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 &#8220;Asthenes&#8221; 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.

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
<ol>
	<li>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.</li>
	<li>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.</li>
	<li>Use two separate rooms or sections for the experimental setting: one for the participant and the other for the experimenter.</li>
	<li>Use one computer with two separate screens: one computer screen for the experimenter, and one larger television screen for the participant.</li>
	<li>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.</li>
	<li>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.
	<ol>
		<li>To establish the API connection, open an internet browser on the computer, and type in the specific API address of the robotic arm.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>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).
	<ol>
		<li>For safety reasons, disable the constant current stimulator output by switching down the orange toggle switch in the upper-right corner of the stimulator&#8217;s front control panel.</li>
		<li>Use the orange toggle switch in the middle of the front control panel to set the output range to x 10 mA.</li>
		<li>Use the black rotary knob in the upper-left corner of the front control panel to set the pulse duration to 2 ms (2000 &#181;s).</li>
		<li>To switch on the constant current stimulator, press the power button in the lower-left corner of the front control panel.</li>
	</ol>
	</li>
</ol>
2. Screening for exclusion criteria and obtaining informed consent
<ol>
	<li>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).</li>
	<li>Screen the participant for exclusion criteria by means of self-report (see step 1.1 for exclusion criteria).</li>
	<li>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.</li>
	<li>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&#8217;s arrival. The participant should be able to access and sign the forms independently using this table.</li>
</ol>
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).
<ol>
	<li>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.</li>
	<li>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.</li>
	<li>Attach the stimulation electrodes over the triceps tendon of the participant&#8217;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.</li>
</ol>
4. Calibrating the pain stimulus
<ol>
	<li>Explain the pain calibration procedure and corresponding scale by presenting it on the television screen (see step 1.4).
	<ol>
		<li>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 &#8220;significantly painful and demanding some effort to tolerate&#8221;.</li>
		<li>Ask the participant to rate each stimulus on the numerical scale presented on the television screen, ranging from 0&#8211;10, where 0 is labeled as &#8220;I feel nothing&#8221;; 1 as &#8220;I feel something, but this is not unpleasant; it is only a sensation&#8221; (i.e., detection threshold), 2 as &#8220;the stimulus is not yet painful, but is beginning to be unpleasant&#8221;; 3 as &#8220;the stimulus starts being painful&#8221; (i.e. pain threshold); and 10 as &#8220;this is the worst pain I can imagine&#8221;.</li>
	</ol>
	</li>
	<li>Enable the constant current stimulator output by switching up the orange toggle switch (see step 1.7.1).</li>
	<li>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.
	<ol>
		<li>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.</li>
	</ol>
	</li>
	<li>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.
	<ol>
		<li>Announce each stimulus to the participant before triggering the constant current stimulator.</li>
	</ol>
	</li>
	<li>Terminate the calibration procedure once the participant reaches a pain intensity level which he/she would describe as &#8220;significantly painful and demanding some effort to tolerate&#8221;. Ideally, this should correspond to a 7&#8211;8 on the pain calibration rating scale.</li>
	<li>Document the participant&#8217;s final pain intensity in mA and his/her pain intensity rating (0&#8211;10) and maintain this intensity for the remainder of the experiment.</li>
</ol>
5. Running the experimental task
<ol>
	<li>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.</li>
	<li>Provide the participant with standardized written instructions of the task on-screen.</li>
	<li>Practice: Via the experimental script, on the television screen, present three arches (T1&#8211;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.
	<ol>
		<li>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.</li>
		<li>Instruct the participant that he/she can freely choose which one of the available movement trajectories to perform on each trial.</li>
	</ol>
	</li>
	<li>Do not administer the pain stimulus (see section 3: Note&#160;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.</li>
	<li>Provide the participant with verbal feedback while they perform the practice phase.
	<ol>
		<li>Ensure the participant does not start moving before the visual and auditory &#8220;start signals&#8221;, and that he/she releases the robotic arm immediately when the visual and auditory &#8220;stop signals&#8221; are presented. 
		NOTE: Two distinct auditory signals (a &#8220;start tone&#8221; and a &#8220;scoring tone&#8221;) and two distinct visual signals (the target and a virtual &#8220;traffic light&#8221; 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.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>At the end of the practice phase, respond to the participant&#8217;s questions. Leave the experimental section/room and dim the lights. The participant starts the experiment himself/herself by pressing the &#8216;Confirm&#8217; foot pedal (see step 5.5.2).</li>
	<li>Acquisition: During avoidance acquisition, similarly to the practice phase, let the participant choose which movement trajectory (T1&#8211;T3) to perform on each trial.
	<ol>
		<li>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.</li>
		<li>Specifically, if the participant performs the easiest movement trajectory (T1), always present the pain stimulus (100% pain/no deviation or resistance).</li>
		<li>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).</li>
		<li>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.</li>
		<li>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.
		<ol>
			<li>If using a Yoked procedure (i.e., each control participant is paired with a participant in the experimental group, such that the two receive 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.</li>
		</ol>
		</li>
		<li>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.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>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).
	<ol>
		<li>On the television screen, present three novel movement trajectories instead of the acquisition trajectories T1&#8211;T3. Ensure that these &#8220;generalization trajectories&#8221; (G1&#8211;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).</li>
	</ol>
	</li>
	<li>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.
	<ol>
		<li>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&#8217;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.</li>
	</ol>
	</li>
	<li>Test of spontaneous recovery: For testing spontaneous recovery of avoidance, administer a 2-day protocol with 24 h &#177; 3 h in between sessions. On day 1, administer the RPE protocol (see section 5.9).
	<ol>
		<li>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.</li>
		<li>Present the three acquisition trajectories (T1&#8211;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.</li>
	</ol>
	</li>
</ol>
6. Concluding the experiment
<ol>
	<li>Once the participant has completed the experiment, detach the stimulation electrodes.</li>
	<li>Provide the participant with a Tablet located on the table in the participant&#8217;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.</li>
	<li>While the participant completes the psychological trait questionnaires, clean off the electrolyte gel from the stimulation electrodes.</li>
	<li>Once the participant has finished completing the psychological trait questionnaires, provide him/her with a debriefing and reimbursement.</li>
	<li>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.</li>
</ol>

The authors have nothing to disclose.

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 se...

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 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&#8217;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&#8217; 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&#8211;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&#8212;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 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&#8211;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&#8212;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.

<table><tbody><tr><td>1 computer and computer screen</td><td>Intel Corporation</td><td>64-bit Intel Core</td><td>Running the experimental script</td></tr><tr><td>40 inch LCD screen</td><td>Samsung Group</td><td></td><td>Presenting the experimental script</td></tr><tr><td>Blender 2.79</td><td>Blender Foundation</td><td></td><td>3D graphics software for programming the graphics of the experiment</td></tr><tr><td>C#</td><td></td><td></td><td>Programming language used to program the experimental task</td></tr><tr><td>Conductive gel</td><td>Reckitt Benckiser</td><td>K-Y Gel</td><td>Facilitates conduction from the skin to the stimulation electrodes</td></tr><tr><td>Constant current stimulator</td><td>Digitimer Ltd</td><td>DS7A</td><td>Generates electrical stimulation</td></tr><tr><td>HapticMaster</td><td>Motekforce Link</td><td></td><td>Robotic arm</td></tr><tr><td>Matlab</td><td>MathWorks</td><td></td><td>For writing scripts for participant randomization schedule, and for extracting maximum deviation from shortest trajectory per trial</td></tr><tr><td>Qualtrics</td><td>Qualtrics</td><td></td><td>Web survey tool for psychological questionnaires</td></tr><tr><td>Rstudio</td><td>Rstudio Inc.</td><td></td><td>Statistical analyses</td></tr><tr><td>Sekusept Plus</td><td>Ecolab</td><td></td><td>Disinfectant solution for cleaning medical instruments</td></tr><tr><td>Stimulation electrodes</td><td>Digitimer Ltd</td><td>Bar stimulating electrode</td><td>Two reusable stainless steel disk electrodes; 8mm diameter with 30mm spacing</td></tr><tr><td>Tablet</td><td>AsusTek Computer Inc.</td><td>ASUS ZenPad 8.0</td><td>For providing responses to psychological trait questinnaires</td></tr><tr><td>Triple foot switch</td><td>Scythe</td><td>USB-3FS-2</td><td>For providing self-report measures on VAS scale</td></tr><tr><td>Unity 2017</td><td>Unity Technologies</td><td></td><td>Cross-platform game engine for writing the experimental script including presentations of electrocutaneous stimuli</td></tr></tbody></table>

investigating pain-related avoidance behavior using a robotic arm-reaching paradigm

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.

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-expecta...

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Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

Avoidance is central to chronic pain disability, yet adequate paradigms for examining pain-related avoidance are lacking. Therefore, we developed a paradigm that allows investigating how pain-related avoidance behavior is learned (acquisition), spreads to other stimuli (generalization), can be mitigated (extinction), and how it may subsequently re-emerge (spontaneous recovery).

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 ...

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JoVE Journal

Behavior

3.9K Views.  Maastricht University. Vermijding staat centraal bij chronische pijnstoornissen, maar adequate paradigma's voor het onderzoeken van pijngerelateerde vermijding ontbreken. Daarom hebben we een paradigma ontwikkeld dat het mogelijk maakt om te onderzoeken hoe pijngerelateerd vermijdingsgedrag wordt aangeleerd (acquisitie), zich verspreidt naar andere stimuli (generalisatie), kan worden verzacht (uitsterven) en hoe het vervolgens opnieuw kan ontstaan (spontaan herstel).

Video: Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Recent research that tests interactive devices for prolonged therapy practice has revealed new prospects for robotics combined with graphical and other forms of biofeedback. Previous human-robot interactive systems have required different software commands to be implemented for each robot leading to unnecessary developmental overhead time each time a new system becomes available. For example, when a haptic/graphic virtual reality environment has been coded for one specific robot to provide haptic feedback, that specific robot would not be able to be traded for another robot without recoding the program. However, recent efforts in the open source community have proposed a wrapper class approach that can elicit nearly identical responses regardless of the robot used. The result can lead researchers across the globe to perform similar experiments using shared code. Therefore modular "switching out"of one robot for another would not affect development time. In this paper, we outline the successful creation and implementation of a wrapper class for one robot into the open-source H3DAPI, which integrates the software commands most commonly used by all robots.

Recently, a vast amount of prospects have come available for human-robot interactive systems. In this paper we outline the integration of a new robotic device with open source software that can rapidly make possible a library of interactive functionality. We then outline a clinical application for a neurorehabilitation application.

Recent research that tests interactive devices for prolonged therapy practice has revealed new prospects for robotics combined with graphical and other ...

Bioengineering

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Skilled reaching tasks are commonly used in studies of motor skill learning and motor function under healthy and pathological conditions, but can be time-intensive and ambiguous to quantify beyond simple success rates. Here, we describe the training procedure for reach-and-pull tasks with ETH Pattus, a robotic platform for automated forelimb reaching training that records pulling and hand rotation movements in rats. Kinematic quantification of the performed pulling attempts reveals the presence of distinct temporal profiles of movement parameters such as pulling velocity, spatial variability of the pulling trajectory, deviation from midline, as well as pulling success. We show how minor adjustments in the training paradigm result in alterations in these parameters, revealing their relation to task difficulty, general motor function or skilled task execution. Combined with electrophysiological, pharmacological and optogenetic techniques, this paradigm can be used to explore the mechanisms underlying motor learning and memory formation, as well as loss and recovery of function (e.g. after stroke).

A paradigm is presented for training and analysis of an automated skilled reaching task in rats. Analysis of pulling attempts reveals distinct subprocesses of motor learning.

Skilled reaching tasks are commonly used in studies of motor skill learning and motor function under healthy and pathological conditions, but can be ...

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the behavior. Several automated versions of skilled reaching have been developed recently. Here, we describe a version that automatically presents pellets to rats while recording high-definition video from multiple angles at high frame rates (300 fps). The paw and individual digits are tracked with DeepLabCut, a machine learning algorithm for markerless pose estimation. This system can also be synchronized with physiological recordings, or be used to trigger physiologic interventions (e.g., electrical or optical stimulation).

Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the behavior. We describe an automated version of skilled reaching with motion tracking and three-dimensional reconstruction of reach trajectories.

Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the ...

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Mirror therapy has been performed as effective occupational therapy in a clinical setting for functional recovery of a hemiplegic arm after stroke. It is conducted by eliciting an illusion through use of a mirror as if the hemiplegic arm is moving in real-time while moving the healthy arm. It can facilitate brain neuroplasticity through activation of the sensorimotor cortex. However, conventional mirror therapy has a critical limitation in that the hemiplegic arm is not actually moving. Thus, we developed a real-time 2-axis mirror robot system as a simple add-on module for conventional mirror therapy using a closed feedback mechanism, which enables real-time movement of the hemiplegic arm. We used 3 Attitude and Heading Reference System sensors, 2 brushless DC motors for elbow and wrist joints, and exoskeletal frames. In a feasibility study on 6 healthy subjects, robotic mirror therapy was safe and feasible. We further selected tasks useful for activities of daily living training through feedback from rehabilitation doctors. A chronic stroke patient showed improvement in the Fugl-Meyer assessment scale and elbow flexor spasticity after a 2-week application of the mirror robot system. Robotic mirror therapy may enhance proprioceptive input to the sensory cortex, which is considered to be important in neuroplasticity and functional recovery of hemiplegic arms. The mirror robot system presented herein can be easily developed and utilized effectively to advance occupational therapy.

We developed a real-time mirror robot system for functional recovery of hemiplegic arms using automatic control technology, conducted a clinical study on healthy subjects, and determined tasks through feedback from rehabilitation doctors. This simple mirror robot can be applied effectively to occupational therapy in stroke patients with a hemiplegic arm.

Mirror therapy has been performed as effective occupational therapy in a clinical setting for functional recovery of a hemiplegic arm after stroke. It is ...

Revolutionizing Stroke Rehabilitation with Enhanced Grasping and Finger Recovery

A Flexible Wearable Supernumerary Robotic Limb for Chronic Stroke Patients

In this study, we present a flexible wearable supernumerary robotic limb that helps chronic stroke patients with finger rehabilitation and grasping movements. The design of this innovative limb draws inspiration from bending pneumatic muscles and the unique characteristics of an elephant's trunk tip. It places a strong emphasis on crucial factors such as lightweight construction, safety, compliance, waterproofing, and achieving a high output-to-weight/pressure ratio. The proposed structure enables the robotic limb to perform both envelope and fingertip grasping. Human-robot interaction is facilitated through a flexible bending sensor, detecting the wearer's finger movements and connecting them to motion control via a threshold segmentation method. Additionally, the system is portable for versatile daily use. To validate the effectiveness of this innovation, real-world experiments involving six chronic stroke patients and three healthy volunteers were conducted. The feedback received through questionnaires indicates that the designed mechanism holds immense promise in assisting chronic stroke patients with their daily grasping activities, potentially improving their quality of life and rehabilitation outcomes.

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs 

This protocol introduces a flexible wearable supernumerary robotic limb tailored to assist in finger rehabilitation for stroke patients. The design incorporates a bending sensor to facilitate seamless human-robot interaction. Validation through experiments involving both healthy volunteers and stroke patients underscores the efficacy and dependability of the proposed study.

In this study, we present a flexible wearable supernumerary robotic limb that helps chronic stroke patients with finger rehabilitation and grasping ...

Engineering

The Combined Use of Transcranial Direct Current Stimulation and Robotic Therapy for the Upper Limb

Neurologic disorders such as stroke and cerebral palsy are leading causes of long-term disability and can lead to severe incapacity and restriction of daily activities due to lower and upper limb impairments. Intensive physical and occupational therapy are still considered main treatments, but new adjunct therapies to standard rehabilitation that may optimize functional outcomes are being studied.
Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that polarizes underlying brain regions through the application of weak direct currents through electrodes on the scalp, modulating cortical excitability. Increased interest in this technique can be attributed to its low cost, ease of use, and effects on human neural plasticity. Recent research has been performed to determine the clinical potential of tDCS in diverse conditions such as depression, Parkinson&#39;s disease, and motor rehabilitation after stroke. tDCS helps enhance brain plasticity and seems to be a promising technique in rehabilitation programs.
A number of robotic devices have been developed to assist in the rehabilitation of upper limb function after stroke. The rehabilitation of motor deficits is often a long process requiring multidisciplinary approaches for a patient to achieve maximum independence. These devices do not intend to replace manual rehabilitation therapy; instead, they were designed as an additional tool to rehabilitation programs, allowing immediate perception of results and tracking of improvements, thus helping patients to stay motivated.
Both tDSC and robot-assisted therapy are promising add-ons to stroke rehabilitation and target the modulation of brain plasticity, with several reports describing their use to be associated with conventional therapy and the improvement of therapeutic outcomes. However, more recently, some small clinical trials have been developed that describe the associated use of tDCS and robot-assisted therapy in stroke rehabilitation. In this article, we describe the combined methods used in our institute for improving motor performance after stroke.

The combined use of transcranial direct current stimulation and robotic therapy as an add-on for conventional rehabilitation therapy may result in improved therapeutic outcomes due to modulation of brain plasticity. In this article, we describe the combined methods used in our institute for improving motor performance after stroke.

Neurologic disorders such as stroke and cerebral palsy are leading causes of long-term disability and can lead to severe incapacity and restriction of ...

Neuroscience

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

To reach towards a seen object, visual information has to be transformed into motor commands. Visual information such as the object&#8217;s color, shape, and size are processed and integrated within numerous brain areas, then ultimately relayed to the motor periphery. In some instances, a reaction is needed as fast as possible. These fast visuomotor transformations, and their underlying neurological substrates, are poorly understood in humans as they have lacked a reliable biomarker. Stimulus-locked responses (SLRs) are short latency (&#60;100 ms) bursts of electromyographic (EMG) activity representing the first wave of muscle recruitment influenced by visual stimulus presentation. SLRs provide a quantifiable output of rapid visuomotor transformations, but SLRs have not been consistently observed in all subjects in past studies. Here we describe a new, behavioral paradigm featuring the sudden emergence of a moving target below an obstacle that consistently evokes robust SLRs. Human participants generated visually guided reaches toward or away from the emerging target using a robotic manipulandum while surface electrodes recorded EMG activity from the pectoralis major muscle. In comparison to previous studies that investigated SLRs using static stimuli, the SLRs evoked with this emerging target paradigm were larger, evolved earlier, and were present in all participants. Reach reaction times (RTs) were also expedited in the emerging target paradigm. This paradigm affords numerous opportunities for modification that could permit systematic study of the impact of various sensory, cognitive, and motor manipulations on fast visuomotor responses. Overall, our results demonstrate that an emerging target paradigm is capable of consistently and robustly evoking activity within a fast visuomotor system.

Presented here is a behavioral paradigm that elicits robust fast visuomotor responses on human upper limb muscles during visually guided reaches.

To reach towards a seen object, visual information has to be transformed into motor commands. Visual information such as the object&#8217;s color, shape, ...

The Impact of Motor Task Conditions on Goal-Directed Arm Reaching Kinematics and Trunk Compensation in Chronic Stroke Survivors

Trunk compensation is the most common movement strategy to substitute for upper extremity (UE) motor deficits in chronic stroke survivors. There is a lack of evidence examining how task conditions impact trunk compensation and goal-directed arm reaching kinematics. This protocol aims to investigate the impact of task conditions, including task difficulty and complexity, on goal-directed arm reaching kinematics. Two non-disabled young adults and two chronic stroke survivors with mild UE motor impairment were recruited for testing the protocol. Each participant performed goal-directed arm reaches with four different task conditions (2 task difficulties [large vs. small targets] X 2 task complexities [pointing vs. picking up]). The task goal was to reach and point at a target or pick up an object located 20 cm in front of the home position as quickly as possible with a stylus or a pair of chopsticks, respectively, in response to an auditory cue. Participants performed ten reaches per task condition. A 3-dimensional motion capture camera system was used to record trunk and arm kinematics. Representative results showed that there was a significant increase in movement duration, movement jerkiness, and trunk compensation as a function of task complexity, but not task difficulty in all participants. Chronic stroke survivors showed significantly slower, jerkier, and more feedback-dependent arm reaches and significantly more compensatory trunk movements than non-disabled adults. Our representative results support that this protocol can be used to investigate the impact of task conditions on motor control strategies in chronic stroke survivors with mild UE motor impairment.

This protocol is intended to investigate the impact of task conditions on movement strategies in chronic stroke survivors. Further, this protocol can be used to examine if a restriction in elbow extension induced by neuromuscular electrical stimulation causes trunk compensation during goal-directed arm reaches in non-disabled adults.

Trunk compensation is the most common movement strategy to substitute for upper extremity (UE) motor deficits in chronic stroke survivors. There is a lack ...

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

A robot-assisted hand is used for the rehabilitation of patients with impaired upper limb function, particularly for stroke patients with a loss of motor control. However, it is unclear how conventional occupational training strategies can be applied to the use of rehabilitation robots. Novel robotic technologies and occupational therapy concepts are used to develop a protocol that allows patients with impaired upper limb function to grasp objects using their affected hand through a variety of pinching and grasping functions. To conduct this appropriately, we used five types of objects: a peg, a rectangular cube, a cube, a ball, and a cylindrical bar. We also equipped the patients with a robotic hand, the Mirror Hand, an exoskeleton&#160;hand that is fitted to the subject&#8217;s affected hand and follows the movement of the sensor glove fitted to their unaffected hand (bimanual movement training (BMT)). This study had two stages. Three healthy subjects were first recruited to test the feasibility and acceptability of the training program. Three patients with hand dysfunction caused by stroke were then recruited to confirm the feasibility and acceptability of the training program, which was conducted on 3 consecutive days. On each day, the patient was monitored during 5 min of movement in a passive range of motion, 5 min of robot-assisted bimanual movement, and task-oriented training using the five objects. The results showed that both healthy subjects and subjects who had suffered a stroke in conjunction with the robotic hand could successfully grasp the objects. Both healthy subjects and those who had suffered a stroke performed well with the robot-assisted task-oriented training program in terms of feasibility and acceptability.

This study reports the development of a novel robot-assisted task-oriented program for hand rehabilitation. The developmental process consists of experiments using both healthy subjects and subjects who have had a stroke and suffered from subsequent motor control dysfunction.

A robot-assisted hand is used for the rehabilitation of patients with impaired upper limb function, particularly for stroke patients with a loss of motor ...

Medicine

Mechanical Conflict-Avoidance Assay to Measure Pain Behavior in Mice

Pain comprises of both sensory (nociceptive) and affective (unpleasant) dimensions. In preclinical models, pain has traditionally been assessed using reflexive tests that allow inferences regarding pain's nociceptive component but provide little information about the affective or motivational component of pain. Developing tests that capture these components of pain are therefore translationally important. Hence, researchers need to use non-reflexive behavioral assays to study pain perception at that level. Mechanical conflict-avoidance (MCA) is an established voluntary non-reflexive behavior assay, for studying motivational responses to a noxious mechanical stimulus in a 3 chamber paradigm. A change in a mouse's location preference, when faced with competing noxious stimuli, is used to infer the perceived unpleasantness of bright light versus tactile stimulation of the paws. This protocol outlines a modified version of the MCA assay which pain researchers can use to understand affective-motivational responses in a variety of mouse pain models. Though not specifically described here, our example MCA data use the intraplantar complete Freund's adjuvant (CFA), spared nerve injury (SNI), and a fracture/casting model as pain models to illustrate the MCA procedure.

The mechanical conflict-avoidance assay is used as a non-reflexive readout of pain sensitivity in mice which can be used to better understand affective-motivational responses in a variety of mouse pain models.

Pain comprises of both sensory (nociceptive) and affective (unpleasant) dimensions. In preclinical models, pain has traditionally been assessed using ...

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

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