Abstract

It is imperative that human observers detect changes instantaneously in the dynamic

environment so that they can plan and execute an appropriate response. To

investigate the extent of the impact of reduced attention on change detection, 360

undergraduate cognitive psychology students completed a modified version of the

canonical ‘flicker’ change detection task, while either conversing on a mobile,

conversing in-person, or while paying full attention to the task. To test whether these

conversational distractions make it more difficult to detect change, only some trials

had ‘flickers’. Results showed that flickers impeded detection and produced slower

reaction times. The control condition had a faster reaction time than the mobile

condition, but the conversation condition did not differ from control or mobile.

These findings have broader implications for real-world activities such as driving.

Therefore, the results and their practical ramifications are considered generally in

terms of dividing attention between driving and conversing.

The extent of conversational distractions on flicker change detection tasks: In-

person conversations versus mobile-phone conversations.

Human observers must be able to detect sudden changes in their visual fields

proficiently in order to interact optimally with dynamic environments (McCarley et

al., 2004). Motion-sensitive receptors in the low-level visual system facilitate

detection by exogenously cueing observers’ attention (Blake & Shiffrar, 2007), but

evidence is accumulating that such higher-order cortical processes as perceptual load

and dual-tasking can override the involuntary system (Lavie, 2000). This paper aims

to investigate the cognitive differences in change-detection performance between

having a remote-person conversation via mobile phone and an in-person one.

Such transient cues in an observer’s field of view as motion mark a change on

the retina in the direction, location, velocity, or colour of an object, or some

combination of these, which may in turn require a rapid and appropriate response

(Blake & Shiffrar, 2007). It is therefore important for the perception of motion to be

an unparalleled mechanism of the human visual system (Albright & Stoner, 1995).

This being the case, such abrupt visual changes as motion engender a transient signal

on the retina, which cues attention and facilitates detection. Such change blindness

(CB) studies as Rensink, O’Regan, and Clark (1997) have, however, found that

observers are remarkably poor at detecting large, salient visual changes when this

transient is unavailable.

Change detection experiments essentially employ the same strategy of using

artificial masks or occlusions to obstruct the retinal signal, which is localisable under

normal conditions (Srinivasan, 2007). They also tend to use the speed and accuracy of

reactions as the determinants of detection performance (Smith & Ratcliff, 2009). Such

studies as Henderson and Hollingworth (2003) and Zelinsky (2001) have also used

eye tracking devices to measure the locations, sequence, and duration of observers’

fixations on a scene.

CB studies have largely employed flicker tasks (Rensink, O'Regan, & Clark,

1997), which insert such visual disruptions as blank screens (Hollingworth, Schrock,

& Henderson, 2001), movie cuts (Levin & Simons, 1997), and mudsplashes

(O’Regan,(O'Regan, Rensink, & Clark, 1999) between an original and a modified

image as they alternate in rapid succession to create a visible flicker (Stirk &

Underwood, 2007). Their intention is to imitate real-world counterparts, saccades, and

blinks, which cause brief periods of visual insensitivity and consequently CB

(Rensink, 2002).

Successful change detection depends on visual attention, which in many

conditions may be insufficient (Simons & Levin, 1998; Scholl, 2000). Levin and

Simons (1997), for example, found that only a third of their sample noticed the salient

change during a motion picture when an actor who was the focus of attention got up

to answer the telephone and became replaced after a scene cut with another actor in

the role. Similarly, Simons and Levin (1998) had an actor ask participants walking

across a university campus for directions. As he did so two men carrying a door

walked between them, and the actor changed places with another actor. Only half of

the subjects reported noticing the change.

That these findings are incongruent with established neuropsychological

evidence makes them questionable. Such studies as Cohen-Kadosh and Johnson

(2007) and Yovel, Tambini and Brandman (2008) have found cortical networks that

specialise in perceiving and encoding faces in what they call the fusiform face area.

The question remains whether the perception of faces is conditional and people are

not as adept at it as once thought or if these striking findings are inapplicable in such

more ecological conditions as real-world situations in which conversational partners

do not change.

The roles of a flicker in change-detection tasks are (a) to mask the transient, as

formerly mentioned, causing the search to be slow and effortful because observers

have to search for changes on an almost an item-by-item basis (Rensink, 2008), and

(b) to wipe the image from iconic memory (Varakinô, Levin, & Collins, 2007), which

refers to comparing and representing failures from one view to the next, whereby

change detection successes only occur if observers encode the objects being changed

from the pre-change images, retain them across the flicker, encode those in the post-

change images, and then compare them (Strayer & Drews, 2007). Change detection is

impossible without undergoing this process (Hyun, Woodman, Vogel, Hollingworth,

& Luck, 2009).

The literature in regard to the flicker paradigm therefore indicates that change

detection without a readily available motion transient is never timely and rarely

successful because of the removal of the exogenous cues. It has also indicated that

success in detecting a change requires that the transient be unique and salient and that

those doing the detecting not expend all of such cognitive resources of theirs as

attention (Macdonald & Lavie, 2008; McCarley et al., 2004; Smith & Ratcliff, 2009).

Under conditions of divided attention or, dual-task conditions, change detection is

slower, success rates decline significantly, and the number of undetected changes

increases (Lavie, 2000; McCarley et al., 2004).

Observers have finely detailed vision in only a sparse region of their visual

field (Hole, 2006; Stirk & Underwood, 2007), defined as the area in which they focus

their attention (Rensink, 2008). It is extremely important for people driving cars to

have the opportunity to process any relevant information so that they can detect

changes and respond to them instantaneously (Hole, 2006; Young & Stanton, 2007).

They can only do this, however, if they do not expend their attentional resources on

non-driving-related tasks. Brain research has been found to support this. Todd,

Fougnie, and Marois (2005) for example, found that having residual attention for the

primary task (e.g. driving) is centrally important because the right temporal parietal

junction (rTPJ) mediates the exogenous orientation of attention toward visual changes

that are salient, unexpected, and, especially, behaviourally relevant, such as stop

signs, traffic lights, and pedestrians. It does this by acting similarly to a circuit-

breaker for top-down processes (Corbetta & Shulman, 2002). Todd et al.’s key

finding, however, was that expending attentional resources on secondary tasking

suppresses rTPJ activation and therefore prevents observers from being automatically

alert to potential hazards (i.e. changes).

Several factors can affect drivers’ attention and consequently reduce their

performance. One of the most controllable factors in both real-world situations and

experimental conditions is their cognitive load, which includes both their perceptual

and attentional loads (Macdonald & Lavie, 2008). Nunes and Recarte (2002) found

that when drivers are engaged in a secondary task that increases their cognitive load

they seldom look in their rear-vision mirrors or at their speedometers and they scan

less of their peripheral field, making their vision more tunnelled to the extent that

their functional field of view, also called their attentional breadth, can be but 87.5% of

its normal size. It is therefore reasonable to assume that this decrease in attentional

breadth increases people’s chances of experiencing CB.

Similarly, Lavie (2006) used a modified version of the flicker paradigm to test

and compare change detection performance under conditions of high and low

perceptual load. Using cluttered scenes rather than the performing of a secondary

cognitive task to increase their mental workloads, Lavie found that the accuracy of the

sample’s change detection declined significantly by 11% from the high-load,

cluttered-scene (natural scenes) task to the low-load, simple-scene (geometric shapes)

task. This indicates that under busy driving conditions drivers need to pay more

attention to the primary task of driving because visually busy environments seriously

impede change detection.

Using a mobile phone while driving is an example of secondary task

interference, and such studies as Patten, Kircher, Östlund, and Nilsson (2004) have

found that having in-person conversations to be just as distracting, as the key factor is

the complexity of the conversation. Although this dual-task combination is cross-

modal, attention is not modality-specific, which means that engaging in such an

auditory cognitive task as having a conversation while visually scanning the traffic

scene requires the use of resources from a single attentional pool (Zimmer &

Macaluso, 2007). As people dual-tasking allocate more resources to one of the tasks,

and most drivers are likely to allocate more to conversation if they are experienced

and driving relatively automatically, performance on that task improves to the

detriment of the other (Strayer & Drews, 2007). Drivers are unlikely to notice any

deterioration in their driving performance because they process and perform such

simple tasks as steering, changing gears, and braking automatically. In an emergency,

however, or when the environment changes suddenly, is when the deterioration

becomes clearly apparent.

Amando and Ulupinar (2005) found that conversation has a negative impact

on detection reaction time and accuracy, but that whether conversations are in person

or over a mobile phone makes no difference. McKnight and McKnight (1993) found

that conversations with passengers do differ from those on mobile phones. Drews,

Pasupathi, and Strayer (2008) concluded that this is the case because such aspects of

the traffic environment as its flow, changes, and other drivers’ behaviour tend to

become topics of conversation, which helps drivers and passengers to share situation

awareness (Strayer & Drews, 2007). Drews et al. (2008) also found that the

complexity of traffic environments reflects the complexity of the conversations,

which ameliorates in-person conversations’ negative impacts. Furthermore, Kuhn,

Tatler and Cole (2009) suggested that people tend to look where other people are

directing their gaze, so in the case of passenger and driver (compared with only

driver), the operator of the vehicle may be prompted by the direction of the

passengers gaze, making it less likely that they will miss critical changes and suffer

CB. To further support the argument that a passenger may enhance the quality of ones

driving, is the understanding that human beings have an inherent limited attention

span (Becker, Pashler, & Anstis, 2000) and therefore, the primary task of driving

simply could not be focused on for an entire trip; it would undoubtedly result in mind-

wondering causing mental distraction that may be equivalent to having a mobile

conversation.

Strayer, Drews, and Johnston (2003) also found that conversations on mobile

phones impair reaction time and accuracy in response to vehicles’ brake lights and

reduce attention to foveal-system information. This indicates that reduced attention to

visual inputs at least partly mediates the reductions in driving performance

engendered by mobile-phone conversations.

This study will modify Rensink et al.’s (1997) flicker task to include mobile-

phone and in-person conversational distractions and a baseline condition. Most

previous studies have measured change detection using either the flicker paradigm or

such apparatus as driving simulators and have not analysed its cognitive effects using

a simple laboratory-based task and modifying it with such real-world distractions as

cross-modal interference. University students will be asked to complete the

experiment either in class or at home according to their conditions, and measurements

of their reactions’ speed and accuracy will be completed with an online programme.

This study will compare the effects of reduced attention by having an in-

person conversation, a conversation on a mobile phone, in comparison to full

attention (without distractions) on a change-detection task using a mixed design. It is

hypothesised, in accordance with the research examined, that the flicker reaction time will

be significantly slower than the reaction time with no flicker. It is also hypothesised that the control

condition will have a significantly faster reaction time than the mobile-phone condition but a slower

reaction time than the conversation condition, and that the conversation condition will have a

significantly faster reaction time than the mobile-phone condition.

Method

Participants

The 360 undergraduate students in this study were all enrolled in a 2010

cognitive psychology course at the University of South Australia and were asked to

participate in this study as a course requirement. The sample included 79 males, 280

females, and 1 unknown, ranging in age from 17 to 66 (Median=21). All participants

agreed for their data to be used in this research.

Design

The current study employed a mixed experimental design. The between

groups dependent variable was reaction time, and the independent variable was the

experimental condition which had 3 levels: control, mobile, and conversation. The

within-groups dependent variable was reaction time, and the independent variable was

masking which had 2 levels: flicker (masking), and no flicker (no masking).

Materials

The change detection task was completed using software by Thomson

Wadsworth: Coglab Online version 2.0. Student data were collected by using Tellus,

an online program that allows participants to login and access questionnaires that a

creator has provided. Demographic questions included age, sex and experimental

condition, and a final question was used to obtain informed consent about using

participant responses in the group data.

Procedure

Participants were randomly assigned to one of mobile, conversation, or control

conditions according to their surnames. During lectures leading up to the experiment,

participants were informed of the study’s purpose and that their results would be used

for class research. Participants were further informed at the beginning of their

practical classes of their specific condition’s instructions. All participants were asked

to alter the default keyboard functions (Change = C and No change = N) so that the

response keys were Change = B, and No change = N; participants could only respond

with their index and middle fingers on their dominant hand, which maintained

homogeneity with the mobile participants whom had only one free hand. The mobile

condition participants were asked to complete the task in their own time when they

were able to engage in a real mobile conversation. They were also instructed to

initiate the phone conversation prior to commencing the task. The conversation group

was asked to leave the room for 30 minutes while the control condition completed the

experiment in silence. After this time, the conditions exchanged. The conversation

participants were told to pair up and hold a conversation while each take turns in

completing the task assuring one partner faced away from the computer. Participants

were asked to calculate their Mean Flicker Correct Reaction Time and Mean No

Flicker Correct Reaction Time scores, and submit these results along with their

demographic information online via Tellus.

Results

Data was entered into a SPSSV17.0 file and screened for normality. Age

flicker different reaction time (FDRT) and no flicker different reaction time (NFDRT)

were all positively skewed. However, after identification and removal of outliers, the

distribution of FDRT and NFDRT were normalised.

A paired-samples t-test revealed that the FDRT was significantly slower than

NFDRT (see Table 1), t(293)=14.95, p<.001 (one-tailed).

A One-way ANOVA revealed that there was a significant difference found

between reaction times of participants in the different experimental conditions on

FDRT, F(2, 310)=3.91, p=0.21. Bonferroni post hoc tests (p<.05) revealed that the

FDRT for the control condition (see Table 1) was significantly faster than the mobile

condition (see Table 1). The conversation condition FDRT did not differ significantly

from FDRT of either the control or mobile conditions (see Table 1).

Discussion

This study’s purpose has been to examine the differences in cognitive impact

on having a remote person conversation on a mobile-phone, and having an in-person

conversation on change detection tasks, in comparison to a baseline condition.

Table 1. Demographic information with means and standard deviations of RTs for 3 conditions Control Mobile Conversation Total Sex Males

Females n=21 n=98

n=34 n=86

n=24 n=96

n=79 n=280

Age Median=21 Min= 18 Max=56 N=119

Median=20 Min= 19 Max=49 n=120

Median=20 Min= 17 Max=65 n=120

Median=21 Min= 17 Max=65 N=359

Flicker Different Reaction Time

M=6783.10 SD=3191.61 n=109

M=8060.51 SD=3715.02 n=101

M7176.04 SD=3188.95 n=103

M=7324.60 SD=3399.89 n=313

No Flicker Different Reaction Time

M=4206.02 SD=1742.06 n=114

M=4908.23 SD=1787.07 n=110

M=4479.52 SD=1608.74 n=114

M=4526.79 SD=1732.46 n=338

Another purpose of the study was to measure the differences between flicker and no

flicker reaction times to determine the degree of differences (if any) found between

the conditions. As anticipated, the flicker different condition had a significantly

slower reaction time than did the no flicker different condition, which supported of

the first hypothesis. These results are in concurrence with previous studies conducted

by Rensink, O’Regan and Clark (1997), Scholl (2000), Hollingworth and Henderson

(2000) and Aginsky and Tarl (2000), which all evidenced the generality of the change

blindness effect when a change is masked by a brief flicker.

As anticipated, the control group had a significantly faster reaction time than

the mobile condition, evidencing that having a conversation on the mobile phone

(completing change detection tasks under conditions of reduced attention) negatively

affects performance. This is in line with such previous studies as Strayer, Drews and

Johnson (2003), and Amado and Ulupmar (2005) who both found that reaction time

and general performance in change detection suffers from concurrently engaging in a

mobile phone conversation.

Findings that the conversation condition and did not significantly differ from

either mobile conversation or control conditions in their reaction times challenges the

extant research on the topic such as Drews, Pasupathi and Strayer’s (2008) findings

that in-person conversations do not interfere with the detection of change because

such aspects of the traffic environment as its flow, changes, and other drivers’

behaviour tend to become topics of conversation. These results can be accounted for

however, by a few factors; for example, the scenes used in the change detection tasks

were not related to driving. Furthermore, Nunes and Recarte’s (2002) finding that a

driver’s functional field of view can be but 87.5% of its normal size when attention is

divided between a primary and a secondary task are not applicable in this study

because the entire scene is in the participant’s foveal vision.

In addition, the complexity of the conversation was not controlled for meaning

that the conversations in this study may have been simpler and less complex than

those in studies finding differences. Further, participants may have allocated most

attentional resources to the primary task because the conversations were not of real

importance to them and also because they knew that their results would be used in this

research, suggesting that participants may have considered the change detection task a

personal test that is an indication of their abilities as an individual. The participants

may not have behaved normally in a driving situation whereby the experimental

design may have forced them to behave atypically, which according to Orne (1962)

are called ‘demand characteristics’.

These results boast theoretical implications that pertain to already large body

of knowledge. For example, as formerly mentioned, change detection is studied using

the typical laboratory-based design that is important for isolating and understanding

visual and cognitive processes as well as testing psychological theories such as

change blindness, but lack ecological validity because of the contrived conditions in

which the tasks are conducted in. This study enhanced ecological validity by

including real-world factors that clearly have an influence on the detection of changes

in one’s visual field, such as conversational distractions while driving. Given the

increase in in-car technologies, this area of study is indeed an important one and

adding to the literature is an imperative feat.

In addition, the real world implications of this study are also key, and not only

in relation to driving (however, this is still highly important). For example, eye-

witness testimony, as noted above that established neuropsychological evidence has

revealed cortical networks that specialise in perceiving and encoding faces (the

fusiform face area) but change blindness challenges this. In addition, aviation, like

driving, is an important area this study has real-world implications for (e.g. Wickens,

2002). With reference to driving, the real-world implications include education of

drivers – particularly novice drivers – about the consequences that can come from

having a mobile conversation while driving. The notion of change blindness blindness

(Levin, Momen, Drivdahl, & Simons, 2000) maintains that individual’s believe that

they are inherently good at detecting changes, but change blindness falsifies this

belief. Naïve observers who have never been involved in, or heard of change

blindness studies are going to continue making the metacognitive error of over-

estimating their abilities, and engage in secondary tasks (like conversations) while

driving.

The limitations of this study are firstly that because the conversation pace or

complexity was not controlled for, differences found between the conditions cannot

be attributed to the type of conversation (e.g. in-person and mobile). Moreover, the

results cannot be extrapolated to real-world driving conditions because of the nature

of the task: the content of the images were non-driving related, and the scenes were

static images. Furthermore the sample was quite limited in its generalisability because

it was taken from one year class of cognitive psychology students who were mostly

females.

The difference between this experiment and real world situations is the

priority placed on each task by the participants. In a real driving situation, an

experienced driver is not being tested on their response time (speed and accuracy) of

detecting changes in their visual field. Furthermore, because the task is automaticed,

they will place more importance on the conversation with the passenger or with the

mobile confederate. Given the results were submitted the task was most likely made

the primary task in this situation. Furthermore, as noted above about participants

possibly seeing the task as a reflection of their own individual abilities, they were

asked to calculate their own mean scores and submit them. This has two implications,

the first is accidental dishonesty whereby students may have miscalculated their

scores, and the second is deliberate, whereby students were embarrassed about their

scores and simply adjusted them to make them better.

Assumptions were also made regarding the attentional capacities and the

vision acuity of the participants; the task required participants to focus and divide

their attention and individual differences that may have affected this such as age, or

attention disorders were not controlled for. Causality can be ascribed to the results, so

many other extraneous variables may have been at play. For example, cultural factors

have been found to influence where in a scene an individual looks first (Masuda &

Nisbett, 2006). As the sample was an undergraduate class, there would have been a

vast range of individuals including exchange students, mature-age students, and

individuals from different cultural backgrounds.

Moreover, there was no way to control for the mobile phone condition, they

may have, again, either accidently performed the task wrong (without the presence of

the instructor), or purposely done it wrong such as not even engaged in a mobile

conversation at all. Further, while the mobile condition were assured that other

conditions would be homogeneous in terms of their response keys, there was no

assuring that the participants did this, meaning that the reaction time results could

have be influenced by participants who used two hands (making it easier on them).

There are a few suggestions for future research to improve this study as well

as suggestions for future research in the area of change blindness and detection. The

two most prominent suggestions are that the content of the images should be driving-

related, and conversation should be controlled for. Suggestions for doing so are

participants should be given a debatable topic of current social concern and made to

discuss, this assures that the conversation is less likely to lapse which affords the

mobile or conversation participant a condition of no distractions (either temporarily or

for a lengthy period of time), and this also controls for any ‘awkward silences’ that

may erupt between two participants that may not be friendly.

The experimental design of any similar change detection tasks should be

altered so that there is a stronger difference between the mobile and control conditions

reaction time. To do so, the conversation partner of the participants should be seated

next to the participant.

To increase ecological validity of this modified version of the flicker change

detection task, additional scales or measures should be used that ascertain cognitive

workload is equal across all participants in different conditions and to neutralise

working memory, neural stimulation, mental effort etcetera.

A final suggestion is for more complex research that would require a re-design

of the flicker task, but may provide further insight into change blindness in real-world

driving conditions; the flicker should mask the change’s signal in the beginning, but

then should be unmasked for the latter part of the motion transient, giving the

observer a limited amount of time to respond appropriately (response keys could be L

to move left, R to move right space bar to brake etcetera) but not give them a

substantiated time to detect. Unlike in the current flicker tasks where changes are

repeated and made to objects that aren’t likely or important (e.g. a lamppost

disappearing). These changes should also be unexpected, as changes in the real-world

are always unexpected, they may be known to be likely (e.g. traffic lights change) but

they are rarely known precisely.

Conclusively, change blindness studies have revealed the incongruity between

what humans believe to be seeing, and what they truly are seeing. While it may not be

an avoidable aspect of human vision, attention, and perception, but indeed making

individuals aware of change blindness is sure to change their behaviour so that they

are more cautious. In addition, the simple laboratory tasks used in this study may

appear to lack ecological validity, but their simplicity makes them easy to conduct in

large numbers (e.g. university classes), and simply being involved in change

blindness tasks and seeing how wrong introspection can be, is a likely way for

individuals to override their erroneous beliefs. The present study has shown that in the

presence of a flicker, which mirrors saccades and blinks, head turns or sneezes

etcetera, without paying attention to the pre-change image, changes are going to go

undetected, causing the driver to be operating a vehicle in an environment they

believe to contain one thing, but in actual fact contains another. The complete

perception that individuals have of their surroundings suggests to them that there is no

need to pay attention to things that they already know (such as the position, speed,

direction of cars), but in reality, one must update the content of their surroundings by

paying attention to them constantly. The very recognition of change blindness is a

remarkable finding, one which holds ramifications for both theory and for practice, so

it is imperative that research continues to uncover the specifics of the phenomenon so

that the consequences that are so common in the real-world can eventually be

eradicated.

References

Albright, T. D., & Stoner, G. R. (1995). Visual motion perception. Proceedings of the National Academy of Sciences of the United States of America, 92(7), 2433-2441.

Amado, S., & Ulupmar, P. (2005). The effects of conversation on attention and peripheral detection: Is talking with a passenger and talking on the cell phone different? Transportation Research Part F: Traffic Psychology and Behaviour, 8(6), 383-395.

Becker, M. W., Pashler, H., & Anstis, S. M. (2000). The role of iconic memory in change-detection tasks. Perception, 29(3), 273-286.

Blake, R., & Shiffrar, M. (2007). Perception of human motion. Psychology, 58, 47-73.

Cohen-Kadosh, K., & Johnson, M. H. (2007). Developing a cortex specialized for face perception. Trends in Cognitive Sciences, 11(9), 367-369.

Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201-215.

Drews, F. A., Pasupathi, M., & Strayer, D. L. (2008). Passenger and Cell Phone Conversations in Simulated Driving. Journal of Experimental Psychology: Applied, 14(4), 392-400.

Henderson, J. M., & Hollingworth, A. (2003). Eye movements and visual memory: Detecting changes to saccade targets in scenes. Perception & psychophysics, 65(1), 58-71.

Hole, G. (2006). The psychology of driving: Lawrence Erlbaum: London.

Hollingworth, A., Schrock, G., & Henderson, J. M. (2001). Change detection in the flicker paradigm: The role of fixation position within the scene. Memory & cognition, 29(2), 296 - 304.

Hyun, J., Woodman, G. F., Vogel, E. K., Hollingworth, A., & Luck, S. J. (2009). The comparison of visual working memory representations with perceptual inputs. Journal of experimental psychology. Human perception and performance, 35(4), 1140 - 1160.

Kuhn, G., Tatler, B. W., & Cole, G. G. (2009). You look where I look! Effect of gaze cues on overt and covert attention in misdirection. Visual Cognition, 17(6), 925-944.

Lavie, N. (2000). Selective attention and cognitive control: Dissociating attentional functions through different types of load. Control of cognitive processes: Attention and performance XVIII, 175-194.

Lavie, N. (2006). The role of perceptual load in visual awareness. Brain Research, 1080(1), 91-100.

Levin, D. T., & Simons, D. J. (1997). Failure to detect changes to attended objects in motion pictures. Psychonomic bulletin & review, 4(4), 501-506.

Macdonald, J. S. P., & Lavie, N. (2008). Load induced blindness. Journal of Experimental Psychology: Human Perception and Performance, 34(5), 1078-1091.

McCarley, J. S., Vais, M. J., Pringle, H., Kramer, A. F., Irwin, D. E., & Strayer, D. L. (2004). Conversation disrupts change detection in complex traffic scenes. Human Factors: The Journal of the Human Factors and Ergonomics Society, 46(3), 424 - 436.

McKnight, A. J., & McKnight, A. S. (1993). The effect of cellular phone use upon driver attention. Accident Analysis & Prevention, 25(3), 259-265.

Nunes, L., & Recarte, M. A. (2002). Cognitive demands of hands-free-phone conversation while driving. Transportation Research Part F: Traffic Psychology and Behaviour, 5(2), 133-144.

O'Regan, J. K., Rensink, R. A., & Clark, J. J. (1999). Change-blindness as a result of ‘mudsplashes’. Nature, 398(6722), 34.

Patten, C. J. D., Kircher, A., Östlund, J., & Nilsson, L. (2004). Using mobile telephones: cognitive workload and attention resource allocation. Accident Analysis & Prevention, 36(3), 341-350.

Rensink, R. A. (2002). Change detection. Annual Review of Psychology, 245-278.

Rensink, R. A. (2008). On the applications of change blindness. Psychologia, 51(2), 100-106.

Rensink, R. A., O'Regan, J. K., & Clark, J. J. (1997). To see or not to see: The need for attention to perceive changes in scenes. Psychological Science, 8(5), 368.

Scholl, B. J. (2000). Attenuated change blindness for exogenously attended items in a flicker paradigm. Visual Cognition, 7(1), 377-396.

Simons, D. J., & Levin, D. T. (1998). Failure to detect changes to people during a real-world interaction. Psychonomic Bulletin and Review, 5, 644-649.

Smith, P. L., & Ratcliff, R. (2009). An integrated theory of attention and decision making in visual signal detection. Psychological Review, 116(2), 283-317.

Srinivasan, N. (2007). Interdependence of attention and consciousness. Progress in Brain Research, 168, 65-75.

Stirk, J. A., & Underwood, G. (2007). Low-level visual saliency does not predict change detection in natural scenes. Journal of Vision, 7(10), 1-10.

Strayer, D. L., & Drews, F. A. (2007). What is attention and why is it important? In Handbook of Applied Cognitive, eds. F. T. Durso. John Wiley: London, 29-54

Strayer, D. L., Drews, F. A., & Johnston, W. A. (2003). Cell phone-induced failures of visual attention during simulated driving. Journal of Experimental Psychology Applied, 9(1), 23-32.

Todd, J. J., Fougnie, D., & Marois, R. (2005). Visual short-term memory load suppresses temporo-parietal junction activity and induces inattentional blindness. Psychological Science, 16(12), 965-972.

Varakinô, D. A., Levin, D. T., & Collins, K. M. (2007). Comparison and representation failures both cause real-world change blindness. Perception, 36, 737-749.

Young, M. S., & Stanton, N. A. (2007). Miles away: determining the extent of secondary task interference on simulated driving. Theoretical Issues in Ergonomics Science, 8(3), 233-253.

Yovel, G., Tambini, A., & Brandman, T. (2008). The asymmetry of the fusiform face area is a stable individual characteristic that underlies the left-visual-field superiority for faces. Neuropsychologia, 46(13), 3061-3068.

Zelinsky, G. J. (2001). Eye movements during change detection: Implications for search constraints, memory limitations, and scanning strategies. Perception & psychophysics, 63(2), 209-225.

Zimmer, U., & Macaluso, E. (2007). Processing of multisensory spatial congruency can be dissociated from working memory and visuo-spatial attention. European Journal of Neuroscience, 26(6), 1681-1691.