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 ...

investigating-pain-related-avoidance-behavior-using-robotic-arm

Research

JoVE Journal

Behavior

3.8K Views.  Maastricht University. 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).

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

The Resident-intruder Paradigm: A Standardized Test for Aggression, Violence and Social Stress

This video publication explains in detail the experimental protocol of the resident-intruder paradigm in rats. This test is a standardized method to measure offensive aggression and defensive behavior in a semi natural setting. The most important behavioral elements performed by the resident and the intruder are demonstrated in the video and illustrated using artistic drawings. The use of the resident intruder paradigm for acute and chronic social stress experiments is explained as well. Finally, some brief tests and criteria are presented to distinguish aggression from its more violent and pathological forms.

This video shows the resident-intruder paradigm in rats. This test is a standardized method to measure offensive aggression, defensive behavior and violence in a semi-natural setting. The use of the paradigm for social stress experiments is explained as well.

This video publication explains in detail the experimental protocol of the resident-intruder paradigm in rats. This test is a standardized method to ...

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

The ability to recall the past allows us to report on details of previous experiences, from the everyday to the significant. Because recall memory is commonly assessed using verbal report paradigms in adults, studying the development of this ability in preverbal infants and children proved challenging. Over the past 30 years, researchers have developed a non-verbal means of assessing recall memory known as the elicited or deferred imitation paradigm. In one variant of the procedure, participants are presented with novel three-dimensional stimuli for a brief baseline period before a researcher demonstrates a series of actions that culminate in an end- or goal-state. The participant is allowed to imitate the demonstrated actions immediately, after a delay, or both. Recall performance is then compared to baseline or to performance on novel control sequences presented at the same session; memory can be assessed for the individual target actions and the order in which they were completed. This procedure is an accepted analogue to the verbal report techniques used with adults, and it has served to establish a solid foundation of the nature of recall memory in infancy and early childhood. In addition, the elicited or deferred imitation procedure has been modified and adapted to answer questions relevant to other aspects of cognitive functioning. The broad utility and application of imitation paradigms is discussed, along with limitations of the approach and directions for future research.

The elicited imitation procedure was established to examine the development of recall memory in infancy and early childhood. This procedure has been widely used to establish a solid foundation of the nature of recall memory in infancy and early childhood.

The ability to recall the past allows us to report on details of previous experiences, from the everyday to the significant. Because recall memory is ...

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 ...

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

In perceptual studies, it is often important to objectively assess the equality of delivered stimulation across participants or to quantify the intra-individual sensation magnitude that is evoked by stimulation over multiple trials. This requires a robust mapping of stimulus magnitude to perceived intensity and is commonly achieved by psychophysical estimation methods such as the staircase procedure. Newer, more efficient procedures like the QUEST algorithm fit a psychophysical function to the data in real time while at the same time maximizing the efficiency of data collection. A robust estimate of the threshold intensity between painful and nonpainful perceptions can then be used to reduce the influence of variations in sensory input in subsequent analyses of oscillatory brain activity. By stimulating at a constant threshold intensity determined by an adaptive estimation procedure, the variance in the ratings can be directly attributed to perceptual processes. Oscillatory activity can then be contrasted between &#34;pain&#34; and &#34;no-pain&#34; trials directly, yielding activity that closely relates to perceptual classification processes in nociception.

Psychophysical methods such as the QUEST estimation procedure can efficiently yield robust estimates of the stimulation intensity at which nonpainful sensations transition into painful sensations. By stimulating repeatedly at the threshold intensity, the variability in rating responses can directly be attributed to perceptual classifications in subsequent analyses.

In perceptual studies, it is often important to objectively assess the equality of delivered stimulation across participants or to quantify the ...

Reinstatement of Drug-seeking in Mice Using the Conditioned Place Preference Paradigm

The present protocol describes the Conditioned Place Preference (CPP) as a model of relapse in drug addiction. In this model, animals are first trained to acquire a conditioned place preference in a drug-paired compartment, and after the post-conditioning test, they perform several sessions to extinguish the established preference. The CPP permits the evaluation of the conditioned rewarding effects of drugs related to environmental cues. Then, the extinguished CPP can be robustly reinstated by the non-contingent administration of a priming dose of the drug, and by exposure to stressful stimuli. Both methods will be explained here. When the animal reinitiates the behavioral response, a reinstatement of the conditioned reward is considered to have taken place.
The main advantages of this protocol are that it is non-invasive, inexpensive, and simple with good validity criteria. In addition, it allows the study of different environmental manipulations, such as stress or diet, which can modulate relapse into drug seeking behaviors. However, one limitation is that if the researcher aims to explore the motivation and primary reinforcing effects of the drug, it should be complemented with self-administration procedures, as they involve operant responses of animals.

This protocol describes the Conditioned Place Preference (CPP) as a model of relapse. This procedure permits the measurement of relapse in laboratory animals, considering the impact of drug-associated environmental cues as craving and relapse in abstaining addicts is currently the focus of drug-abuse treatment programs.

The present protocol describes the Conditioned Place Preference (CPP) as a model of relapse in drug addiction. In this model, animals are first trained to ...

The HoneyComb Paradigm for Research on Collective Human Behavior

Collective human behavior such as group movement frequently shows surprising patterns and regularities, such as the emergence of leadership. Recent literature has revealed that these patterns, often visible at the global level of the group, are based on self-organized, individual behaviors that follow several simple local parameters. Understanding the dynamics of human collective behavior can help to improve coordination and leadership in group and crowd scenarios, such as identifying the ideal placement and number of emergency exits in buildings.
In this article, we present the experimental paradigm HoneyComb, which can be used to systematically investigate conditions and effects of human collective behavior. This paradigm uses a computer-based multi-user platform, providing a setting that can be shaped and adapted to various types of research questions. Situational conditions (e.g., cost-benefit ratios for specific behavior, monetary incentives and resources, various degrees of uncertainty) can be set by experimenters, depending on the research question. Each participant's motions are recorded by the server as hexagonal coordinates with timestamps at an accuracy of 50 ms and with individual IDs. Thus, a metric can be defined on the playfield, and movement parameters (e.g., distances, velocity, clustering, etc.) of participants can be measured over time. Movement data can in turn be combined with non-computerized data from questionnaires garnered within the same experiment setup.
The HoneyComb paradigm is paving the way for new types of human movement experiments. We demonstrate here that these experiments can render results with sufficient internal validity to meaningfully deepen our understanding of human collective behavior.

Here, we present the computer-based, multi-agent game HoneyComb, which enables experimental investigations of collective human movement behavior via black-dot-avatars on a virtual 2D hexagonal playfield. Different experimental conditions, like variable&#160;incentives on goal fields or vision radius, can be set, and their effects on human movement behavior can be investigated.

Collective human behavior such as group movement frequently shows surprising patterns and regularities, such as the emergence of leadership. Recent ...

Assessment of Stress Effects on Cognitive Flexibility using an Operant Strategy Shifting Paradigm

Stress affects cognitive function. Whether stress enhances or impairs cognitive function depends on several factors, including the 1) type, intensity, and duration of the stressor; 2) type of cognitive function under study; and 3) timing of the stressor in relation to learning or executing the cognitive task. Furthermore, sex differences among the effects of stress on cognitive function have been widely documented. Described here is an adaptation of an automated operant strategy shifting paradigm to assess how variations in stress affect cognitive flexibility in male and female Sprague Dawley rats. Specifically, restraint stress is used before or after training in this operant-based task to examine how stress affects cognitive performance in both sexes. Particular brain areas associated with each task in this automated paradigm have been well-established (i.e., the medial prefrontal cortex and orbitofrontal cortex). This allows for targeted manipulations during the experiment or the assessment of particular genes and proteins in these regions upon completion of the paradigm. This paradigm also allows for the detection of different types of performance errors that occur after stress, each of which has defined neural substrates. Also identified are distinct sex differences in perseverative errors after a repeated restraint stress paradigm. The use of these techniques in a preclinical model may reveal how stress affects the brain and impairs cognition in psychiatric disorders, such as post-traumatic stress disorder (PTSD) and major depressive disorder (MDD), which display marked sex differences in prevalence.

Stressful life events impair cognitive function, increasing the risk of psychiatric disorders. This protocol illustrates how stress affects cognitive flexibility using an automated operant strategy shifting paradigm in male and female Sprague Dawley rats. Specific brain areas underlying particular behaviors are discussed, and translational relevance of results are explored.

Stress affects cognitive function. Whether stress enhances or impairs cognitive function depends on several factors, including the 1) type, intensity, and ...

A Novel Pavlovian Fear Conditioning Paradigm to Study Freezing and Flight Behavior

Fear- and anxiety-related behaviors significantly contribute to an organism&#8217;s survival. However, exaggerated defensive responses to perceived threat are characteristic of various anxiety disorders, which are the most prevalent form of mental illness in the United States. Discovering the neurobiological mechanisms responsible for defensive behaviors will aid in the development of novel therapeutic interventions. Pavlovian fear conditioning is a widely used laboratory paradigm to study fear-related learning and memory. A major limitation of traditional Pavlovian fear conditioning paradigms is that freezing is the only defensive behavior monitored. We recently developed a modified Pavlovian fear conditioning paradigm that allows us to study both conditioned freezing and flight (also known as escape) behavior within individual subjects. This model employs higher intensity footshocks and a greater number of pairings between the conditioned stimulus and unconditioned stimulus. Additionally, this conditioned flight paradigm utilizes serial presentation of pure tone and white noise auditory stimuli as the conditioned stimulus. Following conditioning in this paradigm, mice exhibit freezing behavior in response to the tone stimulus, and flight responses during the white noise. This conditioning model can be applied to the study of rapid and flexible transitions between behavioral responses necessary for survival.

Defensive behavioral responses are contingent upon threat intensity, proximity, and context of exposure. Based on these factors, we developed a classical conditioning paradigm that elicits clear transitions between conditioned freezing and flight behavior within individual subjects. This model is crucial for the understanding the pathologies involved in anxiety, panic, and post-traumatic stress disorders.

Fear- and anxiety-related behaviors significantly contribute to an organism&#8217;s survival. However, exaggerated defensive responses to perceived threat ...

Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants

The peripartum period is considered a sensitive period where adverse maternal exposures can result in long-term negative consequences for both mother and offspring, including the development of neuropsychiatric disorders. Risk factors linked to the emergence of affective dysregulation in the maternal-infant dyad have been extensively studied. Exposure to psychosocial stress during pregnancy has consistently emerged as one of the strongest predictors. Several rodent models have been created to explore this association; however, these models rely on the use of physical stressors or a limited number of psychosocial stressors presented in a repetitive fashion, which do not accurately capture the type, intensity, and frequency of stressors experienced by women. To overcome these limitations, a chronic psychosocial stress (CGS) paradigm was generated that employs various psychosocial insults of different intensity presented in an unpredictable fashion. The manuscript describes this novel CGS paradigm where pregnant female mice, from gestational day 6.5 to 17.5, are exposed to various stressors during the day and overnight. Day stressors, two per day separated by a 2 h break, range from exposure to foreign objects or predator odor to frequent changes in bedding, removal of bedding, and cage tilting. Overnight stressors include continuous light exposure, changing cage mates, or wetting bedding. We have previously shown that exposure to CGS results in the development of maternal neuroendocrine and behavioral abnormalities, including increased stress reactivity, the emergence of fragmented maternal care patterns, anhedonia, and anxiety-related behaviors, core features of women suffering from perinatal mood and anxiety disorders. This CGS model, therefore, becomes a unique tool that can be used to elucidate molecular defects underlying maternal affective dysregulation, as well as trans-placental mechanisms that impact fetal neurodevelopment and result in negative long-term behavioral consequences in the offspring.

The chronic psychosocial stress (CGS) paradigm employs clinically relevant stressors during pregnancy in mice to model psychiatric disorders of mothers and infants. Here, we provide a step-by-step procedure of applying the CGS paradigm and downstream assessments to validate this model.

The peripartum period is considered a sensitive period where adverse maternal exposures can result in long-term negative consequences for both mother and ...

Investigating Migraine-Like Behavior Using Light Aversion in Mice

Migraine is a complex neurological disorder characterized by headache and sensory abnormalities, such as hypersensitivity to light, observed as photophobia. Whilst it is impossible to confirm that a mouse is experiencing migraine, light aversion can be used as a behavioral surrogate for the migraine symptom of photophobia. To test for light aversion, we utilize the light/dark assay to measure the time mice freely choose to spend in either a light or dark environment. The assay has been refined by introducing two critical modifications: pre-exposures to the chamber prior to running the test procedure and adjustable chamber lighting, permitting the use of a range of light intensities from 55 lux to 27,000 lux. Because the choice to spend more time in the dark is also indicative of anxiety, we also utilize a light-independent anxiety test, the open field assay, to distinguish anxiety from light-aversive behavior. Here, we describe a modified test paradigm for the light/dark and open field assays. The application of these assays is described for intraperitoneal injection of calcitonin gene-related peptide (CGRP) in two mouse strains and for optogenetic brain stimulation studies.

Rodents are not able to report migraine symptoms. Here, we describe a manageable test paradigm (light/dark and open field assays) to measure light aversion, one of the most common and bothersome symptoms in patients with migraines.