Adriana Salatino
email: adriana.salatino@mil.be

Arthur Prevel
email: arthur.prevel@univ-lille.fr

Emilie Caspar

Salvatore Lo Bue

Arthur Prével

Émilie Caspar

A new paradigm testing how

Keywords: Human-autonomous systems interaction, Human performance, Level of system autonomy, Moral decision-making, Sense

come

Introduction

In the last decades, the use of autonomous systems has become increasingly widespread in various fields of human activity. From driving [START_REF] Ayoub | From manual driving to automated driving: A review of 10 years of autoui[END_REF][START_REF] Chan | Advancements, prospects, and impacts of automated driving systems[END_REF] to aviation [START_REF] Anderson | Levels of aviation autonomy[END_REF][START_REF] Chialastri | Automation in aviation[END_REF][START_REF] Valdés | Aviation 4.0: more safety through automation and digitization[END_REF], medicine [START_REF] Kawamoto | Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success[END_REF][START_REF] Sutton | An overview of clinical decision support systems: benefits, risks, and strategies for success[END_REF], and military defense and security [START_REF] Mayer | The new killer drones: Understanding the strategic implications of nextgeneration unmanned combat aerial vehicles[END_REF], there is almost no area where these technologies are not massively deployed. As these technologies become more widespread, the inevitable trend toward even more machine autonomy will lead to profound changes in the role played by humans during the execution of tasks. From workers in industry, agriculture, and transportation, to consumers in their daily lives, advances observed in technologies show that humans are rapidly moving from the position of direct tasks executors (with the help of mechanistic machines) to supervising tasks performed directly by intelligent machines with high level of autonomy.

The main reason for the massive deployment of autonomous systems resides in the many benefits these systems offer to users. Several laboratory experiments have shown that the introduction of some level of autonomy in tasks can have substantial effects in terms of users' decision-making and performance. For example, studies have shown that autonomous systems can help people in detecting task-relevant cues and ignoring irrelevant cues in the environment [START_REF] Chavaillaz | Automation in visual inspection tasks: X-ray luggage screening supported by a system of direct, indirect or adaptable cueing with low and high system reliability[END_REF][START_REF] Goh | Effects of automation failure in a luggage screening task: a comparison between direct and indirect cueing[END_REF], improve the decisions made by human subjects in complex situations and reduce the number of errors they make [START_REF] Macmillan | A comparison of alternatives for automated decision support in a multi-task environment[END_REF][START_REF] Rovira | Effects of imperfect automation on decision making in a simulated command and control task[END_REF][START_REF] Sarter | Supporting decision making and action selection under time pressure and uncertainty: The case of in-flight icing[END_REF], or even reduce the time to make correct decisions [START_REF] Chavaillaz | Automation in visual inspection tasks: X-ray luggage screening supported by a system of direct, indirect or adaptable cueing with low and high system reliability[END_REF]. In addition, the introduction of autonomy has been shown to reduce users' mental workload and thus increase their ability to monitor multiple tasks simultaneously (e.g. , [START_REF] Chen | Supervisory control of multiple robots: Effects of imperfect automation and individual differences[END_REF][START_REF] Wright | Human -automation interaction for multiple robot control: the effect of varying automation assistance and individual differences on operator performance[END_REF]. Laboratory experiments have also shown that the effects produced by the interaction with autonomous systems on human decision -making and performance depend on several factors [START_REF] Parasuraman | Humans and automation: Use, misuse, disuse, abuse[END_REF][START_REF] Mosier | Humans and automated decision aids: A match made in heaven?[END_REF]. In particular, autonomous systems operating at the decision stage of a task or with a high level of autonomy are generally associated with the largest benefits in terms of human performance (e.g., [START_REF] Endsley | Level of automation effects on performance, situation awareness and workload in a dynamic control task[END_REF][START_REF] Manzey | Human performance consequences of automated decision aids: The impact of degree of automation and system experience[END_REF][START_REF] Rovira | Effects of imperfect automation on decision making in a simulated command and control task[END_REF]. Based on these results, one could easily conclude that more autonomy is always better for the users, whether they are pilots flying on a plane, physicians analyzing the test results from a patient, or military drone operators deployed in a war zone.

However, the involvement of autonomous systems technologies into human activities has not been systematically associated with positive effects. Several studies have shown that their use can also have significant negative effects. [START_REF] Parasuraman | Performance consequences of automation-induced'complacency[END_REF] provided a classic demonstration thereof. They reported low level of detection of automation failures with highly reliable systems, an effect they called automation complacency (also named automation overreliance). Other examples of negative effects are loss of situational awareness [START_REF] Endsley | From here to autonomy: lessons learned from human-automation research[END_REF], skill decay (Haslbeck & Hoemann, 2016;[START_REF] Volz | Evaluation of cognitive skill degradation in flight planning[END_REF], performance decrement in return-to-manual control [START_REF] Endsley | The out-of-the-loop performance problem and level of control in automation[END_REF], or increased workload with too many autonomous systems to monitor [START_REF] Wang | How search and its subtasks scale in N robots[END_REF]. Interestingly, the detrimental effects caused by the cooperation with autonomous systems seem to be directly related with the stage and level of autonomy of those systems. For example, [START_REF] Rovira | Effects of imperfect automation on decision making in a simulated command and control task[END_REF] reported lower rates of correct decisions with highly reliable systems with high levels of autonomy (i.e., decision systems) compared to systems with lower levels of autonomy (i.e., information systems). Thus, while more autonomy seems to be clearly beneficial when the system's recommendations are correct, the negative effects seem also to be more pronounced for higher levels of autonomy in machines that are, most of the time, imperfect.

In this context, another important aspect of human-autonomous systems interaction that has received increasing attention in recent years is the impact of auto nomy on human agency [START_REF] Berberian | Automation technology and sense of control: a window on human agency[END_REF][START_REF] Berberian | Man-Machine teaming: a problem of Agency[END_REF], Coyle et al., 2012;[START_REF] Zanatto | Human-machine sense of agency[END_REF]. The sense of agency (SoA), defined as the feeling of causing changes in the external world by controlling one's own voluntary actions [START_REF] Jeannerod | The mechanism of self -recognition in humans[END_REF][START_REF] Haggard | Sense of agency in the human brain[END_REF][START_REF] Burin | That's my hand! Therefore, that's my willed action: How body ownership acts upon conscious awareness of willed actions[END_REF][START_REF] Pyasik | Shared neurocognitive mechanisms of attenuating self -touch and illusory self-touch[END_REF], is recognized as an important aspect of human consciousness. Because SoA enables us to perceive ourselves as causal agents, it is the basis for intentional behavior [START_REF] Haggard | The experience of agency: Feelings, judgments, and responsibility[END_REF], and is closely related to moral responsibility [START_REF] Moretto | Experience of agency and sense of responsibility[END_REF][START_REF] Caspar | Coercion changes the sense of agency in the human brain[END_REF].

The recent interest in this topic has been triggered by the possibility offered by the "Intentional Binding" effect to implicitly measure the SoA. The Intentional Binding is a phenomenon by which the perceived time between an action and its outcomes is modulated by the intentionality of that action. Time appears compressed in situations where the person is active, while time appears stretched in situations where the person is passive [START_REF] Haggard | Voluntary action and conscious awareness[END_REF].

Measuring the SoA by using this effect usually consists of asking subjects to estimate the time interval between an action they perform and the consequences of that action. Numerous studies have now shown that the time estimation between action and outcome is a valid implicit measure of SoA (e.g., [START_REF] Christensen | I just lost it! Fear and anger reduce the sense of agency: a study using intentional binding[END_REF][START_REF] Imaizumi | Intentional binding coincides with explicit sense of agency[END_REF][START_REF] Malik | Social exclusion reduces the sense of agency: Evidence from intentional binding[END_REF][START_REF] Haggard | Voluntary action and conscious awareness[END_REF][START_REF] Moore | Intentional binding and the sense of agency: a review[END_REF] and is preferable to a subjective measurement of responsibility, which is usually obtained by a direct report of how people attribute the effects of (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. their own actions [START_REF] Saito | Discrepancy between explicit judgement of agency and implicit feeling of agency: Implications for sense of agency and its disorders[END_REF], which is subject to social desirability and other biases (e.g., [START_REF] Blackwood | Self-responsibility and the self-serving bias: an fMRI investigation of causal attributions[END_REF]Wegner & Withley, 1999).

In one of the first studies investigating the effects of autonomy on SoA by using the Intentional Binding paradigm [START_REF] Berberian | Automation technology and sense of control: a window on human agency[END_REF], participants took part in a flight simulation and were assisted in their task by different levels of automation. Berberian and colleagues' results showed a decrease in SoA with increasing levels of automation, suggesting that agency decreases with higher levels of automation. Further evidence using the same paradigm can be found in the study by Coyle and coworkers (2012), who investigated how assistance, in a machine -assisted point-and-click task, affects the user's SoA. Their results suggest that, up to a certain point, the computer could assist users while also allowing them to maintain a sense of control over their actions and outcomes, hence of their agency. More recently, [START_REF] Zanatto | Human-machine sense of agency[END_REF] showed a similar negative impact of automation on SoA, and that the mental workload may also play a role in reducing agency. Taken together, these studies suggest that automation technology may affect the mechanism underlying human agency.

Hence, the evidence of negative effects on human decisions and performance, and the evidence of a decrease in the implicit and explicit SoA [START_REF] Berberian | Automation technology and sense of control: a window on human agency[END_REF]Coyle et al., 2012;[START_REF] Vantrepotte | Leveraging human agency to improve confidence and acceptability in human-machine interactions[END_REF], that might result from the interaction with the autonomous systems, have serious performance and safety implications. Engineers working on the development of new forms of autonomous technologies should be aware of these effects and take them into account.

Furthermore, these results have important implications when those systems are used in sensitive or moral domains such as in medicine or in the military, in which decisions of life and death have to be made.

To date, however, very little is known about how the interaction with an autonomous machine affects SoA and the decisions made by someone engaged in a moral scenario, and how this is influenced by the level of autonomy of the system. Indeed, it is possible that interacting with autonomous systems to make moral decisions negatively affects the moral and ethical decisionmaking process and the resulting actions, particularly in tasks and domains of moral value such as in the military context [START_REF] Christensen | Moral dilemmas in cognitive neuroscience of moral decision-making: A principled review[END_REF][START_REF] Cushman | Action, outcome, and value: A dual-system framework for morality[END_REF][START_REF] Cushman | Moral learning: Psychological and philosophical perspectives[END_REF].

In recent years, research in the field of moral decision making and autonomy has focused mainly on the rules and/or algorithms that can be assigned to an autonomous system to perform ethical responses in moral situations [START_REF] Arkin | Moral decision making in autonomous systems: Enforcement, moral emotions, dignity, trust, and deception[END_REF][START_REF] Jiang | Can machines learn morality? the delphi experiment[END_REF]. Surprisingly, until recently, little attention has been paid to understanding how a human agent's ethical behavior in moral decision-making situations can be influenced by its interaction with an autonomous system [START_REF] Köbis | Bad machines corrupt good morals[END_REF]. The available data suggest a mixed picture of the effects of autonomous systems in social and moral decision-making situations. Indeed, while some recent evidence suggests that interaction with automation could lead to the promotion of prosocial behaviors (such as fairness and cooperation, see, e.g., [START_REF] De Melo | Social decisions and fairness change when people's interests are represented by autonomous agents[END_REF][START_REF] De Melo | Human cooperation when acting through autonomous machines[END_REF], other studies have shown that it could also lead to unethical behaviors (Cohn et al., 2022;[START_REF] Leib | The corruptive force of AI-generated advice[END_REF]. Concerning SoA, while some studies report that a moral context increases it (e.g., [START_REF] Moretto | Experience of agency and sense of responsibility[END_REF] and that a higher SoA is associated with higher prosocial decision-making [START_REF] Caspar | Commanding or being a simple intermediary: how does it affect moral behavior and related brain mechanisms?[END_REF], it is not clear whether this is still true when decisions are made in collaboration with an autonomous intelligent machine.

Considering the lack of research on how autonomous systems impact the SoA and decisionmaking in a moral context, and how this can be modulated by the level of autonomy of the system, in the present study, we aimed to build an ad hoc setup to investigate how the level of system autonomy affects SoA and the moral decision-making. To this end, we developed a task in which participants (military cadets) played the role of drone operators on a simulated battlefield and had to decide whether or not to initiate an attack, based on the presence of enemies and the risk that allies might also be harmed. Participants were exposed to three types of trials representing three types of uncertainty (Moral Decision-Making Trials, No Risk Trials, and No Enemy Trials) with three different levels of system autonomy, including no system assistance, information assistance (i.e., the system gives processed-information on the presence of enemies and the risk for allies), and decision assistance (i.e., the system provides a recommendation on the best decision to make).

In our study, SoA is measured both at the implicit level, using the Intentional Binding paradigm, and at the explicit level through a subjective assessment of responsibility (using an ad-hoc scale).

We also measured performance by using reaction time, the proportion of trials in which participants chose to attack, and the proportion of choices leading to the fewest ally losses (called utilitarian choices in our task).

The primary purpose of this research was to develop and test a new paradigm to investigate how the interaction with autonomous systems can affect the SoA and the decisions made by people when facing moral choices, and how the level of autonomy of the system influences this effect.

Based on previous findings [START_REF] Berberian | Automation technology and sense of control: a window on human agency[END_REF], Coyle et al., 2012;[START_REF] Zanatto | Human-machine sense of agency[END_REF], we hypothesized that agency decreases with increasing levels of system autonomy, as indicated by a longer time estimation between action and outcome, and lower subjective judgments of responsibility at higher levels of system autonomy. We also hypothesized that if the SoA would be affected by the autonomous system, as well as the sense of responsibility associated with SoA, the moral decision-making would also be affected, with the number of attacks increasing as system autonomy increases [START_REF] Caspar | Only giving orders? An experimental study of the sense of agency when giving or receiving commands[END_REF][START_REF] Goh | Effects of automation failure in a luggage screening task: a comparison between direct and indirect cueing[END_REF][START_REF] Chavaillaz | Automation in visual inspection tasks: X-ray luggage screening supported by a system of direct, indirect or adaptable cueing with low and high system reliability[END_REF]. In addition, we expected shorter reaction times and more utilitarian choices with higher levels of system autonomy. Crucially, by validating the new paradigm we propose with the present study, we hope to pave the way for new quantitative studies to understand how the interaction with autonomous systems affects agency and decision-making in a moral context. In turn, understanding these effects better could help in the development of safer and more efficient autonomous systems in the future.

(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Method

Participants

A total of 31 participants took part in the study (M age = 22, SD = 2.28, Range = 19 -36, 7 women, 24 men). Participants were cadets at the Royal Military Academy of Belgium and were recruited with the help of a Master student officer in the course of his thesis. Participants were in their third and fourth year of study, meaning they had notions of International Humanitarian Law, and thus about what is legally allowed and forbidden in the conduct of armed conflicts. The sample size was estimated using G*Power [START_REF] Faul | G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences[END_REF], with a small-tomedium size effect f of 0.2, a threshold for significance  set at 0.05, and a power 1- at .80. Based on these values, the estimated sample size was 28 participants. To compensate for potential data losses and exclusions, a total of 32 participants was targeted. However, considering the limited pool and recruitment period, 31 participants were finally included in this study. The study was performed in accordance with the principles expressed in the declaration of Helsinki and with the protocol of the local ethics review board at the Faculty of Psychology and Educational Sciences at Ghent University. Participants were informed of the general purpose and the duration of the experiment, and about their rights as participants in psychological research before giving their consent. Participation was voluntary and participants could withdraw their participation at any time without justification and without consequences. Written informed consent was obtained from each participant before the experiment.

Stimuli and procedure

All the material can be found on the Open Science Framework [START_REF] Salatino | Fire! Do Not Fire!": A pilotnew paradigm testing how autonomous systems affect agency and moral decision-making[END_REF]. The experiment was programmed and presented using MATLAB 2020b and the Psychophysics Toolbox extension. The experiment was run on a laptop computer (display resolution: 2560*1600 pixels) and responses were collected via an AZERTY keyboard with left and right arrow keys serving as the response keys used in the experiment. The experiment was conducted in an experimental room in the department of Life Sciences at the Royal Military Academy. Participants were seated on a chair, in front of a table with the screen of the laptop being at approximately 40 cm from the participants. Each trial of the task consisted of a 50 by 50 grid with dark grey cells (RGB = [064, 064, 064]) presented on a black screen (RGB = [000, 000, 000]; see Fig. 1, below).

Participants were instructed that the grid represented a radar display used to inform on the position of allies and enemies. Out of the 2500 cells, 100 were colored in light grey (RGB = [192,192,192]) and each light grey cell represented the position of a group of 10 allies. The number of allied forces (i.e., light grey cells) was kept constant across trials, but their position varied randomly from trials to trials. In addition, in 66% of trials one of the 2500 cells was colored in white (RGB = [255,255,255]) to represent the presence and the position of an enemy. Maximum one cell was colored in white during trials (i.e., maximum of one enemy). When an enemy (i.e., a white cell) was presented on a trial, participants were asked to choose between attacking the enemy or not by pushing the left (attack) and right (no attack) arrows of the keyboard (i.e., Action 1-A1, if they decide to attack, and Action 2-A2, if they decide to not attack). Participants were instructed that not pushing the "attack" button whenever an enemy was present would result in the death of five allies because of the continued hostile activities of that enemy. Instead, pushing the "attack" button whenever an enemy was present would result in the death of the enemy, but with sometimes a risk of collateral damages considering the position of the allies on the 50 by 50 grid radar display (see details in the next paragraph below). Collateral damage was likely, but not sure, when grey cells were separated from the enemy by less than five empty cells. When no enemy was shown on the grid, participants were asked to not push on the "attack" button. Moral DM trials an enemy was shown on the radar display and up to three groups of allies were on the first to the fifth range of cells next to the enemy. Here, participants received the instruction that during those trials there was a risk of collateral damages if they decided to attack. More exactly, participants were instructed that allies found on the 1 st range of cells next to the position of the enemy had a .50 probability of being killed if they decided to attack, while the probability of collateral damages decreased with the distance according to the following rangeprobability combinations: 2 nd range -.40, 3 rd range -.30, 4 th range -.20, and 5 th range -.10. The number of allies that could be killed during an attack varied from 10, 20, or 30 allies. Groups of allies were systematically on the same range of cells and Moral DM trials consisted in 15 different number of allies by probabilities of collateral damages combinations. Thus, we expected those trials to be morally challenging because participants were asked to choose between (1) pushing the attack button to neutralize the enemy, but with the risk of killing allies during the attack, or (2) not pushing the attack button and let the enemy kill 5 allies anyway.

The experiment consisted of three blocks, with each block including a specific level of Autonomy from intelligent system (Level 0, Level 1, Level 2). In Level 0, the block with the lowest level of assistance from intelligent system, participants received basic visual assistance to determine the position of allies relatively to the position of an enemy shown on the grid. During

No Risk and Moral DM trials, by clicking with the help of the computer mouse on a white cell while the 50 by 50 grid was shown on the screen, the cells on the 6 th range next to the position of the enemy turned blue (RGB = [000, 000, 255]) until the participants made their decision. This visual information was designed to help the participants to detect how far allied forces were from the enemy's position and to compute the risk of collateral damages if they decided to press the attack button. However, allies found within that area were at risk for collateral damages, with the risk depending on the range.

Then, in Level 1 of Autonomy, in addition to the basic visual assistance found in Level 0, a 12 grade-scale was shown on the right part of the screen (see Fig. 1, right up) participants were instructed that this scale indicated the risk in terms of losses of allied forces if the attack button was pressed. The risk was computed based on the number of allies within the blue area (i.e., 10, 20, or 30) and the probability of collateral damages based on their position (i.e., .50, .40, .30, .20, or .10). The scale ranged from yellow (very low risk) to red (very high risk).

Finally, in Level 2 of Autonomy, the visual assistance of Level 0 was still included, but in addition participants were assisted by a decision-support system that on each trial made a yes/no recommendation on the best decision to make (see Fig. 1, right bottom). This recommendation was based in each trial on the choice associated with the lowest expected losses in terms of allied forces.

When the expected losses were lower for pressing the attack button the 'yes' cue was highlighted, while the 'no' cue was highlighted when the expected losses were lower by pressing the no attack button (except during the trial where the number of allies was 10 and the probability was .50, in which expected losses were equal for both choice). Participants were not informed on the computation on which the recommendations were determined. Overall, each block consisted of 15

No Risk trials, 15 No Enemy trials, and 15 Moral DM trials.

The experimental setup of this task is shown in Fig. 1 (left): (1) First a loading bar was presented for delay chosen randomly between 1000ms and 2000ms to signal a new trial to the participants. (2) This was followed by a blackout screen for 500ms and then the presentation of the 50 by 50 grid, displayed for 15000ms or until the participant pressed one of the two response keys. (3) Participants were asked to confirm their choice by pressing the selected response key again, or they had the possibility to change their choice by pressing the other key. (4) Responses were followed by the presentation of blackout screen for a random duration of either 200, 500, or 800ms, and a tone (frequency: 400Hz) for 200ms. Finally, (5) participants were asked to report the duration of the interval between their confirmation choice and the tone on a horizontal scale ranging from 0 ms to 1000 ms. Trials were separated by an interval of 1000ms.

Measurements and analysis

We used five dependent variables in this study: Decision, Utilitarian Choice, Response Time, Agency, and Subjective Responsibility. Decision (A1) was expressed by the proportion of trials on which participants decided to attack. Utilitarian Choice (UC) was expressed by the proportion of choices implying the lowest expected losses (in percentage). Response Time (RT) was the mean response time (in seconds) on each trial. SoA was measured by Intentional Binding (IB, in milliseconds). IB was computed by subtracting each interval estimate from the mean actual response-tone interval (500ms) and averaged these scores for each Uncertainty X Autonomy condition. Each block of Autonomy ended with a subjective judgment of responsibility (SubjA), in which participants were asked to indicate how much they felt responsible of the decisions they made on a scale from -100 (not responsible at all) to 100 (entirely responsible)1 .

Statistical analyses were performed using JASP version 0.17.2. We performed separate repeated-measures ANOVAs for A1, UC, RT, and IB with Uncertainty (No Risk trials, No Enemy trials, Moral Decision-Making trials) and Autonomy (Level 0, Level 1, Level 2) as within-subject factors. In addition, SubjA was compared by means of a repeated measures ANOVA with Autonomy as within-subject factors. For each dependent variable, only data of participants within +/-2.5 SDs were considered. Greenhouse-Geisser correction was applied where sphericity was violated. We assessed Moral decision-making by several indicators. The primary focus of our analysis concerned the presence of a main effect of Uncertainty on 1) A1, for which we expected a higher percentage of attacks during No Risk trials and a lower percentage during no Enemy trials, 2) UC, for which we expected an increased number in the No during Moral Decision-Making trials in comparison with the two control trials. We also expected a main effect of Autonomy on 1) UC, with an increased rate of UC with the level of autonomy of the task, 2) RT, congruent with the main effect of Uncertainty, 3) IB, with less IB, indicating a decrease of SoA, with increased level of autonomy in line with the conclusions of [START_REF] Berberian | Automation technology and sense of control: a window on human agency[END_REF], and 4) SubjA, with lower SubjA with increased level of autonomy. These effects were expected to provide evidence of the effectiveness of the paradigm we developed. The threshold selected for significance was p < .05 with a two-tailed approach. Raw data, scripts, and processed data can be found on the Open Science Framework [START_REF] Salatino | Fire! Do Not Fire!": A pilotnew paradigm testing how autonomous systems affect agency and moral decision-making[END_REF].

(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Results

Analyses on A1 decisions

The analysis on A1 decisions (i.e. the proportion of attacks) (Fig. 2) revealed a main effect of Uncertainty (F (1.06, 27.72) = 926.70, p < .001, ηp2 = .97) and post hoc tests showed that all comparisons were significant (all ps < .001) with more a1 choices during No Risk trials (mean = 99.48, SEM = .20) in comparison with Moral DM trials (mean = 54.24, SEM = 1.77) and No Enemy trials (mean = .81, SEM = .52). However, the analysis revealed no significant effect of Autonomy (F (1.78, 46.39) = 2.80, p = .07, ηp2 = .09) on A1, as well as no significant interaction between Uncertainty and Autonomy (F (2.76, 71.98) = 1.17, p = .32, ηp2 = .04).

Analyses on Utilitarian Choice (UC)

The analysis on UC (i.e., the choices implying the lowest expected losses, Fig. 3 ). The analysis (Fig. 3, Panel B) also revealed a significant effect of Autonomy (F (1.76, 47.55) = 8.67, p < .001, ηp2 = .24). Post hoc tests showed no significant difference between Level 0 (mean = 94.10, SEM = 1.07) and Level 1 (mean = 92.59, SEM = 1.17) (p = .17) and between Level 0 and Level 2 (mean = 95.84, SEM = .88) (p = .089). A significant difference was found in the proportion of UC between Level 1 and Level 2 (p < .001) with more UC on Level 2. Finally, the analysis revealed no significant interaction between Uncertainty and Autonomy (F (2.36, 63.80) = 2.48, p = .08, ηp2 = .08).

Analyses on Response Time (RT)

The analysis on RT (Fig. 4, Panel A) revealed a significant effect of Uncertainty (F (1.73,46.95) = 68.76, p < .001, ηp2 = .71) with post hoc tests showing that all comparisons were significant (all ps ≤ 0.36), with longer RT during Moral DM trials (mean = 4.62, SEM = .20) than during No Risk (mean = 2.27, SEM = .12) and No Enemy trials (mean = 2.82, SEM = .14). In addition, the analysis revealed no significant effect of Autonomy (F (1.68, 45.36) = 0.667, p = .49, ηp2 = .02), but a significant interaction (Fig. 4, Panel B) between Uncertainty and Autonomy (F (3.43, 92.74) = 3.81, p = .009, ηp2 = .12), with a simple main effect of Autonomy in No Enemy trials (p = 0.03), but not in other Uncertainty conditions (all p > .37).

Analyses on Intentional Binding (IB)

The analysis on IB (Fig. 5) revealed a significant effect of Uncertainty (F (1.56,46. 1.000). Concerning Autonomy, the analysis revealed no significant effect (F (1.76, 52.82) = 0.67, p = .495, ηp2 = .022) and no significant interaction between Uncertainty and Autonomy (F (3.52, 105.86) = 1.29, p = .27, ηp2 = .04).

Analyses of subjective judgment of responsibility (SubjAs)

Discussion

In the present pilot study, we aimed to develop a new paradigm to investigate how the sense of agency and (moral) decision-making are influenced by the type of input received from an intelligent autonomous system. To this end, we programmed a task in which participants, in the role of drone operators, had to decide whether (or not) to initiate an attack on a simulated battlefield, during different trial types and with the support of an intelligent system at three levels of autonomy. Ultimately, the overall goal of this research agenda was to develop a paradigm to investigate the mechanisms involved in human-AI interactions in the context of morally challenging situations and to better understand the determining factors in the effect of (inputs received from) autonomous systems on the decisions of human agents faced with moral choices .

Our results show that our new paradigm was sensitive enough to discriminate between moral and non-moral situations. Indeed, participants in our study had a .54 likelihood to initiate an attack in our moral situations, while they refrained from attacking when there were no enemies, and systematically attacked in situations where an enemy was present, but an attack posed no risk for allied troops. Furthermore, the moral situations were characterized by less utilitarian choices than our control condition, reflecting the uncertainty of the situation. Notably, the assistance of the autonomous system increased the number of utilitarian choices, demonstrating the influence of the machine on decision-making. Finally, the moral situations were characterized by a longer reaction time, indicating the participants' hesitation when they had to decide to fire or to not fire when allies' life were at stake. Thus, this pilot study thus provides a preliminary paradigm for investigating research questions related to the effects of human -machine interactions in moral decision-making situations.

Our results were partially consistent with our expectations, as the number of attacks increased when there was no risk and decreased when there was no enemy, confirming that our control trials were working properly as well. However, no effects related to the level of autonomy were found regarding the proportion of attacks. One possibility is that human choices, when made in the context of moral dilemmas, are not influenced by the level of autonomy of a system, contrary to choices made in a context without these dilemmas. However, this would be surprising, given that previous studies conducted in military scenarios (e.g., [START_REF] Chen | Concurrent performance of gunner's and robotics operator's tasks in a multitasking environment[END_REF][START_REF] Rovira | Effects of imperfect automation on decision making in a simulated command and control task[END_REF] have found an influence of the level of autonomy on human performance and decision.

Another interpretation for the lack of an effect of level of autonomy on the rate of A1 choices relies on the way the task was designed (and in particular the possibility for participants to calculate maybe too easily the expected losses for both alternatives in each choice), resulting in ceiling/floor effects in the control conditions and an A1 rate of around 50% during Moral Decision-Making trials.

With respect to the Utilitarian Choice, i.e., the proportion of choices that lead to the least loss of allies, we expected an increase in UC in trials without moral conflict and an effect of autonomy leading to an increase in these choices at the highest level of Autonomy (as evidence of an effect of autonomous system on moral decision). Our results confirmed our expectations, with utilitarian choices increasing as a function of Uncertainty and Autonomy. Considering that the recommendations made with the highest level of system support were based on the lower expected losses computed, our results showed that our participants' moral choices were significantly changed by the input received from the system. This suggests that human choices can be influenced by the recommendations received from a decision support system, independently of the morallyunmorally challenging nature of the situation.

In relation to the Response Time, we expected that participants would take a longer time to make a decision on trials with a moral conflict and Autonomy would shorten participants' response times. Our results confirmed our expectation, with RTs being longer in moral situations, suggesting that participants took longer to make a decision when a moral conflict was present, and that the highest level of system support shortened their response time in the No-Enemy trials. This last result is consistent with previous findings from laboratory experiments showing that autonomous systems can help users in detection tasks (e.g., [START_REF] Goh | Effects of automation failure in a luggage screening task: a comparison between direct and indirect cueing[END_REF], although it is surprising that this effect was found only for the absence of target but not for the presence of target during trials without risk, which is somehow inconsistent with previous findings (e.g., [START_REF] Chavaillaz | Automation in visual inspection tasks: X-ray luggage screening supported by a system of direct, indirect or adaptable cueing with low and high system reliability[END_REF]. One possible reason for this result could be that the target used in our task (i.e., a white square among grey squares on a black background) was too noticeable and thus too easy to recognize for the Level 1 and Level 2 functions to be useful to participants.

Regarding SoA, given that a decrease in agency in human-machine interactions has been previously reported [START_REF] Berberian | Automation technology and sense of control: a window on human agency[END_REF][START_REF] Vantrepotte | Leveraging human agency to improve confidence and acceptability in human-machine interactions[END_REF], we firstly expected that participants would show a decrease in the Intentional Binding and subjective sense of responsibility at higher levels of support, indicating a decrease in the SoA. Consistent with our hypothesis, our results showed a decrease in the SoA at the explicit level with higher levels of support. This result is also consistent with the recently described human tendency to attribute moral responsibility to non-human agents which may lead people to be willing to blame them [START_REF] Furlough | Attributing blame to robots: I. The influence of robot autonomy[END_REF][START_REF] Kneer | Playing the blame game with robots[END_REF][START_REF] Liu | Blame attribution asymmetry in human -automation cooperation[END_REF]. Nevertheless, our results did not show a decrease in SoA at the implicit level, which is inconsistent with our expectations and previous studies [START_REF] Berberian | Automation technology and sense of control: a window on human agency[END_REF]. However, this discrepancy is not completely surprising considering that a dissociation between the two levels of measures in the SoA has already been reported in previous studies (e.g., [START_REF] Synofzik | I move, therefore I am: A new theoretical framework to investigate agency and ownership[END_REF][START_REF] Moore | Intentional binding and the sense of agency: a review[END_REF][START_REF] Saito | Discrepancy between explicit judgement of agency and implicit feeling of agency: Implications for sense of agency and its disorders[END_REF].

It has been suggested that at the explicit level a higher-order conceptual judgement of being an agent is formed and that this aspect of SoA is closely related to higher-level sources of information such as social and contextual cues [START_REF] Synofzik | I move, therefore I am: A new theoretical framework to investigate agency and ownership[END_REF], suggesting that Intentional Binding and explicit judgments of agency do not share the same processes [START_REF] Dewey | Do implicit and explicit measures of the sense of agency measure the same thing?[END_REF]. Since the two measurement systems are separable [START_REF] Saito | Discrepancy between explicit judgement of agency and implicit feeling of agency: Implications for sense of agency and its disorders[END_REF], it is possible that dissociation between implicit and explicit measurements occurred in our study. In particular, in our new paradigm the three autonomy conditions may not have been so different as to yield a significant difference at the implicit level of agency, but only at the explicit level.

Still regarding SoA, consistent with previous findings [START_REF] Moretto | Experience of agency and sense of responsibility[END_REF], we also expected an increase in SoA during Moral Decision-Making trials. Consequently, we expected that moral decision making would also be affected. Because SoA appears to be closely related to moral responsibility [START_REF] Moretto | Experience of agency and sense of responsibility[END_REF][START_REF] Caspar | Coercion changes the sense of agency in the human brain[END_REF] and this is reduced by the level of autonomy of the machine, we expected that a decrease in the sense of responsibility would lead to a change in the number of attacks at the highest level of system autonomy. However, contrary to our expectations, the results showed a significant decrease in the SoA in the Moral Decision-Making trials at the implicit level (i.e., in the IB). One possible explanation for these results is related to the human tendency to take more responsibility for positive than for negative events, which seems to be a mechanism for increasing self-esteem [START_REF] Bradley | Self-serving biases in the attribution process: A reexamination of the fact or fiction question[END_REF][START_REF] Greenberg | Depression, self-focused attention, and the self-serving attributional bias[END_REF][START_REF] Yoshie | Negative emotional outcomes attenuate sense of agency over voluntary actions[END_REF]. However, it has been pointed out there is a tendency to overestimate one's agency, and that this bias is stronger when the outcome of an action is positive rather than neutral or negative [START_REF] Wegner | Apparent mental causation: Sources of the experience of will[END_REF][START_REF] Haggard | Sense of agency in the human brain[END_REF]. Because the risk of a potential hit to allies was constantly present in the Moral Decision-Making trials in our task, it is possible that participants in these trials had a reduced SoA and responsibility, and disengaged from the situation due to the risk of negative dramatic consequences of their actions. Alternatively, it is also possible that these results are related to the young cadets' lower SoA, which has already been described by Caspar and colleagues [START_REF] Caspar | Only giving orders? An experimental study of the sense of agency when giving or receiving commands[END_REF].However, these results should be taken with caution, considering a flaw in the preparation of the Matlab script used to run the experiment, and the possibility that the time intervals used for the time estimations (200, 500, and 800ms) were not completely assigned equally across Uncertainty conditions (i.e., that each time interval are shown five times per Uncertainty condition). Indeed, the program was designed to generate for each three blocks (corresponding to the three levels of Autonomy) 15 presentations of each interval duration (45 intervals in total) presented in a random order across trials (45 trials/block). Albeit randomly presented across Uncertainty conditions, it is possible that the number of presentations of each interval duration was not perfectly the same across Uncertainty condition. Future investigations will be therefore necessary to determine whether the SoA decreases when human subjects interact with autonomous systems when making moral decisions compared to situations that do not pose a moral challenge, or whether this result is due to the methodological flaw observed in our experiment.

To summarize, our results show that human choices made in morally -challenging scenarios can be differentially influenced by the recommendations received from different level of autonomous systems, replicating and extending previous findings (e.g., [START_REF] Chen | Concurrent performance of gunner's and robotics operator's tasks in a multitasking environment[END_REF][START_REF] Rovira | Effects of imperfect automation on decision making in a simulated command and control task[END_REF] for studies in the military domain). This was measured by the small but significant difference in the number of utilitarian choices between conditions and the decrement in response time in the No Enemy trials. Interestingly, this effect was completed by a decrement in the subjective measure of agency with higher levels of autonomy, which is consistent with previous research (Barberian et al., 2012;[START_REF] Vantrepotte | Leveraging human agency to improve confidence and acceptability in human-machine interactions[END_REF]. At the same time, however, our results show some inconsistencies with previous studies [START_REF] Moretto | Experience of agency and sense of responsibility[END_REF][START_REF] Berberian | Automation technology and sense of control: a window on human agency[END_REF], with less agency measured in the moral decision-Making trials and no significant difference across Level of Autonomy at the implicit level of agency (IB).Thus, further experiments need to be conducted to determine if the inconsistent results we found were related to the design of the experiment as we suggested above. In particular, in addition to the change need on the Intentional Binding measure, the scenario we used was closer to impersonal/neutral stimuli rather than a moral/emotional context. For example, the radar screen shown to participants was quite schematic and likely did not allow participants to properly imagine the context of the choices they were making. In addition, they did not know the number of victims following their decision, and the victims were not clearly shown as individuals. Thus, it could be that the images we used did not have enough emotional content to reinforce/enhance the SoA. Since the sense of agency could be also affected by the actions' outcome, which is missing in the current version of the task, to overcome this issue, future research could improve our paradigm by using a less neutral task and content with more moral and emotional valence.

Conclusion

Considering the increasing presence of intelligent autonomous systems in our daily lives, it is crucial to conduct further research to better understand the implications in sensitive domains such as the military context to provide input for the successful design of innovative automated systems.

Our findings suggest that the level of system autonomy influences participants' moral decisionmaking and that input received from an intelligent autonomous system influences SoA. By developing a valid paradigm for assessing the impact of human-machine interaction on moral decision-making in the military, with the present study we pave the way for further lines of research on the influence of autonomous systems on human moral behavior , considering the current lack of research on this issue. Cohn, A., Gesche, T., & Maréchal, M. A. (2022). Honesty in the digital age. Management Science, 68(2), 827-845.

Coyle, D., Moore, J., Kristensson, P. O., Fletcher, P., & Blackwell, A. (2012, May) In 66% of the trials, one of the 2500 cells was coloured white to represent an enemy and participants were asked to choose whether or not to attack the enemy by pressing the left (attack) or right (no attack) arrow key on the keyboard. The grid was displayed for 15000ms or until the participant pressed one of the two response keys. After the response, a blackout screen was displayed for a random duration (200, 500 or 800ms) and a tone (200ms).

Participants were asked to indicate the duration of the interval between their choice and the tone on a horizontal scale from 0 ms to 1000 ms. Level 1 of system autonomy is shown in the right panel (top), where in addition to the basic visual assistance of Level 0, a scale has been displayed indicating the risk in terms of losses of allied forces in the event of an attack.

In Level 2 (Lower Right Panel), in addition to the visual assistance of Level 0, participants were assisted by a decision support system that gave a yes/no recommendation for the best decision to make.

Fig. 1

 1 Fig. 1, here

  Risk and No Enemy (control) trials compared to the Moral Decision-Making trials, and 3) RT, with expected shorter response time in the No Risk and No Enemy trials compared to the Moral Decision-Making trials. These effects were expected to provide evidence of the moral conflict produced by the scenarios. Regarding IB, following the results of Moretto et al. (2011), we expected a main effect of Uncertainty with shorter time interval, indicating an increase of SoA,

  , Panel A) revealed a significant effect of Uncertainty (F (1.18, 31.96) = 95.76, p < .001, ηp2 = .78). Post hoc tests showed a significant difference between Moral DM trials (mean = 84.02, SEM = 1.09) and No Risk trials (mean = 99.30, SEM = 0.27), and between Moral DM and No Enemy trials (mean = 99.21, SEM =.50) (all ps < .001), with a reduced number of UC during Moral DM trials, but not between No Risk and No Enemy trials (p = 1.000

  87) = 8.12, p = .002, ηp2 = .21) with post hoc tests showing a significant difference between Moral DM trials (mean = 118.85, SEM = 13.58) and No Risk trials (mean = 147.04, SEM = 13.99) and between Moral DM and No Enemy trials (mean = 145.53, SEM = 13.69) (all p < 0.005), with shorter intervals reported in No Risk and No Enemy trials in comparison with Moral Decision-Making trials. No significant differences were found between No Risk and No Enemy trials (p =

Fig. 1 .Fig. 1 .

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Fig. 2 .Fig. 2 .

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Fig. 3 .Fig. 3 .

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Fig. 4 .Fig. 4 .

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Fig. 5 .Fig. 5 .

 55 Fig. 5.

Fig. 6 .Fig. 6 .

 66 Fig. 6.

  . I did that! Measuring users' experience of agency in their own actions. In Proceedings of the SIGCHI Prével, A., Krebs, R. M.,Kukkonen, N., & Braem, S. (2021). Selective reinforcement of ://doi.org/10.1371/journal.pone.0255430.

	conflict	processing	in	the	Stroop	task. PloS	one, 16(7),	e0255430.
	conference	on	human	factors	in	computing	systems (pp.	2025-2034).

https

This range is commonly used in human contingency assessment (for recent examples, seePrével et al.,

[START_REF] Vaghi | Action-outcome knowledge dissociates from behavior in obsessivecompulsive disorder following contingency degradation[END_REF].

Funding: This research was funded by Belgian Defense -Royal Higher Institute of Defense, grant number HFM20-03.

(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.


Gonzalo P Urcelay
email: gonzalo.urcelay@nottingham.ac.uk

Kadell Symmons

Bethany Amos

Hazem Toutounji

Arthur Prével

Avoidance renewal Running Head: Renewal of instrumental avoidance Renewal of instrumental avoidance in humans

Keywords: Instrumental avoidance, extinction, renewal, context Avoidance renewal 3 Pavlovian Conditioning Instrumental Acquisition Instrumental Extinction Tests CTX

The renewal effect, which occurs when extinguished behaviour is tested outside of the extinction context, has been proposed as a model of contextual control that has relevance for the understanding of relapse following exposure therapy. Notably, there are multiple mechanisms by which the renewal effect can be explained. In two experiments, we used within-subjects designs in which participants learned to avoid a loud noise signalled by two discrete visual stimuli (CSs+), by pressing the space bar on the computer keyboard. Each CS+ was trained along with a CS-in a different context. During extinction, CS+ and CS-stimuli were presented in the alternative context from that of training, and participants were allowed to freely respond, but no loud noise was presented. Finally, all CSs were tested in both contexts, resulting in a within-subjects ABA vs ABB comparison. In both experiments, participants increased avoidance responses during training, and decreased responding during extinction, although Experiment 2 revealed less extinction. During test, responding was higher when CS+ stimuli were tested in the training (ABA) vs the extinction context (ABB), thus showing renewal of instrumental avoidance in humans. Additional analyses in Experiment 2 showed a remarkable similarity between avoidance responses and expectancy ratings. This study shows renewal of instrumental avoidance in humans, and the results suggest the operation of a modulatory role for the context in renewal, similar to occasion setting of extinction learning by the context.

for avoidance behaviour may be due in part to the finding that avoidance behaviour itself can prevent fear extinction from happening [START_REF] Lovibond | Safety behaviours preserve threat beliefs: Protection from extinction of human fear conditioning by an avoidance response[END_REF], which is central to exposure-based therapies. It is thought that this can sometimes result in persistent fear and avoidance behaviours [START_REF] Williams | A demonstration of persistent human avoidance in extinction[END_REF].

Extinction learning has inspired exposure-based therapies, in part because of the striking parallels between the two. One of the cardinal features of extinction learning is that, rather than erasing the original memory, it results in new inhibitory learning, which is context dependent (Bouton, Avoidance renewal 4 1993;[START_REF] Bouton | Context and behavioral processes in extinction[END_REF]. This is supported by behavioural evidence showing at least eight phenomena documenting recovery from extinction [START_REF] Urcelay | Exposure techniques: The role of extinction learning[END_REF]. Three of these phenomena, namely spontaneous recovery, reinstatement, and renewal, have been widely studied in humans and other animals [START_REF] Bouton | Context, time, and memory retrieval in the interference paradigms of Pavlovian learning[END_REF][START_REF] Bouton | Context and behavioral processes in extinction[END_REF][START_REF] Urcelay | Exposure techniques: The role of extinction learning[END_REF]. Following conditioning and extinction, spontaneous recovery is the return of excitatory learning that is observed when a retention interval is interposed between extinction and testing. Reinstatement is a similar recovery that results from re-exposure to the aversive event. Finally, renewal is the recovery from extinction that is typically observed when participants are tested outside of the context in which extinction took place. These three phenomena have gained attention because they parallel return of fear following exposure-based therapies, which happens with the passage of time (spontaneous recovery), upon re-exposure to feared stimuli and stress (reinstatement), and when patients leave the therapist's office (renewal). Whilst these three phenomena have been widely documented following extinction of fear and appetitive conditioning, there is a dearth of studies investigating recovery from extinction of instrumental avoidance behaviour [START_REF] Urcelay | Extinction of instrumental avoidance[END_REF].

As mentioned, the renewal effect happens when, following extinction learning, participants are tested in a context different from that of extinction learning. Assuming that excitatory learning happens in context A, extinction can occur in the same (A) or a different context (B), and testing can occur in the same context as acquisition and extinction (AAA), in the context of extinction learning (ABB), in the context of acquisition when extinction was conducted in a different context (ABA), or when test happens in a context different from that of acquisition and extinction (AAB or ABC). All three conditions in which testing is conducted outside of the context of extinction (ABA, ABC, AAB) result in recovery from extinction [START_REF] Bouton | Context and behavioral processes in extinction[END_REF][START_REF] Urcelay | Exposure techniques: The role of extinction learning[END_REF]. Renewal of avoidance has been documented in rodents [START_REF] Nakajima | Renewal of signaled shuttle box avoidance in rats[END_REF], and there are two recent reports with human participants (Schlund et al., 2019;[START_REF] Cobos | Individual Differences in Vulnerability Factors for Anxiety Disorders: A Study on the Acquisition, Extinction, and Renewal of Avoidance, and the Concomitant Dynamics of Relief[END_REF], although both can be explained by either context inhibition (during extinction) or context summation (during test) and do not distinguish between different explanations of the renewal effect (see below and General Discussion).

Avoidance renewal 5

Although numerous different associative mechanisms can account for ABA renewal (see [START_REF] Nelson | Experimental renewal in human participants[END_REF]; Figure 1 and text for elaboration on these), they can be classed into two general categories that map on the notion that contexts can play two fundamental functions, that of a discrete CS and that of a modulator of memory expression [START_REF] Urcelay | Two roles for the context in Pavlovian fear conditioning[END_REF]214). If the context is assumed to play the role of a discrete CS that enters into an association with the outcome, then ABA renewal can be explained in two ways. First, the context B acquires inhibitory associative strength during extinction, thus resulting in disinhibition when testing is conducted in A. Second, because the context A was also present during acquisition, it could contribute to responding to the target stimulus at test by summation. Either alone or together, these two mechanisms can account for ABA renewal and both appeal to the context functioning like any other discrete CS which enters into (excitatory or inhibitory) associations with the US. These mechanisms are consistent with standard associative theories like the Rescorla-Wagner model (Rescorla & Wagner, 1972; also see [START_REF] Delamater | Psychological and neural mechanisms of experimental extinction: a selective review[END_REF] for an elaboration on how it can account for extinction phenomena). An alternative way to explain ABA renewal is offered by Bouton's model (1993;[START_REF] Bouton | Conditioning, remembering, and forgetting[END_REF] which assumesin line with the context functioning like an occasion setter or modulatorthat second learned associations, such as those learned during extinction, are highly context dependent and therefore any change in context from extinction to test results in recovery from extinction. Extinction is thus seen as new learning which is highly context dependent.

One way to distinguish between these explanations of the ABA renewal effect is to run the experiment using multiple CSs and contexts, and a within-subjects design in which the associative history of the context of acquisition and extinction is matched. In this case, the observation of ABA renewal can only be explained by Bouton's explanation. Such a design was proposed by [START_REF] Rescorla | Within-subject renewal in sign tracking[END_REF]Experiment 1a) [START_REF] Bouton | Context, time, and memory retrieval in the interference paradigms of Pavlovian learning[END_REF][START_REF] Bouton | Conditioning, remembering, and forgetting[END_REF], then renewal is anticipated despite the associative histories of the contexts being the same.

In these experiments, the objective was to document ABA renewal of instrumental avoidance in humans, as this would increase the generality of the phenomenon. A second objective was to disambiguate different explanations of the ABA renewal effect, by adopting a within-subjects design developed by [START_REF] Rescorla | Within-subject renewal in sign tracking[END_REF], which can only be accounted for by Bouton's explanation. In order to fulfil these objectives, we adapted the task pioneered by Flores and her colleagues [START_REF] Flores | Intolerance of uncertainty as a vulnerability factor for excessive and inflexible avoidance behavior[END_REF] and used a within-subjects design [START_REF] Rescorla | Within-subject renewal in sign tracking[END_REF]. The unique advantage of this design is that it matches the two contexts for their overall history of reinforcement during acquisition and non-reinforcement during extinction, and the conditioned stimuli (CSs) in terms of histories of reinforcement and non-reinforcement. In addition to reducing the number of participants needed, this design renders any differences observed at test attributable to modulatory effects of the context, which is consistent with the best-developed explanation for the context-dependence of extinction [START_REF] Bouton | Context, time, and memory retrieval in the interference paradigms of Pavlovian learning[END_REF][START_REF] Bouton | Conditioning, remembering, and forgetting[END_REF].

Experiment 1

Method

Participants

The participants were 30 undergraduate students from the University of Leicester that completed the study in exchange for course credit. Twenty-five identified themselves as female and 5 as males, their ages ranged from 18 to 25 years (M= 20.43,SD= 1.41). Participants were recruited via an online system where they signed up in return for course credits. The University of Leicester Ethics Review committee approved the study, in accordance with the Code of Ethics of the World Medical Association. Because the report on which we based the task [START_REF] Flores | Intolerance of uncertainty as a vulnerability factor for excessive and inflexible avoidance behavior[END_REF] was conducted between subjects, we did not have a proper reference to estimate the sample size needed to achieve power in a 2x2 within-subjects interaction (at renewal). We estimated the sample size based on prior experience. The experiments were not preregistered.

Avoidance renewal 7

Apparatus and Materials

The experiments were run in three separate rooms, each containing a chair and desk with a computer. Three Helwet-Packard PCs with Windows operating system were used to run the task, which was programmed in Matlab using the Psychtoolbox interface. The stimuli were presented using 19.0" Neovo F-419 monitors (SXGA 1280 x 1024 resolution), placed roughly 60 cm in front of the participants. Avoidance responses were made through a keyboard by pressing the spacebar. Four fractals (10 x 8.5 cm) were used as stimuli (CS1, CS2, CS3, CS4) and these were randomized across participants by the program. A 3-second tone of 1100 Hz (95 ± 4dB) was presented bilaterally through dynamic stereo headphones and served as the aversive outcome. A few participants mentioned that the noise was too loud, and for these the loud noise was reduced to 90 dB. Before starting the experiment, participants completed Spielberger's State-Trait Anxiety Inventory for adults [START_REF] Spielberger | Manual for the State-Trait Anxiety Inventory[END_REF], which consists of 40 items, 20 of these assessing State Anxiety and the remaining 20 assessing Trait Anxiety. Items are rated on a Lickert scale ranging from 1 to 4.

Design

This study was run using a within-subjects design (see Table 1), adapted from the design used by [START_REF] Rescorla | Within-subject renewal in sign tracking[END_REF]. Two contexts were created by changing the colour of the background (green or pink) of the screen where the stimuli were presented. In Context A, CS1 was always (i.e., continuous reinforcement) paired with the loud tone, whereas CS2 was never paired with the loud tone. In Context B, CS3 was similarly paired with the tone whereas CS4 was not. Following Pavlovian conditioning and Instrumental Acquisition phases, CS1 and CS3 were presented in the alternative context (CS1 was presented in Context B, and CS3 in Context A), and participants were allowed to press the spacebar but the loud tone was never presented (extinction). During this phase, CS2 and CS4 were also presented in the alternative context, in the absence of the loud tone. All stimuli were tested in the Acquisition and Extinction contexts, with the order counterbalanced between participants. Thus, all stimuli were tested (Test phase) in the same context in which extinction took place, or in the Avoidance renewal 8 context where acquisition took place, resulting in a within-subjects ABB vs ABA comparison (where A is the context of acquisition and B the context of extinction).

Procedure

Upon arriving in the laboratory, participants signed a consent form informing them that there will be images that may be paired with a loud noise as part of the experiment, and they would eventually have the opportunity to avoid the loud noise by using the keyboard. After giving informed consent, participants completed the STAI questionnaires, and then started the experiment. The experiment itself was divided into 4 phases: a Pavlovian learning phase, an instrumental learning phase, an extinction phase, and a test phase. Participants began by wearing the headphones given, and reading the instructions pertaining to the first, Pavlovian phase. The instructions read: "In this experiment, you will see different fractal images on the screen. Some of these will be followed by an aversive sound (that will be played through the headphones), but some will not. At this stage, your task is to determine which images are followed by the annoying sound, and which ones are not. Note that there will also be changes in the background colour of the screen. In addition, you should try to determine when the sound is to appear. Press the SPACE bar to continue". During each trial, the context background was the first thing presented for 3 seconds. Following this, one of the four images (CS1, CS2, CS3, and CS4) was displayed in the centre of the screen in front of the context background for 20 seconds. In this and all other phases, the CSs were always presented for 20 seconds, regardless of whether participants avoided the loud noise or not. The onset of the noise was programmed according to a variable time schedule with a mean of 9 seconds (from the onset of the CSs+), which followed a rectangle distribution with range of 15 seconds. This way, the aversive sound could appear randomly at any second between 2 and 16 seconds from the onset of a CS+. The Pavlovian learning phase consisted of 4 trial blocks each including 2 presentations of each CS, the order in which each CS was presented within a block was randomised (as was the case throughout) as well as the specific images that triggered the aversive sound also being randomised. Each CS was presented 8 times during this phase.

Avoidance renewal 9

After the Pavlovian phase, participants read the instructions for the instrumental phase. "This phase is identical to the previous phase, except you can now avoid the sound by pressing the space bar. Critically, for a press to be successful, it has to be emitted within one second before the sound is presented. You can press the space bar as many times as you wish nevertheless, but only those presses within one second of the appearance of the noise will prevent the noise from happening. Your task it to try to avoid as many presentations of the noise as possible. Press the SPACE bar to continue". The instrumental phase also consisted of 4 trial blocks each including 2 presentations of each CS that was randomised, therefore having 8 presentations for each CS during this phase. After the instrumental phase, participants began the extinction phase which did not have any instructions beforehand, so the transition from the instrumental to the extinction phase was seamless. During this phase however, the 2 CSs+ were presented against the opposite context background as was the case with the 2 CS-(see Table 1). During this and the Test phases, the aversive sound was never presented, but spacebar responses were still permitted. The extinction phase consisted of 8 blocks of randomised CS presentations, therefore each CS was presented 16 times during this phase. Lastly, all stimuli were tested in both contexts, with the order of context counterbalanced across participants. This phase consisted of blocks in which there were 2 presentations of each CS per context.

Data Analysis

The main dependent measure in this study was the number of presses per stimulus in each block (each block containing 2 stimulus presentations) across both CSs+ and both CSs-. The reasoning for collapsing across CSs is that these always received the same training across all phases. Thus, during acquisition we summed the avoidance responses across the 2 presentations of each CS, and averaged the two CSs+ and the two CSs-in each block (4 blocks). The same was done for the 8 extinction blocks, and each test block. Space bar presses were analysed with within-subjects ANOVAS with stimulus identity (CSs+ vs CSs-) and blocks (1-4 during training, and 1-8 during extinction) as within-subjects variables. During test, we used within subjects ANOVAS and compared stimuli (CSs+ vs CSs-) and context of Test (Extinction vs Acquisition). When sphericity was violated, we used the Huynh-Feldt adjustment. In all cases we report partial eta squared as a measure of the unbiased, effect size [START_REF] Cohen | A power primer[END_REF]. Confidence intervals on partial-eta squares (90%) were computed using software available in [START_REF] Nelson | A robust function to return the cumulative density of non-central F distributions in Microsoft Office Excel[END_REF].

Transparency and Openness Statement

We report how we determined our sample size, and explain all data exclusions (if any), all manipulations, and all measures in the study. The data reported in this paper are available at DOI 10.17605/OSF.IO/V4D3B. Data were analyzed using IBM SPSS Statistics (Version 27). This study's design and its analysis were not pre-registered. The task was programmed using MATLAB using the Psychophysics Toolbox v. 3 extensions [START_REF] Kleiner | What's new in Psychtoolbox-3?[END_REF], and the materials are available upon request. .42], but no differences were observed for CSs-, F (1, 29) = .96, p = .33, ηp² = 0.03. Thus, consistent with the expectations, we observed a significant renewal effect when testing was conducted in the acquisition context relative to the extinction context. In addition, because this within-subjects design equates the associative strength of both contexts, we did not observe any differences in responding to the CSs-based on test context.

Results

Acquisition

Experiment 2

Experiment 1 revealed convincing evidence for renewal of instrumental avoidance in humans.

The purpose of Experiment 2 was twofold. First, we wanted to replicate the main findings observed in Experiment 1. Second, we wanted to collect self-report measures in addition to instrumental responding to assess whether there is consistency between avoidance responses and measures of Avoidance renewal 12 expectancy, because cognitive models of avoidance assign an important role for expectancy in human avoidance [START_REF] Lovibond | Fear and Avoidance: An Integrated Expectancy Model[END_REF].

Method

Participants

The participants were 30 undergraduate students from the University of Leicester that completed the study in exchange for course credit. Twenty-seven identified themselves as female and 3 as males, their ages ranged from 18 to 39 years (M= 19.7, SD= 3.71). Participants were recruited via an online system where they signed up in return for course credits.

Apparatus and Materials

The apparatus and materials were the same as those described in Experiment 1. In addition to recording responses and expectancy (see below), the program also recorded the number of loud noises (USs) experienced during training. We also asked participants to complete the GAD-7 (Spitzer, Kroenke, Williams & Lowe, 2006).

Procedure

The procedure was the same as in Experiment 1, except for the addition of an expectancy test which was given immediately after the renewal tests. In the expectancy test, participants saw each of the 4 CSs in each of the two contexts and had to rate the extent to which they expected that the loud noise would appear. Before the expectancy test, they received the following instructions. "Now we wish to know your expectation that the loud noise will appear following different stimuli and backgrounds. To indicate your expectation, with the help of the computer mouse you will have to make choices on a scale between 9 responses. 1 = No expectation, 5 = Moderate expectation, 9 = Very high expectation". Immediately after, participants saw all possible combinations of CSs and contexts and gave a rating for each. Following the expectancy test, participants were asked to rate how loud they thought the noise was (scale; 1 = Not loud, 5 = Loud, 9 = Very loud).

Results

Acquisition

Avoidance renewal 13

Instrumental acquisition proceeded as expected, with participants pressing the space bar more in the presence of the CSs+ than in the presence of the CSs-(see Figure 2A). This pattern changed as training progressed, so that the differences became larger. These impressions were confirmed by a 2 (Stimulus: CSs+ vs CSs-) x 4 (Block: 1-4) within-subjects ANOVA. The analysis revealed a main effect of Stimulus, F (1, 29) = 61.02, p < .001, ηp² = 0.678, 90% CIs [.48, .76], an effect of Block, F (1.78, 51.64) = 24.45, p < .001, ηp² = 0.457, 90% CIs [.27, .56], and a Stimulus x Block interaction, F (1.73, 50.18) = 17.7, p < .001, ηp² = 0.379, 90% CIs [.19, .50]. Analyses of simple effects revealed an effect of block for CSs+, F (1.8, 52.22) = 24.25, p < .001, ηp² = 0.455, 90% CIs [.37, .56], but a nonsignificant effect for CSs-F (1.44, 41.81) = 3.29, p = .062, ηp² = 0.102. Thus, the interaction suggests that an increase in responding was observed for CSs+, but not for CSs-. Consistent with the increase in CSs+ responses reflecting avoidance learning, the number of loud noises experienced in each block decreased, as revealed by one-way within-subjects ANOVA that revealed an effect of block, F (2.36, 68.58) = 14.82, p < .001, ηp² = 0.327, 90% CIs [.16, .43]. During Blocks 1, 2, 3 and 4 participants experienced on average 1.1 (SD = .99), 0.47 (SD = .81), 0.2 (SD = .55) and 0.17 (SD = .37) loud noises, respectively.

Extinction

The results of the extinction phase are presented in Figure 2B, which reveal little extinction of responding to the CSs+ in comparison to what was observed in Experiment 1. These impressions were supported by a 2 (Stimulus: CS+ vs CSs) x 8 (Block: 1-8) within-subjects ANOVA revealed a main effect of Stimulus, F (1, 29) = 7.54, p = .011, ηp² = 0.20, 90% CIs [.02, .38], but no effect of Block, F (2.44, 70.87) = 1.42, p = .24, ηp² = 0.047, 95%, and no Stimulus x Block interaction, F (2.82, 82.00) = 0.478, p = .68, ηp² = 0.016. Given the absence of an interaction, we did not assess the change for each pair of CSs separately.

Test

Participants during test responded more to the CSs+ when these were tested in the acquisition context than in the extinction context, a finding that reveals renewal of instrumental avoidance. No differences were apparent in responding to the CSs-, in line with Experiment 1 (see Figure 2C). These 16.26, p < .001, ηp² = 0.359, 90% CIs [.12, .52], but no differences were observed for CSsexpectancy ratings in the different contexts, F (1, 29) = 1.12, p = .29, ηp² = 0.037. Thus, expectancy ratings paralleled the findings observed in instrumental responses. The average rating of noise intensity was 6.63 (SD = 1.47) suggesting that participants perceived the noise as somewhere in between Loud (5) and Very Loud (9).

General Discussion

The purpose of this study was to assess in a within-subjects experimental paradigm the renewal of instrumental avoidance in humans. Both experiments revealed convincing evidence of renewal, and Experiment 2 in addition revealed a striking parallel between renewal of avoidance responses and expectancy ratings. We adopted a within-subjects design proposed by [START_REF] Rescorla | Within-subject renewal in sign tracking[END_REF] and used a task based on that developed by Flores and colleagues [START_REF] Flores | Intolerance of uncertainty as a vulnerability factor for excessive and inflexible avoidance behavior[END_REF] that introduces uncertainty concerning when the aversive outcome is to occur. This results in high levels of avoidance behaviour, as opposed to requiring a single response for successful Avoidance renewal 15 avoidance as often used in these paradigms. In both experiments, during instrumental training, we observed good discrimination between CSs+ and CSs-, and an increase in responding across blocks of training that was selective to the CSs+, as suggested by the interaction between stimuli and block during training. During extinction, Experiment 1 revealed a selective decline in responding to the CSs+ without large changes in responding to the CSs-. There was a small increase in responding to the CSs-that occurred during early blocks of extinction, perhaps because participants that noticed the change in contingency began responding to the previously non-reinforced CSs-, a finding that is also observed in Pavlovian fear extinction experiments (e.g., [START_REF] Haesen | Beyond extinction: Habituation eliminates conditioned skin conductance across contexts[END_REF]. Experiment 2 did not reveal much evidence of extinction, although numerically there was a decline in responding. This could be due to a number of reasons, the most relevant being that the data was collected during February 2020 when the COVID pandemic was imminent and stress levels were highfor it has been documented that stress attenuates extinction in human participants [START_REF] Schwabe | Stress increases behavioral resistance to extinction[END_REF].

Critically, during test both experiments revealed higher levels of avoidance responding when CSs+ were tested in the Acquisition context relative to testing in the Extinction context, whilst no differences were observed in responding to the CSs-.

As described in the introduction, there are several explanations of the renewal effect (see [START_REF] Delamater | Psychological and neural mechanisms of experimental extinction: a selective review[END_REF][START_REF] Nelson | Experimental renewal in human participants[END_REF]; for detailed reviews), and these broadly align with two fundamental roles played by contexts [START_REF] Urcelay | Two roles for the context in Pavlovian fear conditioning[END_REF][START_REF] Nakajima | Renewal of signaled shuttle box avoidance in rats[END_REF]. When the context is assumed to function like any other discrete CS, ABA (and ABC) renewal can be explained by positing that the extinction B context became inhibitory during extinction (because of non-reinforced presentations of the excitor during extinction) and such a release from inhibition when subjects are tested in the A (or C) context results in renewal. A recent report using a task similar to the one used in the present experiments has revealed ABC renewal that can be explained by inhibitory learning by the context during extinction learning [START_REF] Cobos | Individual Differences in Vulnerability Factors for Anxiety Disorders: A Study on the Acquisition, Extinction, and Renewal of Avoidance, and the Concomitant Dynamics of Relief[END_REF]. Similarly, it could be argued that in an ABA vs ABB comparison, the differences in responding observed at test are due to excitatory associative strength of the A context summating with responding to the extinguished CS (such excitation should not be present in the extinction context B) and again that can provide an explanation for the Avoidance renewal 16 observation of renewal. Consistent with this explanation, there is a report that has documented ABA renewal of avoidance behaviour and can be explained by the training context (A) acting as a cue and contributing to responding during the test [START_REF] Schlund | Renewal of fear and avoidance in humans to escalating threat: Implications for translational research on anxiety disorders[END_REF]. Finally, it is possible that during training the combination of the context and the CS became configured (as a unique cue; see Wagner & Rescorla 1972). During extinction of the CS in a different context B, the unique/configural cue is not present and therefore undergoes little extinction (but presentations of the CS alone may disintegrate the configuration) and recovery is observed when the unique/configural cue is presented again during testalthough this explanation does not easily explain AAB renewal. As an alternative to these explanations, [START_REF] Bouton | Context, time, and memory retrieval in the interference paradigms of Pavlovian learning[END_REF][START_REF] Bouton | Conditioning, remembering, and forgetting[END_REF] proposed that extinction is best conceived as new (inhibitory) learning which is highly context dependent. That is, during extinction, the CS becomes associated with the absence of the US and this CS→NoUS association depends on the extinction context for its expression, which means that testing in any other context from that of extinction should result in some recovery from extinction. Because the current within-subjects design equates the associative history of contexts and CSs, that is, all contexts and cues have similar excitatory and inhibitory training, the current results are best explained by Bouton's model that assumes that extinction is contextdependent new learning [START_REF] Bouton | Context, time, and memory retrieval in the interference paradigms of Pavlovian learning[END_REF].

In addition to discriminating between different explanations of the renewal effect, the withinsubjects design offers increased sensitivity to renewal, perhaps superior to that seem in a betweensubjects design. In Experiment 2, for example, there was little evidence that participants decreased responding during extinction, yet testing revealed a strong renewal effect -participants responded less when tested in the extinction context relative to the acquisition context. This suggests that participants did learn something about the extinction phase, otherwise such differences at test would not have been observed. The differences at test observed in responding were also observed in expectancy ratings for each CS and context combination. Although in the expectancy tests we did not manipulate the possibility of respondingthat is we asked participants to provide an expectancy rating, but we did not allude to whether the avoidance response was made or notthe overall pattern of results had a remarkable similarity to the pattern observed in responses. The expectancy data thus Avoidance renewal 17 provides some support to the proposal that cognitive expectations form a strong basis of the avoidance behaviour observed during the avoidance test [START_REF] Lovibond | Fear and Avoidance: An Integrated Expectancy Model[END_REF][START_REF] Seligman | A cognitive theory of avoidance learning[END_REF]. Of course, it is difficult to properly determine what came first. In Experiment 2, participants were first tested on avoidance responding, so it is possible that what they did during the avoidance tests carried over to (or formed the basis of) the expectancy tests. Ultimately, associative and cognitive explanations of the phenomena should not necessarily be seen as incompatible with each other but instead complementary, with associative processes providing the building blocks for cognitive expectations [START_REF] Witnauer | A one-system theory that is not propositional[END_REF].

As previously noted, research on extinction of instrumental avoidance has been growing steadily in the last decade (see [START_REF] Dymond | Overcoming avoidance in anxiety disorders: The contributions of Pavlovian and operant avoidance extinction methods[END_REF][START_REF] Urcelay | Extinction of instrumental avoidance[END_REF] for reviews), however the scarce literature of recovery from extinction in human avoidance behaviour has been somewhat contradictory. In rodents, [START_REF] Nakajima | Renewal of signaled shuttle box avoidance in rats[END_REF] conducted a thorough set of experiments investigating different forms of renewal following extinction, and Tapias-Espinosa and colleagues (Tapias-Espinosa, Kadar, & Segura-Torres (2018) investigated spontaneous recovery following extinction, both using of 2-way shuttle-box avoidance. Both studies revealed clear evidence of recovery from extinction in rodent avoidance. In humans, most studies have observed recovery from extinction assessed by expectancy ratings and skin conductance responses, but only a few reports have observed recovery from extinction as measured by avoidance responses. For example, following extinction of targets (CS+ and CS-) and generalized stimuli, [START_REF] Cameron | Generalization of socially transmitted and instructed avoidance[END_REF] presented 3 unsignaled shocks and observed a moderate reinstatement of avoidance, but to a CS-.

Similarly, [START_REF] Krypotos | Testing a novelty-based extinction procedure for the reduction of conditioned avoidance[END_REF] conducted fear extinction following instrumental avoidance and afterwards they administered 3 unsignaled shocks to the participants. This resulted in reinstatement of shock expectation but because they did not measure avoidance responses during extinction, they could not measure the extent to which this manipulation resulted in reinstatement of avoidance behaviour. Krypotos and colleagues [START_REF] Krypotos | Avoided by association: Acquisition, extinction, and renewal of avoidance tendencies toward conditioned fear stimuli[END_REF] investigated the effect of fear extinction on avoidance tendencies (as distinct from instrumental avoidance responses, avoidance tendencies rely on automatic tendencies acquired through Pavlovian Avoidance renewal 18 conditioning). Using an ABA design, they observed a trend towards more avoidance tendencies in the training context (A) but the specific comparison with avoidance tendencies in the extinction context (B) did not achieve statistical significance. [START_REF] Vervliet | Low-cost avoidance behaviors are resistant to fear extinction in humans[END_REF] tested extinction of instrumental avoidance without the possibility of responding (as an attempt to model exposure with response prevention; ERP) and observed that extinction with response prevention resulted in recovery at test when the response was again available, regardless whether extinction prevention was experienced (i.e., the response was not available during extinction) or informed (i.e., participants were told not to respond during extinction). This finding, which in itself is problematic for ERP (because of the recovery) and expectancy theory (because participants avoided at test despite showing lowextinguishedexpectancy ratings), can be interpreted as renewal. That is, renewal can be assumed following the logic that avoidance acquisition occurred in a context (A) with the presence of the response, removing the response during extinction created a distinct context during extinction (B), which lead to recovery when participants had again the opportunity to avoid during test (A). This finding replicated a study using a platform-avoidance task in rats (Bravo-Rivera, Roman-Ruiz, [START_REF] Bravo-Rivera | Persistent active avoidance correlates with activity in prelimbic cortex and ventral striatum[END_REF]. Finally, the two reports mentioned above [START_REF] Cobos | Individual Differences in Vulnerability Factors for Anxiety Disorders: A Study on the Acquisition, Extinction, and Renewal of Avoidance, and the Concomitant Dynamics of Relief[END_REF][START_REF] Schlund | Renewal of fear and avoidance in humans to escalating threat: Implications for translational research on anxiety disorders[END_REF] have documented renewal of avoidance behaviour in humans, and both of those reports can be explained by standard associative theories.

Given that avoidance is a hallmark of anxiety, PTSD and OCD, and renewal models return of fear (or relapse) that occurs when patients leave the therapist office, the current findings have a number of implications. First, a clear demonstration of renewal in human instrumental avoidance suggests that the principles governing extinction of human instrumental avoidance obey similar principles to those of human fear extinction, and of course extinction (Pavlovian and instrumental) in other animals [START_REF] Todd | Behavioral and neurobiological mechanisms of extinction in Pavlovian and instrumental learning[END_REF]. This means that much of what we know about extinction of fear can also be applied to avoidance behaviour, and this is relevant given recent suggestions that considering avoidance as part of the treatment (or as an adjunct to exposure therapy) can be beneficial [START_REF] Hofmann | Rethinking avoidance: Toward a balanced approach to avoidance in treating anxiety disorders[END_REF][START_REF] Treanor | Treatment of avoidance behavior as an adjunct to exposure therapy: Insights from modern learning theory[END_REF]. Second, a design that provides a clear demonstration of renewal in human instrumental avoidance will enable studies investigating the Avoidance renewal 19 effectiveness of ERP upon changes in contextual background. Given that availability or not of the response can create different contexts, changes in physical background can help to determine whether these effects are additive or not, hence illuminating the interactions between these different factors. Third, safety signals and safety behaviours relief, and safety signals reinforce avoidance behaviour [START_REF] Fernando | Safety signals as instrumental reinforcers during free-operant avoidance[END_REF][START_REF] Vervliet | Temporal dynamics of relief in avoidance conditioning and fear extinction: Experimental validation and clinical relevance[END_REF], but conditioned inhibitors also prevent extinction of fear [START_REF] Lovibond | Safety behaviours preserve threat beliefs: Protection from extinction of human fear conditioning by an avoidance response[END_REF][START_REF] Volders | Safety behavior can hamper the extinction of fear of movement-related pain: An experimental investigation in healthy participants[END_REF], and hence their use in therapeutic settings is poorly understood. Assessing their role using a powerful renewal design will enhance our understanding of these interactions on a design that better models return of fear (i.e., relapse) outside of the extinction context. Finally, a clear renewal design allows to investigate ways of conducting extinction to attenuate recovery from extinction of instrumental avoidance, as it has been done in Pavlovian extinction paradigms [START_REF] Urcelay | Exposure techniques: The role of extinction learning[END_REF]. For example, studies in rodents investigating extinction of fear [START_REF] Urcelay | Spacing extinction trials alleviates renewal and spontaneous recovery[END_REF] and avoidance (Tapias-Espinosa, Kadar, & Segura-Torres, 2018) have found that spacing extinction trials (or sessions) attenuates recovery from extinction, but little is known about this factor in the extinction of human instrumental avoidance.

In summary, in two experiments we observed a clear, within-subjects demonstration of renewal in human instrumental avoidance, using a design that provides unique support for Bouton's proposal (1993;[START_REF] Bouton | Conditioning, remembering, and forgetting[END_REF]. Overall, we believe that this task and design have potential to develop basic research on extinction of human avoidance, with an emphasis of translating basic findings to clinical practice. Whilst there is more research needed in these areas, we believe that this is a first and important step towards the development of such basic knowledge.

Avoidance renewal 28

Figure

  Figure 1A depicts the acquisition of instrumental avoidance (frequency of space bar presses)

  with a 2 (Stimulus: CS+ vs CS-) x 2 (Context: Acquisition vs Extinction) within-subjects ANOVA, which revealed an effect of Stimulus, F (1, 29) = 29.69, p < .001, ηp² = 0.50, 90% CIs [.26, .63], and effect of Context, F (1, 29) = 4.92, p < .05, ηp² = 0.145, 90% CIs [.00, .33], and a Stimulus by Context interaction, F (1, 29) = 8.08, p < .01, ηp² = 0.218, 90% CIs [.03, .40]. A comparison of avoidance responses during CSs+ in both contexts revealed more responding in the Acquisition context, F (1, 29) = 9.73, p < .01, ηp² = 0.251, 90% CIs [.05, .43], but no differences were observed for CSs-, F (1, 29) = 2.39, p = .13, ηp² = 0.076. Analysis of the expectancy ratings collected after the test of instrumental responding revealed a similar pattern as that observed with instrumental responses (Figure 2D). A 2 (Stimulus: CS+ vs CS) x 2 (Context: Acquisition vs Extinction) within-subjects ANOVA, revealed an effect of Stimulus, F (1, 29) = 70.45, p < .001, ηp² = 0.70, 90% CIs [.51, .78], and effect of Context, F (1, 29) = 9.37, p < .01, ηp² = 0.244, 90% CIs [.04, .42], and a Stimulus by Context interaction, F (1, 29) = 13.12, p < .001, ηp² = 0.312, 90% CIs [.09, .48]. A follow up comparison of expectancy ratings to the CSs+ in both contexts also revealed more responding in the Acquisition than in the Extinction context, F (1, 29) =

Figure 1 .

 1 Figure 1. Results of Experiment 1. A) Average responses per block during acquisition. There was an

Figure 2 .

 2 Figure 2. Results of Experiment 2. A) Average responses per block during acquisition. There was an

  . In this design, two different CSs (CS1 and CS2) are trained each in a different context A and B, respectively (CS1 is trained in Context A and CS2 is trained in Context B). Following training, CS1 is extinguished in Context B whereas CS2 is extinguished in Context A. Finally, both CSs are tested in both contexts. Because both contexts received excitatory training during acquisition and both received inhibitory training during extinction, the associative histories of the contexts are the same, and hence no renewal is anticipated. If, however, renewal results from second learned associations being context dependent