Risk and the recognition of driving situations

APPLIED COGNITIVE PSYCHOLOGYAppl. Cognit. Psychol. 18: 1231–1249 (2004)

Published online 15 June 2004 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/acp.1043

Risk and the Recognition of Driving Situations

PETER CHAPMAN1* and JOHN A. GROEGER2

1University of Nottingham, UK2University of Surrey, UK

SUMMARY

This paper reports a series of experiments that explore the relationship between subjective risk whenviewing driving situations and subsequent recognition memory for such films. In the first experimentno simple relationship was found between risk and recognition performance. Instead the experienceof subjective risk was associated with good recognition of generally dangerous situations but poorrecognition of generally safe ones. The remaining experiments replicated this result and exploredpossible reasons for it. An account of the results in terms of schema theory was tested and rejected.The effect is instead interpreted in terms of the types of information that are present and attended toin different kinds of dangerous situation. Overall recognition memory for films of driving situationswas surprisingly poor, but the pattern of results was consistent with drivers showing good memoryfor central details from dangerous situations. Copyright # 2004 John Wiley & Sons, Ltd.

Previous research on memory for driving situations has tended to focus on specific aspects

such as parking locations (Lutz, Means, & Long, 1994; Pinto & Baddeley, 1991), road

signs (e.g. Fisher, 1992), social factors in the recall of traffic accidents (Underwood &

Milton, 1993), or the reliability of self report for particular driving habits (Lajunen &

Summala, 2003). While such studies are clearly of interest, they do not tell us directly

about what people may remember about normal driving situations. Despite occasional

reports of people being unable to recall large sections of drives (Chapman, Ismail, &

Underwood, 1999), it is widely assumed that we remember the driving situations we

encounter and use this experience to govern our future behaviour. Knowledge about any

potential biases in memory is thus extremely important in anticipating how people will

learn from the situations they encounter.

Memory for everyday events and situations is often thought to be schema-driven in the

sense that both encoding and retrieval of information are guided by pre-existing knowl-

edge structures (e.g. Alba & Hasher, 1983; Brewer & Nakamura, 1984). This seems an

especially useful way of considering memory for driving situations since it has been

suggested that the driving task is largely controlled by constantly developing schemata

(Groeger, 1989, 2000; Riemersma, 1988), thus it might be expected that schema

consistency would play a substantial role in determining people’s memories of driving

Copyright # 2004 John Wiley & Sons, Ltd.

*Correspondence to: Peter Chapman, School of Psychology, University of Nottingham, University Park,Nottingham, NG7 2RD, UK. E-mail: [email protected]

Contract grant sponsor: General Accident plc.

situations. However, there are other variables which have more traditionally been thought

to play a central role in the driving task and which may have significant effects on memory.

The experience of subjective risk is often considered to be a central aspect of the driving

task (e.g. Simonet & Wilde, 1997, though cf. Rothengatter, 2002) and there are many

reasons to expect that this experience may relate directly to our memories for the situations

encountered when driving.

Many psychological theories of driving suggest that subjective risk plays a central role

in regulating behaviour, either as a quantity to be controlled (Wilde, 1994; Zuckerman,

1994), avoided (Naatanen & Summala, 1974, 1976; Summala, 1988), or as feedback in a

learning theory approach (Fuller, 1988). Subjective risk in this sense refers to an actual

feeling of danger experienced by the driver but is generally assessed as accident estimates

given at the roadside (Brehmer, 1987) or danger ratings as used in many laboratory tasks

(Groeger & Chapman, 1996). Although subjective risk can be measured using verbal

ratings (e.g. Groeger & Chapman, 1990), when participants are actually driving it has

more often been related to covert measures such as heart rate or electrodermal response.

These are precisely the measures that have been used in much of the laboratory research

looking at the general effects of emotional arousal on memory (e.g. Christianson, 1992;

Heuer & Reisberg, 1992). It thus seems likely that the same types of effects that have been

observed in such studies may appear in memory for driving situations.

Many studies have suggested that emotional arousal influences memory. Researchers

have generally used one of two strategies to investigate these effects (Christianson, 1992).

One approach has been to explore memory for naturally occurring traumatic events

(Christianson & Hubinette, 1993; Wagenaar & Groeneweg, 1990; Winograd & Neisser,

1992). Inevitably such studies suffer from the difficulty of knowing exactly what the

encoding conditions for individual participants were. The other approach has been to

simulate a traumatic event using slides, films or actors (Christianson & Loftus, 1987;

Heuer & Reisberg, 1990). While such techniques allow careful control over the encoding

conditions, it is hard to be sure that the behaviour of participants in such experiments is

really representative of their actions in the situations that are being simulated.

Much of this work has been carried out within the context of eyewitness testimony

research to decide whether the reliability of testimony is likely to be affected by arousal at

the time of encoding. Answering this question has proved to be surprisingly complex;

some studies have suggested that arousal enhances memory (e.g. Heuer & Reisberg, 1990;

Rubin & Kozin, 1984), while others have suggested that it causes an impairment (e.g.

Clifford & Hollin, 1981). There have been a number of attempts to resolve the

inconsistencies in such findings. One approach has been to suggest that the effects

observed depend on the levels of arousal used. Deffenbacher (1983, 1991) has suggested

that increases in arousal may initially enhance memory until some optimal level is reached

(cf. Yerkes & Dodson, 1908); subsequent increases in arousal will then impair memory.

One issue here is how memory is tested; it is for example possible that arousal might

enhance free recall by providing additional retrieval cues without affecting performance as

assessed in recognition tests. To minimize any influences of arousal purely as a retrieval

cue the experiments described in the current paper concentrate on recognition memory.

A different approach has been to concentrate on the possible effects of arousal on

memory for different aspects of a situation. Christianson (1992; Christianson & Loftus,

1987, 1991) has suggested that a major effect of emotional arousal is to cause attention

focusing (cf. Easterbrook, 1959), and that arousal may thus enhance memory for central

information while impairing memory for peripheral information. In the most extreme

1232 P. Chapman and J. A. Groeger

Copyright # 2004 John Wiley & Sons, Ltd. Appl. Cognit. Psychol. 18: 1231–1249 (2004)

cases this could explain reports of ‘weapon focus’—the claim that victims of a violent

crime may focus so completely on the weapon that they are unable to recall other details

(Loftus, 1979). Clearly the variety of effects that have been observed previously makes it

difficult to confidently predict a single relationship between subjective risk and memory in

driving. They do, however, give one reason to suspect that memory for driving situations

may be prone to systematic biases with respect to subjective risk; the studies reported in

this paper explore this possibility.

The first question of interest is simply whether an overall relationship exists between

memory performance and subjective risk in driving situations. Experiments by Hughes

and Cole (1986a, 1986b) have demonstrated that verbal reports and visual attention

watching films of driving in the laboratory closely parallel those obtained during actual

driving. Watts and Quimby (1979) have additionally demonstrated that verbal risk ratings

made in a stationary vehicle with a filmed road projected onto a screen in front of the

vehicle were closely comparable to ratings made by participants actually driving on the

same roads. We thus adopted a procedure similar to that of Watts and Quimby—

participants watching films while seated in a stationary vehicle, making risk ratings

when signalled by a tone. Our films were also similar in content to those used by Watts and

Quimby—representing everyday driving situations, rather than actual accidents or staged

dangerous incidents. The general levels of risk represented in our stimuli were thus

relatively low. Our assumption was that at relatively low levels of subjective risk, increases

in risk would be likely to improve subsequent memory and that none of our situations

would induce high enough levels of emotional arousal to impair subsequent memory

(Deffenbacher, 1991). To encourage our participants to make judgements of subjective

risk (i.e. experienced feelings of danger) rather than basing their ratings on knowledge of

the road environment and location, we had participants make a second rating after the risk

rating which would represent a more objective estimate of the dangers generally present in

each location. This was an estimate of the number of accidents occurring at each junction

over an extended period. Our prediction was that overall both accident estimates and risk

ratings might be correlated with memory performance. However, we expected that within

multiple exemplars of an individual junction (for which accident estimates should be

nearly constant) only risk ratings would predict memory performance.

EXPERIMENT 1

Method

Participants

These were 36 drivers who had responded to a newspaper advertisement. There were 18

females and 18 males. Participants ranged in age from 20 to 62 (mean 42); all had held a

full British driving licence for at least three years and were paid £5 for their participation.

Stimuli

The stimulus materials consisted of video films of the view through a car windscreen while

driving through various junctions. Each film started from a point sufficiently before the

junction to allow signs for the junctions to be seen and lasted until the car had reached a

point approximately 100 m past the junction. Films ranged in length from 16 to 60 s. There

were 60 films in all. These consisted of six exemplars each of drives through 10 different

Risk and recognition 1233


junctions. The 10 different junctions were chosen to represent different levels of potential

risk selected on the basis of actual accident statistics for the junction in question. Because

the junctions used were local, they would be familiar to many of the participants. In each

of the six exemplars the manoeuvre performed at the junction was identical but the traffic

conditions at the junction differed. The exemplars were chosen to represent a range of

everyday traffic conditions and an attempt was made to include some films of potentially

dangerous or unusual situations at each junction.

Apparatus

Films were viewed in a purpose-built driving environment. This consisted of the front half

of a small family car mounted in a darkened room. The participant sat in the driver’s seat.

All the car’s controls were in place but the participant was not required to use them during

the experiment. A video projector beneath the car projected the films onto a screen (1.7 m

horizontal, 1.4 m vertical) approximately 4 m in front of the participant’s head. This

produced a relatively realistic driving environment, though consistent with the limitations

of the original filmed stimuli there is a relatively restricted field of view horizontally.

Design and procedure

The experiment consisted of two phases, a judgement phase in which risk and accident

ratings were made and a recognition phase in which memory was tested. The participant

did not know in advance that there would be a memory test. Each phase lasted

approximately 25 min and the two phases were separated by a pause of approximately

5 min during which the participant filled in two brief questionnaires about driving.

Judgement phase

Participants watched a series of 30 films, three exemplars of each of the 10 junctions.

Although the films were otherwise silent, a 1.5 s tone was recorded on each film at the

point at which the car passed through the centre of the junction. This was the signal for

participants to perform two rating tasks. Participants first gave a rating of the risk they

were feeling at that moment. This was done on a 20-point scale where 1 meant that, ‘there

is no possible way in which an accident could occur in this situation’ and 20 meant that,

‘you feel that you could be involved in an accident at any moment.’ Participants then gave

an estimate of the number of accidents that had occurred at the junction over the preceding

three years. They were told that these accident estimates should not exceed 20 for any

particular junction.

Recognition phase

In the recognition phase participants watched a further 30 films, 15 of which had appeared

in the judgement phase and 15 of which they had not seen before. These 30 films again

consisted of three exemplars of each junction selected randomly with the constraint that

either one or two exemplars of each junction always appeared as a target. The participants’

task was to decide whether or not they had seen each film during the judgement phase; they

also gave a rating of their confidence on a 7-point scale. In both phases films were viewed

in a randomized order.

Participants were divided into four groups balanced by age and their self-reported

driving experience. The four groups watched different films such that each film was



viewed by 18 participants in the judgement phase and 18 in the recognition phase,

appearing to nine of these participants as a target and to the other nine as a distracter.

Results and discussion

The mean risk rating given was 4.76 (individual films received mean ratings ranging from

2.28 to 9.50) and the mean accident estimate was 5.74 (film means ranging from 3.11 to

11.83). Junctions that received high risk rating also generally received high accident

estimates (r(8) ¼ 0.94, p < 0.01) while within each junction there was a more moderate

relationship between risk ratings and accident estimates (mean r(4) ¼ 0.45) indicating

that participants were able to partially separate these two rating scales. The recognition

results were analysed using signal detection theory to obtain recognition scores for

individual films. Locksley, Stangor, Hepburn, Grosovsky, and Hochstrasser (1984)

recommend the use of non-parametric indices of recognition sensitivity in such cases.

The 14 recognition responses possible (Yes or No and a rating from 1 to 7) were

aggregated into six categories such that responses were approximately evenly distributed

across categories. Hits and false alarms were thus calculated as a function of confidence

category and Receiver-Operating Characteristic (ROC) curves were calculated for

individual stimuli. Following McNicol (1972) non-parametric measures of recognition

sensitivity, P(A), and response bias, B, were thus calculated for the 60 films. Values of P(A)

ranged from 0.43 to 0.96 with a mean of 0.70 (chance responding would be 0.5). Values of

B varied from 2.00 to 4.83 with a mean of 3.21 (unbiased responding would give a value of

3.5, lower numbers indicate a tendency to give too many false alarms).

The question of particular interest in this experiment was whether the measure of

recognition sensitivity for a particular film was related to the types of risk ratings it

received. There was no significant correlation across the 60 films between mean risk rating

and either P(A), r(58) ¼ 0.14 or B, r(58) ¼ 0.07, or between accident estimates and either

measure, r(58) ¼ 0.06 and r(58) ¼ 0.03.

One reason for the failure to find any relationship between P(A) and risk ratings may

have been that despite substantial differences among the 10 junctions in risk ratings

(means ranging from 3.44 to 7.25) there was relatively little variation among the 10

junctions in recognition sensitivity (mean P(A) ranges from 0.63 to 0.74). Indeed most of

the variation in P(A) occurs within individual junctions and this variation does appear to be

related to the risk ratings for individual exemplars. Calculating the 10 correlations

between risk and P(A) across the six exemplars of each junction produced correlations

ranging from � 0.67 to 0.88. Although only one of these is significantly different from

zero in its own right (r(4) ¼ 0.88, p < 0.05), the magnitude of these correlations is clearly

related to the mean risk rating given for the junction (r(8) ¼ 0.88, p < 0.01). This is best

described as the three most generally risky junctions producing an average correlation

between risk ratings and P(A) that is significantly greater than zero (r(12) ¼ 0.80,

p < 0.01), while the three least risky junctions show a negative average correlation

(r(12) ¼ � 0.58, p < 0.05) and the remaining four junctions show no significant average

correlation between risk ratings and P(A). No similar relationships are evident with either

accident estimates or B. Note also that the relationship between risk ratings and P(A) is not

evident unless exemplars are grouped by junction first, i.e. there is no evidence for an

overall U-shaped (or inverted U-shaped) relationship between the variables. Note also that

although there is some relationship between the actual length of clips in seconds and both

their judged riskiness (r(58) ¼ 0.32, p < 0.05) and P(A) (r(58) ¼ 0.40, p < 0.01), this



fails to explain either the lack of overall relationship between these two measures or the

emergent relationship within junctions.

EXPERIMENT 2

The results from Experiment 1 were somewhat unexpected in that no overall relationship

between risk and recognition sensitivity was observed. Instead different patterns of results

appeared to emerge for different junctions. Since these results were not predicted a priori,

our first concern was whether the results would replicate with different participants and

stimuli. Experiment 2 thus used films of a wide range of new driving situations in an

attempt to generalize and replicate these results.

Most of the junctions used in the previous experiment were already well known by the

participants. One effect of this is that fixed information in the films (i.e. the appearance of

the actual junctions) may have played a relatively small role in the recognition task

compared to the role of variable information (i.e. the traffic present and events taking

place). Experiment 2 used junctions which the drivers were unlikely to have encountered

previously and investigated the recognition of films of driving situations among distracters

that showed similar manoeuvres at different junctions. The fact that the actual locations

differed between films means that both fixed and variable aspects of the stimuli are

potentially useful to participants in performing the recognition task. An attempt was thus

made to quantify the degree to which fixed and variable information were available in

these new stimuli.

Method

Participants

The participants were 40 new drivers who had not participated in the previous experiment.

There were 14 males and 26 females. Participants ranged in age from 20 to 64 (mean 44);

all had held a full British driving licence for at least three years and were paid £5 for their

participation.

Stimuli

The stimuli in this experiment were 48 video films showing the driver’s view ahead while

driving at a variety of locations. They were similar to those used in the previous

experiments except that each film showed a different location and that they were recorded

in places unlikely to be identified by the participants. Half of the films showed turns at

junctions. The other half showed the driver going straight ahead at junctions, sections of

normal road, and three-lane motorway.

Design and procedure

Films were shown in the laboratory as in Experiment 1. The experiment again consisted of

a judgement phase and a recognition phase. In the judgement phase 24 of the films were

shown and risk ratings and accident estimates were given as in Experiment 1, however, in

this experiment the ratings were both made on 7-point scales. In the recognition phase all

48 films were shown and responses were made in the same way as in the previous

experiments. Participants were divided into two groups who watched different films in the

judgement phase. This meant that each film was rated by 20 participants in the judgement

phase and watched by all 40 participants in the recognition phase, appearing to half the

participants as a target and to half as a distracter.




The mean risk rating given was 2.15 (individual films received mean ratings ranging from

1.30 to 3.95) and the mean accident rating was 2.39 (film means from 1.60 to 3.60). The

recognition results were again analysed using signal detection theory aggregating the 14

responses into the same six response categories that were used in Experiment 1. ROC

curves were again calculated for individual stimuli and values of P(A) and B were

obtained. The mean value of P(A) was 0.77 (film scores ranging from 0.52 to 0.94) and the

mean value of B was 3.07 (range 1.60 to 4.38). The similarity between these values and

those from Experiment 1 shows that the recognition task in this experiment was almost as

difficult as that used in Experiment 1 despite the fact that a different location was used for

every film in the present experiment. There was no significant correlation across the 48

junctions between the mean risk rating and either P(A), r(46) ¼ 0.24, or B, r(46) ¼ 0.12.

The results so far parallel those from Experiment 1 in showing no overall relationship

between risk ratings and recognition sensitivity. Inspection of the data additionally

confirms that there was again no evidence for any U-shaped, or inverted U-shaped

relationship between risk and P(A). In this experiment it was not possible to analyse

relationships within the exemplars of individual junctions since each junction appeared

only once. It was nonetheless possible to look for different types of relationship evident

within generally dangerous situations versus generally safe situations. One way of doing

this was to divide the films on the basis of the accident ratings given to them. The first two

columns of Table 1 show the data split this way. The films were divided around the median

into high versus low accident films (three films that received the median score of 2.3 were

excluded from this analysis). This left 22 films in the high accident group and 23 in the low

accident group. Although there was no reliable difference between the groups in terms of

recognition sensitivity there was a significant difference between the groups in the

correlation between risk and P(A) across the films, z ¼ 1.75, p < 0.05.

One problem with using accident ratings to divide the situations into dangerous versus

safe is that since participants do not know the junctions and see each one only once, their

accident estimates may depend highly on the risks actually visible in the exemplar viewed.

Since objective accident data were not available for the 48 sites used in the study, it is not

possible to divide them a priori into dangerous versus safe; however, it is possible to

characterize some manoeuvres as a priori more risky than others. Hall (1986), for example,

finds that 6.6 times more accidents at signalized cross-roads occur to vehicles turning right

than occur to those turning left. This confirms the impression that right turns on British

roads are, in general, more dangerous than left turns. Since the films used in this study

show equal numbers of left and right turns at similar junctions it is possible to split them on

this basis. The third and fourth columns of Table 1 show the data for the 12 right turns and

Table 1. Results from Experiment 2 for different subsets of the stimuli, divided first into highversus low accident estimates and then into right turns versus left turns at junctions

High accidents Low accidents Right turns Left turns

Mean risk 2.468 1.850 2.392 2.063Mean accidents 2.802 2.004 2.658 2.279Mean P(A) 0.783 0.759 0.809 0.800Mean B 3.113 2.995 3.217 3.145n 22 23 12 12Risk. P(A) correlation 0.428 � 0.105 0.462 � 0.357



the 12 left turns separately. Once again there was a significant difference between the two

groups in the relationship between risk and P(A) across the films, z ¼ 1.85, p < 0.05.

Fixed and variable information

To understand the differences between stimuli a separate group of 24 participants subse-

quently rated the 48 films on two additional rating scales. They answered the questions,

‘how much was there to see in the film in terms of moving objects? (Traffic, pedestrians,

etc.)’ and, ‘how much was there to see in the film in terms of fixed objects? (Buildings, road

signs, etc.)’. They answered both questions on a scale from 1 (nothing to see) to 7 (much to

see). Mean ratings for the number of moving objects were significantly correlated across the

48 films with the previous ratings of both risk, r(46) ¼ 0.469, p < 0.01, and accidents,

r(46) ¼ 0.579, p < 0.01. Mean ratings for the number of fixed objects were not signifi-

cantly correlated with either risk, r(46) ¼ 0.150, or accidents, r(46) ¼ 0.233. There was

also a significant correlation between ratings for moving objects and P(A), r(46) ¼ 0.429,

p < 0.01, though not B, r(46) ¼ 0.145, and a significant correlation between ratings for

fixed objects and B, r(46) ¼ 0.397, p < 0.01, though not P(A), r(46) ¼ 0.034.

The correlations of these two new ratings with risk ratings and accident estimates

suggests that danger perceived in traffic scenes is strongly determined by the number of

moving objects visible. The correlations with P(A) suggest that these moving objects are

also important in determining how easy the film is to subsequently identify. The

correlation between fixed objects and B is rather harder to interpret. It shows that

participants had a tendency to call the films with many fixed objects distracters whether

or not they had seen them before. The reason for this may simply be that in the initial risk

and accident rating tasks participants do not attend to much of the fixed information in the

environment. Information about the actual area around the road may in this sense be

peripheral to the driving task. Because such information will not appear familiar in the

recognition task it may cause participants to incorrectly classify targets as distracters. This

may also be part of the reason that overall recognition sensitivity was not higher in this

task. The additional cues available by virtue of each film showing a unique road location

may simply not have been attended to by participants performing the initial rating tasks.

Despite using films widely differing in the levels of subjective risk reported and

junctions and film types themselves demonstrating significant overall differences in risk

ratings, Experiments 1 and 2 failed to find any simple overall relationship between

subjective risk and recognition sensitivity either for all films or for individual junctions or

film types. Instead subjective risk appears to enhance recognition for the more risky

situations and impair recognition for exemplars of the less dangerous ones. One way of

describing the results might be that participants were good at recognizing situations that

accorded to their expectations (i.e. risky exemplars of risky junctions and safe exemplars

of safe junctions). This appears to be the kind of result that might be predicted from some

versions of schema theory (e.g. Brewer & Treyens, 1981, though see also Brewer &

Nakamura, 1984, for a discussion of cases where schema expectancy may actually impair

recognition). Experiment 3 thus attempted to replicate the recognition results and to

provide a direct measure of schema consistency for individual films.

EXPERIMENT 3

The recognition results in Experiment 1 were based on a relatively small amount of data

(18 responses per film) with participants making explicit ratings of risk for the films. Thus



one of the concerns in Experiment 3 was to see whether the recognition results would

replicate. A second aim of Experiment 3 was to obtain a measure of schema consistency

for the films used in the experiments. This was done by having participants give a rating of

how well each film showed the types of things that normally happen at the junction. To

keep the methodology as similar as possible to that used in Experiment 1 two rating tasks

were again used. In this case a speed rating was given instead of the risk rating and the

normality rating was substituted for the accident estimate. One additional concern with

Experiments 1 and 2 was that having participants give risk ratings and accident estimates

while initially watching the stimuli might cause them to be abnormally sensitive to risky

aspects of the films. Since the participants in Experiment 3 received no mention of risk at

any point in the experiment, this problem was avoided.

Method

The stimuli, apparatus and design of Experiment 3 were identical to those used in

Experiment 1. The procedure differed only in that different judgement tasks were used.

Participants

The participants were 36 drivers who had not participated in either of the previous

experiments. There were 18 females and 18 males. Participants ranged in age from 22 to

65 (mean 43); all had held a full British driving licence for at least four years and were paid

£5 for their participation.

Procedure

In the judgement phase participants made two ratings on 7-point scales after hearing the

tone. The first was a rating of the speed the vehicle was travelling, ‘where 1 would indicate

that the driver is going much too slowly and 7 would indicate that the driver is going much

too fast. A rating of 4 would mean that the driver is going at the correct speed for the

conditions.’ The second was a rating of how well the film showed the types of things that

normally happen at the junction, ‘where 1 would indicate that the situation shown was

extremely unusual and 7 would indicate that the events were not surprising in any way.’


The mean speed rating given was 4.26 (individual films received mean ratings ranging

from 3.11 to 5.17) and the mean normality rating was 6.38 (film means ranging from 5.11

to 6.89). The recognition data were aggregated into the same six categories that were used

for Experiment 1 and ROC curves were again calculated for the individual stimuli. The

mean value of P(A) was 0.75 (range 0.44 to 1) and the mean value of B was 3.49 (range

1.83 to 5.10).

To compare the pattern of recognition results with that observed in Experiment 1,

relationships between risk and recognition were explored using the risk ratings from

Experiment 1 and the recognition results from Experiment 3. Again there was no

significant overall correlation between risk ratings and recognition sensitivity, yet there

was a relationship within individual junctions; again this relationship depended on the

mean risk rating given to the junction, r(8) ¼ 0.66, p < 0.05. For the three most risky

junctions risk ratings were positively correlated with P(A), r(12) ¼ 0.66, p < 0.01, while

for the three least risky junctions the relationship was again negative, though not

significantly in this case, r(12) ¼ � 0.48, p ¼ 0.08.



There was no evidence that recognition sensitivity was related to the normality ratings

given by participants r(58) ¼ � 0.20, although the values of response criterion bias, B,

were significantly negatively correlated with the normality ratings, r(58) ¼ � 0.32,

p < 0.05; thus participants were more likely to feel they had seen a film before if it

scored high on normality (whether or not they had in fact encountered it previously).

Discussion

Experiment 3 replicated Experiment 1 with respect to the recognition results and their

relationship with the risk ratings from that study. Again subjective risk appeared to be

related to enhanced recognition of exemplars of generally risky junctions but impaired

recognition of exemplars of generally safe junctions. Experiment 3 additionally demon-

strated that this result did not depend on the particular rating tasks being performed while

watching the films. It also showed that this result was not simply an example of good

recognition for films that accord with participants’ expectations. There was no evidence

that recognition sensitivity was related to the measure of schema consistency. Thus

although schemata may have affected the recognition results in terms of response criterion

bias, the interesting relationships between subjective risk and recognition sensitivity

observed at different junctions could not be adequately described in these terms.

Risk and attention focusing

It was suggested in the introduction that one effect of emotional arousal was to enhance

memory for central information and impair memory for peripheral information

(Christianson & Loftus, 1987, 1991). What effect this might have on overall recognition

performance is not initially clear, since it depends on the definition of central and

peripheral information that is used. Unfortunately it is difficult to formulate a clear

definition of the types of information in a scene that are central and those which are

peripheral. Christianson (1992) for example describes the number written on the victim’s

shirt in a study by Loftus and Burns (1982) as peripheral information but the colour of the

victim’s coat in another study (Christianson & Loftus, 1991) as central information.

Where apparently similar studies have shown different results it may be this definition of

central and peripheral information that is important (e.g. Burke, Heuer, & Reisberg, 1992;

Christianson & Loftus, 1987; Heuer & Reisberg, 1990). Perhaps the best solution to this

problem is to define central and peripheral with respect to the task that a participant is

performing during encoding (Burke et al., 1992; Christianson, 1992).

The general literature of task performance in dangerous environments would suggest

that arousal causes a person to focus their attention on the aspects of a task that they

perceive to be important (Baddeley, 1972). This may explain why some experiments have

failed to find convincing memory effects of attention focusing. In such studies participants

were often simply watching slides without performing any particular task. In such

circumstances it is difficult to know what information should be regarded as central to

task performance. In the current research, however, it is rather easier to make assumptions

about the task the person is performing.

In Experiments 1 and 2 the task of the viewer was to make judgements about risk and

accidents in driving films. Such an appraisal of risk appears to be a very natural response to

driving situations. If it is assumed that the effect of emotional arousal is thus to enhance

memory for information in the films related to risk itself, it may be possible to explain the

relationship between risk and recognition sensitivity that has been observed. The junctions



that received generally high risk ratings showed difficult manoeuvres at complex

intersections. Since there are likely to be multiple sources of risk in such films it is

understandable that enhanced memory for such information would be likely to improve

recognition sensitivity. The junctions that generally received low risk ratings, however,

showed simpler manoeuvres in less complex situations. It may have been that the dangers

in such situations are much simpler, for example, the driver may be travelling too fast for

the conditions. If attention was focused on such a single item of information it is less clear

that this would enhance subsequent recognition. In fact, since some of the distracters are

also likely to contain such information, it may simply cause the participants to make false

alarms every time they see a distracter containing such information. This could potentially

explain the way in which subjective risk appears to enhance recognition sensitivity in

some situations and impair it in others. Moreover, since it makes predictions about

separate effects on numbers of hits and false alarms, it is possible to test the hypothesis

with the data already available.

Predictions from attention focusing

If the effects of attention focusing and the distribution of risk-related information in the

films were as described above, this would produce the observed effects—risk enhancing

recognition at dangerous junctions but impairing it at safe ones. These effects would come

about because at dangerous junctions experiencing subjective risk would increase the

number of hits participants make, while at safer junctions experiencing risk would

increase the number of false alarms. To test this prediction the 10 junctions were divided

into three high risk junctions, three low risk junctions, and four medium risk junctions, on

the basis of the mean risk ratings received in Experiment 1. The six exemplars of each

junction were then divided into two groups of three on the same basis to give high versus

low risk exemplars. Table 2 presents the recognition data split this way. The recognition

data have been aggregated across Experiments 1 and 3. Mean hits and mean false alarms

are thus presented out of a maximum possible of nine for each film.

For high risk junctions there is a significant difference in the number of hits for high risk

exemplars compared to the number for low risk exemplars, t(34) ¼ 2.64, p < 0.01,

though no significant difference in the numbers of false alarms [t(34) ¼ 0.85]. For low

risk junctions this result reverses and there is a significant difference between low and

high risk exemplars in terms of false alarms, t(34) ¼ 3.57, p < 0.01, but not in hits

[t(34) ¼ 0.33]. There are no significant differences between the rates for exemplars of

the medium risk junctions. Note that this tendency to make false alarms when confronted

with high risk exemplars of low risk junctions is also manifested in the response criterion

bias in this case which is significantly lower than in other conditions, t(58) ¼ 2.58,

p < 0.01.

Table 2. Average number of hits and false alarms (FAs), recognition sensitivity (P(A)), andresponse criterion bias (B) from Experiments 1 and 3. Results are shown separately for high and lowrisk exemplars of the three overall levels of junction risk

Junction risk High Medium Low

Exemplar risk High Low High Low High Low

Mean hits 6.45 4.89 5.62 5.15 5.56 5.73Mean FAs 1.72 2.23 2.71 1.65 3.39 1.61Mean P(A) 0.83 0.67 0.71 0.74 0.65 0.76Mean B 3.40 3.45 3.35 3.51 2.98 3.40



There was an apparent inconsistency in the recognition results in Experiment 2;

although splitting the data with respect to accident estimates gave a dissociation in

recognition results, there was no evidence for an actual reversal in the relationship

between risk and P(A) for low accident situations. Considering only junctions and splitting

the data with respect to direction of turn did appear to produce a full reversal. This may be

because of a lack of peripheral information in some of the stimuli. The full set of 48 films

included films of motorways and bends in roads without junctions. A comparison of these

films with those showing junctions revealed that such films indeed are rated as having less

to see in them. There is a small difference, though not a significant one, in the ratings for

the number of moving objects, mean rating 3.91 versus 4.37 [t(46) ¼ 1.28], but a much

larger difference in the ratings for the number of fixed objects, mean rating 3.31 versus

4.87, t(46) ¼ 6.03, p < 0.01. This confirms the impression that a certain minimum

amount of peripheral information in films may be necessary for risk to produce

impairments in recognition sensitivity in generally safe situations.

Attention focusing in high risk situations appears to provide a coherent explanation for

the recognition effects observed. Note that the differences in judgement tasks between

Experiments 1 and 3 did not appear to alter this result. This may be because risk

assessment is part of a well-practised and natural method of scanning the driving

environment. The weakness in the interpretation of the recognition results in terms of

attention focusing towards risk-related information is that it relies on a number of

assumptions about the distribution of risk-related information among the stimuli. Speci-

fically it has been assumed that risky exemplars of generally risky junctions contain a great

deal of risk-related information whereas risky exemplars of generally safe junctions

contain relatively little risk-related information, and that such information is likely to be

shared by multiple films.

EXPERIMENT 4

Experiment 4 was designed to provide descriptions of the information in films and

specifically of risk-related information. The films were shown in 5 s sections with

participants having unlimited time to describe each section. This allowed participants

sufficient time to appreciate what was happening in the film, but reduced the scope for

forgetting to occur. While this technique worked well for simple descriptions, pilot

participants found it more difficult to describe risks this way because risks often took some

time to develop. To avoid this problem participants were asked to describe potential risks

as well as actual ones and to describe the development of risky situations.

Method

Participants

The participants were 20 drivers who had responded to a newspaper advertisement. There

were eight females and 12 males. Participants ranged in age from 21 to 62 (mean 44); all

had held a full British driving licence for at least four years and were paid £5 for their

participation.

Stimuli/apparatus

The stimuli in this experiment were 24 of the 60 video films used in Experiments 1 and 3.

These constituted three exemplars of each of eight junctions. Four of the junctions were



chosen as high risk junctions and four as low risk junctions on the basis of the mean risk

ratings given in Experiment 1. For each junction three exemplars were chosen to give one

low risk, one medium risk, and one high risk exemplar. Films were divided into 5 s sections

separated by a plain blue field.

Design/procedure

There were two versions of the experiment, a description condition and a potential risks

condition; 10 participants were used in each condition. Films were shown in the laboratory

as in Experiments 1 to 3. Each participant viewed the 24 films in a randomized order. After

5 s of film had been viewed, the projector was paused with only a plain blue field visible

and the participant performed one of two tasks. In the descriptions condition participants

simply described the situation depicted in the film section. In the potential risks condition

participants specifically described risks and potential risks from the film section. The

responses were recorded and subsequently transcribed.

Results

The transcripts for the 20 participants were grouped according to the film section to which

they referred. A coding system was then developed loosely based on the British police

force’s STATS-19 accident recording form. This system allowed comments to be divided

into one of 50 categories. Thirteen of these categories referred to fixed aspects of the

situation (e.g. road width, signposts, junctions), the remaining 37 categories coded

variable aspects of the situation (pedestrians, weather conditions, other vehicles). The

same 50 categories were used for both the description and the potential risks conditions. A

small number of comments (less than 5% of the sentences transcribed) did not refer

directly to any potential or actual driving event (e.g. ‘Having to remain vigilant’), such

comments were not coded.

A total of 6467 comments were coded, 4587 from the description condition and 1880

from the potential risks condition. This corresponded to each participant giving a mean of

19.1 descriptive comments or 7.8 comments relating to potential risks for each film.

Figure 1 shows these data and the corresponding risk and recognition sensitivity data,

divided by junction risk and exemplar risk.

The descriptions and the potential risks conditions were analysed separately. For the

descriptions condition analysis of variance with two within subject factors demonstrated a

significant main effect of junction risk, F(1, 9) ¼ 195.21, p < 0.01, a main effect of

exemplar risk, F(2, 18) ¼ 16.66, p < 0.01, and an interaction between the two,

F(2, 18) ¼ 18.98, p < 0.01. Post hoc multiple comparisons demonstrated that the

difference between high and low risk junctions was significant at all three levels of

exemplar risk, p < 0.01. For high risk junctions both high and medium risk exemplars

were given significantly more descriptive comments than low risk exemplars, p < 0.01.

For low risk junctions high risk exemplars were given significantly fewer descriptive

comments than both medium and low risk exemplars, p < 0.05.

For the potential risks condition a similar analysis again showed a significant main

effect of junction risk, F(1, 9) ¼ 43.01, p < 0.01. There was, however, no significant main

effect of exemplar risk [F(2, 18) ¼ 0.95] or interaction [F(2, 18) ¼ 3.04, p ¼ 0.07].

Although the total amount of comments coded was substantially lower than in the

descriptions condition, the general distribution of comments was nonetheless very similar

to that in the description condition.



A surprising aspect of these results is that there was no significant relationship between

the risk ratings previously given to the films and the amount of information given in the

potential risks condition [r(22) ¼ 0.271]. Again we examined the possibility that there

were different effects at high and low risk junctions; although neither correlation was

significant, there was a generally positive correlation between risk ratings and information

in the potential risks condition for high risk junctions [r(10) ¼ 0.326] and a negative one

for low risk junctions [r(10) ¼ � 0.330].

Discussion

It is readily apparent from Figure 1 that the distribution of information differs at different

types of junctions. For the high risk junctions, risky exemplars are characterized by having

a large total amount of information available and a large number of potential risks. For low

risk junctions this pattern is not evident, there is no tendency for risky exemplars to have

Figure 1. Average recognition sensitivity (P(A)) and risk ratings from Experiment 1 for the 24 filmsused in Experiment 4, and the mean number of coded comments in the description and potential risksconditions of Experiment 4. Results are shown separately for high and low risk exemplars of thethree overall levels of junction risk and comments relating to fixed aspects of the junction are coded

separately from those relating to aspects that potentially change between viewings



more information than less risky exemplars either generally or specifically related to risks.

This is precisely the assumption that was made by the attention focusing interpretation of

the recognition results. It was assumed that risky junctions had numerous sources of risk

and that attention focusing towards risk-related information would thus increase a viewer’s

ability to identify old items (hits) hence improving recognition sensitivity. On the other

hand it was assumed that less risky junctions would contain few sources of risk and

attention focusing towards risk-related information would thus cause participants to make

false alarms hence impairing recognition sensitivity.

GENERAL DISCUSSION

The first thing worthy of note about Experiments 1 to 3 is generally how poor participants’

memory proved to be. Studies of long term visual memory have traditionally concluded

that it is extremely good (e.g. Shepard, 1967; Standing, Conezio, & Haber, 1970). In the

Standing et al. study people viewed more than 2000 pictures and in later memory tests

were approximately 90% accurate in a two-alternative forced choice recognition test. It

might be predicted that with far fewer items, and each involving a protracted video rather

than a single scene, visual memory would be even better than this in our experiments.

Instead, when shown 48 films of different events at different junctions, participants were

only correct in their responses on 72% of occasions, and performance was even worse

when junctions were repeated (66% correct in Experiment 1). Although performance did

vary from exemplar to exemplar, it was nowhere near perfect in any case (in Experiment 2

the best performing junction was recognized correctly on 87.5% of occasions). The

participants in our experiments did not expect their memories to be tested and this may

explain the relatively poor level of recognition achieved. Instead they were presumably

attending to a limited range of information from the scenes, and we have suggested that

this often involved a specific focus on risk.

Another important conclusion from the experiments in this paper is that all risks are not

all equal. The differing relationships between recognition sensitivity and risk ratings in

generally safe and generally dangerous situations reflect the different types of risks present

in such situations. This creates problems for theories of driving that use subjective risk as a

single homogeneous concept in determining behaviour. Imagine the following two

situations: Firstly driving on an empty rural road close behind a fast moving vehicle,

secondly driving down a busy urban road with numerous parked vehicles and pedestrians

present. It is possible that many judges would rate the two situations equally in terms of the

absolute level of risk present (this is just what is demonstrated in Figure 1). However, the

sources of risk and the appropriate behavioural responses to them might be quite different

in the two cases. Indeed, studies recording participants’ eye movements in just such

situations report clear evidence for differences in patterns of visual search. Chapman and

Underwood (1998a, 1998b) found that films of quiet rural roads elicited long fixation

durations generally straight ahead, while films of busy urban roads elicited shorter fixation

durations with greater horizontal scanning. In both cases fixation durations increased and

variance in scanning decreased when the participants recognized a specific hazard present

in the scene. However, variance in horizontal gaze angle (a measure of scanning) in

dangerous urban scenes was still greater than that in safe rural scenes (i.e. ‘focused’

attention in urban scenes still involved greater scanning than the ‘unfocused’ attention in

the generally safer rural scenes). These eye movement results are clearly compatible with



the descriptions and potential risks generated in Experiment 4. Dangerous urban situations

have multiple sources of potential risk and even when attention may be focused on one

particular object in the scene, regular scanning elsewhere for additional risks may still be

necessary. In contrast the dangerous rural scenes may allow much greater attention

focusing. Where a single source of danger is located almost all the driver’s attention can be

devoted to it and other aspects of the scene effectively ignored.

A question that is raised by these experiments is whether memory for driving can be

predicted completely by a recording of drivers’ eye movements. A recent study by

Underwood, Chapman, Berger, and Crundall (2003) sheds light on this question by

recording drivers’ eye movements and then testing their recall of events from driving

scenes. As would be expected recall in dangerous situations did show reduced availability

of peripheral information, but not all information that was fixated by the viewer could be

later recalled, and not all recalled information was actually fixated. This can also be

related to the different types of risk present in various driving situations. Some risks are

sufficiently predictable given the general situation that they can be correctly recalled even

without fixation. Note here the difference in predictions that would be made between recall

and recognition for driving situations. While predictability may apparently enhance recall

by allowing people to guess information that they did not originally attend to, more

rigorous recognition tests are sensitive to such effects and high false alarm rates allow us to

conclude that overall memory performance in such situations may actually be worse.

This series of studies has shown that risk is an important and natural way for our

participants to judge driving situations and that these judgements can be related to the

participants’ subsequent ability to recognize the situations that they have seen. The results

are interpreted with the assumption that subjective risk is generally characterized by a

focusing of attention onto just those aspects of the situation where potential dangers are

anticipated. Such focusing of attention onto risks and potential risks in the driving

environment does not actually require the participant to be driving. Instead it may be a

standard pattern of visual search and information processing that is learned by experienced

drivers; indeed acquiring the ability to search the road environment in this way may be an

important characteristic of driving expertise. It would be of considerable applied interest

to know whether such effects would also be obtained by participants without experience of

driving a motor vehicle.

Our findings are also compatible with studies using peripheral detection tasks to show

that drivers are impaired in detecting peripheral targets at times of high workload or

hazard (e.g. Crundall, Underwood, & Chapman, 1999, 2002; Harms & Patten, 2003). In

applied circumstances the problem with this conclusion is deciding how to categorize

information as central or peripheral. In peripheral detection tasks the distinction is purely

spatial, but the results of the current experiments suggest that a more flexible definition

may be necessary. Here our interpretation is that central information is that which the

driver perceives as relevant to the driving task, particularly that information which may

indicate potential risks to the driver. This will create problems for drivers learning about

risks in the traffic environment. Unless multiple sources of risk are appreciated by drivers

they may selectively focus on the most apparent source of danger (this is similar to the

phenomenon of ‘attentional capture’ sometimes described in novice drivers—Zwahlen,

1993). This may place them at risk of accident from unattended sources and will certainly

impair their ability to learn about the behaviour of such sources in dangerous situations. In

longer term memory tests, however, such problems may not be apparent. The vast majority

of information available while driving is sufficiently mundane that it is rapidly and



routinely forgotten in normal circumstances (e.g. Chapman & Underwood, 2000;

Chapman et al., 1999; Luoma, 1993). What is remembered of everyday driving is that

which is attended to most closely—central information from dangerous situations.

ACKNOWLEDGEMENTS

This research was partially supported by a research grant to the second author and Dr I. D.

Brown from General Accident plc.

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Risk and the recognition of driving situations

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