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Learned predictiveness influences rapid attentional capture: Evidence from the dot probe task

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Learned predictiveness influences rapid attentional capture: Evidence from the dot probe task

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Attentional theories of associative learning and categorization propose that learning about the predictiveness of a stimulus influences the amount of attention that is paid to that stimulus. Three experiments tested this idea by looking at the extent to which stimuli that had previously been experienced as predictive or nonpredictive in a categorization task were able to capture attention in a dot probe task. Consistent with certain attentional theories of learning, responses to the dot probe were faster when it appeared in a location cued by a predictive stimulus compared to a location cued by a nonpredictive stimulus. This result was obtained only with short (250-ms or 350-ms) but not long (1,000-ms) delays between onset of the stimuli and the dot probe, suggesting that the observed spatial cuing effect reflects the operation of a relatively rapid, automatic process. These findings are consistent with the approach to the relationship between attention and learning taken by the class of models exemplified by Mackintosh's (1975) theory. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
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Learned Predictiveness Influences Rapid Attentional Capture:
Evidence From the Dot Probe Task
Mike E. Le Pelley
University of New South Wales and Cardiff University
Miguel Vadillo
University College London
David Luque
University of Málaga
Attentional theories of associative learning and categorization propose that learning about the predic-
tiveness of a stimulus influences the amount of attention that is paid to that stimulus. Three experiments
tested this idea by looking at the extent to which stimuli that had previously been experienced as
predictive or nonpredictive in a categorization task were able to capture attention in a dot probe task.
Consistent with certain attentional theories of learning, responses to the dot probe were faster when it
appeared in a location cued by a predictive stimulus compared to a location cued by a nonpredictive
stimulus. This result was obtained only with short (250-ms or 350-ms) but not long (1,000-ms) delays
between onset of the stimuli and the dot probe, suggesting that the observed spatial cuing effect reflects
the operation of a relatively rapid, automatic process. These findings are consistent with the approach to
the relationship between attention and learning taken by the class of models exemplified by Mackintosh’s
(1975) theory.
Keywords: associative learning, attention, categorization, dot probe task
Supplemental materials: http://dx.doi.org/10.1037/a0033700.supp
Attention provides the gateway between the mass of information
in the world and the relatively small subset of that information that
we select for further analysis or action. What, then, determines the
stimuli that will be selected under a given set of circumstances?
Much of the research related to this issue that is described in the
cognitive psychology literature has focused on the “intrinsic”
perceptual and emotional properties of stimuli. For example, a
stimulus is more likely to capture attention if it is highly percep-
tually salient (e.g., if it has an abrupt onset, or a bright color; Folk,
Remington, & Johnston, 1992) or if it is “emotionally relevant”:
Negative mood states bias attention toward threatening informa-
tion (MacLeod, Mathews, & Tata, 1986), while positive mood
states bias attention to desirable, rewarding stimuli (Tamir &
Robinson, 2007). A second line of research has instead examined
the ability of external events to modulate attention to stimuli. For
example, the influence of attention to a stimulus persists for longer
if selection of that stimulus is highly rewarded than if it is only
weakly rewarded (Della Libera & Chelazzi, 2006).
While the studies described above have considered the effects of
stimulus and reward properties on attentional selection in isolation,
recent research suggests that we should also consider them in
combination (Anderson, Laurent, & Yantis, 2011; Della Libera &
Chelazzi, 2009; Hickey, Chelazzi, & Theeuwes, 2010; Kiss,
Driver, & Eimer, 2009; Le Pelley, Mitchell, & Johnson, 2013). For
example, Della Libera and Chelazzi (2009) gave participants train-
ing on a task in which selection of certain shapes was typically
followed by high reward (0.10), while selection of other shapes
was typically followed by low reward (0.01). After extensive
training, shapes that predicted high-value outcomes were shown to
be easier to select when serving as targets (Experiment 2), and
more difficult to reject when serving as distractors (Experiment 1),
compared to shapes that predicted low-value outcomes. Using a
visual search task, Anderson et al. (2011) similarly demonstrated
that presenting cues previously associated with high-value reward
as distractors led to a slowing of search, compared to cues previ-
ously associated with low-value reward. These findings suggest
that cues associated with high-value outcomes are more likely to
capture attention than those paired with low-value outcomes; that
is, participants learn to attend to cues as a function of the value of
the reward with which they are paired. Consequently these studies
can be described as demonstrating an influence of learned value on
attention.
Other studies of associative learning in both humans and non-
human animals suggest that the learned predictiveness of a stim-
ulus might also be a determinant of attention to that stimulus (for
This article was published Online First July 15, 2013.
Mike E. Le Pelley, School of Psychology, University of New South Wales,
Sydney, Australia, and School of Psychology, Cardiff University, Cardiff,
United Kingdom; Miguel Vadillo, Division of Psychology and Language
Sciences, University College London, London, United Kingdom; David
Luque, School of Psychology, University of Málaga, Málaga, Spain.
This work was supported by Grants FT100100260 from the Australian
Research Council and P11-SEJ-7898 from the Andalusian Government
(Spain).
Correspondence concerning this article should be addressed to Mike E.
Le Pelley, School of Psychology, University of New South Wales, Sydney
NSW 2052, Australia. E-mail: m.lepelley@unsw.edu.au
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Experimental Psychology:
Learning, Memory, and Cognition
© 2013 American Psychological Association
2013, Vol. 39, No. 6, 1888 –1900
0278-7393/13/$12.00 DOI: 10.1037/a0033700
1888
reviews, see Le Pelley, 2004; Mitchell & Le Pelley, 2010). The
predictiveness of a stimulus refers to the accuracy with which the
occurrence of that stimulus allows subsequent events to be pre-
dicted. A predictive stimulus is one that is consistently followed by
the same outcome (which can be of high or low value, such that
predictiveness and learned value are orthogonal); a nonpredictive
stimulus is not. In a typical experiment, participants learn during
Phase 1 that certain stimuli are predictive of outcomes, while
others are not.
1
In a subsequent Phase 2 involving new stimulus–
outcome contingencies, human participants typically learn faster
about stimuli previously experienced as predictive than those
previously experienced as nonpredictive (e.g., Bonardi, Graham,
Hall, & Mitchell, 2005; Kruschke, 1996; Le Pelley & McLaren,
2003; Le Pelley, Suret, & Beesley, 2009; Le Pelley, Turnbull,
Reimers, & Knipe, 2010). Such findings support the suggestion
that attention is modulated by learned predictiveness, as long as it
is assumed that differences in the rate of learning about stimuli
during Phase 2 reflect differences in attention to those stimuli, as
suggested by “attentional” theories of associative learning (e.g.,
Kruschke, 2003; Le Pelley, 2004; Mackintosh, 1975). However,
this assumption is questionable. After all, the putative relationship
between attention and rate of learning has been invoked only to
account for the results of experiments of this kind and has received
little external validation in the cognitive psychology literature.
Consequently, the possibility remains open that what is influenced
by predictiveness, and what in turn influences learning, is not
attention but rather an associability parameter that merely modu-
lates the rate at which stimuli enter into associations; a “nonatten-
tional” model along these lines has been proposed by Honey,
Close, and Lin (2010; see also Oswald et al., 2001). Alternatively,
one could appeal to memory processes, rather than attention.
Perhaps stimuli experienced as predictive during Phase 1 develop
stronger and/or more distinct representations in memory than those
experienced as nonpredictive, and this allows information experi-
enced in Phase 2 to be more accurately addressed to (associated
with) the correct stimulus representation for predictive stimuli than
nonpredictive stimuli (see Le Pelley, Reimers, et al., 2010). Yet
another nonattentional account of these rate-of-learning studies has
recently been proposed, in terms of an “inferential–attribution”
process (Mitchell, Griffiths, Seetoo, & Lovibond, 2012).
A growing set of studies has taken a more direct approach to
investigating the putative relationship between learned predictive-
ness and attention, by looking at the effect of predictiveness on
measures of attention that have previously been validated in the
cognitive psychology literature. In particular, predictiveness has
been shown to influence overt attention, measured in terms of gaze
location (Beesley & Le Pelley, 2011; Kruschke, Kappenman, &
Hetrick, 2005; Le Pelley, Beesley, & Griffiths, 2011; Wills,
Lavric, Croft, & Hodgson, 2007). However, shifting attention does
not necessarily entail eye movements. Visual performance can be
enhanced at the site where attention is directed without changing
fixation (Jonides, 1981; Yeshurun & Carrasco, 1999). Hence, it is
desirable to develop a test of the influence of learned predictive-
ness on attention that does not rely on eye gaze. Moreover, all of
these previous gaze-based studies have demonstrated a bias in
overt attention at the point at which participants made their cate-
gorization response. It is perhaps unsurprising that participants are
more likely to pay attention to a stimulus during a categorization
task if identification of that stimulus is necessary for making a
correct categorization response. A more powerful finding would be
a demonstration that learning about the predictiveness of stimuli
produces a more general attentional bias with regard to those
stimuli that operates even when it is not required, and when it may
even hinder performance. Finally, and more pragmatically, eye
tracking apparatus is typically expensive, intrusive and cumber-
some. Consequently, it is not well-suited for use with large par-
ticipant samples, or for testing outside the laboratory—for exam-
ple, with children in schools, or with patients in in-patient
facilities. This last point is pertinent, given that a dysfunction of
the relationship between learning and attention has been impli-
cated in schizophrenia (Morris, Griffiths, Le Pelley & Weickert,
2012), Parkinson’s disease (Gauntlett-Gilbert, Roberts, & Brown,
1999; Hampshire & Owen, 2010), obsessive-compulsive disorder
(Hampshire & Owen, 2010), as well as certain types of brain injury
(Owen, Roberts, Polkey, Sahakian, & Robbins, 1991). It would
therefore be advantageous to develop a procedure to measure the
relationship between attention and learning that can be imple-
mented on any standard computer without special equipment and
can be used with multiple participants simultaneously.
Toward this end, the current experiments examined the influ-
ence of predictiveness on attentional capture using a variant of the
spatial cuing task (Posner, Nissen, & Ogden, 1978). It is well
established that responses to targets appearing in an attended
location are faster than to targets appearing in an unattended
location (Posner, 1980; Posner, Snyder, & Davidson, 1980). Fol-
lowing this rationale, Folk et al. (1992) demonstrated that re-
sponses to a target were faster when it appeared in a location
previously occupied by a cue defined in terms of an abrupt onset
or a discontinuity in color. Similarly, on each trial of MacLeod et
al.’s (1986) dot probe task, a pair of words appeared— one threat-
related (e.g., injury) and the other neutral. Anxious participants
were faster to identify a target (a small dot) when it subsequently
appeared in the location that had been occupied by the threat-
related word compared to the neutral word. The implication of
these studies is that stimuli defined in terms of abrupt onsets, color
discontinuities, or emotional valence, can capture attentional re-
sources.
This type of cuing procedure is the original and classic
paradigm used for investigating the operation of attentional
processes in humans but has never before been used to assess
the relationship between attention and learned predictiveness.
In the current experiments, we use this approach to investigate
whether stimuli that differ in predictiveness also differ in the
extent to which they capture spatial attention, and in Experi-
ments 2 and 3 we go on to look at the timecourse over which
this capture occurs.
Experiment 1
Experiment 1 involved two tasks. The first was described to
participants as a categorization task, with the design shown in
1
Notably, in such studies all stimuli are typically followed by equally
valued outcomes on all trials and, hence, do not differ in terms of their
learned value. The stimuli differ only in terms of the accuracy with which
they predict which specific outcome will occur, i.e., in terms of their
learned predictiveness. This is also the case in the experiments described in
the current article.
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1889
PREDICTIVENESS AND ATTENTIONAL CAPTURE
Table 1. On each trial, two stimuli appeared: a square filled with
one of two shades of green (labeled Gre1 and Gre2), and a set of
oblique lines at one of two orientations (Lin1 and Lin2; see Figure
S1 in the online supplemental materials). Participants categorized
this pair of stimuli into one of two categories by making an
appropriate response, with immediate corrective feedback pro-
vided. For participants in condition “Green Predictive,” the shade
of green predicted the correct categorization response and the
orientation of the lines was nonpredictive: Presence of Gre1 indi-
cated that response R1 was correct, presence of Gre2 indicated that
response R2 was correct, while Lin1 and Lin2 provided no infor-
mation on the correct response. For participants in condition
“Lines Predictive” the orientation of the lines was predictive of the
correct response and shade of green was nonpredictive. If predic-
tiveness influences attention, then this categorization task should
cause participants to come to attend more strongly to the stimuli
belonging to the predictive dimension than to those belonging to
the nonpredictive dimension.
The second task was designed to assess any such difference in
attention, using a variant of the dot probe procedure. On each trial
one of the green squares and one of the sets of oblique lines
appeared briefly on either side of the screen. A dot probe target
could then appear in the location previously occupied by one of
these cues. This target was equally likely to appear in the location
cued by the stimulus that had been predictive during the catego-
rization task as the location cued by the stimulus that had been
nonpredictive. Hence, both stimuli were equally valid as cues
during the dot probe task. If the stimulus that was predictive during
the categorization task was more likely to capture attention, how-
ever, then responses to the target should be faster when it appeared
in the location cued by this stimulus, compared to the location cued
by the nonpredictive stimulus.
These two tasks alternated across phases—Experiment 1 con-
tained four task phases, in the order: categorization, dot probe,
categorization, dot probe. This procedure meant that learning
about the categorization predictiveness of cues was “topped up”
prior to each iteration of a relatively short test on the dot probe
task.
Method
Participants and apparatus. Eleven Cardiff University stu-
dents (10 female) took part in exchange for course credit and were
tested individually in a dim, quiet room. Stimuli were presented on
a 43.2-cm monitor, and stimulus presentation was controlled by a
Visual Basic program. Timing used Windows API QueryPerfor-
mance functions for millisecond resolution. Responses were made
using the keyboard, and error signals were beeps given over
headphones.
Stimuli. The two green squares had red–green–blue color
values of (0, 255, 0) and (0, 160, 0), with sides subtending 3.42°
visual angle from a viewing distance of 60 cm. The two oblique
line stimuli comprised sets of cyan lines (thickness 0.34° visual
angle) sloping upward to the right at an angle of either 33° or 57°,
enclosed within a black square background with sides 3.42° visual
angle. Stimuli are shown in Figure S1.
These green square and oblique line stimuli were presented
centrally in white square frames with sides subtending 3.76°,
which were positioned either side of a small fixation cross located
in the center of the screen; the distance from the center of the cross
to the center of each box subtended 5.30°. The target in the dot
probe task was a white equilateral triangle with side length sub-
tending 2.22°. This would appear centrally in one of the white
square frames. The screen background was black.
Design. Two between-subjects conditions were created by
varying the predictive dimension during the categorization task.
Participants were initially assigned randomly to conditions, and
following the exclusions described below replacements were run
to ensure equal numbers in each condition. Overall, four partici-
pants were tested in condition Green Predictive and seven in
condition Lines Predictive. Particular values on each stimulus
dimension were randomly assigned to the labels shown in Table 1
for each participant; e.g., for some participants in condition Lines
Predictive, the label Lin1 in Table 1 referred to lines at 33° to the
horizontal and Lin2 referred to lines at 57°, while for others this
assignment was reversed.
Experiment 1 contained four task phases, in the order: catego-
rization task, dot probe task, categorization task, dot probe task.
Each phase of the categorization task was split into blocks. Each
block contained four trials, with each of the stimulus pairs shown
in Table 1 appearing once in random order. The first phase of the
categorization task had 12 blocks, and the second phase had eight
blocks. Across blocks, for each stimulus pair the predictive stim-
ulus appeared equally often on the left and on the right.
On each trial of the dot probe task, one of the four stimulus pairs
shown in Table 1 appeared as the cue. This pair could appear with
the green square on the left or on the right. On target-present trials,
the target could subsequently appear on the left or the right. For
target-present trials, every combination of stimulus pair, stimulus
position, and target position appeared once during each phase of
the dot probe task (giving 4 2 2 16 target-present trials).
Each phase of the dot probe task also included eight target-absent
trials; one for each combination of stimulus pair and stimulus
position. The 24 trials of each phase of the dot probe task were
presented in random order.
Procedure. Participants received written and oral descriptions
of the tasks. Initial instructions described the categorization task.
Participants were told that (a) on each trial a cross would appear in
Table 1
Design of the Categorization Task
Stimulus pair
Correct response
Green predictive Lines predictive
Gre1 & Lin1 R1 R1
Gre1 & Lin2 R1 R2
Gre2 & Lin1 R2 R1
Gre2 & Lin2 R2 R2
Note. Gre1 and Gre2 refer to squares filled with slightly different shades
of green; Lin1 and Lin2 refer to oblique lines at slightly different orien-
tations (Experiment1) or lines with different thicknesses (Experiment 2).
Particular values on each dimension were randomly assigned to these
labels for each participant. R1 and R2 refer to two distinct responses. The
correct response for each stimulus pair is shown for participants in the
“green predictive” condition (in which the shade of green was predictive of
the correct response while the orientation of the oblique lines was nonpre-
dictive) and the “lines predictive” condition (in which the orientation
[Experiment 1] or thickness [Experiment 2] of the oblique lines was
predictive of the correct response while the shade of green was nonpre-
dictive).
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1890
LE PELLEY, VADILLO, AND LUQUE
the center of the screen and that they should keep their eyes fixed
on it throughout the trial; (b) a pair of stimuli would then be
presented; (c) their task was to decide if that pair of stimuli
belonged to Category 1 (in which case they should press the C key)
or to Category 2 (M key); (d) they would start out guessing but that
on the basis of feedback their decisions should become more
accurate; (e) they should make as few errors as possible; (f) they
should respond within 3 s but should not anticipate the stimuli.
There followed four practice trials using stimuli that were not
encountered in the main body of the experiment and with no
feedback provided. Following these practice trials, participants
were asked if they had maintained central fixation throughout each
trial; if they had not, the practice trials were repeated.
Participants then received instructions relating to the dot probe
task as follows: (a) a cross would appear in the center of the screen
and that it was very important that their eyes remained fixed on it;
(b) two stimuli would be presented briefly; (c) after these stimuli
disappeared, the target might appear to the left or right of the
central cross; (d) if this target appeared, they should press the
spacebar, and if it did not appear, they should do nothing; (e) they
should respond as quickly as possible but should not anticipate the
target. There followed four practice trials of this task (three target-
present and one target-absent), using stimuli not encountered in the
body of the experiment. If participants reported not maintaining
fixation on these trials, they were repeated.
Participants were then shown the four stimuli to be used in the
body of the experiment, before the experiment began.
Each trial of the categorization task began with the appearance
of the fixation cross flanked by the two empty white frames. After
1,000 ms, the stimulus pair appeared in these frames. If partici-
pants made the correct category response, the word “Correct”
appeared in the center of the screen; if they made the incorrect
response, “Wrong” appeared. If a response was made within 150
ms, the message “Do not anticipate the stimuli” appeared. If
participants did not make a response within 3 s, the message “You
took too long” appeared. All non-“correct” feedback was accom-
panied by an error signal. Feedback remained on screen for 800
ms; the screen then cleared and the next trial began after 600 ms.
After every eight trials of the categorization task participants were
told how many errors they had made in those eight trials, and their
mean response time (RT; excluding any anticipations or timeouts).
This information remained on-screen for 3 s before the experiment
proceeded.
After the first phase of the categorization task, participants read
a reminder of the instructions for the dot probe task. Figure 1
shows a schematic of a target-present trial of this task. Each trial
began with presentation of the fixation cross flanked by two white
frames. After 1,000 ms, the stimulus pair was presented for 150 ms
in these frames, and then disappeared. On target-present trials, the
dot probe appeared after a delay of 200 ms, giving a stimulus-onset
asynchrony (SOA) of 350 ms between presentation of the stimulus
pair and the dot probe. If participants pressed the spacebar with-
in 2,000 ms, the screen cleared and the next trial began after an
interval of 600 ms. If participants did not press the spacebar
within 2,000 ms on target-present trials, a timeout error signal
was given and the experiment moved to the next trial. Re-
sponses within 150 ms of the dot probe appearing were deemed
anticipations and an error signal was given. On target-absent
trials, no dot probe appeared; if participants pressed the space-
bar during a 2,150-ms window after the cues vanished this was
deemed an anticipation, and an error signal was given. If they
did not respond, then after this window had elapsed the next
trial proceeded. After 16 trials of the dot probe task participants
were told how many anticipations they had made during those
trials, and mean RT on target-present trials.
The procedure for the second categorization and dot probe
phases was the same. Instructions prior to each phase stated which
task participants would be carrying out in that phase, and reminded
them of the importance of maintaining fixation on each trial.
Results
An influence of predictiveness on spatial cuing in the dot probe
task could only be expected if participants managed to learn about
the differential predictiveness of the cues involved during the
categorization task. Following Le Pelley and McLaren (2003),a
selection criterion of 60% correct categorization responses aver-
aged across all blocks of the categorization task was imposed
(chance performance 50% correct). Three participants (all in
condition Lines Predictive) failed to achieve this criterion, and
their data were excluded from all subsequent analyses.
The primary focus of this article is the influence of predictive-
ness on attentional capture, as opposed to any “unlearned” influ-
ence of stimulus salience (in terms of color, intensity, onset, etc.).
In other words, we are interested in the status of cues as predictive
or nonpredictive, rather than in the particular identity of these cues
(green squares or sets of oblique lines). To reflect this focus—and
Figure 1. Schematic example of the sequence of events on a target-
present trial of the dot probe task in Experiment 1. The gray square in the
cue display represents one of the green squares that could be used as cue
stimuli, and the striped square represents one of the sets of oblique lines.
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1891
PREDICTIVENESS AND ATTENTIONAL CAPTURE
since, following the exclusions described above, the number of
participants in each condition was equal—the two between-
subjects conditions (Green Predictive and Lines Predictive) were
combined. In this pooled sample, the label predictive stimuli refers
to green squares for participants from condition Green Predictive,
and sets of oblique lines for participants from condition Lines
Predictive; nonpredictive stimuli refers to oblique lines for partic-
ipants from condition Green Predictive, and green squares for
participants from condition Lines Predictive. (See supplemental
materials for an analysis including condition [Green Predictive vs.
Lines Predictive] as a between-subjects factor.)
Figure 2 shows accuracy across training blocks of the catego-
rization task. One-way analysis of variance (ANOVA) revealed a
significant effect of block, F(19, 133) 2.82,
p
2
.29, p .001,
with accuracy increasing across training (effect size in this and all
subsequent analyses is partial eta-squared,
p
2
). Collapsing across
the blocks of each phase (Phase 1 being Blocks 1–12 and Phase 2
being Blocks 13–20) revealed that mean accuracy was signifi-
cantly greater than chance (50%) in both Phase 1 and Phase 2,
one-sample t(7) 3.39 and 13.7,
p
2
.62 and .96, p .012 and
p .001, respectively.
Accuracy on the dot probe task was very high; across all
participants, only one anticipation was made on a target-absent
trial. The data of main interest relate to the target-present trials.
Anticipation responses and timeouts (which constituted 1.2% and
0% of all target-present trials, respectively) were removed. Several
measures were taken to reduce the impact of any outlying response
times (RTs). First, RTs were log-transformed. Second, any log
RTs lying more than three standard deviations from each partici-
pant’s mean were excluded as outliers (1.95% of all target-present
trials), following Sincich (1986).
Target-present trials of the dot probe task were labeled congru-
ent if the dot probe appeared in the location cued by the predictive
stimulus from the categorization task, and incongruent if the dot
probe appeared in the location cued by the nonpredictive stimulus.
For each participant we calculated the median RT for congruent
and incongruent trials for each phase of the dot probe task; these
data were then averaged across participants and are shown in
Figure 3. ANOVA with factors of congruence and phase revealed
a main effect of congruence, F(1, 7) 15.3,
p
2
.69, p .006,
indicating that across all participants, responses were significantly
faster on congruent trials than incongruent trials. The observed
difference in log RTs between congruent and incongruent trials
corresponds to an RT difference of 14 ms. The main effect of
phase and the phase congruence interaction were nonsignificant,
larger F(1, 7) 0.74, both
p
2
s .1, both ps .40.
Discussion
Participants were faster to respond to the appearance of the dot
probe when it appeared in a location that was cued by a stimulus
that had been predictive in the categorization task, relative to a
stimulus that had been nonpredictive. The implication of this
finding is that the difference in experienced predictiveness of the
stimuli influenced their tendency to capture attention during the
dot probe task. This relationship between predictiveness and at-
tention is exactly that anticipated by attentional theories of asso-
ciative learning (e.g., Kruschke, 2003; Le Pelley, 2004; Mackin-
tosh, 1975).
Notably, the attentional capture observed in Experiment 1 oc-
curred even though there was no advantage to be gained in shifting
attention to the predictive stimulus during the dot probe task. That
is, because the dot probe was equally likely to appear in the
location cued by the predictive stimulus as that cued by the
nonpredictive stimulus, the best strategy during this task was to
maintain central fixation throughout (and participants were explic-
itly instructed to do so). The implication, then, is that the faster
responses to congruent than incongruent dot probes observed in
Experiment 1 might reflect relatively automatic shifts of attention
toward stimuli experienced as being predictive during the catego-
rization task, or away from those experienced as nonpredictive.
This kind of automatic, stimulus-driven or exogenous attentional
orienting can be contrasted with endogenous shifts of attention that
are under the control of the participant (Jonides, 1981; Posner,
1980; Posner & Cohen, 1984).
Experiment 2
The aims of Experiment 2 were twofold. The first aim was to
replicate our novel finding of faster responding in the dot probe
task when the probe appears in a location congruent with a stim-
ulus that has been experienced as predictive of a categorization
response. The second aim relates to the issue of automaticity,
raised in the Discussion of Experiment 1. We argued there that
during the dot probe task, there was no reason for participants to
consciously shift their attention toward one stimulus rather than
the other, and no advantage to be gained in doing so. Nevertheless
it remains possible that they did so regardless, strategically orient-
ing attention toward the predictive stimulus for some reason. If this
is the case, and the dot probe data of Experiment 1 reflect a
conscious strategy of shifting attention toward predictive stimuli,
then providing more time to process the predictive status of the
stimuli (in terms of a longer SOA between stimuli and probe in the
dot probe task) should produce stronger or at least similar effects,
assuming that such controlled strategies are time-consuming (see
Figure 2. Mean percentage correct categorization responses across the 20
blocks of Experiment 1. Error bars show standard error of the mean. Blocks
1–12 constituted Phase 1, and Blocks 13–20 constituted Phase 2. Dotted
line shows level of performance expected by chance (50% correct).
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1892
LE PELLEY, VADILLO, AND LUQUE
De Houwer, Hermans, & Eelen, 1998; Fazio, Sanbonmatsu, Pow-
ell, & Kardes, 1986; Le Pelley, Calvini, & Spears, 2013; Posner &
Snyder, 1975). Experiment 2 investigated this issue by varying the
SOA in the dot probe task as a within-subject variable.
Method
Participants and apparatus. Seventy-two University of
Málaga students (47 female) participated in exchange for course
credit. They were tested in groups of up to 10 at a time in a
room containing 10 semienclosed cubicles, using standard PCs
with 48.3 cm monitors. Stimulus presentation was controlled by
the Cogent 2000 toolbox (http://www.vislab.ucl.ac.uk/Cogent/)
for MATLAB. Participants made all responses with the com-
puter keyboard using their dominant hand.
Stimuli. The two green squares had red–green–blue color
values of (51, 255, 0) and (0, 102, 0), with sides subtending 9.0°
visual angle from a distance of 60 cm. The two oblique line stimuli
comprised sets of thick (width 0.86° visual angle) or thin
(width 0.01°) rightward-slanted blue lines, enclosed within a
black square background with sides subtending 9.0°. Stimuli are
shown in Figure S2 (see online supplemental materials).
These green square and oblique line stimuli were presented
centrally in white square frames with sides subtending 13.1°,
which were positioned either side of a small fixation cross that was
located in the center of the screen; the distance from the center of
the cross to the center of each box subtended 9.0°. The dot probe
was a white square with side length subtending 1.35°. This would
appear superimposed centrally on one of the stimuli. The screen
background was black.
Design. For half of the participants the shade of the green
square determined the correct response in the categorization task
(condition Green Predictive); for the other half, the thickness of the
oblique lines determined the correct response (condition Lines
Predictive). Particular values on each stimulus dimension (shade of
green and thickness of lines) were randomly assigned to the labels
shown in Table 1 for each participant.
Procedure. Initial instructions (in Spanish) described the cat-
egorization task. Participants were told that on each trial a pair of
stimuli would appear and that they were required to make a
response using either the up or down arrow keys and that their task
was to learn the correct response for each stimulus pair. Partici-
pants then completed a first phase of 32 categorization trials. This
phase comprised four, eight-trial blocks, with each of the stimulus
pairs in Table 1 appearing twice per block in random order; for
each stimulus pair, the predictive stimulus appeared once on the
left and once on the right. Incorrect responses produced the feed-
back message “Error! The correct response was [UP/DOWN],”
which remained onscreen for 3 s; correct responses were not
followed by any explicit feedback.
Participants then moved on to the first phase of the dot probe
task. Instructions for this task were similar to those of Experiment
1, but participants were now told explicitly that, in order to
respond to the square (the dot probe target) as quickly as possible,
“it is best to ignore the figures” (i.e., the stimulus pair). Each dot
probe trial began with presentation of a central fixation cross. After
500 ms the stimulus pair appeared to either side of this cross. After
an SOA of either 250 ms or 1,000 ms, the dot probe appeared
superimposed on one of the stimuli. This probe remained until
participants made the correct response (left arrow key for a target
presented on the left; right arrow key for a target presented on the
right). Immediately on making the correct dot probe response, the
screen cleared, and the next trial began after an intertrial interval
of1s.
Each phase of the dot probe task contained 16 trials: 2 SOAs
(250 ms or 1,000 ms) 4 stimulus pairs (see Table 1) 2 trial
types (target congruent with predictive stimulus versus target
incongruent with predictive stimulus). Whether the predictive
stimulus appeared on the left or right was randomly determined on
each trial.
After the first phase of the dot probe task, participants returned
to the categorization task. Experiment 2 comprised eight alterna-
tions of the categorization task with the dot probe task.
Results
Three participants failed to achieve the criterion of 60% correct
averaged over all the trials of the categorization task (two in
condition Green Predictive and one in condition Lines Predictive).
These participants’ data were excluded from further analysis. As
for Experiment 1, data were collapsed across counterbalancing
conditions Green Predictive and Lines Predictive; see supplemen-
tal materials for an analysis including condition (Green Predictive
vs. Lines Predictive) as a between-subjects factor.
Figure 4 shows accuracy across training blocks of the catego-
rization task. One-way ANOVA revealed a significant effect of
block, F(31, 2108) 65.3,
p
2
.49, p .001, with accuracy
increasing across training. Collapsing across the blocks of each
phase revealed that mean accuracy was significantly greater than
chance (50%) in all phases, smallest t(68) 11.3, all
p
2
s .65,
all ps .001.
Dot probe trials were defined as correct if participants’ first
response correctly corresponded to the position of the probe.
Figure 3. Median log-transformed response time to appearance of the
probe on target-present trials of the dot probe task of Experiment 1.
Congruent probe appeared in location cued by predictive stimulus from
categorization task; Incongruent probe appeared in location cued by
nonpredictive stimulus from categorization task.
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PREDICTIVENESS AND ATTENTIONAL CAPTURE
Accuracy on the dot probe task was very high, with a mean of
99.3 0.15% (SEM) correct trials across all participants.
In Experiment 1, the experiment program defined minimum and
maximum RT limits of 150 ms and 2,000 ms for the dot probe task,
as responses faster or slower than these limits, respectively, were
not permitted. For consistency, dot probe RTs in Experiment 2 that
were below 150 ms or above 2,000 ms were therefore excluded
from analysis (0.24% and 0.01% of all trials, respectively). As for
the analysis of Experiment 1, RTs were log-transformed and any
log RTs lying more than three standard deviations from each
participant’s mean were excluded as outliers (1.46% of all trials).
As in Experiment 1, for each participant we calculated the median
log RT for congruent and incongruent trials for each phase of the
dot probe task (using data from correct trials only). These data
were analyzed as four epochs, with each epoch representing the
averaged data from a consecutive pair of dot probe phases (see
Figure 5).
At an SOA of 250 ms, a cuing effect occurred with faster
responses to the probe on congruent trials than incongruent trials
across all epochs. In contrast, at an SOA of 1,000 ms there was no
clear cuing effect; RTs on congruent and incongruent trials were,
on average, more similar. A (2) (2) (4) repeated-measures
ANOVA with factors of SOA, congruence and epoch revealed no
significant main effect of congruence, F(1, 68) 1.71,
p
2
.02,
p .20. Crucially, however, congruence interacted with SOA,
F(1, 68) 4.17,
p
2
.06, p .045, indicating that the influence
of congruence was significantly greater at 250-ms SOA than at
1,000-ms. The main effect of SOA was significant, F(1, 94)
1.85,
p
2
.73, p .001, with faster responses on trials with
1,000-ms SOA (presumably because the longer SOA allowed more
time for response preparation). The main effect of epoch was
significant, F(3, 204) 2.83,
p
2
.04, p .040, although Figure
5 reveals no continuing pattern in changes of RT across epochs.
Other interactions were nonsignificant, largest F(3, 204) 1.04,
p
2
s .012, ps .38.
Analysis of simple effects (collapsing across epochs) revealed
that the effect of congruence at 250-ms SOA was significant, F(1,
68) 4.81,
p
2
.07, p .032, corresponding to an advantage of
5 ms for congruent trials over incongruent trials. There was no
effect of congruence at 1,000-ms SOA, F(1, 68) 0.16,
p
2
.002, p .69.
Discussion
Experiment 2 replicated the cuing effect observed in Experiment
1 (faster responses on congruent than incongruent trials of the dot
probe task), but only for trials with 250-ms SOA. Trials with
1,000-ms SOA showed no difference in response times when the
dot probe appeared in the location of the stimulus that was pre-
dictive in the categorization task, relative to when it appeared in
the location of the nonpredictive stimulus.
In the Introduction to Experiment 2 we argued that, if the cuing
effect observed at short SOA reflected a conscious strategy of
shifting attention toward predictive stimuli, then providing more
time to process the predictive status of the stimuli should produce
stronger or at least similar effects. In contrast, the data reveal that
providing more time led to a significant weakening of the cuing
effect. The implication is that the cuing effect observed at short
SOA is not a consequence of a controlled, strategic process, but
rather reflects an automatic process. This issue is taken up in the
General Discussion.
Experiment 3
Attentional theories of associative learning (e.g., Kruschke,
2003; Le Pelley, 2004; Mackintosh, 1975; Pearce & Hall, 1980)
Figure 4. Mean percent correct categorization responses across the 32
blocks of Experiment 2. Error bars show standard error of the mean. The
level of performance expected by chance is 50% correct. Each four blocks
constituted one phase of the categorization task (i.e., Blocks 1– 4 consti-
tuted Phase 1, Blocks 5–8 constituted Phase 2, etc.).
Figure 5. Median log-transformed response time to the dot probe in
Experiment 2 for trials with a stimulus-onset asynchrony (SOA) of 250 ms
or 1,000 ms. Congruent probe appeared in location cued by predictive
stimulus from categorization task; Incongruent probe appeared in loca-
tion cued by nonpredictive stimulus from categorization task. Each of
Epochs 1–4 comprises two consecutive phases of the dot probe task (i.e.,
Phases 1 and 2 constitute Epoch 1, Phases 3 and 4 constitute Epoch 2, etc.).
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1894
LE PELLEY, VADILLO, AND LUQUE
suggest that changes in attention to stimuli develop as a conse-
quence of learning about the predictive status of those stimuli; that
is, an attentional bias should develop incrementally as a function of
how well participants have learned the various stimulus–response
associations. Experiments 1 and 2 did not provide clear support for
this prediction, in that the size of the attentional bias in the dot
probe task remained similar across the course of each experiment
(see Figures 3 and 5). However, this may be because in each case
participants had received considerable training on the categoriza-
tion task prior to the first phase of the dot probe task, such that the
predictive status of the stimuli was already quite well established
at this point. Figures 2 and 4 show that categorization accuracy in
the final block of the first phase of the categorization task had
reached a similar level to that maintained subsequently.
Experiment 3 used a procedure that combined the categorization
and dot probe task. This allowed us to assess more closely the
development of the dot probe bias across the course of training,
and thus provided a more thorough test of the suggestion that the
size of the attentional bias should increase as function of learning
about the predictive status of the stimuli.
Combining the categorization and dot probe phases brings a
further advantage. While the cuing effect at short SOA observed in
Experiments 1 and 2 was significant, it was numerically small.
This may be because any automatic attentional bias toward the
predictive cue that is learned during the categorization task extin-
guishes to some extent over the trials of the following dot probe
phase. If this was indeed the case, then combining the two tasks
such that every dot probe trial is also a categorization trial would
minimize the influence of extinction and, hence, should result in a
larger cuing effect at short SOA.
Experiment 3 also incorporated the SOA manipulation of Ex-
periment 2, although this was now varied between-subjects rather
than within subject. Given the findings of Experiment 2, we
expected to observe a cuing effect for participants with short SOA
in the dot probe task, but no effect for participants with long SOA.
Method
Participants, apparatus and stimuli. A total of 108 Univer-
sity of Málaga students (86 female) participated in exchange for
course credit. Testing conditions, apparatus and stimuli were as for
Experiment 2.
Design. In Experiment 3, the SOA of the dot probe task was
varied between subjects. For 53 participants, the SOA between
presentation of the stimuli and the dot probe was 250 ms; of these,
27 participants were in condition Green Predictive, and 26 were in
condition Lines Predictive. For the remaining 55 participants (27
in condition Green Predictive and 28 in condition Lines Predictive)
the SOA was 1,000 ms. Particular values on each stimulus dimen-
sion (shade of green and thickness of lines) were randomly as-
signed to the labels shown in Table 1 for each participant.
Procedure. Initial instructions for the categorization task were
as in Experiment 2. After reading these instructions, participants
completed a pretraining phase of 16 categorization trials, with each
of the stimulus pairs in Table 1 appearing four times in random
order; for each stimulus pair, the predictive stimulus appeared
twice on the left and twice on the right. Categorization feedback
was as for Experiment 2.
Following this pretraining phase, participants received further
instructions explaining that subsequent trials would be more com-
plicated: On each trial (a) a pair of stimuli would appear; (b) a
small white square (the dot probe) would then appear superim-
posed on one of these stimuli; (c) participants should press the left
arrow if the square appeared on the left stimulus, and the right
arrow if it appeared on the right; (d) once they had responded to
the square, they should make a response to the stimulus pair using
the up or down arrows as in the pretraining stage. Similar to
Experiment 2, participants were told that they should respond to
the position of the dot probe as rapidly as possible and that “In
order to do so, it is best that you ignore the pair of figures until you
have responded to the location of the square.” Finally, participants
were told that occasionally an arrow would appear in the center of
the screen and that when it did they should press the corresponding
arrow key as rapidly as possible. These “arrow trials” were in-
tended to further encourage participants to maintain central fixa-
tion at the start of each trial, since unexpected targets could
occasionally appear at this location.
Figure 6 shows a schematic of a standard trial. Each such trial
began with presentation of a central fixation cross. After 500 ms the
stimulus pair appeared to either side of this cross. After an SOA of
either 250 ms or 1,000 ms (depending on which between-subjects
condition the participant had been allocated to), the dot probe ap-
peared superimposed on one of the stimuli. This probe remained until
participants made the correct response (left arrow key for a target
Figure 6. Schematic example of the sequence of events on a standard trial
of Experiment 3. The gray square in the cue display represents one of the
green squares that could be used as cue stimuli, and the striped square
represents one of the sets of oblique lines. The duration of the stimulus
display (and, hence, the stimulus-onset asynchrony of the dot probe task)
was varied between subjects.
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1895
PREDICTIVENESS AND ATTENTIONAL CAPTURE
presented on the left; right arrow key for a target presented on the
right). Immediately on making the correct dot probe response, the
probe disappeared and the message “UP or DOWN?” appeared.
Participants then made a categorization response using the up or down
arrow keys; feedback was administered as during the pretraining
phase, and the next trial then began after an intertrial interval of 1 s.
On “arrow trials,” the fixation cross appeared for 500 ms and
was then replaced by a small arrow in the center of the screen,
which remained until participants made a response using one of the
arrow keys. No feedback was provided for these responses. The
next trial then began after an interval of 1 s.
Participants completed 15 blocks of trials. Each block contained
17 trials in random order— one arrow trial (with the direction of
the arrow determined randomly), and every combination of the
four trial types in Table 1 with the predictive stimulus appearing
on the left or the right, and with the dot probe appearing on the left
or the right (4 2 2 16 trials). Hence, as in previous
experiments, the dot probe was equally likely to appear in the
location of the predictive or the nonpredictive stimulus.
Results
Twelve participants failed to achieve the selection criterion of
60% correct when averaged over all the trials of the categorization
task (five in condition Green Predictive and two in condition Lines
Predictive with SOA 250 ms; three in condition Green Predic-
tive and two in condition Lines Predictive with SOA 1,000 ms).
These participants’ data were excluded from further analysis. As
for Experiments 1 and 2, data were collapsed across counterbal-
ancing conditions Green Predictive and Lines Predictive; see sup-
plemental materials for an analysis including condition (Green
Predictive vs. Lines Predictive) as a between-subjects factor. Data
were analyzed as five epochs, with each epoch representing the
averaged data from three training blocks.
Figure 7 shows accuracy of participants’ categorization re-
sponses. Accuracy rose steadily across the course of training and
was similar in participants who experienced an SOA of 250 ms or
1,000 ms in the dot probe task. A 2 (5) ANOVA with factors of
SOA and epoch revealed a significant main effect of epoch, F(4,
376) 50.9,
p
2
.35, p .001, but no main effect of SOA, F(1,
94) 1.58,
p
2
.02, p .21, or interaction, F(4, 376) 0.34,
p
2
.004, p .85. (Note that that this analysis did not include the
data from the pretraining phase because trials in this phase did not
incorporate the dot probe task, and we are interested in how
participants’ accuracy relates to their dot probe performance.)
Accuracy on the dot probe task was very high, with a mean of
99.3 0.10% (standard error of the mean) correct trials across all
participants. Dot probe RT data were processed as for Experiment
2. RTs below 150 ms or above 2,000 ms were excluded (0.06%
and 0.92% of all trials, respectively). RTs were then log-
transformed and any log RTs lying more than three standard
deviations from each participant’s mean were excluded as outliers
(0.63% of all trials). For each participant we calculated the median
log RT for congruent and incongruent trials for each block of
training (using data from correct trials only). These data were then
averaged across epochs of three blocks; see Figure 8.
At an SOA of 250 ms, a clear cuing effect occurred with faster
responses to the probe on congruent trials than incongruent trials,
and the size of this effect increased across epochs. In contrast, at
an SOA of 1,000 ms there was no cuing effect; RTs on congruent
and incongruent trials remained similar to one another across all
epochs but showed a general decrease as the experiment pro-
gressed. A 2 (2) (5) ANOVA with factors of SOA, congru-
ence and epoch revealed a significant main effect of congruence,
F(1, 94) 25.2,
p
2
.21, p .001. Crucially, the effect of
congruence interacted with SOA, F(1, 94) 20.5,
p
2
.18, p
.001, indicating that the influence of congruence was significantly
greater at 250 ms SOA than at 1,000 ms. Congruence also inter-
acted with epoch, F(4, 376) 3.67,
p
2
.04, p .006, with the
influence of congruence tending to increase across epochs. The
main effect of epoch was significant, F(4, 376) 5.08,
p
2
.05,
p .001, with RTs decreasing across epochs as participants
became more familiar with the task. The SOA Epoch interaction
was also significant, F(4, 376) 6.63,
p
2
.07, p .001, with
the decrease in RT across epochs being greater for participants
with 1,000 ms SOA than for those with 250 ms. The main effect
of SOA was significant, F(1, 94) 13.6,
p
2
.13, p .001, with
faster responses in participants with 1,000-ms SOA (presumably
because the longer SOA allowed more time for response prepara-
tion). The three-way interaction did not reach significance, F(4,
376) 1.68,
p
2
.02, p .15.
This omnibus analysis was followed up by separate, preplanned
two-way ANOVAs using the data from each group of participants
defined by SOA. For participants with 250-ms SOA, there was a
significant main effect of congruence, F(1, 45) 32.2,
p
2
.42,
p .001, with shorter RTs on congruent than incongruent trials.
The observed mean difference in log RTs between congruent and
incongruent trials corresponds to an RT difference of 46 ms. The
Congruence Epoch interaction was significant, F(4, 180)
5.20,
p
2
.10, p .001, with the size of the congruence effect
tending to increase across epochs, showing a significant linear
trend, F(1, 45) 14.8,
p
2
.25, p .001. Simple effects analysis
revealed that the effect of congruence was significant in every
epoch, smallest F(1, 45) 4.57, all
p
2
s .09, all ps .038. The
main effect of epoch was not significant, F(4, 180) 1.62,
p
2
.03, p .17. For participants with 1,000-ms SOA, there was no
main effect of congruence, F(1, 49) 0.19,
p
2
.004, p .67,
Figure 7. Mean percent correct categorization responses across Experi-
ment 3 for participants who experienced a stimulus-onset asynchrony
(SOA) of 250 ms or 1,000 ms in the dot probe task. Pre pretraining
phase; each of Epochs 1–5 comprises three blocks of 16 trials. Error bars
show standard error of the mean. The level of performance expected by
chance is 50% correct.
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1896
LE PELLEY, VADILLO, AND LUQUE
and no Congruence Epoch interaction, F(4, 196) 0.69,
p
2
.07, p .60. The main effect of epoch was significant for these
participants, F(4, 196) 10.6,
p
2
.37, p .001.
The analysis described above demonstrated that the cuing effect
at 250-ms SOA increased across epochs. A further analysis tested
the more specific question of whether this effect increased as a
function of accuracy on the categorization task. For each partici-
pant in the 250 ms SOA group, we calculated the Spearman’s rank
correlation between categorization accuracy and the size of the
cuing effect (incongruent RT minus congruent RT) across epochs.
One participant was excluded from this analysis as their categori-
zation accuracy was equal in all epochs, such that no correlation
could be calculated. The mean correlation across remaining par-
ticipants was (5) .20, with a standard error of .07. A one-
sample t test revealed that this mean correlation was significantly
greater than zero, t(44) 2.67,
p
2
.14, p .010, demonstrating
that the cuing effect was indeed correlated with categorization
performance.
Finally, performance on the occasional “arrow trials” was sim-
ilar in both SOA groups. Mean accuracy on arrow trials was
97.8 1.48% for the 250 ms SOA group, and 99.5 .26% for the
1,000-ms SOA group; this difference was not significant, t(95)
1.14,
p
2
.01, p .26. Mean RT on arrow trials was 667 16
ms for the 250-ms SOA group, and 683 14 ms for the 1,000-ms
SOA group; this difference was not significant, t(95) 0.77,
p
2
.006, p .44.
Discussion
Experiment 3 replicated the key findings of Experiment 2: a
significant cuing effect in the dot probe task at 250-ms SOA but no
cuing effect at 1,000-ms SOA. Moreover, the cuing effect at 250
ms was significantly greater than that at 1,000 ms SOA. And
combining the categorization and dot probe tasks produced a cuing
effect at short SOA that was numerically larger than that observed
in Experiments 1 and 2 (46 ms, compared to 14 ms in Experiments
1 and 5 ms in Experiment 2). This is consistent with the suggestion
that the effects in our earlier studies were smaller due to extinction
of previously learned attentional biases during the dot probe task.
Importantly, in Experiment 3 the cuing effect at short SOA
increased over the course of training, and more specifically in-
creased as a function of the accuracy of participants’ categoriza-
tion responses. This finding is consistent with the suggestion made
by attentional theories of associative learning (e.g., Kruschke,
2003; Le Pelley, 2004; Mackintosh, 1975) that learning about the
predictive status of stimuli drives changes in attention to those
stimuli.
General Discussion
Three experiments used a dot probe task to demonstrate that
learning about the predictiveness of stimuli influences attentional
orienting to those stimuli. In all experiments, when a short SOA
was used in the dot probe task (350 ms in Experiment 1; 250 ms
in Experiments 2 and 3), participants were faster to respond to the
dot probe when it appeared in the same location as a stimulus that
had previously been experienced as predictive in a categorization
task, than when it appeared in the location of a stimulus experi-
enced as nonpredictive. Experiment 3 demonstrated that this cuing
effect increased in magnitude as performance on the categorization
task improved.
The cuing effect occurred even though the short SOA meant that
participants had little time to shift attention to the location of the
predictive stimulus, and even though there was no advantage to be
gained in so doing. Indeed, in Experiments 2 and 3 participants
were explicitly informed that the best strategy was to ignore the
stimulus pair until after they had responded to the dot probe, and
unpredictable “arrow trials” were included in Experiment 3 to
encourage participants to try to maintain attention to the central
fixation point during the dot probe task. The fact that a cuing effect
was still observed under these conditions suggests that the source
of this effect was a relatively rapid, automatic process.
This suggestion is further supported by the finding of Exper-
iments 2 and 3 that increasing the SOA to 1,000 ms did not
increase or even maintain the cuing effect, as might be expected
if this effect reflected a conscious strategy of shifting attention
toward predictive stimuli (see De Houwer et al., 1998; Fazio et
al., 1986; Le Pelley, Calvini, & Spears, 2013; Posner & Snyder,
1975). Instead, increasing SOA led to a significant reduction in
the cuing effect (and in fact eliminated it entirely). It is true that
in both Experiments 2 and 3 there was a difference in baseline
response time, with faster responses at long SOA than short
SOA, raising the possibility that the failure to observe a cuing
effect at long SOA might simply be a consequence of a floor
effect in response times. However, this seems unlikely. First,
response times at 1,000-ms SOA decreased significantly across
epochs in Experiment 3. Hence, it is clear that response times
Figure 8. Median log-transformed response time to the dot probe in
Experiment 3 for participants who experienced a stimulus-onset asyn-
chrony (SOA) of 250 ms or 1,000 ms in this task. Congruent probe
appeared in location cued by predictive stimulus from categorization task;
Incongruent probe appeared in location cued by nonpredictive stimulus
from categorization task. Each of Epochs 1–5 comprises three blocks of 16
trials.
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1897
PREDICTIVENESS AND ATTENTIONAL CAPTURE
were not at floor in the earlier epochs, and yet there was no hint
of a congruency effect at 1,000-ms SOA in these epochs (while
there was a significant effect at 250-ms SOA). Second, mean
response time for the dot probe task at 1,000-ms SOA in
Experiment 3 was longer than at 250-ms SOA in Experiment 2
(6.24 log ms vs. 5.92 log ms, or 514 ms vs 374 ms; see Figures
8 and 5). And yet a congruence effect was observed for the short
response times at 250-ms SOA in Experiment 2 but not for the
longer response times at 1,000-ms SOA in Experiment 3.
Hence, it seems unlikely that the failure to observe a congru-
ence effect at 1,000-ms SOA in Experiment 3 reflects a lack of
sensitivity at this level of baseline response time.
Instead, we suggest that the cuing effect at short SOA reflects
the operation of an automatic attentional process. That is,
presentation of the stimuli leads to a rapid, automatic orienting
of attention to the predictive stimulus,
2
producing the cuing
effect at short SOA. One possibility that can account for the
current data is that, during the longer SOA, this initial auto-
matic attentional influence decays and, hence, attention returns
to the center of the display, such that no cuing effect is seen at
long SOA (cf. Fazio et al., 1986). However, our favored account
appeals instead to an interaction between automatic and con-
trolled attentional processes (cf. Klauer, Roßnagel, & Musch,
1997). Participants in the current task knew that the best strat-
egy was to attend centrally during the dot probe task. On this
account, the long SOA provides sufficient time for participants
to use controlled processes to correct for and overcome the
automatic attentional orienting caused by presentation of the
stimuli, returning attention to the center of the display. This
latter account has the advantage that it is also able to account
for the persistence of greater attention to predictive cues that
is typically observed during categorization training using eye
tracking (e.g., Le Pelley et al., 2011). During categorization
training in these studies there is no particular reason for par-
ticipants to try to maintain central fixation. Consequently—and
unlike in the dot probe task of the current study—there is no
drive for them to use controlled processes to overcome an initial
automatic tendency to attend to the predictive stimuli, and,
hence, this tendency will persist over a longer period.
Attentional theories of associative learning (e.g., Kruschke,
2003; Le Pelley, 2004; Mackintosh, 1975; Pearce & Hall, 1980)
share the central dogma that learning about the predictiveness
of stimuli influences attention to those stimuli. As noted in the
introduction, the majority of studies that have been conducted
to test this idea have used the rate of learning about stimuli as
a proxy measure of attention. This approach, however, leaves
such studies open to interpretation in nonattentional terms, for
example in terms of differences in the strength of mnemonic
representations. By using a more direct measure of attentional
orienting, the current studies provide a more direct test—and
confirmation— of this central dogma of attentional theories.
These data add to research that has used eye gaze as a measure
of the influence of learning about predictiveness on overt at-
tention (Beesley & Le Pelley, 2010; Kruschke et al., 2005; Le
Pelley et al., 2011; Wills et al., 2007). However, the current dot
probe procedure has certain important advantages over these
previous studies using eye gaze (see also Livesey, Harris, &
Harris, 2009). In particular, it meets the criteria laid down in the
Introduction. First, we have used the dot probe procedure to
demonstrate the incremental development of an attentional bias
in a task (dot probe) that is incidental to the task that produces
that bias (categorization); indeed, there is no need for partici-
pants to pay attention to the stimuli at all during the dot probe
task. Second, the dot probe procedure does not rely on move-
ments of eye fixation, which may or may not accompany shifts
of attention (Jonides, 1981; Yeshurun & Carrasco, 1999). Third,
it does not require expensive, intrusive or cumbersome equip-
ment and can be implemented on any standard computer (in-
cluding laptops or tablets that could be used outside the labo-
ratory) and used with multiple participants simultaneously.
Beyond these criteria, the dot probe procedure also allowed us
to test the automaticity of the attentional bias produced by
learned predictiveness by varying the SOA; studies of eye gaze
have not so far provided evidence relating to this issue.
Discriminating Between Attentional Theories of
Associative Learning
Up to this point, “attentional theories” of learning have been
treated as a single generic class of model, but, in fact, different
attentional theories take a rather different view of the relation-
ship between predictiveness and attention. The current experi-
ments were introduced from the perspective of the class of
attentional theory exemplified by Mackintosh’s (1975) model
(see also Kruschke, 2003), which proposes that more attention
is devoted to cues that are more accurate predictors of the
current outcome (here category membership) than those that are
less predictive. Clearly models taking this approach are well-
equipped to account for the congruence effect observed in the
current experiments.
However, an alternative attentional account was proposed by
Pearce and Hall (1980), who suggested that more attention will be
devoted to stimuli that are followed by surprising outcomes, than
to stimuli that are followed by well-predicted and hence unsur-
prising outcomes. This account is less successful when applied to
the current data. In its original formulation, this model states that
what is crucial for determining attention is not how surprising the
outcome is given the presence of a particular, individual stimulus,
but rather how surprising the outcome is given the combination
(compound) of all currently presented stimuli. In the categorization
design used here (Table 1), all compounds are equally predictive of
category membership (e.g., the compound “Gre1 & Lin2” belongs
to the same category on all training trials), and hence the outcome
occurring on each trial is equally surprising. Consequently, Pearce
and Hall’s model predicts that all stimuli will maintain equal
attention throughout the experiment, which is at odds with the dot
probe data of the current experiments.
2
Or, potentially, an automatic shift of attention away from the nonpre-
dictive stimulus. The current experiments do not allow us to decide
between an account in which participants learn to shift attention toward
predictive stimuli, or away from nonpredictive stimuli (or both). That said,
one might expect that the cuing effect produced by a shift away from the
nonpredictive stimulus would be very weak, since attention could poten-
tially shift away from this stimulus in any direction. In other words, a shift
away from the nonpredictive stimulus will not necessarily be a shift toward
the location of the predictive stimulus (where the dot probe target will
appear).
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1898
LE PELLEY, VADILLO, AND LUQUE
Certain studies of the influence of predictiveness on the rate
of novel learning about a stimulus, conducted in nonhuman
animals, are consistent with the approach suggested by Pearce
and Hall (1980), and inconsistent with Mackintosh’s (1975)
model (see Le Pelley, 2004, for a review). However, studies
with humans that are able to decide between these models have
generally produced results consistent with Mackintosh’s ac-
count (e.g., Bonardi et al., 2005; Kruschke, 1996; Le Pelley et
al., 2011; Le Pelley & McLaren, 2003; Le Pelley, Turnbull, et
al., 2010; see Le Pelley, 2010, for a review), and the current
data add to this collection.
Conclusion
When considering the factors that influence attention, in addi-
tion to thinking about intrinsic properties of the stimulus (e.g., its
size, color, onset, or emotional valence), and the external factors of
rewarding attention to a particular stimulus, it would seem that we
also need to consider stimulus and reward properties in combina-
tion. That is, learning of an association between a particular
stimulus and a particular reward (or outcome, or category mem-
bership) will also influence the attention that is devoted to that
stimulus in future. The current studies demonstrate that learning
about the predictiveness of stimuli produces a relatively automatic
attentional bias with respect to those stimuli; a bias that is consis-
tent with the pattern anticipated by some of the most influential
attentional theories of associative learning.
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Received December 26, 2012
Revision received April 9, 2013
Accepted May 19, 2013
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LE PELLEY, VADILLO, AND LUQUE
... The Mackintosh (1975a) theory will predict that more attention will be paid towards a cue that has a history of being a good predictor (Food A, B, C and D) and less attention would be paid towards one that was a bad predictor (Food W, V, X and Y). Therefore, if participants showed a Learning about the predictiveness of cues can also influence stimulus processing as demonstrated by studies assessing attention based on eye movement (Beesley, Nguyen, Pearson, Le Pelley, 2015;Le Pelley, Beesley and Griffiths, 2011) and spatial cueing Le Pelley, Vadillo and Luque, 2013). Le Pelley et al. (2011) presented participants with a learned predictiveness task (Le Pelley and McLaren (2003) and tracked their eye movements whilst they performed the task. ...
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Associative learning phenomena have been widely used to understand the deficits in selective attention in schizophrenia by using the personality trait, schizotypy, as a proxy. However, other personality traits such as anxiety and the Big 5 personality traits have been under-looked despite a comorbidity between schizophrenia/schizotypy and anxiety as well as the psychopathology links of the Big 5 traits. Moreover, there is evidence of different thinking styles exhibited by different cultures (e.g., individualistic and collectivistic cultures), where the majority of members in an individualistic culture learn and think in an analytical/elemental manner while the majority of members in a collectivistic culture have a predisposition to think and learn holistically/configurally. It is therefore proposed that other personality traits and cultural differences in thinking/learning can explain conflicting evidence found in the schizotypy and associative learning literature. Previous studies of variations in attention-driven associative learning have demonstrated an emphasis on latent inhibition and less so on blocking and learned predictiveness. Furthermore, there are very few studies that have attempted to reproduce two learning effects within the same individual. Therefore, Study 1 aimed to create a paradigm which can generate the effects of blocking and learned predictiveness within the same participant to first, fill the gap in the literature, and second, to develop a better, converging, understanding of the role of attention in learning. The results of this study found an effect of learned predictiveness but no effect of blocking. It was proposed that the effect of learned predictiveness somehow masked the effect of blocking, so Study 2 aimed to replicate the previous study but with only the blocking trials. The results still showed no blocking effect despite the removal of the learned predictiveness trials. 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The psychological mechanisms underlying infants' selective social learning are currently a subject of controversy. The main goal of the present study was to contribute data to this debate by investigating whether domain‐specific or domain‐general abilities guide infants' selectivity. Eighteen‐month‐olds observed a reliable and an unreliable speaker, and then completed a forced‐choice word learning paradigm, two theory of mind tasks, and an associative learning task. Results revealed that infants showed sensitivity to the verbal competence of the speaker. Additionally, infants with superior knowledge inference abilities were less likely to learn from the unreliable speaker. No link was observed between selective social learning and associative learning skills. These results replicate and extend previous findings demonstrating that socio‐cognitive abilities are linked to infants' selective social learning.
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... Controlled attention will be engaged in order to reduce uncertainty (Pearce & Hall, 1980;, but the ability to do this may be weakened as task difficulty increases. In contrast, attention paid to predictive cues may reflect a more automatic form of attention (Le Pelley, Vadillo, & Luque, 2013;Luque, Vadillo, Le Pelley, & Beesley, 2017; but see Mitchell, Griffiths, Seetoo, & Lovibond, 2012, for a potential role of controlled processing in the predictiveness effect). When task difficulty is low the effect of controlled attention paid to uncertain cues may be greater than the automatic attention paid to predictive cues, but as task difficulty increases the cognitive resources required to engage controlled attention are decreased such that automatic attention wins out over controlled attention. ...
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Attention determines which cues receive processing and are learned about. Learning, however, leads to attentional biases. In the study of animal learning, in some circumstances, cues that have been previously predictive of their consequences are subsequently learned about more than are nonpredictive cues, suggesting that they receive more attention. In other circumstances, cues that have previously led to uncertain consequences are learned about more than are predictive cues. In human learning, there is a clear role for predictiveness, but a role for uncertainty has been less clear. Here, in a human learning task, we show that cues that led to uncertain outcomes were subsequently learned about more than were cues that were previously predictive of their outcomes. This effect occurred when there were few uncertain cues. When the number of uncertain cues was increased, attention switched to predictive cues. This pattern of results was found for cues (1) that were uncertain because they led to 2 different outcomes equally often in a nonpredictable manner and (2) that were used in a nonlinear discrimination and were not predictive individually but were predictive in combination with other cues. This suggests that both the opposing predictiveness and uncertainty effects were determined by the relationship between individual cues and outcomes rather than the predictive strength of combined cues. These results demonstrate that learning affects attention; however, the precise nature of the effect on attention depends on the level of task complexity, which reflects a potential switch between exploration and exploitation of cues. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
... Over the last 20 years or so there has been growing interest in the degree and manner in which the stimulus's reward value influences attention (e.g., Anderson & Yantis, 2013;Chelazzi et al., 2013;Lee & Shomstein, 2014;Shahan & Podlesnik, 2006;Small et al., 2005). In a particularly striking example, reward value undermined the much studied "attentional blink phenomenon" (Raymond & O'Brien, 2009), and several studies show that stimuli that predict rewards can come to control attention in much the same manner as do highly salient physical features, such as abrupt onset times or striking color contrasts (e.g., Le Pelley, Vadillo, & Luque, 2013). In a review of this literature, Chelazzi and his colleagues (2013) concluded with the sentence, "it may seem paradoxical that learning principles developed to explain overt behavior, within a theoretical framework that was skeptical about the hidden and impalpable intricacies of cognition . . . ...
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Many factors affect figure–ground segregation, but the contributions of attention and reward history to this process is uncertain. We conducted two experiments to investigate whether reward learning influences figure assignment and whether this relationship was mediated by attention. Participants learned to associate certain shapes with a reward contingency: During a learning phase, they chose between two shapes on each trial, with subsets of shapes associated with high-probability win, low-probability win, high-probability loss, and low-probability loss. In a test phase, participants were given a figure–ground task, in which they indicated which of two regions that shared a contour they perceived as the figure (high-probability win and low-probability win shapes were pitted against each other, as were high-probability loss and low-probability loss shapes). The results revealed that participants had learned the reward contingencies and that, following learning, attention was reliably drawn to the optimal stimulus. Despite this, neither reward history nor the resulting attentional allocation influenced figure–ground organization.
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We hypothesized that attitudes characterized by a strong association between the attitude object and an evaluation of that object are capable of being activated from memory automatically upon mere presentation of the attitude object. We used a priming procedure to examine the extent to which the mere presentation of an attitude object would facilitate the latency with which subjects could indicate whether a subsequently presented target adjective had a positive or a negative connotation. Across three experiments, facilitation was observed on trials involving evaluatively congruent primes (attitude objects) and targets, provided that the attitude object possessed a strong evaluative association. In Experiments 1 and 2, preexperimentally strong and weak associations were identified via a measurement procedure. In Experiment 3, the strength of the object-evaluation association was manipulated. The results indicated that attitudes can be automatically activated and that the strength of the objectevaluation association determines the likelihood of such automatic activation. The implications of these findings for a variety of issues regarding attitudes—including their functional value, stability, effects on later behavior, and measurement—are discussed.