ORIGINAL ARTICLE

Predicting and manipulating the incidence of inattentionalblindness

Anne Richards • Emily M. Hannon •

Nazanin Derakshan

Received: 26 February 2009 / Accepted: 17 December 2009 / Published online: 9 January 2010

� Springer-Verlag 2010

Abstract Inattentional blindness (IB) occurs when an

observer, who is engaged in a resource-consuming task,

fails to notice an unexpected although salient stimulus

appearing in their visual field. The incidence of IB is

affected by changes in stimulus-driven properties, but little

research has examined individual differences in IB pro-

pensity. We examine working memory capacity (WMC),

processing styles (flicker task), inhibition (Stroop task), and

training in predicting IB. WMC is associated with IB

(Experiments 1 and 2) but neither processing style

(Experiment 1) nor inhibition (Experiment 2) was associ-

ated. In Experiment 2, prior training on a task reduced the

incidence of IB compared to no prior training, and this

effect was significantly larger when trained on the same

tracking task as that used in the IB task rather than a

different task. We conclude that IB is related to WMC and

that training can influence the incidence of IB.

Introduction

Inattentional blindness (IB) occurs when an observer who

is engaged in a resource-consuming task fails to notice an

unexpected stimulus appearing in front of their eyes (Mack

& Rock, 1998; Most, Simons, Scholl, & Chabris, 2000;

Most et al., 2001; Neisser & Becklen, 1975; Simons &

Chabris, 1999). IB occurs in everyday life and can be of

minimal importance (e.g. failing to notice your friend at the

cinema as you search for a vacant seat) or can have cata-

strophic consequences (failing to notice a child crossing the

road). In a series of experiments, Mack and Rock (1998)

presented a primary task in which participants were

required to identify which of two lines of a cross, vertical

or horizontal, was longer. The cross was presented for

200 ms and was followed by a pattern mask. On the third

or fourth trial, an unexpected stimulus was simultaneously

presented in one of the four quadrants of the cross for the

same duration (200 ms). Mack and Rock found that many

people were unaware of the unexpected stimulus on the

critical trial, and depending on the type of visual display,

the incidence of IB ranged from 25 to 85%.

Some early research by Neisser and Becklen (1975)

used a dynamic IB task in which one video was superim-

posed upon another. Participants were required to monitor

one video (a hand slapping game) or the other (a basketball

game). An unexpected event occurred (i.e. the ball was

thrown out of play in the basketball game or the two

players shook hands in the hand slapping game), and most

people remained inattentionally blind to these events.

Later research by Neisser (1979) superimposed videos

of two basketball teams where one team wore white shirts

and the other black shirts. Participants were required to

monitor the ball passes of just one team, either the black

or white shirts. An unexpected, semi-transparent video

with a woman carrying an umbrella appeared after 30 s and

walked across the screen. Again, most participants

remained unaware of this unexpected stimulus (22 out of

28 were IB). Becklen and Cervone (1983) argued that this

failure to notice the unexpected stimulus was unlikely to be

due to memory failure, as when they eliminated the time

delay between the unexpected stimulus disappearing from

the screen and the participant being questioned about their

awareness of any unexpected stimulus, the incidence of IB

remained the same. Simons and Chabris (1999) presented

a more standard video display of two teams playing

A. Richards (&) � E. M. Hannon � N. Derakshan

Psychological Sciences, School of Science, Birkbeck College,

University of London, Malet Street, London WC1E 7HX, UK

e-mail: a.richards@bbk.ac.uk

123

Psychological Research (2010) 74:513–523

DOI 10.1007/s00426-009-0273-8

basketball rather than superimposing two videos together.

Participants were required to count the number of ball

bounces during the video. The unexpected stimulus was a

black gorilla that walked through the scene, stopping for a

few seconds in the centre of the display to thump its chest

before carrying on walking to the other side of the scene.

The basketball game continues for a few seconds after the

gorilla has disappeared. Simons and Chabris found that

only 44% of participants reported seeing the gorilla when

questioned at the end.

Most et al. (2000) later developed a video to measure IB

in a more experimentally controlled manner in which

participants were required to track four white letters and

ignore four black letters. An unexpected gray cross

appeared and traversed the screen during the video, and

participants who were unable to report having seen the red

cross were deemed to be inattentionally blind. Most et al.

(2000) found that levels of IB were highest when the

unexpected stimulus moved either the top or bottom third

of the screen compared to when moved across the centre of

the screen.

Other work has examined the effects of attentional set

on implicit attentional capture (Folk, Remington, &

Johnston, 1992) and attentional set on IB (Most et al.,

2001). Recently, perceptual load on the incidence of IB has

been investigated (e.g. Cartwright-Finch & Lavie, 2007;

Lavie, 2006). However, a question as yet unanswered is

when people are presented with exactly the same physical

display, why some individuals are inattentionally blind

whereas others are not? Very little research has examined

stable differences in individuals that make them susceptible

to IB, and the current paper attempts to examine some of

the possible candidates in this area.

One such candidate is that there may be individual

differences in processing styles that might impact on IB.

Visual attention can be directed at different levels of a

visual scene, with focus on the more holistic, global level

or on the more analytic, local level (Navon, 1977). Navon

(1977) observed a ‘global precedence’ when analysing a

visual scene, with global changes in a display being

detected more accurately and quickly than local changes.

However, other researchers propose that a participant’s

attentional set is a more reliable predictor of performance

on a global/local detection task, and have investigated the

interaction between stimulus-driven (exogenous) properties

with top-down (endogenous) mechanisms. Austen and

Enns (2000) used Navon’s (1977) stimulus items in which

small letters (local component) were configured in the

shape of a large letter (global component). Participants

were presented with alternating displays that were either

identical, or there was a change (global or local) in one of

the letters. Austen and Enns (2000) found that they were

able to bias participants’ attention to either the local or

global component by manipulating the probability of target

level change, which resulted in a corresponding enhance-

ment of performance when there was a change that was

congruent with the induced bias. One possibility is that IBs

(i.e. those who do not notice the unexpected stimulus) are

biased towards a more highly focused but narrowly defined

analysis of visual stimuli than not inattentionally blind

(NIB), rendering the IBs as being less likely to notice the

unexpected stimulus. IBs would have a more focused

processing style than NIBs, with a focus on local compo-

nents making them more likely to be IB.

The relationship between processing resources and the

incidence of IB will also be investigated. Working memory

tasks are assumed to reflect executive functioning of

working memory (Baddeley & Hitch, 1974; Cowan, 2001).

One possibility is that if there are insufficient processing

resources available then the unexpected may not be pro-

cessed to a high level (i.e. will be filtered as a result of

early selection) and IB will result. If IB is associated with

reduced processing resources then it is predicted that those

individuals deemed to be IB will have lower working

memory scores than those deemed to be NIBs. Experiment

1 tests these hypotheses using a global–local flicker task to

measure differences in processing style, an operation span

task (OSPAN) to measure working memory, and an IB

task. If IB individuals are characterised by a bias for a local

level of analysis, they should be more sensitive to local

changes in a visual display compared to NIBs, with NIBs

being more sensitive to global changes. If the limited

resources hypothesis is supported, we would expect lower

working memory capacity (WMC) for IBs compared to

NIBs.

Experiment 1

Method

Participants

A total of 77 participants were tested but 9 were excluded

for misunderstanding the OSPAN task or for reporting that

they were aware of IB research when questioned at the end

of the session. Data from 68 participants were analysed (41

females): mean age 27.43, SD 9.45, range 18–56 years.

Design and procedure

OSPAN of Turner and Engle (1989) In this task, partic-

ipants are required to solve simple mathematical equations

while memorising unrelated words, with word lists varying

between 2 and 5 words per set, e.g. ‘does (2*1) - 1 = 1 ?

pipe.

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Three sets of each list length are presented in apparent

random order but fixed across participants. List length is

unknown to participants until the cue ‘???’ appears, when

they must then write down the words they can remember

from that set, in the exact order they appeared. Scoring on

this task consists of summing the recalled words for only

those sets recalled completely and in the correct order, with

scores having a possible range of between 0 and 42.

Local/global flicker task This task was based on that of

Austen and Enns (2000). Two global letters (a large E and a

large S) were created using a series of small Ss or small Es.

This produced four stimuli: a large E comprising small Es

and a large S comprising small Ss (global and local com-

ponents are congruent), and a large E comprising small Ss

and a large S comprising small Es (local and global com-

ponents are incongruent). In this task, participants were

presented with two alternating displays in which the two

displays were either identical (no change) or one of the

items on the display had either a global change (e.g. small

Es would be presented in the shape of a large S on one

display and a large E on the alternating display) or a local

change (e.g. a large E would be configured by small Es on

one display and by small Ss on the alternating display). 50%

of the local letters were consistent with the global letter and

50% inconsistent. Each stimulus (48 9 78 pixels) was

shown in displays of alternating frames of one, three or five

items. Each frame (display and blank) appeared for 225 ms

(see Fig. 1). This flickering display continued until the

participant made a response (i.e. change/no-change deci-

sion). A change occurred on 50% of trials, and half of these

were local and the other half global. Items appeared ran-

domly in one of nine squares of an imaginary 3 9 3 matrix.

When there was only a single item in the flickering display,

spatial attention will be focused on that one item. However,

with displays of multiple items (3 or 5 flickering items),

spatial attention has to be distributed widely across the

screen in order to perceive a change in one of the items

(Austen & Enns, 2000). There were 48 practice trials and 3

experimental blocks of 80 trials.

IB task A computerised, sustained IB trial identical to

that reported in earlier work (Most et al., 2001; video clip

courtesy of Simons, 2003) was used. The task comprised

four black and four white letters (Ls and Ts) moving

haphazardly around the screen, frequently ‘hitting’ the

borders of the display. Participants tracked the four white

letters (2 Ls and 2 Ts) but ignored the black letters, and

reported the number of ‘hits’ at the end of the 17-s video.

After 5 s, a red cross took 7 s to move across the centre of

the screen from right to left.

At the end of the task, participants were asked if they

had seen anything else in addition to the Ls and Ts. Those

who said ‘yes, a red cross’ were deemed to be NIBS and

those who said ‘no’ were deemed to be IB. If a participant

reported seeing ‘something’ they were asked to specify

what they had seen, and all such participants described the

red cross. Although many participants said they saw black

Ls and Ts, none reported seeing anything else that they

could not identify.

Participants completed the OSPAN and local/global

flicker task in randomised order with the IB task presented

Fig. 1 Schematic display of a

three-item global change trial

(bottom right item changes from

global E to global S. Local

component (E) remains

constant)

Psychological Research (2010) 74:513–523 515

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last to prevent participants adopting a divided attentional

set for later tasks.

Results

Of the 68 participants, 36 (53%) failed to notice the

moving red cross and were therefore classified as IB. The

remaining participants reported seeing the red cross, and

were classified as NIB. There were no differences between

IBs and NIBs in their ability to perform the primary

component of the IB task (means of 11.61, SD = 1.104 and

11.61, SD = 1.25 respectively).

Local/global flicker task

Following Austen and Enns (2000), data were analysed

separately for the focal attention task (set size 1) and for

distributed attention task (set sizes 3 and 5). An examina-

tion of response latencies on the focal attentional task

revealed no differences between IB and NIB individuals

for global or local trials. A comparison of change trials

(collapsed over local and global) and no-change trials

showed faster responses for change than no-change

(1,430 ms, SE = 35 and 1,715 ms, SE = 71, respectively;

F(1, 66) = 32, p \ 0.001, g2p ¼ 0:33). Change detection

during distributed attention was examined. An ANOVA

with display type (local, global) and set size (3, 5) as

within-subjects factors and blindness status (IB, NIB) as a

between-subjects factor revealed faster responses for global

than local changes (2,932 ms, SE = 104, and 3,165 ms,

SE = 116, respectively; F(1, 66) = 5.95, p = 0.02,

g2p ¼ 0:08), and for set size 3 than for 5 (mean of 2,620 ms,

SE = 76 and 3,478 ms, SE = 136, respectively;

F(1, 66) = 84.13, p = 0.02, g2p ¼ 0:56.

A comparison of change trials (collapsed over local and

global) with no-change trials revealed that responses were

faster on change than no-change trials (mean of 3,049 and

4,655 ms, respectively; F(1, 66) = 252, p \ 0.001,

g2p ¼ 0:79), responses to set size 3 were faster than set

size 5 (mean of 3,238 and 4,465 ms, respectively;

F(1, 66) = 227, p \ 0.001, g2p ¼ 0:78). In addition, there

was an interaction between set size and change (F(1, 66) =

65.57, p \ 0.001, g2p ¼ 0:50), showing that the difference

in RTs for change and no-change trials was greater for set

size 5 (mean of 3,478 and 5,453 ms for change and no-

change, respectively) than for set size 3 (mean of 2,620 and

3,857 ms, respectively).

A non-parametric Signal Detection Analysis (Snodgrass

& Corwin, 1988) was applied to the accuracy data for set

sizes 3 and 5 combined (see Table 1). Sensitivity was greater

for global than local displays (mean of 4.12, SE = 0.19,

and 3.75, SE = 0.19, respectively; F(1, 66) = 13.60,

p \ 0.001, g2p ¼ 0:17), but there were no differences

involving IB status (F’s \ 1). There were no significant

effects from the analysis of the response bias scores.

These analyses show that performance is better for all

participants when there is a change in the visual display

compared to when there is no change. When there was a

change in the visual display when attention was distributed,

global changes were detected more rapidly than local

changes. Performance was also better on displays with

fewer items. However, there were no main or interaction

effects involving IB in any of the analyses.

IB and WMC

The IBs had lower OSPAN scores than the NIB individuals

(mean of 15.11, SD = 6.62 and 19.56, SD = 7.69,

respectively; t(66) = 2.57, p = 0.013), indicating lower

WMC is associated with IB. One interpretation of these

data is that individuals with lower WMC did not have

sufficient resources to process the unexpected stimulus.

IB, WMC and sensitivity to local and global visual changes

In order to examine the influence of WMC and sensitivity

to global and local changes in predicting the probability of

IB, a simultaneous entry logistic regression was performed.

IB was the outcome variable and age, sex and latency

differences (global minus local latencies) were predictors

(see Table 2). This analysis revealed that only OSPAN was

a significant predictor.

Discussion

Inattentional blindness was observed in 53% of the sample,

and this is consistent with our own research and that of

Table 1 Mean response latencies (ms), sensitivity, response bias for

the global–local flicker task

NIBs IBs

Set size 3 Set size 5 Set size 3 Set size 5

Latency

Global 2,427 (587) 3,368 (919) 2,563 (711) 3,371 (1,453)

Local 2,655 (691) 3,440 (896) 2,834 (889) 3,732 (1,565)

Sensitivity

Global 4.07 (1.48) 4.18 (1.65)

Local 3.57 (1.55) 3.94 (1.62)

Response bias

Global 0.99 (0.01) 0.98 (0.85)

Local 0.99 (0.03) 0.97 (0.15)

Not inattentionally blind (NIB) and inattentionally blind (IB) indi-

viduals (SDs in parentheses)

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others (e.g. Hannon & Richards, 2009; Most et al., 2000).

There were no differences between local and global

detection when attention was focused but when attention

was divided all participants showed increased sensitivity

for global than local visual changes on the flicker task (in

line with Austen & Enns, 2000; Navon, 1977). There were

no differences between the IBs and NIBs, showing no

support for the notion that IB individuals are characterised

by an increase in analytical processing compared to NIB

individuals. However, the IBs were shown to have lower

WMC than the NIBs. The logistic regression gives weight

to this conclusion, showing that only OSPAN significantly

predicted the probability of IB, with the latency difference

on local and global displays playing no role. Although the

IBs had significantly lower WMC than the NIBs, we cannot

draw any conclusions regarding causality. Being inatten-

tionally blind might cause a reduction in WMC, low WMC

may cause IB, or there may be a third variable (e.g.

motivational component) that may be causing both lower

WMC as measured by the OSPAN task and IB. There is no

evidence for a difference between IBs and NIBs in their

motivation from the current study, as both IBs and NIBs

were equally good at performing the primary task in the IB

phase.

However, this finding does not rule out a motivational

account, as the task might be sufficiently easy so that all

participants were able to perform the task adequately. This

finding supports that of Simons and Jensen (2009). Simons

and Jensen used Most et al.’s (2001) IB task but equated

the difficulty level of the primary task for each individual

participant. IB tasks with differing demands were then

presented. They found that manipulating the demands of

the IB task influenced the incidence of IB (Experiment 1),

so that more difficult IB tasks were associated with an

increase in the incidence of IB. However, in Experiment 2,

they found that individual differences in the ability to

perform the task were not related to their ability to notice

the unexpected stimulus. A motivational account would

predict that IBs should have poorer overall performance

than the NIBs on the flicker task. There were, however, no

differences related to IB on the global/local flicker task.

Response latencies were numerically larger for the IBs

compared to the NIBs but none of these differences

approached significance. Although these differences were

non-significant, a logistic regression was performed with

IB as the outcome variable and speed (mean overall RT on

flicker task) and accuracy (overall accuracy on flicker task)

and OSPAN as the independent variables was performed.

This revealed that only OSPAN was a significant predictor

of IB, with both speed and accuracy making no contribu-

tion to the equation (p [ 0.5).

WMC is very important in IB, but there may be addi-

tional influential variables. One such proposal is that IB

individuals fail to notice the unexpected stimulus because

they have successfully inhibited it, and this proposal will

be examined in Experiment 2.

We also ask, ‘To what extent does experience with a

primary task influence rates of IB?’. This question has

potential implications for employment and training in

many areas of life, such as that given to drivers, pilots, air

traffic controllers, medical professionals, etc. Specifically,

we predict that training on a primary task will make the

task more automatic and thereby free up processing

resources. If processing resources become available, then

the incidence of IB should be reduced.

Experiment 2

The ability to inhibit an irrelevant stimulus is an extremely

useful process in most circumstances, as it ensures that the

individual maintains attentional focus on the goal of the

task thereby avoiding the disruptive influence of irrelevant

information. However, such a process brings with it

potential costs, as in the case of the appearance of an

ostensibly irrelevant stimulus that is highly significant. High

WMC has been shown to be related to the ability to inhibit

or block distracting information (Conway & Engle, 1994;

Table 2 Simultaneous entry logistic regression for Experiment 1 (IB status = outcome variable, OSPAN, age, sex and global–local

index = predictors)

95% CI for Exp b

B(SE) Lower Exp b Upper

Constant 1.43 (1.13)

OSPAN* -0.09 (0.04) 0.85 0.92 0.99

Age 0.01 (0.03) 0.96 1.01 1.06

Sex -0.23 (0.53) 0.28 0.80 2.24

Global–local response latency difference 0.00 (0.00) 1.00 1.00 1.00

R2 = 0.10 (Cox and Snell), R2 = 0.13 (Nagelkereke). Model = v2(4) 7.19, p = 0.13

* p \ 0.05

Psychological Research (2010) 74:513–523 517

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Conway, Tuholski, Shisler, & Engle, 1999; Daneman &

Carpenter, 1980). WMC is viewed by many researchers

as involving controlled attention (e.g. Turner & Engle,

1989). Bleckley, Durso, Crutchfield, Engle, and Khanna

(2003) propose that there are differences in attentional

control between high and low WMC individuals, with the

former having a more flexible attentional allocation whereas

the latter have a spotlight of attention, which is a continuous

but less flexible mode of attentional allocation. They argue

that high WMC individuals are more able to inhibit and

control attention. Kane, Bleckley, Conway, and Engle

(2001) found no difference between high and low

WMC individuals in a prosaccade task, but compared to

low WMC individuals, high WMC individuals had

superior performance on the antisaccade task in which the

saccade towards the cue had to be suppressed in favour of a

saccade in the opposite direction. In addition, Conway,

Kincade, and Shulman (2001) found that high WMC par-

ticipants heard their own name less frequently than low

WMCs in a dichotic listening task, suggesting that they

inhibited this information. If IB is as a result of inhibitory

processes then it might be predicted that individuals with

high levels of inhibition will be more likely to be IB. It might

also be expected from this perspective that IB would be

associated with higher levels of WMC, although it is not

what we found in Experiment 1.

In Experiment 2, we used a task that measures inhibitory

ability on a separate task, i.e. a Stroop task. The Stroop task

comprised four colour–word conditions. In the control

condition, strings of Xs were printed in coloured inks. In the

congruent condition, the word and colour of the ink mat-

ched. In the ignored repeated condition, ink colour and

word colour name conflicted, but in addition, the colour of

the ink on trial n corresponded to the word on trial n - 1. In

the incongruent condition, each word again was the name of

a colour, which conflicted with the coloured ink, but there

was no such relationship between successive trials. As

reading is largely an automatic process, it takes consider-

able attentional effort to ignore the words and identify the

colour of the ink. Facilitation is measured by comparing the

congruent with the control trials. Interference is measured

by comparing responses on the incongruent trials with the

control condition. However, inhibition is measured by

comparing responses on the incongruent trials with those on

the ignored repetition trials. The latter trials are the same as

the incongruent trials (in that the colour and the word are

incongruent) but there is an added inhibitory component, as

participants on trial N have to respond with the colour that

they have just inhibited on the previous trial (N - 1). The

greater the inhibition of the colour word on trial N - 1, the

longer the RT on trial N when this colour now has to be

activated and responded to. The Stroop task has been used

to examine inhibitory processes in schizophrenia (Beech,

Powell, McWilliams, & Claridge, 1989; Boucart et al.

1999), people with Parkinson’s disease (Brown & Mardsen,

1988), the elderly (Hasher & Zacks, 1988), and people with

multiple sclerosis (Vitkovitch, Bishop, Dancey, &

Richards, 2002). If individuals are IB because they have a

tendency to inhibit goal-irrelevant stimuli then we might

expect IB individuals to display stronger inhibition on the

Stroop task.

In the current experiment, we directly compare an

inhibition hypothesis with a reduced capacity hypothesis,

and, in addition, we look at the effects of training on IB. As

participants become more practiced there should be a

corresponding increase in available attentional resources.

We therefore predict that training will decrease the inci-

dence of IB.

Whether practice on video games can improve atten-

tional perceptual tasks is a matter for debate. Green and

Bavelier (2007), e.g., found improvements on such tasks,

whereas Boot, Kramer, Simons, Fabiani, and Gratton

(2008) found no effects after 20 h of practice in non-

gamers. Neisser (1979) describes a study where individuals

were presented with a video scene in which a woman with

an umbrella walks through a basketball game. Prior to this,

participants had completed an easier task, a more difficult

task, or no task. Neisser concludes that people fail to see an

unexpected object in situations where they believe the task

to be difficult. Although essential details are missing from

the account of this study, it does suggest that practice may

have a beneficial effect on reducing incidences of IB. The

current study examines the incidence of IB after (a) no

training, (b) after training on the same task as the primary

task in the final IB task (i.e. counting white Ls and Ts) and

(c) after training on a different task (i.e. counting diamonds

and triangles). We predict that training will reduce the

incidence of IB compared to the no training control con-

dition, as processing resources should be made available by

virtue of the primary task becoming more automatised. By

having a same and different training condition, we will be

able to examine whether general training on the task will

transfer to a different but similar task. We will also

examine whether training, inhibition or WMC predicts the

incidence of IB.

Method

Participants

A total of 89 participants were tested, but 5 were omitted

due to failing to complete all the tasks or for reporting that

they were aware of IB research when questioned at the

end of the session. A final sample of 82 participants

(61 females) with a mean age of 32.48 (SD 7.89, range

21–56 years) is reported.

518 Psychological Research (2010) 74:513–523

123

Design and procedure

Participants performed the automatic operation span

(AOSPAN) task of Unsworth, Heitz, Schrock, and Engle

(2005) and a Stroop task (Stroop, 1935). These two tasks

were counterbalanced across participants. Participants were

then randomly allocated to one of three conditions (control,

same training or different training). Participants in the two

training condition completed a tracking task that was either

the same as the primary task on the IB task or different (but

similar) to the IB tracking task. These two groups then

completed the IB task. The control group did not receive

any training and just completed the same IB task as the

trained groups.

AOSPAN task (Unsworth, Heitz, Schrock, & Engle,

2005) This task is designed to measure an individual’s

WMC, and is less dependent on language abilities as letters

rather than words were used. Participants are presented

with a series of maths problems that they need to solve as

quickly as possible, which is then followed by a letter that

needs to be recalled at a later stage. The practice was

divided into three phases. In the first phase, participants

were presented with a number of letters, each presented for

800 ms (with this being the same for all experimental

blocks). At the end of each trial, participants were pre-

sented with a 4 9 3 matrix of letters (F, H, J, K, L, N, P, Q,

R, S, T, Y). Participants were required to click a box next

to the appropriate letter in the exact order that the letters

had appeared. Accuracy feedback was given.

For the second practice phase, participants were

presented with a series of 15 maths problems, e.g.

(1*2) ? 1 = ?, to be solved as quickly as possible. On

the next screen, a possible answer, e.g. 3 and two boxes

with ‘True’ or ‘False’, was presented, and participants

were required to check the appropriate answer. Accuracy

feedback was also given for this section. During this

phase, the participant’s average solution time was also

calculated. This average time (?2.5 SD) was then used as

a time limit for the maths portion of the task. If, in the

experimental trials, a participant took 2.5 SD longer than

their average solution time to solve a maths problem, the

program skipped the ‘True’/‘False’ part of the maths

problem and moved directly onto the letter part but then

coded this trial as having a speed error. In the third and

final practice sessions, participants performed both the

letter recall and the maths problems together. The

experimental trials consisted of three sets each of set sizes

that ranged from three to seven per set, i.e. sets of 3, 4, 5,

6, 7 letters and maths. Therefore, in total, there were 75

letters and 75 maths problems, with possible scores

ranging between 0 and 75. Order of set sizes was

randomised for each participant.

Stroop task There were four types of trials (congruent,

control, incongruent and ignored repetition). There were

eight experimental blocks, each comprising trials of the

same type (e.g. all control trials). There were, therefore,

two blocks for each of the four types of trials. Within each

block, there were 32 trials, and the 4 colours (red, green,

yellow and blue) appeared an equal number of times. After

each of the eight blocks, participants were allowed to take a

short break. Each trial began with a fixation cross for

500 ms, which was replaced by the target (a colour word or

a row of Xs), presented in one of four colours (red, green,

yellow, blue). Participants were required to identify the

colour of the ink as quickly and accurately as possible by

making a manual response using the colour-coded keys on

the keyboard. After a response had been made, the next

trial began. There was a practice block of 20 trials at the

beginning of the experiment.

IB and training tasks To allow for training effects to be

observed, a similar IB task to that developed by Most et al.

(2001) was created using MATLAB, with the difference

being that the letter and moving cross were slightly smaller

than those in the original task, there was a larger number of

‘hits’ (i.e. bounces off the border) and the scanning area

was slightly larger. The starting positions of the Ls and Ts

on the IB task were changed to create a different starting

position for the targets and distractors for the training

sessions. We were also able to substitute the Ls and Ts with

triangles and diamonds, so that participants in the same and

different training conditions received exactly the same

configuration of targets and distractors with the same

starting positions, movement direction and velocities but

with different identities.

In the control condition, participants completed the IB

task with no training (see Fig. 2). Participants in the

different training condition completed two 17-s training

sessions, similar to the primary task in the IB task, but with

moving diamonds and triangles (and no unexpected stim-

ulus; see Fig. 3). Participants in the same training condition

completed two training sessions on the same primary task

as that used in the IB task (i.e. moving Ls and Ts) but

without the unexpected stimulus. The movement of items

in these two sessions was the same as those of the two same

training sessions. Both of the training groups completed the

IB task immediately after the training sessions.

Results

Training effects on incidence of IB

A Chi-square, performed on the number of IBs and NIBs

in the different training conditions (see Fig. 4), revealed

a significant association between the incidence of IB

Psychological Research (2010) 74:513–523 519

123

and training condition (v2 = 17.01, N = 82, df = 2, p \0.001, U = 0.46). The number of IBs relative to NIBs was

higher in the control condition compared to the same

(v2 = 17.04, N = 57, df = 1, p \ 0.001, U = 0.55) and

compared to the different training condition (v2 = 3.86,

N = 60, df = 1, p = 0.05, U = 0.25). There were also

more IBs than NIBs in the different compared to the same

training conditions (v2 = 4.07, N = 47, df = 1, p = 0.03,

U = 0.32). This showed that the more similar the training

is to the primary task on the IB task, the greater the

reduction in the incidence of IB. These findings support the

proposal that training allows the primary task to become

more automatised resulting in an increase in the available

processing resources to enable the unexpected stimulus to

be fully processed.

Consistent with Experiment 1, the IBs and the NIBs did

not differ in their performance on the primary component

of the IB task with mean number of hits reported being

14.19 (SD 2.47) and 13.89 (SD 3.09) for IBs and NIBs,

respectively.

WMC and IB

An analysis of AOSPAN scores in the three training

conditions revealed that overall the IBs had lower AOSPAN

scores than the NIBs (mean of 44.68, CI95 = 39.57, 49.79

and 55.41, CI95 = 48.10, 63.62, respectively; F(1, 76) =

6.04, p = 0.016, g2p ¼ 0:074). There were no differences

between the training conditions or any interactions involv-

ing training or IB. Thus, it appears that IBs tend to have

lower WMC than NIBs but WMC is not influential in

identifying which individuals would benefit from training in

order to reduce their chances of displaying IB.

Stroop task: facilitation; inhibition and IB

An ANOVA of the Stroop RT data was performed, with

condition (congruent, control, incongruent, ignored repeti-

tion) as a within-subjects factor and training (control, same,

different) and IB status (IB, NIB) as the between-subjects

factors. This revealed a main effect of condition

(F(3, 228) = 11.79, p \ 0.001, g2p ¼ 0:013). Further anal-

yses revealed that RTs were not significantly faster for

congruent compared to control trials (mean of 830 ms,

SD = 155 and 836 ms, SD = 174, respectively), showing

that there was no facilitation observed in the current

study. However, significant inhibition was observed over-

all, with RTs to incongruent being faster than those to

ignored repetition trials (mean of 880 ms, SD = 185 and

910 ms, SD = 199, respectively; t(81) = 2.54, p = 0.013,

g2p ¼ 0:074). There were no other significant effects,

showing that IB individuals do not have a greater tendency

to inhibit irrelevant stimuli compared to the NIBs.

Working memory; inhibition; training, and IB

To test the relative contributions of training, WMC and

inhibition in predicting the probability of IB, simultaneous

entry logistic regression was performed where IB was the

outcome variable and training, inhibition, and AOSPAN

scores were the predictors (Table 3). Both AOSPAN and

training predicted the probability of IB, but inhibition did

not. There were significant effects of different training

compared to control, and for different compared to same

training.

To examine for motivational difference between the

IBs and NIBs, an analysis was performed in which overall

Fig. 2 A still frame from the IB task, showing the unexpected cross

Fig. 3 A still frame from the different training task

Fig. 4 Incidence of inattentionally blind (IBs) and not inattentionally

blind (NIB) by training

520 Psychological Research (2010) 74:513–523

123

RT and overall accuracy were entered as additional

independent variables into the analysis. It was found that

only AOSPAN and training predicted IB with overall RTs

and accuracy not accounting for a significant amount of

the variance (p’s [ 0.15). This analysis again argues

against the proposal that motivational differences predict

IB status.

Discussion

Again, we have demonstrated the relationship between IB

and WMC, with low WMC individuals being more likely

to be IB than high WMC individuals. Although inhibition

was observed in the group as a whole, the IB group did not

display greater inhibition on the Stroop task. This finding

argues against the proposal that individuals who fail to

report the unexpected cross do so because they have a

tendency to inhibit goal irrelevant stimuli. Training had

a significant effect on the incidence of IB, with training on

a task that is similar to the IB task reducing IB compared to

both control (no training) and different training. Training

on a task that is the same as the primary task from the IB

task produces the greatest decrease in the incidence of IB,

but training on a different primary task with the basic

configuration as the primary IB task also produces

decreases in the incidence of IB. These findings suggest

that IB can be manipulated by training, and may have

implications for training of drivers, pilots, etc. Training on

similar tasks is hugely beneficial for later detection of

unexpected stimuli, and there is benefit albeit to a lesser

extent, from training on a different task.

There was no support for a motivational account of IB,

as there were no differences in performance on the primary

component of the IB task, and there was no difference in

overall performance on the Stroop task or any contribution

in terms of speed or accuracy of performance on the Stroop

task in predicting IB in the logistic regression.

General discussion

In two experiments, we have demonstrated robust effects of

WMC on predicting the likelihood of IB. We have shown

this using both the standard OSPAN and the AOSPAN

tasks. There were no differences in processing styles on the

flicker task, and no differences in terms of inhibition as

measured by the Stroop task. Of particular interest were the

effects of training on the incidence of IB, and here we

showed clearly that training on a task similar to the IB task

produced the greatest benefits in terms of reducing IB but

there are also some benefits to be gained from training on a

different IB task.

The findings from the two experiments that low WMC is

associated with IB whereas higher WMC is associated with

NIB suggest that IB individuals do not have sufficient

processing resources to fully process goal-irrelevant stim-

uli. We argue that there is a tendency for low WMC

individuals to not process the unexpected stimulus or to

process the unexpected stimulus and then filter it out at an

early stage of processing. This interpretation is supported

by the findings from the training effects in Experiment 2,

where training might make the processing of the tracking

task more automatic thereby freeing up resources to enable

the unexpected stimulus to be processed. The greater the

similarity of the training task to the primary task in the IB

task, the greater the automatisation of performance result-

ing in a release of processing resources leading to a greater

reduction in the incidence of IB.

If performance on the IB task is seen as resulting from

the interplay between goal-driven and stimulus-driven

attentional control (Egeth & Yantis, 1997), then an indi-

vidual’s propensity towards IB may be the result of the

suppression of stimulus-driven attentional control (Todd,

Fougnie, & Marios, 2005). The intraparietal sulcus (IPS) is

located in the dorsal parietal lobe and has been shown to be

related to goal-driven attentional control and is recruited by

task-driven attention and with the task demands of working

memory (e.g. Cohen et al. 1997; Corbetta, Kincade, &

Shulman, 2002; Todd & Marios, 2004). The temporo-

parietal junction (TPJ) is located at the junction between

the temporal and parietal lobes at the posterior end of the

sylvian fissure, and is associated with stimulus-driven

attentional control (e.g. Ansari, Lyons, van Eitneren, & Xu,

2007; Marois, Leung, & Gore, 2000). Todd et al. (2005)

demonstrated that engagement in a resource-consuming

task was associated with the recruitment of the IPS and

simultaneous suppression of the TPJ. The differential

recruitment of the IPS and the corresponding suppression

of TPJ may offer an explanation for the pattern of results

that we have observed. If the primary task is sufficiently

demanding for a low WMC individual, then there will be

increased suppression of the TPJ resulting in IB. High

Table 3 Simultaneous entry logistic regression for Experiment 2

95% CI for Exp b

B(SE) Lower Exp b Upper

Constant 2.67 (0.96)

AOSPAN* -0.051 (0.02) 0.91 0.95 0.99

Inhibition 0.000 1.00 1.00 1.01

Training*

Control vs. different** 4.47 (1.80) 2.58 87.49 2,968

Same vs. different** -2.79 (1.09) 0.01 0.06 1.00

R2 = 0.28 (Cox and Snell), R2 = 0.39 (Nagelkereke). Model = v2(6)

26.53, p \ 0.001

*p \ 0.05

**p \ 0.001

Psychological Research (2010) 74:513–523 521

123

WMC individuals are likely to find the primary task less

demanding, which would be associated with a reduction in

the suppression of the TPJ and reduced likelihood of IB.

An alternative but related explanation is in terms of

individual differences in selection efficiency. Vogel,

McCollough, and Machizawa (2005) measured contralateral

delay activity (CDA) in an event-related potential (ERP)

study. The CDA is a sustained negative voltage over the

hemisphere that is contralateral to the visual field where

visual items are displayed in order to be memorised, and the

amplitude of this wave increases as the number of items held

in memory increases. Using this methodology, Vogel et al.

(2005) varied the number of components (targets and dis-

tractors) in a visual display, with instructions to participants

to pay attention to the targets. By comparing the amplitude

of the CDA in four-item displays where all items were tar-

gets with four-item displays where two of the items were

targets and the other two distractors, it was possible to infer

how many items were stored in working memory. High

WMC individuals were more efficient at representing only

relevant items in memory whereas low WMC inefficiently

represented both relevant and irrelevant items in memory.

These findings suggest that low WMC individuals not only

have fewer resources available but they also tend to use these

resources less efficiently than high WMC individuals.

Lavie (2006) proposes that when the number of items to

be monitored in a display is low, then perception of irrel-

evant items is more likely to happen than when the number

of items to be monitored is high. She argues that a high

perceptual load results in early selection of relevant items

with no resources left for the processing of irrelevant (or

unexpected) items. Our current studies do not enable us to

examine these predictions, as it is not possible to determine

whether our display (4 targets and 4 distractors) qualifies as

a high or low perceptual load. One possibility is that it

would be a high perceptual load for a low WMC individual

but a low perceptual load for a high WMC individual.

However, Lavie (2005) makes a distinction between per-

ceptual load and cognitive load, proposing that whether

distractors will be processed or not depends on both the

level (high or low) and type (perceptual or cognitive) load.

She proposed that whereas a high perceptual load will

reduce the probability that distractors will be processed due

to limited perceptual capacity, a high-cognitive load would

increase the probability that distractors will be processed.

Lavie argues that a load on executive cognitive control

functions such as working memory results in a failure to

keep activated the processing priorities of the task, and ‘it

increases interference by irrelevant low-priority distractors

rather than decreases it.’ On the basis of this, it might be

predicted that fewer available working memory resources,

as would be the case for low WMC individuals, should

result in a decrease in the incidence of IB. Our studies do

not offer any support for this proposal. In fact, we have

consistently found effects in the opposite direction; with IB

rather NIB being associated with reduced working memory

resources.

It is clear, however, that there are limitations associated

with correlational studies. First, there may be a third factor

that is causally related to both the tendency to be IB and

low WMC. One possible ‘third variable’ is that of moti-

vation. It might be the case that a lack of motivation to

perform the task well could produce non-optimal perfor-

mance on both the OSPAN and the IB tasks. If this was the

case, then we might expect performance on other tasks,

such as the local/global flicker task and the Stroop task, to

be performed more slowly and/or less accurately by the IBs

compared to the NIBs. We might also expect differences in

terms of performance on the primary component of the IB

task. We did not find support for any of these predictions.

However, it cannot be asserted that WM is causally related

to the occurrence of IB. Cognitive load needs to be

manipulated directly in order to test for a possible causal

link between WM and IB. Although Experiment 2 indi-

rectly manipulated the resources available by training

participants in order to free up some resources, a more

direct manipulation is needed.

Further research is necessary to examine different types

of training over different time scales. For example, do the

effects endure over long intervals? Systematic variations of

these variables need to be performed to examine their

effects of IB. Despite finding robust support for the rela-

tionship between low WMC and the incidence of IB, there

are a small number of NIBs who have very high WMC.

Clearly, this group of participants have ample resources to

process all the stimuli in the display, so why are these

individuals IB? We put forward a dual route model, which

proposes that individuals may demonstrate IB but for dif-

ferent reasons. On the basis of this model, we predict that

low WMC individuals fail to notice the irrelevant stimulus

because they do not have sufficient resources to process

information outside the goals of the primary task, whereas

high WMC individuals, who do show IB, do so because they

are actively inhibiting the irrelevant stimulus. Research is

currently underway to examine these proposals.

Acknowledgments This work is supported by the Leverhulme

Foundation awarded to the first author under grant number F/07 112/R.

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