executive dysfunction in a survival environment

APPLIED COGNITIVE PSYCHOLOGYAppl. Cognit. Psychol. 24: 41–66 (2010)Published online 7 January 2009 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/acp.1542
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Executive Dysfunction in a Survival Environmenty

HEATHER PORTER1 and JOHN LEACH2*1Defence Science & Technology Ltd, Human Systems Group, Farnborough, UK

2FSES (NorDISS), Oslo/mil Akershus, N-0015, Oslo, Norwayz

SUMMARY

Victims often respond to survival incidents with maladaptive behaviours that suggest impairment inexecutive function. To examine this hypothesis the authors tested sub-components of executivefunction during an intensive military survival exercise. Compared to a control group the survivalcourse participants showed significant impairment in the incongruent condition of the Stroop task; themean repetition gap and adjacent letter pair components of the random letter generation task; and theplanning and action components of the Tower of London task. No impairment was found in dual-taskperformance nor in verbal fluency. The pattern of the data suggests that the maladaptive behaviourfrequently observed in survival incidents may be explained by dysfunction in the supervisory system-contention scheduler interface. Copyright # 2009 John Wiley & Sons, Ltd.

A former Chief of a Coastguard search and rescue team recently expressed his opinion to

one of us that ‘under stress people do stupid things’. His view was based on years of

experience in recovering victims of accident and disaster on both land and sea. Certainly, it

has long been known that, when faced with a hostile situation, people engage in behaviours

that seem counter-indicated for survival (Leach, 2004). Furthermore, there appears to be a

pattern to these behaviours (Leach, 1994) that begs the question about the nature of their

underlying cognitive processes.

Intuitively, a threatening situation would place a high information load on the cognitive

system. To enhance survival this information must be processed, interpreted and responded

to in a timely and appropriate manner. Yet various official investigations clearly indicate

impairment in cognitive functioning under threat (e.g. the Boeing 737 fire at Manchester

Airport, 1985; the explosion and capsize of Piper Alpha, 1988; the Estonia sinking, 1994).

For example, witnesses report passengers during the Estonia sinking just standing still,

seemingly apathetic and bewildered whilst others appeared paralysed (JAIC, 1997).

Such behaviours can also be observed in situations which, whilst providing environmental

duress, do not actually threaten life or limb. We observed a serviceman, participating in an

intensive military survival exercise, repeatedly give his radio call-sign when prompted for his

name. He commented later that, whilst he knew his responseswere incorrect, hewas unable to

break out of his response rut (personal debrief). Perseveration was also observed in a woman

passenger in the Boeing-737 fire atManchester airport (1985) who, to aid evacuation, was told

Correspondence to: John Leach, FSES(NORDISS), Oslo mil/Akershus, 0015 Oslo, Norway & Department ofsychology, University of Lancaster, Lancaster, LA1 4YF, UK. E-mail: [email protected] article was published online on 7 January 2009. An error was subsequently identified. This notice is includedthe online and print versions to indicate that both have been corrected [date to be added later].orrection made here after initial online publication.

opyright # 2009 John Wiley & Sons, Ltd.

42 H. Porter and J. Leach

to open the emergency overwing exit door positioned next to her seat. The opening of the door

involved lifting a lever upwards, but the woman was seen instead to be pulling repeatedly on

the armrest of her seat (AAIB, 1988). Other passengers attempted to remove their luggage

from the overhead bins before evacuating the aircraft suggesting stereotypical behaviour.

Other indications of cognitive impairment come from survivors who report difficulties in

formulating and implementing plans during an emergency as well as impaired judgement and

decision-making ability (Leach, 1994).

Reactions such as stereotypy, perseveration and loss of initiative, have been frequently

observed amongst victims and provide a possible clue to the source of this cognitive

impairment, particularly as the same types of behaviour are observed in patients suffering

from disorders affecting executive function, for example, Parkinson’s disease, Alzheimer’s

type dementia, schizophrenia and Korsakoff syndrome. This concordance led to the

proposition that victim behaviour witnessed under threat arises through impairment in

executive function (Leach, 2005). The idea that survival behaviour may involve the

supervisory system has received some support from a recent empirical study (Leach &

Ansell, 2008) which showed impairment in both selective and sustained attention in RAF

personnel undergoing a military survival exercise which, taken together, suggests

dysfunction in executive attention (Engle, 2002; Kane & Engle, 2002). Further implication

of the supervisory system under intrinsic threat comes from the finding in a study looking at

working memory during parachuting (Leach & Griffith, 2008) which suggested that the

search and retrieval of information from long-term memory (a supervisory function) was

impaired during the jump. Also, the supervisory system may not be not transferring

information from working memory to LTM whilst parachuting (e.g. Breivik, Roth, &

Jørgensen, 1998; Thompson, Williams, L’Esperance, & Cornelius, 2001).

One of the more influential models of executive function is that by Norman and Shallice

(1980, 1986) who proposed the concept of a supervisory attentional system (SAS) that

modulates the attentional processes involved in the selection and execution of actions and

thoughts. The SAS is called upon in situations that involve planning, novelty, trouble

shooting, error correction or the inhibition of an habitual response. Normal routine

operations, actions and thoughts are represented by a series of schemata that are run off

consecutively. These routines are capable of realising relevant goals effectively and are

selected through the automatic triggering of well-learned perceptual or cognitive cues

(Shallice & Burgess, 1996). In routine situations environmental stimuli are sufficient to

trigger the activation of relevant schemata that are under the control of a relatively

automatic system called the contention scheduler (Cooper, 2002; Cooper & Shallice, 2000).

In normal circumstances, the contention-scheduling component of the model selects a

schema and it remains active until it has attained its goal or is actively inhibited by a

competitor or higher level control. Without SAS control a selected schema remains

continuously active. Similarly, distractibility may be explained by the fact that the SAS is

failing to select a single action schema amongst a number of competing ones and,

therefore, the contention scheduler becomes easily and briefly activated by any number of

schemata. Therefore, the failure of the supervisory component of the SAS can result in

perseverative behaviour. Other behaviours exhibited by survivors, such as stereotypical

behaviour, may also be explained in terms of the SAS model. During threat, impairment of

the SAS may prevent individuals from constructing and executing new, appropriate actions

to aid their survival. Instead, well-rehearsed, stereotyped action schemata are

implemented, being selected by the contention scheduler because they share characteristics

similar to, but clearly not identical to, the threat environment. Consequently, the SAS

Copyright # 2009 John Wiley & Sons, Ltd. Appl. Cognit. Psychol. 24: 41–66 (2010)

DOI: 10.1002/acp

Executive dysfunction in survival 43

model seems to account for the impairments observed both by individuals with executive-

based clinical disorders and individuals exposed to hostile situations who exhibit

maladaptive behaviour.

Attempts have been made to fractionate the executive system within the SAS

framework. According to Shallice and Burgess (1996), if the processes associated with the

supervisory system are indeed separable, then a dissociation between certain tasks should

be expected whereby individuals might perform well on one executive task that taps into

one process, but poorly on another task that taps into a different process. Evidence for the

differential involvement of separable executive functions has been provided by, for

example, Baddeley, Della Sala, Papagno, and Spinnler (1997b); Alderman (1996); Burgess

and Shallice (1996); Owen, Downes, Sahakian, Polkey, and Robbins (1990).

In summary, existing research on the behaviour of survivors indicates that certain

individuals experience cognitive dysfunction when exposed to threat. Evidence points

towards this dysfunction being executive in nature, although it is not known which specific

executive processes are vulnerable. Current investigations into the structure of the

executive system suggests that the system may comprise a number of independent sub-

processes. It has been proposed that the central executive construct, originally proposed as

a global supervisory system, may be replaced with an integrated but separable system

comprising at least four executive sub-processes (Baddeley, 1996; Baddeley & Della Sala,

1996). These functions include the capacity to co-ordinate simultaneous activities, to

switch attention from one activity to another, to attend selectively to one activity whilst

suppressing the processing of irrelevant stimuli, and the capacity to encode (learn), access,

retrieve and manipulate information in long-term memory. The functions outlined by

Baddeley share similarities with the functions discussed by others (Burgess and Shallice,

1996; Shallice and Burgess, 1996; Fournier and Larigauderie, 2001; Ward, Roberts, &

Philips, 2001). To this list we would also add planning (Shallice, 1982; Newman,

Carpenter, Varma, & Just, 2003; Carpenter, Just, & Reichle, 2000). Given the apparent

separability of processes within the executive system, it is not yet clear whether the

observed cognitive dysfunction in survivors is selective (specific to certain sub-functions)

or global (affecting the entire executive system).

The armed forces run intensive courses that realistically simulate the novel and

frequently harsh conditions of a survival situation. These courses provide a setting with

controlled risks to study cognitive function in a field environment and, as Barnard, Scott, &

May (2001) have pointed out, such situations are likely to expose any limited capacity of

the central executive for examination. Whilst observing these courses, both the course

instructors and ourselves have noted behaviours that reflect those reported by people in

hostile situations and indicative of environmentally induced cognitive impairment. Many

of the components of a real incident are experienced by those who undertake these courses

and several participants have previously reported difficulties in adapting to an environment

that is both unfamiliar and stressful.

METHOD

Participants

All participants were RAF aircrew. The experimental sample comprised a total of

58 participants (male:female, 52:6), age range 19–35 years (mean¼ 24.34 years) whowere

drawn from four survival courses that ran during winter months in the north of England.


DOI: 10.1002/acp


The control group comprised 20 participants (18:2), age range 19–30 (mean¼ 23.65 years).

Although it is an operational requirement for aircrew to complete the military survival

course, individual participation in this experimental study was voluntary and conducted in

accordance with the British Psychological Society ethical guidelines for experiments with

human participants (BPS, 1993). Of a total of 81 course attendees only three declined to

take part and were excluded from the study.

Equipment and test materials

To test the proposition that impaired cognitive function is consequent upon executive

dysfunction, the following areas of executive function were probed: Dual task co-

ordination, selective attention, response suppression and manipulation of information in

long-term memory (Baddeley, 1996) and planning (Shallice, 1982; Newman et al., 2003;

Carpenter et al., 2000). Five tasks were included in the executive function test battery: The

Stroop test, a pen-and-paper dual-task test, a verbal fluency task, a modified Tower of

London (ToL) test and a random letter generation task.

Dual task test (Baddeley et al., 1997a)

This test is designed to be an indicator of a person’s ability to deploy attentional resources

during single and dual task conditions and comprises three components: Memory span, a

tracking task and a combined span and tracking task. Each component takes 2minute to

complete. Firstly, each participant’s maximum digit span length was determined. For the

tracking task, participants were presented with A4-size white laminated sheets showing a

configuration of 80 1 � 1 cm2 boxes connected by single lines. ‘Start’ and ‘finish’ boxes

were indicated at each end of the chain. With their non-dominant hand, and using a felt-tip

pen, participants were required to write a cross in each box beginning at the start box and

working along the chain in order towards the finish box inserting as many crosses as

possible within 2minutes. The score was the total number of correctly marked boxes

achieved within 2minutes. In the dual-task component each participant was required to

perform both the digit span and the box crossing tasks simultaneously within 2minutes.

Different span lists and different box configurations were used for re-tests.

Stroop test (Stroop, 1935)

This was the standard Stroop test in which participants had to recite aloud the names of

colours written in coloured inks that were either congruent or incongruent with the word.

There was also a control trial in which names of colours printed in black ink were

presented. The words were printed in a list of 25 colour words with 5 different lists

representing one for each condition.

Verbal fluency

Eleven categories (e.g. names of countries, types of bird, etc.) were selected, based on

category norm data reported by Battig and Montague (1969) and previously used in verbal

fluency research (e.g. Moscovitch, 1994). One category was used for a practice trial and the

remaining 10 categories were used for the test phase. The score was the number of category

items generated within 1minute for each of two categories per session.

Random letter generation task (Baddeley, 1966, 1991)

This task required the participants to recite aloud alphabet letters in as random a manner as

possible for 1minute at speeds of 0.5, 1 and 2 seconds. To regulate the pace of recitation an


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electronic timer was used with a red light-emitting diode that flashed at either 0.5, 1 or

2 second intervals.

The Stroop test and random letter generation tasks required verbal responses that were

recorded using a cassette recorder and tie-pin microphone.

The tower of London task (Shallice, 1982)

The TOL task comprised two identical peg boards each with a flat base (30� 10� 1.5 cm3)

and three vertical pegs arranged in a straight line and spaced 10 cm apart. Five different

coloured circular discs (2.5 cm diameter) slotted onto the pegs. One board represented a

‘start’ state and the other board the ‘end’ state. The task of the participant was to move pegs

on the start board to match the arrangement of those on the end state board in as few moves

as possible. There were two levels of difficulty: The less difficult task involved the goal

state being achieved in five moves with no sub-goals and no sub-goal chunks (see Ward &

Allport, 1997), whilst the more difficult task required seven moves and involved three sub-

goals and three sub-goal chunks.

Procedure

The survival course comprises an initial 4 days of classroom theory followed by a practical

field phase that is designed to provide a realistic simulation of an ‘aircraft down’ survival

incident. Course students were dressed in flying suits and carried only the standard issue kit

found in their aircraft personal survival pack.

The test battery was administered to the experimental groups as follows: Participants on

the first two survival exercises (n¼ 32) completed the Stroop and dual task tests whilst

participants on the last two courses (n¼ 26) completed the random letter generation, verbal

fluency and ToL tests. The control group received all five tests. All participants were given

practice trials on the test battery. Baseline data were obtained from all participants during

the classroom phase of the course. The experimental groups were retested in the field on

four consecutive days whilst the control group were retested in the classroom also on four

consecutive days.

Different versions of the dual-task, Stroop, verbal fluency and ToL tests were

constructed for use across the five test days. The order and presentation of the tests were

randomised across participants and days. In the RLG task the order in which the pacing

intervals were presented was randomised. Responses to the verbal fluency and RLG tests

were recorded using a tie-pin microphone and cassette recorder for later transcription.

RESULTS

Stroop test

The Stroop test generated three scores per day: The time taken to read a word list under

control, congruent and incongruent word forms. Summaries of descriptive statistics are given

in Table 1. The Stroop test was analysed using a 5 (day)� 3 (Stroop)� 2 (group) ANOVA.

Analyses revealed significant overall main effects for day, Stroop and group along with a

significant interaction for day� Stroop. Summary ANOVA statistics are given in Table 2.

We analysed the data further under each of the Stroop word forms: Control, congruent and

incongruent. Under each form we compared the performance of both the experimental and


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Table 1. Mean times for Stroop test completion (second)

Form Group 0 1 2 3 4

Control Control 6.51 (1.89) 6.28 (1.06) 5.95 (0.73) 5.85 (0.83) 5.76 (0.79)Experimental 6.95 (1.69) 6.81 (1.51) 6.61 (1.41) 6.39 (1.36) 6.27 (0.93)

Congruent Control 6.52 (0.82) 6.76 (1.23) 6.62 (1.34) 6.31 (0.81) 6.18 (1.11)Experimental 7.58 (2.08) 7.56 (1.96) 7.65 (2.25) 6.77 (1.43) 6.85 (1.54)

Incongruent Control 15.06 (3.68) 13.44 (1.95) 12.34 (2.66) 11.66 (2.63) 11.09 (2.09)Experimental 15.92 j(2.91) 15.96 (3.20) 14.61 (2.82) 13.39 (2.42) 12.86 (2.39)

SDs in parentheses. Group N: Control¼ 20, experimental¼ 32.


control groups under baseline (classroom) and environmental (survival exercise for the

experimental group and continuing classroom for the control group) conditions.

Stroop (control) revealed no significant differences between the experimental and

control groups (F(1, 50)¼ 3.162, p> 0.05) and no interaction was found between group

and day (F(1, 50)¼ 0.048, p> 0.05). A significant within-subjects effect was seen across

the days suggesting that both groups were improving with practice (F(1, 50)¼ 27.742,

p< 0.001).

Stroop (congruent) revealed a significant difference between the experimental and

control groups (F(1, 50)¼ 4.348, p< 0.05), however, this difference was confined to the

baseline condition (F(1, 51)¼ 4.723, p< 0.05). This difference is attributed to the fact that

both groups inadvertently received two different versions of instructions on conducting the

congruent Stroop test. This discrepancy was identified and rectified before the field phase

began. No difference between the two groups was found during the field phase (F(1,

50)¼ 3.743, p> 0.05). Significant within subjects effects were found for both the

experimental group (F(1, 31)¼ 6.606, p< 0.05) and the control group (F(1, 19)¼ 12.012),

p< 0.01) suggesting that both groups were again improving with practice.

Stroop (incongruent) revealed a significant difference between the experimental and

control groups (F(1, 50)¼ 7.678, p< 0.01). No significant difference was found between

the control and experimental groups in the baseline phase (F(1, 51)¼ .892, p> 0.05). The

Table 2. Summary analysis of variance statistics for Stroop test

Source SS df MS F

Within-subjectsDay 274.335 4 68.584 40.638��

Day� group 8.375 4 2.094 1.241Error(day) 337.536 200 1.688Stroop 8148.514 2 4074.257 654.542��

Stroop� group 55.317 2 27.659 4.443Error(Stroop) 622.459 100 6.225Day� Stroop 186.858 8 23.357 19.818��

Day� Stroop� group 13.703 8 1.713 1.453Error(day� Stroop) 471.428 400 1.179

Between-subjectsGroup 216.388 1 216.388 7.434��

Error 1455.369 50 29.107

��p< 0.01.


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experimental group performed significantly worse than the control group during the field

phase (F(1, 50)¼ 10.757, p< 0.01).

Dual task

The dual-task test provides a measure of the participant’s ability to deploy attentional

resources during dual-task operations. The response measure m is given as the proportion

of boxes crossed to lists recalled under dual compared to single task conditions (Baddeley

et al., 1997a) and is defined as

m ¼ 1� ðPm þ PtÞ2

� �� 100

where Pm is the proportional loss of memory performance and Pt is the proportional loss

of spatial performance under dual task conditions. These are calculated as follows: For the

memory task, if ns and nd are the number of lists correctly recalled under single and dual

task conditions then the total number of lists presented under each condition is given by Ns

and Nd. The proportion of lists recalled under single and dual task conditions, ps and pd, are

calculated by ps¼ ns/Ns and pd¼ nd/Nd and the proportional loss of memory performance is

given by ps� pd¼Pm. Similarly, for the spatial tracking task, if ts and td are the number of

boxes crossed in single and dual task conditions then the proportion of single-task

performance lost during dual task conditions is given by Pt¼ (ts� td)/ts. The dual task

measure (m) is expressed as a percentage of single task performance on both tasks. The

descriptive statistics are given in Table 3.

A 5 (day)� 2 (group) ANOVA revealed no significant main effects. Summary ANOVA

statistics are given in Table 4.

Table 3. Mean scores for dual-task test

Day

Group 0 1 2 3 4

Control 94.90 (16.75) 89.85 (10.94) 91.25 (10.66) 90.80 (10.73) 90.85 (10.38)Experimental 93.25 (13.00) 89.75 (13.86) 88.50 (11.75) 89.56 (11.50) 90.44 (9.46)


Table 4. Summary analysis of variance statistics for dual-task test

Source SS df MS F

Within-subjectsDay 636.179 4 159.045 1.300Day� group 54.548 4 13.637 .111Error(day) 24465.890 200 122.329

Between-subjectsGroup 93.102 1 93.102 .392Error 11883.710 50 237.674

All p non-significant.


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Table 5. Mean scores for verbal fluency test

Day

Condition Group 0 1 2 3 4

No words Control 45.45 (10.06) 45.00 (13.09) 43.85 (11.03) 42.75 (12.66) 43.10 (9.54)Experimental 45.42 (12.39) 45.96 (9.46) 42.19 (10.58) 41.46 (8.17) 44.69 (12.27)

Switch Control 9.68 (2.05) 8.65 (2.54) 8.33 (2.00) 8.25 (1.80) 8.48 (2.72)Experimental 8.59 (2.04) 8.35 (1.99) 8.69 (2.57) 7.96 (2.00) 8.17 (1.86)

Cluster Control 3.58 (1.01) 3.52 (1.33) 3.99 (1.75) 4.05 (1.87) 3.58 (1.26)Experimental 3.81 (1.05) 4.01 (1.30) 3.80 (1.34) 3.83 (1.33) 4.55 (1.98)



Verbal fluency

Each participant generated two different subject categories for each of the 5 days. Category

intrusions, that is, items that did not fall within a correct category, were excluded as were

category repetitions. The descriptive statistics are given in Table 5. Both groups performed

similarly on the task, generating between 42–45 items per day.

A 5 (day)� 2 (group) ANOVA revealed no significant main effects in verbal fluency.

Summary ANOVA statistics are given in Table 6a.

Two further measures were obtained from the data: A mean cluster score and a category

switching score. A cluster is defined as a group of contiguous words belonging to the same

semantic sub-category, for example, gooseberry–blackberry–strawberry all of which

belong to the subcategory ‘berry-fruits’. Associations were considered to be clusters only

when at least three consecutive words were semantically related (Robert et al., 1998). For

each response set a mean cluster size was calculated by summing the number of items

within each cluster and dividing this number by the number of clusters generated within the

response set. A 5 (day)� 2 (group) ANOVA revealed no significant main effects for the

cluster data. Summary ANOVA statistics are given in Table 6b.

The category switching score was the number of transitions that arose between clusters,

that is, in the response set England–Ireland–Scotland–Wales–Norway–Finland a switch

lies between the responses Wales and Norway. A 5 (day)� 2 (group) ANOVA revealed no

significant main effects for category switching. Summary ANOVA statistics are given in

Table 6c.

Table 6a. Summary analysis of variance statistics for verbal fluency test

Source SS df MS F

Within-subjectsDay 398.485 4 99.621 .932Day� group 88.554 4 22.139 .207Error(day) 18806.063 176 106.853

Between-subjectsGroup .397 1 .397 .002Error 7762.333 44 176.417



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Table 6b. Summary analysis of variance statistics for verbal fluency: Cluster score

Source SS df MS F

Within-subjectsDay 3.825 4 .956 .429Day� group 11.263 4 2.816 1.264Error(day) 391.951 176 2.227

Between-subjectsGroup 3.645 1 3.645 2.206Error 72.707 44 1.652


Table 6c. Summary analysis of variance statistics for verbal fluency: Category switching

Source SS df MS F

Within-subjectsDay 26.650 4 6.663 1.978Day� group 11.867 4 2.967 .881Error(day) 592.767 176 3.368




Random letter generation

The data for each participant comprised a set of letters (response set) generated during a 1-

minute period for each of the three conditions (0.5, 1 and 2 seconds) for each day. The

response sets were analysed for randomness using the software program RgCalc (Towse &

Neil, 1998). Summaries of descriptive statistics are given in Table 7. The following indices

were calculated as reflections of stereotypy, perseveration and inhibition:

N

N represents the proportion of items produced during the 1minute time period relative to

expected, or ‘perfect’, performance. For the 0.5 second condition, in which participants had

to generate letters at a speed of one per half second for 1minute, the expected number of

items generated would be 120 (60/0.5). For the 1 and 2 second conditions, the expected

number of items would be 60 (60/1) and 30 (60/2) respectively. The value ofN is calculated

as a percentage of the expected score.

Random number generation (RNG)

The random number generation score represents the distribution of response pairs, or

digrams, that is, how often any response alternative follows any other response alternative.

The RNG score has a range between 0 and 1, with 0 indicating an equal distribution of

response pairs and scores nearing 1 indicating a bias of particular response pairs.


DOI: 10.1002/acp

Table

7.Meanscoresforrandom

letter

generation

Day

Condition

Group

01

23

4

N0.5second

Control

68.55(17.84)

78.90(14.64)

85.10(15.74)

88.75(19.90)

89.95(18.27)

Experim

ental

68.96(10.01)

76.23(12.95)

84.35(16.06)

88.15(15.87)

90.96(15.49)

1second

Control

52.80(7.35)

56.45(5.28)

57.10(4.08)

57.90(4.92)

57.15(4.44)

Experim

ental

54.15(7.57)

55.96(4.42)

58.31(8.04)

58.50(4.58)

58.46(5.49)

2second

Control

29.30(1.17)

29.85(0.49)

29.95(0.22)

30.00(0.00)

29.95(0.22)

Experim

ental

29.46(1.24)

29.35(0.75)

29.35(0.85)

29.77(0.43)

29.81(0.57)

RNG 0.5second

Control

0.19(0.07)

0.19(0.06)

0.20(0.06)

0.21(0.08)

0.20(0.05)

Experim

ental

0.19(0.07)

0.17(0.08)

0.19(0.05)

0.19(0.07)

0.18(0.06)

1second

Control

0.14(0.07)

0.14(0.07)

0.11(0.06)

0.13(0.05)

0.13(0.05)

Experim

ental

0.12(0.06)

0.12(0.05)

0.12(0.06)

0.19(0.16)

0.12(0.05)

2second

Control

0.08(0.07)

0.06(0.06)

0.05(0.07)

0.07(0.08)

0.07(0.08)

Experim

ental

0.04(0.05)

0.08(0.06

0.06(0.05)

0.06(0.06)

0.07(0.08)

TPI 0.5second

Control

82.47(15.53)

90.77(14.24)

89.69(12.18)

89.74(10.92)

92.16(10.96)

Experim

ental

68.96(10.01)

76.23(12.95)

84.35(16.06)

88.15(15.87)

90.96(15.49)

1second

Control

88.01(14.24)

90.05(13.10)

90.70(9.72)

91.04(15.63)

93.81(13.93)

Experim

ental

54.15(7.57)

55.96(4.42)

58.31(8.04)

58.50(4.58)

58.46(5.49)

2second

Control

96.33(17.58)

101.25(13.00)

95.77(15.63)

91.10(27.26)

92.57(11.30)

Experim

ental

29.46(1.24)

29.35(0.75)

29.35(0.85)

29.77(0.43)

29.81(0.57)

MRG

0.5second

Control

14.52(2.99)

14.82(2.06)

15.28(2.95)

14.97(2.56)

15.15(1.74)

Experim

ental

15.60(2.11)

16.46(2.41)

15.70(2.43)

15.99(2.12)

16.26(2.59)

1second

Control

13.37(2.23)

13.83(1.84)

13.62(2.27)

13.77(1.80)

13.11(1.48)

Experim

ental

14.65(1.47)

15.22(2.13)

14.52(1.89)

14.93(1.72)

14.41(1.66)

2second

Control

11.34(2.38)

11.08(1.48)

11.56(1.85)

10.59(1.84)

11.01(1.98)

Copyright # 2009 John Wiley & Sons, Ltd. Appl. Cognit. Psychol. 24: 41–66 (2010

DOI: 10.1002/acp


)

Experim

ental

11.50(1.93)

11.77(2.12)

11.31(2.04)

12.06(1.66)

11.79(1.44)

ADJA 0.5second

Control

17.13(8.14)

14.76(4.14)

13.25(4.87)

11.59(5.16)

10.67(5.88)

Experim

ental

16.33(5.74)

14.63(6.13)

15.99(6.45)

12.92(6.29)

13.30(5.55)

1second

Control

11.22(5.69)

8.96(4.34)

7.51(3.50)

6.79(4.17)

5.91(3.23)

Experim

ental

12.60(5.87)

9.90(5.46)

8.78(4.79)

8.89(4.45)

8.40(7.21)

2second

Control

3.30(3.29)

3.01(3.06)

4.52(4.11)

3.67(3.73)

3.83(3.79)

Experim

ental

4.51(4.73)

5.79(5.38)

5.81(5.90)

5.54(6.04)

4.51(4.89)

ADJD 0.5second

Control

5.57(3.89)

6.50(3.70)

5.87(3.82)

6.40(3.16)

5.58(1.74)

Experim

ental

8.67(3.91)

7.54(3.30)

6.63(3.13)

7.05(4.06)

6.37(3.37)

1second

Control

5.02(3.46)

4.45(3.59)

5.61(3.90)

5.84(3.37)

5.17(3.24)

Experim

ental

6.30(3.54)

6.01(3.32)

6.20(4.01)

7.79(4.29)

5.51(3.20)

2second

Control

2.18(3.47)

3.50(2.75)

2.99(2.12)

2.33(2.44)

3.01(3.58)

Experim

ental

3.51(4.38)

4.08(4.13))

3.27(3.24)

3.75(3.84)

4.76(5.36)

ALP 0.5second

Control

10.65(7.39)

10.15(4.34)

9.85(5.28)

9.15(5.41)

8.20(5.87)

Experim

ental

10.73(4.07)

10.31(4.59)

12.00(5.71)

11.08(6.48)

11.38(5.49)

1second

Control

4.80(3.22)

4.15(2.50)

3.00(1.97)

3.15(2.43)

2.80(1.70)

Experim

ental

6.69(3.44)

5.04(3.41)

4.96(2.68)

4.77(2.45)

4.54(3.91)

2second

Control

0.85(0.86)

0.75(0.85)

1.05(1.23)

0.70(0.92)

0.80(0.77)

Experim

ental

1.19(1.29)

1.69(1.59)

1.35(1.55)

1.50(1.77)

1.31(1.46)

DIG

R0.5second

Control

9.75(4.76)

11.35(4.36)

12.90(4.19)

11.85(5.67)

13.25(4.64)

Experim

ental

7.92(3.84)

9.15(4.32)

10.00(4.20)

11.96(4.38)

12.23(5.01)

1second

Control

7.90(2.92)

8.00(2.99)

8.45(3.91)

8.55(3.52)

8.15(3.01)

Experim

ental

6.77(2.27)

6.77(3.13)

6.92(3.19)

7.23(2.93)

7.62(2.97)

2second

Control

4.00(1.86)

3.90(1.17)

4.05(2.48)

4.05(1.96)

3.90(1.48)

Experim

ental

3.77(1.99)

3.58(1.92)

3.50(1.96)

3.50(1.82)

3.42(2.08)

SDsin

parentheses.GroupN:Control¼

20,experim

ental¼

26.


DOI: 10.1002/acp



Turning point index (TPI)

The TPI refers to the number of responses that indicate a change in direction between

ascending and descending sequences, that is, in the sequence ‘A, C, E, F, D, B, G’ there are

two turning points (on the responses ‘F’ and ‘B’). The TPI is compared against a theoretical

distribution of random responses and calculated as a percentage score indicating the

correspondence between the observed value and an expected value. Values greater than

100% indicate more turning points in a response set compared to expectation whilst a value

lower than 100% indicates fewer turning points than expected.

Mean repetition gap (MRG)

The MRG refers to the average distance between repetitions, that is, in the sequence ‘A, H,

Y, U, R, L, A, U’, the letter ‘A’ is repeated after a lag of six items and the letter ‘U’ is

repeated after four items with an overall MRG being 5. According to Towse and Neil

(1998), a high MRG indicates less randomness within a response set. Individuals often

deliberately avoid repetitions because a repetition is falsely regarded as a non-random

response so items that are repeated with less frequency within a sequence are associated

with less randomness.

Adjacency-ascending (ADJA)

According to Towse and Neil (1998) the RNG index accounts for all possible response

pairings. However, certain response types (e.g. letters of the alphabet, numbers) follow an

ordinal sequence and these response sets may contain digrams comprising adjacent items

(e.g. ‘M, N’, ‘X, Y’, 3, 4’ etc.). The ADJAmeasure is, therefore, a more specific measure of

digram frequency and is calculated by dividing the number of adjacent pairs found in the

response set by the total number of adjacent response pairs from the alphabet. This score is

then represented as a percentage, with a score of 0% indicating a dataset with no

neighbouring pairs in an ascending order and a score of 100% indicating an entire dataset

that comprises neighbouring pairs in an ascending order.

Adjacency-descending (ADJD)

Similar to the ADJAmeasure, the ADJD index indicates the number of adjacent digrams in

a response set but this time in a descending direction, for example ‘N, M’, ‘T, S’, ‘6, 5’, etc.

Adjacent-letter pairs (ALP)

The adjacent-letters pair index (ALP) measures the total number of letter pairs found

within a response set that are adjacent to each other in the alphabet in an ascending order

(e.g. ‘J, K’, ‘S, T’). For instance, in a string of adjacent letters, such as ‘B, C, D’, each

adjacent association would be considered separately, so the pairs ‘B, C’, and ‘C, D’ were

counted as individual scores. The ALP index is very similar to the ADJA index calculated

by the RgCalc program, however, whilst the ALP index represents the raw number of

adjacent pairs the ADJA index is calculated as a proportion of all possible pairs. The raw

number of pair scores indicates the number of times the individual exhibits difficulty in

inhibiting a prepotent response.

Digrams (DIGR)

The Digram measure was devised by us to asses the frequency with which semantic

digrams, for example ‘U, K’, ‘O, K’, appear in a response set. Whilst the RNG index

measured the frequency distribution of any digram within a dataset and the adjacency


DOI: 10.1002/acp


indices (ADJA, ALP) accounted for the frequency of digrams according to their ordinal

sequence they do not address the presence of semantic digrams that may appear in a dataset

only once yet still indicates redundancy. Furthermore, the participants have been drawn

from a population (the armed forces) in which abbreviations are used routinely, for

example ‘R, A, F,’, ‘O, C’, ‘P, M, C’, and, therefore, this index should provide an insight

into the frequency with which known, non-random digrams appear in a response set.

The field and control groups generated letters at a similar rate and the mean N scores and

standard deviations are similar for both groups within each of the different rates of recall

(RoR) conditions across the 5 days. A 5 (day)� 3 (rate of recall)� 2 (group) ANOVAwas

conducted on each of the above indices.

N. Analyses revealed significant overall main effects for day and RoR but not for group.

There was a significant interaction for day�RoR. Summary ANOVA statistics are given in

Table 8a. Further analyses showed no effect of group forN in either the 0.5 or 1 second RoR

(F(1, 44)¼ .017, p> 0.05; F(1, 44)¼ .368, p> 0.05). However, the 2 second condition

revealed a significant difference between the experimental and control groups during the

field phase (F(1, 44)¼ 22.319, p< 0.01) but not between the two groups on the baseline

condition (F(1, 44)¼ .201, p> 0.05). This difference is the result of a slight decrease in N

in the experimental group during days 1 and 2 in the field followed by a recovery to baseline

level by day 5.

RNG. Analyses revealed significant main effects for RoR but not for day or group. There

was no significant interactions. Summary ANOVA statistics are given in Table 8b.

TPI. Analyses revealed significant main effects for day and RoR but not for group. There

were no significant interactions. Summary ANOVA statistics are given in Table 8c. Further

analysis showed that the significant effect for day was confined to the 2.0 second RoR

where on day 2 of the study the control group showed an increase in the TPI whilst the

experimental group showed a corresponding decrease in TPI. Following this initial

divergence both groups subsequently realigned.

Table 8a. Summary analysis of variance statistics for RLG: N

Source SS df MS F

Within-subjectsDay 7259.899 4 1814.975 48.015�

Day� group 82.705 4 20.676 .547Error(day) 6652.869 176 37.800RoRa 309459.669 2 154729.834 598.903�

RoR� group 55.066 2 27.533 .107Error(RoR) 22735.276 88 258.355Day�RoR 7702.841 8 962.855 32.242�

Day�RoR� group 38.226 8 4.778 .160Error(Day�RoR) 10511.939 352 29.863


�p< .05.aRate of recall.


DOI: 10.1002/acp

Table 8b. Summary analysis of variance statistics for RLG: RNG

Source SS df MS F

Within-subjectsDay .024 4 .006 1.395Day� group .018 4 .004 1.048Error(day) .765 176 .004RoRa 1.759 2 .880 197.985��

RoR� group .003 2 .001 .409Error(RoR) .391 88 .004Day�RoR .040 8 .005 1.174Day�RoR� group .054 8 .006 1.592Error(day�RoR) 1.499 352 .004

Between-subjectsGroup .003 1 .003 .293Error .509 44 .011

��p< 0.01.aRate of recall.


MRG. Significant main effects were found for group and RoR. Summary ANOVA

statistics are given in Table 8d. Further analyses revealed no differences in performance

between the two groups during the baseline condition for either 0.5, 1 or 2 second RoR

(F(1, 45)¼ 2.084, p> 0.5; F(1, 45)¼ 3.756, p> 0.05; F(1, 45)¼ .060, p> 0.05,

respectively). The experimental group scored significantly higher than the control group

on MRG during the field phase for both 1 and 2 second RoR (F(1, 44)¼ 8.183, p< 0.01);

(F(1, 44)¼ 6.388, p< 0.05, respectively). No differences were found between the two

groups in the 0.5 second RoR (F(1, 44)¼ 3.691, p> 0.05) during the field phase.

ADJA. No main effects were found for the ADJA index under any RoR: 0.5 sec (F(1,

44)¼ .876, p> 0.05), 1 sec (F(1, 44)¼ 2.649, p> 0.05), 2 sec (F(1, 44)¼ 2.151, p> 0.05).

Summary ANOVA statistics are given in Table 8e.

Table 8c. Summary analysis of variance statistics for RLG: TPI

Source SS df MS F


Day� group 648.384 4 162.096 1.174Error(day) 24294.373 176 138.036RoRa 6642.689 2 3321.345 19.769��

RoR� group 254.471 2 127.236 .757Error(RoR) 14784.513 88 168.006Day�RoR 1761.869 8 220.234 1.696Day�RoR� group 1423.426 8 177.928 1.370Error(day�RoR) 45712.325 352 129.865


�p< .05; ��p< 0.01.aRate of recall.


DOI: 10.1002/acp

Table 8d. Summary analysis of variance statistics for RLG: MRG

Source SS df MS F

Within-subjectsDay 9.776 4 2.444 .866Day� group 18.037 4 4.509 1.598Error(day) 496.789 176 2.823RoRa 1951.452 2 975.726 212.898��

RoR� group 12.580 2 6.290 1.372Error(RoR) 403.311 88 4.583Day� RoR 18.205 8 2.276 .762Day� RoR� group 11.293 8 1.412 .473Error(day� RoR) 1051.463 352 2.987

Between-subjectsGroup 150.809 1 150.809 7.568��

Error 876.851 44 19.928



ADJD. Similarly, no main effects were found for the ADJD index under any RoR: 0.5 sec

(F(1, 44)¼ 3.411, p> 0.05), 1 sec (F(1, 44)¼ 3.723, p> 0.05), 2 sec (F(1, 44)¼ 3.089,

p> 0.05). Summary ANOVA statistics are given in Table 8f.

ALP. Significant differences were found between the experimental and control groups

during the field phase for the 1 and 2 sec RoR (F(1, 44)¼ 6.746, p< 0.05; F(1, 44)¼ 4.126,

p< 0.05) with the experimental groups producing higher ALP scores. No difference was

found between the two groups for either RoR for the baseline conditions (F(1, 45)¼ 3.615,

p> 0.05; F(1, 45)¼ 1.030, p> 0.05). The groups did not differ on the 0.5 sec RoR (F(1,

44)¼ 2.181, p> 0.05). Summary ANOVA statistics are given in Table 8g.

Table 8e. Summary analysis of variance statistics for RLG: ADJA

Source SS df MS F


Day� group 41.781 4 10.445 .509Error(day) 3613.200 176 20.530RoRa 10458.530 2 5229.265 202.515��

RoR� group 7.849 2 3.925 .152Error(RoR) 2272.306 88 25.822Day�RoR 590.768 8 73.846 4.58��

Day�RoR� group 121.337 8 15.167 .942Error(day�RoR) 5668.157 352 16.103




DOI: 10.1002/acp

Table 8f. Summary analysis of variance statistics for RLG: ADJD

Source SS df MS F

Within-subjectsDay 19.810 4 4.952 .412Day� group 34.259 4 8.565 .712Error(day) 2116.969 176 12.028RoRa 1314.812 2 657.406 48.107��

RoR� group 1.118 2 .559 .041Error(RoR) 1202.564 88 13.665Day�RoR 136.079 8 17.010 1.687Day�RoR� group 51.331 8 6.416 .636Error(Day�RoR) 3549.838 352 10.085

Between-subjectsGroup 228.460 1 228.460 5.692�

Error 1766.066 44 40.138



DIGR. No main effects were found between the two groups in DIGR scores for any RoR:

0.5 sec (F(1, 44)¼ 2.181, p> 0.05), 1 sec (F(1, 44)¼ 3.998, p> 0.05) and 2 sec (F(1,

44)¼ 2.662, p> 0.05). Summary ANOVA statistics are given in Table 8h.

Tower of London

Each participant completed one five-move and one seven-move ToL task per day. Each task

involved two components: The planning stage, which was measured by the time taken to

plan a sequence of moves, and the action stage in which the time taken to move the discs

was measured. In addition the number of actual moves taken to complete each task was also

Table 8g. Summary analysis of variance statistics for RLG: ALP

Source SS df MS F

Within-subjectsDay 74.245 4 18.561 1.823Day� grroup 33.010 4 8.253 .811Error(day) 1791.941 176 10.181RoRa 9905.340 2 4952.670 216.776��

RoR� group 36.767 2 18.383 .805Error(RoR) 2010.535 88 22.847Day�RoR 89.084 8 11.135 1.451Day�RoR� group 61.049 8 7.631 .994Error(Day�RoR) 2701.939 352 7.676


Error 2433.827 44 55.314



DOI: 10.1002/acp

Table 8h. Summary analysis of variance statistics for RLG: DIGR

Source SS df MS F


Day� group 25.962 4 6.490 .896Error(day) 1275.615 176 7.248RoRa 5982.918 2 2991.459 157.341��

RoR� group 37.602 2 18.801 .989Error(RoR) 1673.108 88 19.013Day�RoR 257.085 8 32.136 4.797��

Day�RoR� group 41.821 8 5.228 .780Error(day�RoR) 2357.933 352 6.699




recorded. All data were analysed using a 5 (day)� 2 (ToL task)� 2 (group) ANOVA.

Descriptive statistics are given in Table 9.

Analyses of the planning time revealed significant effects for day, ToL task and group.

Summary ANOVA statistics are given in Table 10a. Further analyses showed no difference

between the two groups on the baseline condition for either the five-move (F1, 45)¼ 1.120,

p> 0.05), or seven-move tasks (F1, 45)¼ 0.809, p> 0.05). However, significant

differences in performance were found between the experimental and control groups

during the field phase on the five-move task (F(1, 44)¼ 11.335, p< 0.01) and repeated

measures showed a significant effect of day for the control group (F(1, 19)¼ 4.423,

p< 0.05) with the control group taking less time to plan over the course of the week. No

such effect was observed for the experimental group (F(1, 25)¼ 1.565, p> 0.05).

Analyses of the field phase of the seven-move task revealed a significant effect for group

(F(1, 44)¼ 4.174, p< 0.05) with the experimental group consistently taking longer to plan

than the control group. Repeated measures showed no significant effect of day for either the

control group (F(1, 19)¼ .224, p> 0.05) or the experimental group (F(1, 25)¼ .002,

p> 0.05).

Analyses of the action time revealed significant effects for day, ToL task and group.

There was also a significant interaction between day and group. Summary ANOVA

statistics are given in Table 10b. Further analyses showed no difference between the two

groups on the baseline conditions for either the five-move (F1, 45)¼ 2.955, p> 0.05), or

seven-move tasks (F1, 45)¼ 3.576, p> 0.05).

A significant difference was found in performance between the control and experimental

groups during the field phase on the five-move task (F(1, 44)¼ 11.882, p< 0.01) with the

experimental group taking longer to move the discs than the control group. Repeated

measures showed a significant effect of day for the control group (F(1, 19)¼ 8.742,

p< 0.01) which did not occur in the experimental group (F(1, 25)¼ 1.339, p> 0.05) with

the control group taking increasingly less time to move the discs over the course of the

week. This suggests an improvement with practice in the control group that is lacking in the

experimental group.


DOI: 10.1002/acp

Table

9.MeanscoresforTower

ofLondontest

Day

Condition

Group

01

23

4

Nomoves

Five-move

Control

5.25(1.12)

5.15(0.67)

5.20(0.89)

5.05(0.22)

5.10(0.31)

Experim

ental

5.08(0.39)

5.58(1.36)

5.12(0.33)

5.23(0.99)

5.04(0.20)

Seven-m

ove

Control

8.75(1.80)

8.80(2.26)

8.30(1.26)

7.65(1.09)

7.45(0.76)

Experim

ental

8.12(1.34)

8.69(1.96)

8.42(1.65)

8.35(1.72)

7.35(0.63)

Planning

Five-move

Control

5.15(4.15)

4.10(3.50)

3.68(3.39)

3.07(3.98)

2.37(2.31)

Experim

ental

6.26(3.03)

5.92(3.26)

6.29(2.81)

4.95(2.71)

5.14(3.68)

Seven-m

ove

Control

21.58(14.97)

13.68(11.60)

13.22(9.32)

12.06(11.00)

14.81(12.29)

Experim

ental

26.54(20.87)

21.05(19.66)

22.40(17.37)

22.33(20.03)

21.27(19.32)

Action

Five-move

Control

7.99(1.97)

7.33(1.47)

6.97(1.52)

6.64(1.57)

6.30(1.31)

Experim

ental

7.08(12.39)

9.02(9.46)

7.88(10.58)

8.31(8.17)

7.77(12.27)

Seven-m

ove

Control

17.21(4.32)

15.12(4.59)

14.87(3.71)

12.74(3.40)

11.93(2.66)

Experim

ental

15.02(12.39)

16.70(9.46)

16.32(10.58)

15.97(8.17)

15.34(12.27)

SDsin

parentheses.Tim

ein

seconds.GroupN:Control¼

20,experim

ental¼

26.


DOI: 10.1002/acp


Table 10a. Summary analysis of variance statistics for ToL: Planning

Source SS df MS F


Day� group 135.281 4 33.820 .528Error(day) 11279.026 176 64.085ToL 22794.678 1 22794.678 58.146��

ToL� group 889.654 1 889.654 2.269Error(ToL) 17249.034 44 392.023Day� ToL 533.716 4 133.429 2.029Day� ToL� group 87.520 4 21.880 .333Error(day� ToL) 11571.571 176 65.748


Error 23383.624 44 531.446

�p< .05; ��p< 0.01.


Analyses of the field phase of the seven-move task revealed a similar performance to the

five-move task with a significant difference in performance between the experimental and

control groups (F(1, 44)¼ 8.408, p< 0.01) with the control group taking less time to move

the discs than the field group. Repeated measures also showed no significant effect of day

for the experimental group (F(1, 25)¼ .657, p> 0.05) but a significant effect for day with

the control group (F(1, 19)¼ 11.425, p< 0.01) again suggesting improvement with

practice in the control group that is lacking in the experimental group.

Analyses of the number of moves made revealed no significant difference between the

experimental and control groups on either the five- or seven-move tasks. There was a

significant effect for day and ToL task. Summary ANOVA statistics are given in Table 10c.

Further analyses showed that the effect of day only existed for the seven-move task in both

control group (F(4, 76)¼ 3.872, p< 0.01) and experimental group (F(4, 100)¼ 2.985,

Table 10b. Summary analysis of variance statistics for ToL: Action

Source SS df MS F


Day� group 264.574 4 66.144 4.849��

Error(day) 2400.517 176 13.639ToL 6562.987 1 6562.987 443.652��

ToL� group 6.276 1 6.276 .424Error(ToL) 650.897 44 14.793Day�ToL 71.065 4 17.766 1.716Day�ToL� group 44.231 4 11.058 1.068Error(Day�ToL) 1821.902 176 10.352


Error 1154.982 44 26.250

�p< .05; ��p< 0.01.


DOI: 10.1002/acp

Table 10c. Summary analysis of variance statistics for ToL: Number of moves

Source SS df MS F


Day� group 8.695 4 2.174 1.536Error(day) 249.092 176 1.415ToL 1023.139 1 1023.139 571.402��

ToL� group .112 1 .112 .063Error(ToL) 78.785 44 1.791Day�ToL 16.523 4 4.131 3.052�

Day�ToL� group 4.462 4 1.116 .824Error(day�ToL) 238.238 176 1.354


�p< .05; ��p< 0.01.


p< 0.05). This appeared to be due to participants moving towards an optimal number of

moves in the more complex seven-move task. There was no comparable effect on the less

complex five-move tasks for either the control group (F(4, 76)¼ .227, p> 0.05) nor the

experimental group (F(4, 100)¼ 1.876, p> 0.05).

DISCUSSION

This study arose from an attempt to identify the processes underlying maladaptive

behaviour reported by victims and witnesses of survival incidents. We speculated that the

reported behaviour was consistent with executive dysfunction and this hypothesis was

tested by probing subcomponents of the executive system in participants undergoing the

duress of a demanding survival environment. The results provided some evidence to

suggest that individuals exposed to the type of environmental duress associated with a

survival situation appear to exhibit problems with some, but not all, executive processes. In

particular, the incongruent condition of the Stroop task, the MRG and ALP components of

the random letter generation task and the planning and action components of the ToL task

were all prone to impairment. No impairment was found in dual-task performance nor in

verbal fluency. Considering these results in turn:

Stroop task

The Stroop test showed no difference in performance between the control and experimental

groups in the control form. No difference was found between the groups on the baseline

session for the incongruent form, however, the experimental group performed significantly

worse on the incongruent form than the control group during the field phase. The fact that

the control and experimental groups performed comparably on the control and congruent

tasks suggests that environmental duress is impairing still further the ability to inhibit pre-

potent responses.


DOI: 10.1002/acp


Dual task

The dual-task test provides a measure of a participant’s ability to deploy attentional

resources during single and dual-task conditions (Baddeley, 1996). Previous studies using

this pen-and-paper dual task test have found that it can distinguish successfully between

patients with dysexecutive syndrome and nondysexecutive patients with frontal focal

lesions (Baddeley et al., 1997b). It has been argued (Duncan, 1995) that dual tasking

involves, not only attention, but the ability to shift mental sets rapidly between tasks.

However, our finding that the experimental and control groups did not differ in their ability

to co-ordinate simultaneous tasks in either the baseline or field phases supports Miyake,

Friedman, Emerson, Witski, & Howerter (1980) who found no evidence that shifting

contributed to dual task performance.

Verbal fluency

Both the control and experimental groups generated a similar number of items during the

week. No difference between the two groups was found on either clustering or switching

data. The absence of a significant difference in switching activity suggests that exposure to

environmental duress does not impair either the ability to access information from LTM or

the capacity to switch retrieval programmes. This is consistent with our findings from the

dual-task test. Experimental participants did not generate more repetitions compared to the

control group suggesting an ability unimpaired by environmental demands.

Random letter generation

Both the control and experimental groups produced a similar number of letters within the

time limits indicating that both groups were behaving similarly and neither was reducing

the rate of output to try to maintain randomness. Of interest was the performance of the

experimental group on those indices that reflected the use of pre-potent responses, that is,

stereotypical responses that may indicate impairment of those processes involved in

inhibition. The findings from the Stroop test suggest that the participants exposed to the

survival environment experienced problems with inhibiting pre-potent behaviours and this

is consistent with the impairment found in the MRG and ALP indices of the random letter

generation task.

Problems with inhibition and the suppression of pre-potent responses is in keeping with

the hypothesis that difficultly in adapting to a survival environment is consequent upon

executive impairment. Such impairment would be reflected in the experimental group

demonstrating poorer performance on those indices requiring the suppression of pre-potent

(non-random) responses. In particular, the experimental group would be expected to score

significantly higher on the adjacent letter-pairs index, which measures a failure to suppress

pre-potent responses, and this was found to be the case.

Another index that indicates difficulties in inhibiting pre-potent responses is the MRG

index which measures the size of the gap between response repetitions. If the experimental

group are producing more stereotyped responses then this would be reflected in a higher

score on the MRG index (Baddeley, Emslie, Kolodny, & Duncan, 1998; Baddeley, 1966).

The results of the MRG data reveal that the experimental group does score significantly

higher on this index compared to the control group in line with predictions. This might be

explained by the supervisory system needing to evaluate and then suppress a letter whilst


DOI: 10.1002/acp


also initiating a search for more appropriate alternatives that are not stereotyped. Initiating

a search for a random response is a complex task that involves the participant keeping in

memory all or part of the response range, selecting a candidate at random, evaluating it for

its suitability and then articulating it. This activity is made harder by enforcing a time

constraint on the task. Therefore, for those individuals who have difficulty suppressing a

pre-potent response (i.e. the experimental group), they are most likely to generate a

stereotyped response because no other more suitable candidate can be selected in the given

time. Suppression activity is either taking longer than usual or is not functioning normally.

This slowing down of the system is also reflected in the ALP data.

In summary, the random letter generation data reveal that the experimental group were

prone to producing more stereotyped responses compared to the control group under field

conditions, which is indicative of difficulties with the suppression of pre-potent responses.

It is possible that the survival environment constricts the resource capacity necessary for

the search, selection and evaluation of a potential response.

Tower of London

The ToL data showed that, whilst the control group and the experimental group both

executed a comparable number of moves to achieve the goal state, the experimental group

took significantly longer to plan and execute their sequences in both the five- and seven-

move trials. The finding that the two groups did not differ in the number of moves used was

contrary to expectation as it was anticipated that, if the experimental group were

experiencing problems with planning, then they would make more mistakes and not

achieve the goal state using the optimal number of moves. Indeed, previous research has

shown a negative correlation between planning time and the number of moves made

(Morris, Ahmed, Syed, & Toone, 1993). However, no such correlation was observed in the

present data and no relationship was found between the number of moves and the time

taken to plan. Consequently, it would appear that the experimental group were maintaining

accuracy on the task by increasing planning time. This supports our hypothesis that the

survival environment reduces the availability of executive resources, which in turn leads to

an increase in the latency associated with performing the task (Baddeley, Lewis, Eldridge,

& Thomson, 1984). Accuracy remains unaffected, as the ToL task does not impose a time

constraint on the participants.

The picture that is emerging is that environmental duress, of the type common to survival

situations, produces differential impairment in executive function. Interestingly, and

unexpectedly, those tests that showed impairment during the field phase tend to be

associated with the left dorsolateral prefrontal cortex (DLPFC). Perret (1974) reports that

patients with lesions in the left frontal lobe were poorer at suppressing habitual responses

on the Stroop test than patients with lesions elsewhere. McDonald, Cohen, Stenger, and

Carter (2000) reports fMRI findings that show activation in the left DLPFC when

participants were told to name the colour but not read the word, whilst Jahanshahi, Profice,

Brown, Ridding, Dirnberger, and Rothwell (1998) found activity in the left DLPFC during

the suppression of the reading of words. Using PET imaging during random number

generation, Jahanshahi, Dirnberger, Fuller, and Frith (1997) also found left DLPFC acti-

vation during the active suppression of habitual counting tendencies. Furthermore, applying

transcranial magnetic stimulation over the left DLPFC (Jahanshahi & Dirnberger, 1999:

Jahanshahi et al., 1998) produced an increase in stereotypy of digrams with both random

number and random letter generation, which is consistent with our findings showing an


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increase in production of adjacent letter pairs during the field phase compared with con-

trols. No such increase in stereotypy was found when TMS was applied to the right DLPFC.

Data from both the Stroop and the RLG tasks suggest that the processes involved in

inhibiting pre-potent responses are impaired during exposure to survival conditions

although it is difficult to determine whether the impairment lies in the ability to suppress a

pre-potent response or in the ability to initiate the correct response. The results of the verbal

fluency test revealed that the control and experimental groups generated approximately the

same number of switches. This suggests that the ability to suppress a previously activated

schemata and switch to a new schema is not impaired. However, in both the Stroop and

RLG tasks the automatic activation of stereotyped responses needs to be inhibited. The

results indicate that the ability to suppress pre-potent schemata is impaired in the

experimental group. These findings are consistent with Ward et al. (2001) who found

that the ability to suppress a pre-potent response is an independent process separate

from the ability to suppress a previously activated response. This was further supported by

TMS studies (Jahanshahi et al., 1998) that were found to affect response suppression in

random number generation tasks only when applied to the left DLPFC. This again suggests

an increase in stereotypy and impaired suppression which is consistent with our field

findings. From their studies, Jahanshahi et al. (1997, 1998) have argued that the left and

right DLPFC play different roles: The left DLPFC being responsible for suppressing

habitual response, whilst the right DLPFC is responsible for monitoring non-routine

processes.

The impairment in performance on the ToL tasks is also consistent with left DLPFC

dysfunction. Planning cognition has been found to be impaired in patients with left anterior

damage compared to right sided patients (Shallice, 1982; Owen et al., 1990). Specific left

side activation in normal participants during the ToL task has been found with both

regional cerebral blood flow (Morris et al., 1993) and with PET imaging (Owen, Doyon,

Petrides, & Evans, 1996). Newman et al. (2003) has suggested that left and right DLPFC

may be performing distinguishable functions with the right side being differentially

involved in constructing a plan for solving the ToL problem whilst the left side is involved

in control processes for supervising the execution of the plan. Using fMRI techniques they

found that significantly more activation occurred in the left DLPFC than the right during

easy ToL tasks whilst the right becomes involved with more difficult ToL tasks.

The suggestion that selective impairment occurs in the left, but not right, DLPFC can

also explain the apparent lack of practice effects recorded in the field group. Encoding in

episodic memory appears to activate the left prefrontal cortex, whilst retrieval from

memory activates the right frontal cortex (Kapur, Craik, Tulving, Wolsin, Houle, & Brown,

1994; Shallice, Fletcher, Frith, Grasby, Frackowiak, &Dolan, 1994; Tulving, Kapur, Craik,

Moskovitch, & Houle, 1994). Consequently, impairment in the left DLPFC would impair

learning and produce the observed lack of practice effects in the field group. A lack of

impairment in the right frontal cortex would leave intact the ability to retrieve information

from LTM resulting in the observed comparable performance between the field and control

groups on the verbal fluency test. The finding of no difference between the two groups on

either switching or clustering in verbal fluency suggests that the search strategies in LTM

also remained unimpaired. Rogers, Sahakian, Hodges, Polkey, Kennard, and Robbins

(1998) have pointed out that practice should reduce involvement of the frontal cortex whilst

enhancing the involvement of other neural systems. This does not appear to be happening

in the field group. In fact, it is as though the survival environment has to be lived anew each

day until a learning threshold is passed.


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Interestingly, Newman et al. (2003) have suggested that the left DLPFC may be the

location of the contention scheduler. If this is so, then our findings suggest that the

maladaptive behaviours witnessed in survival situations can be explained by postulating

dysfunction within the contention scheduler component of the supervisory system.

Another possibility reflects the suggestion by Frith (2000) that the role of the DLPFC is

in the modulation of the contention scheduler by the SAS, in which case the impairment

witnessed in the field could be the result of a normally functioning contention scheduler but

one lacking appropriate SAS control. If so, then impairment would lie at the SAS-

contention scheduler interface.

ACKNOWLEDGEMENTS

We thank Sqn Ldr Ray Pelcot RAF, Survival School (RAF Cranwell), Sqn Ldr Rob

Webster RAF School of Combat Survival & Rescue (RAF St Mawgan) and all the

instructors and students.

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