# Relationship between reward-related brain activity and opportunities to sit

Juliana Parma, Mariane F.B. Bacelar, Daniel A.R. Cabral, Robyn Recker, Dan Orsholits, Olivier Renaud, David Sander, Olav Krigolson, Matthew Miller, Boris Cheval, et al.

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## To cite this version:

Juliana Parma, Mariane F.B. Bacelar, Daniel A.R. Cabral, Robyn Recker, Dan Orsholits, et al. Relationship between reward-related brain activity and opportunities to sit. Cortex, 2023, 167, pp.197-217. 10.1016/j.cortex.2023.06.011. hal-05017562

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1 Relationship between reward-related brain activity and opportunities to sit

2

3 Juliana O. Parma¹,†, Mariane F. B. Bacelar²,†, Daniel A. R. Cabral¹, Robyn S. Recker¹, Dan Orsholits³,⁴,

4 Olivier Renaud⁵, David Sander⁶,⁷, Olav E. Krigolson⁸, Matthew W. Miller¹,⁹,††,*, Boris Cheval¹⁰,¹¹,††,*,

5 Matthieu P. Boisgontier¹²,¹³,††,*

6

7 ¹School of Kinesiology, Auburn University, AL, USA

8 ²Department of Kinesiology, Boise State University, ID, USA

9 ³Centre for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva,

10 Switzerland

11 ⁴Swiss National Centre of Competence in Research LIVES—Overcoming Vulnerability: Life Course

12 Perspectives, Lausanne and Geneva, Switzerland

13 ⁵Methodology and Data Analysis, Department of Psychology, University of Geneva, Geneva, Switzerland

14 ⁶Swiss Center for Affective Sciences, University of Geneva, Switzerland

15 ⁷Laboratory for the Study of Emotion Elicitation and Expression (E3Lab), Department of Psychology,

16 University of Geneva, Switzerland

17 ⁸Centre for Biomedical Research, University of Victoria, Canada

18 ⁹Center for Neuroscience, Auburn University, USA

19 ¹⁰Department of Sport Sciences and Physical Education, Ecole Normale Supérieure Rennes, Bruz, France

20 ¹¹Laboratory VIPS2, University of Rennes, Rennes, France

21 ¹²School of Rehabilitation Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, Canada

22 ¹³Bruyère Research Institute, Ottawa, Canada

23 *Corresponding authors: mwm0024@auburn.edu (Matthew W. Miller); boris.cheval@ens-rennes.fr

24 (Boris Cheval); matthieu.boisgontier@uOttawa.ca (Matthieu P. Boisgontier).
25 JOP and MFBB contributed equally to this work.

26 MWM, BC, MPB contributed equally to this work.
27 Abstract

28 The present study tested whether energy-minimizing behaviors evoke reward-related brain activity that
29 promotes the repetition of these behaviors via reinforcement learning processes. Fifty-eight healthy
30 young adults in a standing position performed a task where they could earn a reward either by sitting
31 down or squatting while undergoing electroencephalographic (EEG) recording. Reward-prediction errors
32 were quantified as the amplitude of the EEG-derived reward positivity. Results showed that reward
33 positivity was larger on reward versus no reward trials, confirming the validity of our paradigm to
34 measure evoked reward-related brain activity. However, results showed no evidence that sitting (vs.
35 standing and squatting) trials led to larger reward positivity. Moreover, we found no evidence
36 suggesting that this effect was moderated by typical physical activity, physical activity on the day of the
37 study, or energy expenditure during the experiment. However, at the behavioral level, results showed
38 that the probability of choosing the stimulus more likely to lead to sitting than standing increased as the
39 number of trials increased. In addition, results revealed that the probability of changing the selected
40 stimulus was higher when the previous trial was a stand trial relative to a sit trial. In sum, neural results
41 showed no evidence supporting the theory that opportunities to minimize energy expenditure are
42 rewarding. However, behavioral findings suggested participants tend to choose the less effortful
43 behavioral alternative and were therefore consistent with the theory of effort minimization (TEMPA).
44

44

45 Keywords

46 Exercise; Physical activity; Sedentary behaviors; Reward; Electroencephalography; Reinforcement
47 learning
48 **1. INTRODUCTION**

Imagine your supervisor calls you to their office to give you a bonus check. Upon learning that you earned the reward, would its value change if you knew you had to walk several flights of stairs as opposed to being able to take an elevator ride, equal in time, to retrieve it? The answer to this question has implications for one's level of physical activity. Most individuals are now cognizant of the positive effects of regular physical activity and have the intention to be active (Martin, Morrow, Jackson, & Dunn, 2000; Canadian Fitness and Lifestyle Research Institute, 2008). Yet, this intention is often not sufficient to engage in physical activity (Rhodes & Dickau, 2012). A recent study involving 1.9 million participants showed that more than a quarter of all adults are physically inactive, which extrapolates to more than 1.4 billion adults when considering the world population (Guthold, Stevens, Riley, & Bull, 2018). Some other results are even more concerning, especially in the United States, where more than 95% of adults fail to accumulate the recommended 30 minutes of moderate-to-vigorous physical activity on at least 5 days per week (Troiano et al., 2008). This high prevalence is concerning because physical inactivity involves higher risks of cardiovascular disease (Wahid et al., 2016), hypertension (Liu et al., 2017), diabetes (Aune, Norat, Leitzmann, Tonstad, & Vatten, 2015), cancer (Moore et al., 2016), depression (Schuch et al., 2017; Boisgontier et al., 2020), obesity (Bleich, Vercammen, Zatz, Frelier, Ebbeling, & Peeters, 2018), and mortality (Ekelund et al., 2019) with 6 to 10% of all deaths from non-communicable diseases worldwide attributed to physical inactivity (Lee et al., 2012).

It has been speculated that this failure to be physically active may be explained by automatic reactions toward stimuli that are related to physical activity behaviors (Conroy and Berry, 2017). These automatic reactions may disrupt the implementation of behavioral goals grounded in reflective motivation (Strack & Deutsch, 2004). Experimental studies testing these automatic reactions show that stimuli related to physical activity automatically attract attention (Berry, 2006; Berry, Spence, & Stolp, 2011; Calitri, Lowe, Eves, & Bennett, 2011; Cheval et al., 2020c), and trigger automatic affective
reactions (Bluemke, Brand, Schweizer, & Kahlert, 2010; Conroy, Hyde, Doerksen, & Ribeiro, 2010; Rebar, Ram, & Conroy, 2015) as well as approach tendencies (Cheval, Sarrazin, & Pelletier, 2014; Cheval, Sarrazin, Isoard-Gautheur, Radel, & Friese, 2015; Cheval, Sarrazin, Boisgontier, & Radel, 2017; Cheval et al., 2018b; Farajzadeh et al., 2023). These effects are stronger in active individuals, but inactive individuals generally demonstrate similar positive automatic reactions toward physical activity. Taken together, these results suggest that automatic reactions can support physical activity behaviors in both active and inactive individuals, which contrasts with the current pandemic of physical inactivity (Kohl 3rd et al., 2012). These results also suggest that automatic reactions toward physical activity can hardly explain this pandemic.

The recent theory of effort minimization in physical activity (TEMPA) suggests that an automatic attraction toward behaviors minimizing energetic cost, which may be inherently rewarding, could explain the inability to transform intentions to be physically active into actions (Cheval et al., 2018a; Cheval & Boisgontier, 2021). The repeated failure in counteracting this automatic attraction may partly explain the pandemic of physical inactivity (Boisgontier & Iversen, 2020). A positive bias toward lower energy expenditure has been evidenced in decision-making and learning tasks (Klein-Flügge, Kennerley, Friston, & Bestmann, 2016; Palidis & Gribble, 2020; Prévost, Pessiglione, Météreau, Cléry-Melin, & Dreher, 2010; Skvortsova, Palminteri, & Pessiglione, 2014). In the study by Klein-Flügge et al. (2016), participants were asked to make a series of choices between two options, which independently varied in required grip force and reward magnitude. The monetary reward ranged from 10 to 40 pence and required effort ranged from 20% to 80% of maximum grip force. Similarly, Skvortsova et al. (2014) used a probabilistic instrumental learning task with binary choices (left or right) and four possible outcomes: two reward levels (20¢ or 10¢) times two effort levels (80% and 20% of maximal force). Participants were encouraged to accumulate as much money as possible and to avoid making unnecessary effort. In the study by Palidis and Gribble (2020), participants made binary choices that probabilistically affected
whether they were asked to accurately produce a low or high level of quadriceps activation to earn a reward. Electroencephalographic (EEG) activity time-locked to feedback about whether they earned the reward for accurate force production was assessed. Results showed participants were more likely to change their response from the previous trial if it led to high effort. Results also showed that reward-related brain activity was greater when participants received reward feedback on high effort trials.

These results are consistent with findings showing individuals learn to make decisions to avoid high physical effort but, paradoxically, value rewards obtained with high effort more than those obtained with low effort (Inzlicht, Shenhav, & Olivola, 2018). In the study by Prévost et al. (2010), participants decided whether it was worth investing in a stronger effort using a hand grip to see an erotic picture clearly for 3 s or to invest in a small effort to see the picture for 1 s. These four studies showed that during choices involving monetary or erotic reward and physical effort the brain serves as a choice comparator for effort-reward trade-offs (Klein-Flügge et al., 2016) with behaviors associated with higher physical effort being avoided (Paladis & Gribble, 2020) and devalued (Prévost et al., 2010; Skvortsova et al., 2014). In line with the theory of effort minimization, experimental results suggest that a high tendency to approach stimuli related to sedentary behaviors can contribute to explain the gap between intentions to be physically active and actual physical activity (Cheval et al., 2015). Other results suggest sedentary stimuli require more inhibitory control to avoid relative to physical activity stimuli (Cheval et al., 2020a) and that avoiding sedentary stimuli requires higher brain activity linked to inhibitory control and conflict monitoring than approaching sedentary stimuli (Cheval et al., 2018b). These results are consistent with the notion that such stimuli are attractive and, thus, difficult to avoid. Finally, epidemiological research shows that declines in cognitive functioning, which may be necessary to avoid sedentary stimuli, precede declines in physical activity (Cheval et al., 2020c).

An untested corollary from the theory of effort minimization is that energy-minimizing behaviors elicit reward-related brain activity that promotes the repetition of such behaviors via
reinforcement learning processes (Rescorla & Wagner, 1972; Sutton & Barto, 1998). One of the crucial processes underlying reinforcement learning is the brain's computation of positive and negative reward-prediction errors, which represent the degrees to which actual outcomes are better or worse than expected, respectively. Positive reward-prediction errors act as signals within the brain to increase the value of decisions and actions that led to the errors, thus 'stamping in' such decisions and actions. Conversely, negative reward-prediction errors act as signals within the brain to decrease the value of decisions and actions that led to the errors, thus 'stamping out' such decisions and actions. Reward-prediction errors in humans can be quantified using the reward positivity component of the event-related potential (ERP) derived from the EEG (Krigolson, 2018; Proudfit, 2015; Sambrook & Goslin, 2015). The reward positivity manifests as a positive deflection in the ERP 250-350 ms following rewarding feedback and is maximal at midline frontocentral electrode sites. Based on the theory of effort minimization and reinforcement learning theory, experiencing a positive reward-prediction error from taking the elevator or a negative reward-prediction error from taking the stairs should reinforce behaviors that optimize opportunities to take the former, such as choosing to enter a building through a specific door known to have easy access to an elevator.

In the present research, we tested hypotheses consistent with the theory of effort minimization in physical activity (Cheval et al., 2018a; Cheval & Boisgontier, 2021) and reinforcement learning theory (Rescorla & Wagner, 1972; Sutton & Barto, 1998). Specifically, participants performed a doors task inspired by Hassall, Hajcak, and Krigolson (2019) and crossed with a movement-incentive delay task (Cheval, Boisgontier, Bacelar, Feiss, & Miller, 2019), both of which have been used to study reinforcement learning brain activity (i.e., reward positivity). On each trial, participants in a standing position chose one of two stimuli ("doors") on the screen. Following this choice, they were first informed whether they had to sit down and squat, should they earn a reward on the trial. Next, participants were informed whether they earned the reward or not. If they earned the reward, they had
144 to retrieve it by implementing the behavior indicated in the first step (i.e., sitting down or squatting and returning to the standing position). Unbeknownst to participants, both doors were equally likely to lead to a reward, but one door was programmed to lead to an opportunity to sit 3.5 times more often than the other door. As such, since choices were unrelated to the probability of receiving a reward, we could test whether participants learned to make choices based on the likelihood of sitting.

149 Our primary hypothesis was that opportunities to sit lead to more positive reward-prediction errors, as expressed by a larger reward positivity (H1). To test this hypothesis, we examined whether the 150 opportunity to sit versus stand (Trial Type) and the presence of absence of reward (Reward) was 151 associated with reward positivity amplitude and whether these variables interacted with each other 152 (Trial Type x Reward). This hypothesis followed directly from the theory of effort minimization's 153 prediction that opportunities to minimize energy expenditure are rewarding. We also investigated 154 whether the effect of opportunities to sit tested in H1 was moderated by factors related to energy 155 expenditure. Specifically, we hypothesized that the effect was larger in participants who were typically 156 less physically active (H2.1), in participants who were physically active on the day of the experiment 157 prior to the experiment (H2.2), and after energetically demanding behavior (i.e., squatting) during the 158 experiment (H2.3). These predictions followed from the theory of effort minimization's contention that 159 opportunities to minimize energy expenditure are particularly rewarding for individuals who are 160 typically physically inactive, and that the reward of effort minimization increases when an individual 161 spends energy. A third hypothesis was that the probability of choosing the stimulus more likely to lead 162 to sitting than standing increased together with the increase in trials (H3). This followed from the theory 163 of effort minimization's claim that opportunities to minimize energy expenditure are rewarding, and 164 reinforcement learning theory's claim that decisions that lead to rewards are repeated. Finally, our 165 fourth hypothesis was that reward positivity predicted subsequent decisions about whether one 166 chooses the same or a different stimulus. Consistent with reinforcement learning theory, we 167
hypothesized that a large positive reward-prediction error reinforced the decision that led to it (i.e., the participant should choose the same stimulus) (H4). This experiment was conducted as a registered report, and the approved protocol (stage 1 of the registered report) can be found at https://osf.io/tcr7f (Miller et al., 2021).

172

## 2. METHODS

We report how we determined our sample size, all data exclusions, all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study.

### 2.1. Population

Adults 19 to 40 years of age were recruited from the College of Education Research Participant Pool at Auburn University (USA) and by word-of-mouth to participate in the study in exchange for course credit, if applicable. This demographic was convenient to the investigators and had been used in similar studies (e.g., Cheval et al., 2019). To be included in the study, participants had to report an absence of physical impairment and disabilities that would make repeatedly standing and sitting difficult (yes vs. no), an absence of skin allergies or sensitivity to lotions or cosmetics, and an absence of neurological impairment.

185

### 2.2. Sample Size Calculation

To estimate the sample size required for sufficient power (90%) with an alpha level lowered to 2%, we focused on the linear mixed-effects model (MEM) used to test H1, our primary hypothesis. In general, sample size calculation is difficult and sensitive since it depends on the values of all (fixed and random) parameters. However, in a fully balanced case, such as the current design (40 trials per trial type/reward combination [condition]), repeated-measures ANOVA and linear MEM will be nearly
identical. For repeated-measures ANOVA, we know the main effects and interaction tests will be independent; the distribution under the alternative hypothesis is a non-central F with non-centrality parameter:

$$ \lambda = \frac{n \sum_{j=1}^{2} \sum_{k=1}^{2} \beta_{jk;\text{interest}}^{2}}{\frac{1}{R} \sigma_{\varepsilon}^{2} + 2\sigma_{\text{interest}}^{2}} $$

where “interest” corresponds either to the main effect of trial type and, thus, $\beta_1$ and $\sigma_1^2$, to the main effect of reward and, thus, $\beta_2$ and $\sigma_2^2$, or to the Trial Type x Reward interaction and, thus, $\beta_3$ and $\sigma_3^2$. R is the number of repetitions per participant and per condition. Based on H1, our primary hypothesis, our effect of interest is the Trial Type x Reward interaction. Our pilot data results showed a Cohen’s f = .516 (see 3.2 Pilot Results). However, we decided to use a more conservative f = .25, representing a medium effect size (Cohen, 1962), because pilot study results are unlikely to yield accurate estimates of effect sizes (Albers & Lakens, 2018). An f = .25, where $f = \sqrt{\lambda/n}$, implies that $\beta$ should be equal to 0.25 times the squared root of the denominator in the definition of $\lambda$. To take realistic values, we based our values on the pilot study and used R = 34, $\sigma_{\varepsilon}^2 = 108$, and $\sigma_{\text{interest}}^2 = 2.5$. This implies a value for $\beta$s of .715. To ensure this approach was also valid for linear MEM for our design, we ran simulation studies that showed, as in repeated-measures ANOVA, that the main effects and the interaction tests would be independent and, for example, the power for $\beta_1$ depends only on $\sigma_1^2$ (the variance of $u_{1j}$) and $\sigma_{\varepsilon}^2$. The values of $\sigma_2^2$ and $\sigma_3^2$ have almost no influence on this power. The power is guided by $\lambda$, as defined above. To evaluate the power for different sample sizes, we ran a MEM Monte Carlo simulation based on the model planned to address H1 with 500 samples of each size and with the above values. It was accomplished with the lmer R functions and simulated from the lme4 package. With these settings, for all effects, with $\alpha$ = .02, the number of participants needed to detect a medium effect size was $\ge$ 56. Based on the pilot study where 1 of 9 participants had a poor EEG recording, we expected poor EEG recordings from 11.11% of participants. Therefore, we recruited 64 participants but ensured that we had
215 quality data in a sufficient number of trials (n ≥ 20 condition; Marco-Pallares, Cucurell, Münte, Strien, & 216 Rodriguez-Fornells, 2011) from at least 56 participants.

217 For the first secondary analysis (H2), the same reasoning and computations as the ones used for 218 H1 was made for all effects and, with α = .02, the number of participants needed to detect a medium 219 effect size was also ≥ 56. Power calculation for secondary analyses addressing H3 and H4 was attempted 220 but not completed because the calculations failed to yield reliable results, possibly due to the increased 221 complexity of the models.

222

223 **2.3. Experimental setup**

224 Each trial of the task began with the participant standing and facing a table upon which was a 225 computer monitor, approximately eye level to the participant (Figure 1). There was a blue container 226 holding plastic coins next to the monitor and approximately arm-level with the participant when 227 standing. A foldup butterfly chair was positioned immediately behind the participant. Another blue 228 container holding plastic coins and an empty red (collection) container were positioned next to the chair 229 and approximately arm-level with the participant when seated. A recording device (e.g., iPAD) was 230 positioned on the ground facing the participant's legs. Participants were told their lower body 231 movements was recorded to confirm that they were standing as still as possible, which they were 232 instructed to do to facilitate EEG recording. The participant held a wireless game controller throughout 233 the experiment.
<figure><img src="image_3.png" /></figure>

**Figure 1. Experimental Setup.** The participants used a game controller to respond to stimuli on a computer monitor. They had the opportunity to win plastic coins from the blue container at arm-level while standing or the blue container at arm-level while seated, based on probabilistic learning and chance. The participant deposited the coins won in the red container.

**2.4. Experimental protocol**

Data were collected at a single testing site. Participants' height and weight was measured with a stadiometer and scale. They were asked to rate how fatigued they felt using the Multidimensional Fatigue Inventory (Smets, Garssen, Bonke, & De Haes, 1995) and three custom items (see Appendix B) prior to starting the task. Participants began each trial standing and were prompted to hit a game controller button to start the trial (Figure 2). Next the participant saw two squares (or “doors”) appear on the computer monitor, one to the left and one to the right. One of the squares was burnt orange (RGB: 205, 85, 0) and one was navy blue (RGB: 0, 0, 128). The color of the square appearing on the left or right varied randomly with equal probability. Participants were instructed to select one of the squares by pressing the game controller button corresponding with the side of the monitor containing their square of choice (i.e., the left button if the square they choose is on the left side, and the right button if the square they choose is on the right side). After a choice was made, a fixation cross appeared for 300 – 500 ms followed by a stimulus depicting two lines, an upper line and a lower line, with a container depicted upon one of the lines. If the container was upon the upper line (stand trial), it indicated that, if the participant earned a reward on the trial, it would result in them retrieving coins from the upper blue
256 container that was arm-level when standing. If the container was upon the lower line (sit trial), it
257 indicated that, if the participant earned a reward on the trial, it would result in them retrieving coins
258 from the lower blue container that was arm-level when sitting. The lines and container stimuli remained
259 on the monitor for 2000 ms and were followed by a fixation cross for 300 - 500 ms. Next, participants
260 saw a feedback stimulus informing them whether they earned the reward or not. They either saw a "$"
261 sign for 1000 ms indicating that they earned a reward, or a "0" for 1000 ms if they did not. Then,
262 participants saw the word "WAIT" appear on the monitor for 3000 ms. Then, on stand reward trials,
263 participants heard a tone indicating that they should take a coin from the upper container, squat to
264 touch their butt to the chair while placing the coin in the red collection container, then return to a
265 standing position. This process was repeated after a 6000 ms interval before the next tone, until a total
266 of 5 coins had been retrieved. On sit reward trials, participants sat down in the chair upon hearing the
267 tone and took a coin from the lower container, then placed the coin in the red collection container. The
268 participant remained seated until the next tone, at which time they retrieved another coin from the
269 lower container by simply reaching into the container. This process was repeated until the participant
270 retrieved five coins in total. Participants were told to remain seated after retrieving the fifth coin until
271 prompted to start the next trial.

272 On no-reward trials ("0" sign), participants remained standing for 30 s, irrespective of the
273 information provided to them in the first step (i.e., sit vs. stand trial). Thus, participants should have set
274 expectations about whether they would sit or squat to retrieve coins in the first step, then compute a
275 reward-prediction error based on the feedback stimulus ("$" vs. "0") in the second step, which informed
276 them whether they indeed sat or squatted to retrieve coins.

277 Prior to starting the task, participants were told that each coin represents a raffle ticket to win
278 $10 [USD]; the more coins they earned, the more likely they were to win $10; on each trial, a certain
279 color square gave them a certain probability of winning, so they should focus on choosing a square
based on color; and there was no strategy for selecting a color square to win. Please see Appendix A for
complete instructions that were given to the participants. Unbeknownst to participants, each color
square had a 50% probability of resulting in a reward on each trial, but one square had a 70% chance of
resulting in a sit trial, whereas the other square had a 20% chance of resulting in a sit trial. This
procedure allowed to test whether participants began to choose the square more likely to minimize
effort (H3) while avoiding having them choose a square based on its likelihood of resulting in a reward
(coins). Through preliminary pilot testing, we established that these probabilities should lead to at least
*n* = 25 of each trial type (sit reward, sit no-reward, stand reward, stand no-reward), which past research
has revealed leads to a reliable reward positivity (Marco-Pallares et al., 2011). The median and minimum
number of trials per condition and dependability (reliability) are reported for both the pilot and main
study (Table 1). Reliability was obtained using generalizability theory (Carbine, Clayson, Baldwin,
LeCheminant, & Larson, 2021; Clayson & Miller, 2017b), and using the ERP reliability analysis toolbox
implemented in Matlab software (Clayson & Miller, 2017a, 2017b). We used reliability to contextualize
results from our primary experiment (reliability is associated with standard error of measurement and
effect size; Clayson & Miller, 2017) and inform future research (e.g., how many trials per condition
researchers should try to obtain).
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Experimental protocol and stimuli. There were four types of trials, each of which began with the participant standing. For each participant, one of the colored squares had a 70% chance of resulting in a sit trial and the other square had a 20% chance of resulting in a sit trial. Each square and each type of trial had a 50% chance of resulting in a reward, which determined whether the behavior had to be performed or not.</figcaption></figure>

yThe color square with the higher probability of resulting in a sit trial varied randomly between participants. Participants completed a total of 160 trials, which took about 110 min. Participants were given breaks approximately every 22 min and remained standing during the breaks.

After finishing the task, participants completed questionnaires. The Borg scale (Borg, 1982) was used to rate the perceived level of exertion they typically experienced when retrieving coins and waiting for the next trial from the sitting vs. standing position. Participants were asked whether they preferred to retrieve coins by sitting or standing. The custom fatigue questions were asked again (Appendix B). The International Physical Activity Questionnaire (IPAQ) (Craig et al., 2003) was used to assess the level of energy expenditure during a typical week and on the current day. Dependence on exercise was assessed with the Exercise Dependence Scale-21 (Hausenblas & Symons Downs, 2002) and their affective attitudes toward exercise were also assessed (Courteya & Bobick, 2000). Participants provided information related to handedness (Oldfield, 1971). Finally, participants were informed that one of the
squares was more likely to result in stand trials and asked to rate their awareness of this manipulation of likelihood on a 0 ("not aware at all") to 10 ("fully aware") scale.

317

## 2.5. EEG recording and signal processing

Scalp EEG was collected from a BrainVision actiCAP system (Brain Products GmbH, Munich, Germany) labeled in accord with an extended international 10-20 system (Oostenveld & Praamstra, 2001) and sampled at 250 Hz. Data were collected from the following electrodes: FP1, FP2, F3, Fz, F4, FC3, FCz, FC4, C3, Cz, C4, CP3, CPz, CP4, P3, Pz, and P4. EEG data were referenced online to the left earlobe and a common ground was employed at the FPz electrode site. Electrode impedances were maintained below 25 kΩ throughout the study and a high-pass filter was set at 0.016 Hz. The EEG signal was transmitted via the BrainVision wireless MOVE add-on (Brain Products GmbH) to a BrainAmp DC amplifier (Brain Products GmbH) that amplified and digitized the signal. The amplifier was linked to a computer running BrainVision Recorder software (Brain Products GmbH) that recorded the signal. EEG data processing was conducted with BrainVision Analyzer 2.2 software. Data was visually inspected to determine whether any electrode needed to be interpolated, for example due to recording failure (e.g., 1-s or longer periods of voltage changing by less than 0.5 µV) and/or electrical noise (e.g., sharp changes in voltage of more than 200 µV). Next, data were re-referenced to an average ears montage. Then, data were prepared for independent component analysis (ICA) cleaning. First, a 1 – 40 Hz band-pass filter with 4th order roll-offs and a 60 Hz notch filter was applied. Next, data were visually inspected and non-stereotypical artifacts were marked. Then, an ICA was conducted to identify stereotypical artifacts, such as blinks and saccades. We identified stereotypical artifacts, such as blinks and saccades, by looking for components that exhibited relatively sharp changes in frontopolar voltage (e.g., more than 200 µV) that decreased in amplitude from anterior to posterior electrode sites (blinks), or exhibited broad frontopolar changes in voltage (e.g., more than 200 µV) that were larger in a hemisphere than in the
339 other hemisphere and decreased in amplitude from anterior to posterior electrode sites (saccades). This
340 ICA was applied to the unfiltered data to remove identified artifacts. This cleaned data was band-passed
341 filtered between 0.1 and 30 Hz with 4th order roll-offs, and a 60 Hz notch filter was applied.

342

343 **2.6. Measures**

344 **2.6.1. Reward-prediction errors: "Reward Positivity"**

345 The reward positivity was extracted from an epoch beginning 200 ms prior to the onset of the
346 feedback stimulus, indicating whether the participant earned the reward or not, and ending 800 ms
347 after this stimulus. Then, the epoch was baseline corrected with respect to the pre-stimulus interval (-
348 200 - 0 ms). Next, epochs containing a change of more than 50 µV from one data point to the next, a
349 change of 100 µV within a moving 200-ms window, or a change of less than 0.5 µV within a moving 200-
350 ms window in any of the midline electrodes (Fz, FCz, Cz, CPz, and Pz) were excluded from subsequent
351 analysis. Next, we determined the time window for reward positivity quantification. Specifically, epochs
352 time-locked to reward feedback were averaged separately for reward and no-reward trials. Then, the
353 average of the no-reward feedback epochs was subtracted from the average of the reward feedback
354 epochs to create a difference wave for each participant. In our pilot data, difference waves exhibited
355 substantial interindividual variability in reward positivity peak latency (the positive peak 230 - 350 ms
356 after feedback onset). Thus, we adaptively centered each participant's reward positivity time window
357 (length = 40 ms) on their reward positivity peak latency at the electrode at which it peaked (Fz, FCz, or
358 Cz) (Clayson, Baldwin, & Larson, 2013). We also confirmed that this window included a negative
359 deflection in the no-reward feedback waveforms (Krigolson, 2018). If it did not, we centered the window
360 on the maximal negativity between 230 and 350 ms in the no-reward feedback waveforms. Of note, we
361 originally planned to identify each participant's reward positivity between 250 and 350 ms, but, after
362 collecting data from 9 participants, we noticed that the reward positivity was peaking as early as 230 ms.
Thus, we consulted the editorial team and were advised to adjust our reward positivity window to 230 – 350 ms and conduct sensitivity analyses with the original time window. Then, we computed mean amplitude in each participant's time window at Fz, FCz, and Cz for each epoch (i.e., the non-averaged data) and then averaged across these electrodes. That is, the mean amplitude, pooled across Fz, FCz, and Cz, in each participant's reward positivity time window for each trial served as the reward positivity. If one of the electrodes malfunctioned during recording, it was not included in the average. Finally, since the reward positivity exhibited an unexpected posterior scalp distribution (i.e., maximal voltage at electrode CPz or Pz), we quantified the component by averaging across electrodes Cz, CPz, and Pz, and submitted this reward positivity to a sensitivity analysis.

372

373 **2.6.2. Energy expenditure**

The typical level of energy expenditure was assessed using the IPAQ (Craig et al., 2003) assessing moderate-to-vigorous physical activity undertaken during a typical week ("**typical MVPA**"). Typical MVPA was computed using the Metabolic Equivalent of Task (MET) associated with moderate (6 METs) and vigorous physical activity (8 METs) (IPAQ Research Committee, 2005). Specifically, based on the IPAQ protocol, the formula we used was: typical MVPA in MET minutes per week = 4.0 x minutes of moderate physical activity per week + 8.0 x minutes of vigorous physical activity per week (IPAQ Research Committee, 2005). The level of energy expenditure prior to the experiment on the day of the experiment ("**today MVPA**") was also assessed using the IPAQ assessing moderate-vigorous physical activity in MET-minutes. Finally, the level of energy expenditure during the experiment ("**study energy expenditure**") was computed by summing the METs spent on each trial up to the current trial. To compute the energy expended on each trial, we considered the actions performed during the trial and the time spent performing these actions. Specifically, participants spent 28 s standing on sit/stand no-reward trials; 26 s sitting down and 2 s squatting (sitting down to retrieve coins and standing up to begin
the next trial) on sit reward trials; and 12 s squatting and 16 s standing on stand reward trials. 1.50 MET was assigned for sitting; 1.75 MET was assigned for standing; and 4 METs was assigned for squatting, which we consider moderate-intensity exercise (Mansoubi et al., 2015). After converting METs from min to s, the trial types were determined to have the following energy expenditure: sit reward trial = 1.30 MET; sit/stand no-reward trial = 1.36 MET; and stand reward trial = 2.11 METs.

392

### 2.6.3. Behavioral measures

The first behavioral measure was the stimulus participants chose on each trial ("stimulus chosen"), which was either the stimulus with the higher or lower probability of resulting in a sit trial. The second behavioral measure was whether a participant changed their response (what stimulus they chose) from the previous trial ("changed response").

398

## 2.7. Statistics

Factors, designs, and formal tests used to investigate the hypotheses are summarized in Supplemental Table 1. If a variable was not normally distributed, as tested by the Shapiro-Wilk normality test, the variable was normalized using the Box-Cox transformation (Box and Cox 1964), which represents a family of power transformations that incorporates and extends the traditional methods (e.g., square root, log, inverse) to find the optimal normalizing transformation for each variable. As such, Box-Cox represents a potential best practice to normalize data (Osborne, 2010).

MEMs were used to test the hypotheses. The mixed-effect approach provides a type I error rate that corresponds to its expected level (Boisgontier & Cheval, 2016; Lachaud & Renaud, 2011) and is useful when modeling effects predicted to change over time (e.g., H3; Lohse, Shen, & Kozlowski, 2020). In several research fields, the use of MEM is promoted as a better alternative than traditional statistical models (Boisgontier & Cheval, 2016). Unlike traditional approaches (e.g., ANOVA), which require
411 averaging trials within each condition, MEM preserve all the information (i.e., for each participant, these
412 models keep the variability of the responses within each condition). Therefore, the number of data
413 points in the model increases, which contains type I error rate without compromising the power (Judd,
414 Westfall, & Kenny, 2012). The MEMs were built and fit by maximum likelihood in R using the lme4 and
415 lmerTest packages and p-values were approximated using the Satterthwaite's method (Bates, Mächler,
416 Bolker, & Walker, 2015; Kuznetsova, Brockhoff, & Christensen, 2016; R Core Team, 2019). An estimate of
417 the effect size of the fixed effects was reported using the marginal pseudo R² computed with the MuMln
418 package (Barton, 2018). Statistical assumptions associated with MEMs (normality of the residuals,
419 homogeneity of variance, linearity, multicollinearity exclusion, and control of undue influence) were
420 checked for all models. If some observations exerted undue influence on the model estimation (i.e.,
421 outliers), the models were tested with and without them to ensure results' robustness. Alpha was set to
422 .02 for all analyses. To interpret significant interactions, simple-effect analyses were conducted.
423

423

424 2.7.1. Primary analysis

425 H1 was tested with the following linear MEM:

$$
\begin{equation}
\begin{aligned}
\text{Reward Positivity}_{ij} = & \\
& \quad (\beta_0 + u_{0j}) + (\beta_1 + u_{1j})\text{Trial Type (stand vs. sit)}_{ij} + (\beta_2 + \\
& \quad u_{2j})\text{Reward (no reward vs. reward)}_{ij} + \beta_3 \text{Trial Type}_{ij} \times \text{Reward}_{ij} + \epsilon_{ij}
\end{aligned}
\tag{1}
\end{equation}
$$

426 where Reward Positivity$_{ij}$ is the participant's reward positivity in condition i, β₀ to β₃ are the fixed
427 effect coefficients, u₀j to u₂j are the random effects for participant j (random intercepts and slopes),
428 εᵢⱼ is the error term, u₁j, u₂j and εᵢⱼ are Gaussian and independent.

429 To test H1, we checked and ensured that reward positivity was larger on reward versus no reward trials
430 as this condition must be satisfied to demonstrate the presence of a reward positivity that could
431 potentially be moderated by other factors, such as trial type.
432

433 **2.7.2. Sensitivity analyses**

434 As mentioned in section 2.6.1, a sensitivity analysis was conducted with the window centered on the
435 maximal negativity between 250 and 350 ms in the no-reward feedback waveforms. In addition, a
436 sensitivity analysis was conducted with reward positivity averaged across electrodes Cz, CPz, and Pz (and
437 centered on a peak between 230 and 350 ms).

438

439 **2.7.3. Secondary analyses**

440 H2.1, H2.2, and H2.3 were tested with the following linear MEM:

$$
\begin{align*}
\text{Reward Positivity}_{ij} &= (\beta_0 + u_{0j}) + (\beta_1 + u_{1j})\text{Trial Type (stand vs. sit)}_{ij} + \tag{2.1-2.3} \\
&\quad (\beta_2 + u_{2j})\text{Reward (no reward vs. reward)}_{ij} + \\
&\quad (\beta_3 + u_{3j})\text{Energy Expenditure}_{ij} + \beta_4\text{Trial Type}_{ij} \times \text{Reward}_{ij} + \\
&\quad \beta_5\text{Trial Type}_{ij} \times \text{Energy Expenditure}_{ij} + \beta_6\text{Reward}_{ij} \times \\
&\quad \text{Energy Expenditure}_{ij} + \beta_7\text{Trial Type}_{ij} \times \text{Reward}_{ij} \times \text{Energy Expenditure}_{ij} + \\
&\quad \epsilon_{ij}
\end{align*}
$$

441 where *Reward Positivity*<sub>ij</sub> is the participant's reward positivity in condition i, β<sub>0</sub> to β<sub>7</sub> are the fixed
effect coefficients, *u*<sub>0j</sub> to *u*<sub>3j</sub> are the random effects for participant j (random intercepts and slopes),
442 *ε*<sub>ij</sub> is the error term, *u*<sub>1j</sub>, *u*<sub>2j</sub>, *u*<sub>3j</sub>, and *ε*<sub>ij</sub> are Gaussian and independent, *Energy Expenditure* is the
443 score on typical MVPA, today MVPA, and study energy expenditure for model 2.1, 2.2, and 2.3,
444 respectively.
445

446

447 H3 was tested with the following logistic MEM:

$$
\operatorname{logit}(E_j(\text{Stimulus Chosen}_{ij})) = \beta_0 + \tag{3}
$$
$$ (\beta_1 + u_{1j}) \text{Trial Number}_{ij} + u_{0j} $$

448 where *Stimulus Chosen* is the stimulus chosen by the *j*th participant on trial *i*, $E_j$ is the conditional expectation, $\beta_0$ and $\beta_1$ are the fixed effect coefficients, $u_{0j}$ and $u_{1j}$ are the random intercepts and slopes for the *j*th participant and are Gaussian and independent.

451
452 H4 was tested with the following logistic MEM:

$$ \operatorname{logit}(E_j(\text{Changed Response}_{ij})) = \qquad (4) $$

$$ \beta_0 + (\beta_1 + u_{1j}) \text{Reward Positivity}_{i-1j} + u_{0j} $$

453 where *Changed Response* is whether the *j*th participant changed their response from trial *i* -1 to trial *i*, $\beta_0$ and $\beta_1$ are the fixed effect coefficients, $u_{0j}$ and $u_{1j}$ are the random intercepts for the *j*th participant and are Gaussian and independent.

456
457 Several variables were added to determine if they explained residual variance. For models 1 and 2, the outcome variable, reward positivity, is sensitive to whether a reward is predicted on a trial.
458
459 Although each trial (1, 2, 3, etc.), each stimulus chosen (burnt-orange square vs. navy-blue square), and each type of trial (sit vs. stand) was programmed to have 50% chances of resulting in a reward, it was
460
461 possible that rewards occurred more or less frequently at times. Thus, we added variables reflecting the probability of receiving a reward on the current trial given how frequently (1) a reward had been received up to the current trial (“reward probability”); (2) a reward had been received when choosing a certain stimulus up to the current trial (“stimulus reward probability”); and (3) a reward had been received on a certain trial type up to the current trial (“trial type reward probability”). We also added interaction terms between these variables and those in the primary models.
For *model 3*, the choice of the stimulus should also be sensitive to reward probability based on the stimuli chosen up to the current trial. Therefore, we added stimulus reward probability in this model. Stimulus chosen should also be sensitive to trial type given the stimulus chosen. Although one stimulus was programmed to lead to sit trials 70% of the time and the other stimulus only 20%, the actual difference may have departed from 50% at times. Thus, we added a variable reflecting the probability that one stimulus led to a sit trial relative to the probability that the other stimulus led to a sit trial, up to the current trial ("stimulus trial type probability"). We also added interaction terms between these variables and those in the primary models.

For *model 4*, trial number may have predicted changed response, with participants changing their responses less often across trials as they learned the stimuli-trial type relationship (e.g., Lohse, Miller, Daou, Valerius, & Jones, 2020). Additionally, trial type (sit vs. stand) on the prior trial ("previous trial type") and reward (reward vs. no-reward) on the prior trial ("previous reward") may have predicted changed response. We also added interaction terms between these variables and those in the primary models.

481

### 2.7.4. Non-registered analyses

Non-registered analyses using data from questionnaire responses were conducted to determine whether independent variables that have been shown, or could reasonably be expected, to be related to physical activity or sedentary behavior could contribute to explaining the variability in the following dependent variables: (i) reward positivity, (ii) the probability of choosing the stimulus with a higher probability of sitting, and (iii) the probability of changing the chosen stimulus compared to the previous trial. The independent variables examined in these exploratory analyses were age (Cheval et al., 2018c), gender (Chalabaev et al., 2022), body mass index (BMI; computed from height and weight) (Cheval et al., 2018c; Klimentidis et al., 2018), typical sitting time (Craig et al., 2003), exercise dependence (Hausenblas
491 & Symons Downs, 2002), affective attitudes toward exercise (Farajzadeh et al., 2023; Rhodes & Kates, 2015), instrumental attitudes toward exercise (Rollo, Gaston, & Prapavessis, 2016), fatigue (Multidimensional Fatigue Inventory [Smets et al., 1995] as well as pre- and post-task custom questions), IPAQ scores ranked by quartiles (Sagelv et al., 2020), rating of perceived exertion associated with retrieving coins on sit reward trials and stand reward trials, preference for the sit vs. stand trials, and awareness about the fact that one stimulus led to a higher probability of stand (vs. sit) trials relative to the other stimulus.

498 When estimating the effect on reward positivity (i), we tested the interaction between reward, type of stimulus, and each independent variable in models that accounted for the random effect of trial type and reward (reward vs. no reward) at the participant level. When estimating the effect on the probability of choosing the stimulus with the higher probability of sitting (ii), we tested the interaction between trial number and each independent variable in models that accounted for the random effect of trial at the participant level. When estimating the effect on the probability of changing chosen stimulus (iii), we tested the interaction between reward positivity and each independent variable in models that accounted for the random effect of reward positivity at the participant level.

506

507 **3. PILOT STUDY**

508 After conducting several preliminary pilot studies aiming to refine the paradigm (e.g., number of trials, probabilities that each stimulus leads to a sit trial), we conducted our main pilot study with two objectives. First, we sought to determine whether we could observe a reward positivity in our data that could potentially be moderated by trial type. Such effect would be observed if there was a frontocentral positive deflection in the ERP time-locked to feedback onset for reward trials in comparison to no-reward trials. Second, we sought to determine whether the rating of perceived exertion (Borg, 1982) was lower for trials in which participants sat to retrieve rewards versus squatted to retrieve rewards. No

509

510

511

512

513

514
persistent movement artifact was observed in the segments of pilot EEG data from which the reward
positivity was extracted (i.e., the data time-locked to feedback presentation). This was expected because
participants were motionless when feedback was presented. Additionally, despite participants
squatting, no sweat artifact was observed in the pilot EEG data, which was expected because the testing
room temperature was kept at 20°C. The pilot data informed the sample size calculation, which was
conducted with a simulation informed by the data (see 2.2 Sample Size Calculation). Regarding the
number of trials for each condition, the medians were as follows: sit reward: 36.5 (minimum = 24), sit
no-reward: 32.5 (minimum = 26), stand reward: 39 (minimum = 30), and stand no-reward: 39 (minimum
= 29).

524

525 **3.1. Pilot population**

526 We recruited nine participants from the College of Education Research Participant Pool at
527 Auburn University (USA) (5 males; age = 21.2 ± 1.2 years, BMI = 24.7 ± 4.8 kg/m², mean ± SD). We
528 determined seven participants were required to detect a main effect of reward, based on an effect size
529 observed in our past research (Meadows, Gable, Lohse, & Miller, 2016), but chose to recruit at least
530 eight participants in case of data loss due to poor EEG recording, which did occur for one participant.

531

532 **3.2. Pilot results**

533 ERP waveforms and scalp topographies for the pilot data are depicted in Supplemental Figure 1.
534 The figure suggested that we were able to obtain clean data, which was further evidenced by the fact
535 that we lost only 11.4% (SD = 10.8%) of trials per participant due to artifacts in the EEG. As expected, a 2
536 (Trial Type: Sit vs. Stand) x 2 (Reward: Reward vs. No-Reward) repeated-measures ANOVA revealed a
537 main effect of reward, F(1, 7) = 16.2, p = .005, f = 1.52, such that reward positivity was larger for reward
538 trials (M = 11.8 µV, SD = 8.48 µV) than no-reward trials (M = 5.51 µV, SD = 5.86 µV). The Trial Type x
539 Reward interaction was F(1, 7) = 1.86, p = .215, f = .516, and the main effect of trial type was F(1, 7) =
540 0.851, p = .387, f = .348. Regarding the second objective of the pilot data, as expected, a paired-sample
541 t-test revealed that rating of perceived exertion was lower when retrieving rewards on sit trials (M =
542 7.33, SD = 1.41) than stand trials (M = 11.1, SD = 2.20), t(8) = 4.09, p = .004, d = 1.36. The primary
543 statistical models were also tested with the pilot study data and results shown in Supplemental Table 2,
544 3, and 4.

545

546 **4. RESULTS**

547 All the models can be tested using the data and R code available in a public repository (Parma et
548 al., 2023). In lieu of a laboratory log, readers can note the data of each data collection by viewing the log
549 files at this repository.

550 MEM do not require Gaussianity of the dependent and independent variables, but of the
551 residuals and of the random effects, which can be checked only after a model has been fitted.
552 Accordingly, results in the main text reflect the initial results, except in the few cases where results
553 changed with transformed variables. In these cases, the main text shows results based on transformed
554 variables. For transparency, complete results based on non-transformed variables can be found in Table
555 2 and those based on transformed variables can be found in Supplementary Table 5.

556 Since outliers have an influence on the choice of the best transformation made by the
557 traditional Box-Cox likelihood-based transformation, this method may not always provide the correct
558 transformation. Therefore, when the Box-Cox transformation did not visually improve the distribution of
559 the variable, other transformations were tested.

560 All independent variables treated as continuous (i.e., trial, typical MVPA, today MVPA, study
561 energy expenditure, reward probability, stimulus reward probability, trial type reward probability,
562 typical sitting time, exercise dependence, affective attitudes, instrumental attitudes, fatigue, rating of
perceived exertion, awareness) were standardized to facilitate interpretation and to simplify the random structure. Standardization was conducted using the scale() function in the R base package (R Core Team, 2021). Thus, all the continuous independent variables included in the models have a mean of zero and a standard deviation of one.

Since the dependent variables were not standardized, the b-values reported in the results can be interpreted as follows: If the b-value is positive, then for each one-standard-deviation increase in the independent variable, the dependent variable increases by the value of the b coefficient. This strategy was chosen to facilitate comparison of the effects of the independent variables, to reduce potential multicollinearity problems, and to improve model convergence.

## 4.1. Descriptive statistics

Table 1 shows the characteristics of the participants. Sixty-four participants began the study, but the final sample included 58 participants (30 women; mean age 20.5 ± 1.1 years; mean body mass index 25.6 ± 3.9 kg/m²), two of whom had their EEG data discarded due to excessive artifact. Six participants did not complete the study due to experiment equipment failure or not meeting the minimum age criterion. The typical level of moderate-to-vigorous physical activity was 4396.6 ± 2831.9 MET minutes per week, which is above the threshold for a high level of physical activity (3000 MET-mins per week) (IPAQ Research Committee, 2005). This result is likely due to our sample consisting of young adults who were enrolled in Auburn University College of Education courses, many of which are about exercise science and physical activity. It should also be noted that the IPAQ is prone to overestimating actual levels of physical activity (e.g., Dinger, Behrens, & Han, 2006; Lee, Macfarlane, Lam, & Stewart, 2011). The level of moderate to vigorous physical activity during the day of the study was 314.4 ± 420.2 MET minutes, and the energy expenditure during the study was 122.3 ± 3.2 METs. The mean rating of perceived exertion was 7.4 (± 1.3) for sit trials and 12.5 (± 1.8) for stand trials. The mean reward
positivity amplitude was of 6.8 µV (± 4.6), the mean reward positivity amplitude in the no reward condition was of 4.6 µV (± 4.6), and the mean reward positivity amplitude in the reward condition was of 9.1 µV (± 15.1). On average, participants chose the stimulus with the higher probability to sit rather than to stand 58% (± 15%) of the trials. Finally, participants changed the stimulus chosen in 38% (± 14%) of the trials. The median number of trials per condition were as follows: sit reward = 37 (minimum = 23), sit no-reward = 40.5 (minimum = 21), stand reward = 40 (minimum = 24), and stand no-reward = 37 (minimum = 21). Other variables that were used for exploratory analyses are described in Table 1.

594

## 4.2. Reward Positivity

Table 2 and Figure 3 show the results for reward positivity as a function of the reward and the type of trial. Results showed that reward positivity was larger on reward versus no reward trials (b = 2.29; 95% confidence interval [95CI] = [1.74; 2.84]; *p* = 2.0 × 10⁻¹¹). This result demonstrated the presence of a reward positivity that could potentially be moderated by other factors, such as trial type. Contrary to H1, results showed no evidence of a two-way interaction between reward (reward vs. no reward) and the type of trial (sit vs stand), suggesting that the effect of reward on reward positivity did not significantly vary with the type of trial (b = 0.10; 95CI = [-0.18; 0.40]; *p* = 0.482). Contrary to H2.1, H2.2, and H2.3, results showed no evidence suggesting that typical MVPA (b = 0.14; 95CI = [-0.15; 0.45]; *p* = 0.338) or the energy expended across the task (b = -0.07; 95CI = [-0.37; 0.21]; *p* = 0.604) moderated the interaction effect of reward × type of trial on reward positivity. However, in line with H2.2, today MVPA moderated this interaction effect of reward × type of trial on reward positivity (b = 0.38; 95CI = [0.08; 0.683], *p* = 0.012). Simple analyses further showed that when today MVPA was high (+ 1SD), the two-way interaction between reward (reward vs. no reward) and the type of trial (sit vs. stand) was in a different direction (b = 0.47; 95CI = [0.047; 0.839], *p* = 0.029) than when today MVPA was low (-1 SD) (b = -
0.29; 95CI = [-0.723; 0.127], *p* = 0.170). Specifically, when today MVPA was high, the effect of reward in the seated trial was lower than in the stand trial (b = 2.03; 95CI = [1.135; 2.932], *p* = 1.97 × 10⁻⁵ vs. b = 2.97; 95CI = [2.098; 3.854], *p* = 1.6 × 10⁻⁹ for sit and standing trials, respectively).

In contrast, when today MVPA was low, the effect of reward in the sit trial was higher than in the stand trial (b = 2.37; 95CI = [1.135; 2.932], *p* = 8.94 × 10⁻⁷ vs. b = 1.78; 95CI = [2.098; 3.854], *p* = 1.4 × 10⁻⁴ for sit and standing trials, respectively). Of note, when today MVPA was transformed using the Box Cox transformation, the moderation did not stand (b = 0.28; 95CI = [-0.01; 0.58]; *p* = 0.061), but it did when a log₁₀₀ transformation that provided a better distribution of today MVPA was conducted (b = 0.37; 95CI = [0.07; 0.67]; *p* = 0.014). Since both the analysis with non-transformed today MVPA and the analysis with transformed MVPA with the better distribution (log1000) were significant with a p-value below 0.02 (*p* = 0.012 and *p* = 0.014) and a b-value above 0.36 (b = 0.38 and b= 0.37), we considered this moderation effect in the discussion. However, this result should be treated with caution, as the third analysis using another, yet less optimal, transformation (Box Cox) showed no significant effect (*p* = 0.061), although the b-value was similar and in the same direction (b = 0.28).

626

<table><thead><tr><th></th><th>n</th><th>Mean</th><th>SD</th></tr></thead><tbody><tr><td>Age (years)</td><td>58</td><td>20.5</td><td>1.1</td></tr><tr><td>BMI (kg/m²)</td><td>58</td><td>25.6</td><td>3.9</td></tr><tr><td>Typical MVPA (MET minutes per week)</td><td>54</td><td>4396.6</td><td>2831.9</td></tr><tr><td>Today MVPA (MET minutes)</td><td>57</td><td>314.3</td><td>420.2</td></tr><tr><td>Study-related energy expenditure (METs)</td><td>58</td><td>122.3</td><td>3.2</td></tr><tr><td>Perceived Exertion for the sit trial (range 6-20)</td><td>58</td><td>7.4</td><td>1.3</td></tr><tr><td>Perceived Exertion for the stand trial (range 6-20)</td><td>58</td><td>12.5</td><td>1.8</td></tr><tr><td>Reward Positivity (μV)</td><td></td><td></td><td></td></tr><tr><td>Average</td><td>56</td><td>6.8</td><td>4.6</td></tr><tr><td>Reward trials</td><td>56</td><td>9.2</td><td>5.5</td></tr><tr><td>No-reward trials</td><td>56</td><td>4.6</td><td>4.6</td></tr><tr><td>Probability to choose the sit vs. stand stimulus (%)</td><td>58</td><td>58</td><td>15</td></tr></tbody></table>
<table><tbody><tr><td>Change of chosen stimulus (vs. previous trial) (%)</td><td>58</td><td>38</td><td>14</td></tr><tr><td>Typical sitting time on a weekday (min/day)</td><td>54</td><td>389.2</td><td>209.3</td></tr><tr><td>Fatigue</td><td></td><td></td><td></td></tr><tr><td>Multidimensional Fatigue Inventory (range 1-5)</td><td>58</td><td>3.9</td><td>0.5</td></tr><tr><td>Pre-task custom score (range 0-10)</td><td>58</td><td>1.8</td><td>1.6</td></tr><tr><td>Post-task custom score (range 0-10)</td><td>58</td><td>4.7</td><td>2.0</td></tr><tr><td>Exercise dependence (range 1-6)</td><td>58</td><td>2.7</td><td>0.8</td></tr><tr><td>Affective attitudes toward exercise (range 1-9)</td><td>58</td><td>7.0</td><td>1.5</td></tr><tr><td>Instrumental attitudes toward exercise (range 1-9)</td><td>58</td><td>8.5</td><td>0.7</td></tr><tr><td>Awareness that a stimulus led more to sit than stand trials (range 0-10)</td><td>58</td><td>6.3</td><td>3.4</td></tr><tr><td>Number of trials per participant and EEG reliability</td><td></td><td>Median</td><td>Minimum</td><td>Reliability [95% CI]</td></tr><tr><td>Sit reward trials</td><td>56</td><td>37</td><td>23</td><td>.86 [.81, .91]</td></tr><tr><td>Sit no-reward trials</td><td>56</td><td>40.5</td><td>21</td><td>.78 [.68, .86]</td></tr><tr><td>Stand reward trials</td><td>56</td><td>40</td><td>24</td><td>.84 [.78, .90]</td></tr><tr><td>Stand no-reward trials</td><td>56</td><td>37</td><td>21</td><td>.85 [.78, .90]</td></tr></tbody></table>

627

628

629

630

631

**Table 1. Sample description.** Note. BMI = body mass index; 95% CI = 95% confidence intervals. Reliability estimates were computed using the ERP Reliability Analysis (ERA) Toolbox (v0.5.2; 1000 iterations), which uses generalizability theory (Clayson & Miller, 2017a).
<table><thead><tr><th rowspan="2">Reward positivity</th><th colspan="2">Without MVPA<br>(n = 56)</th><th colspan="2">With typical MVPA<br>(n = 52)</th><th colspan="2">With today MVPA<br>(n = 55)</th><th colspan="2">With study energy expenditure<br>(n = 56)</th></tr><tr><th>b (CI)</th><th>p</th><th>b (CI)</th><th>p</th><th>b (CI)</th><th>p</th><th>b (CI)</th><th>p</th></tr></thead><tbody><tr><td><strong>Fixed Effects</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>6.94 (5.71;8.16)</td><td>5.0 x 10<sup>-16</sup></td><td>7.09 (5.78;8.39)</td><td>5.7 x 10<sup>-15</sup></td><td>7.02 (5.79;8.25)</td><td>4.1 x 10<sup>-16</sup></td><td>6.95 (5.72;8.18)</td><td>4.9 x 10<sup>-16</sup></td></tr><tr><td><strong>Reward (ref. no reward)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Reward</td><td>2.29 (1.74;2.84)</td><td>2.0 x 10<sup>-11</sup></td><td>2.35 (1.78;2.93)</td><td>5.7 x 10<sup>-11</sup></td><td>0.19 (1.73;2.85)</td><td>4.1 x 10<sup>-11</sup></td><td>2.28 (1.74;2.83)</td><td>1.9 x 10<sup>-11</sup></td></tr><tr><td><strong>Type of trial (ref. sit)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Stand</td><td>-0.19 (-0.24;0.63)</td><td>.388</td><td>0.20 (-0.25;0.66)</td><td>.384</td><td>1.91 (-0.25;0.64)</td><td>.398</td><td>0.22 (-0.21;0.67)</td><td>.307</td></tr><tr><td><strong>Reward x Type of trial</strong></td><td>0.10 (-0.18;0.40)</td><td>.482</td><td>0.11 (-0.19;0.43)</td><td>.449</td><td>0.08 (-0.21;0.38)</td><td>.573</td><td>0.09 (-0.20;0.39)</td><td>.531</td></tr><tr><td><strong>Typical level of physical activity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Typical MVPA</td><td></td><td></td><td>0.33 (-0.95;1.62)</td><td>.607</td><td></td><td></td><td></td><td></td></tr><tr><td>Typical MVPA x Reward</td><td></td><td></td><td>0.41 (-0.15;0.98)</td><td>.151</td><td></td><td></td><td></td><td></td></tr><tr><td>Typical MVPA x Type of trials</td><td></td><td></td><td>-0.36 (-0.81;0.09)</td><td>.119</td><td></td><td></td><td></td><td></td></tr><tr><td>Typical MVPA x Reward x Type of trial</td><td></td><td></td><td>0.14 (-0.15;0.45)</td><td>.338</td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Today level of physical activity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Today MVPA</td><td></td><td></td><td></td><td></td><td>0.56 (-0.65;1.77)</td><td>.361</td><td></td><td></td></tr><tr><td>Today MVPA x Reward</td><td></td><td></td><td></td><td></td><td>0.21 (-0.34;0.76)</td><td>.448</td><td></td><td></td></tr><tr><td>Today MVPA x Type of trials</td><td></td><td></td><td></td><td></td><td>-0.12 (-0.56;0.32)</td><td>.591</td><td></td><td></td></tr><tr><td>Today MVPA x Reward x Type of trial</td><td></td><td></td><td></td><td></td><td>0.38 (0.08;0.68)</td><td>.012</td><td></td><td></td></tr><tr><td><strong>Study energy expenditure</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.80 (0.51;1.10)</td><td>7.1 x 10<sup>-8</sup></td></tr><tr><td>Study energy expenditure</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.11 (-0.40;0.17)</td><td>.445</td></tr><tr><td>Study energy expenditure x Reward</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.03 (-0.25;0.33)</td><td>.797</td></tr><tr><td>Study energy expenditure x Type of trial</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.07 (-0.37;0.21)</td><td>.604</td></tr><tr><td><strong>Random Effects</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Participants</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>15.559</td><td></td><td>16.481</td><td></td><td>15.066</td><td></td><td>15.599</td><td></td></tr><tr><td>Reward for subject</td><td>5.955</td><td></td><td>6.019</td><td></td><td>6.076</td><td></td><td>5.902</td><td></td></tr><tr><td>Type of trial for subject</td><td>2.924</td><td></td><td>2.899</td><td></td><td>3.003</td><td></td><td>2.974</td><td></td></tr><tr><td>Residual</td><td>191.342</td><td></td><td>195.568</td><td></td><td>193.195</td><td></td><td>190.664</td><td></td></tr></tbody></table>

Table 2. Results of the mixed-effects models predicting reward positivity. Notes. CI = confidence interval at 95%; MVPA =

moderate-to-vigorous physical activity.

635
<figure><img src="image_5.png" /><figcaption>Figure 3. ERP waveforms and difference wave scalp topographies.</figcaption></figure>

Left panel: Grand average waveforms by trial type and reward. Right panel: Scalp topographies for reward minus no reward difference wave (top) and sit minus stand difference wave (bottom). Topographies are shown for a window that spanned 252 to 292 ms after feedback onset, as this was the average timing of the window in which reward positivity was analyzed.
<figure><img src="image_6.png" /><figcaption>Figure 4. Odds Ratio for choosing the stimulus that was more likely to lead to sitting (vs. standing) as a function of trial number. *Notes.* Errors bars = confidence interval at 95%. Figure shown with uncentered Trial Number.</figcaption></figure>

<table><caption>Table 3. Results of the mixed effects models predicting the probability of choosing the stimulus with the higher sitting likelihood. *Notes.* OR = odds ratio. 95 CI = confidence interval at 95%. Trial number was centered at the middle (i.e., 80th trial) of the task.</caption><thead><tr><th>Probability of choosing the "sit" stimulus</th><th colspan="2">(n = 58)</th></tr><tr><th></th><th>OR (95 CI)</th><th>p</th></tr></thead><tbody><tr><td><strong>Fixed Effects</strong></td><td></td><td></td></tr><tr><td>Intercept</td><td>0.42 (0.20;0.65)</td><td>1.7 x 10<sup>-4</sup></td></tr><tr><td>Trial number</td><td>0.32 (0.16;0.49)</td><td>1.0 x 10<sup>-4</sup></td></tr><tr><td><strong>Random Effects</strong></td><td></td><td></td></tr><tr><td>Participants</td><td></td><td></td></tr><tr><td>Intercept</td><td>0.710</td><td></td></tr><tr><td>Trials number</td><td>0.371</td><td></td></tr><tr><td>Corr. (Intercept, trial number)</td><td>0.30</td><td></td></tr></tbody></table>

4.3. Probability of choosing the stimulus with the higher sitting likelihood
Table 3 and Figure 4 show the results for the probability of choosing the stimulus that was more likely to lead to sitting (vs. standing). Consistent with H3, results showed that the probability of choosing the stimulus more likely to lead to sitting increased as the number of trials increased (OR = 0.32; 95CI = [0.16; 0.49], *p* = 1.0 × 10⁻⁴). For example, at the 20th trial of the task, participants odds of choosing the stimulus more likely to lead to sitting than standing was not significant (OR = 0.99; 95CI = [0.76; 1.30], *p* = 0.990), this odd was higher and significant in the 140th trial of the task (OR = 2.33; 95CI = [1.64; 3.35], *p* = 2.0 × 10⁻⁶). An exploratory analysis showed no evidence of a quadratic effect of trial number (OR = -0.006; 95CI = [-0.05; 0.04], *p* = 0.813).

### 4.3. Influence of reward positivity on the subsequent decision to choose the same (vs. different) stimulus.

Table 4 shows the results for influence of reward positivity on the subsequent decision about whether a participant chose the same or different stimulus. Contrary to H4, MEM results showed no evidence that the reward positivity in a given trial predicted subsequent decision to change (vs. keep) the selected stimulus in the subsequent trial.

### Probability of changing of stimulus
(n = 56)

b (95CI)

p

**Fixed Effects**

Intercept
-0.51 (-0.70;-0.31)
1.4 × 10⁻⁷

Reward positivity preceding trial
-0.001 (-0.005;0.001)
.301

**Random Effects**

Participants

Intercept
0.490

Reward positivity preceding trial
2.9 × 10⁻⁵

Corr. (Intercept, trial number)
-0.47

Table 4. Results of the mixed effects models predicting the influence of reward positivity on the subsequent decision to choose the same (vs. different) stimulus. Notes. 95 CI = confidence interval at 95%.
697 **4.4. Sensitivity analyses related to reward positivity**

698 Overall, results of the sensitivity analyses were consistent with those of the main analyses.
699 Specifically, for the reward positivity centered on a peak between 250 and 350 ms, results showed that
700 reward positivity was larger in reward than no reward trials (b = 2.25; 95CI = [1.71; 2.79]; p = 2.3 x 10⁻¹¹),
701 but this effect was not significantly moderated by the type of trial (b = 0.19; 95CI = [-0.10; 0.49]; p =
702 0.198). Results showed that reward positivity averaged across electrodes Cz, CPz, and Pz (and centered
703 on a peak between 230 and 350 ms) was larger in reward than no reward trials (b = 2.29; 95CI = [1.68;
704 2.90]; p = 5.1 x 10⁻¹⁰). Moreover, as the significance threshold was set to .02, the type of trials did not
705 significantly moderate the main effect of reward (b = 0.28; 95CI = [0.01; 0.56], p = 0.039). As the p-value
706 was significant under a less stringent threshold (0.05), we explored the simple effects, which confirmed
707 the absence of a meaningful moderation pattern as they showed no evidence of a difference between
708 stand and sit trials, be it in the reward (b = 0.20, 95CI = [-0.28; 0.69], p = 0.411) or no reward condition
709 (b = -0.36, 95CI = [-0.85; 0.11], p = 0.137). Thus, in line with the main analyses, the effect of reward was
710 not significantly more pronounced in the sitting vs. standing trials.

711

712 **4.5. Secondary analyses**

713 <u>**4.5.1. Reward positivity**</u>

714 As registered, we tested whether the frequency a reward had been received (1) up to the
715 current trial, (2) when choosing a certain stimulus up to the current trial, and (3) on a certain trial type
716 up to the current trial explained residual variance. For the frequency related to the type of stimulus or
717 the type of trial, we built two indicators. The indicator related to the type of stimulus contrasted the
718 probability of obtaining a reward when choosing the stimulus with the higher probability to sit minus
719 the probability of obtaining a reward when choosing the stimulus with the higher probability to stand. A
720 higher value indicates a higher reward probability for the sit relative to the stand stimulus. Likewise, for
721 the frequency related to the type of trial, we built a variable contrasting the probability of obtaining a
722 reward following sit trials minus the probability of obtaining a reward following stand trials. A higher
723 value indicates a higher reward probability for the sit relative to the stand trials.

724 Results testing each indicator separately showed no evidence that the indicator related to
725 reward probability (b = -0.20; 95CI = [-0.50; 0.08]; p = 0.172), reward frequency associated to the type of
726 stimulus (b = -0.001; 95CI = [-0.30; 0.29]; p = 0.992), or reward frequency associated to the type of trial
727 (b = 0.09; 95CI = [-0.20; 0.39]; p = 0.526) moderated the two-way interaction between reward and the
728 type of trial.

729

<figure><img src="image_7.png" /><figcaption>Figure 5. Odds for choosing the sit stimulus as a function of the probability that the stimulus leads to a stand or a sit trial (A) and odds for changing of stimulus as a function of choice on the previous trial (B). Notes. Error bars and blue area = confidence interval at 95%. The results illustrated in Figure 5A should be considered with caution as the p value (p = 0.029) was above the alpha level of the study (0.02).</figcaption></figure>

736

### 4.5.2. Choice of the stimulus

738 As registered, for the model testing the probability of choosing the stimulus more likely to lead
to sitting than standing, we built a variable reflecting the probability that the stimulus with the higher sit

739
probability led to a sit trial relative to the probability that the other stimulus, with the higher stand probability, led to a sit trial, up to the current trial. Specifically, this variable (stimulus trial type probability) as well as its interaction with the number of trials were included in the model. Consistent with the main analyses, results showed that the probability of choosing the stimulus more likely to lead to sitting than standing increased as the number of trials increased (OR = 0.30; 95CI = [0.15; 0.46], *p* = 6.3 × 10⁻⁵). However, and based on a stringent alpha level (i.e., .02), we found no statistically significant evidence that the actual probability to sit affected the probability of choosing the sit stimulus relative to the stand stimulus (OR = 0.18; 95CI = [0.01; 0.34], *p* = 0.029) or interacted with the number of trials (OR = 0.08; 95CI = [-0.03; 0.19], *p* = 0.152). Because a less stringent statistical threshold (0.05) would have suggested a positive relationship between the probability to sit and the probability of choosing the sit stimulus, this relationship is illustrated in Figure 5A.

751

752 **4.5.3. Influence of the reward positivity and the subsequent stimulus choice**

As registered, for the model testing the influence of reward positivity on the subsequent decision about whether a participant chose the same or different stimulus, we added in separate models the previous trial type and the probability of reward up to the current trial, as well as their interactions with the number of trials. In the model adjusting for the previous trial type, results showed that the probability of changing of stimulus was higher when the previous trial was a stand trial relative to a sit trial (OR = 0.35; 95CI = [0.26; 0.44], *p* = 4.8 × 10⁻¹⁴) (Figure 5B). However, results showed no evidence of an association between reward positivity and the probability of changing stimulus (OR = -0.07; 95CI = [-0.14; 0.001], *p* = 0.052). The two-way interaction between previous reward positivity and previous trial type was not significant (OR = 0.07; 95CI = [-0.01; 0.16], *p* = 0.122). In the model adjusting for the probability of reward up to the current trial, no main or interactive effects were observed (*ps* > 0.196).

763
764 **4.6. Non-registered analyses**

765 None of the exploratory analyses showed significant main or interactive effects on reward
766 positivity. The main effect of typical physical activity ranked by quartiles (OR = -0.22; 95CI = [-0.41; -
767 0.03], p = 0.019) and the interaction effect between trial and awareness of the experimental
768 manipulation (OR = 0.25; 95CI = [0.10; 0.40], p = 7.4 x 10⁻⁴) were the only significant effects on the
769 probability of choosing the stimulus with the higher probability of sitting. Importantly, in the latter
770 model, the main effect of trial remained significant (OR = 0.32; 95CI = [0.17; 0.47], p = 1.9 x 10⁻⁵). The
771 main effect of preference for the sit vs. stand trials (OR = -1.70; 95CI = [-3.13; -0.28], p = 0.017) and
772 awareness (OR = -0.21; 95CI = [-0.39; -0.03], p = 0.017) as well as the interaction effect between reward
773 positivity and typical physical activity ranked by quartiles (OR = -0.05; 95CI = [-0.10; -0.01], p = 0.009)
774 were the only significant effects on the change in chosen stimulus. The simple effects of the latter 2-way
775 interaction showed that higher reward positivity was significantly associated with a lower probability of
776 changing the stimulus chosen but only among individuals in the highest quartile (Q4: OR = 0.88; 95CI =
777 [0.81; 0.96], p = 0.004). In the lower quartiles, the effect of reward positivity on the probability of
778 changing the stimulus chosen was not significant (for Q1: OR = 1.05; 95CI = [0.96; 1.29], p = 0.215; for
779 Q2: OR = 0.99; 95CI = [0.94; 1.05], p = 0.855; for Q3: OR = 0.93; 95CI = [0.88; 0.99], p = 0.025).

780

781 **5. DISCUSSION**

782 **5.1. Main findings**

783 The theory of effort minimization in physical activity (TEMPA) suggests that sedentary behaviors
784 are rewarding (Cheval & Boisgontier, 2021; Cheval et al., 2018a). However, direct evidence supporting
785 its rewarding value is lacking. Here, the objective of this registered report was to test whether sedentary
786 behaviors (i.e., retrieving a reward while sitting down) evoke reward-related brain activity (reward
787 positivity), and whether this effect is moderated by factors related to energy expenditure (typical
788 physical activity, physical activity on the day of the study, physical activity during the experiment).
789 Moreover, based on reinforcement learning theory (Sutton & Barto, 2018), we tested whether decisions
790 leading to sedentary behaviors are “learned” (i.e., participants learned to choose a stimulus likely to
791 lead to sitting down), and whether reward positivity is linked to subsequent decisions (i.e., whether
792 reward positivity associated with sitting down after choosing a stimulus increased the likelihood of
793 choosing the same stimulus).

794 At the neural level, results showed that reward positivity was larger on reward versus no-reward
795 trials, thereby confirming the validity of the experimental procedure we used to evoke reward-related
796 brain activity. However, contrary to our main hypothesis, we found no evidence that this reward
797 positivity effect was significantly moderated by the type of trial (sit vs. stand). At the behavioral level,
798 results showed that the probability of choosing the stimulus more likely to lead to sitting than standing
799 and squatting increased when the number of trials increased. In addition, participants were more likely
800 to change the selected stimulus if the stimulus they chose on the previous trial led to a stand (vs. a sit)
801 trial. Hence, in line with the theory of effort minimization in physical activity, and consistent with sitting
802 serving as a reward in reinforcement learning, our study confirms that people choose the options
803 associated with the least effort. Yet, our results showed no evidence suggesting this behavioral pattern
804 could be explained by the reward positivity. Likewise, we found no evidence suggesting that typical
805 physical activity, physical activity on the day of the study, or physical activity during the experiment
806 influenced reward positivity.

807 Several factors can explain why sitting reward trials did not lead to larger reward positivity than
808 standing reward trials. First, our paradigm crossing a doors task (Hassall et al., 2019) with a movement-
809 incentive delay task (Cheval et al., 2019) may not be suitable for measuring the rewarding value of
810 sedentary behaviors. In particular, a process of justification of effort, “a form of cognitive dissonance in
811 which one gives greater value to outcomes that require greater effort to obtain, to justify the greater
effort" (Aronson & Mills, 1959), may explain why trials associated with standing (vs. sitting) were not processed as less rewarding in our paradigm. Indeed, in our task, participants already knew that they would have to expend energy (i.e., to stand and squat) if they earned a reward (see Figure 2, column 4) before finding out whether they indeed received the reward (see Figure 2, column 6). Accordingly, after learning that they would have to expend energy to retrieve the reward, participants may have raised the rewarding value of the energy expenditure, midtrial, through a process of justification of effort (Alessandri, Darcheville, & Zentall, 2008) before finding out whether they would have to actually expend energy to retrieve the reward, which was when the reward positivity was measured.

At the theoretical level, an evolutionary account of human behavior can largely explain this process of effort justification. Specifically, since people minimize unnecessary energy expenditure, investing effort into a given behavior should be justified by a reward that is worth the investment. As such, through a rationalization mechanism aiming to reduce the risk for cognitive dissonance, a conflict that occurs when beliefs do not line up with behaviors, people may increase the value of a reward associated with a behavior as soon as they engage in this behavior. Of note, this mechanism is not unique to physical effort and can be extended to other costs, such as the money invested in an object (e.g., wine) (Schmidt, Skvortsova, Kullen, Weber, & Plassmann, 2017).

Noteworthy, for physical effort at least, this reasoning highlights the need to dissociate the mechanisms associated with the anticipation phase (i.e., the incentive value of a given potential reward) from those associated with the consummatory phase (i.e., the rewarding activity while the reward is obtained) (Novak & Foti, 2015). In other words, the effect of physical effort intensity on the reward associated with this effort during the anticipation phase (i.e., negative relationship) could be different from its effect during the consummatory phase (i.e., positive relationship). While people typically behave in a way that minimizes effort (Klein-Flügge et al., 2016; Prévost, et al., 2010; Skvortsova et al., 2014) – which confirms that effort is essentially processed as a cost and as an aversive experience to
avoid –, once they engage in an effortful behavior, the subjective value of the behavior becomes higher
836
through this effort justification to reduce cognitive dissonance (“I have engaged a lot of effort in this
837
behavior, but it is not worth the effort” vs. “I have engaged a lot of effort in this behavior, and it was
838
worthwhile”). For example, in the study by Palidis and Gribble (2020), reward-related brain activity was
839
greater when participants received reward feedback on high-effort trials, an observation aligned with
840
other studies showing that individuals value rewards obtained during high effort more those obtained
841
during low effort (Inzlicht et al., 2018). In sum, the more a behavior involves effort, the less people are
842
likely to engage in it, but, paradoxically, once people are committed to the effort, the more this effortful
843
behavior is valued. Thus, physical effort can be avoided (anticipation phase) or valued (consummatory
844
phase), depending on the behavior phase. The observation that the effect of reward in the stand trials
845
(vs. sit trials) was higher when today MVPA was high was consistent with this reasoning. Indeed, when
846
people already engaged in physical activity on the day of the study, the standing trials may be perceived
847
as more effortful, thereby potentially explaining the higher rewarding value of such trials via the effort
848
justification process.
849

Second, EEG primarily records cortical activity (Krishnaswamy, Obregon-Henao, Ahveninen, et
850
al., 2017). Yet, it is possible that the brain regions underpinning the rewarding value of sedentary
851
behaviors are subcortical and not accessible by EEG. For example, regions typically involved in reward
852
processing, such as amygdala, nucleus accumbens, and the ventral striatum (Corbit & Balleine, 2011;
853
Gottfried, O'Doherty, & Dolan 2003; Knutson, Adams, Fong, & Hommer, 2001; Prévost, Liljeholm,
854
Tyszka, & O'Doherty, 2012; Roesch & Olson, 2004; Schultz, Tremblay, & Hollerman, 2000), may not be
855
reflected in EEG. However, it is important to note that combined ERP-functional magnetic resonance
856
imagining (fMRI) research has shown that the blood-oxygen-level-dependent signal in the ventral
857
striatum is correlated with the amplitude of the reward positivity, and EEG source localization analysis
858
suggests the striatum may be the neural generator of the reward positivity (Carlson et al., 2011; Foti et
859
al., 2011). Thus, variations in reward positivity amplitude may correspond with changes in the activation of subcortical regions associated with reward processing, providing indirect measures of subcortical activities involved in reward processing. Nonetheless, future studies using fMRI, possibility in conjunction with EEG, could be useful to measure the subcortical regions that may process the rewarding value of sedentary behaviors. Another avenue for future research may involve examining the role of ERP components other than the reward positivity in processing the rewarding value of sedentary behaviors and influencing the likelihood of choosing stimuli associated with such behaviors. For example, Meadows et al. (2016) found the P3b ERP component was sensitive to reward value, and Fischer and Ullsperger (2013) observed the P3b predicted future choices in an experimental task. Notably, in the present study, the P3b seems sensitive to reward feedback, with reward trials exhibiting higher amplitude over parietal cortex than no reward trials (Figure 3).

At the behavioral level, results showing that the probability of choosing the stimulus more likely to lead to sitting than standing increased as the number of trials increased was consistent with existing literature, the theory of effort minimization in physical activity, and the corollary that decisions leading to sedentary behaviors are reinforced (Cheval et al., 2018a; Cheval & Boisgontier, 2021). Experimental works have shown that humans favor lower rather than higher effort, everything else being equal (Prévost et al., 2010; Skvortsova et al., 2014; Klein-Flügge et al., 2016; Palidis & Gribble, 2020). For example, findings have robustly confirmed that humans process physical effort as a cost in decision-making tasks and minimize the physical effort required to obtain a specific reward (Prévost et al., 2010; Skvortsova et al., 2014; Klein-Flügge et al., 2016; Bernacer et al., 2019). Morevoer, from an health psychology perspective, our current behavioral findings are consistent with the observation that sedentary-related stimuli act as temptations (Cheval et al., 2017), and that not engaging in such behaviors require higher inhibitory and cognitive function (Cheval et al., 2020a; Cheval et al., 2020c). In
sum, the current study provides additional behavioral evidence, based a whole-body exercise task, that
individuals favor the behavioral alternative associated with the least effort.

Regarding the secondary analyses, tests focusing on the neural outcomes showed no evidence
that the frequency at which a reward has been received 1) up to the current trial, 2) when choosing a
certain stimulus up to the current trial, or 3) on a certain trial type up to the current trial were related to
the reward positivity or moderated the effect of reward depending on the type of trial. Analyses
focusing on behavioral outcomes showed that the probability of changing the selected stimulus was
higher when the previous trial was a stand trial relative to a sit trial. This finding was consistent with
some empirical studies (Palidis & Gribble, 2020). For example, in the study by Palidis and Gribble (2020),
results showed participants were more likely to change their response from the previous trial if it led to
high effort. Morevoer, although not significant based on the stringent alpha level required by the
current journal (i.e., *p* < .02), participants were more likely to choose the sit stimulus when the actual
probability of this stimulus leading to a sit trial was relatively high (*p* = 0.029). If this finding would have
been considered significant, it would have provided additional evidence that people tend to behave in a
way that maximizes the probability to conserve energy.

Regarding the non-registered analyses, if our alpha level would have been less stringent (e.g.,
0.05), our results would have suggested that stronger affective attitudes toward physical activity
reduced the probability of choosing the sit (vs. stand) stimulus (p= 0.044), and that higher perceived
exertion associated with the stand trials (i.e., higher peevied effort for the squats) was associated with
an increased probability of choosing the sit (vs. stand) stimulus (p = 0.033). Although not planned and
above the stringent significance threshold, these findings are consistent with the existing literature –
improved affective experience associated with physical activity should favor engagement in physically
active behaviors (Maltagliati, Sarrazin, Fessler, Lebreton, & Cheval, 2022). Finally, results revealed that
the increased probability of choosing the stimulus more likely to lead to sitting than standing as a
function of trial number was more pronounced in people who were aware that one stimulus led to more
sit than stand trials compared to the other stimulus. Notably, the main effect of trial number was
significant even after accounting for this awareness. Thus, this effect was observed even in people who
were unaware that their decisions were influencing their energy expenditure. That is, the selection of
the behavioral option minimizing effort can also take place at a rather automatic, unconscious level.

912

913 **5.2. Limitations and strengths**

The present study has some limitations. First, as explained above, the used paradigm may have
confounded the potential devaluation of the reward by the physical effort to obtain it because of a
process of effort justification. To reduce the risk of this potential confounding, future studies need to
disentangle the effect of effort at different stages of the decision process: before (i.e., effort avoidance),
during (i.e., effort minimization), and after (i.e., effort justification) physical activity behavior. Such a
design may allow for disentangling the differential effects of effort across stages of behavioral
regulation, thus allowing for a more nuanced and accurate assessment of the effects of effort and
reward processing. Second, while EEG provides an advantage over other brain imaging techniques in
terms of temporal resolution, which was essential in our study, the use of this technique may have
precluded accurate assessment of subcortical brain regions that may process the rewarding value of
sedentary behaviors as EEG primarily records cortical activity (Krishnaswamy et al., 2017). Studies based
on another non-invasive brain imaging technique, magnetic resonance imaging (MRI), can overcome this
limitation and provide images with higher spatial resolution, but lower temporal resolution. Third,
typical levels of physical activity were assessed using a self-report questionnaire, which may not
accurately capture the actual levels of physical activity, as correlations between self-report and direct
measures of physical activity are low to moderate (Lee et al., 2011; Prince et al., 2008). Assessment of
usual physical activity using device-based measures, such as accelerometers, would have provided more
reliable and valid information, as they have shown greater validity and reliability than self-report measures (Dowd et al., 2018). Fourth, the sample was young, healthy, and physically active, which may have biased the current results as this population is likely to be less attracted toward effort minimization than adults who are older, more sedentary adults, or adults with a health condition, as the latter populations' perception of the same level of effort is likely higher than the former population's due to greater fatigability (LaSorda et al., 2020) or chronic pain (Shupler, Kramer, Cragg, Jutzeler, & Whitehurst, 2019). Therefore, energy-minimizing behaviors are more likely to elicit reward-related brain activity in older, more sedentary, and/or less-healthy adults.

939

940 **5.3. Conclusion**

This registered report showed evidence that people behave in the way that minimizes the effort to invest in the task to obtain the reward, consistent with the theory that opportunities to minimize energy expenditure are rewarding. However, we found no evidence that reward-related brain activity underlies these behavioral manifestations. Future studies using other EEG paradigms or relying on other methodologies (e.g., magnetic resonance imaging) are warranted to better capture the neural mechanisms at works.

947

948 **Acknowledgements**

Boris Cheval is supported by an Ambizione grant (PZ00P1_180040) from the Swiss National Science Foundation (SNSF). Matthieu P. Boisgontier is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC; RGPIN-2021-03153), the Canada Foundation for Innovation (CFI), Mitacs, and the Banting Research Foundation.

953

954 **Open data and code**
955 The data, materials and code are available at: https://doi.org/10.5281/zenodo.8111833 (Parma et al.,
956 2023).

957

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1300 **Appendix A: Task instructions read to participants**

1301 "To start each trial, press the bottom (A) button. Each trial begins with a burnt-orange and a navy-blue
1302 square. Select which color square you want to choose by pressing the left (X) button or the right (B)
1303 button. So, on this trial, if you choose the burnt-orange square, you would press the ____ button. If you
1304 choose the navy-blue square, you would press the ____ button. YOU SHOULD FOCUS ON SELECTING A
1305 SQUARE BASED ON COLOR, NOT BASED ON LOCATION. In other words, select a square because it is
1306 burnt-orange or navy-blue, not because it is on the left or right. After making your selection, you will see
1307 a stimulus indicating whether you will retrieve your reward from the upper or lower container, if you
1308 win a reward. If you see a stimulus with the container on the upper line, then you will be retrieving your
1309 reward from the upper container. If you see a stimulus with the container on the lower line, then you
1310 will be retrieving your reward from the lower container. Next, you will see if you actually won a reward
1311 or not. If you see a dollar sign, then you won a reward. If you see a zero, then you did not win a reward.
1312 If you win a reward from the upper container, then you will wait until you hear a tone. When you hear a
1313 tone, you will take a coin from the upper container, touch your butt to the chair, then place the coin in
1314 the upper collection container. You will repeat this sequence four more times when prompted by a
1315 tone. If you win a reward from the lower container, then you will wait until you hear a tone. When you
1316 hear a tone, you will sit down in the chair and take a coin from the lower container, then place the coin
1317 in the lower collection container. You will remain seated and reach into the lower container to retrieve a
1318 coin each time you hear a tone (you will hear four more tones). When you are prompted to start the
1319 next trial, return to a standing position. If you get feedback that indicates a zero instead of a dollar sign,
1320 then simply remain standing. Each coin represents a raffle ticket to win $10, so the more coins you earn,
1321 the more likely you are to win $10. On each trial, a certain color square will give you a certain probability
1322 of winning, so, again, FOCUS ON CHOOSING A SQUARE BASED ON COLOR. However, there is no strategy
1323 for selecting a color square in order to win. In other words, there is no pattern as to which color square

1324 will give you the best chance at winning from trial to trial.”
Appendix B: Fatigue Questions

1. Right now, how fatigued are you?

0 1 2 3 4 5 6 7 8 9 10
Not At All

Very Much

2. Right now, I have no energy

0 1 2 3 4 5 6 7 8 9 10
Completely Disagree

Completely
Agree

3. Right now, I feel physically exhausted

0 1 2 3 4 5 6 7 8 9 10
Completely Disagree

Completely
Agree