Predictability of Painful Stimulation Modulates Subjective and Physiological Responses

Predictability of Painful Stimulation Modulates Subjective and Physiological Responses

The Journal of Pain, Vol 11, No 3 (March), 2010: pp 239-246 Available online at www.sciencedirect.com Predictability of Painful Stimulation Modulates...

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The Journal of Pain, Vol 11, No 3 (March), 2010: pp 239-246 Available online at www.sciencedirect.com

Predictability of Painful Stimulation Modulates Subjective and Physiological Responses Shunichi Oka,* C. Richard Chapman,y Barkhwa Kim,* Osamu Shimizu,x Noboru Noma,{ Osamu Takeichi,# Yoshiki Imamura,{ and Yoshiyuki Oi* * Department of Dental Anesthesiology, Nihon University School of Dentistry, Tokyo, Japan. y Pain Research Center, University of Utah School of Medicine, Salt Lake City, Utah. x Department of Oral and Maxillofacial Surgery, Nihon University School of Dentistry, Tokyo, Japan. { Department of Oral Diagnosis, Nihon University School of Dentistry, Tokyo, Japan. # Department of Endodontics, Nihon University School of Dentistry, Tokyo, Japan.

Abstract: Clinical observations suggest that the perceived intensity of a painful event increases as the unpredictability of its occurrence increases. We examined the effect of varying stimulus predictability on the Somatosensory Evoked Potential (SEP), Pupil Diameter Response (PDR), Pain Report (PR), and Fear Report (FR) in 25 healthy female volunteers experiencing repeated noxious fingertip shocks. Each volunteer underwent high- and low-stimulus intensities in 4 stimulus patterns defined by stimulus sequence (SEQ) and interstimulus interval (ISI) as follows: A) serial stimulus intensity SEQ with fixed ISI; B) serial stimulus intensity SEQ with varied ISI; C) random stimulus intensity SEQ with fixed ISI; and D) random stimulus intensity SEQ with varied ISI. Results revealed that: (1) lower stimulus predictability led to higher PR and FR, greater PDR magnitude, and greater SEP amplitude; and (2) the 4 dependent measures showed the same response pattern across levels of stimulus predictability. These findings support the hypothesis that lower stimulus predictability is associated with higher reported pain and fear as well as greater physiological arousal. Perspective: Patients undergoing painful procedures experience more distress when the occurrence of a painful event is unpredictable. Poor predictability increases pain, fear, and associated physiological arousal. Maximizing the predictability of painful events may improve the quality of patient care by minimizing associated levels of pain and fear. ª 2010 by the American Pain Society Key words: Somatosensory evoked potentials, pupil dilation response, visual analogue scale, predictability, pain, human.

H

uman pain is a complex experience with sensory, affective, and cognitive features. Studies of clinical and laboratory pain have demonstrated consistently that pain varies in response to cognitive changes. Attention,30 event cuing,9,36,42,46,50 expectancy,37,41,48 memory,29 and appraisal,4,22,47 for example, can shape the experience of pain. Moreover, functional brain imagReceived July 8, 2009; Revised July 14, 2009; Accepted July 18, 2009. Support for this research came from the following grants: 1) Japanese Grant-in-Aid for Exploratory Research (No. 14657535); 2) Grant-in-Aid for Scientific Research (C) (No. 16592026) from the Ministry of Education, Culture, Sports, Science Technology; and 3) the Sato Fund, Nihon University School of Dentistry(S. O., 2009). Address reprint requests to Dr. Shunichi Oka, Department of Dental Anesthesiology, Nihon University School of Dentistry, 1-8-13, Kanda Surugadai Chiyoda-Ku, Tokyo, 101-8310, Japan. E-mail: [email protected] ac.jp 1526-5900/$36.00 ª 2010 by the American Pain Society doi:10.1016/j.jpain.2009.07.009

ing studies reveal that cognitive manipulations result in different patterns of central processing.21,34 Appraisal of a recurring painful event generates negative emotion such as fear when the timing or intensity of the event is uncertain. In the dynamic event stream of daily living, the cognitive process of prediction reduces such uncertainty. Accurate prediction facilitates adaptive reactions to injury or threatening situations. In the laboratory, the brain uses prediction to appraise and adjust to controlled event sequencing. In clinical settings, acute pain often accompanies diagnostic procedures, manipulations, dressing changes, and interventions. Patients with chronic musculoskeletal pain are sometimes fearful that certain activities will exacerbate their pain, and their inability to predict the painfulness of an activity contributes to their disability.24 The influence of prediction on pain merits further inquiry because increasing predictability is a common denominator 239

240 for many cognitive pain-control strategies. Skillful control of the predictability of noxious events in clinical settings could potentially prevent or reduce patient distress. Evidence defining the relationship of stimulus predictability to pain is encouraging but sparse. Human cognition is inseparable from emotion, and negative emotion accentuates pain through autonomic arousal. How the predictability of a noxious stimulus influences the subjective experiences of fear and pain is reasonably clear, but the psychophysical relationship of fear and pain to sympathetic and cortical somatosensory responses is still insufficiently defined. Characterizing the psychophysiological relationship of subjective experience to sympathetic and central activation requires pain-related physiological markers sensitive to cognitive manipulations along with pain and fear ratings. Using noxious fingertip electrical shock to create brief painful events, we previously studied pupil dilation response (PDR) and brain somatosensory evoked potential (SEP) amplitudes to determine their relationship to pain report. With stronger stimulus intensities, the PDR increases, SEP amplitude increases, and subjects report more intense pain. Subjects breathing varied concentrations of nitrous oxide in oxygen showed a significant decrement in all 3 response measures at the strongest concentration.33 The consistent multivariate psychophysiological response pattern suggests that the measures are different facets of an integrated defense response to threatening events.11,15 Sokolov44 proposed that the hypothalamus produces an orchestrated, adaptive, and complex response to threat. This response has central nervous system, autonomic and endocrine components. The reliable multivariate psychophysiological response pattern that we have previously observed is consistent with Sokolov’s defense response model.44,45 In this study, we investigated the influence of varying noxious stimulus predictability on the multivariate psychophysiological response pattern. Our purpose was to examine the effect of varying stimulus predictability on the SEP, PDR amplitude, pain report (PR), and fear report (FR) in healthy volunteers undergoing repetitive painful stimulation. By varying the sequencing and timing of repetitive painful fingertip electrical shocks, we created conditions of low, moderate and high stimulus predictability. The dependent measures were SEP, PDR amplitude, PR, and FR. The literature leads us to propose that lower stimulus predictability is associated with greater PDR magnitude and SEP amplitude, as well as higher PR and FR. Therefore we tested the null hypothesis that PDR magnitude, SEP amplitude, PR and FR do not differ across conditions of varying stimulus predictability. We also predicted that the 4 measures would show the same pattern of response to variation in stimulus predictability.

Methods Subjects Gender differences influence pupil reactions to painful stimulation,16,32 and we therefore limited the study to 1

Predictability and Physiological Response gender, female. The subjects were 25 healthy female volunteers aged 24 to 32 years. All subjects were in good health, reported being well rested at the time of testing, and all reported that they were not taking analgesic or psychoactive medications. The Nihon University School of Dentistry Human Subjects Review Committee approved the project and each subject gave signed, informed consent. The study was conducted in accordance with the Declaration of Helsinki. Subjects received no compensations for participation.

Dolorimetry We repeatedly delivered noxious stimuli to subjects’ finger tips using the standard method first introduced by Bromm and Meier.5,10,33,43 With this method, preparing the subject for stimulation requires creating a crater on a fingertip followed by insertion and fixation of a metal electrode. A small stainless drill (diameter 1.5 mm) carefully applied to a fingertip removed the epidermal layers of the glabrous skin and created a crater for the insertion of the cathode. The anode was 3-cm diameter electrocardiograph monitoring electrode taped to the volar surface of the ipsilateral forearm. The resistance between electrodes was always less than 20 KU and most preparations had impedances less than 10 KU. The stimuli consisted of single square wave pulses of 5-ms duration each, delivered by a SEN-3301 stimulator (Nihon Kohden, Tokyo, Japan), stimulus isolation unit, with a constant current unit connected in series. We set 2 stimulus levels for each subject by working with each individual’s subjective range of stimulus intensities. We delivered stimuli with slowly increasing intensity to each subject until she identified a faint pain (rated 3 on a 10-point scale) and strong pain (rated 7 on a 10-point scale). After 3 repetitions, the average of the intensities became the low and high intensities for subsequent testing. The mean stimulus intensities 6SD that subjects chose for themselves were 1.78 6 .9 mA for faint pain and 3.34 6 1.35 mA for strong pain. We controlled the predictability of the strong stimulus. The low- and high-intensity stimuli occurred across trials in either ordered (predictable) or random sequences, hereafter serial vs random delivery, and with either fixed (predictable) or varied interstimulus intervals (ISIs), hereafter fixed vs varied ISIs. With serial stimulus delivery, subjects received the same intensity over blocks of repeated trials whereas with random stimulus delivery, the low and high intensities occurred over repeated trials in a random, unpredictable pattern. Similarly with fixed ISI, the stimuli occurred at a predictable time across repeated trials but with varied ISI intervals between stimuli varied in an unpredictable way. Each subject experienced 4 stimulus patterns defined on the basis of sequence (SEQ) and ISI as follows: A) Serial stimulus intensity 3 with fixed 10 seconds ISI (25 trials) followed by serial stimulus intensity 7 with fixed 10 seconds ISI (25 trials); B) Serial stimulus intensity 3 with varied 520 seconds ISI (25 trials) followed by serial stimulus intensity 7 with varied 520 seconds ISI (25 trials); C) Random stimulus intensity 3 (25 trials) or 7 with fixed 10

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241 Assessed for eligibility (n= 26)

Refused to participate (n= 1)

Enrollment

Randomized (n= 25)

Allocated to intervention (n= 6)

Allocated to intervention (n= 6)

A1

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Allocated to intervention (n= 6) C1 C2

D1 D2

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C1 C2 A1

B2 D1 D2

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Allocated to intervention (n= 7)

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Analysis (n= 25)

Figure 1. The consort flow diagram. The letters show the 4 conditions and the numbers show the 2 intensity levels of the noxious stimulus (1 = weak; 2 = strong). An arrow in the box indicates the order of the condition with the stimulus.

seconds ISI (25 trials); and D) Random stimulus intensity 3 (25 trials) or 7 (25 trials) with varied 520 seconds ISI. The combination of fixed intensity sequences and fixed ISI in cell A created maximum predictability, while the combination of random presentation of stimulus intensity and varied ISIs in cell D provided a condition of minimum predictability. The remaining 2 cells, B and C, represent moderate predictability. Fig 1 provides the consort diagram for this study. The order of cells A, B, C, and D varied in a counterbalanced pattern across subjects. Each subject had 8 repeated measures on each dependent variable because there were 4 stimulus pattern conditions and 2 stimulus intensity levels.

Procedure Subjects sat in a dental chair inside a sound-attenuated shield testing chamber with ambient light set at approximately 150 lux and fixed their gaze at a picture on the test chamber wall about 3 m away. One of 2 investigators administered the painful fingertip shocks and monitored subjects on a video screen. Subjects had voice contact with this investigator at all times. The other investigator controlled the order of the stimulus patterns over blocks of trials in a blinded fashion so that neither the subject

nor the other investigator knew which stimulus pattern the subject was receiving. During the experiment, each subject experienced 4 blocks of 50 stimulus trials each. To minimize fatigue and habituation, each subject took a 5-minute break after the first 25 trials of each block. Thus, in conditions A and B, each subject experienced 25 trials with serial stimulus intensity 3 first, took a break, and experienced 25 trials with serial stimulus intensity 7 last. With conditions C and D, each subject experienced 25 trials with randomly sequenced stimulus intensities 3 or 7 first, took a break, and then underwent 25 trials with randomly sequenced stimulus intensities again. Each of the 25 subjects completed 4 blocks of 50 trials for a total of 200. Thus, each subject experienced all levels of treatment in a single day.

Electroencephalographic Recording We recorded the electroencephalogram (EEG) at the vertex (Cz) using an 8-mm diameter gold disk electrode, with reference to linked earlobe electrodes, and an electrode taped to the forehead served as ground. Electrooculogram (EOG) leads affixed above and below the external canthus of 1 eye detected eye blinks and ocular rotation artifacts. EEG filtering employed a 2- to 100-Hz

242 band pass, digitizing at a sampling rate of 250 Hz. To create late near-field SEPs, we sampled the EEG 200 ms before and 500 ms after the application of each stimulus and averaged the samples. As in other studies, this produced a waveform with a prominent negative deflection at approximately 150 ms and a prominent positive deflection at approximately 250 ms. These peaks are correlated, and for expediency we took as our dependent measure the peak-to-peak value of the N150-P250 deflection (SEP Amplitude).

Pupil Diameter Recording To measure pupil size, we used an ISCAN RK 406 Infrared Pupillometer System (ISCAN Inc, Cambridge, MA). This system is a real-time digital image processor consisting of a camera, infrared light source, video monitor, and analogue video processing unit. The Pupillometer sent a continuous reading of pupil diameter signals to a SONY VAIO PCG-FX77S/BP computer, which sampled pupil diameter beginning 1 second prestimulus through 2 seconds poststimulus at 256 Hz. To calibrate prior to testing each subject, we measured 2 black dots of known size that are maximum and minimum measurable, immediately adjacent to the target pupil and calculated a linear correction function. The EEG and EOG leads connected to a 12-bit A/D board on the computer. This computer coordinated and controlled stimulus presentation, sampling and processing signals of EP and pupil diameter via a customdesigned program called SMI (Shimizu Inc, Tokyo, Japan) running in the National Instruments Lab View programming environment. Once initiated, SMI controlled the experiment, stimulating the subject and recording responses while screening for EEG and EOG out-of-range signals, eye blinks, and pupil measurement artifacts on a trial-by-trial basis. SMI rejected and subsequently repeated flawed trials, deferring repetition until the end of the trial block.

Signal Processing Artifact identification Before testing, the experimenter adjusted the gain of the individual subject’s EOG signal to produce an outof-range condition during gross eye movement, thus insuring that the computer could identify ocular artifacts. When an out-of-range signal occurred in the EOG channel during testing, the computer designated the trial as contaminated, rejected it, and scheduled a latter repetition of that trial.

Pupil Diameter Artifact Removal Most eye blinks affected pupil diameter to some degree for a few milliseconds, thereby imbedding spikes in small portions of the records for the trials on which they occurred. The computer identified and corrected blink effects whenever blinks created outlier spikes that might distort averaging of the record. A spike qualified as an outlier when it caused a pupil diameter value

Predictability and Physiological Response to differ from the median of a 5-point moving window by a set amount: 6.6  (median – minimum).

Pain Rating and Fear Rating Pain report and the fear associated with each trial were assessed after each stimulus pattern measurement using a 10-cm horizontal line. For the pain rating, this line was anchored with no pain at all on the left end and the worst pain imaginable at the right end. For the fear rating the anchors were no fear at all at the left end and the the worst fear imaginable at the right end for the fear scale. Immediately after each stimulus, subjects reported the intensities of the pain and fear caused by the electrical stimuli by placing vertical marks on the separate pain and fear analogue scales.

Statistical Analysis Prior to data analysis, we averaged the 25 responses to each of the 8 stimulus predictability conditions for each subject. To evaluate the significance of these observations, we performed a mixed-effects analysis of variance (SPSSPC, v.16; SPSS, Inc, Chicago, IL) on each dependent measure. The fixed effects were Stimulus Intensity (SI), SEQ, and ISI, with Blocks (specific combination of SEQ and ISI) as the repeated effect. The hypothesis predicts that the 2- way interaction of SEQ and ISI should be significant because manipulation of these 2 conditions produced variations in stimulus predictability ranging from high to low. Reported correlations are Pearson product-moment coefficients.

Results Fig 2 provides a visual summary of the data. The box plots show the effects of varying stimulus predictability on the medians of the 4 dependent measures. In each case, the median response was lower for high predictability and highest for low predictability, with moderate levels of predictability falling between these extremes. These response patterns appear to conform to the hypothesis put forward above. Table 1 reports the main effects as well 2-way and 3-way interactions in the mixed-effects analysis of variance. The 4variables show nearly identical patterns of statistical significance. In addition to the main effect significance, each variable has a statistically significant SEQ*ISI interaction. To further examine the SEQ by ISI interaction for SEP, PDR, PR and FR, we performed pairwise post hoc comparisons of ISI fixed and varied conditions under random and serial SEQ conditions. All contrasts were significant at # P = .001, except for the SEP in the serial contrast (P = .034). Collectively, these outcomes refute the null hypothesis of no difference in response across levels of stimulus predictability. To compare the magnitude of the effect of predictability across the 4 outcomes, we put the variables into a common scale by calculating standardized mean differences. The difference between high- and low-predictability conditions for each outcome variable divided by the pooled standard deviation estimates effect size.12

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Figure 2. The effects of varying predictability for low- and high-stimulus intensities on the dependent measures SEP is the peak-topeak amplitude of the somatosensory evoked potential between the negative peak at approximately 150 ms poststimulus and the positive peak at approximately 250 ms poststimulus (N150-P250). PDR is the change in the diameter of the pupil immediately following the stimulus for each individual trial. Each variable has 2 graphs that compare the effect across low- and high-stimulus intensity trials. Box plots display indicators of dispersion for each variable: range, median, and quartiles. The box represents the interquartile range, and the line in its center is the median. The whiskers are the lines extending from the box to the highest and lowest values. The circles and stars represent outlier scores. SEP, Somatosensory Evoked Potential; PDR, Pupil Dilation Response; PR, Pain Report; FR, Fear Report. High = Condition A; Left Moderate = Condition B; Right Moderate = Condition C; Low = Condition D. An effect size greater than .80 is a large effect according to the Cohen standard. The comparative effect sizes are: SEP = 1.13; PDR = 1,24; PR = .82; and FR = 1.4. While all of these are large effect sizes, PR has notably smaller effect size than the other 3 variables. Table 2 shows the pair-wise correlation coefficients for PDR, SEP, FR and PR for both the low- and high-stimulus intensity levels. Overall, the correlations are substantial and statistically significant at P < .001, with the psychophysiological measures correlating more highly with one another than with the subjective report measures, and conversely the subjective report measures correlating more highly with one another than with the physiological measures. These findings indicate that the 4 outcome measures tend to respond in similar ways to the various experimental conditions.

Discussion Physicians and dentists who perform minor invasive procedures know that the distress a painful event causes worsens when the timing or the intensity of the event is unpredictable for the patient. Pain-related predictability lends itself readily to investigation in the human studies laboratory because stimulus probability quantifies it. An investigator can precisely manipulate the probability of an intense vs weak painful stimulus occurring or the probability of the painful event occurring at a given time. We created 4 conditions of predictability by manipulating these factors and examined the effect of predictability on both subjective experience (PR, FR) and psychophysiologi-

cal arousal (SEP, PDR). Broadly, both subjective discomfort and physiological arousal increased as the predictability associated with a noxious event decreased. This finding is consistent with everyday clinical observation.

The Nonlinear Relationship of Pain and Fear The relationship of fear to pain is complex3,31,38 and the literature contains paradoxes. Emotion modulates pain,26,40 but some papers indicate that negative emotions inhibit pain1,39 while others report that negative emotions enhance pain under some conditions.13,51 Fear in particular has an impact on pain perception,

The Main Effects, 2-Way, And 3-Way Interactions in the Mixed Effects Analysis of Variance

Table 1.

SOURCE Fixed Effects Intercept SI SEQ ISI SI  SEQ SI  ISI SEQ v ISI SI  SEQ  ISI Repeated Blocks Effects

SEP

PDR

PR

FR

<.0001* <.0001* <.0001* <.0001* .403 .596 .002* .357 <.0001*

<.0001* <.0001* <.0001* <.0001* .029* .302 .018* .620 <.0001*

<.0001* <.0001* <.0001* <.0001* .351 .627 .025* .798 <.0001*

<.0001 <.0001* <.0001* <.0001* .801 .389 .006* .375 <.0001*

*Designated statistical significance

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Pair-Wise Correlations Among the Four Outcome Variables for Low- And High-Stimulus Intensity Trials

Table 2.

VARIABLE BY VARIABLE SEP  PDR SEP  PR PDR  PR SEP  FR PDR v FR PR  FR

LOW STIMULUS INTENSITY

HIGH STIMULUS INTENSITY

.609 .412 .505 .409 .340 .566

.574 .460 .529 .538 .539 .572

and numerous studies in experimental animals and some in humans have shown that fear decreases pain sensitivity,38 which would increase the likelihood of survival in nature. In our study, subjects perceived a safe pain that did not threaten survival, and so fear of it did not reach a level sufficient to activate endogenous pain modulation. Medical procedures, like laboratory pain, do not threaten survival and so laboratory models may generalize well to medical settings. All together, the literature suggests that the relationship of pain to fear may be nonlinear, following the inverted U pattern. Pain increases as fear increases up to a point, after which endogenous modulation processes begin to attenuate pain, reducing sensitivity to noxious stimulation as fear increases further. Predictability and fear of pain are related.28 Carleton et al7 recently proposed that intolerance of the uncertainty associated with a painful event may exacerbate the experience of pain. Carlsson et al8 reported that, compared to a condition in which pain stimuli were predictable by means of a visual cue, unpredictable pain stimuli increased ratings of anxiety, pain intensity, and negative valence. Their results are consistent with our study showing that high fear elicited by low predictability enhances laboratory pain. In clinical studies, patients with pain frequently report that they suffer more from the cognitive and emotional distress related to pain, ie, fear or anxiety of pain, than from the pain itself.2,14,35

Psychophysiological Measures: Attention and Processing Load Numerous investigators have used the SEP extensively to study both pain and fear. The peak-to-peak SEP component, N150-P250, comprises 2 base-to-peak deflections, each of which may reflect different psychological processes. In a study of pain set in the context of negative emotional arousal, the N150 deflection varied with the emotional valence of emotional pictures presented to subjects in association with painful shocks while P250 varied as a function of arousal.23 Decomposing N150P250 to its 2 components—N150 base-to-peak and P250 base-to-peak—revealed that the P250 SEQ  ISI interaction was significant (P = .006) but the N150 component was not (P = .06). The pattern of response was in the expected direction for N150 and its failure to reach significance may reflect the small sample size and insufficient power.

Nociceptive processing intrinsically involves attentional factors.6 The pain-related SEP may indicate, in part, attention to a threatening stimulus.17,52 Some investigators have related the N150 base-to-peak-amplitude to attentional processing.19,27 Others report that the pain-related P250 is sensitive to attention.25 Yamasaki et al52 found that distraction tasks reduced the peak-to-peak amplitude of the N140-P230 SEP significantly. Our second physiological measure, the PDR, gauges general arousal, attention, mental effort, stress, and anxiety20,49 in widely varied investigations. Granholm and Steinhauer18 reviewed the use of the PDR in psychological vs psychophysiological studies. They asserted that pupillary responses can index the extent of central nervous system processing allocated to a task. The extent of pupil dilation evoked by a task reflects brain activation regardless of whether the activation stems from information processing or emotional processing. The Granholm and Steinhauer18 framework suggests that lower predictability imposes a greater processing burden on the subject in a pain experiment than higher predictability, and this in turn increases the PDR. This framework is parsimonious in its assertion that all cognitive activity incurs a processing load. The seemingly disparate constructs of information processing and emotional arousal have a common denominator in this framework because each incurs a processing load. The concept of processing load is essentially a resource-utilization construct, and this suggests that the lower the predictability of a painful event, the greater the resource utilization, or cost in brain activation, to the subject. Brain activation involves both preconscious stimulus processing and conscious evaluation of the stimulus. Our psychophysiological outcome measures reflect preconscious processing of the stimulus while the subjective report variables reflect the postconscious evaluation of the experienced stimulus. Comparison of effect sizes in our study revealed that predictability influenced all of the outcome measures. PR had the smallest effect size perhaps because subjects chose their stimulus intensities at the beginning of the experiment, setting them at 3 and 7 for low and high intensities. For Conditions B and C, the mean values of the PR were approximately 3 and 7 for low- and high-stimulus intensities respectively. For Condition A where predictability was high, the mean PR was lower, while in Condition D where predictability was lowest, mean PR increased by almost 1 unit. The other 3 variables had no constraints, and so they were free to be lower with the highest predictability condition and higher with the lowest predictability condition.

Study Limitations We used only females and it is unclear whether the results will generalize perfectly to males. In related studies there are few gender differences, but we have previously observed that N150 in the SEP is smaller in females than males11 and PDR amplitude is larger in females than males.32 It is possible that the effect on N150 and PDR would be show a more robust effect in males than in females alone.

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Conclusions We examined the effect of varying painful stimulus predictability on both subjective experience (PR, FR) and psychophysiological arousal (SEP, PDR) in healthy volunteers. In each case, the mean response was lower for high predictability and highest for low predictability, with moderate levels of predictability falling between these extremes. These response patterns would conform to the hypothesis that lower stimulus predictability is associated with higher reported pain and fear, greater PDR magnitude, and greater EP amplitude. Furthermore, the 4 dependent measures are correlated and

245 show the same response pattern across levels of predictability, and this is consistent with the defense response model.

Acknowledgment We thank Mr. Yuki Shimizu for programming of the pupillometry equipment and to Professor Gary W. Donaldson, PhD at the Pain Research Center, University of Utah, for statistical guidance with mixed effect modeling. None of the authors has any financial interest in this research.

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