(C) PLOS One [1]. This unaltered content originally appeared in journals.plosone.org.
Licensed under Creative Commons Attribution (CC BY) license.
url:https://journals.plos.org/plosone/s/licenses-and-copyright

------------



Locus coeruleus neurons encode the subjective difficulty of triggering and executing actions

['Pauline Bornert', 'Motivation', 'Brain', 'Behavior Team', 'Institut Du Cerveau Et De La Moelle Épinière', 'Icm', 'Inserm Umrs', 'Cnrs Umr', 'Pitié-Salpêtrière Hospital', 'Paris']

Date: 2022-01 

We will first present behavioral data that provide information about effort production across the 3 tasks and then describe the dynamic modulation of LC activity across these 3 tasks.

Behavior

We compared behavior in the 3 tasks, all involving a manual response to a visual stimulus (go signal, a red point turning green). In each of these tasks, monkeys completed multiple trials across which actions varied in terms of sensory-motor requirements and/or in terms of reward contingencies. At the beginning of each trial, monkeys received information about the current task condition using a specific visual cue and had to adjust their behavior accordingly. We measured 2 behavioral responses: RT (the interval between the go signal and action onset) and engagement (whether they attempted to perform the trial or not). Thus, in each condition, the proportion of trials in which monkeys engaged (engagement rate) reflected their average WTW given the expected sensory-motor constraints and reward contingencies. Critically, since trials were repeated until correct completion, there was no instrumental interest in refusing to perform any trial. In such task, the optimal behavior would be to perform all trials regardless of the associated costs and benefits, because refusing to engage only increased the delay until reward delivery. But still, monkeys failed to initiate the action more often in trials associated with higher cost and/or lower rewards, which indicates that they could not repress a natural tendency to disengage in such conditions, even if in that situation if was counterproductive. Thus, we assumed that in task conditions associated with lower WTW, engaging in the task and performing the action required a higher level of cognitive control to overcome the stronger tendency to disengage, compared to conditions in which average WTW was higher. Even if the absence of engagement could also be taken as a passive process (a lack of motivation, rather than an urge to disengage), it would still require cognitive control to override that suboptimal behavior. Hence, we used the contrast in WTW across conditions to evaluate the influence of these conditions on cognitive difficulty and, potentially, on the amount of cognitive effort mobilized to overcome that difficulty. Since all these tasks involved triggering a response to a visual target, we could also measure RT, i.e., how quickly the animal responded to the stimulus. In line with previous studies, we assumed that RTs could be affected both by sensory effects (how difficult it was to identify the target stimulus), motor effects (how difficult it was to execute the action), and cognitive effects, namely the amount of cognitive control required to trigger the action in the current condition (here, as a function of the natural tendency to disengage from the task in the current condition) [33]. Critically, the increase in RT in conditions associated with greater cognitive control could reflect both the difficulty itself and/or the mobilization of resources (i.e., time) in order to overcome that difficulty [7,34,35]. To capture the potential influence of sensory-motor and cognitive effects on RT, we compared behavior across 3 tasks manipulating both sensory-motor and reward parameters across conditions (Fig 1).

The different features of the 3 tasks are summarized in Table 1, together with the predicted influence of sensory-motor and cognitive effects on RT. In short, all 3 tasks involved detecting a simple visual target (a red dot turning green). In the delay discounting and in the force discounting task, the target stimulus was always presented in the middle of the screen and only one responding device was available. In the target detection task, the target stimulus could be presented in 1 of 9 possible positions on the screen, and there were 3 response devices (left, middle and right). In the delay discounting task, the action was a simple bar release, whereas in force discounting and target detection task, monkeys had to squeeze a grip and exert a given level of force. In the force discounting task, the level of force necessary to complete the trial was varied systematically across trials according to 3 difficulty levels, whereas in the target detection task, the level of force required was set to a minimum and equivalent across all task conditions. Finally, we also manipulated reward parameters: In both the delay discounting and force discounting tasks, we systematically varied the size of the reward (volume of juice) according to 3 levels. In the delay discounting task, we also systematically varied the delay between correct responses and reward delivery, according to 3 levels.

Based on these features, we predicted that, across task conditions, WTW should differ only in delay discounting and force discounting tasks, but not in the target detection task. Accordingly, RTs should be affected only by cognitive effects in the delay discounting task. In the target detection task, however, RTs should only be affected by sensory-motor effects. Finally, in the force discounting task RTs should display both sensory-motor and cognitive effects. To test these predictions, we compared engagement rates and RT modulations across conditions in the 3 tasks.

In the delay discounting task, there were clear differences in WTW across conditions: Engagement rates showed a significant positive modulation by reward and a significant negative modulation by delay in both monkeys. For each monkey, we fit a logistic regression for WTW with reward and delay as parameters. The reward effect was significantly positive for both monkeys (Monkey T: beta = 0.40; p < 10-11; Monkey L: beta = 0.23; p < 10-4), and the delay effect was significantly negative for both monkeys (Monkey T: beta = −0.42; p < 10-14; Monkey L: beta = −0.31; p < 10-8; Fig 2A and 2B). Since in this task sensory-motor constraints were equivalent across conditions, we expected task conditions to affect RTs through cognitive effects and thus according to their influence on WTW. Indeed, in both monkeys, task parameters had significant opposite effects on RT, as measured using linear modeling (GLM; Fig 2C and 2D). Reward had a negative effect on RT (Monkey T: beta = −0.12; p < 10−34; Monkey L: beta = −0.23; p < 10−33), and delay had a positive effect (Monkey T: beta = 0.22; p < 10−95; Monkey L: beta =0.27; p < 10−52). Thus, task parameters clearly affected WTW and consequently the cognitive difficulty associated with engaging in the task and triggering the action. The corresponding differences in RTs across conditions confirmed this interpretation in terms of cognitive difficulty to trigger the actions, but also in terms of resources (time, at least) invested in order to overcome the difficulty and perform the action.

PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 2. Behavior in the delay discounting task, the target detection task, and the force discounting task. (A–D) Behavior in the delay discounting task. (A, B) Coefficients of the logistic regression for engagement in trials (0 if the monkey made no action, 1 if he performed an action) with task parameters (reward and delay) as regressors, by monkey. For both monkeys, reward had a positive effect, and delay had a negative effect on the probability to engage in trials. (C, D) Coefficients for the GLM for RT with task parameters (reward and delay) as regressors, by monkey. For both monkeys, reward had a negative effect, and delay had a positive effect. (E–H) Behavior in the target detection task. (E) Mean engagement rate across sessions by dot position on screen. There was no difference in engagement rate across the 9 conditions (ANOVA). (F) Mean RT across sessions by dot position on screen. A 2 way-ANOVA with vertical and horizontal dot coordinates showed that RTs were significantly shorter for middle grip presses. (G) Time course of the exerted force on each grip across sessions after action onset. Thick lines represent the mean exerted force and the thinner lines represent one the SEM above and below the mean. The maximum exerted force was higher for middle grip presses (blue) than for right grip (green) or left grip (red) presses. (H) Coefficients for the GLM for RT with only the maximum exerted force as parameter. RT was longer if the maximum exerted force would be stronger. (I–N) Behavior in the force discounting task. (I, J) Coefficients of the logistic regression for engagement in trials (0 if the monkey made no action, 1 if he performed an action) with task parameters (reward and force) as regressors, by monkey. For both monkeys, reward had a positive effect, and force had a negative effect on the probability to engage in trials. (K, L) Coefficients for the GLM for RT with task parameters (reward and force) as regressors, by monkey. For both monkeys, reward had a positive effect (only a tendency for Monkey D), and force had a negative effect. (M, N) Coefficients for the GLM for RT with only the maximum exerted force as parameter. For both monkeys, there was a strong negative relationship between RT and the maximum exerted force. **: p < 0.01; ***: p < 0.0001; n.s., nonsignificant; error bars represent SEM. Underlying data in 10.17605/OSF.IO/PYVSA. GLM, generalized linear model; RT, response time; SEM, standard error of the mean. https://doi.org/10.1371/journal.pbio.3001487.g002

In the target detection task, sensory-motor constraints did vary across task conditions (positions of targets and of response levers), but they did not affect WTW (1-way ANOVA on session-by-session rates of engagement in each condition, dot position as parameter, p >> 0.05; Fig 2E). By contrast, these sensory-motor constraints did affect RTs: We fit a 2-way ANOVA for RT with the horizontal position of the dot (i.e., position of the grip to be used: left, middle, and right) and vertical position of the dot as parameters (low, intermediate, and high). The effect of the grip used was significant (F [2] = 27.06; p < 10−4; Fig 2F), with lower RTs when using the middle grip (post hoc t test with correction for multiple comparison, p < 0.05), but no effect of the vertical position of the dot (p >> 0.05). These constraints were also associated with differences in terms of force production: we fitted an ANOVA for the exerted force with the side of the grip as a parameter. The effect of side was significant (F [2] = 858.6; p < 10−20) with the force applied on the middle grip being the highest (multiple comparison of means, p < 0.01; Fig 2G). We finally confirmed the link between RT and exerted force by looking at the relationship between RT and the peak of exerted force by fitting RT and exerted force with a linear model. The effect of the exerted force on RT was significant and negative (p < 1014; Fig 2H). In summary, in the target detection task, the absence of WTW contrast across conditions indicated that the amount of cognitive control required to trigger the action (press) was probably equivalent across conditions. Additionally, RTs were clearly modulated by sensory-motor constraints, but there is no reason to interpret differences in RT in terms of cognitive difficulty and/or associated cognitive effort.

In the force discounting task, as described in the original paper [5], WTW was strongly modulated across conditions. Using a logistic regression, we found that engagement was modulated positively by reward and negatively by force category (p < 0.01 for both parameters for both monkeys; Fig 2I and 2J). Thus, as in the delay discounting task, the amount of cognitive control required to perform the task should be modulated across task conditions, and RTs should be influenced by cognitive effects. We then examined the modulations of RTs across task conditions by fitting a GLM for RTs, with the offered reward and the category of force requested as parameters. In both monkeys, RTs were positively modulated by reward (Monkey A: beta = 0.58; p < 10−4; Monkey D: beta = 0.00076; p = 0.47) and negatively modulated by force category (Monkey A: beta = −0.13; p < 10−20; Monkey D: beta = −0.087, p < 10−15), which is the opposite of the pattern expected for pure cognitive effects, given the influence of task parameters on WTW (Fig 2K and 2L). But these effects of task parameters could also be accounted for in terms of simple motor constraints, rather than cognitive constraints. To explore that possibility, we examined the relationship between trial-by-trial RTs and exerted force (maximum exerted force on the grip during press). As was the case in the target detection task, there was a significant negative relation between RT and the amount of exerted force (GLM, p < 0.01 for both monkeys; Fig 2M and 2N). Thus, in this task, RTs were clearly affected by sensory-motor constraints. However, even if the pattern of WTW suggests that they might also be affected by cognitive constraints, we could not find evidence for it.

In short, even if all 3 tasks involved triggering an action in response to a visual target, the 3 tasks differed in the relative weight of sensory-motor versus cognitive constraints on RT. In the delay discounting task, responses were mostly influenced by cognitive constraints, with virtually no difference in sensory-motor processes across conditions. By contrast, in the target detection task, conditions clearly differed in terms of sensory-motor constraint but not in terms of value. In the force discounting task, the difference in behavior across conditions suggests that it involved both sensory-motor and cognitive constraints, but the relative weight of the 2 remains difficult to evaluate.

[END]

[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001487

(C) Plos One. "Accelerating the publication of peer-reviewed science."
Licensed under Creative Commons Attribution (CC BY 4.0)
URL: https://creativecommons.org/licenses/by/4.0/


via Magical.Fish Gopher News Feeds:
gopher://magical.fish/1/feeds/news/plosone/