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Running head: SOMATOSENSORY DECISION-MAKING 1

Effects of Learning on Somatosensory Decision-Making and Experiences

Akib-ul-Huque, Ellen Poliakoff and Richard J. Brown

Faculty of Biology, Medicine and Health, University of Manchester, Zochonis Building, Brunswick Street, Manchester M13 9PL

Address for correspondence: Dr Akib-ul-Huque, University of Dhaka, Bangladesh, Dhaka-1000, Bangladesh. E-mail: [email protected]

Accepted for publication in Journal of Experimental Psychology: General, 21 st July 2017


Abstract

Operant conditioning has been shown to influence perceptual decision making in the auditory

and visual modalities but the effects of conditioning on touch perception are unknown. If

conditioning can be used to reduce the tendency to misinterpret somatic noise as signal

(tactile false alarms), there may be the potential to use similar procedures in the treatment of

excessive physical symptom reporting in clinical settings. We explored this possibility in four

experiments investigating whether the false alarm (FA) rate in a somatosensory signal

detection task (SSDT) could be altered with operant conditioning, and whether the resultant

learning would transfer to other sensory decisions. In Experiments 1a and 2a, non-clinical

participants were rewarded for hits and punished for misses on the SSDT, with a view to

increasing their FA rate. In Experiments 1b and 2b, participants were rewarded for correct

rejections and punished for FAs, with a view to decreasing their FA rate. Control participants

received no treatment in Experiments 1a and 1b and underwent sham training in Experiments

2a and 2b. As predicted, operant conditioning increased (Experiments 1a, 2a) and decreased

(Experiments 1b, 2b) FAs on the SSDT. Training effects did not transfer to an unrelated

somatosensory task and there was only weak evidence for transfer to an auditory task in

Experiment 2a. Auditory and tactile FAs correlated positively in the baseline phase. The

results indicate that the tactile false alarm rate is trainable, but that the conditioning effect

does not transfer across sensory decisions with this brief training paradigm.

Keywords: signal detection, operant conditioning, transfer of perceptual learning, tactile

perception, medically unexplained symptoms


Effects of Learning on Somatosensory Decision-Making and Experiences

Signal detection theory posits that both stimulus and perceiver characteristics determine

perception (Green & Swets, 1966). Sensitivity and response criterion are two observer

characteristics that are independent but which greatly influence perceptual decisions, such

that the same stimulus produces different perceptual experiences in each case. Disturbances

in perceptual decision making are thought to play an important role in the development and

maintenance of certain clinical phenomena, including psychotic experiences, body image

problems and excessive somatic symptom reporting (Albert III, Kelley, & Corlett, 2016;

Brown et al., 2012; Slade, 1994). Signal detection theory provides a useful framework for

understanding and studying these phenomena. In order to maintain our health, for example,

we need to be able to detect sensory signals coming from our body and then decide whether

any variation in these might indicate disease and warrant further investigation. Whether an

appropriate decision is made will depend partly on the criterion that the individual adopts for

judging whether a sensation represents meaningful signal or irrelevant noise. If the individual

sets a very strict criterion, then they may miss a possible threat to their health; if they set a

very liberal criterion, they risk interpreting normal perceptual variations as potentially

dangerous, generating alarm and unnecessary help-seeking as a result.

A variation on this idea is central to so-called somatosensory amplification (Barsky,

1992; Rief & Barsky, 2005), which has been linked to increased health anxiety, excessive

symptom reporting and the experience of “medically unexplained” or functional somatic

symptoms (Witthöft, Fischer, Jasper, Rist, & Nater, 2016). The latter constitute up to three

quarters of the symptoms encountered in hospital settings (Körber, Frieser, Steinbrecher, &

Hiller, 2011) and are a major societal problem. At present, the mechanisms of excessive

symptom reporting and functional symptoms are poorly understood and existing treatments

are inadequate (van Dessel et al., 2014).


From a signal detection perspective, functional symptoms are akin to somatosensory

false alarms (cf. Brown, 2004), where bodily noise is misinterpreted as disease signal.

Although numerous factors are likely to contribute to this process (e.g., trait anxiety/negative

affectivity, self-focus, avoidance, rumination; Rief & Barsky, 2005), there is some evidence

that physical symptom reporting correlates with a more general tendency to false alarm

during the detection of somatic signals (Brown, Brunt, Poliakoff, & Lloyd, 2010; Brown et

al., 2012; Katzer, Oberfeld, Hiller, & Witthöft, 2011). Studies also suggest that problems

perceiving the internal bodily state (i.e., interoceptive dysfunction) may influence medical

and psychological problems, such as gastrointestinal and cardiac disease, anxiety disorders,

somatoform disorders and chronic pain (Cameron, 2001). If problems with somatic

perception are central to functional symptoms and excessive symptom reporting, then

ameliorating any perceptual difficulties may be an important part of treatment for these

conditions. Consistent with this, interoceptive accuracy training significantly reduced

symptom reports in patients with functional complaints in one recent study (Schaefer, Egloff,

Gerlach, & Witthöft, 2014).

Numerous studies have found that it is possible to manipulate signal detection

performance using training methods such as operant conditioning (e.g., Johnstone & Alsop,

2000; Lie & Alsop, 2009; Szalma, Hancock, Warm, Dember, & Parsons, 2006). The vast

majority of studies have considered the effects of training in the auditory and visual

modalities, however, and there are few training studies on somatosensory perception (e.g.,

Brown et al., 2010; McKenzie, Lloyd, Brown, Plummer, & Poliakoff, 2012; Mirams,

Poliakoff, Brown, & Lloyd, 2012, 2013). It therefore remains unclear whether it is possible to

train somatic perception, although learning theories (Tazaki & Landlaw, 2006), chronic pain

research (Rief & Broadbent, 2007), and child illness behavior studies (Walker & Zeman,


1992) suggest that operant conditioning might contribute to the perception and reporting of

somatic symptoms.

In this paper, we describe four experiments investigating the effect of operant

conditioning on perception in the tactile modality. As a proximal sense with spatial referents

on the body, touch is more directly related to somatic experience than audition or vision, but

is much easier to manipulate than the perception of internally generated stimuli (i.e.,

interoception). We sought to establish whether reward and punishment could be used both to

increase and decrease the false alarm rate on the Somatic Signal Detection Task (SSDT;

Lloyd, Mason, Brown, & Poliakoff, 2008), which has previously been linked to physical

symptom reporting. We also sought to establish whether this training would transfer to other,

unrelated stimuli and tasks (Bratzke, Schröter, & Ulrich, 2014; Liu & Weinshall, 2000),

which would give some indication of the potential clinical utility of the training paradigm..

Experiments 1a and 1b: Method

In Experiments 1a and 1b, we attempted to condition participants to report more or

fewer false alarms on the SSDT respectively using a within-subjects design. We predicted

that rewarding and punishing certain responses on the task would lead to an enduring change

in the false alarm rate post-training, and that this effect would transfer to perception on a

different body-relevant task, the spontaneous sensation (SpS) test (Michael & Naveteur,

2011).

Overview

Experiments 1a and 1b were run in parallel (Figure 1). The control condition was

identical in each case and consisted of eight blocks of the SSDT. In the experimental

condition of Experiment 1a, a conditioning procedure was introduced on blocks 3-6 of the

SSDT aimed at increasing the false alarm rate. In Experiment 1b, blocks 3-6 of the SSDT in

the experimental condition incorporated conditioning aimed at decreasing the false alarm


rate. The experimental condition in both cases was otherwise identical to the control

condition, where no training was given. Participants with relatively low (<0.16) and high (>=

0.16) false alarm rates1 were automatically allocated to either Experiment 1a or 1b

respectively, depending on their SSDT performance in the first two blocks of the

experimental condition; this was to eliminate the potential impact of ceiling and floor effects

in Experiments 1a and 1b respectively. Neither the researchers nor the participants knew who

was in which study at the time of participation. Participants completed the two conditions on

separate days at least a week apart.

Design

A mixed design was used for Experiments 1a and 1b, with condition (experimental vs.

control) and phase (baseline vs. manipulation vs. follow-up for SSDT response outcomes;

baseline vs. follow-up for SpS) as within-group variables. Session order (experimental

session first vs. control session first) was included as a between-group variable to test for any

enduring changes in signal detection performance related to the training. The dependent

variables were the SSDT (i.e., hit rate, false alarm rate, response bias [c], and sensitivity [d´])

and SpS response outcomes (total number of SpSs). In Experiment 1a, we predicted that

there would be an increase in both hits and false alarms, resulting in a more liberal response

bias, in the training condition compared to the control condition during both the manipulation

and follow-up phases. We also predicted that there would be an increase in spontaneous

sensations on the SpS test in the training condition compared to the control condition. In

Experiment 1b, we predicted that there would be a decrease in hits, false alarms and

spontaneous sensations in the manipulation and follow-up phases of the experimental

condition compared to the control. We did not expect any impact of the training on perceptual

sensitivity in either study. Given the potential impact of sleepiness and state anxiety on tactile

signal detection (Gillberg & Åkerstedt, 1998; Malow, 1981) we also measured these variables


to establish whether the two conditions were comparable in these respects. In addition, we

measured the relationship between somatic symptom reporting and SSDT false alarm rates to

determine the comparability of our sample with previous studies (Brown et al., 2012; Katzer

et al., 2011).

Participants received either 12 academic credits or £15 as compensation. We planned to

recruit 78 participants in total, giving adequate power for both studies assuming the cut-off

false alarm rate of 0.16 resulted in equal allocation in each case. This sample size was

determined using G*Power (Faul, Erdfelder, Lang, & Buchner, 2007) with power = .90,

alpha = .05 (two-tailed), and effect size = .50. The inclusion criteria were: aged 18-40 years

and ability to understand English instructions. The exclusion criterion was having any

medical condition that might affect the sense of touch. Ethical approval was given by the

relevant University committee.

Materials, Measures and Procedures

All study materials, measures and procedures were the same for Experiments 1a and 1b

apart from the conditioning procedure.

SSDT

Thresholding. The vibrotactile perceptual threshold of each participant was first

determined using a forced-choice adaptive procedure. A centrally aligned computer monitor

was used to provide instructions and present a green arrow (962×722 pixels) that cued the

start of each of two 1020ms consecutive intervals that comprised a trial. The arrow appeared

in the middle of the monitor for 250ms, pointing downwards towards the participant’s finger.

The first and second arrows contained the numbers 1a and 1b respectively to designate which

interval was being presented. In each trial, a 20ms tactile pulse (painless vibration) was

delivered through a bone conductor (vibrating device) by amplifying the sound output from a

computer using a custombuilt amplifier (Dancer Design, Merseyside, United Kingdom). A


double-sided adhesive circle was used to attach the bone conductor to the pad of the

participant’s non-dominant index finger. The bone conductor was mounted on a foam cube

alongside a 5mm red LED, which was not used during the thresholding. The vibration

appeared randomly in the middle of one of the two intervals. After each trial, participants

were asked to indicate using a computer keyboard which of the two intervals contained a

short tactile vibration. Participants were prompted to rest after 80 trials but could do so at any

time. During both the thresholding and SSDT proper, participants wore headphones that

delivered white noise to mask background noise and the sound of the bone conductor.

A parameter estimation by sequential testing (PEST) computer algorithm was used to

determine the participant’s tactile threshold for use in the SSDT. The PEST began with a

strong, easily perceptible, but painless vibration of 31.16 dB that was equal to a pressure of

274 m/s. A Wald SPRT [N(c) (number of correct responses) - Pt. N (T) (probability threshold

value (0.75) multiplied by current trials completed) W (W’s limits were: 1 to -1)] was used to

change the vibration strength. Selection of the vibration level depended on the responses

given on all trials since it reached its current intensity level. In this process, the threshold set

for each participant was the intensity of vibration that they correctly detected in

approximately 75% of the trials; this is significantly above chance level performance (50%)

but ensures that the stimulus in the main task is sufficiently ambiguous for misperception to

occur. Though the number of trials required for the determination of threshold varied between

individuals, the computer algorithm was programmed to run a maximum of 250 trials. If the

maximum was reached, the average vibration intensity of the last 50 trials was taken as the

participant’s threshold level. E-Prime (Psychology Software Tools, Inc., Pittsburgh, PA) was

used to deliver stimuli and record responses.

Main task. The same apparatus was used for the main SSDT task, which comprised

blocks of 80 trials consisting of four, randomly interspersed trial types (touch only, light only,


light and touch and no stimulus), each lasting 1020ms. The start of each trial was signalled by

the green arrow, presented for 250ms. In touch only trials, a threshold level tactile vibration

was presented for 20ms in the middle of the trial interval. In light only trials, the LED light

was flashed for 20ms in the middle of the trial interval. In light and touch trials, both the

vibration and light flash appeared simultaneously. In no stimulus trials, nothing was

presented. After each trial, participants indicated whether they felt any vibration by pressing

1, 2, 3, and 4 on the keyboard number pad corresponding to definitely yes, maybe yes, maybe

no, and definitely no respectively2. The light was included to maximise the false alarm rate

(Lloyd et al., 2008) but was otherwise unrelated to the purpose of the experiment. There were

12 practice trials. Participants were kept naive about the significance of the light and

informed that the vibration would not be present in all trials. Both control and experimental

conditions consisted of eight blocks. The first two blocks were the baseline blocks. The

average false alarm rate in light present trials in these blocks of the experimental condition

was used to determine whether the participant had a high or low false alarm rate and therefore

entered into Experiment 1a or 1b. Blocks 3-6 were the manipulation blocks that delivered

reinforcement and punishment in the training condition but no manipulation in the control

condition. Blocks 7 and 8 were follow-up blocks consisting of regular SSDT trials without

reward or punishment. Participants were encouraged to rest briefly between blocks.

Conditioning. During blocks 3-6 of Experiment 1a, participants in the experimental

condition received reward and punishment with a view to conditioning the “yes” response.

Participants received 10 points in half of the hit trials (i.e., where they correctly indicated a

stimulus was present) and lost 10 points in half of the miss trials (i.e., where they incorrectly

indicated a stimulus was absent). In Experiment 1b, participants in the experimental condition

received reward and punishment during blocks 3-6, with a view to conditioning the “no”

response. Participants received 10 points in half of the correct rejection trials (i.e., where they


correctly indicated a stimulus was absent) and lost 10 points in half of the false alarm trials

(i.e., where they incorrectly indicated a stimulus was present).

Before starting the conditioning trials, participants were informed that they would

receive 1p for every point they accumulated during this part of the experiment. A random

selection process (built into the E-Prime programme) was used to select half of the hits and

misses in Experiment 1a, and correct rejections and false alarms in Experiment 1b, to

condition them with reward and punishment respectively. This was done to control

participants’ expectancy about specific trials or responses that might result in consequences.

The random selection process also allowed for a variable ratio training schedule, which tend

to produce more enduring effects (Reynolds, 1975). Participants were told whether they had

won or lost points by 3-second feedback messages on the computer monitor, which also

indicated their running point total (e.g., “You have won 10 points. Your total score is 300”;

“You have lost 10 points. Your total score is 280”). Different colours were used to present the

messages (yellow = win; red = loss). Instructions about the possibility of winning or losing

points and obtaining corresponding feedback messages were given at the start of this phase.

At the end of each experiment, participants received 1p for every point that they accumulated

during this phase (in cases where they had fewer than zero points they did not receive

anything apart from the usual honorarium for taking part). Participants were encouraged to

win as many points as possible to maximise motivation on the task.

Data processing. SSDT data were processed to derive four SSDT variables (hit rate, false

alarm rate, bias and sensitivity) for each block using standard formulae with the log-linear

correction (Snodgrass & Corwin, 1988). For each statistic, the means of Blocks 1a and 1b

were calculated to get baseline measures, the means of Blocks 3, 4, 5, and 6 were calculated

to obtain measures for the manipulation phase, and the means of Blocks 7 and 8 were

calculated to obtain measures for the follow-up phase. Means for the experimental and


control conditions were calculated separately, as were those for light present and light absent

trials. We present the data separately for Experiments 1a and 1b below.

SpS test. An adapted version of the protocol and procedure described by Michael and

Naveteur (2011) was used to measure spontaneous sensations. Participants were asked to

relax with their non-dominant hand palm down on a piece of smooth white A4 paper. At this

time, the wrist of their other hand remained at resting position on the corresponding thigh

under the table so that they could not see it. Participants were then asked to focus their

attention on their non-dominant hands for 10 seconds, indicated by verbal start and stop

signals from the experimenter. Immediately after the stop signal, participants were given a

standard picture of a non-dominant hand to mark any areas where they felt sensations during

the 10-second attention period, and to write names (e.g., tingling, throbbing) for these. The

SpS test was repeated twice, making three trials overall in a phase, which were then

combined to get the average number of SpSs reported in the baseline and follow-up phases of

the control and experimental conditions. There was a practice trial on the first administration

of the SpS test to familiarize participants with the task.

Karolinska sleepiness severity scale (KSS). Akerstedt and Gillberg (1990) developed

this one-item scale to measure current level of sleepiness. The scale ranges from 1 (very

alert) to 9 (very sleepy, great effort to keep awake, fighting sleep). A score of 7 or more

indicates excessive sleepiness.

Spielberger state-trait anxiety inventory (STAI) – state short form. This six-item

scale assesses how anxious someone is at that moment (Marteau & Bekker, 1992). There are

four response options for each item ranging from 1 (not at all) to 4 (very much). Total scores

vary between 6 and 24 with higher scores indicating elevated anxiety. The scale is sensitive

to fluctuations in state anxiety and it has acceptable reliability and validity (Marteau &


Bekker, 1992; Tluczek, Henriques, & Brown, 2009). The average internal consistency

coefficients were .70 and .75 in Experiments 1a and 1b respectively.

Patient health questionnaire-15 (PHQ-15). The PHQ-15 (Kroenke, Spitzer, &

Williams, 2002) was used to assess somatic symptom severity. Respondents indicated how

bothered they had been about 15 physical symptoms during the last 4 weeks (not bothered,

bothered a little, or bothered a lot corresponding to scores of 0, 1, and 2 respectively). The

scale is known to have excellent psychometric properties (Zijlema et al., 2013). Internal

consistency was .83 and .75 in Experiments 1a and 1b respectively.

Procedure

The order of conditions was determined randomly for each participant. Both sessions

were carried out in a quiet, light-attenuated room. At the start of each session, participants

were asked to remove any jewellery from the fingers and wrists of their non-dominant hand.

Some participants wore wristbands that could not be removed without cutting or breaking

them; in these cases, they were asked to ensure that these bands were worn in both sessions.

Approximately 15 seconds before the tasks, participants were asked to clean their hands with

alcohol-based hand rub. The control condition started with the baseline SpS test followed by

the baseline SSDT (two blocks of 80 trials), manipulation SSDT (four blocks of 80 trials with

no training), follow-up SpS test, and follow-up SSDT (two blocks of 80 trials). The

procedures for the experimental conditions were identical to that of the control condition

apart from the manipulation SSDT, where the conditioning manipulations (i.e., operant

training) were delivered. Participants responded to the PHQ-15 online (at least a week) before

participating in the first session. Sleepiness was measured after the SSDT threshold task, after

the baseline phase, and after the manipulation phase. State anxiety was measured before the

baseline and after the manipulation phase.

Experiment 1a: Results


Participants

Thirty three (65.2%) female and 19 (36.53%) male student (n = 50) and staff (n = 2)

volunteers of the University of Manchester, aged between 18 and 38 years (M = 22.15, SD =

4.22) had relatively low false alarm rates and were allocated to Experiment 1a. Most

(84.62%) were right-handed on the Edinburgh Handedness Inventory (EHI; Oldfield, 1971).

Data Preparation

Data were initially examined for consistency with the assumptions of mixed Analysis of

Variance (ANOVA) for the SSDT and SpS test and of dependent t-test for sleepiness and

state anxiety. Standard correction procedures (e.g., replacing extreme outliers, transforming

scores, Huynh-Feldt correction; Field, 2009) were followed where needed. Due to the

violation of parametric assumptions, Wilcoxon signed-rank test was used to examine the

difference in change scores in state anxiety between the conditions. Four participants with an

extremely high (> 95%) baseline hit rate in the experimental condition were excluded from

the analysis on the grounds that the task had been insufficiently ambiguous for these

individuals. Two more participants were excluded for falling asleep during the experiment.

The final ANOVAs were therefore carried out with 46 individuals with a low false alarm rate

on the SSDT, of whom 22 completed the experimental condition first. As changes in false

alarm rates and spontaneous sensations were our primary outcomes, an alpha value of .05 was

used for these variables. A Bonferroni corrected alpha of .01 was adopted for the remaining

analyses. Analyses pertaining to the light were performed to identify any interactions with

possible training effects. As will become apparent, there were very few significant

interactions between the light and training variables in any of the studies in this paper; these

are reported below where relevant. As all other analyses involving the light are peripheral to

our main purpose they are presented as supplementary materials and not discussed further

here.


Effects of Conditioning on SSDT

Descriptive statistics and phase by condition interactions for the SSDT outcomes are

presented in Tables 1 and 2 and Figure 2. In each case, the main effects of phase and

condition for false alarms, hits and response bias were subsumed under significant condition

by phase interactions so are not reported here; full details of the analyses are presented in the

supplementary materials. In describing the results, we focus mainly on those that pertain to

likely training effects.

--- INSERT TABLES 1 AND 2 AND FIGURE 2 ABOUT HERE ---

False alarms, hit rate and response bias. Both the false alarm and hit rates were

significantly higher during and after the conditioning procedure than before, and were

significantly higher in the experimental than the control condition in both the manipulation

and follow-up phases. Participants in the training condition were significantly more likely to

say yes during and after the conditioning procedure than before, and significantly more so

than the control participants in the experimental and follow-up phases. Control participants

became significantly less likely to say yes over time, with a corresponding decrease in false

alarms and hits. There was a significant light x phase x condition interaction, F(2, 88) = 4.60,

p = .01, η2p = .10, for the response criterion variable, which was attributable to a minor

variation for the control condition when comparing the manipulation and follow-up phases

(see supplementary analyses); this was not considered theoretically important and is not

discussed further here.

Sensitivity. The main effect of phase was significant, F(1.78, 78.19) = 8.26, p = .001,

η2p = .16. Bonferroni corrected post-hoc tests revealed that participants’ sensitivity

(regardless of condition) did not differ between the baseline and manipulation phases, mean

difference = .07, 95% CI [-.13, .27], p = 1.0. However, sensitivity was lower in the follow-up


compared to the baseline, mean difference = -.32, 95% CI [-.57, -.08], p = .007, and

manipulation phases, mean difference = -.25, 95% CI [-.42, -.08], p = .002.

Transfer of Conditioning to SpSs

Descriptive statistics are presented in Table 3. Contrary to our hypothesis, the main

effect of condition, F(1, 44) = 1.46, p = .23, η2p = .03, and interaction between phase and

condition, F(1, 44) = 1.40, p = .24, η2p= .03, were not significant. There was a significant

main effect of phase, F(1, 44) = 5.00, p = .03, η2p = .10, with a Bonferroni corrected post-hoc

test revealing that sensations were significantly more common at follow-up (M = .82, SD

= .47) than at baseline (M = .71, SD = .41), regardless of condition, mean difference = .11,

95% CI [.01, .21], p = .03.

--- INSERT TABLE 3 ABOUT HERE ---

There was a significant interaction between condition and session, F(1, 44) = 6.54, p

= .01, η2p = .13. It was found that the total number of SpSs in the experimental condition (M

= .85, SD = .42) was higher than in the control condition (M = .68, SD = .44) when the

experimental condition was the first session, mean difference = .17, 95% CI [.04, .31], p

= .01. This difference was not found when the control condition was the first session, mean

difference = .06, 95% CI [-.07, .19], p = .34 (Control: M = .79, SD = .44; Experimental: M

= .73, SD = .38).

Sleepiness and State Anxiety

Change in sleepiness (i.e., sleepiness at the follow-up – baseline phase) differed

significantly between the conditions, t(45) = 4.02, p < .001, r = .51. Participants became

sleepier in the control (M = 1.78, SE = .29) than in the experimental (M = .30, SE = .34)

condition. Change in state anxiety (i.e., state anxiety at the follow-up – baseline phase),

however, did not differ between the conditions (Mdn = .00 for both the conditions), z = -.39,

p = .70, r = -.04.


Experiment 1b: Results

Participants

Thirty student (n = 27) and staff (n = 3) volunteers from the University of Manchester,

aged between 18 and 39 years (M = 22.97, SD = 5.07), were identified as having a high false

alarm rate and were automatically allocated to Experiment 1b. Of these, 21 (70%) were

female and 27 (90%) were right-handed on the EHI.

Data Preparation and Statistical Analysis

Data were prepared and analysed as for Experiment 1a. None of the participants had an

extremely low (<.05) or high (>0.95) hit rate in the baseline phase of the experimental

condition, indicating that they all understood the task and the vibration level was sufficiently

ambiguous. Data from both Experiments 1a and 1b were used to determine the non-

parametric (Spearman’s) correlation between SSDT false alarms, total SpSs and PHQ-15

scores in the baseline phase of the first session.



presented in Tables 1 and 4 and Figure 2.

--- INSERT TABLE 4 ABOUT HERE ---


significantly lower during and after the conditioning procedure than before, and were

significantly lower in the experimental than the control condition in both the manipulation

and follow-up phases. Participants in the training condition were significantly less likely to

say yes during and after the conditioning procedure than before, and significantly less so than

the control participants in both the experimental and follow-up phases. Participants in the

control condition exhibited more false alarms, and were more likely to say yes, when the

control session was first than when it was second. The difference in false alarms between the


two conditions was also significantly greater when the control session was first, as was the

tendency to say yes.

Sensitivity. The main effect of phase was significant, F(2, 56) = 17.64, p = .001, η2p

= .39. Bonferroni corrected post hoc tests indicated that sensitivity in the follow-up phase was

significantly lower than in the baseline, mean difference = -.07, 95% CI [-.10, -.03], p = .001,

and manipulation phases, mean difference = -.06, 95% CI [-.09, -.03], p = .001. Sensitivity

did not differ between the baseline and manipulation phases, mean difference = .01, 95% CI

[-.02, .03], p = 1.00. The remaining main effects and interactions did not reach significance

(Fs < 5, ps > .01).

Transfer of Conditioning to SpSs

Descriptive statistics are presented in Table 3. The main effect of phase was significant,

F(1, 28) = 8.73, p = .01, η2p = .24. Contrary to expectation, a Bonferroni corrected post hoc

test indicated that participants reported significantly more SpSs in the follow-up (M = 1.02,

SD = .71) than in the baseline phase (M = .76, SD = .55), mean difference = .26, 95% CI [.08,

.43], p = .006. There was a significant interaction between condition and session, F(1, 28) =

6.18, p = .02, η2p = .18. Bonferroni corrected post hoc tests indicated that reporting of SpSs

did not differ between the conditions when the control condition was the first session (Ms

= .78 and .60, SDs = .59 and .78 for the control and experimental conditions respectively),

mean difference = .18, 95% CI [-.11, .47], p = .21, but when the experimental condition was

the first session, participants reported significantly more SpSs in the experimental (M = 1.22,

SD = .68) than the control condition (M = .94, SD = .52), mean difference = .28, 95% CI [.03,

.54], p = .03. In the control condition, there was no significant difference between the groups

with regard to the number of SpSs reported, mean difference = -.16, 95% CI [-.60, .29], p

= .47. In the experimental condition, the participants who completed the experimental

condition in the first session reported significantly more SpSs than the participants who


completed the control condition in the first session, mean difference = .62, 95% CI [.11,

1.14], p = .02. The remaining main effects and interactions did not reach significance (Fs < 4,

ps > .05).


The difference between changes in sleepiness in the two conditions (Ms = 1.90 and

1.27, and SEs = .31 and .46 for the control and experimental conditions respectively) was not

significant, t(29) = 1.27, p = .21, r = .23. Changes in state anxiety also did not differ between

the conditions (Ms = .20 and .00, and SEs = .21 and .30 for the control and experimental

conditions respectively), t(29) = -.61, p = .55, r = .11.

Correlations Between Baseline False Alarms, SpSs and PHQ-15 Scores

The correlation coefficients of SpS with SSDT false alarm rate, rs(76) = .13, p = .28, hit

rate, rs(76) = .09, p = .42, sensitivity, rs(76) = -.004, p = .97, and response bias, rs(76) = -.15,

p = .19, were not significant at baseline. Similarly, there was no significant correlation

between SSDT false alarm rates and PHQ-15 scores, rs(76) = -.04, p = .73.

Discussion: Experiments 1a and 1b

Operant conditioning had the expected effects on false alarms, hits and response

criterion (i.e., their overall tendency to say yes vs. no) in both experiments, with these effects

manifesting both whilst reward and punishment were being delivered in the manipulation

phase and afterwards in the follow-up phase. There was no effect on sensitivity in either

study. Contrary to expectation, neither study revealed a significant effect of conditioning on

the total number of spontaneous sensations, suggesting that the training did not generalize to

subsequent somatosensory decision-making on an unrelated task.

Since false alarm rates are highly reliable (McKenzie, Poliakoff, Brown, & Lloyd,

2010) it was expected that the baseline false alarm rates would be comparable across the

conditions in each study. In both studies, however, it was found that baseline false alarm rates


differed significantly between the conditions, with the control condition having significantly

higher rates in Experiment 1a and lower rates in Experiment 1b. Evidently, those participants

who received training in the first session showed a carry-over of training effects to the second

session, with overall baseline response criterion appearing either relatively liberal or

relatively stringent accordingly. Although this was always possible in theory, we did not

anticipate that the effects of a brief conditioning procedure would persist for such a long

period without further training. This unwanted effect of session order suggests that a

between-subjects design may be more appropriate for research of this sort.

Another limitation of both experiments is that the conditioning may have influenced

participants’ mood and motivation during the procedure. The fact that the majority of

participants in the experimental condition won money may explain the sleepiness scores of

Experiment 1a, which indicated that participants were more alert in the experimental than in

the control condition (a similar, although non-significant, effect was also found in

Experiment 1b). Reward-related changes in mood and motivation could have systematically

affected participants’ subsequent experiences of SpSs, which might explain why participants

reported more SpSs following the training condition when it was presented first and

motivation was likely at its highest. Though no study has yet investigated possible

relationships between affect-related physiological arousal and the reporting of SpSs on this

paradigm, it is well established that affective states influence whether we notice and attend to

normal body sensations (Watson & Pennebaker, 1989). One way of dealing with this possible

confounding effect would be to introduce rewards into the control condition to make the

conditions more comparable, in a manner that creates a pleasant experience of winning

without introducing any form of learning.

One possibility is that the use of an alcohol-based hand-rub immediately prior to the

SpS procedure obscured many of the more subtle sensations in the participants’ hand by


inducing much less ambiguous feelings of coldness and/or tingling3. The SpS test is also

potentially problematic because the focus for participants’ responses on this task (i.e., the

non-dominant hand) is also the site of stimulation on the SSDT task. As the SSDT involves

attending to and detecting tactile stimulation in the index finger over many trials, it is likely

that prolonged attention to the hand will result in the perception of more SpSs (Michael et al.,

2012; Michael & Naveteur, 2011), consistent with the main effect of phase observed in both

studies. Fatigue in the non-dominant hand may also have contributed to this effect. An

alternative approach would be to ask participants to focus on the entire body instead of just

on the non-dominant hand to identify and report SpSs.

It is also possible that the SpS test is not the most appropriate test to detect whether

SSDT conditioning generalises. Compared to the SSDT, it is much less clear what

participants should attend to during the SpS test and when such experiences might arise. With

this in mind, a task more similar to the SSDT in a different sensory modality might be more

suitable to study the presumed transfer of any conditioning effects.

Contrary to the findings of previous SSDT studies (Brown et al., 2012; Katzer et al.,

2011), the correlation between the baseline false alarm rate and physical symptom reporting

(as measured by the PHQ-15) was not significant. Katzer, Oberfeld, Hiller, Gerlach, and

Witthöft (2012) also reported a non-significant relationship between the variables. One

possible explanation for the absence of the relationship in Experiments 1a and 1b might be

that participants answered the PHQ-15 online at least one week before performing the SSDT,

and that the previously observed correlations are a context rather than a trait effect.

Experiments 2a and 2b

To address the limitations of Experiments 1a and 1b, replicate the SSDT findings, and

further investigate the possible transfer effects of SSDT conditioning, two further

experiments were carried out. In Experiment 2a, we sought to replicate the findings of


Experiment 1a by conditioning participants with a low baseline false alarm rate to exhibit

more false alarms. In Experiment 2b, we sought to replicate the findings of Experiment 1b by

conditioning participants with a high baseline false alarm rate to exhibit fewer false alarms.

In both experiments, we adopted a between-subjects design, attempted to control for possible

differences in motivation by providing sham reinforcement and punishment in the control

condition, and replaced the original SpS with a modified version of the task focusing on the

whole body. We also included an auditory signal detection (voice-hearing) task to investigate

whether somatosensory training generalizes to a different modality.

Method

Design

A mixed design was used for both experiments, with condition (control vs.

experimental) as the between-subject variable and phase (baseline vs. manipulation vs.

follow-up) as the within-subject variable. False alarm rate, hit rate, response bias, and

sensitivity on the SSDT, total number of SpSs, and false alarm rate, hit rate and total number

of voice responses on the voice hearing task were the dependent variables. We also measured

sleepiness, state anxiety and symptom reporting. As before, both experiments were carried

out in parallel, with participants being automatically allocated to studies according to their

baseline false alarm rate. Participants with low (< 0.15) and high false alarm rates (>= 0.15)

in light present trials in the experimental condition were allocated to Experiments 2a and 2b

respectively. The new false alarm criterion was approximate to the median false alarm rate

found in Experiments 1a and 1b combined and was expected to ensure approximately equal

numbers of participants in each study. A power calculation based on the effect sizes observed

in Experiments 1a and 1b (1.44 and .80 respectively) indicated that 18 participants (9 in each

group) in Experiment 2a and 52 participants (26 in each group) in Experiment 2b would give

80% power to detect the expected changes in false alarm rate with alpha = .05 (two-tailed).


Inclusion criteria were being aged between 18 and 40 years and having a good understanding

of English instructions. Exclusion criteria were non-corrected visual impairment, having a

medical condition that might affect the sense of touch or hearing, and participation in

previous SSDT studies. Participants received an honorarium of £10 or eight experimental

credits (psychology students only) for taking part.

Materials, Measures and Procedures

The SSDT setup and procedure and the questionnaires were the same as those in

Experiments 1a and 1b.

SpS test. The SpS test used in Experiments 1a and 1b was modified to address the

limitations identified previously. Participants were asked to relax and focus on their whole

body (instead of the non-dominant hand) for 20 seconds (compared to 10 seconds previously;

the duration was increased to allow participants to have adequate time to attend to the entire

body). There was a practice trial followed by three main trials, each indicated by a verbal

start and stop signal. After each trial, participants were given a printed body figure to circle

the areas where they felt the sensations. The three trials were averaged to obtain mean

baseline and follow-up SpSs for each participant.

Voice hearing task (VHT). The VHT was built using E-prime and consisted of a

continuous, 4.5 minute stream of white noise over which nonsense speech stimuli of different

amplitudes were randomly presented. Each of the speech stimuli consisted of seven random

English letters (e.g., oppvqsc) generated by PassMaker ( version 1.2; Rohr, 2013). These

were then converted into IVONA voice Brian (2014; WAV speech file) using a text-to-

speech software program (Balabolka version 2.9; Morozov, 2014) with volume = 100, rate =

0, pitch = 0, and duration = 800ms. Participants’ task was to press the spacebar every time

they thought they heard speech. The amplitude of the voices was determined by a pilot study

and ranged between three standard deviations of the mean auditory threshold of the pilot


sample; this range was selected on the assumption that it would result in a set of stimuli that

ranged from barely perceptible to clearly audible for most young adult participants. This

made the VHT sufficiently ambiguous (and thereby increased the likelihood of false alarms)

whilst remaining face valid to participants. We opted not to use a formal thresholding

procedure for this task to minimise the apparent similarity between the VHT and SSDT. The

main VHT was preceded by a one-minute practice version of the task using the same

structure and similar stimuli, as well as high amplitude sounds that were used to calculate

each participant’s mean reaction time (RT) to clearly audible voices. The RT was then used

to determine whether a given spacebar press was classed as a hit (within two standard

deviations of the mean practice RT excluding outliers) or a false alarm (beyond two standard

deviations of the mean practice RT excluding outliers) in the main task. Pseudorandom

intervals (1-2 seconds in the practice phase and 3-10 seconds in the main task) were

maintained between each presentation of the voice to ensure that they were spread over the

entire duration without overlapping or becoming predictable. False alarm rates on this task

are highly reliable over a period of 3 weeks (Huque, Heaney, Poliakoff, & Brown, 2016).

Procedure

The procedure was the same as that in Experiments 1a and 1b except that the VHT was

included in the task sequence and each participant took part in a single (randomly

determined) testing session only. As before, the only difference between the control and

experimental conditions was in the manipulation phase when conditioning was delivered

according to study allocation. The control condition started with the baseline VHT followed

by the baseline SpS test, baseline SSDT (two blocks of 80 trials), manipulation SSDT (four

blocks of 80 trials), follow-up VHT, follow-up SpS test, and follow-up SSDT (two blocks of

80 trials). As before, the short-form of the STAI (alpha coefficients were .68 and .66 in

Experiments 2a and 2b respectively) and KSS were administered before and after the SSDT


manipulation phase. In addition, participants answered the PHQ-15 (Cronbach’s alphas = .68

and .56 in Experiments 2a and 2b respectively) and MPPS at the end of the experiments. At

the start of the manipulation phase in the control condition, participants were informed that

they would get regular feedback about their performance after a certain number of trials and

told that this would improve their tactile perception and decision making. After every 40

trials, they saw a message on a computer screen stating how many points they had won or lost

and what the cumulative score was (e.g., after the 160th trial, “You have won 140 points; you

have lost 130 points; your cumulative total point is 80”). These messages were the same for

all participants in the control condition and were unrelated to their actual performance on the

task. At the end of this phase, participants were informed that their total score was 250 points

for which they received £2.50 (the average amount of money won in Experiments 1a and 1b)

in addition to their usual honorarium. These messages were included as a general motivator

but were not expected to train particular responses.

Conditioning. In Experiment 2a, the procedure for the experimental condition was the

same as Experiment 1a, which sought to increase the false alarm rate by rewarding 50% of

the hit trials and punishing 50% of the miss trials in blocks 3-6 of the SSDT. In Experiment

2b, the procedure for the experimental condition was the same as Experiment 1b, which

sought to decrease the false alarm rate by rewarding 50% of the correct rejection trials and

punishing 50% of the false alarm trials in blocks 3-6 of the SSDT. In this study, participants

won or lost five points (i.e., five pence), with the accompanying messages now including

either a yellow smiley-face emoticon (win) or a red sad-faced emoticon. It was expected that

the use of happy and sad emoticons would strengthen the reward and punishment and would

also act as a visual aid to the feedback message (Derks, Bos, & von Grumbkow, 2007;

Huang, Yen, & Zhang, 2008). Participants received one penny for each point they won in the

manipulation phase of the experimental condition plus their usual honorarium.


Data preparation and statistical analysis. Like Experiments 1a and 1b, data from the

SSDT, SpS test, VHT, sleepiness, and state anxiety were examined to identify outliers and

violation of mixed ANOVA assumptions, and standard procedures were followed to correct

problems. As before, a significance level of .05 was adopted for hypothesis testing

concerning the false alarms on the SSDT and VHT, and total SpS; a level of .01 was adopted

for all other comparisons.

Experiment 2a: Results

Participants

Seventy five (Female = 41, 54.67%) student (n = 65, 86.67%) and staff volunteers from

the University of Manchester took part. Age ranged between 19 and 39 years (M = 23.92. SD

= 4.96). All but four (94.67%) were right handed on the EHI. None of the participants’ hit

rates in the baseline phase was excessively high or low; data from all participants were

therefore used to analyse the SSDT responses. Though we originally intended to recruit a

much smaller sample (N = 18), the cut-point of 0.15 for a high false alarm rate proved to be

unexpectedly stringent, meaning we ended up testing many more participants in Experiment

2a to allow for adequate recruitment to Experiment 2b.


Descriptive statistics on the SSDT response outcomes and phase by condition

interactions are presented in Tables 5 and 6 and Figure 3.

---INSERT TABLE 5 AND 6 AND FIGURE 3 ABOUT HERE---


significantly higher during and after the conditioning procedure than before, and were

significantly higher in the experimental than the control condition in both the manipulation

and follow-up phases. Participants in the training condition were significantly more likely to


say yes during and after the conditioning procedure than before, and significantly more so

than the control participants in the manipulation and follow-up phases.

Sensitivity. The main effect of phase was significant, F(1.84, 134.38) = 6.26, p = .003,

η2p = .08, but the main effect of condition, F(1, 73) = .22, p = .64, η2

p = .003, and the phase

by condition interaction, F(2, 146) = .39, p = .66, η2p = .01, were not. Bonferroni post-hoc

tests indicated that baseline sensitivity did not differ from sensitivity in the manipulation

phase, mean difference = .004, 95% CI [-.01, .02], p = 1.00, but dropped significantly in the

follow-up phase, mean difference = -.02, 95% CI [-.03, -.004], p = .01. Sensitivity in the

manipulation phase was significantly higher than that in the follow-up phase, mean difference

= .01, 95% CI [.002, .02], p = .01.

Transfer of Conditioning to SpSs and Auditory Modality

Descriptive statistics are presented in Table 7.

---INSERT TABLE 7 ABOUT HERE---

SpS. Contrary to expectation, the main effects of phase, F(1, 73) =.39, p = .54, η2p

= .01, condition, F(1, 73) = 1.56, p = .22, η2p = .02, and the interaction between them, F(1,

73) =.25, p = .62, η2p = .003, were not significant.

Voice false alarms. The main effect of phase was significant, F(1, 73) = 57.49, p

= .001, η2p= .44, but the main effect of condition, F(1, 73) = 3.25, p = .08, η2

p= .04, and the

interaction between phase and condition, F(1, 73) = 2.51, p = .12, η2p= .03, were not (see

Figure 4). Regardless of condition, participants produced significantly more false alarms in

the follow-up than in the baseline phase, mean difference = .92, 95% CI [.68, 1.16], p = .001.

Total voices. The main effect of phase was significant, F(1, 73) = 25.4, p = .001, η2p

= .26, but the main effect of condition was not, F(1, 73) = 1.58, p = .21, η2p= .02. Regardless

of condition, participants reported significantly more voices in the follow-up than in the


baseline phase, mean difference = .48, 95% CI [.29, .67], p = .001. The interaction between

phase and condition (see Figure 4) was close to significant at the Bonferroni corrected alpha

value, F(1, 73) = 4.50, p =.037, η2p= .06 .

Total hits. None of the main or interaction effects reached significance (F < 3, p > .05).


There was no significant difference in changes in sleepiness between the conditions (Ms

= 1.18 and 1.00, SEs = .28 and .42 for the control and experimental conditions respectively),

t(73) = 1.78, p = .08, r = .20. Changes in state anxiety also did not differ between the

conditions (Ms = -.05 and .26, SEs = .33 and .31 for the control and experimental conditions

respectively), t(73) = -.68, p = .50, r = .08. Main and interaction effects for the SSDT

variables remained unchanged when the influences of sleepiness and state anxiety were

controlled for.

Results: Experiment 2b

Participants

Seventy six student (n = 67, 88.16%) and staff volunteers from the University of

Manchester participated (female = 39, 51.31%), aged between 19 and 37 years (M = 22.89,

SD = 3.66); nine (11.84%) were left-handed on the EHI.

Effect of Conditioning on the SSDT


presented in Tables 5 and 8 and Figure 3.

---INSERT TABLE 8 ABOUT HERE---

False alarms, hit rate and response bias.

Both the false alarm and hit rates were significantly lower during and after the

conditioning procedure than before, and were significantly lower in the experimental than the

control condition in both the manipulation and follow-up phases. Participants in the training


condition were significantly less likely to say yes during and after the conditioning procedure

than before, and significantly less so than the control participants in both the experimental

and follow-up phases. The tendency to say yes decreased further in the follow-up phase

compared to the manipulation phase in the experimental condition. Participants in the control

condition were less likely to say yes over time, with a corresponding descrease in their false

alarms and hit rate. In addition, there was a significant light × condition interaction, F(1, 74)

= 9.36, p < .01, ηp2 = .11, such that the hit rate was higher in the control than the experimental

condition but only for light present trials (light absent mean difference = .07, 95% CI

[-.04, .18], p = .20; light present mean difference = .15, 95% CI [.05, .25], p < .005). As this

has no obvious theoretical importance in this context it is not discussed further here.

Sensitivity. The main effects of phase, F(1.72, 127.60) = 2.60, p = .09, η2p= .03, and

condition, F(1, 74) = .26, p = .61, η2p= .003, were not significant and neither was the

interaction between them, F(2, 148) = 2.00, p = .15, η2p= .03.

Transfer of Conditioning to SpSs and Auditory Modality

Descriptive statistics are presented in Table 7.

SpS. Contrary to expectation, the main effects of phase, F(1, 74) = .46, p = .50, η2p

= .01, and condition, F(1, 74) = .04, p = .83, η2p= .001, and the interaction between them, F(1,

74) = 2.24, p = .14, η2p= .03, were not significant.

Voice false alarms. The main effect of phase was significant, F(1, 74) = 34.28, p

= .001, η2p= .32. Bonferroni corrected post-hoc tests revealed that participants produced

significantly more false alarms in the follow-up than in the baseline phase, mean difference

= .82, 95% CI [.54, 1.09], p = .001. The main effect of condition was not significant, F(1, 74)

= .24, p = .63, η2p= .003, and neither was the phase by condition interaction (see Figure 4),

F(1, 74) = 2.16, p = .15, η2p= .03.


Total voices. The main effect of phase was significant, F(1, 74) = 6.98, p = .01, η2p

= .09. A Bonferroni-corrected post-hoc test revealed that participants reported more voices in

the follow-up than in the baseline phase, mean difference = .28, 95% CI [.07, .49], p = .01.

The main effect of condition, F(1, 74) = .00, p = 1.00, η2p= .00, and the phase by condition

interaction were not significant, F(1, 74) = .28, p = .42, η2p= .01 (see Figure 4).

Voice hits. The main effect of phase was significant, F(1, 74) = 23.30, p = .001, η2p

= .24. Bonferroni post-hoc tests revealed that participants made fewer hits in the follow-up

than in the baseline phase, mean difference = -2.44, 95% CI [-3.45, -1.44], p = .001. The

main effect of condition, F(1, 74) = .19, p = .66, η2p= .003, and the phase by condition

interaction, F(1, 74) = .45, p = .50, η2p= .01, were not significant.


There was no significant difference between the conditions in changes in sleepiness (Ms

= 1.43 and .38, SEs = .37 and .39 for the control and experimental conditions respectively),

t(74) = 1.94, p = .06, r = .22, and state anxiety (Ms = .62 and -.08, SEs = .36 and .46 for the

control and experimental conditions respectively), t(74) = 1.18, p = .24, r = .14. Main and

interaction effects for the SSDT variables remaind unchanged when the influences of

sleepiness and state anxiety were controlled for.

Correlations Between Baseline False Alarms, SpSs and PHQ-15 Scores

As predicted, SSDT false alarm rate correlated positively with voice false alarms, rs

(149) = .21, p = .01. The correlation between SSDT hit rate and total SpSs was close to

significant, rs (149) = .14, p = .08. Contrary to expectation, the relationship between PHQ-15

and SSDT false alarm rates, was not significant, rs (149) = .09, p = .25. All the correlation

coefficients are presented in Table 5.

Discussion: Experiments 2a and 2b


Operant conditioning had the expected effects on false alarm rate in both studies,

replicating the findings of Experiments 1a and 1b using a more robust design that controlled

for order effects and between group differences in motivation and sleepiness. It is noteworthy

that the same effects pertained even though the monetary value of the reward and punishment

was half that used in Experiments 1a and 1b. However, neither study showed a significant

effect of SSDT conditioning on SpS. There was a near-significant trend for a larger increase

in voice false alarms in the conditioning group, suggesting that training may transfer between

tasks; there was no evidence of transfer on the VHT in Experiment 2b, however.

It is possible that the between-groups difference at VHT follow-up in Experiment 2a

would become significant with additional SSDT training. Indeed, our training was brief in

comparison to other studies on cross-modal transfer, which can involve up to 1000 training

trials over several days (e.g., Bratzke et al., 2014; Nagarajan, Blake, Wright, Byl, &

Merzenich, 1998). In the present experiment, there was a maximum of 80 trials resulting in

reward and punishment depending on participant responses. The strength of the effect on

SSDT performance and the apparent transfer to VHT responses is striking given the small

number of training trials used. It is less likely that a lack of training trials alone could account

for the absence of transfer on the VHT in Experiment 2b. It may be that the general increase

in voice false alarms over time, which was apparent in both studies and unrelated to

condition, concealed any countervailing training effects.

General Discussion

The primary aim of these experiments was to investigate whether it is possible to train

the tendency to report somatosensory misperceptions (i.e., tactile false alarms). As predicted,

we found reliable evidence that operant conditioning can both increase and decrease the

tendency to report false alarms on the SSDT. These findings are consistent with previous

perceptual training studies focusing on other sensory modalities (e.g., Johnstone & Alsop,


1996, 2000; Lie & Alsop, 2010) and, to our knowledge, are the first showing that

conditioning can also be used to manipulate tactile perception. Although we did not study

patients and our vibrotactile stimuli are not clinically relevant per se, the results of our

experiments provide proof of concept that learning can contribute to somatosensory

misperception and therefore have important implications for models of symptom reporting

and functional symptoms that draw on this notion (e.g., Brown, 2004). Our findings are

consistent with studies demonstrating the effect of classical conditioning on symptom reports

(e.g., Klinger, Soost, Flor, & Worm, 2007; Van den Bergh et al., 1999), whilst showing that

somatosensory false alarms can effectively be changed in both directions using operant

condition, and that this persists over time (for as long as a week, as suggested by the carry

over to the following session in Experiments 1a and 1b). They also raise the possibility that

operant conditioning could be used to reduce excessive symptom reporting. Future studies

should investigate whether it is possible to use a similar procedure to that studied here to

reduce the perceived unpleasantness of aversive stimuli (e.g., painful laser or electric pulses),

and thereby its potential as a clinical intervention in this sphere.

Contrary to expectation, we found no evidence that perceptual training on the SSDT

transfers to an unrelated task in which participant are asked to report SpSs in their hand or

their body. There was some weak evidence that training participants to say “yes” more on the

SSDT (i.e., exhibit more false alarms and hits) increased their tendency to make perceptual

errors (i.e., hear illusory voices) on the VHT. Although modest, this effect is noteworthy

given the extent of the training and the fact that the VHT and SSDT were strucuturally and

procedurally distinct, meaning that our findings are more likely to reflect common perceptual

mechanisms than shared-method variance. This is in marked contrast to other studies that

have used different versions of the same task to investigate possible cross-modal training

transfer effects (e.g., Bratzke et al., 2014; Lapid, Ulrich, & Rammsayer, 2009; Nagarajan et


al., 1998). We also found a significant correlation between false alarm rates on the SSDT and

VHT across Experiments 2a and 2b, suggesting some overlap between the two tasks despite

their procedural differences. Given the modest nature of these effects, however, it is evident

that further research is required before firm conclusions can be drawn regarding perceptual

transfer following operant conditioning in the tactile modality.

We have suggested previously that variations in a common perceptual decision process

could confer vulnerability to medically unexplained symptoms (MUS) as well as other

perceptual disturbances seen in clinical settings (e.g., hallucinations, body image

disturbance), with the precise nature of the experience depending on the individual’s beliefs,

preoccupations and focus of attention (Brown et al., 2012). Although some previous studies

have found support for this idea in the form of a correlation between false alarm rate on the

SSDT and physical symptom reporting (Brown et al., 2012; Katzer et al., 2011), no such

correlations were found in the studies reported here. Indeed, the relationship between these

variables appears to be highly inconsistent (Katzer et al., 2012) and presumably dependent on

other, as yet unidentified, factors. Any apparent variation in this mechanism also had little or

no effect on judgements about the presence of spontaneous bodily sensations in our studies.

We have already noted the small number of training trials in the conditioning phase, which

may have been inadequate to bring about a significant transfer effect and which should be

considered in future studies. It may also be that the SpS task, which asks participants to

describe all emerging sensations in their hand/body, is too non-specific to show what may be

a relatively small training effect. If so, categorising qualitatively similar sensations or asking

participants to detect and report specific sensations may address this issue.

One limitation of the present study is that we do not know whether our training

procedure changed the quality of perception or simply decision processes. To disentangle

this, the activation of sensory areas and higher-level processing areas of the brain in false


alarm trials can be examined with brain imaging techniques before, during, and after SSDT

training.

In summary, this is the first published study to demonstrate the effects of operant

conditioning on tactile perception and misperception. Our findings agree with previous

studies on signal detection training and provide proof of concept that similar training

procedures could have utility as treatments for excessive symptom reporting and other related

phenomena. Further research using more intensive training methods is required to establish

whether these effects transfer to other perceptual decisions and therefore have true clinical

potential.


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Footnotes

1Determined by the median false alarm rate in previous SSDT studies (Katzer, Oberfeld,

Hiller, & Witthöft, 2011; Lloyd, Mason, Brown, & Poliakoff, 2008; McKenzie, Lloyd,

Brown, Plummer, & Poliakoff, 2012; McKenzie, Poliakoff, Brown, & Lloyd, 2010).

2For the purposes of analysis, definitely yes and maybe yes responses were combined as

were definitely no and maybe no responses.

3We are grateful to an anonymous reviewer for this suggestion.


Table 1 Estimated Marginal Means (Standard Deviations) of the SSDT Outcomes in the Baseline,

Manipulation, and Follow-Up Phases of the Experimental and Control Conditions in


Condition FA rate Hit rate Bias Sensitivity

Experiment 1a (LFA)

Control condition

Baseline .36 (.12)a .63 (.24) 1.53 (.16)a 1.57 (.83)

Manipulation .34 (.14)a .54 (.27) 1.60 (.16)a 1.35 (.94)

Follow-up .32 (.14)a .47 (.26) 1.64 (.15)a 1.20 (.94)

Experimental condition

Baseline .29 (.09)a .54 (.22) 1.62 (.13)a 1.55 (.71)

Manipulation .46 (.22)a .75 (.22) 1.40 (.20)a 1.63 (1.13)

Follow-up .41 (.18)a .58 (.26) 1.52 (.19)a 1.27 (1.07)

Experiment 1b (HFA)

Control condition

Baseline -.88 (.37)b .63 (.22) .32 (.62) .55 (.11)b

Manipulation -1.03 (.45)b .51 (.26) .59 (.70) .54 (.09)b

Follow-up -1.01 (.45)b .40 (.25) .75 (.70) .49 (.11)b


Baseline -.64 (.18)b .62 (.20) .15 (.38) .54 (.09)b

Manipulation -1.27 (.39)b .37 (.25) .99 (.58) .54 (.12)b

Follow-up -1.47 (.37)b .17 (.15) 1.45 (.58) .47 (.11)b

Note. SSDT = somatosensory signal detection task, FA = false alarm, LFA = low false alarm

participants, HFA = high false alarm participants.a Square root transformed score; bLog transformed score.


Table 2 Effects of Training Designed to Increase the False Alarm Rate in Experiment 1a

Significant interactions Source of interaction

False alarm rate

Phase × Condition: F(2, 88) = 29.32, p = .001, η2

p = .40

Baseline phaseExperimental < Control (diff. = .08, 95% CI [.04, .11], p = .001)

Manipulation phaseExperimental > Control (diff. = .12, 95% CI [.06, .19], p = .001)

Follow-up phaseExperimental > Control (diff. = .09, 95% CI [.04, .14], p = .001)

Control conditionFollow-up < Baseline (diff. = .04, 95% CI [.01, .07], p = .003)

Experimental conditionManipulation > Baseline (diff.= .18, 95% CI [.10, .25], p = .001)Follow-up > Baseline (diff. = .12, 95% CI [.06, .19], p = .001)Manipulation > Follow-up (diff. = .05, 95% CI [.00, .10], p = .05)

Hit rate

Phase × Condition: F(1.90, 83.80) = 19.81, p = .001, η2

p = .31



Experimental conditionManipulation > baseline (diff. = .21, 95% CI [.13, .29], p = .001)Manipulation > follow-up (diff. = .17, 95% CI [.10, .24], p = .001)

Control conditionBaseline > Manipulation (diff. = .09, 95% CI [.03, .15], p = .001)Baseline > Follow-up (diff. = .16, 95% CI [.07, .24], p = .001)

Response bias (tendency to say ‘yes’)a

Phase × Condition:F(2, 88) = 36.92, p = .001, η2

p = .46

Baseline phaseControl > Experimental (diff. =-.09, 95% CI [-.15, -.04], p = .001)

Manipulation phaseExperimental > Control (diff. = -.20, 95% CI [-.28, -.13], p = .001)

(continued)Significant interactions

Source of interaction


Follow-up phaseExperimental > Control (diff. = -.12, 95% CI [-.18, -.05], p = .001)

Control conditionBaseline > Manipulation (diff. = -.07, 95% CI [-.11. -.04], p = .001)Baseline > Follow-up (diff. = -.11, 95% CI [-.16, -.06], p = .001)

Experimental conditionManipulation > baseline (diff. = -.23, 95% CI [-.30, -.15], p = .001)Manipulation > follow-up (diff. = -.13, 95% CI [-.19, -.06], p = .001)Follow-up > baseline (diff. = -.10, 95% CI [-.17, -.03], p = .002)

Note. Only significant interaction effects are presented; see supplementary materials for full

details of analysis. aTendency to say yes is associated with lower scores of c (i.e. response bias).


Table 3

Mean Number (Standard Deviation) of Spontaneous Sensations in the Baseline and Follow-

up SpS Tests in the Experimental and Control Conditions Across Different Session Orders in

Experiments 1a and 1b.

First session

Experiment Condition Phase Control condition Experimental condition

Experiment 1a (LFA)a Experimental Baseline .67 (.44) .76 (.47)

Follow-up .79 (.48) .95 (.47)

Control Baseline .74 (.38) .67 (.48)

Follow-up .84 (.36) .70 (.64)

Experiment 1b (HFA) Experimental Baseline .59 (.63) .98 (.67)

Follow-up .62 (.62) 1.47 (.92)

Control Baseline .67 (.54) .80 (.62)

Follow-up .90 (.67) 1.08 (.79)

Note. SpS = spontaneous sensation, LFA = low false alarm participants, HFA = high false

alarm participants.aSquare root transformed data.


Table 4

Effects of Training Designed to Decrease the False Alarm Rate in Experiment 1b

Significant interactions Source of interaction

False alarm rate


p = .64

Condition × Session: F(1, 28) = 18.30, p = .001, η2

p = .40

Baseline phaseExperimental > Control (diff. = .24, 95% CI [.07, .40], p = .006)

Manipulation phaseExperimental < Control (diff. = .24, 95% CI [.06, .41,], p = .01)

Follow-up phaseExperimental < Control (diff. = .47, 95% CI [.33, .60], p = .001)

Control conditionManipulation < Baseline (diff. = .15, 95% CI [.03, .27], p = .01)

Experimental conditionFollow-up < Manipulation (diff. = .21, 95% CI [.07, .35], p = .003) < Baseline (diff.= .62, 95% CI [.44, .80], p = .001)

Control session firstControl > Experimental (diff. = .44, 95% CI [.24, .65], p = .001)

Control conditionControl first > Experimental first (diff. =.48, 95% CI [.18, .78], p = .003)

Hit rate


p = .31

Manipulation phaseExperimental < Control (diff. = .14, 95% CI [.04, .24], p = .007)

Follow-up phaseExperimental < Control (diff. = .23, 95% CI = [.13, .33], p = .001)



p = .52

Condition × Session: F(1, 28) = 6.27, p = .02, η2

p = .18

Manipulation phaseExperimental < Control (diff. = -.40, 95% CI [-.62, -.18], p = .001)

Follow-up phaseExperimental < Control (diff. = -.70, 95% CI [-.95, -.45], p = .001)

Control session firstControl > Experimental (diff. = -.57, 95% CI [-.89, -.25], p = .001)

(continued)Control condition


Significant interactions Source of interactionControl second < Control first (diff. = -.60, 95% CI [-1.08, -.11], p = .02)




Table 5

Estimated Marginal Means (Standard Deviations) of the SSDT Outcomes in the Baseline,

Manipulation, and Follow-Up Phases of the Experimental and Control Conditions in


Condition FA rate Hit rate Bias Sensitivity

Experiment 2a (LFA)

Control condition

Baseline -1.24 (.33)b .72 (.13)c 1.65 (.15)a .88 (.05)b

Manipulation -1.15 (.46)b .71 (.12)c 1.62 (.17)a .87 (.06)b

Follow-up -1.30 (.42)b .68 (.12)c 1.70 (.18)a .86 (.05)b


Baseline -1.19 (.33)b .70 (.13)c 1.66 (.15)a .87 (.05)b

Manipulation -.68 (.46)b .83 (.12)c 1.35 (.17)a .87 (.06)b

Follow-up -.70 (.42)b .80 (.12)c 1.43 (.18)a .85 (.05)b

Experiment 2b (HFA)

Control condition

Baseline -.64 (.20)b .61 (.19) 1.46 (.12)a 1.40 (.27)a

Manipulation -.74 (.30)b .57 (.25) 1.51 (.15)a 1.39 (.30)a

Follow-up -.80 (.38)b .51 (.26) 1.54 (.18)a 1.34 (.32)a


Baseline -.68 (20)b .59 (.20) 1.49 (.12)a 1.38 (.27)a

Manipulation -1.25 (.30)b .41 (.25) 1.70 (.15)a 1.46 (.29)a

Follow-up -1.34 (.38)b .36 (.26) 1.75 (.18)a 1.40 (.32)a

Note. SSDT = somatosensory signal detection task, FA = false alarm, LFA = low false alarm

participants, HFA = high false alarm participants.a Square root transformed score; bLog transformed score; cReciprocal transformed score.


Table 6

Effects of Training Designed to Increase the False Alarm Rate in Experiment 2a

Significant interactions Source of interactionFalse alarm rate

Phase × Condition: F (2, 146) = 18.21, p = .001, η2

p = .20



Control conditionManipulation > Follow-up (diff. = .15, 95% CI [.04, .26], p = .003)

Experimental conditionManipulation > Baseline (diff.= .51, 95% CI [.31, .72], p = .001)Follow-up > Baseline (diff. = .49, 95% CI [.31, .67], p = .001)

Hit ratePhase × Condition: F(2, 146) = 24.54, p = .001, η2

p = .25



Experimental conditionManipulation > baseline (diff. = .13, 95% CI [.09, .18], p = .001)Follow-up > baseline (diff. = .10, 95% CI [.05, .15], p = .001)Manipulation > Follow-up (diff. = .03, 95% CI [.00, .07], p = .05)


Phase × Condition:F(2, 146) = 33.28, p = .001, η2

p= .31

Manipulation phaseExperimental > Control (diff. = -.27, 95% CI [-.35, -.19], p = .001)

Follow-up phaseExperimental > Control (diff. = -.27, 95% CI [-.35, -.19], p = .001)

Control conditionManipulation > Follow-up (diff. = -.08, 95% CI [-.13, -.03], p = .001)

Experimental conditionManipulation > baseline (diff. = -.30, 95% CI [-.38, -.22], p = .001)Follow-up > baseline (diff. = -.22, 95% CI [-.30, -.15], p = .001)Manipulation > follow-up (diff. = -.08, 95% CI [-.13, -.02], p = .002)


details of analysis.


aTendency to say yes is associated with lower scores of c (i.e. response bias).

Table 7

Mean Number (Standard Deviation) of Spontaneous Sensations, Voice False Alarms, Total

Voices, and Voice Hits in the Baseline and Follow-up Phases of the Experimental and

Control Conditions in Experiments 2a and 2b.

Control condition Experimental condition

Variable Baseline Follow-up Baseline Follow-up

Experiment 2aSpontaneous sensationsa 1.45 (.25) 1.46 (.28) 1.37 (.26) 1.40 (.24)

Voice false alarmsa 2.44 (.99) 3.17 (1.24) 2.71 (1.12) 3.82 (1.50)

Total voicesa 4.50 (.79) 4.78 (1.19) 4.55 (.74) 5.24 (1.07)

Voice hits 13.93 (5.82) 13.15 (6.77) 12.68 (6.07) 11.74 (6.81)

Experiment 2bSpontaneous sensationsb .33 (.17) .31 (.19) .29 (.16) .33 (.15)

Voice false alarmsa 2.59 (1.14) 3.61 (1.53) 2.92 (1.09) 3.53 (1.24)

Total voicesa 4.78 (.85) 5.15 (1.30) 4.87 (.82) 5.06 (.98)

Voice hits 15.62 (6.38) 12.84 (6.31) 14.72 (5.88) 12.62 (5.60)

Note. aSquare root transformed data. b Log transformed data.


Table 8

Effects of Training designed to Decrease the False Alarm Rate in Experiment 2b

Significant interactions Source of interactionFalse alarm rate


p= .31


Follow-up phaseExperimental < Control (diff. = .55, 95% CI [-.72, -.37], p = .001)

Control conditionFollow-up < Baseline (diff. = -.15, 95% CI [-.30, -.01], p = .05)

Experimental conditionManipulation < Baseline (diff.= -.57, 95% CI [-.67, -.47], p = .001)Follow-up < Baseline (diff.= -.66, 95% CI [-.80, -.53], p = .001)

Hit ratePhase × Condition: F(2, 148) = 7.67, p = .001, η2

p= .09


Follow-up phaseExperimental < Control (diff. = -.15, 95% CI [-.27, -.03], p = .02)

Control conditionFollow-up < Baseline (diff. = -.10, 95% CI = [-.18, -.02], p = .01)Follow-up < Manipulation (diff. = -.06, 95% CI [-.11, -.001], p = .05)

Experimental conditionManipulation < Baseline (diff.= -.18, 95% CI [-.25, -.12], p = .001)Follow-up < Baseline (diff.= -.23, 95% CI [-.31, -.15], p = .001)



p= .23

Manipulation phaseControl > Experimental (diff. = -.19, 95% CI [-.26, -.12], p = .001)

Follow-up phaseControl > Experimental (diff. = -.21, 95% CI [-.29, -.13], p = .001)

Control conditionBaseline > Follow-up (diff. = -.08, 95% CI [-.14, -.01], p = .01)

Experimental conditionBaseline > Manipulation (diff. = -.21, 95% CI [-.27, -.16], p = .001)Baseline > Follow-up (diff. = -.26, 95% CI [-.32, -.20], p = .001Manipulation > Follow-up (diff. = -.05, 95% CI [-.09, -.01], p = .01)


Table 9

Summary of Medians, Ranges, and Intercorrelations for the SSDT and VHT Outcomes, and Total SpSs in the Baseline Phase of Experiments 2a

and 2b Combined (N = 151)

Variables Mdn Range 1 2 3 4 5 6 7

1. False alarm rate .13 .74 --

2. Hit rate .57 .94 .21** --

3. Sensitivity 1.27 4.01 -.41*** .76*** --

4. Bias .40 2.81 -.68*** -.83*** -.30*** --

5. Total SpSs 1 4.06 .02 .14 .08 -.126 --

6. Voice false alarms 7 41 .21* .10 -.04 -.21* -.05 --

7. Total voices 22 41 .21* .02 -.09 -.14 -.06 .51*** --

Note. SSDT = Somatosensory signal detection task; VHT = Voice hearing task; SpS = Spontaneous sensation.

*p < .05. **p < .01. ***p < .001.


Figure 1. Experimental condition of Experiments 1a and

1b. The same sequence of tasks was followed in the control condition except that SSDT

responses did not produce any consequence. SpS = spontaneous sensation; SSDT =

somatosensory signal detection task; FA = false alarm; CR = correct rejection.

Follow-up SpS test

Experiment 1a (Low FA participants)

Baseline SSDT (2 X 80) trials

Baseline SpS test

Experiment 1b (High FA participants)

Manipulation phase SSDT (4 X 80) trials Won 10 points for a CR Lost 10 points for a FA

Manipulation phase SSDT (4 X 80) trials Won 10 points for a hit Lost 10 points for a miss

Follow-up SSDT(2 X 80) trials


Control conditionExperimental condition

Figure 2. Phase by condition interactions for the SSDT outcomes of Experiments 1a and 1b. False alarm rate, hit rate, response bias, and

sensitivity are depicted in 1a-i, 1a-ii, 1a-iii, 1a-iv respectively for Experiment 1a, and in 1b-i, 1b-ii, 1b-iii, and 1b-iv respectively for Experiment

1b. Error bars are standard errors.

Control conditionExperimental condition

*

* ****

*

*

1a-iv1a-iii1a-ii1a-i

**

***

*

2a-iv2a-iii2a-ii2a-i

*

*

****

*

1b-iv1b-iii1b-ii1b-i


Figure 3. Phase by condition interactions for the SSDT outcomes of Experiments 2a and 2b. False alarm rate, hit rate, response bias, and

sensitivity are depicted in 2a-i, 2a-ii, 2a-iii, 2a-iv respectively for Experiment 2a, and in 2b-i, 2b-ii, 2b-iii, 2b-iv respectively for Experiment 2b.

Error bars are standard errors.

**

**

**

2b-iv2b-iii2b-ii2b-i


Figure 4. Interactions between phase and condition for voice false alarms and total voices in

Experiment 2a (i, ii) and Experiment 2b (iii, iv). Error bars are standard errors.

Baseline Follow-up2

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