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 exam-

ined. 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 RescorlaWagner (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 RescorlaWagner 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 (FWOFlanders, Belgium). We

thank Tom Randell and Isabel Muyllaert for their help in collecting the data.

2003 The Experimental Psychology Societyhttp://www.tandf.co.uk/journals/pp/02724995.html DOI:10.1080/02724990244000296

THE QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2003, 56B (4), 345357

Q1183QJEP(B)02b26 / Oct 7, 03 (Tue)/ [13 pages 2 Tables 0 Figures 2 Footnotes 0 Appendices]. .

Centre single caption. Shortcut keys. READ AS KEYED [rj]

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 infer-

ences (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 presum-

ably 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 contin-

gency 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.

346 DE HOUWER AND BECKERS

1When 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 probabilis-

tic 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 intention-

ally act in ways consistent with this knowledge.

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 effec-

tively 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, rule-

based 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 manip-

ulating 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 explo-

sions 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

BLOCKING 347

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 asso-

ciative 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 when-

ever 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), partici-

pants 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 confi-

dence 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 confi-

dence 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).

348 DE HOUWER AND BECKERS

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 draw-

ings 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 immedi-

ately 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

BLOCKING 349

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 contin-

gency 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. Partici-

pants 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 func-

tioned 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 effec-

tive; 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

350 DE HOUWER AND BECKERS

score between 0 (very unsure) and 100 (very sure). All participants first rated the cue that was repre-

sented 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 (0100) 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 confi-

dence 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 differ-

ence cues, we first conducted ANOVAs with condition (easy or difficult secondary task) and

cue (A, T, K, L, Z) as variables. Where necessary, GreenhouseGeisser 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 condi-

tion was not significant, Fs < 1. Contrary to the predictions, the interaction was also not signif-

icant in neither of the ANOVAs, Fs < 1.

To explore the data in more detail, t tests were performed on the blocking scores. One-

sample 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)

BLOCKING 351

TABLE 1

Mean effectiveness ratings, confidence ratings, and blocking scores as a function of secondary

task difficulty in Experiment 1

Cue

Secondary A T K L Z Blocking

task

Ratings difficulty M SE M SE M SE M SE M SE M SE

Effectiveness Easy 79 6 14 4 39 3 42 3 0 0 26 4

Difficult 74 6 23 6 39 5 39 5 2 2 16 7

Confidence Easy 81 6 72 7 40 6 38 6 89 6 33 9

Difficult 75 6 66 6 50 5 49 6 89 6 16 7

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.

= 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 NewmanKeuls 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 condi-

tion (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 anal-

yses. 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.

352 DE HOUWER AND BECKERS

Method

Participants

Seventeen first-year psychology students at Ghent University and 15 students from various facul-

ties 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,

BLOCKING 353

TABLE 2

Mean effectiveness ratings, confidence ratings, and blocking scores as a function of secondary

task difficulty in Experiment 2

Cue

Secondary A T K L Z Blocking

task

Ratings difficulty M SE M SE M SE M SE M SE M SE

Effectiveness Easy 78 6 7 3 34 4 48 6 0 0 34 4

Difficult 79 6 28 6 48 3 41 4 1 1 16 7

Confidence Easy 84 7 74 8 45 7 44 6 83 9 30 12

Difficult 79 7 58 10 55 7 44 7 74 9 8 8

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.

t(15) = 2.51, p < .05, but not in the difficult secondary task condition, t(15) = 1.09. Never-

theless, the magnitude of the confidence blocking effect did not differ significantly between

both conditions, t(30) = 1.53, p = .14.

Additional NewmanKeuls 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 influ-

enced 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 diffi-

cult than the easy secondary task (M = 44, SE = 6), t(30) = 3.07, p = .005. Likewise, the contin-

gency 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