
In order to examine whether condition had a differential effect on the ratings for the difference cues, we first conducted ANOVAs with condition (easy or difficult secondary task) and
cue (A, T, K, L, Z) as variables. Where necessary, Greenhouse–Geisser corrections were
performed. Both the ANOVA on the effectiveness ratings and the ANOVA on the confidence
ratings revealed a main effect of cue: F(2.65, 103.48) = 89.37, p < .001, for the effectiveness
ratings; F(2.55, 99.37) = 34.61, p < .001, for the confidence ratings. The main effect of condition was not significant, Fs < 1. Contrary to the predictions, the interaction was also not significant in neither of the ANOVAs, Fs < 1.
To explore the data in more detail, t tests were performed on the blocking scores. Onesample t tests confirmed that the blocking scores were significantly different from zero, both in
the easy secondary task condition, t(20) = 6.01, p < .001, for the effectiveness ratings, and t(20)
TABLE 1
Mean effectiveness ratings, confidence ratings, and blocking scores as a function of secondary
task difficulty in Experiment 1
Secondary
task
difficulty
Cue
——————————————————————————
A
T
K
L
Z
———–
———–
———–
———–
———–
M SE
M SE
M SE
M SE
M SE
Blocking
———–
M SE
Effectiveness
Easy
Difficult
79
74
6
6
14
23
4
6
39
39
3
5
42
39
3
5
0
2
0
2
26
16
4
7
Confidence
Easy
Difficult
81
75
6
6
72
66
7
6
40
50
6
5
38
49
6
6
89
89
6
6
33
16
9
7
Ratings
Note: The blocking score for the effectiveness ratings corresponds to the mean effectiveness rating for K and L
minus the effectiveness rating for T. The blocking score for the confidence ratings corresponds to the confidence
rating for T minus the mean confidence rating for K and L.
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DE HOUWER AND BECKERS
= 2.35, p < .05, for the confidence ratings, and in the difficult secondary task condition, t(19) =
3.76, p < .005, for the effectiveness ratings, and t(19) = 2.41, p < .05, for the confidence ratings.
Independent-samples t tests showed that neither the mean blocking scores, t(39) = 1.29, nor
the mean confidence blocking scores, t(39) = 1.53, differed significantly between the two
conditions. Additional Newman–Keuls tests on the effectiveness and confidence ratings for
each of the separate cues showed that none of the ratings was affected by condition, all ps > .15.
As a manipulation check, we examined whether condition had an effect on the ratings of the
experienced difficulty of the secondary task and the contingency learning task, as well as on
secondary task performance. Participants in the difficult secondary task condition rated the
secondary task to be more difficult (mean rating = 58, SE = 7) than did participants in the easy
secondary task condition (M = 36, SE = 6), t(39) = 2.56, p < .05. The contingency learning
task was also rated as being more difficult by participants in the difficult secondary task condition (M = 64, SE =5) than for those in the easy secondary task condition (M = 49, SE = 6), but
this difference was only marginally significant, t(39) = 1.93, p = .06. To compare secondary
task performance, we calculated for each participant the mean reaction time of the responses to
the tones as well as the percentage of correct responses. One secondary task data file was stored
incorrectly due to a computer error. These data could therefore not be included in the analyses. Analyses of the data of the remaining participants showed that neither the mean reaction
time nor the mean percentage of correct responses differed between the easy secondary task
condition (mean reaction time = 321 ms, SE = 26; mean percentage of correct responses =
91.73%, SE = 2) and the difficult secondary task condition (M = 365 ms, SE = 22; M =
88.34%, SE = 3), ts < 1.30.
Discussion
The analysis of the effectiveness and confidence ratings showed that blocking was significant
in both the easy and the difficult secondary task condition and did not differ in magnitude
between conditions. These results thus fail to support the hypothesis that deductive reasoning
(partially) underlies forward blocking. It is, however, possible that the impact of the difficulty
of the secondary task on the opportunity for deductive reasoning during the learning phase
was compensated for by deductive reasoning that took place during the test phase. It is indeed
conceivable that during the test phase, participants recalled the events of the learning phase
and at that time formally deduced that T was ineffective in destroying tanks. If this hypothesis
is correct, then blocking in the two conditions should differ if the secondary task was also
presented during the test phase. We tested this prediction in Experiment 2.
EXPERIMENT 2
Experiment 2 was identical to Experiment 1 apart from the fact that participants now also
performed the secondary task during the test phase. Because secondary task difficulty should
influence the opportunity for engaging in rule-based reasoning during both the learning and
the test phase, we predicted less blocking when the secondary task was difficult than when it
was easy.
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Method
Participants
Seventeen first-year psychology students at Ghent University and 15 students from various faculties at the University of Southampton took part in the experiment. Students from Ghent participated
for partial fulfilment of course requirements; students from Southampton received £6 for their help.
None had participated in the previous experiment. Half of the participants were randomly assigned to
the easy secondary task condition, and the others were assigned to the difficult secondary task
condition.
Materials and procedure
Experiment 2 was identical to Experiment 1 except for the fact that the secondary task was started
again after participants indicated that they understood the instructions for the test phase. The secondary
task was stopped once the participants had entered all effectiveness and confidence ratings.
Results
The mean effectiveness and confidence ratings for each cue as well as the blocking scores can
be found in Table 2. We again first examined whether condition had a differential effect on
the ratings for the different cues by conducting ANOVAs with condition and cue as factors.
The ANOVA on the effectiveness ratings revealed a main effect of cue, F(3.12, 93.6) =
107.33, p < .001, a marginally significant main effect of condition, F(1, 30) = 2.95, p = .10,
and, most importantly, a significant interaction between condition and cue, F(3.12, 93.6) =
4.11, p < .01. The ANOVA on the confidence ratings only revealed a main effect of cue,
F(2.73, 81.88) = 14.66, p < .001, all other Fs < 1.30. As was the case in Experiment 1, the
blocking effect in the effectiveness ratings was significant both in the easy secondary task
condition, t(15) = 8.43, p < .001, and in the difficult secondary task condition, t(15) = 2.44,
p < .05. However, an independent-samples t test showed that blocking was significantly
stronger in the easy than in the difficult secondary task condition, t(30) = 2.32, p < .05. The
blocking effect in the confidence ratings was significant in the easy secondary task condition,
TABLE 2
Mean effectiveness ratings, confidence ratings, and blocking scores as a function of secondary
task difficulty in Experiment 2
Secondary
task
difficulty
Cue
——————————————————————————
A
T
K
L
Z
———–
———–
———–
———–
———–
M SE
M SE
M SE
M SE
M SE
Blocking
———–
M SE
Effectiveness
Easy
Difficult
78
79
6
6
7
28
3
6
34
48
4
3
48
41
6
4
0
1
0
1
34
16
4
7
Confidence
Easy
Difficult
84
79
7
7
74
58
8
10
45
55
7
7
44
44
6
7
83
74
9
9
30
8
12
8
Ratings
Note: The blocking score for the effectiveness ratings corresponds to the mean effectiveness rating for K and L
minus the effectiveness rating for T. The blocking score for the confidence ratings corresponds to the confidence
rating for T minus the mean confidence rating for K and L.
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t(15) = 2.51, p < .05, but not in the difficult secondary task condition, t(15) = 1.09. Nevertheless, the magnitude of the confidence blocking effect did not differ significantly between
both conditions, t(30) = 1.53, p = .14.
Additional Newman–Keuls tests showed that T received a lower effectiveness rating when
the secondary task was easy than when it was difficult, p < .001. The rating for K also tended to
be lower in the easy secondary task condition, p = .07. However, the rating for L was not
affected by condition, p > .20, even though K and L were equivalent apart from the fact that K
was always rated before L (see Procedure). None of the other effectiveness ratings was influenced by condition, all ps > .20. Condition also did not influence the confidence ratings for the
cues, all ps > .14.
Participants found the difficult secondary task (M = 72, SE = 6) to be markedly more difficult than the easy secondary task (M = 44, SE = 6), t(30) = 3.07, p = .005. Likewise, the contingency learning task was rated as being more difficult when the secondary task was difficult
(M = 62, SE = 47) than when the secondary task was easy (M = 39, SE = 4), t(30) = 2.75, p <
.05. We also calculated mean reaction times and percentages of correct responses for the
secondary task, separately for the trials presented during the learning phase and for the trials
presented during the test phase. Unlike in Experiment 1, in the learning phase of Experiment
2 participants were slower, t(30) = 6.21, p < .001, and gave fewer correct responses, t(30) =
4.96, p < .001, in the difficult secondary task condition (M = 443 ms, SE = 8; M = 86%, SE =
2) than in the easy secondary task condition (M = 295 ms, SE = 22; M = 98%, SE = 1). The
same pattern emerged during the test phase: M = 541 ms, SE = 11 versus M = 408 ms, SE =
16, for the difficult and easy secondary task condition, respectively, t(30) = 6.70, p < .001; M =
54%, SE = 4, versus M = 91%, SE = 2, for the difficult and easy secondary task conditions,
respectively, t(30) = 7.26, p < .001.
Discussion
The most important result of the present experiment was that the magnitude of the forward
blocking effect was modulated by the difficulty of the secondary task that participants
performed during the learning and test phases. This result supports the hypothesis that
effortful deductive reasoning processes contribute to forward blocking in human contingency
learning. The modulation of blocking by secondary task difficulty could not have resulted
from a general effect of secondary task difficulty on associative processes because secondary
task difficulty did not systematically influence the ratings of the other cues. The ratings for cue
K did tend to be lower when the secondary task was easy than when it was difficult. However,
neither the rating of the equivalent cue L nor the ratings of the other cues were affected. Moreover, on the basis of an associative account, one would predict, if anything, higher ratings for K
when the secondary task was easy than when it was difficult.
Although the pattern in the confidence ratings was that predicted on the basis of the deductive reasoning account, one should note that the critical differences were not statistically
significant. This fact detracts somewhat from the conclusiveness of our results. However, the
confidence results were highly similar to those observed in Experiment 1, which suggests that
the failure to obtain significant effects on the confidence data might be due to a lack of statistical power.
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GENERAL DISCUSSION
Previous studies have provided mixed support for an associative account of forward blocking
in human contingency learning. Some of the findings that are problematic for associative
models (e.g., De Houwer et al., 2002; Lovibond et al., 2002; Waldmann, 2000) can be
explained by supposing that participants engage in deductive reasoning in order to arrive at
contingency judgements. Experiments 1 and 2 represent the first direct tests of the idea that
deductive reasoning contributes to forward blocking in human contingency learning. By
varying the difficulty of a secondary task that participants performed simultaneously with the
contingency learning task, we attempted to influence the opportunity to engage in deductive
reasoning. Experiment 2 showed that forward blocking was significantly reduced when participants performed a difficult secondary task during the learning and test phases than when they
performed a less difficult secondary task. This result supports the hypothesis that blocking
effects are at least partially due to deductive reasoning.
The modulatory effect of secondary task difficulty on forward blocking could, in principle,
be explained on the basis of associative theories if one assumes that less attention is paid to the
contingency learning task under difficult than under easy secondary task conditions.
According to such an account, less attention during the A+ trials would reduce associative
learning about A and thus the ability of A to block associative learning about T during the
AT+ trials (Rescorla & Wagner, 1972). However, the ratings for cues A, K, and L should then
also be lower with a difficult secondary task, which was not the case.
One should note that a reduced but significant blocking effect was observed even when the
secondary task was difficult. Moreover, in Experiment 2, this blocking effect occurred despite
the fact that participants were not significantly more confident in their rating for T than in
their rating for the control cues K and L. According to a deductive reasoning account,
blocking in effectiveness ratings should be accompanied by an increase in the confidence in the
rating for T, relative to the confidence in the ratings for K and L. One could thus argue that the
residual blocking effects in the difficult secondary task conditions were due to the operation of
associative processes. However, it is also conceivable that the difficult secondary task did not
eliminate the opportunity for deliberate deductive reasoning and that the blocking effects in
the difficult secondary task conditions were due to the partial deductive reasoning that did
take place. Our results therefore do not provide conclusive evidence about whether associative
processes are important in forward blocking. They do strongly suggest, however, that associative processes are not the only mechanism through which blocking effects are produced.
Although the pattern of results was similar in both experiments, the crucial comparisons
were significant in Experiment 2 only. Participants in Experiment 2 performed an easy or
difficult secondary task during both the learning phase and the test phase, whereas those of
Experiment 1 were given a secondary task only during the learning phase. In principle, deductive reasoning can take place both during the learning phase and during the test phase. It is
therefore possible that participants in the difficult secondary task condition of Experiment 1
compensated for the lack of opportunity for deductive reasoning during the learning phase by
engaging in more deductive reasoning during the test phase. Such a compensatory strategy
was less likely in Experiment 2 because a secondary task was also present during the test phase.
It was for these reasons that we expected a more pronounced impact of secondary task difficulty in Experiment 2 than in Experiment 1.
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DE HOUWER AND BECKERS
One could also argue, however, that the secondary task manipulation during the learning
phase was more effective in Experiment 2 than in Experiment 1, and that this was the true
reason for the fact that the effects were more pronounced in the second experiment. Such an
argument is supported by the fact that in Experiment 2, both performance on the secondary
task during learning and ratings of task difficulty were clearly influenced by secondary task
difficulty. In contrast, in Experiment 1, secondary task performance during learning did not
differ between conditions, and the impact of condition on ratings of task difficulty appeared to
be smaller. Also note that the blocking effects in the two experiments mainly differed with
regard to the easy secondary task condition rather than the difficult secondary task condition.
Apart from this, the pattern of results was similar in both experiments. This may indicate that
the effect of secondary task difficulty on blocking was larger in Experiment 2, not because
deductive reasoning in the difficult secondary task condition was prevented even more than in
Experiment 1, but because participants in Experiment 2 for some reason engaged in more
deliberate processing during the easy secondary task.2 However, comparisons between experiments are always hazardous. We thus refrain from drawing strong conclusions about whether
performing a difficult secondary task during the test phase has an effect on forward blocking
over and above the effect of such a task during the learning phase.
Regardless of this issue, our results are clearly consistent with the hypothesis that effortful
deductive reasoning plays a role in forward blocking. It would be interesting to investigate
whether similar results can be obtained when the opportunity for deductive reasoning is
manipulated in other ways than through the difficulty of a secondary task. For instance, one
might vary the number of cues in a contingency learning task. As this number increases, it
seems likely that it would become more difficult for participants to keep track of all events and
thus to arrive at rational inferences. The idea that the number of cues might be important
receives support from the fact that only a few cues were presented in those studies that
produced strong evidence against an associative account of blocking (e.g., De Houwer, 2002;
De Houwer et al., 2002; Lovibond et al., 2002; Waldmann, 2000) whereas many cues were
used in studies that provided strong support for associative models (e.g., Dickinson & Burke,
1996).
REFERENCES
Braine, M. D. S. (1990). The “natural logic” approach to reasoning. In W. F. Overton (Ed.), Reasoning, necessity, and
logic: Developmental perspectives (pp. 133–157). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367–405.
Cheng, P. W., & Holyoak, K. J. (1995). Complex adaptive systems as intuitive statisticians: Causality, contingency,
and prediction. In J.-A. Meyer & H. Roitblat (Eds.), Comparative approaches to cognition (pp. 271–302).
Cambridge, MA: MIT Press.
Dawson, M. E., & Shell, A. M. (1987). Human autonomic and skeletal classical conditioning: The role of conscious
factors. In G. Davey (Ed.), Cognitive processes and Pavlovian conditioning in humans (pp. 27–57). New York: Wiley.
2
Note that in both conditions, participants rated the easy secondary task as being somewhat difficult (a rating of 36
and 44 on a scale from 0 to 100). It is thus likely that we would have found bigger effects of secondary task difficulty on
blocking if we would have used a condition without a secondary task as the baseline for the difficult secondary task
condition.
BLOCKING
357
De Houwer, J. (2002). Forward blocking depends on retrospective inferences about the presence of the blocked cue
during the elemental phase. Memory & Cognition, 30, 24–33.
De Houwer, J., & Beckers, T. (2002). A review of recent developments in research and theory on human contingency
learning. Quarterly Journal of Experimental Psychology, 55B, 289–310.
De Houwer, J., Beckers, T., & Glautier, S. (2002). Outcome and cue properties modulate blocking. Quarterly Journal
of Experimental Psychology, 55A, 965–985.
Dickinson, A. (2001). The 28th Bartlett memorial lecture: Causal learning: An associative analysis. Quarterly Journal
of Experimental Psychology, 54B, 3–25.
Dickinson, A., & Burke, J. (1996). Within-compound associations mediate the retrospective revaluation of causality
judgements. Quarterly Journal of Experimental Psychology, 49B, 60–80.
Dickinson, A., Shanks, D., & Evenden, J. (1984). Judgement of act–outcome contingency: The role of selective
attribution. Quarterly Journal of Experimental Psychology, 36A, 29–50.
Evans, J. St. B. T., & Over, D. E. (1996). Rationality and reasoning. Hove, UK: Psychology Press.
Johnson-Laird, P. N., & Byrne, M. J. (1991). Deduction. Hove, UK: Lawrence Erlbaum Associates Ltd.
Lovibond, P. F., Been, S.-L. Mitchell, C. J., Bouton, M. E., & Frohardt, R. (2002). Forward and backward blocking
of causal judgement is enhanced by additivity of effect magnitude. Manuscript submitted for publication.
Mackintosh, N. J. (1995). Categorization by people and pigeons: The twenty-second Bartlett memorial lecture.
Quarterly Journal of Experimental Psychology, 48B, 193–214.
McLaren, I. P. L., Green, R. E. A., & Mackintosh, N. J. (1994). Animal learning and the implicit/explicit distinction.
In N. C. Ellis (Ed.), Implicit and explicit learning of languages (pp. 313–332). New York: Academic Press.
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of
reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current
research and theory (pp. 64–99). New York: Appleton.
Shanks, D. R., & Darby, R. J. (1998). Feature- and rule-based generalization in human associative learning. Journal of
Experimental Psychology: Animal Behavior Processes, 24, 405–415.
Sloman, S. A. (1996). The empirical case of two systems of reasoning. Psychological Bulletin, 119, 3–22.
Szmalec, A., Vandierendonck, A., & Kemps, E. (2002). Response-selection as a component of executive processing.
Manuscript in preparation.
Wagner, A. R. (1981). SOP. A model of automatic memory processing in animal behaviour. In N. E. Spear & R. R.
Miller (Eds.), Information processing in animals: Memory mechanisms (pp. 5–47). Hillsdale, NJ: Lawrence Erlbaum
Associates, Inc.
Waldmann, M. R. (2000). Competition among causes but not effects in predictive and diagnostic learning. Journal of
Experimental Psychology: Learning, Memory, & Cognition, 26, 53–76.
Waldmann, M. R., & Hagmayer, Y. (2001). Estimating causal strength: The role of structural knowledge and processing effort. Cognition, 82, 27–58.
Waldmann, M. R., & Holyoak, K. J. (1992). Predictive and diagnostic learning within causal models: Asymmetries in
cue competition. Journal of Experimental Psychology: General, 121, 22–236.
Original manuscript received 22 July 2002
Accepted revision received 4 November 2002