Christophe Collewet

Eric Marchand

Modeling complex luminance variations for target tracking

Lambert's model is widely used in low level computer vision algorithms such as matching, tracking or optical flow computation for example. However, it is well known that these algorithms often fail when they face complex luminance variations. Therefore, we revise in this paper the underlying hypothesis of its temporal constancy and propose a new optical flow constraint. To do that, we use the Blinn-Phong reflection model to take into account that the scene may move with respect to the lighting and/or to the observer, and that specular highlights may occur. To validate in practice these analytical results, we consider the case where a camera is mounted on a robot end-effector with a lighting mounted on this camera and show experimental results of target tracking by visual servoing. Such an approach requires to analytically compute the luminance variations due to the observer motion which can be easily derived from our revised optical flow constraint. In addition, while the visual servoing classical approaches rely on geometric features, we present here a new method that directly relies on the luminance of all pixels in the image which does not require any tracking or matching process.

Introduction

Most of computer vision-based applications rely on low level algorithms such as matching, features tracking, or optical flow computations for example. Such approaches are based on a relation that links the luminance of a physical point P at time t to its luminance at a later time t + dt due to a relative motion of the observer with respect to that scene or to other events like illumination changes. Nevertheless, due to the complexity of this relation, most often, the algorithms aforementioned are based on the temporal luminance constancy hypothesis:

I(x + dx, t + dt) = I(x, t) (1)
where x = (x, y, 1) is the perspective projection of P assuming that x has a displacement dx = (dx, dy, 0) in the time interval dt. A first order Taylor series expansion of (1) around x yields the so-called optical flow constraint equation (OFCE) [START_REF] Horn | Determining optical flow[END_REF] İ = ∇I ẋ + I t = 0

with I t = ∂I/∂t and ∇I the spatial gradient of I. However, it is well known that this constraint can be easily violated [START_REF] Verri | Motion field and optical flow: qualitative properties[END_REF], e.g. if the orientation of a Lambertian surface is changing with respect to the lighting. Consequently, many authors have addressed this issue. The basic assumption of the luminance constancy, can be extended by the spatial gradient constancy assumption as proposed in [START_REF] Tistarelli | Multiple constraints for optical flow[END_REF]. Dealing with target tracking, under the assumption of a Lambertian scene, illumination variations can be taken into account [START_REF] Hager | Efficient region tracking with parametric models of geometry and illumination[END_REF]. This Lambertian hypothesis may be removed when modeling the illumination changes as a surface that evolves over time [START_REF] Silveira | Real-time visual tracking under arbitrary illumination changes[END_REF]. This latter work can be seen as related to [START_REF] Black | Robustly estimating changes in image appearance[END_REF] where the intensity variation between dt is expressed as a mixture of causes.

A general framework has been proposed in [START_REF] Negahdaripour | Revised definition of optical flow: Integration of radiometric and geometric cues for dynamic scene analysis[END_REF] leading to the following relation:

I(x + dx, t + dt) = 1 + m(x, t) I(x, t) + c(x, t). (3)
However, the coefficients m(x, t) and c(x, t) are considered as locally constant, and then are estimated numerically.

Nevertheless, all these works do not rely on a timevarying physical reflection model.

A very interesting approach, close to our paper, can be found in [START_REF] Haussecker | Computing optical flow with physical models of brightness variation[END_REF]. Indeed, a generalization of ( 2) is presented leading to:

∇I ẋ + I t = dI dt (4)
where dI/dt is analytically computed according to several physical models of brightness variation. In particular, they study the case of a non planar Lambertian surface undergone to a rotation motion.

In this paper, we will analytically compute the term dI/dt involved in (4) in the more general case where the scene is not Lambertian. Indeed, we will use the Blinn-Phong reflection model [START_REF] Blinn | Models of light reflection for computer synthesized pictures[END_REF] to tackle more complex illumination changes such as the occurrence of specularities. This will consequently yield a revised optical flow constraint. To validate this new constraint, we will consider a 3D target tracking task by visual servoing. Briefly, it consists in using the data provided by a camera in order to control the motion of a robotic system to achieve positioning or tracking tasks [START_REF] Hutchinson | A tutorial on visual servo control[END_REF]. Note that, since our goal is to maintain a rigid link between the camera and the target to track, such task requires to control the 6 degrees of freedom of the camera. In addition, this paper proposes a new way to achieve target tracking by visual servoing. Indeed, instead of using geometric features as it is usually done, we use directly the luminance of all pixels in the image. Since our approach relies directly on image intensity, it does not require any more any 2D tracking nor matching.

In this case, since the surface is moving with respect to the lighting, it is clear that the assumption of temporal luminance constancy is violated and that, consequently, a more complex modeling is necessary. The term dI/dt we propose in this paper is also necessary to compute the so-called interaction matrix which is in the heart of the matter. Indeed, it is the relation that links the variation of the image intensity to the camera motion.

The remainder of this paper is organized as follows. First, we revise the optical flow constraint in the case of the Blinn-Phong reflection model in Section 2. We then recall some fundamentals of visual servoing and derive the control law to achieve the tracking task in Section 3. Section 4 shows experimental results related to this task.

Revised optical flow constraint

To derive the term dI/dt involved in (4), we have to consider a more realistic reflection model than the Lambert's one. We first consider the well known Phong model [START_REF] Phong | Illumination for computer generated pictures[END_REF] (see Fig. 1)

I(x) = K s cos k α + K d cos θ + K a . (5)
It is composed of a diffuse, a specular and an ambient component and assumes a point light source. The scalar k is inversely proportional to the roughness of the object surface;

K s describes the specular component of the lighting; K d describes the part of the diffuse term which depends on the albedo in P; K a is the intensity of ambient lighting in P. Note that K s , K d and K a only depend on P. θ is the angle between the normal to the surface n in P and the direction of the light source L; α is the angle between R (which is L mirrored about n) and the viewing direction V. Although empirical, this model is widely used thanks to its simplicity, and because it is appropriate for various types of materials, whether they are rough or smooth.

L n R V θ α θ P Figure 1.
Phong model in the general case [START_REF] Phong | Illumination for computer generated pictures[END_REF].

Considering that R, V and L are normalized, we thus have

I(x) = K s (R V) k + K d n L + K a . (6)
Since R may be hard to compute, following Blinn work [START_REF] Blinn | Models of light reflection for computer synthesized pictures[END_REF], this equation can be simplified (see Fig. 2) by considering for α the angle between n and H where H is the halfway vector between L and V. This leads to

I(x) = K s (n H) k + K d n L + K a (7)
with

V = - x x and H = L + V L + V . ( 8
)
Denoting

u 1 = n H and u 2 = n L, (7) becomes
I(x) = K s (u 1 ) k + K d u 2 + K a (9)
which gives

İ = kK s u k-1 1 u1 + K d u2 . (10)
Consequently, (4) becomes

∇I ẋ + I t = kK s u k-1 1 u1 + K d u2 . (11)
Although, it is possible to explicitly compute ul (l = 1 or 2) in the general case, we will restrict ourselves to more specific cases that will prove to be useful during the experiments.

Light source on the camera

We focus in this section on the case where the light source is mounted on the camera with the same direction as the optical axis (see Fig. 3). We thus have L = -k (where i, j, k are the unit vectors corresponding to the axis of the camera frame).

To explicitly compute [START_REF] Negahdaripour | Revised definition of optical flow: Integration of radiometric and geometric cues for dynamic scene analysis[END_REF], we have to compute ul :

ul = ∂u l ∂x ẋ + ∂u l ∂y ẏ + ∂u l ∂t . ( 12
)
Considering the specular term u 1 , we have while considering u 2 , we have (since ∂L/∂x = ∂L/∂y = 0)

               ∂u 1 ∂x = n ∂H ∂x + H ∂n ∂x ∂u 1 ∂y = n ∂H ∂y + H ∂n ∂y ∂u 1 ∂t = H ∂n ∂t (13)
               ∂u 2 ∂x = -k ∂n ∂x ∂u 2 ∂y = -k ∂n ∂y ∂u 2 ∂t = -k ∂n ∂t . (14)
Note that we clearly see on the above equations the contribution introduced by a non planar surface (i.e., when ∂n/∂x = 0 and ∂n/∂y = 0).

To compute ∂n/∂t, we introduce the matrix c R o which describes the rotation between the camera and the object frames such that n = c R o o n where o n express n in the object frame. Therefore,

∂n ∂t = c Ṙo o n = c Ṙo c R o n = -ω × n ( 15
)
where ω is the camera angular velocity expressed in its own frame. Consequently, by using ( 13) or ( 14) in ( 12), we have

u1 = n ∂H ∂x ẋ + n ∂H ∂y ẏ + (H × n) ω + H ∂n ∂x ẋ + H ∂n ∂y ẏ non planar component (16) and u2 = (n × k) ω -k ∂n ∂x ẋ -k ∂n ∂y ẏ non planar component . ( 17
)
Finally, if we use ( 16) and ( 17) in [START_REF] Phong | Illumination for computer generated pictures[END_REF], we obtain the revised optical flow constraint when the light source direction coincides with the observer direction.

Light source on the camera and the image plane parallel to a planar target

In this section, we consider the following particular case where the light source is still mounted on the camera (with the same direction than the optical axis) and where the image plane is parallel to a planar target, i.e. L = n = -k. It becomes then possible to explicitly compute n ∂H/∂x, n ∂H/∂y and (H × n )ω involved in (16). We obtain after tedious computations

               n ∂H ∂x = - 1 D x x 2 n ∂H ∂y = - 1 D y x 2 (H × n) ω = 2 D (yω x , -xω y , 0) (18)
with

D = 2 (2 x (1+ x )) 1/2
leading finally to

   u1 = - 1 D x x 2 ẋ + y x 2 ẏ -2yω x + 2xω y u2 = 0
(19) that we can plug in [START_REF] Phong | Illumination for computer generated pictures[END_REF] to obtain the revised optical flow constraint for a planar object parallel to the image plane when the light source direction coincides with the observer direction.

Before presenting some experimental results of target tracking, we first recall some fundamentals in visual servoing.

Visual servoing

Visual servoing techniques requires to design a set of visual features s from the visual measurements x(r k ) (s = s(x(r k )) where r k is the camera pose at time k). A control law has to be designed also so that these features s reach a desired value s * obtained for r = r * , defining a correct realization of the task. The control principle is thus to regulate the error vector ss * to zero. Such an approach needs the knowledge of the interaction matrix which describes the relationship between the relative camera-object velocity v and the time variation of the selected visual features ṡ

ṡ = L s v + ∂s ∂t (20)
with v = (v, ω) where v = (v x , v y , v z ) is the camera linear velocity and ω = (ω x , ω y , ω z ) its angular velocity, while ∂s/∂t represents the time variation of s due to potential motions of the object. This matrix is usually used to design a control law whose aim is to cancel the vision-based task function [START_REF] Espiau | A new approach to visual servoing in robotics[END_REF]

e = L + s (s -s * ). ( 21
)
where L s is a model or an approximation of L s . A classical control law is then given by

v = -λe - ∂e ∂t ( 22
)
where λ is a positive gain. Various ways to estimate ∂e/∂t exist. It is for example possible to use, assuming a constant target velocity, a simple integrator to vanish the steady state tracking error, leading at time k to

∂e ∂t k = ∂e ∂t k-1 + µe k = µ k-1 i=0 e j with ∂e ∂t 0 = 0 (23)
with µ a positive gain that we have to tune.

Choice of the visual feature

In practice, the choice of the visual features s is a key point of this approach. With a vision sensor providing 2D measurements x(r k ), numerous potential visual features s exist, since 2D data (coordinates of feature points in the image, moments, ...) as well as 3D data provided by a localization algorithm exploiting 2D data can be considered. Anyway, a robust extraction, matching (between x(r k ) and

x * = x(r * )) and real-time spatio-temporal tracking (between x(r k-1 ) and x(r k )) have to be achieved. However, these tasks have proved to be difficult, as testified by the abundant literature on the subject.

In this paper we propose to radically modify the procedure by removing the extraction of geometric measurements and consequently the matching and tracking processes. To achieve this goal, we use as visual features the simplest feature that can be considered: the image intensity. The visual feature vector s is nothing but the image while s * is the desired image. The error ss * is then only the difference between the current and desired image, that is I -I * where I is a vector containing image intensity of all pixels. Thus, we have:

s(r) = I(r) = (I 1• , I 2• , • • • , I N • ) (24)
where I k• is the k-th line of the image. I(r) is then a vector of size N×M where N×M is the image size. Consequently, we have first to compute the interaction matrix related to the luminance I at a pixel x in the image.

Interaction matrix related to the luminance in the general case

In fact, it is straightforward to compute it from our revised optical flow constraint. We simply have to express I t from ( 11)

I t = -∇I ẋ + kK s u k-1 1 u1 + K d u2 , (25)
writing from ( 12)

u1 = M 1 v and u2 = M 2 v (26)
and introducing the interaction matrices L x and L y related to the coordinates x and y of x [4]

L x = L x , L y (27) = -1/Z 0 x/Z xy -(1 + x 2 ) y 0 -1/Z y/Z 1 + y 2 -xy -x
and we obtain the interaction matrix related to the intensity at pixel x in the general case

L I = -∇I L x + kK s n H k-1 M 1 + K d M 2 . ( 28
)
Note that the interaction for a Lambertian scene is simply

L I = -∇I L x . (29)
However, as already stated, we cannot evaluate most of the components involved in the matrices M l (e.g., n, L, or even V). We will therefore focus on the case when the light source is mounted on the camera as we did in section 2.1.

Light source on the camera

In that case, we can easy compute the matrices M l involved in (26) by explicitly use ( 27) in ( 16) and ( 17). However, it is easier to introduce in M l the planar M lp and non planar

M lnp components such that M l = M lp + M lnp where M 1p =                     - n Z ∂H ∂x - n Z ∂H ∂y x Z n ∂H ∂x + y Z n ∂H ∂y xyn ∂H ∂x + (1 + y 2 )n ∂H ∂y + (H × n) i -(1 + x 2 )n ∂H ∂x -xyn ∂H ∂y + (H × n) j 0                    
(30) and

M 2p = 0 0 0 (n × k) i (n × k) j 0 , (31)
while we have for the non planar part

M 1np =                       - 1 Z H ∂n ∂x - 1 Z H ∂n
                      (32)
and

M 2np =                       1 Z k ∂n ∂x 1 Z k ∂n ∂y - x Z k ∂n ∂x - y Z k ∂n ∂y -xyk ∂n ∂x -(1 + y 2 )k ∂n ∂y (1 + x 2 )k ∂n ∂x + xyk ∂n ∂y -yk ∂n ∂x + xk ∂n ∂y                       . (33)

Light source on the camera and image and target planes parallel

For experimental issue, we are interested in the case where the light source is mounted on the camera with the same direction than the optical axis and where the image plane is parallel to a planar target. Here, we simply explicitly use (27) in ( 19) which gives

M 1p = 1 D 1 x 2 x Z 1 x 2 y Z -1 x 2
x 2 + y 2 Z y -x 0 (34) and M 2p = 0.

Experimental results

Our goal is to perform, using the visual servoing scheme detailed in section 3, a tracking task with respect to a moving object. Therefore, as already stated, we have to maintain a rigid link between the target to track and the camera.

The camera is mounted on a 6 degrees of freedom gantry robot and a light-ring is located around the camera lens (see Figure 4). Therefore the light direction is aligned with the camera optical axis as on Figure 3. This is the unique light in the scene. The considered object is planar (a photo), it is attached to a motorized rail that allows to control its motion. Although only one d.o.f of the object is controlled (with a motion that is completely unknown from the tracking process), the 6 d.o.f of the robot are controlled. The interaction matrix has been computed using equation ( 28) with M 1 given by (34) and M 2 = 0. Object velocity is 1 cm/s. Control law is computed on a Core 2 Duo 3Gz PC running Linux. Image are acquired at 66Hz using an IEEE 1394 camera with a resolution of 320x240. The size of the vector s is then 76800. Despite this size, the interaction matrix L I can be recomputed at each iteration. As can be seen on the images of Figures 6 and5 (first rows), specularities can be seen in the image acquired by the camera. The first experiment shows the behavior of the control law when the interaction matrix was computed only under Lambertian hypothesis (given by equation ( 29)). Images on Figures 6 show that the tracking quickly failed leading to incoherent robot motion (the second row shows the error I -I * ; when the error is null the image must be completely gray).

The same experiment was now considered but with a full illumination model. As shown in Figure 5 the tracking is perfectly achieved since the error I -I * is almost null despite the occurrence of a specularity which shows the importance of such terms in the tracking process (we have k = 100 and K s = 200). When the velocity is constant the object is perfectly tracked as can be seen on Figure 7c where I -I * is depicted. Error in the image remains small except when the object stops or accelerates (see the peaks in Figure 7c). The camera velocity (see Figure 7a) shows a pure motion along the x (± 1cm/s) axis that corresponds to the motion of the motorized rail. For each pixel except during accelerations and decelerations, |I -I * | < 5. Figure 7b depicts the camera position in the robot frame reference. It shows that despite only one degree of freedom of the object is controlled, a 6 dof motion of the robot has to be performed.

In the second experiment, we move the object by hand as seen on the first row of Figure 9. The related images acquired by the camera are shown on the second row. We then have a more complex 3D object motion. Note that all the 6 dof of the robot are controlled (see Fig. 8b). The error I -I * is shown in Figure 8c while I -I * is shown on the third line of Figure 9. When the object is moving the error is more important than during the previous experiment. This is due to the fact that, since the object velocity is obviously no more constant (as testified by Figure 9a), it is no longer possible to consider the integral term involved in (23) leading to classical tracking errors. In contrast, when the motion stops the camera moves to reduce these errors (iterations 5300 and 9500).

Conclusion

In this paper we have extended the optical flow constraint equation in order to consider illumination model that removes the classical Lambertian hypothesis. We have also proposed a general framework to compute the variation of the image intensity due to camera and light motions and have provided the complete model when the light source is mounted on the camera. We then have applied this framework to a visual servoing context. We have also shown in this paper that it is possible to use the luminance of all the pixels in an image as visual features in visual servoing. To the best of our knowledge, this is the first time that visual servoing has been handled without any image processing (except the image spatial gradient required for the computation of the interaction matrix) nor learning step. Experimental results of tracking planar objects have been presented which validate our approach.

Figure 2 .Figure 3 .

 23 Figure 2. Blinn-Phong model in the general case [2].

Figure 4 .

 4 Figure 4. Camera and light-ring mounted on the robot end-effector and view of the object to track and of the camera. The object (a photo) is attached to a motorized rail that allows to control it motion.

Figure 5 .

 5 Figure 5. First experiment: Tracking considering the complete interaction matrix that integrates specularity, diffuse and ambient terms.

Figure 6 .

 6 Figure 6. First experiment: Tracking considering the interaction matrix under Lambertian hypothesis. As can be seen tracking failed quickly leading to incoherent robot motion. First row shows the image acquired by the camera while I -I * is shown on the second row.

Figure 7 .

 7 Figure 7. First experiment: Tracking considering the complete interaction matrix that integrates specularity, diffuse and ambient terms. (a) camera velocity (m/s and radian/s) (b) camera position in the robot reference frame (meter and radian) (c) error I -I *

Figure 9 .

 9 Figure 9. Second experiment: Tracking considering the complete interaction matrix that integrates specularity, diffuse and ambient terms.

Figure 8 .

 8 Figure 8. Second experiment: Tracking considering the complete interaction matrix that integrates specularity, diffuse and ambient terms. (a) camera velocity (m/s and radian/s) (b) camera position in the robot reference frame (meter and radian) (c) error I -I *


Chiara Fini

Maja Fischer

Lara Bardi

Marcel Brass

Agnes Moors
email: agnes.moors@kuleuven.be.

A DIRECT LINK BETWEEN STIMULUS VALENCE AND ACTION TENDENCIES 1 Title: "Support from a TMS/MEP study for a direct link between positive/negative stimuli and approach/avoidance tendencies"

Keywords: valence, approach-avoidance, action tendencies, motor preparation, automatic

Previous behavioral studies using stimulus-response compatibility tasks have shown that people are faster to carry out instructed approach/avoidance responses to positive/negative stimuli. This result has been taken as evidence that positive/negative stimulus valence directly activates a tendency to approach/avoid, which in turn, facilitates execution of instructed approach/avoidance behavior. In these studies, however, it cannot be excluded that the results reflect a purely semantic link between stimulus valence and instructed responses. According to this alternative interpretation, positive/negative stimuli do not elicit an approach/avoidance tendency, but instead they interact with the positive/negative valence of the instructed responses, and in this way, produce the observed compatibility effect. To circumvent this possible disadvantage of compatibility tasks, we used a novel method for the measurement of early action tendencies: TMS induced MEPs. In two experiments, participants were first trained to abduct the index finger to approach and the thumb to avoid. Then, they observed a series of positive and negative stimuli. Each stimulus was followed by a TMS pulse (at 400 ms post-stimulus onset) and MEPs were measured continuously on the muscles of both fingers. These observation trials were randomly intermixed with response trials, in which neutral stimuli were presented and participants were instructed to approach/avoid the stimuli.

In Experiment 1, participants received clear visual feedback on the outcome of their response in the response trials. In Experiment 2, we omitted this feedback to test whether it was necessary for the effect to occur. The results indicated higher MEPs for the approach/avoidance finger after positive/negative stimuli in Experiment 1 but not in Experiment 2. Analyses on the data aggregated over both experiments suggest that the visual feedback was necessary for stimulus valence to elicit action tendencies. Taken together, the results are in line with the results of behavioral studies with compatibility tasks, suggesting that stimulus valence directly elicits specific action tendencies already at 400 ms but they indicate that clear visual feedback is necessary for this effect to occur.

Support from a TMS/MEP study for a direct link between positive/negative stimuli and approach/avoidance tendencies In order to deal with environmental challenges, humans perform adaptive responses allowing to obtain rewards and escape from threats. This observation has led scholars, from ancient Greek philosophy (e.g., Democritus, 460-370 BC; Aristippus; 430-360 BC, cited by [START_REF] Covington | Approach and avoidance motivation[END_REF] throughout the history of psychology [START_REF] Arnold | Emotion and personality[END_REF][START_REF] Darwin | The expression of emotions in animals and man[END_REF][START_REF] Chen | Consequences of automatic evaluation: Immediate behavioral predispositions to approach or avoid the stimulus[END_REF][START_REF] Pavlov | Conditioned reflexes, translated by GV Anrep[END_REF] to postulate that positive stimuli elicit an approach tendency, aimed at getting closer to them, whereas negative stimuli elicit an avoidance tendency, aimed at taking distance from them. Several researchers, moreover, postulate that the link between valenced stimuli and action tendencies is an automatic link (e.g., [START_REF] Chen | Consequences of automatic evaluation: Immediate behavioral predispositions to approach or avoid the stimulus[END_REF][START_REF] Lang | Emotion, attention, and the startle reflex[END_REF][START_REF] Neumann | Motor compatibility: The bidirectional link between behavior and evaluation. The psychology of evaluation: Affective processes in cognition and emotion, 371-391. and evaluation[END_REF][START_REF] Rutherford | Thriving and surviving: Approach and avoidance motivation and lateralization[END_REF]. This is based on the idea that stimuli activate a direct association between the representation of positive/negative stimulus valence and a representation or tendency to approach/avoid [START_REF] Neumann | Motor compatibility: The bidirectional link between behavior and evaluation. The psychology of evaluation: Affective processes in cognition and emotion, 371-391. and evaluation[END_REF]. Once an action tendency is activated it may or may not translate into overt behavior (depending on other, competing action tendencies).

To test whether there is indeed a link between positive/negative stimuli and the tendencies to approach/avoid, researchers have used stimulus-response compatibility tasks in which participants were instructed to approach positive stimuli and avoid negative ones in half of the trials (compatible block), and to approach negative stimuli and avoid positive ones in the other half of the trials (incompatible block). Results typically show that participants are faster in the compatible than in the incompatible block. This effect is referred to as the valence-approach/avoidance compatibility effect (VAAC-effect; [START_REF] Krieglmeyer | On the nature of automatically triggered approach-avoidance behavior[END_REF] or the affective stimulusresponse compatibility effect (e.g., [START_REF] Kozlik | Contrasting motivational orientation and evaluative coding accounts: on the need to differentiate the effectors of approach/avoidance responses[END_REF]. It has been observed across studies in which action tendencies of approach/avoidance were measured using different response modalities, such as pulling/pushing a joystick closer towards/away from themselves or the stimuli (e.g., [START_REF] Chen | Consequences of automatic evaluation: Immediate behavioral predispositions to approach or avoid the stimulus[END_REF][START_REF] Da Gloria | Evidence for a motor mechanism of pain-induced aggression instigation in humans[END_REF][START_REF] Duckworth | The automatic evaluation of novel stimuli[END_REF][START_REF] Lavender | Affect and action: Towards an event-coding account[END_REF], pressing a key to move a manikin toward/away from the stimuli (De Houwer, Crombez, Baeyens, & Hermans, 2001;[START_REF] Krieglmeyer | Being moved: Valence activates approach-avoidance behavior independently of evaluation and approach-avoidance intentions[END_REF], nodding/shaking the head (e.g., [START_REF] Förster | Influence of overt head movements on memory for valenced words: A case of conceptual-motor compatibility[END_REF], contracting the zygomaticus/corrugator muscles of the face (e.g., [START_REF] Dimberg | Facial reactions to emotional stimuli: Automatically controlled emotional responses[END_REF][START_REF] Neumann | Automatic behavioural responses to valence: Evidence that facial action is facilitated by evaluative processing[END_REF][START_REF] Neumann | Not all behaviors are controlled in the same way: Different mechanisms underlie manual and facial approach and avoidance responses[END_REF][START_REF] Otte | Interference in simultaneously perceiving and producing facial expressions-Evidence from electromyography[END_REF], and full-body movements such as stepping forward/backward (e.g., [START_REF] Stins | Walk to me when I smile, step back when I'm angry: emotional faces modulate whole-body approach-avoidance behaviors[END_REF].

To add further support to the idea that the link between valence and action tendencies is direct and automatic, researchers have also reported evidence for the VAAC effect when the valence of the stimuli was task-irrelevant, and participants had to make their approach/avoidance responses dependent on a non-valenced stimulus-feature (e.g., [START_REF] Chen | Consequences of automatic evaluation: Immediate behavioral predispositions to approach or avoid the stimulus[END_REF]De Houwer et. al., 2001;[START_REF] Krieglmeyer | On the nature of automatically triggered approach-avoidance behavior[END_REF][START_REF] Neumann | Automatic behavioural responses to valence: Evidence that facial action is facilitated by evaluative processing[END_REF][START_REF] Otte | Interference in simultaneously perceiving and producing facial expressions-Evidence from electromyography[END_REF][START_REF] Stins | Walk to me when I smile, step back when I'm angry: emotional faces modulate whole-body approach-avoidance behaviors[END_REF].

Early VAAC effects obtained with the joystick task were explained with a specific muscle activation account (Cacioppo, Priester, & Berntson, 2013;[START_REF] Chen | Consequences of automatic evaluation: Immediate behavioral predispositions to approach or avoid the stimulus[END_REF][START_REF] Rotteveel | Automatic affective evaluation does not automatically predispose for arm flexion and extension[END_REF][START_REF] Simons | Latency of instrumental responses as a function of compatibility with the meaning of eliciting verbal signs[END_REF] according to which positive stimuli facilitate an arm flexion movement (as involved in pulling a lever) and negative stimuli an arm tension movement (as involved in pushing a lever). Recent studies, however, were more in line with a distance regulation account (De Houwer, Crombez, Baeyens, & Hermans, 2001;[START_REF] Krieglmeyer | How farsighted are behavioral tendencies of approach and avoidance? The effect of stimulus valence on immediate vs. ultimate distance change[END_REF][START_REF] Markman | Constraining theories of embodied cognition[END_REF][START_REF] Seibt | Movement direction or change in distance? Self-and object-related approach-avoidance motions[END_REF][START_REF] Schneirla | An evolutionary and developmental theory of biphasic processes underlying approach and withdrawal[END_REF] according to which positive/negative stimuli lead to approach/avoidance tendencies understood as tendencies to decrease/increase distance irrespective of the specific muscle movements involved. In/compatibility between these action tendencies and the action tendencies induced by the instructions leads to relative inhibition/facilitation to execute the instructed action tendencies.

Another competing account, called the evaluative coding account [START_REF] Lavender | Affect and action: Towards an event-coding account[END_REF][START_REF] Eder | When do motor behaviors (mis) match affective stimuli? An evaluative coding view of approach and avoidance reactions[END_REF], states that positive/negative stimuli do not spontaneously lead to approach/avoid tendencies and hence VAAC effects do not result from the in/compatibility between stimulus-induced and instruction-induced action tendencies in terms of approach/avoidance. Instead, these effects result from the in/compatibility between stimulus-induced and instructioninduced action tendencies in terms of valence. [START_REF] Eder | When do motor behaviors (mis) match affective stimuli? An evaluative coding view of approach and avoidance reactions[END_REF] framed inherently ambiguous movements of pulling/pushing of the joystick either as positive/negative (pulling as approach, pushing as avoidance) or as negative/positive (pulling as avoidance, pushing as approach). In support of their account, they found facilitation of responses to positive/negative stimuli when the instructed response was framed as positive/negative, regardless of the direction (pulling/pushing) of the movement. They found similar effects when pushing/pulling of the joystick was framed as up (positive)/down (negative).

Stimulus-response compatibility tasks are behavioral tasks in which participants are instructed to respond to stimuli. This method carries the risk that the compatibility effects obtained do not stem from the in/compatibility between stimulus-induced and instruction-induced action tendencies, but instead from the in/compatibility between stimulus features and features of instruction-induced action tendencies. In particular, the compatibility effect can stem from an overlap in the valence of stimuli and the valence of the instruction-induced action tendencies, for instance, via a semantic matching mechanism (e.g., [START_REF] Van Dantzig | Unconstraining theories of embodied cognition[END_REF][START_REF] Zhang | Approach-avoidance actions or categorization? A matching account of reference valence effects in affective S-R compatibility[END_REF]. The implication is that the compatibility effect could occur even if a stimulus-induced action tendency was never activated. In other words, compatibility effects are not airtight diagnostic instruments for detecting stimulus-induced action tendencies. In order to escape from this impasse and to more directly examine whether valenced stimuli spontaneously elicit approach/avoidance tendencies, research methods are needed in which the role of instructions is minimized.

To meet this requirement, we developed a new method based on single-pulse Transcranial Magnetic Stimulation (TMS) of the primary motor cortex (M1) and the measurement of Motor Evoked Potentials (MEPs; [START_REF] Hoshiyama | Reciprocal change of motor-evoked potentials preceding voluntary movement in humans[END_REF][START_REF] Hasbroucq | The time-course of preparatory spinal and cortico-spinal inhibition: an H-reflex and transcranial magnetic stimulation study in man[END_REF][START_REF] Michelet | Response competition in the primary motor cortex: corticospinal excitability reflects response replacement during simple decisions[END_REF]. TMS is a non-invasive technique that uses a rapidly changing magnetic field applied to the skull to produce an electrical current in underlying neural tissue. When TMS is applied to M1, it can stimulate the cortico-spinal tract, inducing a MEP in the contralateral hand (a peak in EMG activity 20 to 50 ms after the pulse). The amplitude of this MEP is considered as an index of the level of excitability of the cortico-spinal tract [START_REF] Rossini | Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: basic principles and procedures for routine clinical and research application. An updated report from an IFCN Committee[END_REF].

If a stimulus (e.g., an image) activates an action tendency, this should lead to higher TMS-induced MEPs registered from the muscle that would carry that same action. This method allows the detection of action tendencies in the absence of overt behavior, and can thus be used when participants merely observe stimuli. The method is also especially suitable to probe for automatic processes (in the sense of fast, and relatively unintentional).

Prior research with single-pulse motor TMS already showed that valenced stimuli can modulate cortico-spinal excitability. Studies vary, however, with regard to (a) the nature of the stimuli presented (e.g., pictures of valence scenes: [START_REF] Coelho | Increased corticospinal excitability induced by unpleasant visual stimuli[END_REF] pictures of body postures: [START_REF] Borgomaneri | Temporal dynamics of motor cortex excitability during perception of natural emotional scenes[END_REF][START_REF] Borgomaneri | Seeing fearful body language rapidly freezes the observer's motor cortex[END_REF]; positive or negative words: Gough, Campione, & Buccino, 2013; selfexperienced pain stimuli [START_REF] Farina | Pain-related modulation of the human motor cortex[END_REF]; third-person observation of pain in others: Avenanti, Minio-Paluello, Bufalari, & Aglioti, 2006, 2009;noxious stimuli: Farina et al. 2001;[START_REF] Tamburin | Cutaneomotor integration in human hand motor areas: somatotopic effect and interaction of afferents[END_REF][START_REF] Urban | Different short-term modulation of cortical motor output to distal and proximal upper-limb muscles during painful sensory nerve stimulation[END_REF]loud acoustic stimuli: Furubayashi et al. 2000;monetary rewards and punishments: Suzuki et al., 2014;[START_REF] Thabit | Momentary reward induce changes in excitability of primary motor cortex[END_REF][START_REF] Vicario | Counterfactual thinking affects the excitability of the motor cortex[END_REF], (b) the task that participants have to perform on the stimuli (valence categorization: [START_REF] Borgomaneri | Seeing fearful body language rapidly freezes the observer's motor cortex[END_REF]reading: Gough et al., 2013;pure observation: Baumgartner et al., 2007) ,(c) the time at which the TMS pulse is delivered after stimulus onset (Borgomaneri et al., 2013: 150 ms and 300 ms;Oliveri et al., 2003: 300 ms;van Loon et al., 2010: 400-500 ms;Hajcak et al., 2007: 3-5 s), and (d) the effectors from which the MEPs are registered (index finger: [START_REF] Coelho | Increased corticospinal excitability induced by unpleasant visual stimuli[END_REF]forearm: Gough et al., 2013;thumb: Hajcak et al., 2007).

Because of this variation, results have been mixed. For instance, it is unclear whether cortico-spinal modulation (decrease or increase) at various pulse timings is caused by negative stimuli (compared to positive and neutral stimuli; [START_REF] Schutter | Fearful faces selectively increase corticospinal motor tract excitability: a transcranial magnetic stimulation study[END_REF] or by both positive and negative stimuli [START_REF] Hajcak | Emotion facilitates action: a transcranial magnetic stimulation study of motor cortex excitability during picture viewing[END_REF][START_REF] Van Loon | Emotional stimuli modulate readiness for action: a transcranial magnetic stimulation study[END_REF], and whether prior response preparation related to the experimental task is required [START_REF] Coelho | Increased corticospinal excitability induced by unpleasant visual stimuli[END_REF]. Some researchers have interpreted a general decrease and increase in MEPs in terms of specific action tendencies. For instance, [START_REF] Borgomaneri | Seeing fearful body language rapidly freezes the observer's motor cortex[END_REF] and [START_REF] Avenanti | The pain of a model in the personality of an onlooker: influence of state-reactivity and personality traits on embodied empathy for pain[END_REF] have interpreted a decrease in MEPs in terms of the tendency to orient or freeze and an increase in MEPs in terms of the tendency to avoid (see also [START_REF] Coelho | Increased corticospinal excitability induced by unpleasant visual stimuli[END_REF][START_REF] Oathes | Worry facilitates corticospinal motor response to transcranial magnetic stimulation[END_REF]. The problem, however, is that a mere decrease in MEPs would also fit with any other passive tendency (e.g., a submissive tendency) and a mere increase in MEPs with any other active tendency (e.g., an antagonistic tendency; see [START_REF] Moors | The role of stimulus-driven versus goal-directed processes in fight and flight tendencies measured with motor evoked potentials induced by Transcranial Magnetic Stimulation[END_REF]. Other researchers have interpreted the increase in MEPs in specific muscles in terms of specific action tendencies. For instance, [START_REF] Gough | Fine tuned modulation of the motor system by adjectives expressing positive and negative properties[END_REF] interpreted an increase in MEPs in the index finger to approach because it contributes to grasping, and MEPs in the forearm to avoidance because it contributes to the release of one's grasp. The problem, however, is that grasping can also figure in avoidance (e.g., grasping an object of safety) and releasing one's grasp can also figure in approach (e.g., releasing an object of safety to approach).

The current studies went beyond previous TMS studies in that we tried to measure not just general action readiness, but the specific action tendencies of approach and avoidance (see also [START_REF] Moors | The role of stimulus-driven versus goal-directed processes in fight and flight tendencies measured with motor evoked potentials induced by Transcranial Magnetic Stimulation[END_REF], for a study in which this method was applied to the specific action tendencies of fight and flight). While previous studies have inferred the activation of specific action tendencies either from a general increase or decrease in MEPs or from MEPs in specific muscles, we opted to install the meaning of specific muscle movements by establishing connections between these movements and approach/avoidance responses during a training phase. In particular, participants were trained to use the index finger to approach and the thumb to avoid. During the experimental phase, positive and negative stimuli were presented, followed by a TMS pulse and continuous registration of MEPs. Comparison of the MEP amplitudes in the muscles used to make the muscle movements allowed us to infer which action tendency (approach or avoidance) was activated most strongly in response to the valenced stimuli.

We conducted two single pulse TMS experiments. In the first experiment, we examined whether passive observation of positive/negative stimuli led to higher MEPs in the fingers used to let a manikin approach/avoid these stimuli. The second experiment was set up as a replication of the first experiment, in which we further examined the role of action feedback in the elicitation of approach/avoidance tendencies.

Experiment 1

The experiment was presented as a multiple-trial computer game, comprised of a training phase and an experimental phase. In the training phase, participants learned to use their index finger to approach pressing one key and their thumb to avoid pressing another key. In each trial, a manikin representing the participant appeared in the lower half of the screen and a neutral dot in the middle of the screen and participants received auditory instructions to approach or avoid the dot. In the experimental phase the manikin was presented in the lower half of the screen together with a negative, positive, or neutral picture in the middle of the screen. This phase consisted of observation trials and occasional response trials. During observation trials, positive and negative stimuli were presented and participants were merely asked to observe them without moving their index finger and thumb. Each stimulus was followed by a single TMS pulse at 400 ms post-stimulus onset, and electromyogram (EMG) activity was recorded continuously from effectors of the index finger and the thumb. In the response trials, participants received an auditory instruction to approach or avoid a neutral picture at 500 ms post-stimulus onset. These trials were simply added to reactivate the meanings of the keypress responses that were installed during the first training phase.

Method

Ratings of the stimulus material, the data file of the study, and the syntax used for the analysis are available from the Open Science Framework database (URL: https://osf.io/2p8dv/).

Design and participants.

The experiment had a 2 x 2 design with stimulus valence (positive, negative) and response (approach, avoid) as within-subject factors. Thirty-four participants took part in the experiment. Five participants were excluded because they did not follow the instructions correctly or due to problems in saving EMG data. Four additional participants were excluded because they had more than 2 SDs less valid MEPs than the other participants. This resulted in a final sample of 25 participants (mean age 21.33 years ± 7.15; all but 4 right handed; 14 females). This sample size was determined based on the results reported by [START_REF] Gough | Fine tuned modulation of the motor system by adjectives expressing positive and negative properties[END_REF] to detect an effect with a power of 80% and a significance level of .05 using the method of [START_REF] Anderson | Sample-size planning for more accurate statistical power: A method adjusting sample effect sizes for publication bias and uncertainty[END_REF], which adjusts for publication bias and uncertainty. Participants had no history of neurological problems or psychiatric disorders, had normal or corrected-to-normal vision, and were prescreened for risks associated with TMS [START_REF] Rossi | Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research[END_REF]. The study was granted ethical approval by the Ethical Medical review board of Ghent University Hospital. Participants gave written informed consent and received a compensation of 15 euros.

TMS administration and MEP recordings. TMS pulses were delivered with a biphasic magnetic stimulator (Rapid2, The Magstim Company Ltd.). A 70 mm eight coil was held tangentially to the skull at the level of the left motor cortex with the handle pointing backward and laterally at a 45° angle to the sagittal plane. The coil was positioned in correspondence with the optimal scalp position defined as the coil location eliciting the largest and more reliable MEPs in both the right first dorsal interosseous (FDI) and the right opponent pollicis (OP). The stimulation intensity was determined based on the resting motor threshold (rMT) of both muscles, which is defined as the intensity that evokes a MEP larger than 50 µV in 50 % of the cases in FDI and OP simultaneously [START_REF] Rossini | Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: basic principles and procedures for routine clinical and research application. An updated report from an IFCN Committee[END_REF]. Participants were equipped with a swimming cap on which the optimal location for the stimulation was marked so that the experimenter could easily track the correct position of the coil. During the experiment, a mechanical arm held the TMS coil in the correct position, but the experimenter also continuously monitored the coil position during the sessions. In participants who were moving too much, the experimenter directly held the coil on the hot spot during the entire experiment. Stimulation intensity during the recording session was set to 110% of the rMT. Average intensity was 67.78 % (range 51%-80%). Electromyographical (EMG) activity was recorded with the ActiveTwo system (www.biosemi.com ). Sintered 11x17 mm active Ag-AgCl electrodes were placed over two muscles: FDI and OP of the right hand. FDI contributes to abduct the index finger from the ring finger, and OP enables to abduct the thumb away from the index finger. The active electrodes were placed over the belly of the right FDI and OP muscles and the reference electrodes over the ipsilateral proximal interphalangeal joints (belly-tendon montage).

The ground electrode was placed on the back of the hand, near the wrist joint. The EMG signal was amplified (internal gain scaling), digitized at 2Hz, filtered at 3Hz, and stored on a PC for offline analysis.

Stimuli and procedure. Stimuli in the experimental phase were positive, negative, and neutral pictures, all depicting humans and sized 328 x 246 px, selected from the International Affective Picture System (IAPS, [START_REF] Lang | International affective picture system (IAPS): Technical manual and affective ratings[END_REF], as well as from a new online database created by [START_REF] Dillen | IAPS 2.1. Ontwikkeling en validatie van een fotoset om emoties te induceren[END_REF]. We chose to only present pictures with humans to avoid a possible confound of animacy observed in previous research [START_REF] Borgomaneri | Motor mapping of implied actions during perception of emotional body language[END_REF].

Presentation of the stimuli and registration of the responses were controlled using Affect 4.0 software [START_REF] Spruyt | Affect 4.0: A free software package for implementing psychological and psychophysiological experiments[END_REF]. Participants were seated at a distance of 60 cm from a 17 inch computer monitor in a dimly lit room. An azerty keyboard was vertically located and two keys (an upper key "J" and a lower key "G") were marked with a blue spot, while two other keys (key "U" left of "J", and key "F" below "G") were marked respectively with the labels "AP" (for approach) and "AV" (for avoidance). Participants were asked to place the tip of the right index finger on the upper key "J" and the tip of the right thumb on the lower key "G".

The experiment was composed of a training phase and an experimental phase. The training phase consisted of 30 trials. On each trial, a white dot was presented in the middle of a black background together with an avatar (stick figure) representing the participant located below the dot (see De Houwer, Crombez, Baeyens, & Hermans, 2001). Participants wore headphones through which they received the auditory instructions to "approach" or "avoid" 250 ms after stimulus onset.

When they received the auditory approach/avoidance cue, they had to move their index finger/thumb as fast as possible from the blue spot towards the approach/avoidance key and press it.

When participants pressed the approach key, the avatar moved toward the dot; when they pressed the avoid key, the avatar moved away from the dot. If participants pressed a key later than 2000 ms after stimulus onset, the error feedback "too late" was displayed on the screen. If participants pressed the wrong key, an error sound was delivered.

The experimental phase consisted of 100 randomized trials (see Figure 1). Instead of the white dot, a picture was now presented in the middle of the screen with the manikin again located below the picture. The majority of the trials (90) were observation trials. In half of them, the picture had a positive valence; in the other half it had a negative valence. The ITI was on average 1000 ms (with a range of 500 ms until 1500 ms) with an initial delay of 1500 ms. Each stimulus remained on screen for 3250 ms. A single TMS pulse was delivered to M1 at 400 ms post-stimulus onset and EMG activity was recorded continuously from effectors of the index finger and the thumb of the right hand. A small number of trials (10) were response trials, during which participants received instructions to approach or avoid the stimuli via auditory cues presented 500 ms post-stimulus onset. The pictures presented during the response trials were neutral because we wanted to avoid installing associations between positive/negative stimuli and the instructed responses. Participants also received a single TMS pulse in the response trials but these were not analyzed. Response trials were aborted 3000 ms after response execution or at 3250 ms after stimulus onset if no response was executed until then. The entire experiment including the TMS/MEP preparation lasted one hour.

Results and Discussion

The raw EMG data were processed for the observation trials only using MATLAB® software.

Epochs of 1000 ms around the TMS pulse (500 ms before and 500 ms after) were extracted from the stream of data. The peak-to-peak amplitude of EMG activity for each trial was calculated for the 20-50 ms window following the TMS pulse (i.e., the common time window during which MEPs are manifested). Furthermore, trials were rejected if the background EMG activity (measured for each trial during a time window of 500 ms preceding the TMS pulse) was above 200 µV. In addition, results from participants with few valid trials (less than 2 SDs below the average number of valid trials) were excluded from the analysis. In the final sample, trials were furthermore discarded with MEPs above or below ± 2 SDs from the average MEP of each muscle in each participant. This resulted in on average 9% discarded trials in the FDI and in 6% discarded trials in the OP.

In each participant, the remaining MEPs were normalized (z score) separately for the two muscles (FDI and OP).The z scores were entered in a repeated measures ANOVA with as withinparticipants factors stimulus valence (positive, negative) and response (approach, avoid). This analysis yielded a significant valence x response interaction, F(1, 24) = 13.30, p = .001, ƞp 2 = .36 (see Figure 2). Pairwise comparisons on the term of the interaction revealed a higher motor responsiveness in the muscle (FDI) of the approach finger (index) (M = 0.05, SD = 0.09) than in the muscle (OP) of the avoidance finger (thumb) (M = -0.03, SD = 0.10) when presented with positive stimuli, F(1, 24) = 12.97, p = .001, ƞp 2 = .35, and a higher motor responsiveness in the OP (M = 0.02, SD = 0.10) than in the FDI (M = -0.06, SD = 0.09) when presented with negative stimuli, F(1, 24) = 10.26, p = .004, ƞp 2 = .30. No other effects were significant, all Fs < 1.38.

The results of Experiment 1 indicate that positive/negative stimuli lead to a spontaneous tendency to approach/avoid. Positive images led to higher motor activation for FDI, the index finger muscle recruited to approach, whereas negative images led to higher motor activation for OP, the thumb muscle recruited to avoid. These results support the idea that positive/negative stimuli spontaneously elicit the tendency to approach/avoid.

Experiment 2

The aim of the second experiment was twofold. First, we set out to replicate the results obtained in the first experiment. Second, we wanted to clarify the impact of the visual outcomes of the approach/avoidance responses that participants received in the response trials on the links between positive-approach and negative-avoidance. [START_REF] Van Dantzig | Approach and avoidance as action effects[END_REF] proposed a motivational account in which approach/avoidance tendencies are conceived of as "flexible, action plans that are represented in terms of their effects" (p. 17). In the same vein, Krieglmeyer and Deutsch (2010) considered the effects of distance regulation as a pre-requisite for the compatibility effect to occur. Also in the evaluative coding account [START_REF] Eder | When do motor behaviors (mis) match affective stimuli? An evaluative coding view of approach and avoidance reactions[END_REF], anticipated action consequences are supposed to be involved in the generation of motor

responses. An open question that is worth investigating empirically is whether valenced stimuli can elicit the tendencies to approach/avoid (in the observation trials) in the absence of visual feedback of the outcome of the approach/avoidance responses (received in the response trials).

The method was the same as in the first experiment, except for two crucial factors. First, images were presented on a horizontally-placed flatscreen monitor and participants were asked to keep the index finger and thumb of their right hand directly on the screen in resting positions. To approach and avoid in the response trials, they had to move these fingers from the resting positions to two nearby positions. Second, the approach/avoidance behaviors were not followed by a manikin walking toward/away from the stimuli during the experimental phase, so that a clear visual feedback of the outcomes of these behaviors was absent. By no longer providing this visual feedback, we could examine whether the mental representation of this outcome mediated the influence of stimulus valence on the approach/avoidance tendencies.

Method

Design and participants. The experiment had a 2 x 2 design with stimulus valence (positive, negative) and response (approach, avoid) as within-subject factors. Forty-one participants took part in the experiment. Five participants were excluded because they did not follow the instructions correctly or due to problems in saving EMG data and three participants were excluded because they had less valid trials than 2 SDs below the average valid trials for all participants. This resulted in a final sample of 33 participants (mean age 24.15 years ± 2.89; all but 4 right handed; 19 females). Participants had no history of neurological problems or psychiatric disorders, had normal or corrected-to-normal vision, and where prescreened for risks associated with TMS [START_REF] Rossi | Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research[END_REF]. The study was granted ethical approval by the Ethical Medical review board of Ghent University Hospital. Participants gave written informed consent and received a compensation of 15 euros.

TMS administration, MEP recordings, stimuli, and procedure. The procedure for TMS administration and MEP recordings was the same as for the first experiment. The average intensity of stimulation was 68.29 % (range 50%-84%). The stimuli were also identical to those used in the first experiment. This time, stimuli were presented on a flatscreen placed horizontally on a table.

Responses were recorded via a custom-made response box that registered button presses from two FSR-sensors and transmitted them via a USB channel. The FSR-sensors were directly fixated on the monitor via double sided adhesive rings. Two resting spots equally distant from the FSR-sensors were marked with adhesive tape, one on the upper part of the screen and one on the lower part of the screen. Participants were asked to place the tip of the right index finger on the upper spot and the tip of the thumb on the lower spot. Only when they were instructed to approach/avoid (during the training phase and the response trials of the experimental phase), they had to move their index finger/thumb from their resting spot to press the FSR-sensor buttons. The training phase and experimental phase were the same as in Experiment 1, except that participants kept their hand directly on the screen. In the response trials of the experimental phase, there was the additional difference that approach/avoidance responses were no longer followed by feedback in the form of a manikin approaching/avoiding the stimulus. This feedback was still present during the training phase.

Results and discussion

The same data processing steps and filters were applied as in Experiment 1. This resulted in the removal of 6% of trials in both FDI and OP, equally distributed across conditions. In each participant, the remaining MEPs were normalized (z score) separately for the two muscles (FDI and OP). The z scores were entered in a repeated measures ANOVA with as within-participants factors stimulus valence (positive, negative) and response (approach, avoid). The analyses revealed no significant interaction between valence and response, F(1, 32) = 2.40, p = .131, ηp 2 = .07. However, pairwise comparisons indicated that MEPs were significantly higher in the FDI (M = 0.02, SD = 0.08) than in the OP (M = -0.01, SD = .12) when positive stimuli were presented, F(1, 32) = 4.26, p = .047, ηp 2 = .12, but did not differ significantly between FDI (M = -0.01, SD = 0.07) and OP (M = 0.01 , SD = .11) when negative stimuli were presented, F(1, 32) = 0.80, p = .378, ηp 2 = .02 (see Figure 2). No other effects were significant, all Fs < 0.42. This suggests that the direct link between stimulus valence and action tendencies that we observed in Experiment 1 is not maintained in the absence of a clear visual outcome of the responses (i.e., the walking manikin).

Results from aggregated data over both experiments and discussion

The evidence for the link between stimulus valence and approach/avoidance tendencies was only found in Experiment 1 but not in Experiment 2. To analyze whether this effect was modulated by the methodological differences between both experiments, we combined their data. A mixedmodel ANOVA on the aggregated data revealed a significant three-way interaction between experiment, valence, and response, F(1, 56) = 4.30, p = .043, ηp 2 = .07, as well as a significant twoway interaction between valence and response, F(1, 56) = 15.614, p < .001, ηp 2 = .22. No other effects were significant, all Fs < .86. This suggests that the visual feedback was indeed necessary for stimulus valence to elicit an action tendency.

General discussion

In two experiments, we examined whether positive/negative stimuli spontaneously elicit the tendencies to approach/avoid by using a neurophysiological technique-TMS induced MEPs-to measure action tendencies, and thereby circumventing some of the problems related to previous behavioral studies that used stimulus-response compatibility tasks. Compatibility tasks do not allow determining whether the compatibility effect is caused by feature overlap between (a) the stimulusinduced action tendencies and the instruction-induced action tendencies or (b) the stimuli themselves and the instruction-induced action tendencies. If the latter case obtains, it is possible that the stimuli never elicited any action tendencies, which makes compatibility tasks unsuitable for examining whether the mere presentation of stimuli with a positive/negative valence is sufficient to induce action tendencies to approach/avoid. This is why we turned to single pulse motor TMS induced MEPs as a method for investigation.

Previous studies using this technique inferred specific action tendencies from general corticospinal modulation (e.g., [START_REF] Borgomaneri | Motor mapping of implied actions during perception of emotional body language[END_REF][START_REF] Borgomaneri | Seeing fearful body language rapidly freezes the observer's motor cortex[END_REF] or from corticospinal modulation in specific muscles [START_REF] Gough | Fine tuned modulation of the motor system by adjectives expressing positive and negative properties[END_REF]. Because these inferences may not always be warranted, we chose to install the meanings of specific muscle movements during a training phase in which participants learned how to perform an approach and avoidance response (approach with index finger and avoid with thumb). After that, participants observed stimuli with a positive or negative valence while they received a single TMS pulse to M1 at 400 ms post-stimulus onset. Comparison of the peak-to-peak amplitudes of the MEPs on the muscles of both fingers (FDI on the index finger and OP on the thumb) allowed us to infer the action tendency that was spontaneously elicited by the stimuli. In the first experiment, we showed that the passive observation of positive/negative stimuli led to higher MEPs in the fingers used to let a manikin approach/avoid the stimuli. The second experiment was set up as a replication of the first experiment and to further examine the role of the representations of the response outcomes in the elicitation of approach/avoidance tendencies. In this experiment, participants held their fingers directly on a horizontally placed flatscreen and the response outcome (manikin walking towards/away from the stimuli) was no longer shown. The results of Experiment 1 were not replicated in Experiment 2, which was confirmed by analyzing the aggregated data. Taken together, the results of both experiments support the idea that stimulus valence can elicit the tendencies to approach or avoid [START_REF] Krieglmeyer | How farsighted are behavioral tendencies of approach and avoidance? The effect of stimulus valence on immediate vs. ultimate distance change[END_REF]-or any positive or negative action tendencies more generally [START_REF] Eder | When do motor behaviors (mis) match affective stimuli? An evaluative coding view of approach and avoidance reactions[END_REF]-, but that representations of clear visual action outcomes are necessary for this effect to occur.

A few potential limitations deserve attention. A first potential limitation has to do with the timing of the TMS pulse. Some previous studies using motor TMS reported evidence that valenced stimuli can modulate cortico-spinal excitability already at very early stages. For instance, using a pulse timing of 150 ms post-stimulus onset, Borgomaneri, Gazzola, and Avenanti (2015) observed an increase in MEPs in the left hemisphere when negative body postures were present, and a decrease in the right hemisphere when both negative and positive body postures were presented.

They interpreted the increase in MEPs as an avoidance response and the decrease in MEPs as an orienting response, but these interpretations may not be warranted. Using a pulse timing of 300 ms post-stimulus onset, these researchers also observed an increase in MEPs in dynamic compared to static body postures, which they interpreted as an effect of resonance or motor simulation. We chose a timing of 400 ms post-stimulus onset as the optimal temporal window to register the motor activity after conducting several pilot tests. It is possible that this increased latency between the stimulus onset and the pulse was necessary to allow specific action tendencies to develop and not merely a general motor activation or a simulation of an implied motion in the pictures. Other relevant differences between [START_REF] Borgomaneri | Seeing fearful body language rapidly freezes the observer's motor cortex[END_REF] and our studies are the type of stimuli that they used and the task that participants had to perform. [START_REF] Borgomaneri | Seeing fearful body language rapidly freezes the observer's motor cortex[END_REF] presented body postures whereas we presented pictures of valenced scenes with humans (e.g., in interactions). It could be that the detection of valence from body postures requires less time that of the valenced scenes that we used. Future research could examine whether the effects we obtained can also be observed at early stages.

A second potential limitation is that we stimulated the hand region of the left motor cortex for both the approach response (measured from the FDI muscle of the right-hand index) and the avoidance response (measured from the OP muscle of the right-hand thumb). Previous research has shown that approach and avoidance behavior are preferentially executed by the right and left hand respectively because of the lateralization of approach and avoidance tendencies in the brain [START_REF] Maxwell | Emotion as motion: Asymmetries in approach and avoidant actions[END_REF][START_REF] Rutherford | Thriving and surviving: Approach and avoidance motivation and lateralization[END_REF][START_REF] Moors | The role of stimulus-driven versus goal-directed processes in fight and flight tendencies measured with motor evoked potentials induced by Transcranial Magnetic Stimulation[END_REF]. By assigning both responses to the same hand, we avoided potential confounds of this lateralization, but we cannot exclude that a different pattern of results would have been obtained if we would have stimulated the right hemisphere instead or another motor area.

A third potential limitation is that our studies do not allow disambiguating between the scenario in which positive/negative stimuli lead to the more specific tendencies to approach/avoid, as suggested in the distance regulation account [START_REF] Krieglmeyer | On the nature of automatically triggered approach-avoidance behavior[END_REF] or the scenario in which positive/negative stimuli lead to the more general tendencies to engage in any positive/negative behavior, as suggested in the evaluative coding account [START_REF] Eder | When do motor behaviors (mis) match affective stimuli? An evaluative coding view of approach and avoidance reactions[END_REF]. This is because we did not train participants to engage in other positive or negative behaviors (e.g., fight, praise). Future research could examine this issue further.

In conclusion, the present neurophysiological evidence suggests that our motor system is activated at 400 ms post-stimulus onset without instructions to move towards/away from pictures depicting positive/negative human scenes. Future studies could further examine whether these findings generalize to other, less intense stimulus material (e.g., facial expressions), other positive and negative action tendencies (e.g., fight, praise), and earlier pulse timings (e.g., 200-300 ms).

FIGURES

Figure 1. Sequence of events in one observation and one response trial. In an observation trial, a valenced stimulus was followed by a TMS pulse at 400 ms post-stimulus onset and disappeared at 3250 ms post-stimulus onset. In a response trial, a neutral stimulus was followed by a TMS pulse at 400 ms post-stimulus onset and an auditory instruction to approach or avoid the stimulus at 500 ms post-stimulus onset. The trial was aborted 3000 ms after response execution or at 3250 ms poststimulus onset if no response was given.

Figure 2 .

 2 Figure 2. Average standardized MEP amplitudes in Experiment 1 and 2 for positive and negative stimuli.

Preparation of this paper was supported by Research Programmes G022313N and G073317N of the

Research Foundation -Flanders (FWO) and the Research Fund of KU Leuven (C14/17/047).