Abstract

Avoidance behavior is a key contributor to the transition from acute pain to chronic pain disability. Yet, there has been a lack of ecologically valid paradigms to experimentally investigate pain-related avoidance. To fill this gap, we developed a paradigm (the robotic arm-reaching paradigm) to investigate the mechanisms underlying the development of pain-related avoidance behavior. Existing avoidance paradigms (mostly in the context of anxiety research) have often operationalized avoidance as an experimenter-instructed, low-cost response, superimposed on stimuli associated with threat during a Pavlovian fear conditioning procedure. In contrast, the current method offers increased ecological validity in terms of instrumental learning (acquisition) of avoidance, and by adding a cost to the avoidance response. In the paradigm, participants perform arm-reaching movements from a starting point to a target using a robotic arm, and freely choose between three different movement trajectories to do so. The movement trajectories differ in probability of being paired with a painful electrocutaneous stimulus, and in required effort in terms of deviation and resistance. Specifically, the painful stimulus can be (partly) avoided at the cost of performing movements requiring increased effort. Avoidance behavior is operationalized as the maximal deviation from the shortest trajectory on each trial. In addition to explaining how the new paradigm can help understand the acquisition of avoidance, we describe adaptations of the robotic arm-reaching paradigm for (1) examining the spread of avoidance to other stimuli (generalization), (2) modeling clinical treatment in the lab (extinction of avoidance using response prevention), as well as (3) modeling relapse, and return of avoidance following extinction (spontaneous recovery). Given the increased ecological validity, and numerous possibilities for extensions and/or adaptations, the robotic arm-reaching paradigm offers a promising tool to facilitate the investigation of avoidance behavior and to further our understanding of its underlying processes.
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 1 of 26
Investigating Pain-Related Avoidance Behavior using a
Robotic Arm-Reaching Paradigm
Eveliina Glogan1,2, Rena Gatzounis1, Kristof Vandael1,3, Mathijs Franssen2, Johan W. S. Vlaeyen1,2, Ann Meulders1,2
1Experimental Health Psychology, Maastricht University 2Research Group Health Psychology, KU Leuven 3Laboratory of Biological Psychology, KU
Leuven
Corresponding Author
Ann Meulders
ann.meulders@kuleuven.be
Citation
Glogan, E., Gatzounis, R., Vandael, K.,
Franssen, M., Vlaeyen, J.W.S.,
Meulders, A. Investigating Pain-Related
Avoidance Behavior using a Robotic
Arm-Reaching Paradigm. J. Vis. Exp. (),
e61717, doi:10.3791/61717 (2020).
Date Published
October 2, 2020
DOI
10.3791/61717
URL
jove.com/t/61717
Abstract
Avoidance behavior is a key contributor to the transition from acute pain to chronic pain
disability. Yet, there has been a lack of ecologically valid paradigms to experimentally
investigate pain-related avoidance. To fill this gap, we developed a paradigm
(the robotic arm-reaching paradigm) to investigate the mechanisms underlying the
development of pain-related avoidance behavior. Existing avoidance paradigms
(mostly in the context of anxiety research) have often operationalized avoidance as an
experimenter-instructed, low-cost response, superimposed on stimuli associated with
threat during a Pavlovian fear conditioning procedure. In contrast, the current method
offers increased ecological validity in terms of instrumental learning (acquisition)
of avoidance, and by adding a cost to the avoidance response. In the paradigm,
participants perform arm-reaching movements from a starting point to a target using
a robotic arm, and freely choose between three different movement trajectories to
do so. The movement trajectories differ in probability of being paired with a painful
electrocutaneous stimulus, and in required effort in terms of deviation and resistance.
Specifically, the painful stimulus can be (partly) avoided at the cost of performing
movements requiring increased effort. Avoidance behavior is operationalized as the
maximal deviation from the shortest trajectory on each trial. In addition to explaining
how the new paradigm can help understand the acquisition of avoidance, we describe
adaptations of the robotic arm-reaching paradigm for (1) examining the spread of
avoidance to other stimuli (generalization), (2) modeling clinical treatment in the
lab (extinction of avoidance using response prevention), as well as (3) modeling
relapse, and return of avoidance following extinction (spontaneous recovery). Given
the increased ecological validity, and numerous possibilities for extensions and/or
adaptations, the robotic arm-reaching paradigm offers a promising tool to facilitate the
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 2 of 26
investigation of avoidance behavior and to further our understanding of its underlying
processes.
Introduction
Avoidance is an adaptive response to pain signaling bodily
threat. Yet, when pain becomes chronic, pain and pain-
related avoidance lose their adaptive purpose. In line with this,
the fear-avoidance model of chronic pain1 , 2 , 3 , 4 , 5 , 6 , 7 , 8
posits that erroneous interpretations of pain as catastrophic,
trigger increases in fear of pain, which motivate avoidance
behavior. Excessive avoidance can lead to the development
and maintenance of chronic pain disability, due to physical
disuse and decreased engagement in daily activities
and aspirations1 , 2 , 3 , 4 , 5 , 9 . Furthermore, given that the
absence of pain can be misattributed to avoidance rather
than recovery, a self-sustaining cycle of pain-related fear and
avoidance can be established10 .
Despite recent interest in avoidance in the anxiety
literature11 , 12 , research on avoidance in the pain domain
is still in its infancy. Previous anxiety research, guided
by the influential two-factor theory13 , has generally
assumed fear to drive avoidance. Correspondingly, traditional
avoidance paradigms12 entail two experimental phases,
each corresponding to one factor: the first to establish
fear (Pavlovian conditioning14 phase), and the second
to examine avoidance (Instrumental15 phase). During
differential Pavlovian conditioning, a neutral stimulus
(conditioned stimulus, CS+; e.g., a circle) is paired with
an intrinsically aversive stimulus (unconditioned stimulus,
US; e.g., an electric shock), which naturally produces
unconditioned responses (URs, e.g., fear). A second control
stimulus is never paired with the US (CS-; e.g., a triangle).
Following pairings of the CSs with the US, the CS+ will elicit
fear in itself (conditioned responses, CRs) in the absence
of the US. The CS- comes to signal safety and will not
trigger CRs. Afterwards, during instrumental conditioning,
participants learn that their own actions (responses, R; e.g.,
button-press) lead to certain consequences (outcomes; O,
e.g., the omission of shock)15 , 16 . If the response prevents
a negative outcome, the chance of that response recurring
increases; this is referred to as negative reinforcement15 .
Thus, in the Pavlovian phase of traditional avoidance
paradigms, participants first learn the CS-US association.
Subsequently, in the instrumental phase, an experimenter-
instructed avoidance response (R) is introduced, canceling
the US if performed during CS presentation, establishing
a R-O association. Thus, the CS becomes a discriminative
stimulus (SD), indicating the appropriate moment for, and
motivating the performance of, the conditioned R15 . Apart
from some experiments showing instrumental conditioning
of pain reports17 and pain-related facial expressions18 ,
investigations into the instrumental learning mechanisms of
pain, in general, are limited.
Although the standard avoidance paradigm, described
above, has elucidated many of the processes underlying
avoidance, it also has several limitations5 , 19 . First, it
does not allow examining the learning, or acquisition,
of avoidance itself, because the experimenter instructs
the avoidance response. Having participants freely choose
between multiple trajectories, and, therefore, learn which
responses are painful/safe and which trajectories to avoid/
not avoid, more accurately models real-life, where avoidance
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 3 of 26
emerges as a natural response to pain9. Second, in
traditional avoidance paradigms, the button-press avoidance
response comes at no cost. However, in real life, avoidance
can become extremely costly for the individual. Indeed,
high-cost avoidance especially disrupts daily functioning5.
For example, avoidance in chronic pain can severely limit
people’s social and working lives9. Third, dichotomous
responses such as pressing/not pressing a button also do
not very well represent real life, where different degrees
of avoidance occur. In the following sections, we describe
how the robotic arm-reaching paradigm20 addresses these
limitations, and how the basic paradigm can be extended to
multiple novel research questions.
Acquisition of avoidance
In the paradigm, participants use a robotic arm to perform
arm-reaching movements from a starting point to a target.
Movements are employed as the instrumental response
because they closely resemble pain-specific, fear-evoking
stimuli. A ball virtually represents participants’ movements on-
screen (Figure 1), allowing participants to follow their own
movements in real-time. During each trial, participants freely
choose between three movement trajectories, represented
on-screen by three arches (T1–T3), differing from each other
in terms of how effortful they are, and in the likelihood that
they are paired with a painful electrocutaneous stimulus (i.e.,
pain stimulus). Effort is manipulated as deviation from the
shortest possible trajectory and increased resistance from
the robotic arm. Specifically, the robot is programmed such
that resistance increases linearly with deviation, meaning
that the more participants deviate, the more force they
need to exert on the robot. Furthermore, pain administration
is programmed such that the shortest, easiest trajectory
(T1) is always paired with the pain stimulus (100% pain/no
deviation or resistance). A middle trajectory (T2) is paired
with a 50% chance of receiving the pain stimulus, but more
effort is required (moderate deviation and resistance). The
longest, most effortful trajectory (T3) is never paired with
the pain stimulus but requires the most effort to reach
the target (0% pain/largest deviation, strongest resistance).
Avoidance behavior is operationalized as the maximum
deviation from the shortest trajectory (T1) per trial, which is
a more continuous measure of avoidance, than for example,
pressing or not pressing a button. Furthermore, the avoidance
response comes at the cost of increased effort. Moreover,
given that participants freely choose between the movement
trajectories, and are not explicitly informed about the
experimental R-O (movement trajectory-pain) contingencies,
avoidance behavior is instrumentally acquired. Online self-
reported fear of movement-related pain and pain-expectancy
have been collected as measures of conditioned fear toward
the different movement trajectories. Pain-expectancy is also
an index of contingency awareness and threat appraisal21 .
This combination of variables allows scrutinizing the interplay
between fear, threat appraisals, and avoidance behavior.
Using this paradigm, we have consistently demonstrated the
experimental acquisition of avoidance20 , 22 , 23 , 24 .
Generalization of avoidance
We have extended the paradigm to investigate generalization
of avoidance23 —a possible mechanism leading to excessive
avoidance. Pavlovian fear generalization refers to the
spreading of fear to stimuli or situations (generalization
stimuli, GSs) resembling the original CS+, with fear
declining with decreasing similarity to the CS+ (generalization
gradient)25 , 26 , 27 , 28 . Fear generalization minimizes the
need to learn relationships between stimuli anew,
allowing swift detection of novel threats in ever-
changing environments25 , 26 , 27 , 28 . However, excessive
generalization leads to fear of safe stimuli (GSs similar to
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 4 of 26
CS-), thus causing unnecessary distress28 , 29 . In line with
this, studies using Pavlovian fear generalization consistently
show that chronic pain patients excessively generalize
pain-related fear30 , 31 , 32 , 33 , 34 , whereas healthy controls
show selective fear generalization. Yet, where excessive
fear causes discomfort, excessive avoidance can culminate
in functional disability, due to avoidance of safe
movements and activities, and increased daily activity
disengagement1 , 2 , 3 , 4 , 9 . Despite its key role in chronic
pain disability, research on the generalization of avoidance is
scarce. In the paradigm adapted for studying generalization
of avoidance, participants first acquire avoidance, following
the procedure described above20 . In a subsequent
generalization phase, three novel movement trajectories
are introduced in the absence of the pain stimulus.
These generalization trajectories (G1–G3) lie on the same
continuum as the acquisition trajectories, resembling each
of these trajectories, respectively. Specifically, generalization
trajectory G1 is situated between T1 and T2, G2 between T2
and T3, and G3 to the right of T3. In this way, generalization
of avoidance to novel safe trajectories can be examined.
In a previous study, we showed generalization of self-
reports, but not avoidance, possibly suggesting different
underlying processes for pain-related fear- and avoidance
generalization23 .
Extinction of avoidance with response prevention
The primary method of treating high fear of movement
in chronic musculoskeletal pain is exposure therapy35
the clinical counterpart to Pavlovian extinction36 , i.e.,
the reduction of CRs through repeated experience with
the CS+ in the absence of the US36 . During exposure
for chronic pain, patients perform feared activities or
movements in order to disconfirm catastrophic beliefs and
expectations of harm34 , 37 . Since these beliefs do not
necessarily concern pain per se, but rather underlying
pathology, movements are not always carried out pain-free
in the clinic34 . According to inhibitory learning theory38 , 39 ,
extinction learning does not erase the original fear memory
(e.g., movement trajectory-pain); rather, it creates a novel
inhibitory extinction memory (e.g., movement trajectory-
no pain), which competes with the original fear memory
for retrieval40 , 41 . The novel inhibitory memory is more
context-dependent than the original fear memory40 , deeming
the extinguished fear memory susceptible to re-emergence
(return of fear)40 , 41 , 42 . Patients are often prevented
from performing even subtle avoidance behaviors during
exposure treatment (extinction with response prevention,
RPE), to establish genuine fear extinction by preventing the
misattribution of safety to avoidance10 , 43 .
Return of avoidance
Relapse in terms of return of avoidance is still common in
clinical populations, even after extinction of fear43 , 44 , 45 , 46 .
Although multiple mechanisms have been found to result
in the return of fear47 , little is known about those relating
to avoidance22 . In this manuscript, we specifically describe
spontaneous recovery, i.e., return of fear and avoidance
due to the passage of time40 , 47 . The robotic arm-reaching
paradigm has been implemented in a 2-day protocol to
investigate return of avoidance. During day 1, participants
first receive acquisition training in the paradigm, as described
above20 . In a subsequent RPE phase, participants are
prevented from performing the avoidance response, i.e., they
can only perform the pain-associated trajectory (T1) under
extinction. During day 2, to test for spontaneous recovery, all
trajectories are available again, but in the absence of pain
stimuli. Using this paradigm, we showed that, one day after
successful extinction, avoidance returned22 .
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 5 of 26
Protocol
The protocols presented here meet the requirements of the
Social and Societal Ethics committee of the KU Leuven
(registration number: S-56505), and the Ethics Review
Committee Psychology and Neuroscience of Maastricht
University (registration numbers: 185_09_11_2017_S1 and
185_09_11_2017_S2_A1).
1. Preparing the laboratory for a test session
1. Before the test session: Send the participant an e-mail
informing him/her about the delivery of pain stimuli, of
the general outline of the experiment, and the exclusion
criteria. Exclusion criteria for healthy participants
comprise: being under 18 years of age; chronic pain;
analphabetism or diagnosed dyslexia; pregnancy; left-
handedness; current/history of cardiovascular disease,
chronic or acute respiratory disease (e.g., asthma,
bronchitis), neurological disease (e.g., epilepsy), and/
or psychiatric disorder (e.g., clinical depression, panic/
anxiety disorder); uncorrected problems with hearing or
vision; having pain in the dominant hand, wrist, elbow or
shoulder that may hinder performing the reaching task;
presence of implanted electronic medical devices (e.g.,
cardiac pacemaker); and presence of any other severe
medical conditions.
2. Due to COVID-19 safety precautions, ask the participant
to wash/disinfect his/her hands upon arrival at the
lab, and do so yourself. Wear a disposable facemask
throughout the duration of the test session, and latex
gloves whenever physical contact with the participant is
required.
3. Use two separate rooms or sections for the experimental
setting: one for the participant and the other for the
experimenter.
4. Use one computer with two separate screens: one
computer screen for the experimenter, and one larger
television screen for the participant.
5. To turn on the robotic arm (e.g., HapticMaster), press
the power switch in the front of the robot (specific to
this robot). Subsequently, turn on the emergency switch,
which may later be used to turn off the robot if necessary.
6. Recalibrate the robotic arm before each test day. This is
done via a direct application programming interface (API)
connection with the robotic arm, and only needs to be
done once, at the beginning of the test day.
1. To establish the API connection, open an internet
browser on the computer, and type in the specific API
address of the robotic arm.
2. On the webpage, select State under HapticMASTER.
Subsequently, press the Start button next to Init (for
initialize).
NOTE: This is the standard calibration procedure
for this robot. Different robots may require different
calibration procedures.
7. Use a constant current stimulator, which is connected
to the computer (see step 1.4). During the experiment,
the pain stimulus is delivered via the experimental
script, which runs on the computer. The experiment is
programmed using a cross-platform game engine (see
Table of Materials).
1. For safety reasons, disable the constant current
stimulator output by switching down the orange toggle
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 6 of 26
switch in the upper-right corner of the stimulator’s
front control panel.
2. Use the orange toggle switch in the middle of the front
control panel to set the output range to x 10 mA.
3. Use the black rotary knob in the upper-left corner of
the front control panel to set the pulse duration to 2
ms (2000 µs).
4. To switch on the constant current stimulator, press
the power button in the lower-left corner of the front
control panel.
2. Screening for exclusion criteria and obtaining
informed consent
1. Position the participant approximately 2.5 m from the
television screen (see step 1.4), at a comfortable distance
(~15 cm) from the handle (sensor) of the robotic arm, in
a chair with arm rests (Figure 1).
2. Screen the participant for exclusion criteria by means of
self-report (see step 1.1 for exclusion criteria).
3. Inform the participant about the delivery of pain stimuli
and of the general outline of the experiment. Also, inform
him/her that he/she is free to withdraw participation at any
point during the experiment, without any repercussions.
Obtain written informed consent.
4. To minimize physical contact with the participant, ensure
that the participant section of the lab includes a table on
which exclusion and informed consent forms, as well as a
Tablet for questionnaires (see step 6.2) are placed before
the participant’s arrival. The participant should be able to
access and sign the forms independently using this table.
3. Attaching the stimulation electrodes
NOTE: The pain stimulus is a 2 ms square-wave electrical
stimulus delivered cutaneously through two stainless steel
bar stimulation electrodes (electrode diameter 8 mm,
interelectrode distance 30 mm).
1. If the participant is wearing long sleeves, ask him/her to
roll up the sleeve on his/her right arm at least 10 cm above
his/her elbow.
2. Fill the center of the stimulation electrodes with
conductive electrolyte gel and plug the electrode cables to
the emergency switch, which is connected to the constant
current stimulator in the experimenter section of the lab.
3. Attach the stimulation electrodes over the triceps tendon
of the participant’s right arm using a strap. Make sure the
strap is neither too tight nor too loose. Once the electrodes
have been attached, tell the participant to relax his/her
arm.
4. Calibrating the pain stimulus
1. Explain the pain calibration procedure and corresponding
scale by presenting it on the television screen (see step
1.4).
1. Clarify to the participant that he/she may choose
the stimulus which he/she will receive during the
experiment, but explain that for data integrity he/she is
asked to select a stimulus that he/she would describe
as “significantly painful and demanding some effort to
tolerate”.
2. Ask the participant to rate each stimulus on the
numerical scale presented on the television screen,
ranging from 0–10, where 0 is labeled as “I feel
nothing”; 1 as “I feel something, but this is not
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 7 of 26
unpleasant; it is only a sensation” (i.e., detection
threshold), 2 as “the stimulus is not yet painful, but is
beginning to be unpleasant”; 3 as “the stimulus starts
being painful” (i.e. pain threshold); and 10 as “this is
the worst pain I can imagine”.
2. Enable the constant current stimulator output by switching
up the orange toggle switch (see step 1.7.1).
3. During the pain calibration procedure, manually increase
the intensity of the pain stimuli using the rotary knob on
the front control panel of the constant current stimulator.
The intensity of the pain stimulus can be seen above this
knob.
1. Start with an intensity of 1 mA, and gradually increase
the intensity in a stepwise manner, with increases of
1, 2, 3, and 4 mA increments. Use the following order
of stimulus presentations in mA: 1, 2, 4, 6, 8, 11, 14,
17, 20, 24, 28, 32, 36, 40, 44, 48, 52, etc.
4. To deliver the pain stimuli one stimulus at a time, manually
trigger the constant current stimulator by pressing the
orange trigger button on the front control panel.
1. Announce each stimulus to the participant before
triggering the constant current stimulator.
5. Terminate the calibration procedure once the participant
reaches a pain intensity level which he/she would
describe as “significantly painful and demanding some
effort to tolerate”. Ideally, this should correspond to a 7–
8 on the pain calibration rating scale.
6. Document the participant’s final pain intensity in mA and
his/her pain intensity rating (0–10) and maintain this
intensity for the remainder of the experiment.
5. Running the experimental task
1. Verbally inform the participant that he/she will receive
instructions about the robotic arm-reaching paradigm on
the television screen in front of him/her, after which he/
she will be able to practice the task under the supervision
of the experimenter.
2. Provide the participant with standardized written
instructions of the task on-screen.
3. Practice: Via the experimental script, on the television
screen, present three arches (T1–T3) situated midway
through the movement plane. The easiest arm movement
(T1) is paired with no deviation or resistance, the middle
arm movement (T2) is paired with moderate deviation and
resistance, and the furthest arm movement (T3) is paired
with the largest deviation and strongest resistance.
1. Instruct the participant to use his/her dominant hand
to operate the sensor of the robotic arm, represented
by a green ball on the television screen, and to move
the ball/sensor from a starting point at the lower-
left corner of the movement plane, to a target at the
upper-left corner of the movement plane.
2. Instruct the participant that he/she can freely choose
which one of the available movement trajectories to
perform on each trial.
4. Do not administer the pain stimulus (see section 3:
Note and step 5.7.6) during the practice phase. However,
ensure that the relationship between deviation and
resistance (see step 5.3) is in place.
5. Provide the participant with verbal feedback while they
perform the practice phase.
1. Ensure the participant does not start moving before
the visual and auditory “start signals”, and that he/she
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 8 of 26
releases the robotic arm immediately when the visual
and auditory “stop signals” are presented.
NOTE: Two distinct auditory signals (a “start tone”
and a “scoring tone”) and two distinct visual signals
(the target and a virtual “traffic light” turning green and
red, respectively; Figure 1) have been used as start
and stop signals. Auditory and visual start signals are
presented simultaneously, as are auditory and visual
stop signals.
2. Instruct the participant to provide self-report
measures of pain-expectancy and fear of movement-
related pain on a continuous rating scale, by scrolling
to the left and right on the scale using two respective
foot pedals on a triple foot switch. Instruct him/her to
confirm his/her answer using a third foot pedal.
NOTE: Present self-report questions on fixed,
predetermined trials, for each movement trajectory
separately. Ensure, via the experimental script, that
the robotic arm is immobilized and remains fixed
during the time the participant is responding to the
questions.
6. At the end of the practice phase, respond to the
participant’s questions. Leave the experimental section/
room and dim the lights. The participant starts the
experiment himself/herself by pressing the ‘Confirm’ foot
pedal (see step 5.5.2).
7. Acquisition: During avoidance acquisition, similarly to
the practice phase, let the participant choose which
movement trajectory (T1–T3) to perform on each trial.
1. During avoidance acquisition, subject the participant
to the experimental Response-Outcome (movement
trajectory-pain) contingencies, and to the avoidance-
costs, i.e., the tradeoff between pain and effort, via
the experimental script.
2. Specifically, if the participant performs the easiest
movement trajectory (T1), always present the pain
stimulus (100% pain/no deviation or resistance).
3. If he/she performs the middle movement trajectory
(T2), present the pain stimulus with a 50% chance, but
ensure he/she will have to exert more effort (moderate
deviation and resistance).
4. If the participant performs the furthest, most effortful
movement trajectory (T3), do not present the pain
stimulus at all, but ensure that he/she will have to
exert the most effort to reach the target (0% pain/
largest deviation, strongest resistance).
NOTE: If applicable to the design, a Yoked Group
can be used as control. In yoked procedures, each
control participant is paired with a participant in
the experimental group, such that the two receive
the same reinforcement schedules48 . Thus, in the
current paradigm, each Yoked Group participant
receives pain stimuli on the same trials as his/
her Experimental Group counterpart, regardless of
the trajectories he/she chooses. No acquisition of
avoidance behavior is expected in the Yoked Group,
given the lack of manipulated Response-Outcome
(movement trajectory-pain) contingencies.
5. Where applicable, save data from each Experimental
Group participant on the computer (see section 1.4),
and use as reference for the reinforcement schedules
of each Yoked (control) Group participant.
1. If using a Yoked procedure (i.e., each control
participant is paired with a participant in the
experimental group, such that the two receive
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 9 of 26
the same reinforcement schedules48 ), allocate
participants to groups using a randomization
schedule with the rule that the first participant
must be in the Experimental Group. Following
this, participants are assigned to either group
randomly, as long as, at each point, the number
of Experimental Group participants exceeds the
number of Yoked Group participants.
6. On trials with a pain stimulus, present the pain
stimulus once two-thirds of the movement has
been performed, i.e., once the participant has
moved through a trajectory arch. The constant
current stimulator is automatically triggered via the
experimental script.
7. Successful trial-completion is indicated by the
presentation of visual and auditory stop signals.
Subsequently, ensure, via the experimental script,
that the robotic arm automatically returns to its starting
position where it remains fixed. After 3,000 ms,
present the visual and auditory start signals, and the
participant can start the next trial.
NOTE: Trial duration differs between trials and
participants, due to differences in movement speeds.
The number of trials per experimental phase can
also change between experiments. We recommend a
minimum of 2 x 12 trials for successful acquisition of
avoidance. Including the steps described above, the
acquisition protocol lasts approximately 45 min.
8. Generalization: In the generalization protocol, test for
generalization of avoidance after the acquisition phase
(see section 5.7).
NOTE: When testing for generalization of avoidance,
the on-screen trajectory arches are separated during
acquisition, to leave room for the generalization trajectory
arches, which are positioned between the acquisition
trajectory arches (see Figure 1).
1. On the television screen, present three novel
movement trajectories instead of the acquisition
trajectories T1–T3. Ensure that these “generalization
trajectories” (G1–G3) are located adjacent to the
acquisition trajectories. Specifically, G1 is situated
between T1 and T2, G2 between T2 and T3, and
G3 to the right of T3 (see Figure 1). Do not pair
generalization trajectories with the pain stimulus.
NOTE: Including the steps described above, with a
generalization phase of 3 x 12 trials, the avoidance
generalization protocol lasts approximately 1.5 h. A
Yoked Group48 is required for testing generalization
of avoidance (see step 5.7.5). However, different
controls can be used depending on the specific
research question (cf. context modulation of
avoidance in a within-subjects design24 ).
9. Extinction with response prevention (RPE): In the
RPE protocol, after the acquisition phase (see section
5.7), provide the participants with standardized written
instructions stating that in the upcoming phase they are
only allowed to perform T1.
1. During the RPE phase, via the experimental script,
visually (e.g., blocking the trajectory arches with a
gate) and/or haptically (e.g., block participant’s arm
movement with a haptic wall) block T2 and T3, so
that only T1 is available. T1 is not paired with the
pain stimulus during this phase. Including the steps
described above, with an RPE phase of 4 x 12 trials,
this session lasts approximately 60 min.
10. Test of spontaneous recovery: For testing spontaneous
recovery of avoidance, administer a 2-day protocol with
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 10 of 26
24 h ± 3 h in between sessions. On day 1, administer the
RPE protocol (see section 5.9).
1. On day 2, attach the stimulation electrodes
(see section 3). Provide brief on-screen refresher
instructions of the task. Do not include any information
regarding the pain stimuli.
2. Present the three acquisition trajectories (T1–T3, cf.
acquisition phase, see section 5.7), in the absence
of the pain stimulus. Including the post-experimental
questionnaire (see section 6.2), and a spontaneous
recovery phase of 4 x 12 trials, this session lasts
approximately 45 min.
NOTE: To prevent reinstatement of fear (i.e., return
of fear following unexpected encounters with the pain
stimulus42 ; see discussion), do not recalibrate the
pain stimulus on day 2.
6. Concluding the experiment
1. Once the participant has completed the experiment,
detach the stimulation electrodes.
2. Provide the participant with a Tablet located on the table
in the participant’s section of the lab (see section 2.4),
for responding to an exit questionnaire inquiring about
the intensity and unpleasantness of the pain stimulus
and avoidance-costs, as well as awareness of the
experimental Response-Outcome (movement trajectory-
pain) contingencies.
3. While the participant completes the psychological trait
questionnaires, clean off the electrolyte gel from the
stimulation electrodes.
4. Once the participant has finished completing the
psychological trait questionnaires, provide him/her with a
debriefing and reimbursement.
5. Clean the stimulation electrodes thoroughly with a
disinfectant solution appropriate for cleaning medical
instruments; remove all the gel inside and around the
electrodes. Dry the electrodes with soft tissue paper.
Clean the sensor of the robotic arm with disinfectant wipes
or spray.
Representative Results
Acquisition of avoidance behavior is demonstrated by
participants avoiding more (showing larger maximal
deviations from the shortest trajectory) at the end of
an acquisition phase, compared to the beginning of the
acquisition phase (Figure 2, indicated by A)20 , or as
compared to a Yoked control group (Figure 3)23 , 48 .
Acquisition of fear and pain-expectancy is evidenced by
participants reporting lower fear for T3 compared to T1 and
T2, and expecting the pain stimulus less during T3 compared
to T1 and T220 . Differential self-reports between T1 and
T3 are of primary interest, because T2 is ambiguous. Non-
differential self-reports between T1 and T2 have also been
found, with both differing from T323 (Figure 4A, Figure 5A,
Figure 6A, and Figure 7A).
Acquisition is a prerequisite for generalization. Generalization
of avoidance behavior is indicated by participants in the
Experimental Group avoiding (deviating) more than the
Yoked Group48 at the beginning of the generalization phase.
Given that generalization is tested in the absence of pain
stimuli, avoidance behavior may decrease throughout the
generalization phase. Furthermore, a general decrease in
avoidance behavior between the end of the acquisition phase
and the beginning of the generalization phase (generalization
decrement) can be expected. This is a result of the
introduction of novel movement trajectories, which may
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 11 of 26
constitute a context-switch49 , 50 . In a previous study, we did
not find generalization of avoidance, possibly due to specific
parameters of the paradigm23 .
Generalization of fear and pain-expectancy is indicated by
a similar pattern to that of the acquisition phase, i.e., by
participants in the Experimental Group reporting lower fear
to G3 compared to G1 and G2, and expecting the pain
stimulus less during G3 compared to G1 and G2, at the
beginning of the generalization phase. As in the acquisition
phase, differential self-reports between G1 and G3 are of
primary interest (Figure 4B and Figure 5B). Non-differential
self-reports between G1 and G2 have been reported so
far, with both differing from G323 . Furthermore, given that
generalization is tested in the absence of pain stimuli,
participants may report less fear and pain-expectancies
throughout the generalization phase. Furthermore, a general
decrease in fear and pain-expectancies toward the novel
generalization trajectories, compared to the acquisition
trajectories (generalization decrement) can be expected. In
a previous study, we found generalization of fear and pain-
expectancies, despite avoidance not generalizing23 .
Acquisition is a prerequisite for extinction. During extinction
of avoidance behavior with response prevention, participants
are only allowed to perform the previously painful movement
trajectory (T1), whereas the other two trajectories (T2 and
T3) are prohibited. Therefore, given that participants only
have the option of performing T1, and thus the observed
data pattern does not reflect their own choices, i.e., genuine
extinction of avoidance behavior, extinction of avoidance is
not included in the analyses (Figure 2).
Extinction of fear and pain-expectancies is evident when
participants report lower fear for T1 and expect the pain
stimulus less when performing T1, at the end of the RPE
phase, compared to the end of the acquisition phase. (Figure
6B and Figure 7B).
Extinction of self-report measures is a prerequisite for
spontaneous recovery. Spontaneous recovery of avoidance
behavior is indicated by participants avoiding more at the
beginning of the test of spontaneous recovery, compared to
the end of the RPE phase (Figure 2B).
Spontaneous recovery of fear and pain-expectancy is
indicated by participants reporting higher fear and pain-
expectancy for T1, during the beginning of the test of
spontaneous recovery, compared to the end of the RPE
phase (Figure 6C and Figure 7C).
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 12 of 26
Figure 1: The experimental set-up and outlook of the experimental task. The participant is seated in front of the
television screen, at reaching distance from the sensor of the robotic arm. The electrodes are placed on the triceps tendon
of the right arm, where the pain stimuli are delivered (red circle), and the triple foot switch is used to give fear of movement-
related pain and pain-expectancy ratings. The acquisition phase of the experimental task is shown on the television screen
and magnified in the white box. The ball is situated in the lower-left corner, and the target in the upper-left corner (green
arch). T1–T3 are situated midway through the movement-plane, from left to right, respectively. Spaces are left between T1–
T3 specifically in avoidance generalization protocols, in order to leave room for the subsequent generalization trajectory
arches (G1–G3). Please click here to view a larger version of this figure.
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 13 of 26
Figure 2: Representative data of avoidance behavior during the acquisition, extinction with response prevention,
and test of spontaneous recovery phases22 . Mean maximum deviation (in centimeters) from the shortest trajectory to the
target during acquisition (ACQ1–2), extinction with response prevention (RPE1–4), and spontaneous recovery (TEST1–2).
Note that, participants are only allowed to perform the shortest trajectory (T1) during the RPE phase. Error bars represent
standard error of the mean (SEM). Data in this figure are from 30 participants (9 men, 21 women; mean age = 21.90)22 . This
figure is modified with permission from ref.22 . Please click here to view a larger version of this figure.
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 14 of 26
Figure 3: Representative data of avoidance behavior during the acquisition phase20 . Relative proportions of
movements between the Experimental and Yoked48 Groups, within the experimental movement plane. Top, yellow patterns
represent movements predominantly performed by the Experimental Group, and bottom, blue patterns represent movements
predominantly performed by the Yoked Group. “Direction from starting point to target” indicates the shortest possible
trajectory from the starting point to the target. “Horizontal deviation” indicates deviation from the shortest possible movement
trajectory. Data in this figure are from 50 participants (36 men, 14 women; mean age = 24.92)20 . This figure is reprinted with
permission from ref.20 . Please click here to view a larger version of this figure.
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 15 of 26
Figure 4: Representative data of fear of movement-related pain during the acquisition and generalization
phases23 . Mean fear of movement-related pain toward the acquisition trajectories in the Experimental and Yoked48 groups
during the acquisition blocks (ACQ1–3), and generalization blocks (GEN1–3). Note that during the acquisition phase, self-
reports are provided for trajectories T1–T3 and during the generalization phase, for G1–G3. Error bars represent SEM. Data
in this figure are from 64 participants (32 per group; Experimental Group: 10 men, 22 women, mean age = 22.88; Yoked
Group: 12 men, 20 women; mean age = 23.44)23 . This figure is modified with permission from ref.23 . Please click here to
view a larger version of this figure.
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 16 of 26
Figure 5: Representative data of pain-expectancy during the acquisition and generalization phases23 . Mean pain-
expectancy toward the acquisition trajectories in the Experimental and Yoked48 groups during the acquisition blocks (ACQ1–
3), and generalization blocks (GEN1–3). Note that during the acquisition phase, self-reports are provided for trajectories T1–
T3 and during the generalization phase, for G1–G3. Error bars represent SEM. Data in this figure are from 64 participants
(32 per group; Experimental Group: 10 men, 22 women, mean age = 22.88; Yoked Group: 12 men, 20 women; mean age =
23.44)23 . This figure is modified with permission from ref.23 . Please click here to view a larger version of this figure.
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 17 of 26
Figure 6: Representative data of fear of movement-related pain during the acquisition, extinction with response
prevention, and test of spontaneous recovery phases22 . Mean fear of movement-related pain toward the different
trajectories (T1–T3) during acquisition (ACQ1–2), extinction with response prevention (RPE1–4), and spontaneous recovery
(TEST1–2). Error bars represent SEM. Data in this figure are from 30 participants (9 men, 21 women; mean age = 21.90)22 .
This figure is modified with permission from ref.22 . Please click here to view a larger version of this figure.
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 18 of 26
Figure 7: Representative data of pain-expectancy during the acquisition, extinction with response prevention,
and test of spontaneous recovery phases22 . Mean pain-expectancy toward the different trajectories (T1–T3) during
acquisition (ACQ1–2), extinction with response prevention (RPE1–4), and spontaneous recovery (TEST1–2). Error bars
represent SEM. Data in this figure are from 30 participants (9 men, 21 women; mean age = 21.90)22 . This figure is modified
with permission from ref.22 . Please click here to view a larger version of this figure.
Discussion
Given the key role of avoidance in chronic pain
disability1 , 2 , 3 , 4 , 5 , and the limitations faced by traditional
avoidance paradigms19 , there is a need for methods to
investigate (pain-related) avoidance behavior. The robotic
arm-reaching paradigm presented here addresses a number
of these limitations. We have employed the paradigm in
a series of studies, which have consistently demonstrated
acquisition of avoidance, and these effects have extended
to our self-report measures of pain-expectancy and fear
of movement-related pain20 , 22 , 23 , 24 . However, we have
also found dissociations between fear and avoidance23 that
may be genuine and informative, suggesting that the two do
not always share a one-to-one relationship5 , 12 , 43 , 44 , 45 .
Additionally, the paradigm presents multiple opportunities for
investigating different aspects of avoidance behavior, such
as generalization23 , extinction with response prevention22 ,
and post-extinction return of avoidance22 , as described in the
current manuscript.
The current method offers many advantages over traditional
avoidance paradigms. First, instead of performing an
experimenter-instructed avoidance response, participants
in the robotic arm-reaching paradigm acquire avoidance
behavior themselves. The paradigm thus better models real
life situations, where avoidance behavior emerges naturally
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 19 of 26
as a response to pain9. Understanding the processes
underlying how avoidance is acquired, can provide insight
into how avoidance can subsequently become pathological,
and inspire ways in which these processes can be directly
targeted during treatment51 . For example, methodological
modifications, such as manipulating experimental reward to
increase approach and reduce avoidance tendencies52 , 53 ,
can allow closer investigation of the behavioral and
cognitive processes underlying the acquisition of maladaptive
avoidance. With regard to this, the acquisition of avoidance
demonstrated with the robotic arm-reaching paradigm can
be easily applied to investigate excessive generalization
of avoidance to safe stimuli23 . A second advantage is
that the continuous nature of the avoidance response in
the current paradigm allows us to examine for whom
avoidance might become excessive, as it provides more
detailed data than a dichotomous measure. This increased
detail in the data allows heightened sensitivity for picking
up individual differences, by means of comparing deviation
scores between participants. Such a continuous measure
is also more ecologically valid, as avoidance in real life
can occur at varying degrees. For example, pain-related
avoidance can range from subtle (e.g., postural changes
or changed breathing when performing a movement) to
complete avoidance (e.g., being bedridden). Furthermore, in
addition to incorporating a cost to avoidance, the current
avoidance response demands some physical effort, meaning
that costs increase with time throughout the task. This
accurately models real life, where avoidance can become
increasingly costly for the individual over a period of time9.
For example, prolonged or regular absenteeism becomes
costly from a financial point of view54 , 55 . Finally, given
the low cost associated with the previously used instructed
button-press response, it is hard to disentangle whether
participants in traditional avoidance paradigms avoid due to
genuine fear, or simply due to automatic following of task
instructions. In contrast, given the high-effort and uninstructed
nature of the avoidance response in the current paradigm,
it seems likely that any avoidance behavior observed
models genuine self-motivated avoidance.
In addition to addressing limitations of previous
methodologies, the robotic arm-reaching paradigm offers
many opportunities for investigating further aspects of
avoidance behavior, as demonstrated in the current
manuscript by the avoidance generalization and RPE
protocols. It is noteworthy that, we previously observed
a dissociation between self-reports and avoidance, with
fear and pain-expectancies generalizing to the novel
movement trajectories, while avoidance did not. There are
several plausible explanations for the observed discrepancy
between fear and avoidance23 , which we are currently
investigating. However, this dissociation may also be a
genuine and informative finding, which in fact adds to
previous literature suggesting that fear and avoidance do
not always occur in synchrony5 , 12 , 43 , 44 , 45 , especially
when the avoidance response is costly56 , 57 . This finding
emphasizes the importance of experimentally investigating
avoidance behavior itself, as distinct processes most likely
contribute to different aspects of fear learning58 , 59 , and
these processes would be difficult to uncover by solely
measuring self-reports and psychophysiological indices of
fear. In addition to generalization of avoidance to novel
movements, the robotic arm-reaching paradigm has also
been applied to study generalization of avoidance to
novel contexts24 . So far, context-based generalization of
avoidance has been investigated using different colored
screens as contextual cues24 . However, Virtual Reality (VR)
could be easily implemented with the current paradigm to
increase the ecological validity of the experimental contexts.
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 20 of 26
VR could also be applied to study category-based avoidance
generalization, such as generalization of avoidance between
different action categories60 , 61 . Additional adaptations may
also be implemented in the RPE protocol. Besides using
a 2-day protocol for the investigation of spontaneous
recovery22 , we have also investigated whether pain-related
avoidance behavior returns not with the passage of time,
but after unexpected encounters with the pain stimulus
(reinstatement)42 in a 1-day protocol. Furthermore, to
examine the proprioceptive underpinnings of pain-related
avoidance behavior more closely, the paradigm can be
modified to include less or no visual information. This is
something we are currently investigating in our lab. Finally,
given that physically moving away from an aversive stimulus
represents a species-specific defensive response62 , not
unique to fear and pain, this type of operationalization of
avoidance permits investigation of many different types of
avoidance as well. For example, the paradigm can potentially
be applied to examine, not only avoidance of painful stimuli,
but also avoidance of other types of aversive stimuli, such as
those inducing disgust or embarrassment63 , 64 .
The described protocol can also be easily extended to
include psychophysiological fear measures. Although not
described here, we have incorporated eye-blink startle
responses, as well as electroencephalography (EEG), into
the robotic arm-reaching paradigm. The eye-blink startle
measure offers a fear-specific measure of reflexive defensive
responses65 , 66 , which can provide additional insight into
the mechanisms underlying avoidance behavior and its
relationship to fear, whereas implementing EEG to the
paradigm enables investigation into specific neural correlates
of avoidance behavior67 . Additionally, the skin-conductance
response (SCR)68 , as well as online self-report ratings of
relief-pleasantness69 , 70 could be included as measures of
relief71 . SCRs have been previously found to correlate with
relief72 —a proposed reinforcer of avoidance69 , 70 given its
inherent positive valence in response to the omission of
negative events73 , 74 . Finally, heart rate (HR) and heart rate
variability (HRV) are easily implementable measures that
have been linked to multiple aversive emotions associated
with avoidance, such as fear, disgust, and embarrassment75 .
Despite its strengths, we acknowledge that the robotic arm-
reaching paradigm also has its limitations. For example, the
paradigm is not easily transferable to other laboratories, as
the equipment used in, and required for the paradigm (e.g.,
robot and constant current stimulator) are expensive, limiting
the widespread use of the paradigm and its implementation
by other laboratories. However, note that similar robots,
which are relatively common in rehabilitation clinics, can be
programmed in the same way, and more affordable constant
current stimulators are available as well. It is also noteworthy
that, in the current method the discriminative stimulus (SD)
and the instrumental response are intertwined. This is in
contrast to traditional avoidance paradigms, where fear is
first acquired towards the CS during the Pavlovian phase,
and avoidance is examined in a subsequent instrumental
phase. However, the temporal relationship between fear
and avoidance is not strictly unidirectional51 . Although
the current paradigm allows closer investigation of the
temporal dynamics of avoidance-emergence in relation to
fear-emergence, the measures we have employed so far
do not allow us to accurately disentangle the temporal
dynamics of fear and avoidance. Currently, avoidance
behavior in the paradigm can be examined at a trial-
by-trial basis, whereas fear and expectancy ratings are
only collected at discrete, specific time points during the
task, to not interfere with task flow. However, to allow
precise comparisons between fear and avoidance, a future
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 21 of 26
study could use a more continuous measure of fear, for
example, by means of a dial76 , single-sensor EEG77 , or
fear-potentiated startle, to allow a detailed understanding of
fear-emergence towards the different trajectories, in relation
to avoidance. Finally, only electrocutaneous stimuli have
so far been used in the robotic arm-reaching paradigm as
pain stimuli, for reasons of consistency and comparability
with previous studies of pain-related fear78 , 79 , 80 . However,
electrocutaneous stimuli may not fully mimic the more
tonic pain experienced by chronic pain patients, given that
they produce a relatively phasic, uncommon, and unnatural
pain experience81 . Other pain-induction methods, such as
ischemic stimulation82 and exercise-induced (e.g., delayed
onset muscle soreness, DOMS)83 , 84 pain have been argued
to be better experimental analogues of musculoskeletal pain,
given their natural and endogenous nature81 . These pain-
induction methods could be employed in the robotic arm-
reaching paradigm in the future. Despite these limitations, the
ability of the current paradigm to consistently demonstrate
acquisition of fear and avoidance using such entwined SDs
and Rs is in itself interesting and novel. Furthermore, we
believe that the robotic arm-reaching paradigm can in and of
itself further the discussion of the need for more ecologically
valid avoidance paradigms19 . In addition, the paradigm has
the potential to pave the way for developing better avoidance
paradigms in general, by providing an example of how
problems in the field can be tackled in an innovative manner.
In conclusion, the robotic arm-reaching paradigm offers
a promising route to improving the ecological validity of
investigations into avoidance behavior, and to furthering
our understanding of the underlying processes. Using the
paradigm, we have already obtained interesting results, which
may not have been uncovered by solely assessing passive
correlates of fear such as verbal reports and physiological
arousal. Yet, extensions to the paradigm have provided some
inconclusive results, which require further investigation and
refinement of the procedure. Despite this, the robotic arm-
reaching paradigm is a huge leap forward with respect to
ecological validity in the paradigms used to study avoidance
behavior.
Disclosures
The authors have nothing to disclose.
Acknowledgments
This research was supported by a Vidi grant from the
Netherlands Organization for Scientific Research (NWO), The
Netherlands (grant ID 452-17-002) and a Senior Research
Fellowship of the Research Foundation Flanders (FWO-
Vlaanderen), Belgium (grant ID: 12E3717N) granted to Ann
Meulders. The contribution of Johan Vlaeyen was supported
by the “Asthenes” long-term structural funding Methusalem
grant by the Flemish Government, Belgium.
The authors wish to thank Jacco Ronner and Richard Benning
from Maastricht University, for programming the experimental
tasks, and designing and creating the graphics for the
described experiments.
References
1. Crombez, G., Eccleston, C., Van Damme, S., Vlaeyen,
J. W., Karoly, P. Fear-avoidance model of chronic pain:
the next generation. The Clinical Journal of Pain. 28 (6),
475-483 (2012).
2. Leeuw, M. et al. The fear-avoidance model of
musculoskeletal pain: current state of scientific evidence.
Journal of Behavioral Medicine. 30 (1), 77-94 (2007).
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 22 of 26
3. Vlaeyen, J., Linton, S. Fear-avoidance model of chronic
musculoskeletal pain: 12 years on. Pain. 153 (6),
1144-1147 (2012).
4. Vlaeyen, J., Linton, S. Fear-avoidance and its
consequences in chronic musculoskeletal pain: a state of
the art. Pain. 85 (3), 317-332 (2000).
5. Meulders, A. From fear of movement-related pain and
avoidance to chronic pain disability: a state-of-the-art
review. Current Opinion in Behavioral Sciences. 26,
130-136 (2019).
6. Kori, S. H., Miller, R. P., Todd, D. D. Kinesophobia: a
new view of chronic pain behavior. Pain Management. (3),
35-43 (1990).
7. Lethem, J., Slade, P. D., Troup, J. D., Bentley, G.
Outline of a Fear-Avoidance Model of exaggerated pain
perception-I. Behaviour Research and Therapy. 21 (4),
401-408 (1983).
8. Waddell, G., Newton, M., Henderson, I., Somerville,
D., Main, C. J. A Fear-Avoidance Beliefs Questionnaire
(FABQ) and the role of fear-avoidance beliefs in chronic
low back pain and disability. Pain. 52 (2), 157-168 (1993).
9. Volders, S., Boddez, Y., De Peuter, S., Meulders, A.,
Vlaeyen, J. W. Avoidance behavior in chronic pain
research: a cold case revisited. Behaviour Research and
Therapy. 64, 31-37 (2015).
10. Lovibond, P. F., Mitchell, C. J., Minard, E., Brady, A.,
Menzies, R. G. Safety behaviours preserve threat beliefs:
Protection from extinction of human fear conditioning
by an avoidance response. Behaviour Research and
Therapy. 47 (8), 716-720 (2009).
11. Hofmann, S. G., Hay, A. C. Rethinking avoidance: Toward
a balanced approach to avoidance in treating anxiety
disorders. Journal of Anxiety Disorders. 55, 14-21 (2018).
12. Krypotos, A. M., Effting, M., Kindt, M., Beckers, T.
Avoidance learning: a review of theoretical models
and recent developments. Frontiers in Behavioral
Neuroscience. 9, 189 (2015).
13. Mowrer, O. H. Two-factor learning theory: summary and
comment. Psychological Review. 58 (5), 350-354 (1951).
14. Pavlov, I. P. Conditioned reflexes: An investigation of
the physiological activity of the cerebral cortex. Oxford
University Press. (1927).
15. Skinner, B. F. Science and human behavior. Macmillan.
(1953).
16. Thorndike, E. L. Animal intelligence: An experimental
study of the associative processes in animals. The
Psychological Review: Monograph Supplements. 2 (4),
i-109 (1898).
17. Linton, S. J., Götestam, K. G. Controlling pain reports
through operant conditioning: a laboratory demonstration.
Perceptual and Motor Skills. 60 (2), 427-437 (1985).
18. Gatzounis, R., Schrooten, M. G., Crombez, G., Vlaeyen,
J. W. Operant learning theory in pain and chronic pain
rehabilitation. Current Pain and Headache Reports. 16
(2), 117-126 (2012).
19. Krypotos, A. M., Vervliet, B., Engelhard, I. M. The validity
of human avoidance paradigms. Behaviour Research and
Therapy. 111 99-105 (2018).
20. Meulders, A., Franssen, M., Fonteyne, R., Vlaeyen,
J. Acquisition and extinction of operant pain-related
avoidance behavior using a 3 degrees-of-freedom robotic
arm. Pain. 157 (5) (2016).
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 23 of 26
21. Boddez, Y. et al. Rating data are underrated: Validity of
US expectancy in human fear conditioning. Journal of
Behavior Therapy and Experimental Psychiatry. 44 (2),
201-206 (2013).
22. Gatzounis, R., Meulders, A. Once an Avoider Always
an Avoider? Return of Pain-Related Avoidance After
Extinction With Response Prevention. The Journal of
Pain. (2020).
23. Glogan, E., Gatzounis, R., Meulders, M., Meulders, A.
Generalization of instrumentally acquired pain-related
avoidance to novel but similar movements using a
robotic arm-reaching paradigm. Behaviour Research and
Therapy. 124, 103525 (2020).
24. Meulders, A., Franssen, M., Claes, J. Avoiding Based
on Shades of Gray: Generalization of Pain-Related
Avoidance Behavior to Novel Contexts. The Journal of
Pain. (2020).
25. Kalish, H. I. in Learning: processes. ed M. Marx. 207-297.
Macmillan (1969).
26. Honig, W. K., Urcuioli, P. J. The legacy of Guttman and
Kalish (1956): Twenty-five years of research on stimulus
generalization. Journal of the Experimental Analysis of
Behavior. 36 (3), 405-445 (1981).
27. Ghirlanda, S., Enquist, M. A century of generalization.
Animal Behaviour. 66 (1), 15-36 (2003).
28. Dymond, S., Dunsmoor, J., Vervliet, B., Roche, B.,
Hermans, D. Fear generalization in humans: Systematic
review and implications for anxiety disorder research.
Behavior Therapy. 46 (5), 561-582 (2015).
29. Lissek, S., Grillon, C. Overgeneralization of conditioned
fear in the anxiety disorders. Zeitschrift für Psychologie/
Journal of Psychology. 218 (2), 146-148 (2010).
30. Meulders, A. et al. Contingency learning deficits and
generalization in chronic unilateral hand pain patients.
The Journal of Pain. 15 (10), 1046-1056 (2014).
31. Meulders, A., Jans, A., Vlaeyen, J. Differences in
pain-related fear acquisition and generalization: an
experimental study comparing patients with fibromyalgia
and healthy controls. Pain. 156 (1), 108-122 (2015).
32. Meulders, A., Meulders, M., Stouten, I., De Bie, J.,
Vlaeyen, J. W. Extinction of fear generalization: A
comparison between fibromyalgia patients and healthy
control participants. The Journal of Pain. 18 (1), 79-95
(2017).
33. Harvie, D. S., Moseley, G. L., Hillier, S. L., Meulders,
A. Classical Conditioning Differences Associated With
Chronic Pain: A Systematic Review. The Journal of Pain.
18 (8), 889-898 (2017).
34. Meulders, A. Fear in the context of pain: Lessons learned
from 100 years of fear conditioning research. Behaviour
Research and Therapy. 131, 103635 (2020).
35. Vlaeyen, J., Morley, S., Linton, S., Boersma, K., de Jong,
J. Pain-Related Fear: Exposure Based Treatment for
Chronic Pain. IASP Press, (2012).
36. Scheveneels, S., Boddez, Y., Vervliet, B., Hermans,
D. The validity of laboratory-based treatment research:
Bridging the gap between fear extinction and exposure
treatment. Behaviour Research and Therapy. 86, 87-94
(2016).
37. den Hollander, M. et al. Fear reduction in patients
with chronic pain: a learning theory perspective. Expert
Review of Neurotherapeutics. 10 (11), 1733-1745 (2010).
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 24 of 26
38. Craske, M. G. et al. Optimizing inhibitory learning during
exposure therapy. Behaviour Research Therapy. 46 (1),
5-27 (2008).
39. Quirk, G. J., Mueller, D. Neural mechanisms of extinction
learning and retrieval. Neuropsychopharmacology: An
Official Publication of the American College of
Neuropsychopharmacology. 33 (1), 56-72 (2008).
40. Bouton, M., E. Context, ambiguity, and unlearning:
sources of relapse after behavioral extinction. Biological
Psychiatry. 52 (10), 976-986 (2002).
41. Bouton, M. E., Winterbauer, N. E., Todd, T. P. Relapse
processes after the extinction of instrumental learning:
renewal, resurgence, and reacquisition. Behavioural
processes. 90 (1), 130-141 (2012).
42. Haaker, J., Golkar, A., Hermans, D., Lonsdorf, T. B. A
review on human reinstatement studies: an overview and
methodological challenges. Learning & Memory. 21 (9),
424-440 (2014).
43. Mineka, S. The role of fear in theories of avoidance
learning, flooding, and extinction. Psychological Bulletin.
86 (5), 985-1010 (1979).
44. Bravo-Rivera, C., Roman-Ortiz, C., Montesinos-
Cartagena, M., Quirk, G. J. Persistent active avoidance
correlates with activity in prelimbic cortex and ventral
striatum. Frontiers In Behavioral Neuroscience. 9,
184-184 (2015).
45. Vervliet, B., Indekeu, E. Low-cost avoidance behaviors
are resistant to fear extinction in humans. Frontiers In
Behavioral Neuroscience. 9, 351 (2015).
46. Solomon, R. L., Kamin, L. J., Wynne, L. C. Traumatic
avoidance learning: the outcomes of several extinction
procedures with dogs. The Journal of Abnormal and
Social Psychology. 48 (2), 291-302 (1953).
47. Bouton, M., E., Swartzentruber, D. Sources of relapse
after extinction in Pavlovian and instrumental learning.
Clinical Psychology Review. 11 (2), 123-140 (1991).
48. Davis, J., Bitterman, M. E. Differential reinforcement
of other behavior (DRO): a yoked-control comparison.
Journal of the Experimental Analysis of Behavior. 15 (2),
237-241 (1971).
49. Bouton, M., E., Todd, T., P. A fundamental role for context
in instrumental learning and extinction. Behavioural
Processes. 104, 13-19 (2014).
50. Bouton, M., E., Todd, T., P., Leon, S., P. Contextual
control of discriminated operant behavior. The Journal
of Experimental Psychology: Animal Learning and
Cognition. 40 (1), 92-105 (2014).
51. Pittig, A., Wong, A. H. K., Glück, V. M., Boschet, J.
M. Avoidance and its bi-directional relationship with
conditioned fear: Mechanisms, moderators, and clinical
implications. Behaviour Research and Therapy. 126,
103550, (2020).
52. Pittig, A., Dehler, J. Same fear responses, less avoidance:
Rewards competing with aversive outcomes do not buffer
fear acquisition, but attenuate avoidance to accelerate
subsequent fear extinction. Behaviour Research and
Therapy. 112, 1-11 (2019).
53. Van Damme, S., Van Ryckeghem, D. M., Wyffels, F.,
Van Hulle, L., Crombez, G. No pain no gain? Pursuing
a competing goal inhibits avoidance behavior. Pain. 153
(4), 800-804 (2012).
54. Langley, P. et al. The impact of pain on labor force
participation, absenteeism and presenteeism in the
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 25 of 26
European Union. Journal of Medical Economics. 13 (4),
662-672 (2010).
55. Breivik, H., Collett, B., Ventafridda, V., Cohen, R.,
Gallacher, D. Survey of chronic pain in Europe:
prevalence, impact on daily life, and treatment. European
Journal of Pain. 10 (4), 287-333 (2006).
56. Claes, N., Crombez, G., Vlaeyen, J. W. Pain-avoidance
versus reward-seeking: an experimental investigation.
Pain. 156 (8), 1449-1457 (2015).
57. Claes, N., Karos, K., Meulders, A., Crombez, G., Vlaeyen,
J. W. S. Competing goals attenuate avoidance behavior
in the context of pain. The Journal of Pain. 15 (11),
1120-1129 (2014).
58. Soeter, M., Kindt, M. Dissociating response systems:
erasing fear from memory. Neurobiology of Learning and
Memory. 94 (1), 30-41 (2010).
59. LeDoux, J., Daw, N. D. Surviving threats: neural circuit
and computational implications of a new taxonomy of
defensive behaviour. Nature Reviews Neuroscience. 19
(5), 269-282 (2018).
60. Glogan, E., van Vliet, C., Roelandt, R., Meulders, A.
Generalization and extinction of concept-based pain-
related fear. The Journal of Pain. 20 (3), 325-338 (2019).
61. Meulders, A., Vandael, K., Vlaeyen, J. W. Generalization
of Pain-Related Fear Based on Conceptual Knowledge.
Behavior Therapy. 48 (3), 295-310 (2017).
62. Bolles, R. C. Species-specific defense reactions and
avoidance learning. Psychological Review. 77 (1), 32-48
(1970).
63. Shook, N. J., Thomas, R., Ford, C. G. Testing the
relation between disgust and general avoidance behavior.
Personality and Individual Differences. 150, 109457
(2019).
64. McCambridge, S. A., Consedine, N. S. For whom
the bell tolls: Experimentally-manipulated disgust and
embarrassment may cause anticipated sexual healthcare
avoidance among some people. Emotion. 14 (2), 407-415
(2014).
65. Lipp, O. V., Sheridan, J., Siddle, D. A. Human
blink startle during aversive and nonaversive Pavlovian
conditioning. The Journal of Experimental Psychology:
Animal Learning and Cognition. 20 (4), 380-389 (1994).
66. van Well, S., Visser, R. M., Scholte, H. S., Kindt, M.
Neural substrates of individual differences in human fear
learning: evidence from concurrent fMRI, fear-potentiated
startle, and US-expectancy data. Cognitive, Affective, &
Behavioral Neuroscience. 12 (3), 499-512 (2012).
67. Davidson, R. J., Jackson, D. C., Larson, C. L. in
Handbook of psychophysiology, 2nd ed. Cambridge
University Press. 27-52 (2000).
68. Benedek, M., Kaernbach, C. A continuous measure of
phasic electrodermal activity. Journal of Neuroscience
Methods. 190 (1), 80-91 (2010).
69. Leknes, S., Lee, M., Berna, C., Andersson, J., Tracey,
I. Relief as a reward: hedonic and neural responses to
safety from pain. PloS One. 6 (4), e17870-e17870 (2011).
70. Vervliet, B., Lange, I., Milad, M. R. Temporal dynamics
of relief in avoidance conditioning and fear extinction:
Experimental validation and clinical relevance. Behaviour
Research and Therapy. 96, 66-78 (2017).
71. Leknes, S. et al. The importance of context: When relative
relief renders pain pleasant. PAIN. 154 (3), 402-410
(2013).
Copyright © 2020 JoVE Journal of Visualized Experiments jove.com October 2020 • • e61717 Page 26 of 26
72. Vervliet, B., Lange, I., Milad, M. R. Temporal dynamics
of relief in avoidance conditioning and fear extinction:
Experimental validation and clinical relevance. Behaviour
Research and Therapy. 96, 66-78 (2017).
73. Deutsch, R., Smith, K. J. M., Kordts-Freudinger, R.,
Reichardt, R. How absent negativity relates to affect
and motivation: an integrative relief model. Frontiers in
Psychology. 6 (152), (2015).
74. Vlemincx, E. et al. Why do you sigh? Sigh rate during
induced stress and relief. Psychophysiology. 46 (5),
1005-1013 (2009).
75. Kreibig, S. D. Autonomic nervous system activity in
emotion: A review. Biological Psychology. 84 (3), 394-421
(2010).
76. Pappens, M., Smets, E., Vansteenwegen, D., Van Den
Bergh, O., Van Diest, I. Learning to fear suffocation:
a new paradigm for interoceptive fear conditioning.
Psychophysiology. 49 (6), 821-828 (2012).
77. de Man, J., Stassen, N. Analyzing fear using single sensor
EEG device. in International Conference on Intelligent
Technologies for Interactive Entertainment. eds R Poppe,
Meyer J.J., Veltkamp R., Dastani M. 86-96 Springer.
(2016).
78. Meulders, A., Vandebroek, N., Vervliet, B., Vlaeyen,
J.W.S. Generalization Gradients in Cued and Contextual
Pain-Related Fear: An Experimental Study in Healthy
Participants. Frontiers in Human Neuroscience. 7, 345
(2013).
79. Meulders, A., Vansteenwegen, D., Vlaeyen J. W. S.
The acquisition of fear of movement-related pain and
associative learning: a novel pain-relevant human fear
conditioning paradigm. Pain. 152 (11), 2460-2469 (2011).
80. Meulders, A., Vlaeyen, J. W. S. The acquisition and
generalization of cued and contextual pain-related fear:
an experimental study using a voluntary movement
paradigm. Pain. 154 (2), 272-282 (2013).
81. Moore, D. J., Keogh, E., Crombez, G., Eccleston, C.
Methods for studying naturally occurring human pain and
their analogues. Pain. 154 (2), 190-199 (2013).
82. Lewis, T. Pain in muscular ischemia: its relation to anginal
pain. Archives of Internal Medicine. 49 (5), 713-727
(1932).
83. Niederstrasser, N. G. et al. Pain catastrophizing and fear
of pain predict the experience of pain in body parts not
targeted by a delayed-onset muscle soreness procedure.
The Journal of Pain. 16 (11), 1065-1076 (2015).
84. Niederstrasser, N. G. et al. An experimental approach
to examining psychological contributions to multisite
musculoskeletal pain. The Journal of Pain. 15 (11),
1156-1165 (2014).
... Despite its core role in disability, avoidance is understudied compared to fear, given the tacit assumption of fear being a valid proxy of avoidance 29, 38 . However, avoidance behavior, specifically, does not occur in a void of competing motivations and goals, and individuals may choose not to avoid, even when they are afraid, particularly when avoidance is costly 7,22 . This is highly relevant, since avoidance can become extremely costly in chronic pain (e.g. ...
... Two participants' data were excluded prior to data analysis due to technical difficulties experienced during data collection. Thus, 60 participants were included in the analyses (34 female, M ± SD (range) age = 24.25 ± 5 years (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)). The sample size was determined by a previous a priori power calculation (using G*Power; α = .05, ...
... preference for G4 over G3) was observed. Therefore, although participants 22 labeled G4 as the safest generalization trajectory, they did not prefer this movement. ...
Article
Pain-related fear and –avoidance crucially contribute to pain chronification. People with chronic pain may adopt costly avoidance strategies above and beyond what is necessary, aligning with experimental findings of excessive fear generalization to safe movements in these populations. Furthermore, recent evidence suggests that, when avoidance is costly, it can dissociate from fear. Here, we investigated whether concurrently measured pain-related fear and costly avoidance generalization correspond in one task. We also explored whether healthy participants avoid excessively despite associated costs, and if avoidance would decrease as a function of dissimilarity from a pain-associated movement. In a robotic arm-reaching task, participants could avoid a low-cost, pain-associated movement trajectory (T+), by choosing a high-cost non-painful movement trajectory (T-), at opposite ends of a movement plane. Subsequently, in the absence of pain, we introduced three movement trajectories (G1-3) between T+ and T-, and one movement trajectory on the side of T- opposite to T+ (G4), linearly increasing in costs from T+ to G4. Avoidance was operationalized as maximal deviation from T+, and as trajectory choice. Fear learning was measured using self-reported pain-expectancy, pain-related fear, and startle eye-blink EMG. Self-reports generalized, both decreasing with increasing distance from T+. In contrast, all generalization trajectories were chosen equally, suggesting that avoidance-costs and previous pain balanced each other out. No effects emerged in the EMG. These results add to a growing body of literature showing that (pain-related) avoidance, especially when costly, can dissociate from fear, calling for a better understanding of the factors motivating, and mitigating, disabling avoidance.
... This study aimed (1) at extending previous findings that pain-related avoidance returns with the passage of time ("spontaneous recovery"; by demonstrating that is also returns after unpredictable pain flare-ups ("reinstatement"; Bouton, 2002;Haaker et al., 2014), and (2) at investigating whether the return of pain-related avoidance is modulated by positive affect during extinction. In an experiment with healthy volunteers, we used a validated Running head: POSITIVE AFFECT AND PAIN-RELATED AVOIDANCE 6 operant pain-related avoidance paradigm Glogan et al., 2020;Meulders et al., 2016), an experimental relapse model (Haaker et al., 2014), and an established positive affect induction (Peters et al., 2010). We expected (1) return of pain-related fear and avoidance in participants who, right before extinction with response prevention, performed an exercise that typically does not alter positive affect, and (2) less (or no) return in participants who, right before extinction with response prevention, performed an exercise known to increase positive affect. ...
... Our robotic arm-reaching task is based on that described by (see also Glogan et al., 2020). Participants performed a series of arm-reaching movements on a 2dimensional horizontal movement plane, which was presented on the screen as a rectangular area (see Figure 1). ...
... The furthest point registered on the x-axis in the zone from 3 cm before to 3 cm after the midplane arches was considered as the maximal deviation in that trial (cf. Glogan et al., 2020). Avoidance behaviour is presented in cm, with 40.5 cm being the maximum possible value (41 cm total width of the movement plane, minus 0.5 cm, which was the radius of the green ball). ...
Article
Positive affect is hypothesized to improve safety learning taking place during extinction (i.e., the core mechanism of exposure treatment), therefore improving the maintenance of treatment outcomes. We investigated whether positive affect during extinction attenuates the return of pain-related avoidance and fear. In an operant pain-related avoidance conditioning paradigm, sixty healthy volunteers performed arm-reaching movements using a robotic arm. During acquisition, they learned to avoid an easy but painful movement (T1) by choosing more effortful movements that were sometimes (T2) or never (T3) painful. Then, the Positive affect group wrote about and imagined their best possible self, which is known to induce positive affect, whereas the Control group wrote about and imagined a typical day. During extinction with response prevention (RPE), participants were only allowed to perform T1, which was no longer paired with pain. Next, two painful stimuli were presented when participants were not moving (i.e., reinstatement manipulation). During test, all movements were available, and we examined whether fear and avoidance of the previously painful movements would re-emerge. Pain-related avoidance returned in both groups. The Positive affect group reported increased positive affect, though not more than the Control group. Nevertheless, they generalized the learned safety of T1 to the other movements during RPE, whereas they also retrospectively rated the pain as less intense and less unpleasant. These results add to the literature of positive affect as a resilience factor.
... [21]). This procedure consisted of the presentation of a series of electrical stimuli, starting at 1mA and increasing in intensity in a stepwise manner [15]. Participants were asked to reach an intensity that they would describe as "significantly painful and demanding some effort to tolerate", approximating an "8" on a numeric rating scale ranging from 0 to 10. ...
... during acquisition and G1-3 during generalization), the HM automatically returns to its starting position, remains fixed for 3s, after which the start signals are presented again, and the next trial starts. This figure is adapted with permission from [15]. Response-Incongruent G3 G2 G1 T1 T2 T3 G3 G2 G1 T1 T2 appears on the screen, and the counter is reset to 0. odds of both groups to choose T3 over T1, and of the Response-Incongruent group to choose G1 over G3, and of the Response-Congruent group to choose G3 over G1. ...
Article
People with chronic pain often fear and avoid movements and activities that were never paired with pain. Safe movements may be avoided if they share some semantic relationship with an actual pain-associated movement. The current study investigated whether pain-associated operant responses (movements) can become categorically associated with perceptually dissimilar responses, thus motivating avoidance of new classes of safe movements - a phenomenon known as category-based avoidance generalization. Using a robotic arm, two groups were trained to categorize arm-movements in different ways. Subsequently, the groups learned through operant conditioning, that an arm-movement from one of the categories was paired with a high probability of pain, while the others were paired with either a medium probability, and no pain (acquisition phase). Self-reported pain-related fear and pain-expectancy were collected as indices of fear learning. During a final generalization test phase, the movements categorically related to those from the acquisition phase were made available but in the absence of pain. Results showed that the generalization of outcome measures depended on the categorical connections between arm-movements, that is, the groups avoided and feared the novel generalization movement categorically related to the pain-associated acquisition movement, depending on how they had previously learned to categorize the movements. This suggests that operant pain-related avoidance can generalize to safe behaviors, which are not perceptually, but categorically, similar to a pain-associated behavior. This form of pain-related avoidance generalization is problematic because category-based relations can be extremely wide reaching and idiosyncratic. Thus, category-based generalization of operant pain-related avoidance merits further investigation.
... Most studies rely on instructed avoidance behavior and likely fail to capture the core mechanisms underlying avoidance as overt behavior (75,133,134). To target this issue, pain research addressed costly pain-related fear and avoidance more directly by implementing operant learning paradigms (115,135,136), documenting that sustained avoidance behavior is continued despite being no longer adaptive, and can even increase fear and pain sensitivity (137). It further underscores a key role of threat-related uncertainty, which has recently been identified as a putative vulnerability factor for maladaptive avoidance behavior (138), and may constitute a promising target for behavioral treatments in psychosomatic disorders (115). ...
... Finally, approaches in experimental and clinical research alike should aim at bridging the gap between models applied in laboratory settings and patients' clinical reality and to more closely integrate the concepts of the FAM into the broad field of psychosomatic medicine. To achieve this goal, clinically-relevant and phenomenologically valid models are needed, capturing different facets as well as the specificity of fear and avoidance in psychosomatic disease, as first innovative attempts in the fields of muscoskeletal (115,(135)(136)(137)163) and interoceptive visceral pain (20,110,111,164,165) have previously demonstrated. These experimental settings provide an ideal opportunity to overcome some common limitations of avoidance research, and to operationalize and assess the complex phenomenon of avoidance in its multiple facets, incorporating behavioral, cognitive, but also neural levels (39). ...
Article
Full-text available
Avoidance behaviors are shaped by associative learning processes in response to fear of impending threats, particularly physical harm. As part of a defensive repertoire, avoidance is highly adaptive in case of acute danger, serving a potent protective function. However, persistent or excessive fear and maladaptive avoidance are considered key factors in the etiology and pathophysiology of anxiety- and stress-related psychosomatic disorders. In these overlapping conditions, avoidance can increase the risk of mental comorbidities and interfere with the efficacy of cognitive behavioral treatment approaches built on fear extinction. Despite resurging interest in avoidance research also in the context of psychosomatic medicine, especially in conditions associated with pain, disturbed interoception, and disorders of the gut-brain axis, current study designs and their translation into the clinical context face significant challenges limiting both, the investigation of mechanisms involved in avoidance and the development of novel targeted treatment options. We herein selectively review the conceptual framework of learning and memory processes, emphasizing how classical and operant conditioning, fear extinction, and return of fear shape avoidance behaviors. We further discuss pathological avoidance and safety behaviors as hallmark features in psychosomatic diseases, with a focus on anxiety- and stress-related disorders. Aiming to emphasize chances of improved translational knowledge across clinical conditions, we further point out limitations in current experimental avoidance research. Based on these considerations, we propose means to improve existing avoidance paradigms to broaden our understanding of underlying mechanisms, moderators and mediators of avoidance, and to inspire tailored treatments for patients suffering from psychosomatic disorders.
... The stoplight served as start signal (green light) and stop signal (red light). This figure was adapted from Glogan, Gatzounis, Vandael, et al. (2020). ...
Article
Fear-avoidance models of chronic pain consider excessive spreading (or overgeneralization) of pain-related avoidance toward safe activities to play a crucial role in chronic pain disability. This study (N = 96) investigated whether avoidance generalization is mitigated by positive affect induction. Pain-free, healthy participants performed an arm-reaching task during which certain movements were followed by pain, while another was not. One group then performed an exercise to induce positive affect (positive affect group), while another group performed a neutral exercise (neutral group). A third group also performed the neutral exercise, but did not learn to avoid pain during the arm-reaching task (yoked neutral group). To test generalization, we introduced novel but similar movements that were never followed by pain in all groups. Results showed no differences in generalization between the positive affect and neutral groups; however, across groups, higher increases in positive affect were associated with less generalization of avoidance, and less generalization of pain-expectancy and pain-related fear. Compared to the yoked neutral group, the neutral group showed avoidance generalization, as well as pain-expectancy and pain-related fear generalization. These results point toward the potential of positive affect interventions in attenuating maladaptive spreading of pain-related avoidance behavior to safe activities.
... Sixty-five pain-free volunteers participated in this study. One participant was excluded prior to data analysis due to technical difficulties during data collection, amounting to 64 participants being included in the analyses (52 female, M ± SD (range) age = 22 ± 4 years, (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)). The sample size was based on the same a priori power calculation as that of Glogan et al. 19 (using G*Power; α = .05, ...
Article
Full-text available
Excessive generalization of fear and avoidance are hallmark symptoms of chronic pain disability, yet research focusing on the mechanisms underlying generalization of avoidance specifically, is scarce. Two experiments investigated the boundary conditions of costly pain-related avoidance generalization in healthy participants who learned to avoid pain by performing increasingly effortful (in terms of deviation and force) arm-movements using a robot-arm (acquisition). During generalization, novel, but similar arm-movements, without pain, were tested. Experiment 1 (N=64) aimed to facilitate generalization to these movements by reducing visual contextual changes between acquisition and generalization, whereas Experiment 2 (N=70) aimed to prevent extinction by increasing pain uncertainty. Both experiments showed generalization of pain-expectancies and pain-related fear. However, Experiment 2 was the first and only to also demonstrate generalization of avoidance, i.e. choosing the novel effortful arm-movements in the absence of pain. These results suggest that uncertainty about the occurrence of pain may delay recovery, due to reduced disconfirmation of threat beliefs when exploring, resulting in persistent avoidance. Perspective This article demonstrates generalization of instrumentally acquired costly pain-related avoidance in healthy people under conditions of uncertainty. The results suggest that targeting pain-related uncertainty may be a useful tool for clinicians adopting a psychological approach to treating excessive pain-related avoidance in chronic pain.
Article
Pain-related avoidance of movements that are actually safe (i.e., overprotective behavior) plays a key role in chronic pain disability. Avoidance is reinforced through operant learning: after learning that a certain movement elicits pain, movements that prevent pain are more likely to be performed. Proprioceptive accuracy importantly contributes to motor learning and memory. Interestingly, reduced accuracy has been documented in various chronic pain conditions, prompting the question whether this relates to avoidance becoming excessive. Using robotic arm-reaching movements, we tested the hypothesis that poor proprioceptive accuracy is associated with excessive pain-related avoidance in pain-free participants. Participants first performed a task to assess proprioceptive accuracy, followed by an operant avoidance training during which a pain stimulus was presented when they performed one movement trajectory, but not when they performed another trajectory. During a test phase, movements were no longer restricted to two trajectories, but participants were instructed to avoid pain. Unbeknownst to the participants, the pain stimulus was never presented during this phase. Results supported our hypothesis. Furthermore, exploratory analyses indicated a reduction in proprioceptive accuracy after avoidance learning, which was associated with excessive avoidance and higher trait fear of pain. Perspective: This study is the first to show that poorer proprioceptive accuracy is associated with excessive pain-related avoidance. This finding is especially relevant for chronic pain conditions, as reduced accuracy has been documented in these populations, and points toward the need for research on training accuracy to tackle excessive avoidance.
Article
Full-text available
Human fear conditioning research since Watson's case study on "Little Albert" has vastly evolved and its impact today is reaching far beyond phobic anxiety. This review focuses on how fear conditioning research, mainly using exteroceptive conditioned stimuli (CSs) and aversive, non-noxious stimuli as unconditioned stimuli (USs), has been extended and translated to chronic pain research. We describe the different pain-related fear conditioning paradigms using proprioceptive and interoceptive CSs and painful stimuli as USs that have been developed to study specific forms of pain-related fear (i.e. fear of movement, fear of touch, fear of visceral sensations, and fear of penetration) that are relevant for different chronic pain conditions (i.e. musculoskeletal pain, neuropathic pain, visceral pain, and genital pain). We present evidence that patients with chronic pain demonstrate impaired safety learning and excessive fear generalization; learning anomalies that have also been observed in anxiety disorders. Extinction-based protocols (exposure in vivo) have been developed to reduce pain-related fear and increase daily functioning in various chronic pain disorders. Finally, we outline some challenges and future directions to further our understanding of learning mechanisms underlying the development, persistence, and treatment of chronic pain disability.
Article
Full-text available
Fear motivates different types of defensive behaviors. These behaviors are, however, not merely byproducts of fear. In this review, we highlight a bi-directional relationship between conditioned fear and instrumental defensive behavior in humans. We discuss mechanisms involved in the link from fear to goal-directed avoidance (e.g., relief, generalization), that may become habitual. These defensive behaviors may in turn reduce, preserve, or amplify fear responding (e.g., protection-from-extinction, behavior-asinformation). Multiple factors moderate this bi-directional relationship. Evidence for amplifying and dampening effects of inter-individual differences (e.g., trait anxiety, distress tolerance), intra-individual states (e.g., stress), and external factors (e.g., incentives for competing behavior) on goal-directed and/or habitual avoidance is reviewed. However, the exact mechanisms by which these factors moderate the bi-directional relationship between fear and instrumental defensive behavior are still largely unknown (e.g., modulating avoidance directly vs. indirectly via conditioned fear). Finally, we discuss major implications: First, understanding factors moderating the bi-directional relationship provides insights into risk and resilience factors for anxious psychopathology. Second, specific experimental models and clinical interventions can be mapped onto distinct defensive behaviors (e.g., goal-directed vs. habitual avoidance). More precise matching will help to develop nuanced models and interventions to reduce pathological behaviors and individualize treatments.
Article
Avoidance behavior is protective, yet in the absence of genuine bodily threat, it may become disabling. Therefore, we investigated whether avoidance generalizes to novel safe contexts based on the similarity with the acquisition context. Healthy participants performed arm movements using a robotic arm to reach a target. Three trajectories (T1-3) led to the target. During acquisition, a painful stimulus could be partly/completely prevented by performing more effortful trajectories (i.e. longer and more force needed), T2/T3, in the pain-avoidance context (e.g. black background); in the yoked context (e.g. white background), the same reinforcement schedule was applied irrespective of the chosen trajectories. Generalization of avoidance was tested in two novel contexts (e.g. shades of grey backgrounds). We assessed self-reported pain-expectancy and pain-related fear for all trajectories, and avoidance behavior (i.e. maximal deviation from T1). Results confirm that fear and expectancy ratings reflect the response-outcome contingencies and differential learning selectively generalized to the novel context resembling the original pain-avoidance context. Furthermore, a linear trend in avoidance behavior across contexts emerged, which is indicative of a generalization gradient. Participants avoided more in the context resembling the original pain-avoidance context than in the one resembling the yoked context, but this effect was not statistically significant. Perspective We demonstrated acquisition of pain-related avoidance behavior in a within-subjects design, showing modulation of pain-related fear and pain-expectancy by context and providing limited evidence that avoidance selectively generalizes to novel, similar contexts. These results provide insight regarding the underlying mechanisms of the spreading of protective behavior in chronic pain patients.
Article
In exposure for chronic pain, avoidance is often forbidden (extinction with response prevention; RPE) to prevent misattributions of safety. Although exposure is an effective treatment, relapse is common. Little is known about the underlying mechanisms of return of pain-related avoidance. We hypothesized that pain-related avoidance would recover when becoming available again after RPE and after unexpected pain episodes (“reinstatement”), especially when restricting avoidance during RPE (compared to instructing not to use it). In an operant pain-related avoidance conditioning paradigm, healthy volunteers used a robotic arm to perform various arm reaching movements differing in pain-effort trade-off. During acquisition, participants learned to avoid pain by performing more effortful movements. During RPE they only performed the formerly pain-associated movement under extinction, and were either forbidden (Restricted group) or merely instructed (Instructed group) not to perform other movements. One day later, we tested spontaneous recovery and reinstatement of pain-related fear and avoidance with availability of all movements. Results showed that pain-related fear and avoidance re-emerge after RPE, though not to pre-treatment levels. The reinstatement manipulation had no additional effect. No group differences were observed. We discuss findings in the context of learning processes in (chronic) pain disability and relapse prevention in chronic pain treatment. Perspective: Using experimental models of relapse, we investigated the return of pain-related avoidance behaviour after extinction with response prevention. Findings are potentially informative for clinicians performing exposure treatment with chronic pain patients.
Article
Avoidance is considered a key contributor to the development and maintenance of chronic pain disability, likely through its excessive generalization. This study investigated whether acquired avoidance behavior generalizes to novel but similar movements. Using a robotic arm, participants moved their arm from a starting to a target location via one of three possible movement trajectories. For the Experimental Group, the shortest, easiest trajectory was always paired with pain (T1 = 100% reinforcement/no resistance and deviation). Pain could be partly or completely avoided by choosing increasingly effortful movements (T2 = 50% reinforcement, moderate resistance/deviation; T3 = 0% reinforcement, strongest resistance/largest deviation). A Yoked Group received the same number of painful stimuli irrespective of their own behavior. Outcomes were self-reported fear of movement-related pain, pain-expectancy, avoidance behavior, (maximal deviation from the shortest trajectory), and trajectory choice behavior. We tested generalization to three novel trajectories (G1-3) positioned next to the acquisition trajectories. Whereas acquired fear of movement-related pain and pain-expectancy generalized in the Experimental Group, avoidance behavior did not, suggesting that threat beliefs and high-cost avoidance may not be directly related. The lack of avoidance generalization may be due to a perceived context-switch in the configurations of the acquisition and the generalization phases.
Article
Disgust is posited to serve a disease-avoidance function, signaling the potential presence of a contaminant and encouraging prophylactic behavior. Indeed, some research indicates that disgust sensitivity is associated with avoidance of specific, disgust-related stimuli. However, disgust may be related to more general behavioral avoidance tendencies, but relatively little research has empirically tested this link. Across two studies, we sought to fill this gap. In Study 1, participants completed measures of disgust sensitivity, as well as a performance-based task and a self-report measure of approach-avoidance tendencies. Greater disgust sensitivity was generally correlated with more self-reported avoidance tendencies and more avoidance behavior of positive stimuli in the performance-based task. In Study 2, participants were randomly assigned to either a disgust or control condition, which entailed consuming disgusting flavored or normally flavored jellybeans, respectively. They then completed the performance-based measure of avoidance. Participants in the disgust condition exhibited more avoidance behavior than participants in the control condition. Across both studies, disgust was associated with avoidance tendencies. These findings have broad implications for the role of disgust is shaping worldview and attitudes, in addition to disease avoidance.
Article
In chronic pain, pain-related fear seems to overgeneralize to safe stimuli, thus contributing to excessive fear and avoidance behavior. Evidence shows that pain-related fear can be acquired and generalized based on conceptual knowledge. Using a fear conditioning paradigm, we investigated whether this concept-based pain-related fear could also be extinguished. During acquisition, exemplars of 1 action category (conditioned stimuli [CSs]; eg, opening boxes) were followed by pain (CS+), whereas exemplars of another action category were not (CS–; eg, closing boxes). Participants reported more pain-related fear and expectancy toward exemplars of the CS+ category compared with those of the CS– category. During generalization, fear and expectancy spread to novel exemplars (generalization stimuli [GSs]) of the CS+ category (GS+), but not to those of the CS– category (GS–). During extinction, exemplars of both categories were presented in the absence of pain. At the end of extinction, participants no longer reported elevated fear or expectancy toward CS+ exemplars compared to CS– exemplars. These findings were not replicated in either the eye-blink startle or skin conductance measures. This is the first study to demonstrate extinction of concept-based pain-related fear, thus providing evidence for the potential of extinction-based techniques in the treatment of conceptual pain-related fear. Perspective: This study demonstrates the acquisition, generalization, and extinction of concept-based pain-related fear in healthy participants. These are the first results to show that concept-based pain-related fear can be extinguished, suggesting that conceptual relationships between fear-inducing stimuli may also be important to consider in clinical practice.
Article
Research on defensive behaviour in mammals has in recent years focused on elicited reactions; however, organisms also make active choices when responding to danger. We propose a hierarchical taxonomy of defensive behaviour on the basis of known psychological processes. Included are three categories of reactions (reflexes, fixed reactions and habits) and three categories of goal-directed actions (direct action–outcome behaviours and actions based on implicit or explicit forecasting of outcomes). We then use this taxonomy to guide a summary of findings regarding the underlying neural circuits.
Article
Perspective: The review revealed preliminary evidence that people with chronic pain may exhibit less differential US expectancy and fear learning. This characteristic may contribute to widespread fear-avoidance behavior. The assumption that altered classical conditioning may be a predisposing or maintaining factor for chronic pain remains to be verified.
Article
Increasing evidence suggests that pain-related fear is key to the transition from acute to chronic pain. Previous research has shown that perceptual similarity with a pain-associated movement fosters the generalization of fear to novel movements. Perceptual generalization of pain-related fear is adaptive as it enables individuals to extrapolate the threat value of one movement to another without the necessity to learn anew. However, excessive spreading of fear to safe movements may become maladaptive and may lead to sustained anxiety, dysfunctional avoidance behaviors, and severe disability. A hallmark of human cognition is the ability to extract conceptual knowledge from a learning episode as well. Although this conceptual pathway may be important to understand fear generalization in chronic pain, research on this topic is lacking. We investigated acquisition and generalization of concept-based pain-related fear. During acquisition, unique exemplars of one action category (CS +, e.g., opening boxes) were followed by pain, whereas exemplars of another action category (CS-, e.g., closing boxes) were not. Subsequently, spreading of pain-related fear to novel exemplars of both action categories was tested. Participants learned to expect the pain to occur and reported more pain-related fear to the exemplars of the CS + category compared to those of the CS- category. During generalization, fear and expectancy generalized to novel exemplars of the CS + category, but not to the CS- category. This pattern was not corroborated in the eyeblink startle measures. This is the first study that demonstrates that pain-related fear can be acquired and generalized based on conceptual knowledge.