1 Introduction
In both clinical and experimental situations, qualitative and quantitative aspects of the pain experience are routinely assessed by having subjects generate a subjective report. At the most fundamental level, the cognitive process of making a subjective report of pain involves an introspective assessment of selected qualia of the pain experience. In most cases, subjects are required to retrospectively reflect upon their experience of pain over an interval of time and to provide a single rating that best represents their experience. This interval of time may range from seconds to days depending on the assessment paradigm employed and may encompass significant fluctuations in the magnitude of pain (Algom and Lubel, 1994; Babul et al., 1993; Feine et al., 1998; Smith and Safer, 1993).
A number of studies have examined real-time changes in the magnitude of perceived pain using continuous ratings of pain (Apkarian et al., 1999; Coghill et al., 1993; Davis et al., 2002; Harrison and Davis, 1999; Kwan et al., 2002; LaMotte et al., 1984; Strigo et al., 2002). Using such tools, components of the real-time response, such as peak pain, mean pain, and duration of pain, can be readily assessed (Harrison and Davis, 1999). Little is known, however, about the manner in which stimulus duration interacts with stimulus intensity to influence these real-time components.
How individuals use components of the real-time experience to construct a post-stimulus pain rating remains unknown. Do subjects place more emphasis on the peak pain magnitude experienced in a given block of time than they place on the average pain magnitude experienced over the entire block of time? Additionally, numerous studies have shown that individuals can reliably distinguish between the magnitudes of sensory aspects of pain (i.e. intensity) and the magnitudes of affective components of pain (i.e. unpleasantness) in post-stimulus ratings of pain (Price et al., 1983, 1994a). However, little data exists as to the degree of this distinction in real-time pain ratings (Harrison and Davis, 1999) and nothing is known as to whether specific aspects of the real-time pain experience are weighed differently in the construction of intensity versus unpleasantness ratings.
In order to better characterize temporal aspects of the real-time experience of both pain-intensity and pain-unpleasantness, subjects were recruited to evaluate different combinations of stimulus intensities and durations using continuous pain ratings. Both real-time and post-stimulus assessments of pain-intensity and pain-unpleasantness were obtained to determine the relative contribution of discrete aspects of the real-time pain experience to post-stimulus assessments of pain.
2 Methods
2.1 Subjects
Twelve healthy volunteers (seven male and five female), 18–35 years old (mean 22.9), participated in this study. All subjects gave informed consent acknowledging that they understood: (1) that they would experience experimental painful stimuli, (2) that all methods and procedures were clearly explained, and (3) that they were free to withdraw from the experiment at any time without prejudice. All procedures were approved by the Institutional Review Board of Wake Forest University School of Medicine.
2.2 Stimulation procedures
We employed graded heat as the noxious stimulus for this investigation since it has been widely used in experimental studies of pain and since it can be readily delivered in a highly accurate fashion. Thermal stimuli were delivered to the ventral surface of the non-dominant forearm via a 16×16-mm2 peltier device (Medoc TSA II, Ramat Yishai, Israel). This device was attached to the forearm with a Velcro strap and was maintained at a baseline temperature of 35 °C. Stimulus temperatures were delivered with rise and fall rates of 6 °C/s and were feedback controlled. Sixteen different types of stimulus trials were used. For each stimulus trial, one of 16 combinations of plateau temperatures (43, 45, 47 or 49 °C) and plateau durations (5, 10, 15 or 30 s) was presented in pseudo-random fashion. Each subject received four trials for each stimulus condition (total 64 trials per individual subject). To minimize sensitization habituation or hyperalgesia, all trials were separated by a minimum of 30 s and were performed on previously unstimulated sites of the skin (Pedersen and Kehlet, 1998a,b).
2.3 Psychophysical assessment of pain
2.3.1 Post-stimulus pain ratings
Pain-intensity and pain-unpleasantness were assessed with a visual analogue scale (VAS) that consisted of a 15-cm plastic sliding scale device (Parisian Novelty Co., Chicago, IL; Price et al., 1994a). For pain-intensity, the minimum was anchored with ‘No pain sensation’ and the maximum was anchored with ‘Most intense pain imaginable’. For pain-unpleasantness, the minimum was anchored with ‘Not at all unpleasant’ and the maximum was anchored with ‘Most unpleasant imaginable’. To obtain static VAS ratings, the subjects were requested to rate both overall pain-intensity and pain-unpleasantness in a conventional fashion (Price et al., 1983, 1994a). These VAS ratings were made immediately after termination of the stimulus (post-stimulus ratings).
2.3.2 Real-time pain ratings
In addition to providing post-stimulus ratings at the end of each stimulus trial, subjects also rated their pain in a dynamic real-time fashion throughout the stimulus presentation. Such continuous VAS ratings have been utilized for the measurements of pain (Harrison and Davis, 1999; LaMotte et al., 1984), itch (Stahle-Backdahl et al., 1989) and appetite (Stubbs et al., 2000). To obtain the real-time ratings, we used a custom-made computer-digitized VAS. This device consisted of the same 15-cm VAS plastic sliding scale that was used in the post-stimulus ratings, except that it was coupled to a potentiometer. The output voltage of this device was proportional to position of the VAS scale. Using a digital chart recorder (PowerLab: ADInstruments, Mountain View, CA), the voltage time course was digitized, converted into VAS units, and stored on a computer. For each stimulus trial, real-time ratings of either pain-intensity or pain-unpleasantness were obtained in a pseudo-random fashion (ratio, 1:1). Thus, two trials of real-time ratings were obtained for each pain-dimension (intensity or unpleasantness) for the same stimulus condition.
These real-time data were then processed using customized programs within the IDL software package (Research Systems, Inc., CO). The following five components were examined for each single stimulus trial: peak response (VAS score), mean response (VAS score), latency to peak (s), response duration (s), and full width half-maximum (FWHM, s). The definitions of these components are schematically illustrated (Fig. 1). The mean responses were the average of all non-zero pain ratings (i.e. the average of ratings from the beginning to the end of the psychophysical response). The latencies to peak were defined as the time from the onset of the target temperature to the maximal psychophysical response.
;)
Fig. 1:
Definitions of components for real-time ratings. The solid line shows the pain-response curve and the thick dashed line shows the stimulus temperature. Mean responses were derived by dividing the area under the curve (shown in gray) by response duration. FWHM, full width half-maximum; Max., maximum value; Temp., temperature.
2.4 Statistical analysis
All statistical analyses were performed using the StatView software package (SAS Institute, Cary, NC) on Macintosh computers. To assess the effects of stimulus temperature and stimulus duration on post-stimulus ratings, we performed a two-factor analysis of variance (ANOVA) repeated within subjects. Post hoc tests (Bonferroni/Dunn) were performed to identify differences between data obtained with 5 s stimulus durations and all other durations. In addition, to evaluate the difference between post-stimulus intensity ratings and post-stimulus unpleasantness ratings, we performed a paired t-test. For these analyses, we employed post-stimulus data that were averaged within four trials of the same stimulus condition (e.g. 49 °C, 30 s stimulus) for each subject.
To assess the effects of stimulus temperature and stimulus duration on real-time ratings, we performed a two-factor ANOVA repeated within subjects separately on the five components derived from real-time ratings (peak response, mean response, latency to peak, response duration, and FWHM). Post hoc tests (Bonferroni/Dunn) were performed to identify differences between data obtained with 5 s stimulus durations and all other durations. In addition, to determine the difference of these five components between real-time intensity ratings and real-time unpleasantness ratings, we also performed paired t-tests. For these analyses, we employed data that were averaged across the two trials of the same pain-dimension and stimulus condition (e.g. real-time intensity ratings within 49 °C, 30 s stimulus) for each subject.
To further clarify effects of stimulus duration in the real-time data, we examined VAS responses at 5, 10, 15 and 30 s after the onset of the stimulus plateau in trials using 30 s stimulus durations. Single factor ANOVA's examined effects of stimulus duration and post hoc tests (Bonferroni/Dunn) were used to identify differences between VAS ratings at 5 s and all later time points.
To assess the relationship between post-stimulus and real-time ratings, we performed forward stepwise regression analyses (F-to-enter >4.0). For these analyses, we employed data that were obtained on each individual stimulus trial. The five components derived from real-time ratings (peak response, mean response, latency to peak, response duration, and FWHM) were assigned as independent variables, and the post-stimulus ratings (either intensity or unpleasantness) were assigned as the dependent variable. These regression analyses were used to determine the relationship between: (1) real-time intensity and post-stimulus intensity ratings, (2) real-time unpleasantness and post-stimulus unpleasantness ratings, (3) real-time intensity and post-stimulus unpleasantness ratings, and (4) real-time unpleasantness and post-stimulus intensity ratings. These relationships among pain ratings were defined as a linear combination of weighted factors (components of the real-time response), each of which accounted for a statistically significant portion of the variability of the dependent variable (post-stimulus ratings; Table 4). Not all factors included in the analysis accounted for a significant amount of variability and are thus excluded from the final regression models of the relationship between real-time and post-stimulus pain ratings. To visualize the accuracy of the regression models, the actual post-stimulus pain ratings are plotted against predicted post-stimulus ratings that were calculated according to the regression equations (models) (Figs. 5 and 6). Results obtained from within-dimension analyses (i.e. real-time intensity versus post-stimulus intensity) were contrasted with those obtained by cross-dimension analyses (i.e. real-time intensity versus post-stimulus unpleasantness) to determine the degree of reciprocity between intensity and unpleasantness ratings. Highly reciprocal relationships would be suggestive of largely parallel processing of these two dimensions of pain, while non-reciprocal relationships would be indicative of a more serial processing scheme (Price, 2000).
Table 4: Relationships between real-time and post-stimulus pain ratings
Fig. 5:
Scatter plots showing the relationships between predicted post-stimulus VAS ratings and actual VAS ratings. Predicted ratings were derived from weighted components of the real-time VAS ratings (see
Table 4). Within-dimension models accounted for the most variability in post-stimulus ratings of pain, although post-stimulus ratings of unpleasantness were predicted very well from real-time ratings of intensity. VAS, visual analogue scale.
A schema used to illustrate how components of real-time ratings contribute to post-stimulus ratings. Coefficients are shown in two digit decimals. Intercepts and components that made statistically reliable but minimal contribution were omitted for clarity (see
Table 4). Between real-time and post-stimulus ratings, solid arrows indicate predictability of the model with
R 2≥0.8, and a dashed arrow indicates that with
R 2<0.8.
3 Results
3.1 Effects of stimulus duration on post-stimulus pain ratings
Although pain-intensity ratings were statistically greater than pain-unpleasantness ratings (P<0.0001), post-stimulus ratings of both pain-intensity and pain-unpleasantness showed similar patterns in response to various combinations of stimulus temperatures and durations (Fig. 2). ANOVA revealed that the effects of both stimulus temperature and duration were statistically significant. Importantly, these two factors interacted significantly. Thus the effect of stimulus duration on post-stimulus pain ratings was highly dependent on stimulus temperature (Table 1). Post hoc tests revealed that when stimulus temperatures were low to moderate (43–47 °C), post-stimulus pain ratings did not change significantly as stimulus durations increased. In contrast, when stimulus temperatures were high (49 °C), substantial temporal summation was noted (Fig. 2).
Fig. 2:
Post-stimulus pain ratings responding to variations of stimulus temperature and stimulus duration (mean±SD). The effects of stimulus duration were minimal when the stimulus temperatures were low to moderate (43–47 °C), however, substantial temporal summation was evident when the stimulus temperature was high (49 °C). Bars show the significant differences (P<0.01). VAS, visual analogue scale.
Table 1: Effects of stimulus temperature and stimulus duration on post-stimulus pain ratings for intensity and unpleasantness
3.2 Effects of stimulus duration on real-time pain ratings
The averaged time courses of the real-time ratings are shown in Fig. 3, and the five real-time components derived from each subject's real-time data are presented in Fig. 4. In response to various levels of stimulus temperatures and durations, both real-time intensity and unpleasant ratings showed similar patterns (Figs. 3 and 4). However, the peak response (P<0.0187) and response duration (P<0.0143) of real-time pain-intensity data were statistically greater than those of real-time unpleasantness data while the mean response (P<0.0811), latency to peak (P<0.9584) and FWHM (P<0.3156) were not.
Fig. 3:
Time course of real-time pain ratings responding to variations of stimulus temperature and stimulus duration (mean). Relatively high noxious intensities evoked pain which was sustained across all stimulus durations while relatively weak stimuli evoked pain which adapted with increasing stimulus durations. VAS, visual analogue scale.
Fig. 4:
Five components of real-time pain ratings (mean±SD). The definitions for five components are illustrated in
Fig. 1. For both pain-intensity and pain-unpleasantness, peak responses and mean responses increased as stimulus duration increased at high stimulus temperature. Latency to peak, response duration and FWHM increased relatively proportionally to stimulus durations at moderate to high temperatures. Bars show significant differences (
P<0.01). FWHM, full width half-maximum; VAS, visual analogue scale.
ANOVA confirmed statistically significant effects of stimulus temperature, stimulus duration and their interaction on real-time pain ratings (Table 2). In the case of pain-intensity ratings, post hoc tests revealed that peak response, mean responses and latencies to peak all increased significantly with increasing stimulus durations when the stimulus temperature was 49 °C. However, at lower stimulus temperatures, no significant effects of stimulus durations were detected for these parameters (Fig. 4). In contrast, significant increases in response durations and FWHM were noted for intensity ratings of all stimulus temperatures greater than 43 °C (Fig. 4). Pain-unpleasantness ratings exhibited temporal responses that were generally similar to those of pain-intensity ratings. Post hoc tests detected significant temporal summation in the peak and mean unpleasantness ratings of 49 °C stimuli. Also, latencies to peak, response durations and FWHM increased with increasing stimulus durations when stimulus temperatures were either 47 or 49 °C. In contrast to pain-intensity ratings, significant adaptation was observed in both the peak and mean unpleasantness ratings of the 45 °C stimulus.
Table 2: Effects of stimulus temperature and stimulus duration on five components of real-time pain ratings for intensity and unpleasantness
A detailed analysis of selected time points of the real-time VAS ratings confirmed the interaction between stimulus duration and stimulus temperature (Table 3). At low to moderate stimulus temperatures (43–47 °C), real-time VAS ratings exhibited significant adaptation after 15–30 s of stimulation. In contrast, at the highest stimulus temperature (49 °C), real-time VAS ratings exhibited significant temporal summation after 10–15 s of stimulation, with a non-significant trend towards adaptation after 30 s of stimulation.
Table 3: Comparison between four time points (5, 10, 15 and 30 s) of real-time ratings of 30 s stimuli (
Fig. 3)
3.3 Relationships between real-time and post-stimulus ratings
Regression analyses detected strong relationships between real-time and post-stimulus ratings. In the within-dimension relationships (Fig. 5 and Table 4), much of the variability in the post-stimulus ratings could be explained by discrete components of the real-time response (intensity, adjusted R2=0.894; unpleasantness, adjusted R2=0.841). The mean real-time responses made the most substantial contribution to post-stimulus ratings. Peak responses in the real-time data accounted for the majority of the remaining variability. The relative contribution of peak and mean real-time responses was generally similar for both pain-intensity and pain-unpleasantness. Post-stimulus ratings were formed by 0.6–0.7 of mean response plus 0.3–0.4 of peak response given in VAS ratings (Fig. 6). Latency to peak (given in s) made a statistically reliable, but minimal contribution to post-stimulus ratings of pain-intensity, but not unpleasantness. Other components related to temporal aspects of the real-time response (response duration and FWHM) made no significant contribution to post-stimulus VAS ratings (Table 4).
In the cross-dimension relationships (Fig. 5 and Table 4), post-stimulus ratings of pain-unpleasantness could be reliably predicted by discrete components of the real-time ratings of pain-intensity (Fig. 6). Nearly as much variability was explained by this model (R2=0.829) as by the model describing the within-dimension relationships (Fig. 5). The relative contribution of peak and mean real-time ratings of intensity to post-stimulus unpleasantness ratings, however, differed from that in the within-dimension relationships (Fig. 6 and Table 4). Peak and mean real-time values of intensity were weighed nearly equally when used to predict post-stimulus unpleasantness ratings, unlike the within-dimension relationships in which the peak response was weighed much less than the mean. In contrast, real-time ratings of unpleasantness accounted for a somewhat smaller percentage of the variability of post-stimulus intensity ratings (R2=0.760) than the variability of post-stimulus ratings of unpleasantness explained by real-time ratings of intensity (R2=0.829; Fig. 5). In addition, the contributing components were different from those of other three relationships (Fig. 6 and Table 4). For this relationship, the mean response of real-time ratings was most strongly related to post-stimulus VAS ratings among the four relationships. Latency to peak and FWHM also made minor, but statistically reliable contributions to post-stimulus ratings of intensity.
4 Discussion
Pain is a temporally dynamic experience, however, temporal aspects of pain have remained poorly characterized. In this study, continuous ratings of pain intensity and unpleasantness revealed that the time course of pain was highly dependent on the interaction between stimulus intensity and duration: pain ratings showed progressive decreases when stimulus intensities were low to moderate, and progressive increases when stimulus intensities were high. Although the terms ‘adaptation’ and ‘temporal summation’ are typically applied to repeated stimuli (Coghill et al., 1993; Price et al., 1977, 1994b; Vierck et al., 1997), we observed temporal decreases and increases with maintained stimuli that are consistent with such phenomena. Accordingly, these temporal changes will be referred to as adaptation and temporal summation, respectively. Despite these different temporal effects of pain, subjects could readily distinguish between intensity and unpleasantness while continuously rating their pain. Furthermore, regression analyses of these real-time responses provide insight into the aspects of a moment-by-moment pain experience that subjects use to formulate a post-stimulus rating of pain.
4.1 The effects of stimulus duration on real-time pain ratings
Real-time ratings of both pain-intensity and pain-unpleasantness showed temporal summation at the highest stimulus temperature (49 °C) and adaptation at low to moderate stimulus temperatures (43–47 °C; Table 3). These different temporal effects can be accounted for by temporal summation at multiple levels of neuraxis and by the characteristics of both peripheral nociceptors and second order central nervous system neurons.
Although increasing stimulus durations will increase the transfer of heat to subcutaneous receptors, temporal summation of heat pain has been clearly shown to occur without substantial increases in skin temperature over the course of repeated stimulation (Vierck et al., 1997). However, changes in the temporal responses of both primary afferents and central neurons can account for such temporal summation. During prolonged high intensity noxious stimulation (LaMotte et al., 1982; Meyer and Campbell, 1981; Price, 1999), both Aδ and C nociceptors can become spontaneously active, have lowered thresholds to mechanical and thermal stimuli, and show enhanced responses to supra-threshold stimulation. In addition to such primary afferent changes, dorsal horn neurons also undergo temporal summation during tonic input from C nociceptive afferents (Mendell, 1966; Price, 1972).
In the case of adaptation, a substantial body of evidence conversely suggests that decreased perceptions of pain magnitude may arise primarily from decreased primary afferent input. During prolonged noxious stimulation, both Aδ and C nociceptors exhibit robust activity during the early periods of stimulation, and lesser, but sustained responses in later periods of stimulation (Beitel and Dubner, 1976; Croze et al., 1976). However, central mechanisms have been clearly shown to compensate for this diminished input (Price, 1999). Alternatively, at mildly noxious temperatures, both nociceptors and warm afferents are likely to be activated (Hallin et al., 1982; LaMotte and Campbell, 1978; Torebjork and Hallin, 1974). In C warm afferents, stimuli in the low end of the noxious range (i.e. 45 °C) have been demonstrated to initially evoke brief periods of high discharge frequencies which are followed by a prolonged suppression of activity (LaMotte and Campbell, 1978). Thus, early perceptions of heat pain in the low end of the noxious range may be amplified by the central convergence of both warm afferent and nociceptive afferent information (Defrin et al., 2002). If this is the case, then suppression of warm afferents by prolonged noxious stimuli would be expected to contribute to the progressive decreases in perceived pain magnitude that we observed during longer duration stimulation at the low end of the noxious range.
4.2 Within-dimension relationship between real-time and post-stimulus ratings
Post-stimulus pain ratings are closely related to real-time pain ratings across all combinations of stimulus intensities and durations (Fig. 5). This close correlation between real-time and retrospective ratings of subjective magnitude is consistent with a previous study for itch sensation in patients on maintenance hemodialysis (Stahle-Backdahl et al., 1989) and suggests that post-stimulus ratings of pain are a valid method of assessing pain. Consistent with post-stimulus ratings of pain intensity and unpleasantness, peak ratings of real-time pain intensity and unpleasantness ratings were significantly different, indicating that subjects can distinguish between these two aspects of pain, even when they are evaluated on a moment-by-moment basis. Other studies have similarly shown that subjects can use real-time ratings to evaluate discrete sensory aspects of the experience of pain (Davis et al., 2002).
Subjects weighted discrete components of the real-time experience differently to formulate post-stimulus ratings of pain. For both pain-intensity and pain-unpleasantness, the relative contribution of peak (0.6–0.7) and mean (0.3–0.4) real-time responses to post-stimulus ratings was generally similar (Fig. 6). Since the mean response is derived from the area under the curve divided by the response duration, it reflects the temporal effects (adaptation and temporal summation) to a greater extent than the peak response. Thus, the mean response contributed more significantly than did the peak response. Although there is a great difference in the time span and settings, Smith and Safer (1993) reported that mean pain contributed to retrospective recall of pain in chronic pain patients. In their study, patients were requested to record their pain ratings every 30 min while awake using electric pain diary. On the next day, the patients were asked to recall the overall pain ratings that they felt in the previous day. Comparison of these two types of pain ratings revealed that mean pain ratings of the previous day were tightly related to overall pain ratings that were recalled retrospectively (Smith and Safer, 1993).
In sharp contrast to aspects of pain related to magnitude (peak and mean response), temporal components (response duration and FWHM) of the real-time pain ratings contributed minimally to post-stimulus ratings of pain (Table 4). Although there is a difference in time-span and other parameters, clinical reports of pain support the minimal contribution of temporal components (duration, etc.) to retrospective assessments of pain. For example, the duration of uterine contractions is poorly correlated with VAS ratings of labor pain, while peak magnitude and area under the curve of uterine contractions are closely related to within-individual differences in VAS pain ratings (Algom and Lubel, 1994; Corli et al., 1986). Thus, temporal components contributed to retrospective pain ratings significantly, however, magnitude components contributed to them much more significantly.
4.3 Distinction between pain-intensity and pain-unpleasantness ratings
Although all four relationships between real-time and post-stimulus ratings were relatively strong (adjusted R2, 0.760–0.894; as shown in Table 4), within-dimension relationships were stronger (adjusted R2, 0.841–0.894) than cross-dimension relationships (adjusted R2, 0.760–0.829). Consistent with previous literature, this observation strongly suggests that sensory (intensity) and affective (unpleasantness) dimensions of pain are separable but not independent dimensions of subjective pain report (Duncan et al., 1989; Fernandez and Turk, 1992; Gracely and Dubner, 1987; Gracely et al., 1978; Price et al., 1987; Smith et al., 1998; Wade et al., 1996). In cross-dimension analyses, we observed very different relationships between the real-time and post-stimulus ratings (Fig. 6 and Table 4). Real-time intensity ratings accounted for nearly as much variability in the post-stimulus unpleasantness ratings (adjusted R2, 0.829) as in the post-stimulus intensity ratings. Compared with within-dimension relationships, the significantly contributing components of the real-time ratings were same (peak and mean) although peak response was more heavily weighted (Fig. 6 and Table 4). In contrast, real-time unpleasantness ratings accounted for somewhat less variability in the post-stimulus intensity ratings (adjusted R2, 0.760; Figs. 5 and 6 and Table 4). Moreover, the subsets of contributory components and their coefficients of this relationship were quite different from other three relationships; the mean response contributed more than in any other relationship (Fig. 6 and Table 4). This disparity between the two cross-dimension relationships implies a lack of reciprocity between pain-intensity and pain-unpleasantness processing. Several lines of evidence suggest that pain-unpleasantness is processed subsequently to and in series with pain-intensity (Price, 2000). First, structural equation modeling of chronic pain data indicates that pain is processed in linear stages, with pain sensation intensity forming the lowest level of processing before multiple levels of pain affect (Wade et al., 1996). Secondly, using hypnotic suggestion, Rainville et al. (1997, 1999) manipulated perceived pain-intensity and pain-unpleasantness during noxious stimulation (hand immersion into hot water). They observed that only pain-unpleasantness ratings were changed in the case of hypnotic manipulation of pain-unpleasantness, whereas both pain intensity and unpleasantness ratings changed in parallel in the case of hypnotic manipulation of pain-intensity (Rainville et al., 1997, 1999). Finally, functional imaging studies using the same hypnotic paradigm as above confirm that brain regions important in both sensation and affect are altered during hypnotic modulation of pain sensation, but only regions likely to be involved in affective processing are altered by hypnotic modulation of pain-unpleasantness (Hofbauer et al., 2001; Rainville et al., 1997). The regression model fit of the present data (Fig. 6) is in line with the suggested direction of causation; pain-intensity is in series with and is a cause of pain-unpleasantness and not vise versa (Price, 2000).
In summary, the present findings indicate that post-stimulus ratings of pain acquired immediately after termination of a noxious stimulus are closely related with and can be largely explained by both the peak and the mean of the real-time pain experience. Such post-stimulus measures are highly sensitive to both temporal summation and adaptation that may occur over various combinations of stimulus temperatures and durations.
Acknowledgements
This study was supported by NIH RO1 NS39426.
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