ORI GIN AL PA PER

Physiological Responses to Near-Miss Outcomesand Personal Control During Simulated Gambling

Luke Clark • Ben Crooks • Robert Clarke • Michael R. F. Aitken •

Barnaby D. Dunn

Published online: 24 April 2011� Springer Science+Business Media, LLC 2011

Abstract Near-miss outcomes during gambling are non-win outcomes that fall close to a

pay-out. While objectively equivalent to an outright miss, near-misses motivate ongoing

play and may therefore be implicated in the development of disordered gambling. Given

naturalistic data showing increases in heart rate (HR) and electrodermal activity (EDA)

during periods of real gambling play, we sought to explore the phasic impact of win, near-

miss and full-miss outcomes on physiological arousal in a controlled laboratory environ-

ment. EDA and HR were monitored as healthy, student participants (n = 33) played a

simulated slot-machine task involving unpredictable monetary wins. A second gambling

distortion, perceived personal control, was manipulated within the same task by allowing

the participant to select the play icon on some trials, and having the computer automati-

cally select the play icon on other trials. Near-misses were rated as less pleasant than full-

misses. However, on trials that involved personal choice, near-misses produced higher

ratings of ‘continue to play’ than full-misses. Winning outcomes were associated with

phasic EDA responses that did not vary with personal choice. Compared to full-misses,

near-miss outcomes also elicited an EDA increase, which was greater on personal choice

trials. Near-misses were also associated with greater HR acceleration than other outcomes.

Near-miss outcomes are capable of eliciting phasic changes in physiological arousal

consistent with a state of subjective excitement, despite their objective non-win status.

Keywords Risk-taking � Win � Loss � Arousal � Heart rate � Skin conductance

L. Clark (&) � B. Crooks � R. Clarke � M. R. F. AitkenDepartment of Experimental Psychology, University of Cambridge,Downing St, Cambridge CB2 3EB, UKe-mail: lc260@cam.ac.uk

B. D. DunnMRC Cognition and Brain Sciences Unit, Cambridge, UK

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J Gambl Stud (2012) 28:123–137DOI 10.1007/s10899-011-9247-z

Introduction

Naturalistic gambling is associated with robust changes in peripheral arousal. During

several minutes of real blackjack or slot machine play, regular gamblers display marked

elevations in a range of physiological parameters including heart rate (HR), blood pressure,

electrodermal activity (EDA), and cortisol (e.g. Anderson and Brown 1984; Meyer et al.

2000; Coventry and Hudson 2001). Similar changes can be elicited by laboratory gambling

protocols, particularly when genuine monetary rewards are at stake (Sharpe et al. 1995;

Ladouceur et al. 2003; Wulfert et al. 2005). The causal significance of these changes in

arousal to the maintenance of gambling behaviour is unclear. It has been suggested that

positively reinforcing properties of this arousal may be more important even than the

monetary wins in the maintenance of gambling behaviour (e.g. Brown 1986; Wulfert et al.

2005). Effectively, excitement may represent ‘the gambler’s drug’ (Boyd 1982). In support

of this idea, some studies have reported greater gambling-related increases in physiological

responses (Leary and Dickerson 1985; Sharpe et al. 1995; Moodie and Finnigan 2005) (but

see Griffiths 1993) and subjective excitement or arousal (Brown et al. 2004; Linnet et al.

2010; but see Sodano and Wulfert 2010) in problem gamblers or high frequency gamblers,

compared to individuals without gambling problems.

A distinct approach to understanding gambling behaviour emphasises the distorted

styles of thinking that are evident during gambling (Ladouceur and Walker 1996; Clark

2010). Research with ‘think aloud’ protocols indicates that around 75% of verbalisations

made by regular gamblers during gambling sessions can be classified as erroneous

(Gaboury and Ladouceur 1989; Delfabbro and Winefield 2000). A number of distinct types

of gambling-related cognitive distortions have been identified (Toneatto et al. 1997; Raylu

and Oei 2004), which converge on a general over-estimation of one’s chances of winning.

One well-known distortion is the ‘illusion of control’ (Langer 1975), where the gambler

mistakenly believes that personal skill influences a game determined by chance alone. The

level of gambling distortions and the frequency of irrational cognitions are increased in

individuals with disordered gambling (Coulombe et al. 1992; Raylu and Oei 2004; Miller

and Currie 2008), they are reduced following treatment for gambling problems (Breen

et al. 2001) and predict likelihood of relapse in Gamblers Anonymous attendees (Oei and

Gordon 2008).

Our recent research has focussed on two structural characteristics of gambling games

that are thought to promote gambling distortions: the effects of near-miss outcomes and the

influence of perceived personal control. Near-misses (more accurately, near-wins) are non-

win outcomes that are, in some sense, close to a significant payout. In games of skill, a

near-miss might indicate better performance than a full-miss (Reid 1986). However, in

games of chance, near-misses are objectively no different from outright misses in the

information they provide to the player. Nevertheless, gamblers report motivational effects

of near-misses (Reid 1986), and laboratory studies have shown that gambling persistence

varies with the frequency of near-misses, with maximal persistence observed at near-miss

rates around 30% (Kassinove and Schare 2001; Cote et al. 2003). The effect of personal

control refers to the enhanced feeling of confidence (measured by bet size, for example)

that gamblers display when they have personal responsibility for a choice or action. In

games of chance, this element of personal control is again unrelated to the actual likelihood

of winning. Nevertheless, craps players will frequently blow on dice, and use stronger arm

movements when trying to roll high numbers (Henslin 1967), and roulette players place

higher bets if they are given the opportunity to personally throw the ball onto the wheel,

compared to a croupier condition (Ladouceur and Mayrand 1987). Langer (1975)

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conceptualised personal control (‘involvement’) as one of several factors fuelling the

illusion of control in gamblers (see Thompson et al. 1998 for review). It is hypothesised

that near-misses also feed into the illusion of control, as these events may be interpreted

(falsely) as signals of skill acquisition (Reid 1986; Clark 2010).

We have previously used a simplified slot-machine task to measure subjective and

neural responses to win, near-miss and full-miss outcomes (Clark et al. 2009; Chase and

Clark 2010). Some trials involved perceived personal choice, where the participant was

able to select their ‘play icon’, and on other trials this choice was taken automatically, by

the computer. We observed that near-misses were rated as less pleasant than full-misses,

but simultaneously produced a greater desire to continue with the game. These effects were

restricted to trials with personal control, supporting the hypothesis that the near-miss effect

operates via enhancement of the illusion of control. In a functional neuroimaging exper-

iment with this task, near-misses recruited brain regions that also responded to the mon-

etary wins, including the ventral striatum and anterior insula (Clark et al. 2009). The insula

response was correlated with the motivational impact of the near-misses on the ratings of

‘continue to play’ acquired during scanning, as well as with levels of gambling distortions

on the Gambling-Related Cognitions Scale (Raylu and Oei 2004). Given the known role of

the insula in the representation and awareness of physiological states (Craig 2009), it was

hypothesised that near-misses might invigorate gambling behaviour via the induction of

peripheral arousal.

According to Griffiths (1991, 1993), the ability of wins and near-misses to elicit

physiological arousal means the gambler believes that ‘‘they are not constantly losing but

constantly nearly winning’’. However, the evidence for this statement to date comes largely

from self-report data, as few studies have explored the links between gambling-related

cognitive distortions and physiological arousal. While one study reported a positive cor-

relation between HR increases and erroneous statements during slot machine play (Cou-

lombe et al. 1992), the direction of causality is opaque: it is possible that a state of high

arousal increases cognitive distortions via an effect on decision-making (e.g. Porcelli and

Delgado 2009), or that the cognitive distortions induce an unrealistic expectation of

winning that heightens arousal (Dickerson and Adcock 1987). A major caveat with the

naturalistic studies of physiological arousal during gambling is their poor temporal reso-

lution, as physiological activity is typically averaged across several minutes of play. By

studying gambling in the laboratory, it is possible to measure phasic, event-related,

changes in arousal (see also Dixon et al. 2010; Wilkes et al. 2010). The aim of the present

study was to measure outcome-related physiological activity during our simplified slot

machine task. We hypothesised that EDA and HR responses would be (1) sensitive to

winning outcomes, (2) differentially sensitive to near-miss and full-miss outcomes, and (3)

enhanced under conditions of personal choice (i.e. perceived personal control).

Methods

Participants

We recruited 33 healthy volunteers (16 female) from the student population at the Uni-

versity of Cambridge. Our recruitment strategy sought to identify a broad range of gam-

bling involvement, including participation adverts asking ‘Do you enjoy gambling?’.

Disordered gambling was assessed using the South Oaks Gambling Screen (Lesieur and

Blume 1987), and the Gambling-Related Cognitions Scale (Raylu and Oei 2004) assessed

J Gambl Stud (2012) 28:123–137 125

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susceptibility to gambling distortions. The protocol was reviewed by the University of

Cambridge Psychology Research Ethics Committee, and written informed consent was

obtained from all participants.

Procedure

Volunteers attended individual testing sessions of 1 h duration, where they completed a

computerised slot-machine task with concurrent recording of EDA and HR. Psychophys-

iology was recorded using a BIOPAC MP150 (Biopac Systems Ltd, Goleta, CA), recording

at 1000 samples per second. The BIOPAC was connected to a stimulus delivery computer

and a second administrator computer running Acqknowledge v3.9.0. Events occurring on

the stimulus delivery computer (including the outcomes on the task) were synchronised to

the psychophysiology recording using digital channels. EDA was measured using two

grounded Ag–AgCl electrodes (a BIOPAC TSD203 transducer with a GSR100C amplifier

module; gain = 5 V, low pass filter 1.0 Hz, high pass filters DC), secured on the distal

phalange of the index and middle fingers of the non-dominant hand. Isotonic paste (BI-

OPAC Gel 101, with recommended NaCl concentration of 0.05 M) was used as the

electrolyte, and participants washed their hands prior to attachment of the electrodes. The

EDA signal was transformed into micro-Siemens units in Acqknowledge. HR was recorded

using disposable Ag–AgCl ECG electrodes (Vermed EL503 patches), with clip-on shielded

leads running to an ECG100C module (gain = 1,000, R wave detector = On, filter = On,

high pass filter = 0.05 Hz). Electrode sites were prepared with an ethanol swab before

electrodes were secured to the right dorsal forearm and left ankle. The ECG waveform was

transformed into HR online in Acknowledge. Following attachment of the EDA and HR

electrodes, 2 min of resting state data were acquired, before the instructions for the slot

machine task were read to the participant.

Slot Machine Task

Participants completed 60 experimental trials on a computerised task resembling a two-

reel slot machine (Clark et al. 2009) (see Fig. 1). There were six different icons displayed

Fig. 1 The slot machine task(reprinted with permission ofCell Press). The displayed trial isan example of a near-miss, wherethe chosen icon on the left reel(the bananas) has fallen oneposition from the payline on theright reel

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on each reel, in the same order, and a horizontal payline was visible centrally. In essence,

each gambling trial involved one of the six icons being chosen on the left reel, by

scrolling that icon into position on the payline. The right reel would then spin for a few

seconds, and if the right reel stopped on the chosen icon (i.e. matching icons were

displayed in the payline), a win was delivered. All other outcomes were non-wins. We

distinguish non-wins where the chosen icon stopped one position from the payline on the

right reel (near-misses), from ‘full-misses’ where the reel stopped in one of the three

remaining positions.

The specific trial timings are as follows. Each trial began with presentation of the two

reels for a 5 s viewing period when no motor response was possible. This was followed by

a selection phase where one of the six icons was selected. On participant-chosen trials

(signalled by a white background and the message ‘Please Select’), the subject used

labelled keys on the keyboard to scroll up or down through the shapes, and a third key to

select. On computer-chosen trials (signalled by a black background and the message

‘Computer Selects’), the play icon was chosen automatically by the computer, but a

confirmatory response was required to maintain participants’ attention. There was a 5 s

window to make selection/confirmation responses on both types of trial. Following

selection, the right reel spun for an anticipation interval (2.8–6.0 s), during which time the

reel decelerated to a standstill. As the reel stopped moving, this began the outcome phase

(4 s), where a message displayed either ‘No Win’ or the win amount. Following the

outcome, the reels disappeared for a variable inter-trial interval (8–12 s) to allow for

recovery of EDA and HR, when only the points total was visible. On each trial, three

subjective ratings were taken, using onscreen 21-point visual analogue scales. After the

selection phase, the subject was asked: ‘‘How do you rate your chances of winning?’’.

Following the outcome phase, the subject was asked: ‘‘How pleased were you with the

result?’’ and ‘‘How much do you want to continue to play?’’. No time constraints were

imposed on the subjective ratings.

Participants completed 4 practice trials (including 1 hypothetical win) before com-

mencing the main task. The task was presented on a desktop PC with responses registered

using the keyboard. Task duration was approximately 35 min. At the start of the task, in

order to maximise a sense of involvement, each participant selected the six icons to be

displayed on the reels (e.g. bananas, a cowboy boot, some grapes) from a 4 9 4 matrix (i.e.

16 alternatives).

Participants were reimbursed a fixed amount for participation (£5) with a further bonus

payment on the task. There was a minor variation in the pay-off structure for this bonus,

across two subgroups of participants. Thirteen participants played an identical task to that

used in our previous studies (Clark et al. 2009; Chase and Clark 2010), with no wagers

placed on any trials, and a fixed £.50 win amount. For the remaining 20 participants, a

modification of the payoff structure was introduced to enhance the ecological validity of

the task. Participants were given a £5 endowment, and a fixed wager of £.20 was placed on

every trial, for a fixed £1 win amount. Given that the reels were fair, i.e. the participant

won on 1/6 trials, this latter pay-off structure resulted in a gradual loss of money over the

course of the 60 trials, such that subjects completed the task with approximately £3 (the

exact win amount varied slightly, depending on whether the subject committed any

selection omissions on trials that would have won). Initial analyses included the payoff

structure manipulation as a between-subjects factor; these revealed no statistically reliable

influence on subjective ratings nor on psychophysiological responses, thus data are pre-

sented pooled across this factor.

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Data Processing and Analysis

Statistical tests were conducted two-tailed at the P \ .05 threshold, with Greenhouse-

Geisser corrections applied to all within-subject analysis of variance (ANOVA) tests. The

raw subjective ratings on the task (see Table 1) were also converted to a standardised z

score, based on each individual’s mean and standard deviation for that rating. These

standardised values account for inter-subject variability in anchoring and distribution of

ratings, and enable a comparison against the ratings data in our previous reports (Clark

et al. 2009; Chase and Clark 2010).

Psychophysiological data were extracted using bespoke software programmed in Visual

Basic (v6.0). EDA data were logarithmically transformed given their typical positive skew.

For both EDA and HR, a trial-specific baseline was calculated from the average value in

the final 2 s of the anticipation phase (i.e. the 2 s immediately prior to the outcome). For

EDA, time-course data were extracted using the mean EDA change from baseline in

6 9 2 s bins from the onset of the outcome phase. An EDA summary measure was

calculated, as the maximum positive change from baseline in bins 2–4 (i.e. 2–8 s

post-outcome) given the standard time course of skin conductance responses (Dawson

Table 1 Subjective ratings data on the slot machine task [mean (SD)]

Participant-chosen Computer-chosen

‘‘How do you rate your chances of winning?’’(0 = very low, 100 = very high)

Raw 34.4 (14.3) 31.1 (12.6)

Z .13 (.15) -.13 (.15)

‘‘How pleased are you with the result?’’(-100 = very unhappy, 0 = neutral, ?100 = very happy)

Wins

Raw 38.6 (31.5) 38.4 (31.1)

Z 1.71 (.56) 1.70 (.46)

Near-misses

Raw -40.6 (23.4) -40.7 (23.9)

Z -.39 (.24) -.40 (.18)

Full-misses

Raw -38.5 (21.9) -34.8 (22.3)

Z -.35 (.11) -.25 (.17)

‘‘How much do you want to continue to play?’’(0 = not at all, ?100 = a lot)

Wins

Raw 51.1 (20.1) 50.1 (18.6)

Z .65 (.73) .52 (.61)

Near-misses

Raw 44.0 (19.8) 40.5 (20.1)

Z .01 (.20) -.23 (.21)

Full-misses

Raw 41.8 (19.7) 42.4 (19.9)

Z -.13 (.16) -.11 (.21)

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et al. 2000). For HR, time-course data were plotted using the median HR change from

baseline in 12 9 0.5 s bins, from the onset of the outcome phase. HR summary measures

were calculated, based on work by Hodes et al. (1985): the initial deceleration component

was calculated from the minimum value in bins 1–4 (i.e. 0–2 s post-outcome) minus the

baseline. The subsequent acceleration component was calculated as the maximum value in

bins 5–12 (i.e. 2–6 s post-outcome) minus the deceleration minimum value. For both EDA

and HR, time-course data were analysed initially using repeated-measures ANOVA with

bin (EDA: 6 levels, HR: 12 levels), Outcome (3 levels: win, near-miss, full-miss) and

Choice (2 levels: participant-chosen, computer-chosen) as factors. Follow-up ANOVA

models were run on the psychophysiological summary measures.

EDA data were screened for inclusion given that a proportion of healthy participants

show minimal EDA responsivity to emotional stimuli (Venables and Mitchell 1996). To

identify non-responsive individuals, we assessed whether each participant showed EDA

‘peaks’ to win outcomes (e.g. Fairchild et al. 2008). The temporal order of the minimum

and maximum EDA values during a time window 1–6 s after each win outcome was

recorded. If the maximum value occurred after the minimum value, we inferred that EDA

was increasing during the outcome period (i.e. a ‘peak’ had occurred). In contrast, if the

maxima occurred before the minima, we inferred that no peak had occurred (EDA may

display gradual downward drift due to boredom or task habituation). This method of peak

definition is expected to detect peaks on 50% of trials in EDA data fluctuating randomly. In

the overall sample (n = 33), the mean percentage of winning outcomes accompanied by an

EDA peak was 69.4% (SD 25.0, range 20–100). Taking a threshold of 44% on this variable

(as 1 SD below the overall mean for peak percentage after wins), seven participants were

considered non-responsive to monetary wins, and excluded from the reported EDA anal-

yses. Nevertheless, inclusion of these EDA non-responders did not qualitatively alter any

findings. In addition, one subject was excluded from the HR analysis for high baseline HR

(115 bpm, more than 2.5 SD above group mean).

Error bars in Figs. 2, 3, 4 display twice the standard error of the difference of the means

(SED), which is the appropriate index of variation in within-subjects designs where one is

interested in the differences between variables rather than the variables themselves; SED

was calculated by H([2 9 MSe]/n), where MSe is the mean square for the error term in the

ANOVA model, and n is the number of subjects (Cardinal and Aitken 2006).

Results

Subjective Ratings on the Gambling Task

Analyses are reported for the raw ratings data, but equivalent analyses run on the z

transformed ratings were qualitatively identical (see Table 1). On the ratings of ‘chances of

winning’ taken after the selection phase, subjects reported higher scores on participant-

chosen trials compared to computer-chosen trials (t32 = 4.92, P \ .001), consistent with

an effect of personal choice.

On the ratings of ‘pleased with outcome’, a 3 (Outcome: win, near-miss, full-miss) 9 2

(Choice: participant-chosen, computer-chosen) repeated-measures ANOVA revealed a

significant main effect of Outcome (F(2,64) = 105.4, P \ .001, gp2 = .767), but no main

effect of Choice or Outcome 9 Choice interaction (Fs \ 1, larger gp2 = .021). Collapsing

across the Choice factor, the winning outcomes (mean 38.5, SD 29.2) were significantly

more pleasant than both the near-miss (mean -40.7, SD 23.1; t32 = 10.2, P \ .001) and

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full-miss (mean -36.7, SD 21.7; t32 = 10.5, P \ .001) outcomes, and the near-misses

were significantly less pleasant than the full-misses (t32 = 2.91, P = .007).

On the ratings of ‘continue to play’, a 3 (Outcome) 9 2 (Choice) ANOVA revealed a

significant Outcome 9 Choice interaction (F(2,64) = 4.65, P = .014, gp2 = .127). There

were also significant main effects of both Outcome (F(2,64) = 17.7, P \ .001, gp2 = .357)

and Choice (F(1,32) = 15.1, P \ .001, gp2 = .321), reflecting a greater desire to continue

after wins, and on participant-chosen trials. To decompose the interaction, we first looked

at the effect on Choice of winning outcomes; there was no significant difference between

participant-chosen wins and computer-chosen wins (t32 = .995, P = .327). A 2 (Out-

come: near-miss, full-miss) 9 2 (Choice) ANOVA model restricted to the non-win out-

comes indicated a significant interaction term (F(1,32) = 9.56, P = .004, gp2 = .230). On

participant-chosen trials, near-misses were associated with greater desire to continue than

full-misses (t32 = 3.17, P = .003). By contrast, on computer-chosen trials, near-misses

were associated with reduced desire to continue (t32 = -2.42, P = .021).

Electrodermal Activity

The first stage of analysis was a repeated-measures ANOVA model of Out-

come 9 Choice 9 Bin (6 9 2 s bins). The model revealed a significant Outcome 9 Bin

interaction (F(10,250) = 13.8, P \ .001, gp2 = .356), as well as main effects of Outcome

(F(2,50) = 27.7, P \ .001, gp2 = .525) and Bin (F(5,125) = 8.61, P \ .001, gp

2 = .256).

The main effect of Choice was not significant (F(1,25) = 3.56, P = .071, gp2 = .125), and

no Choice interaction terms approached significance, including the 3-way interaction

(F(10,250) = .264, P = .847, gp2 = .010).

These data were explored further by analysing the maximum EDA change from

baseline, in two separate models. The first ANOVA model compared the two types of win

(participant-chosen, computer-chosen) against all non-win outcomes, as 3 levels of a

repeated-measures factor. These data are shown in Fig. 2, right panel. The effect of

Outcome was highly significant (F(2,50) = 12.4, P \ .001, gp2 = .331). Post-hoc tests

indicated higher EDA after both participant-chosen wins (t25 = 5.61, P \ .001) and

computer-chosen wins (t25 = 3.82, P = .001) compared to non-wins, but no difference

between the two types of win (t25 = .763, P = .453). Thus, monetary wins had a robust

effect of increasing EDA, but these responses did not vary with personal choice.

The second simple effects analysis focussed on the maximum EDA change from

baseline on non-win outcomes (see Fig. 3, right panel). A 2 (Outcome: near-miss, full-

miss) 9 2 (Choice) repeated-measures ANOVA revealed significant main effects of

Outcome (F(1,25) = 5.22, P = .031, gp2 = .173) and Choice (F(1,25) = 8.27, P = .008,

gp2 = .249), with no significant Outcome 9 Choice interaction (F(1,25) = 1.19, P = .286,

gp2 = .045). On participant-chosen trials, max EDA was higher after near-misses than full-

misses (t25 = 2.51, P = .019). Participant-chosen near-misses were associated with

higher EDA than computer-chosen near-misses (t25 = 2.25, P = .033) Near-misses and

full-misses did not differ significantly on computer-chosen trials (t25 = 1.26, P = .221).

Thus, participant-chosen near-misses were associated with greater EDA change than other

non-win outcomes.

Heart Rate

An initial repeated-measures ANOVA model of Outcome 9 Choice 9 Bin (12 9 0.5 s

bins) revealed a significant Outcome 9 Bin interaction (F(22,682) = 2.98, P = .010,

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gp2 = .088), as well as the anticipated main effect of Bin (F(11,341) = 32.1, P \ .001,

gp2 = .509). The main effects of Outcome (F(2,62) = 2.49, P = .101, gp

2 = .074) and

Choice (F(1,31) = 1.73, P = .198, gp2 = .053) were non-significant, and no further inter-

action terms approached significance (3-way: F(22,682) = .531, P = .790, gp2 = .017).

Fig. 2 Change in electrodermal activity (EDA) following gambling wins where the participant had personalchoice over the gamble (participant-wins), wins where the gamble was selected automatically by thecomputer (computer-wins), and all non-win outcomes. a time-course data showing change from baselinevalues in 6 9 2 s bins following the presentation of the outcome. b the maximum EDA value in the window2–8 s post-outcome, minus the baseline period. Error bars indicate twice the standard error of the differenceof the means

Fig. 3 Change in electrodermal activity (EDA) following gambling near-misses and full-miss outcomes. Asin Fig. 2, on some trials, the participant had personal choice over the gamble, and on other trials, the gamblewas selected automatically by the computer. a time-course data showing change from baseline values in6 9 2 s bins following the presentation of the outcome. b the maximum EDA value in the window 2–8 spost-outcome, minus the baseline period. Error bars indicate twice the standard error of the difference of themeans

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From the time-course data displayed in Fig. 4 (left panel; collapsed across Choice),

there was a clear biphasic cardiovascular response in the post-outcome period, with an

initial deceleration to all outcomes in the initial 2 s, followed by an acceleration phase

from 2 to 6 s. The different outcomes diverged in this acceleration phase. Further analysis

of HR was conducted using summary measures for the deceleration and acceleration

components. A 3 (Outcome) 9 2 (Choice) repeated-measures ANOVA on HR decelera-

tion revealed no effects of Outcome (F(2,62) = 1.05, P = .355, gp2 = .033), Choice

(F(1,31) = .136, P = .715, gp2 = .004) or Outcome 9 Choice interaction (F(2,62) = 1.11,

P = .329, gp2 = .034). The equivalent model for the HR acceleration indicated a significant

effect of Outcome (F(2,62) = 6.68, P = .005, gp2 = .177), with no main effect of Choice

(F(1,31) = 1.65, P = .208, gp2 = .051) or Outcome 9 Choice interaction (F(2,62) = .312,

P = .733, gp2 = .010). Direct comparisons of outcomes (collapsed across Choice; see

Fig. 4, right panel) showed that wins were associated with smaller HR accelerations

compared to near-misses (t31 = 3.08, P = .004), and that near-misses were associated

with significantly larger HR accelerations than full-misses (t31 = 2.93, P = .006). The

difference between wins and full-misses was not significant (t31 = 1.38, P = .178).

Associations with Gambling Involvement

Scores on the SOGS varied from 0 to 7 (mean = .94, SD = 1.4, median = 0). Sixteen of

33 subjects had scores above zero, although only 3 participants scored in the range for

problem or ‘at risk’ gambling (C3). In addition, the mean score on the GRCS was 47.4

(range 23–92, SD 19.5, median = 42). Exploratory non-parametric correlations were run

to investigate whether individual differences in disordered gambling and trait susceptibility

to gambling distortions were associated with the subjective effects of the near-misses

(variable 1: ratings of ‘continue to play’ after participant-chosen near-misses minus par-

ticipant-chosen full-misses; variable 2: ratings of ‘pleased with outcome’ after near-misses

Fig. 4 Change in heart rate (HR) following gambling wins, near-misses and full-misses, collapsed acrossthe personal choice conditions. a time-course data showing change from baseline values in 12 9 0.5 s binsfollowing the presentation of the outcome. b the HR acceleration component, defined as the maximum HRvalue in the window 2–6 s post-outcome, minus the minimum value in window 0–2 s post-outcome. Errorbars indicate twice the standard error of the difference of the means

132 J Gambl Stud (2012) 28:123–137

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minus full-misses), the EDA responses (variable 3: max on participant-chosen near-misses

minus max on participant-chosen full-misses; variable 4: averaged max on both types of

win minus max on all non-wins), and the HR acceleration change scores (variable 5:

acceleration on near-misses minus full-misses; variable 6: acceleration on wins minus full-

misses; variable 7: acceleration of wins minus near-misses). There were no significant

associations (all P [ .10).

Discussion

Participants completed a simulated gambling task that delivered unpredictable monetary

wins as well as two types of non-win outcomes: near-misses and full-misses. As expected,

winning outcomes were associated with significantly higher ratings of pleasure and desire

to continue to play the game, compared to the non-win outcomes. Wins also increased

EDA, with a prototypical time-course peaking 4–6 s after outcome delivery (Dawson et al.

2000). EDA is an index of sympathetic nervous system activity, previously shown to be

sensitive to gambling exposure (Sharpe et al. 1995) and monetary reinforcement in

gambling tasks (Camille et al. 2004; Crone et al. 2004; Dixon et al. 2010; Wilkes et al.

2010). The novel aspect of the present study was the separation of the near-miss and full-

miss outcomes; events of equivalent objective value. As we have seen previously (Clark

et al. 2009), near-misses were perceived as more aversive than full-misses, but simulta-

neously increased the desire to play the game; the latter effect was observed only on trials

involving personal choice of the play icon. Like wins, the near-misses also triggered an

EDA increase. As with the invigorating effect of near-misses on the ‘continue to play’

rating, the EDA increases were observed primarily on the trials involving personal choice.

Our data extend a recent study from Dixon et al. (2010), who measured EDA and HR

responses during play on a real multi-line video slot machine (‘Lobstermania’). By

allowing players to bet on several lines per spin, these modern games deliver a moderate

frequency of ‘wins’ that do not even compensate for the wager placed on that trial. Dixon

et al. report that these ‘losses disguised as wins’ exerted a similar effect to outright wins,

increasing EDA compared to full losses (see also Wilkes et al. 2010). The ‘loss disguised

as a win’ is a complex outcome with a configuration resembling a classic near-miss, but

where a concrete pay-out and the associated sensory feedback (i.e. flashing lights, jingles)

is delivered. Our data confirm that novice gamblers even display phasic arousal responses

to evident loss events that lack the sensory feedback associated with winning, providing

these outcomes fall close to the pay-out configuration. By monitoring trial-by-trial sub-

jective ratings, our laboratory data also confirm that these arousal responses to near-misses

are accompanied by an enhanced motivation to gamble.

Gambling outcomes were also associated with cardiovascular changes. HR responses to

emotional stimuli are more complex than uniphasic EDA signals (Bradley 2000): there is

an acute bradycardic response within 1 s of the stimulus onset, linked to attentional ori-

enting. This is followed by a subsequent acceleration from 2 to 6 s post-stimulus, which is

sensitive to emotional valence and the ensuing preparatory responses (e.g. approach vs

freezing) (Bradley 2000). We observed this biphasic signal following all three types of

gambling outcome, but critically, the HR response diverged between near-misses and wins.

The acceleration component was greatest following near-misses, compared to both win and

full-miss outcomes. We infer that this heightened HR acceleration may be associated with

the frustration and negative affect that accompanies the near-miss (Reid 1986; Amsel

1992). There was no statistical difference between wins and full-misses, and numerically,

J Gambl Stud (2012) 28:123–137 133

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HR was suppressed following wins. Dixon et al. (2010) reported significant HR slowing

following gambling wins (but not in response to losses disguised as wins). These brady-

cardic effects may reflect a consummatory response to winning; for example, there are also

post-reinforcement pauses in response latency following slot machine wins (Delfabbro and

Winefield 1999), that are not consistently observed following near-miss outcomes (Dixon

and Schreiber 2004).

Our slot machine task included a second manipulation, of personal choice. It was

hypothesised that psychophysiological responses would be exacerbated by personal choice,

consistent with the illusion of control. However, the pronounced subjective and EDA

responses to winning outcomes did not vary significantly between choice and no-choice

trials, and personal choice exerted no statistical influence on outcome-related HR changes.

However, EDA changes to non-wins were greatest on near-miss outcomes that involved

choice. An impact of personal choice was also observed on the ‘continue to play’ ratings

following non-win outcomes, where the interaction between Outcome and Choice

observed in our earlier study (Clark et al. 2009) was replicated. We previously speculated

that near-misses are taken as evidence of skill acquisition (see also Reid 1986; Griffiths

1991); this appraisal is only valid on trials that allow personal control, and by this

mechanism, near-misses may foster the illusion of control. In our functional imaging study

with this task (Clark et al. 2009), the ventral striatum was responsive to near-misses under

both conditions of control, but the interaction between near-miss outcomes and personal

control was expressed in the rostral anterior cingulate cortex, a region that is implicated in

the integration of recent outcomes with ongoing strategic choice (Rushworth and Behrens

2008). The observation in the present data that both the EDA increases and changes in

‘continue to play’ ratings following a near-miss vary with personal control suggests a

possible mediatory role of sympathetic arousal in the motivational effects of near-misses,

and a potential physiological link to these appraisals of skill in games of chance.

It is well established that naturalistic gambling play is associated with increases in

peripheral arousal over periods of several minutes (Goudriaan et al. 2004 for review).

Naturalistic designs do not readily permit the event-related analysis of phasic arousal that

we report here. The characterisation of phasic changes in peripheral arousal during gam-

bling decisions (Bechara et al. 1996; Crone et al. 2004), and in response to gambling

outcomes (Dixon et al. 2010; Wilkes et al. 2010), constitutes an important step forward in

understanding the precise role that arousal plays in the maintenance of gambling behavior

and development of disordered gambling. Dickerson and Adcock (1987) proposed that

persistent gambling is maintained by bi-directional relationships between states of

increased arousal and illusory control, such that false appraisals of skill may heighten

arousal, which in turn distort ongoing decisional processes. As evidence for such a model,

white noise stress during a gambling session increased bet size in participants with minimal

levels of disordered gambling (Rockloff et al. 2007). Our data show that gambling wins

and near-misses are themselves sufficient to induce moderate changes in physiological

state. This arousal may drive ongoing gambling via a number of distinct mechanisms: there

may be a direct effect on risk-taking tendencies (Rockloff et al. 2007; Lighthall et al. 2009;

Porcelli and Delgado 2009), an effect via positive mood (Cummins et al. 2009), or a

disruption of inhibitory control (Scholz et al. 2009).

Some limitations should be noted. Our participants were student volunteers with modest

gambling involvement, and we observed no significant associations between outcome-

related activity and SOGS scores. As such, the question of whether these effects generalize

to disordered gambling requires further study. Second, our slot machine task was greatly

simplified compared to real-world electronic gaming machines. There is no direct analogue

134 J Gambl Stud (2012) 28:123–137

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to the personal choice manipulation, and the task involved two reels, only one of which

spun. This design enables straightforward modelling of phasic outcome-related activity, in

comparison to a standard three reel machine with sequential stopping, where near-misses

are determined by the adjacent reels that may individually elicit some psychophysiological

response. A further strength of the simulated task is that it does allow exact locking of the

gambling events to the psychophysiology trace; the recent study by Wilkes et al. (2010)

employed a real electronic gaming machine (‘‘Alchemy’’) but used manual event marking,

from the experimenter’s observation of video records. It is unlikely that manual event-

marking is sufficient to capture the rapid time-course of the phasic HR response (see

Fig. 4). In an effort to maintain ecological validity, the structural configuration and fre-

quency of near-misses was similar to real machines. We detected no influence of intro-

ducing a fixed wager into the game, and we considered the availability of real monetary

reinforcers to be important (Ladouceur et al. 2003). A third point is that our findings

pertain to a single example of a gambling near-miss, in the context of a slot-machine task.

Indeed, the majority of the published literature on gambling near-misses has studied slot-

machines, although in a study where participants gambled on a video-taped horse-race,

subjects who experienced a narrow neck-to-neck finish (another classic near-miss) also

displayed HR increases that were well above baseline (Wulfert et al. 2005).

To conclude, the present data indicate that non-win but near-miss outcomes are suffi-

cient to directly elicit physiological arousal consistent with a state of excitement. Of

course, this induction of arousal by near-misses occurs at no cost to the gambling industry,

with possible ramifications for gambling legislation. In slot machine games, the frequency

of near-misses is subject to certain restrictions, but legal techniques exist that ensure a

higher rate of near-misses than one would expect by chance alone (Harrigan 2008). The

observation that near-misses are maximally effective at rates around 30% (Kassinove and

Schare 2001) imposes an inherent cap upon their exploitation, but concerns have been

expressed about the introduction of novel types of near-miss event into electronic

machines, such as the ‘‘trail’’ or ‘‘feature board’’ (Parke and Griffiths 2004). The moti-

vational impact of near-miss outcomes may also be associated with disordered gambling

(Chase and Clark 2010), with implications for treatment based around the modification or

re-appraisal of peripheral arousal.

Acknowledgments This work was completed within the Behavioural and Clinical Neuroscience Institute,supported by a consortium award from the MRC and Wellcome Trust (director: TW Robbins). LC receivedfunding from the ESRC and Responsibility in Gambling Trust (RES 164-25-0010) and the British Academy(SG 52374). BD’s involvement in the project was funded by the UK Medical Research Council(U1055.02.002.00001.01). We thank Mr Aaron Louv for assistance with testing. The authors have noconflicts of interest to declare.

References

Amsel, A. (1992). Frustration theory. Cambridge, UK: Cambridge University Press.Anderson, G., & Brown, R. I. (1984). Real and laboratory gambling, sensation-seeking and arousal. British

Journal of Psychology, 75, 401–410.Bechara, A., Tranel, D., Damasio, H., & Damasio, A. R. (1996). Failure to respond autonomically to

anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex, 6, 215–225.Boyd, W. H. (1982). Excitement: The gambler’s drug. In W. R. Eadington (Ed.), The gambling papers.

Reno, NV: University of Nevada.Bradley, M. M. (2000). Emotion and motivation. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson

(Eds.), Handbook of Psychophysiology (2nd ed.). New York: Cambridge University Press.

J Gambl Stud (2012) 28:123–137 135

123

Breen, R. B., Kruedelbach, N. G., & Walker, H. I. (2001). Cognitive changes in pathological gamblersfollowing a 28-day inpatient program. Psychology of Addictive Behaviors, 15, 246–248.

Brown, R. I. (1986). Arousal and sensation-seeking components in the general explanation of gambling andgambling addictions. International Journal of the Addictions, 21, 1001–1016.

Brown, S. L., Rodda, S., & Phillips, J. G. (2004). Differences between problem and nonproblem gamblers insubjective arousal and affective valence amongst electronic gaming machine players. AddictiveBehaviors, 29, 1863–1867.

Camille, N., Coricelli, G., Sallet, J., Pradat-Diehl, P., Duhamel, J. R., & Sirigu, A. (2004). The involvementof the orbitofrontal cortex in the experience of regret. Science, 304, 1167–1170.

Cardinal, R. N., & Aitken, M. R. F. (2006). ANOVA for the behavioural sciences researcher. New Jersey,USA: Lawrence Erlbaum Associates.

Chase, H. W., & Clark, L. (2010). Gambling severity predicts midbrain response to near-miss outcomes.Journal of Neuroscience, 30, 6180–6187.

Clark, L. (2010). Decision-making during gambling: an integration of cognitive and psychobiologicalapproaches. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences,365, 319–330.

Clark, L., Lawrence, A. J., Astley-Jones, F., & Gray, N. (2009). Gambling near-misses enhance motivationto gamble and recruit win-related brain circuitry. Neuron, 61, 481–490.

Cote, D., Caron, A., Aubert, J., Desrochers, V., & Ladouceur, R. (2003). Near wins prolong gambling on avideo lottery terminal. Journal of Gambling Studies, 19, 433–438.

Coulombe, A., Ladouceur, R., Desharnais, R., & Jobin, J. (1992). Erroneous perceptions and arousal amongregular and occasional video poker players. Journal of Gambling Studies, 8, 235–244.

Coventry, K. R., & Hudson, J. (2001). Gender differences, physiological arousal and the role of winning infruit machine gamblers. Addiction, 96, 871–879.

Craig, A. D. (2009). How do you feel–now? The anterior insula and human awareness. Nature ReviewsNeuroscience, 10, 59–70.

Crone, E. A., Somsen, R. J., Van Beek, B., & Van Der Molen, M. W. (2004). Heart rate and skinconductance analysis of antecedents and consequences of decision making. Psychophysiology, 41,531–540.

Cummins, L. F., Nadorff, M. R., & Kelly, A. E. (2009). Winning and positive affect can lead to recklessgambling. Psychology of Addictive Behaviors, 23, 287–294.

Dawson, M. E., Schell, A. M., & Filion, D. L. (2000). The electrodermal system. In J. T. Cacioppo, L.G. Tassinary, & G. G. Berntson (Eds.), Handbook of Psychophysiology (2nd ed.). New York, USA:Cambridge University Press.

Delfabbro, P., & Winefield, A. H. (1999). Poker-machine gambling: An analysis of within session char-acteristics. British Journal of Psychology, 90, 425–439.

Delfabbro, P., & Winefield, A. H. (2000). Predictors of irrational thinking in regular slot machine gamblers.Journal of Psychology, 134, 117–128.

Dickerson, M., & Adcock, S. (1987). Mood, arousal and cognitions in persistent gambling: Preliminaryinvestigation of a theoretical model. Journal of Gambling Studies, 3, 3–15.

Dixon, M. J., Harrigan, K. A., Sandhu, R., Collins, K., & Fugelsang, J. A. (2010). Losses disguised as winsin modern multi-line video slot machines. Addiction, 105, 1819–1824.

Dixon, M. R., & Schreiber, J. E. (2004). Near-miss effects on response latencies and win estimations of slotmachine players. Psychological Record, 54, 335–348.

Fairchild, G., Van Goozen, S. H., Stollery, S. J., & Goodyer, I. M. (2008). Fear conditioning and affectivemodulation of the startle reflex in male adolescents with early-onset or adolescence-onset conductdisorder and healthy control subjects. Biological Psychiatry, 63, 279–285.

Gaboury, A., & Ladouceur, R. (1989). Erroneous perceptions and gambling. Journal of Social Behavior andPersonality, 4, 411–420.

Goudriaan, A. E., Oosterlaan, J., de Beurs, E., & Van den Brink, W. (2004). Pathological gambling: Acomprehensive review of biobehavioral findings. Neuroscience and Biobehavioral Reviews, 28,123–141.

Griffiths, M. (1991). Psychobiology of the near-miss in fruit machine gambling. Journal of Psychology, 125,347–357.

Griffiths, M. (1993). Tolerance in gambling: An objective measure using the psychophysiological analysisof male fruit machine gamblers. Addictive Behaviors, 18, 365–372.

Harrigan, K. A. (2008). Slot machine structural characteristics: Creating near misses using high awardsymbol ratios. International Journal of Mental Health and Addiction, 6, 353–368.

Henslin, J. M. (1967). Craps and magic. American Journal of Sociology, 73, 316–330.

136 J Gambl Stud (2012) 28:123–137

123

Hodes, R. L., Cook, E. W., 3rd, & Lang, P. J. (1985). Individual differences in autonomic response:Conditioned association or conditioned fear? Psychophysiology, 22, 545–560.

Kassinove, J. I., & Schare, M. L. (2001). Effects of the ‘‘near miss’’ and the ‘‘big win’’ on persistence at slotmachine gambling. Psychology of Addictive Behaviors, 15, 155–158.

Ladouceur, R., & Mayrand, M. (1987). The level of involvement and the timing of betting in roulette.Journal of Psychology, 121, 169–176.

Ladouceur, R., Sevigny, S., Blaszczynski, A., O’Connor, K., & Lavoie, M. E. (2003). Video lottery:Winning expectancies and arousal. Addiction, 98, 733–738.

Ladouceur, R., & Walker, M. (1996). A cognitive perspective on gambling. In P. M. Salkovskis (Ed.),Trends in cognitive and behavioural therapies. Chichester, UK: Wiley.

Langer, E. J. (1975). The illusion of control. Journal of Personality and Social Psychology, 32, 311–328.Leary, K., & Dickerson, M. (1985). Levels of arousal in high- and low-frequency gamblers. Behaviour

Research and Therapy, 23, 635–640.Lesieur, H. R., & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the

identification of pathological gamblers. American Journal of Psychiatry, 144, 1184–1188.Lighthall, N. R., Mather, M., & Gorlick, M. A. (2009). Acute stress increases sex differences in risk seeking

in the balloon analogue risk task. PLoS ONE, 4, e6002.Linnet, J., Thomsen, K. R., Moller, A., & Callesen, M. B. (2010). Event frequency, excitement and desire to

gamble among pathological gamblers. International Gambling Studies, 10, 177–188.Meyer, G., Hauffa, B. P., Schedlowski, M., Pawlak, C., Stadler, M. A., & Exton, M. S. (2000). Casino

gambling increases heart rate and salivary cortisol in regular gamblers. Biological Psychiatry, 48,948–953.

Miller, N. V., & Currie, S. R. (2008). A Canadian population level analysis of the roles of irrationalgambling cognitions and risky gambling practices as correlates of gambling intensity and pathologicalgambling. Journal of Gambling Studies, 24, 257–274.

Moodie, C., & Finnigan, F. (2005). A comparison of the autonomic arousal of frequent, infrequent and non-gamblers while playing fruit machines. Addiction, 100, 51–59.

Oei, T. P., & Gordon, L. M. (2008). Psychosocial factors related to gambling abstinence and relapse inmembers of gamblers anonymous. Journal of Gambling Studies, 24, 91–105.

Parke, A., & Griffiths, M. (2004). Gambling addiction and the evolution of the ‘‘near miss’’. AddictionResearch and Therapy, 12, 407–411.

Porcelli, A. J., & Delgado, M. R. (2009). Acute stress modulates risk taking in financial decision making.Psychological Science, 20, 278–283.

Raylu, N., & Oei, T. P. (2004). The gambling related cognitions scale (GRCS): Development, confirmatoryfactor validation and psychometric properties. Addiction, 99, 757–769.

Reid, R. L. (1986). The psychology of the near miss. Journal of Gambling Behaviour, 2, 32–39.Rockloff, M. J., Signal, T., & Dyer, V. (2007). Full of sound and fury, signifying something: The impact of

autonomic arousal on EGM gambling. Journal of Gambling Studies, 23, 457–465.Rushworth, M. F., & Behrens, T. E. (2008). Choice, uncertainty and value in prefrontal and cingulate cortex.

Nature Neuroscience, 11, 389–397.Scholz, U., La Marca, R., Nater, U. M., Aberle, I., Ehlert, U., Hornung, R., et al. (2009). Go no-go

performance under psychosocial stress: Beneficial effects of implementation intentions. Neurobiologyof Learning and Memory, 91, 89–92.

Sharpe, L., Tarrier, N., Schotte, D., & Spence, S. H. (1995). The role of autonomic arousal in problemgambling. Addiction, 90, 1529–1540.

Sodano, R., & Wulfert, E. (2010). Cue reactivity in active pathological, abstinent pathological, and regulargamblers. Journal of Gambling Studies, 26, 53–65.

Thompson, S. C., Armstrong, W., & Thomas, C. (1998). Illusions of control, underestimations, and accu-racy: A control heuristic explanation. Psychological Bulletin, 123, 143–161.

Toneatto, T., Blitz-Miller, T., Calderwood, K., Dragonetti, R., & Tsanos, A. (1997). Cognitive distortions inheavy gambling. Journal of Gambling Studies, 13, 253–266.

Venables, P. H., & Mitchell, D. A. (1996). The effects of age, sex and time of testing on skin conductanceactivity. Biological Psychology, 43, 87–101.

Wilkes, B. L., Gonsalvez, C. J., & Blaszczynski, A. (2010). Capturing SCL and HR changes to win and lossevents during gambling on electronic machines. International Journal of Psychophysiology, 78,265–272.

Wulfert, E., Roland, B. D., Hartley, J., Wang, N., & Franco, C. (2005). Heart rate arousal and excitement ingambling: Winners versus losers. Psychology of Addictive Behaviors, 19, 311–316.

J Gambl Stud (2012) 28:123–137 137

123