Passer domesticus) to investigate the factors determining group-size distribution. Over a wide range of conditions, we observed that animal group sizes followed a single parameter distribution known as the logarithmic distribution. This single parameter is the mean group size experienced by a randomly chosen individual . For sparrows, the experienced mean group size, and hence the distribution, was affected by four factors: morning temperature, place, behavior and the degree of food spillage. Our results further indicate that the sparrows regulate the mean group size they experience, either by groups splitting more or merging less when local densities are high. We suggest that the mean experienced group size provides a simple but general tool for assessing the ecology and evolution of grouping.One of the most striking aspects of animal groups is their remarkable variation in size, both within and between species. While a number of mechanistic models have been proposed to explain this variation, there are few comprehensive datasets against which these models have been tested. In particular, we only vaguely understand how environmental factors and behavioral activities affect group-size distributions. Here we use observations of House sparrows ( Groups of animals are seen engaged in behaviors as diverse as social foraging N is given byr = 1, and he presented a number of empirical cases where this relationship held r go to infinity while holding the distribution mean constant.The theoretical study of animal group sizes can be approached both in terms of function and mechanism N isa>0 and 0<c<1 are constants, should be wide spread in nature. The parameter a determines the slope of the power law and c determines the point at which the power law is truncated. Similar results were found by Sjöberg While the negative binomial distribution does fit some datasets, the most striking aspect of many empirical observations is the large variance and long tail of group-size distributions (i.e. the occurrence of very large groups) W(N) of observing a group of size N is proportional tog is the maximum observed group size, and W(N) is the observed fraction of groups of size N. logarithmic distribution, can be found in Recently, Niwa proposed a simple distribution of animal grouping and tested it against fish schooling data see also . The keya = 1 and c = . House sparrows in a rural valley in southern France were chosen as a study system due to their tendency to form non-familial groups outside of the breeding season. This small-sized passerine generally lives in close proximity with humans and benefits from feeding on food spills that result from agricultural practices Understanding why groups have certain typical sizes and distributions and how these change with external factors is central to understanding the social dynamics of groups. Jovani et al. 2 test would lead us to reject all these theoretical distributions as perfectly describing the data, the logarithmic distribution provides the best single parameter description of the data.Sparrow group-size distribution over all distributions varied between 1 and 46  while th). The maximum likelihood estimated parameters of the truncated power law were a = 0.99 and c = 0.84. These values are almost identical to those given by the logarithmic distribution  and a lower In terms of AIC, the logarithmic distribution also outperformed both of the alternative two parameter models: the negative binomial distribution (equation 1) and a truncated power law (equation 2). The best fit for the negative binomial distribution was consistent with that predicted by geometric distribution  also resulted in a good fit. It even has a higher Although the logarithmic distribution with  factors  and 4.To assess further which environmental factors affected the group-size distribution we first inverse transformed the data and used a generalized linear mixed model . The invSparrows aggregated in larger groups on cold days than on warmer days. To illustrate this effect we split the data set into two halves in respect to morning temperatures. On cold days with low morning temperature, the average group size experienced by an individual was more than 50% larger . However Do the sparrows actively regulate their group sizes or is it simply determined by the density of the birds in a particular area? This question goes to the heart of stable group size theory. If group size is simply proportional to the number of birds available to form a group then this would suggest that the birds' aggregations result only from a common attraction to particular features in the environment, rather than an active regulation in response to other individuals. In particular, Niwa Our results support the robustness of the logarithmic distribution for describing animal group-sizes The relationship established by Niwa between mean group size experienced by an individual and the distribution of group sizes observed holds for groups of sparrows. The underlying biology of a species determinesN, independent of the truncation in the distribution determined by The environmental and social factors do not affect the shape of the distribution of group sizes, but instead the parameter ted data   = 7.3 lOur study suggests that animals combine the group size they experience with environmental factors to make grouping decisions. Earlier studies of animal grouping have emphasized the use of optimal and stable group sizes in the functional interpretation of data. Optimal and stable points of a distribution are obtained by finding the maximum or a particular extreme of group-size distributions. Niwa's model and our data show that even if individuals change their rate of leaving and joining groups as a function of environmental and social cues, we still expect to observe a wide distribution of different group sizes. As a result We collected data for this study in a population of House Sparrows between November 2007 and March 2008 in Lantabat, about 40 km to the east of Biarritz, Southern France. The community of Lantabat is located in a well confined valley that is surrounded by a mountain ridge on three sides. The landscape structure is characterized by small scale agriculture, in particular by traditional sheep herding on small meadows as well as cattle production. Maize is the only cereal crop cultivated in the valley and is done so on a small scale for livestock use. The majority of farm houses have traditional open maize storages where whole maize cobs are stored in an outdoor frame, that the birds take advantage of for foraging.The settlements in the valley range from single houses (≈ 50) to three larger hamlets with up to 30 houses. For our sparrow surveys, we selected 36 settlements that were at least 100 m apart from each other . The size of the surveyed settlements varied between one and 30 buildings (mean  = 4.6).Preliminary surveys showed that sparrows were not active on days with high wind or rain levels and thus data were not collected under these conditions. Each of the three observers surveyed the same settlements and used always the same observation location within the settlements. We choose different routes through the study site to sample data in the same location at different times during the day. To assess group sizes in the different locations, we counted the number of groups in each location 10 times and recorded the group size, place and activity (see below for detailed definitions).We used extensive observations before the onset of data collection to come up with a meaningful definition of a group For the places we used the following categories:air  =  sparrow flyingground  =  sparrow located on ground, in a field or a meadowhedge  =  sparrow located in a hedge (branches provide cover down to the ground)tree  = sparrow located in a tree (lowest part of tree without cover)house  =  sparrow located on a building wire  =  sparrow located on a wire, power line or phone lineFor each group, we assessed the main activity of the group members. In cases where sparrows in a group were displaying more than one activity, we chose the activity in which most members were engaged. For the assessment of the activities we used the following categories:fight  =  sparrows interacting aggressively either on the ground or in the air, see fly  =  sparrow flyingforage  =  sparrow foraging or handling foodperch  =  sparrow perched.In cases where the sparrows were hiding in dense vegetation making it difficult to assess their behavior, we classified their activity as out of sight.We used a scan-sampling protocol To assess the effect of environmental variation between the locations on group sizes, we surveyed the whole study site and assessed if the settlement contained an active farm, a partially active farm (farmers that were not actively farming large numbers of livestock but still had a few chicken and/or ducks on their farm), or if there was no active farm present. We also assessed the number of livestock, the degree of animal food spillage categorized as locations without food spillage , locations with minor food spillage  but no livestock), locations with intermediate food spillage (farms with livestock some food spillage in a few places), and farms with livestock with a large degree of food spillage in the whole location. In addition we also counted the number of cats present in each location as they can prey upon sparrows.During the observations, we noted all disturbances ) and the weather conditions. On each observation day we recorded the morning temperature before starting the surveys.We used maximum likelihood estimation (MLE) to obtain the parameters for all the proposed models. In fitting the distributions we used all available data, i.e. all of the one minute observations within each 15 minute period. The decision to use all the data is based on the assumption that the group size distribution is in equilibrium, whereby each leaving or joining event takes the group from one point in the distribution to another. In any case, group composition changed rapidly, so there was seldom replication of group sizes from one minute to the next and large groups rapidly split in to smaller groups. Although (as we state above) sampling biases are likely to be small, we note that any potential bias would occur for larger group sizes, thus weakening the fit of logarithimic or power law distributions and strengthening the fit of the negative binomial distribution.r and p for the negative binomial distribution NB (i.e. equation 1). However, MLE gave i and the theoretical probability density 2, we set 10 size classes, the first class consisted of groups with size from 1 to 4, the second was from 5 to 8, the third was from 9 to 12, …, and so on for the first nine classes. The last class contained all groups whose size is no smaller than 37. The third statistic, AIC, is the Akaike information criteria Estimation results and details are shown in For high skewed distribution like exponential and power law distribution, usually the error for data fitting is not normally distributed, errors in the tail are underestimated by normal scale, therefore we also calculated a further two Given that the individual group sizes followed a negative exponential curve, we used the reciprocal transformation. This transformation resulted in group sizes that followed an exponential function. We used the GLIMMIX module in SAS 9.1  to analyze the data. We tested for the effect of the environmental variables, temperature, cluster size against an exponential error distribution. We included minute of the sampling event nested within location identity and date as random effects into the model. This allowed us to control for the repeated sampling on each observation and the nested data structure. We added in all models all explanatory terms of interest and possible interactions, and subsequently dropped all terms that did not influence the explanatory power of the model (a priori α = 0.05).


In this paper, we study nonverbal listener responses on a corpus with multiple parallel recorded listeners. These listeners were meant to believe that they were the sole listener, while in fact there were three persons listening to the same speaker. The speaker could only see one of the listeners. We analyze the impact of the particular setup of the corpus on the behavior and perception of the two types of listeners: the listeners that could be seen by the speaker and the listeners that could not be seen. Furthermore, we compare the nonverbal listening behaviors of these three listeners to each other with regard to timing and form. We correlate these behaviors with behaviors of the speaker, like pauses and whether the speaker is looking at the listeners or not. In a conversation, participants display when they are listening various behaviors in response to the contributions to the conversation of the speaker. These take the form of nonverbal behaviors such as head nods and shakes, various kinds of facial expressions or vocalisations such as uh-huh, hmm, etcetera. These so-called listener responses , eye gaze (looking at the listener or not), mouth gestures , and eye brows . More details on the annotations can be found in de Kok and Heylen . FurtherAs we pointed out in the introduction, there is no hard-and-fast requirement on a listener to produce a response at a given time. Listener responses are optional. Even though listener responses are important for a successful completion of an interaction, it does not mean that when a listener will not provide a response at a certain moment the conversation will immediately break down. It is known that individuals differ in their choice in timing and type of listener responses, but we have no real understanding yet of the causes of these differences.response opportunities. If there had been more listeners in each interaction, then we would probably identify more response opportunities. So, even though the corpus will not provide us a complete coverage of all the possible response opportunities in the interaction , the coverage is a lot wider than when we only have one listener.The MultiLis corpus allows us to look into these causes. The corpus offers three listeners who react to the exact same context. In the corpus, the onset of each listener response is annotated. The moments where at least one listener produces a response can be called For each response opportunity, we annotated how many listeners have provided a listener response  = 0.01, with an a priori chance of 33%. The amount of correct answers varied from 0 to 6. There were 8 segments where both participants identified the correct listener, 27 where one participant identified the correct listener and 13 where none of the participants identified the correct listener. Informal interviews with the four participants who identified the correct displayed listener at least 4 out of 6 times revealed that their strategy was to look for reactions of the speaker to one of the listeners. The listener to which the speaker reacted was chosen to be the displayed listener. They especially paid attention to the timing of smiles. They looked for moments where a speaker reacts to a smile by one of the listeners (by smiling or any other type of reaction). The listener who the speaker reacted to must have been the displayed listener.In total, the participants answered correctly in 43 of 96 segments (45%), which is significantly better than chance [As we mentioned in the introduction, several studies have investigated how the placement of listener responses may be related to behavior of the speaker’s turn. In this section, we look at the notion of graded optionality of response opportunities and how this correlates with behaviors of the speakers. Are there cues the speaker provides, like pausing and/or looking at the listener, that encourage listeners to provide a response at a certain moment? To do this, we will compare the speaker’s behavior at response opportunities to which 1, 2, and 3 listeners responded to each other. We will also look into agreement in the type of gestures the listeners have performed at these response opportunities.To ensure we can regard the responses of the displayed and the concealed listeners as equal, we will check before each analysis, whether the disruption of the closed interaction loop for the concealed listener has resulted in a difference in behavior on the specific aspect we analyze. We do this by comparing the displayed and concealed listener responses to each other.The first feature we looked at is pauses. The left bar graph in Fig. 2 = 39.0, p < 0.01, but there is no significant difference between response opportunities with a different number of responses, χ2 = 0.05, p < 0.97.In the left bar graph in Fig. 2 = 113.74, p < 0.01. For response opportunities with one response, the percentage remains 42%, since start and end times are the same for those, but end times of response opportunities of two responses are 60% of the time during a pause of the speaker. For response opportunities of three responses this even increases to 81%. Between response opportunity groups, the results are also significant, χ2 = 113.74, p < 0.01.Interestingly, when one looks at the end times of the response opportunities, one can see a big difference , the mean overlap between the response opportunity and the utterance is 217 ms.To go into this a little more deeply, we looked at the distance in ms from the start of a response and the closest start time of a pause. If the start of the response is before the start of a pause, a negative distance is recorded.p = 0.19) between the two groups of listeners.The mean location of the 2,433 responses in this corpus is 81 ms after the start of a pause, with a standard deviation of 809 ms. A random distribution of responses would result in a mean of 23 ms before the start of a pause, with a standard deviation of 956 ms. In Fig. If we compare the individual responses in response opportunities with one response to the individual responses in response opportunities with two or three responses, we can see that the more listeners reacted at the same time, the later the responses are with regard to the start of a pause  and also the standard deviation decreases . The histogram distributions are plotted in Fig. The pause itself is probably not the cue to which the listeners respond. It is more likely that the close of a grammatical clause is the cue, as mentioned by Dittman and Llewllyn . Since tThe second feature we looked at is gaze. The right bar graph in Fig. 2 = 139.94, p < 0.01. For response opportunities with one response, the percentage of response opportunities which start while the speaker is looking at the listener is 81%. For response opportunities with two responses, this percentage increases to 90% and for response opportunities with three responses even to 95%. These increases are significant, χ2 = 30.6, p < 0.01. For gaze during end, the percentages are a little lower for response opportunities with two responses (86%) and for response opportunities with three responses (94%).In the right bar graph in Fig. These results support previous findings that looking at the listener as a speaker is a cue for a listener to respond , Backnod (characterized by an upward stroke), and Double nod (two nods in quick succession with the same amplitude). Of each of these head gesture type, there is a Lingering variant. These head gestures continue for a period of time in decreasing amplitude. If the amplitude is increased, a new head gesture is annotated. The corpus also included the labels Downstroke and Upstroke, which are single up or down movements. For the next analyses, these are considered as Nod and Backnod, respectively. All the other labels are combined in the label Other.The main head gesture types available in the corpus are For the first analyses, we looked whether the three listeners used the same head gesture when they reacted at the same time. For each response opportunity with at least two responses, we noted the head gestures type the listener used in their response. This was Nod, Backnod, Double Nod, or Other. Then, we calculated Krippendorff’s α coefficient .So, since there is no agreement between listeners in the head gesture type selection, this selection does not seem to be determined by the context of the response opportunities in which the response is placed, but rather by internal motivators. Which internal motivator  is the key factor in this choice is yet to be determined.In this paper, we have studied nonverbal listener responses on a corpus with multiple parallel recorded listeners. To check whether the particular construction of the corpus had an influence on the behavior of the concealed listeners, we have looked into the differences between displayed and concealed listeners. We have found that displayed listener reported statistically significant higher rapport than concealed listeners. The objective data, however, shows no statistically significant decrease in amount of responses for concealed listeners. In the perceptive study, some observers are able to discriminate the displayed listeners from the other two listeners by paying close attention to the timing of individual behaviors of the listeners  and the reactions of the speaker to these behaviors, while most observers are unable to discriminate them.When we look at the speaker behaviors near response opportunities, we have seen that response opportunities with responses from three listeners are situated around the end of a grammatical clause more often than response opportunities with a response from one or two listeners. Furthermore, they are more often while the speaker is looking at the listener. Analysis of the form of the listener response of different listeners to the same response opportunity showed that there is no agreement between the displayed behavior. This suggests that the form of the listener response is not directly dictated by the speaker’s behavior and context, but more by the listener’s characteristics. More research is needed to understand which factors influence the form of listener responses as the studies presented in this paper are inconclusive.


There is a need in critical care units for continuous cardiopulmonary monitoring techniques. ECG gated electrical impedance tomography is able to localize the impedance variations occurring during the cardiac cycle. This method is a safe, inexpensive and potentially fast technique for cardiac output imaging but the spatial resolution is presently low, particularly for central locations such as the heart. Many parameters including noise deteriorate the reconstruction result. One of the main obstacles in cardiac imaging at the heart location is the high impedance of lungs and muscles on the dorsal and posterior side of body. In this study we are investigating improvements of the measurement and initial conductivity estimation of the internal electrode by modelling an internal electrode inside the esophagus. We consider 16 electrodes connected around a cylindrical mesh. With the random noise level set near 0.05% of the signal we evaluated the Graz consensus reconstruction algorithm for electrical impedance tomography. The modelling and simulation results showed that the quality of the target in reconstructed images was improved by up to 5 times for amplitude response, position error, resolution, shape deformation and ringing effects with perturbations located in cardiac related positions when using an internal electrode. In electrical impedance tomography (EIT) 16–32 skin contact electrodes are typically connected around the chest and current is injected through pairs of electrodes in a circular shifting format (in the adjacent stimulation pattern) with simultaneous measurement of the voltages on other electrode pairs. Gabriel et al. (1996) calculated and reported that tissues and different organs inside the body display different electrical properties . Borsic Despite all the efforts on EIT image quality, spatial resolution is still poor. What makes EIT so hard is related to the nonlinear inverse boundary reconstruction, which is highly unstable with respect to the measurement and finite element modeling errors , 9. The The method of only arranging electrodes around the medium also could affect the result of the reconstruction especially for cardiac output measurement. Patterson et al. used the Sheffield EIT portable system DAS-01 to determine the change in the cardiac image with electrode position, lung volume, and body position. Results showed significant individual variability with electrode position and air volume. The middle electrode most consistently showed an increase in impedance in the region of the heart during systole. In their study, the pattern of variability with electrode position was not consistent among subjects . The senIn this study we are investigating improvement of the reconstruction and initial conductivity estimation of the internal electrode by modeling this technique in Electrical Impedance Tomography, Diffuse Optical Tomography Reconstruction Software (EIDORS), which is a linear finite element solver written in Matlab. This software was developed to promote collaboration between research groups working on EIT- and diffusion-based optical tomography, in medical and industrial settings . The simΩ as a given body that is closed and bounded in a subset of 3D space with a boundary of  δΩ, σ as conductivity, and zc as contact impedance between the object and the electrodes, then the forward model can be shown as [The EIT problem can be developed from Maxwell's equations. If we consider shown as u(x) is the voltage distribution in the body and n is the outward unit normal vector at  Ω. Uℓ is the voltages measured on the surface of the electrodes and Iℓ is the current injected through electrodes into the body. Nel is the number of electrodes connected around the body (in our experiment  Nel = 16).  eℓ is the surface under the electrodes.ℓ=1NelIℓ = 0 and choice of a ground  ∑ℓ=1NelUℓ = 0.The partial differential equations for the forward model converge to a unique solution considering two conditions of conservation of charge ∑If we simplify the observation model as follows:V is the vector of observation voltage around the boundary, the measurement noise n is modeled as Gaussian and independent from conductivity ~N, and conductivity changes are expressed as  δσ ~ N, then the posterior density can be shown asδσ* is a prior estimate of the distribution of conductivity changes.Cn−1 = LnTLn, and Cδσ−1 = LδσTLδσ.Equation  leads toD = 2, Z = 0.8), with circular electrodes (D = 0.1). An empty cylinder in the middle of the boundary cylinder was the location of the internal electrode with the same diameter as normal electrodes and height of the entire boundary . We used the same size of the electrodes for internal and boundary electrodes to decrease variability in contact impedance between electrodes. All electrodes were located with their centers at  Z = 0.4. The red perturbation inside the medium  and the conductivity of left and right lungs was modeled as a two-compartment cylindrical shape in blue . We adopted widely used values for the conductivity of the blood and the inflated lungs at a frequency of 50 kHz (http://niremf.ifac.cnr.it/tissprop/) [−1, the lung inflated is 0.13 s m−1 while other chest tissue is assigned a conductivity of 0.48 s m−1. The common method for solving the partial differential equation in EIT is the finite element method (FEM) , 28. We e medium  with thessprop/) . The conhttp://mayavi.sourceforge.net/) for data visualization.For the normal simulation all 16 electrodes were located around the cylinder and for the model with internal electrode we moved the electrode number 16 inside the cylinder. The ground electrode for both simulations was considered on the bottom and middle of the cylinder. Simulation of thorax as a cylindrical model is reasonable for EIT simulation and easily can be experimented with phantom tanks . The FEMEIT methods for image reconstruction can be evaluated by the following parameters that describe the quality of the reconstructed images. Amplitude response (AR) measures the ratio of image pixel amplitudes in the target to that in the reconstructed image. Position error (PE) measures the distance error between the center of the gravity (COG) in the target and the reconstructed image. Resolution (RES) measures the size of reconstructed targets (number of pixels) as a fraction of the whole reconstructed pixel area. In reconstructed images of EIT the target is often surrounded with impedance changes in the opposite direction of the simulated target, which is called overshoot or ringing. This can also be interpreted as the point spread function (PSF). Shape deformation (SD) measures the artefacts of the reconstructed image with the real target.V − U(δσ)) and the solution (δσ) with different regularization parameters. The red dashed line on the curve shows the L-corner, which is the best trade-off between residual and solution in inverse problems (optimum regularization parameter). The good regularization parameter λ is one that corresponds to a regularized solution near the corner of the L-curve because in this region there is a good compromise between achieving a small residual norm and keeping the solution seminorm reasonably small [Often the regularization parameter is adjusted manually. Here for a fair comparison between the images of the methods we used automatic selection of the optimum regularization parameter as the L-corner of the L-curve. ly small . We repoly small . We expely small . For morly small , 22. To For evaluating the internal electrode with the GREIT parameters we evaluate the situation where we have small changes related to blood conductivity . This does not affect the main concept as our region of interest is the cardiac location, and we assume the ventilation can be controlled in the critical care unit to prevent lung changes. For a fair comparison between methods this removes the nonlinearity related to the shape of the lungs and also rules out any possible effects on the evaluated parameters caused by lung artefacts. In our evaluation the target was located 0.2 units from center of the cylinder and each time the distance from the center increased by 0.1 unit in the direction of electrode 9.r = 0.1). The left column shows the result when the blood perturbation is located 0.2 units from the center of the cylinder (close to the oesophagus) and the right column shows the result when the blood perturbation was located 0.8 units from the center of the cylinder (the apex of the heart near). Images in the top row used an internal electrode and images in the bottom row used an external arrangement of the electrodes. On the bottom of each reconstruction a colour scale represents the intensity of the reconstructed signal inside of the cylinder. The blood changes located 0.2 units from the center are hardly detected with external arrangement of electrodes (bottom row) when surrounded with high resistivity area of left and right lungs. With 16 electrodes and an adjacent stimulation pattern the number of measured voltages for each frame is  16 × 13 = 208. The typical simulated voltages related to one frame have been shown in The change in the L-curve and thus the robustness of the solutions with position changes is demonstrated in In For position error also the curves show that the maximum PE for internal electrode simulation is in the middle near 0.5 and the PE improves near the electrodes. For the external electrodes arrangement the maximum PE is for the target near the middle of the cylinder, which is the furthest location from all electrodes. The PE curves for both methods show a convergence when the target moves to the boundary electrodes with a small improvement for the method with internal electrode relative to the external electrodes case.D = 0.1 unit.In The current gold standard in critical care units for cardiac function measurement is a gated heart pool scan, which provides a good assessment of global heart function, but with low resolution, long lead time, and radiation risks. Less accurate alternatives include transoesophageal echocardiography, where the ultrasonic probe is inserted into the oesophagus behind and close to the heart, and transthoracic echocardiography, where the probe observes the heart through the intercostal spaces. These echo modalities often do not provide satisfactory results in these patients due to lung and rib artefact, and the size of probe currently used for the transoesophagus echocardiogram is large, inconvenient for patients, and unable to be left indwelling.In critical care units mostly the subject is ventilated and the lungs function could pause for impedance measurements. So we expect that the artefacts of lungs movement decrease significantly in this area.Our study shows that using an internal electrode for cardiac function imaging can improve the GREIT evaluation parameters such as AR, PE, RES, and SD in reconstructed images. The internal electrode can provide more information about the inner area of the heart in comparison with the external arrangement of electrodes. EIT is a fast method for global cardiac output imaging in this area. A stainless steel internal electrode similar to the ablation catheters is smaller than ultrasonic probes and can easily be inserted through oesophagus and remain indwelling for significant periods of time.Two practical issues that need further consideration in developing this method are safety and artefacts generated by the catheter movement.Regarding safety, several groups, including our own, have performed practical impedance measurements with oesophagus electrodes in humans and animals –16, 18. Regarding movement artefacts, we have modelled the location of the internal electrode in the plane of external ones and suggest that this can be confirmed in practice by other imaging methods such as fluoroscopy. As we insert the internal electrode close to heart, the internal electrode movements related to the heart are a significant artefact that should be carefully considered. In previous practical experiments we tried the oesophagus and superior vena cava (SVC) electrodes. Our evaluation shows that the electrode located in SVC is less affected by the heart movement and this electrode could be used as a reference to remove the artifacts generated by movements in the esophagus internal electrode.There is a need in critical care units for continuous cardiopulmonary monitoring techniques. One attractive technique is EIT. In critical care units specialists have access to the esophageal area through catheters and previously practical measurements indicated that the contact impedance of electrodes is decreased in this area . In addi


Many theories of driver behaviour suggest that unconscious or implicit emotions play a functional role in the shaping and control of behaviour. This has not been experimentally tested however. Therefore, in this study the effects of emotive masked images on driver behaviour were examined. While driving a simulator, participants were repeatedly exposed to negative or neutral emotionally laden target images that were sandwich masked by emotionally neutral images. These images were encountered across two different trials each of which consisted of 3–4 minutes of driving on a rural road. The results indicate an effect of the negative target images primarily in reducing the extent of familiarisation occurring between the first and second experimental drives. This is evident in a reduced decrease in heart rate and a reduced increase in high band heart rate variability and actual travelling speed from the first to second drives if the negative target image was presented in the second drive. In addition to these findings there was no clear effect of the target image on subjective ratings of effort or feelings of risk. There was however an effect of gender, with the majority of the effects found in the study being limited to the larger female dataset. These findings suggest that unconscious or implicit emotional stimuli may well influence driver behaviour without explicit awareness. Many recent models that seek to explain driver behaviour have come to incorporate functional views on the role of emotions in driving. These theories include Task Difficulty Homeostasis theory (TDH) Most of the above models approach the issue of emotion via reference to the Somatic Marker Hypothesis The main experimental evidence for the Somatic Marker Hypothesis comes from a study carried out using the Iowa Gambling Task that compared neurologically ‘normal’ people with patients who had impaired emotional systems due to ventromedial prefrontal damage Outside of the Somatic Marker Hypothesis, the majority of the above driver behaviour models also owe some of their structure in terms of how they refer to emotions to the work of Taylor Putting aside the differences between the particular theories, there is no doubt that driver behaviour models are trending towards a functional role of emotion and feelings in the control of driving, particularly when it comes to risk judgment. This trend in traffic psychology is a reflection of a wider trend in psychology where the functional importance of emotions and feelings in decision making is being stressed While the idea of implicit or unconscious emotional effects on decision making has become popular, it presents an interesting challenge to experimentally test in a complex task like driving. Exactly how can an emotion be generated in a participant without it entering into awareness and becoming a feeling? And then, how can its impact be tested without again explicitly alerting the participant?One possible solution to this challenge is the use of masked images. This is where emotionally charged images are very briefly shown to participants with other, longer lasting images shown before and after the emotionally charged image in order to ‘mask’ it from the participant's conscious perception. The idea is that the image can still generate an emotive response, but it does so without entering into the conscious awareness of the participant, or at least without the participant becoming explicitly aware of it. This process is also referred to as priming, in that the target images used are associated with situations that prime or trigger certain emotions, feelings, and cognitions in individuals via preconscious processing. In the case of this study for instance it was hoped that the negative images used would prime reactions in the physiology of participants and lead to slower speeds and faster reaction times to road safety relevant stimuli, such as a stop sign. The idea that emotionally negative images can be processed preconsciously is well in line with functional thinking in terms of emotions and feelings, in that it would be potentially evolutionary advantageous to react to threatening or emotionally negative situations as fast as possible. However, it should also be noted that whether masked images are truly unconscious, or subliminal, is a matter of great debate Using masked images has certain merits. Past studies have shown that emotionally negative masked images produce skin conductance responses Masked images have also been shown to impact on attitudes and judgements about others Ultimately the above studies suggest that masked images can have some influence on an individual's decision making or attention. However, these studies have been carried out with relatively easy tasks in relatively simple conditions, and we are unaware of any research examining the influence of masked images on a complex task such as driving.th leading cause of death overall across all age groups Driving is generally viewed as a self-paced task The study described in this paper therefore sets out to experimentally test the influence of implicit or unconscious emotional signals on driver decision making, with a focus on driver speed choice. Speed is one of the most prominent road safety issues. Not only because of the influence of inappropriate speed choices on the chance of having an accident, but also because of the undeniable influence of velocity on physical trauma and property damage when an accident does occur Participants in the present study were asked to drive a simple rural road in a driving simulator while paying attention to a series of images presented just below the rear view mirror. The images were presented under the pretence of carrying out a memory task. Each participant drove the road twice, with one drive involving the presentation of emotionally negative target images and the other of emotionally neutral target images. These images were in both cases backwards and forwards masked by different emotionally neutral masking images. At some point these masking images would change to include a stop sign. When this occurred participants had to stop the car as quickly as possible. Information on speed and stopping time along with subjective impressions of effort and feeling of risk were collected alongside physiological measures of skin conductance, heart rate, and respiration.In line with the functional account of emotion provided by the various models of driver behaviour discussed above Ethics approval, including permission to deceive the participants, was gained from the University of Groningen Psychology Ethics Committee. Participants were informed that their information would be treated anonymously and that they could withdraw from the experiment at any time with no penalty. Participants were also debriefed at the end of the study, and the masking procedure was fully explained.Participants were recruited through the English speaking University of Groningen participant pool and given course credit for participation. Participants were required to have held a valid car drivers licence for at least one year. This resulted in 74 females and 39 males being recruited for the study. However, one female reported feeling uncomfortable with the simulator during the practice drive and therefore did not progress in the study. In addition, two female, and six male participants reported being explicitly aware of the target images and were also removed from the sample. Furthermore, seven males and 12 females mentioned that they thought that perhaps there was an image being shown that they could not see even though they could not report what it was. In order to present results that are as conservative as possible in terms of the awareness of the target images these participants have also been excluded.This results in a final sample size of 85 participants. The remaining 59 females were 21.09 years old on average (SD 2.07) and had held their licence for an average of 3.19 years (SD 1.97). The remaining 26 males were 21.62 years old on average (SD 1.83) and had held their licence for an average of 3.44 years (SD 1.41).The University of Groningen driving simulator was used in this study. It is a fixed base simulator running on STSoftware software©. The simulator consists of three high definition plasma screens, all set to a refresh rate of 60 Hz. The graphics engine of the simulator software itself runs at 60 frames per second, which was confirmed via the FRAPS© software package. In total the simulator provided participants with a 210-degree view of the road environment.The road environment resembled a simple rural road with a consistent gentle s-curve in order to create some steering demand during the task. During all drives speed information was concealed through the use of a cardboard cut-out. This was to force participants to rely on their own perception of speed, rather than the speed provided by the speedometer. The simulated car was set to operate in automatic gear mode in order to minimise any movement related artefacts in the collection of the psychophysiological data.The images used during the task were taken from the International Affective Picture Set (IAPS) Negative target images: IAPS numbers 3000, 3010, 3015, 3053, 3060, 3064, 3068, 3069, 3080, 3102, 3110, 3120, 3150, 3400, 9410, 9433, 9901, 9910, 9911, 9920Neutral target images: IAPS numbers 5130, 6150, 7000, 7009, 7010, 7020, 7030, 7037, 7040, 7050, 7060, 7080, 7090, 7190, 7217, 7234, 7235, 7500, 7700, 7705Masking images: IAPS numbers 7002, 7004, 7006, 7031, 7035, 7036, 7038, 7042, 7052, 7055, 7056, 7057, 7059, 7100, 7150, 7175, 7224, 7233, 7491, 7950The negative target images mostly consisted of mutilated and deceased humans with some images of car accidents. According to the IAPS standardised scores, the negative images had valence ratings between 1.31 and 2.5 (mean 1.80), and arousal ratings between 5.70 and 7.26 (mean 6.59). The neutral target images and masking images consisted mostly of household items such as mugs, bowls, and forks, along with some pictures of buildings. The neutral target images had valence ratings between 4.23 and 5.55 (mean 4.86), and arousal ratings between 2.17 and 3.84 (mean 2.78).The images used for masking (the masking images) had valance ratings between 4.45 and 5.55 (mean 4.97), and arousal ratings between 1.72 and 4.02 (mean 2.76). It was these masking images that were used to forward and backwards mask the above neutral and negative target images during the trials. Variations of the masking images were also created for the reaction time task that had standard stop street signs placed in the centre of the images see . All of Participants were asked to wash and dry their hands and Tin (Sn) electrodes with some saline paste where taped to the distal phalanxes of the index and third finger of their left hand to measure skin conductance. This method of attachment is somewhat unusual, but has been used successfully in the past and allows for good control of steering without creating interference in the skin conductance measures Due to the variation in the moment the stop sign was shown, the psychophysiological data was shortened to only account for the first 180 seconds of driving. Furthermore, there is an immediate effect of beginning to drive on all the physiological measures, so the first 30 seconds of data was also removed to eliminate any biasing effect that this may have caused. This left 150 seconds of data for use in later analysis. In addition four males and 16 females had distorted or missing psychophysiological data and had to be excluded from analyses, resulting in a sample size of 65 participants for the analysis of the psychophysiological data. The participants in this smaller sample did not significantly differ (p>0.05) in gender, age, licence status, speed of driving or any of the subjective measures from the larger 85 person sample, and therefore are assumed to have come from the same underlying population.Participants first filled out a demographic questionnaire to gather data on their age, gender, and driving experience. The experimental procedure was then described to them under the pretence of being a study about the effect of memory tasks on driving. Participants were told that they were to drive at whatever speed they found comfortable, but that while doing so they were to carry out a memory task. This memory task involved paying attention to constantly changing images that were presented in the upper centre of the screen  images and that when they saw this sign they were to stop counting repeats and bring the car to a full stop as quickly as possible.The images that the participants were instructed to pay attention to were the masking images mentioned above, and were presented on the screen for 50 frames (800 ms). Unbeknownst to the participants in between each masking image a target image was presented for 2 frames (32 ms). With the timing of the image presentation confirmed through the use of a high speed digital camera over three four-minute periods during the initial setup of the experiment. The target images shown were either negative or neutral in emotive content depending on the trial. The presentation of the images began only once the participants had driven 100 metres, and continued until the participants brought the vehicle to a complete stop. After three minutes a random timer was started that triggered the presentation of the stop sign variant images to replace the masking images within 1–60 seconds.The masking and target emotive images were selected randomly every time they were presented and programmed to usually avoid directly repeating themselves so that the same image would not often be shown twice in a row. However, this was allowed to randomly occur on occasion, creating the supposed ‘memory task’ for the participants when the masking images would repeat two times in a row.Participants were asked to sit in the driving simulator and adjust the seat so that it was comfortable. They were then given a practice drive to get familiarised with the simulator and the memory task. During this practice task no data was recorded. The practice drive lasted 3–4 minutes, depending on the timing of the stop sign, and was identical to a neutral image experimental trial. Also at the start of the practice drive the experimenter verbally pointed out the first directly repeating image to the participant. This was done to make sure that the exact nature of the memory task was understood.After the practice drive participants were asked if they felt comfortable with the simulator and the memory task. At this point all participants stated that they were comfortable and wished to continue. Then the participants were asked to hold the steering wheel, but to otherwise sit still and quietly while baseline physiological measures were taken. These baseline measures were then collected for 3 minutes.After the baseline data collection period participants were asked to drive the road again twice, while carrying out the memory task. During these two drives data on travelling speed was collected at a rate of 10 Hz. Also the braking reaction time to the stop sign image was calculated as the time from when the first stop sign appeared until the participant had depressed the brake pedal by more than 5 percent. One trial for each participant involved the neutral target images and the other the negative target images. The order was counterbalanced across participants, however due to scheduling issues, and loss of participants due to the earlier mentioned incomplete data sets the end result was that 43 participants  drove with the negative target image trial first and then the neutral, and 42  with the neutral target image trial first and then the negative.After each trial participants were asked to fill in a questionnaire containing the following open ended questions:How many times during the drive did you see paired presentations of images?On average, what speed (in km/h) do you think you drove at during the last trial?On average, what speed (in km/h) would you typically drive while following the route you just saw?Then participants were also asked to provide a driving effort rating for the trial they had just completed on the rating scale for mental effort (RSME) What was/were the difference(s), if any, that you noticed between the first and second roads you drove?Did you notice any images during either drive that seemed out of place, unusual or particularly disturbing? If so, what were they?Once participants had completed the practice drive and both experimental trials, the psychophysiological recording equipment was removed and they were fully debriefed about the use of the hidden, neutral and negative images in the experiment. This included an additional verbal check to see if they had detected the negative or neutral images, and an opportunity for participants to ask any questions that they may have had.Due to the variable nature of when the stop sign was displayed (1–60 seconds after the first 3 minutes of driving) only the first 3 minutes (180 seconds) of driving were used for analysis of the effects of the images on speed of travel. This is in contrast to the 150 seconds of data used in the analysis of the psychophysiological measures mentioned in section 2.2.3 aboveThe dependent measures analysed in this experiment were actual speed driven, stopping reaction time, the subjective ratings of speed, effort and risk, performance on the memory task, and the psychophysiological measures of heart rate, heart rate variability, skin conductance and respiration. In addition qualitative data on differences between the roads and on whether the participants noted anything unusual were also examined.Using PASW SPSS 18.0.3 for Windows, individual full factorial repeated measures analyses were carried out to examine the effect of the independent variable of the target image type within the subjects . In addition the between subjects factors of condition order  and gender were also examined. Possible interactions effects were examined and post-hoc tests with a Bonferroni correction were used where appropriate. The above analyses were run for both the total dataset, and separately for the males and females.In general the participants gave higher ratings for effort than for feeling of risk, with average scores between 53.77 and 59.12, corresponding approximately with a level of ‘Rather much effort” on the RSME. Whereas feeling of risk scores averaged between 30.50 and 34.93, placing them somewhere between ‘A little risk’ and ‘Some risk’ on the modified version of the scale. As shown on 2 = .35) with the second trial generally resulting in higher average perceived speeds. In combination with this, trials where the negative target image condition was second resulted in larger increases in ratings of subjective travelling speed between the two drives. This indicates that the participants perceived a speed increase between the first and second drive, but perceived it as being a greater increase if they had been exposed to the negative image in the second, rather than first, trial.There was however a significant interaction of target image type and condition order for the subjective ratings of travelling speed  = 8.21, p<.001, η2 = .05) interaction of target image type, gender and order was also found for ratings of feeling of risk. This appears to have resulted from the males tending to give higher average ratings of feeling of risk for the negative target images than the females . This also points to a different effect in terms of the order of image presentation, in that the males appear to decrease ratings of risk from the first to the second trial by 6.64 points if the negative target images were presented first, and increase ratings of risk by 3.86 if the negative target images were presented second. Conversely the females always increased their ratings of feeling of risk from the first to the second drive, and did so more if the neutral target images were presented second (an increase of 6.59 versus an increase of 3.86 if the negative target images were second).A significant  = 4.16, p<.05, η2 = .10) order and target image type interaction on ratings of effort, with ratings of effort generally decreasing in the second trial, but doing so more if the negative target images were second . However if the male and female datasets are examined separately then this significant order and target image type interaction is only apparent in the male data  = 6.19, p<.05, η2 = .21) and is different from the combined dataset. In that the decreases in effort ratings from the first to second drive are still apparent in the male data set but are greater when the neutral target images were second (a decrease of 9.76 compared with a decrease of 3.10 when the negative target images were second). No significant main  = .64, p = .43, η2 = .01) effects of the images or interaction effects  = 2.48, p = .12, η2 = .04) with order on ratings of effort were found for the female participants However, the females did tend to also decrease effort ratings from the first to the second trial, and did so somewhat less if the neutral images were second (a decrease of 1.35 compared with a decrease of 4.11 if the negative images were second), although again this effect was not significant.There was also a significant  = 8.54, p<.01, η2 = .0.16) was found on ratings of typical speed for the males (N = 26), which is not in the combined dataset. In addition in the male dataset significant target image type and order interactions were found on typical speed  = 7.45, p<.05, η2 = .24), subjective travelling speed  = 8.87, p<.01, η2 = .27), and effort  = 6.19, p<.05, η2 = .21). Finally in the male data there was no significant main effect  = 2.81, p = .81, η2 = .11) nor any interaction effects with order  = .20, p = .66, η2 = .01) of target image type on feelings of risk. In case of the above effects it seems that the males tended to report higher subjective travelling speeds in the second trial, although more so when the negative target images were second. With an increase of 11.07 km/h on average compared with an increase of 3.64 km/h if the neutral target images were second. The impacts in terms of effort are discussed above, with lower effort being generally reported in the second trial, but more so if the neutral target image trial was second. The effects seen for typical speed seems to have arisen because the male participants who received the neutral and then negative target image trial order on average only increased their ratings of typical speed by 0.33 km/h between trials. Whereas those who experienced the neutral and then negative target image trial order increased their ratings by 2.73 km/h between the first and second trial. This result is unlikely to be meaningful.When data from the males and females was examined separately then a main effect of target image type  = 4.56, p<.05, η2 = .49) and feeling of risk  = 9.98, p<.01, η2 = .15). Again, it seems that for the females their perceptions of the subjective travelling speed increased from the first to the second drive with larger increases when the negative target images were presented second (12.22 km/h versus 11.48 km/h when the negative images were first). As described above, the effect on feeling of risk for the females was an increase between the first and second drive, with a larger increase if the neutral target image was second . As mentioned above no significant main or interaction effects of the target image type or order were found for ratings of effort in the female dataset. As with the combined dataset there were also no significant main  = .11, p = 0.74, η2 = .74) or interaction  = 2.16, p = .15, η2 = .04) effects of the target images on ratings of typical speed either.In terms of the results for the females (N = 59), they were similar to those of the total sample, with significant target image and order interactions for subjective travelling speed  = 54.37, p<.001, η2 = .04) main effect of target image type on stopping reaction time in reaction to the stop sign, with an average reaction time of 2.04 (SD = 1.03) seconds for the negative target image trial and 1.87 (SD = .61) seconds for the neutral target image trial. There were also no significant interactions or main effect of order or gender  = .12 to 2.59, p>.11, η2 = .00 to .03).There was no significant  = 2.98, p = .09, η2 = .02) on average speed driven during the first 3 minutes of the experimental trials. There was however a significant main effect of gender  = 8.21, p<.01, η2 = .09), with males tending to drive faster on average (114.61 km/h) than the females (96.47 km/h) across all trials.As shown in 2 = .20). This means that speed during the second trial tended to be higher than during the first, but also that this effect did not appear to be as pronounced when the negative target images were presented to participants second. Specifically, when the negative target images were second there was an increase of only 3.43 km/h on average from the first to second trial compared with a 7.08 km/h increase in speed when the neutral target images were second.There was also a significant interaction effect of target image type and condition order  = 20.25, p<.001, η2 = .00) in the speed they drove when exposed to either the negative (113.29 km/h on average) or neutral target images (113.19 km/h on average). Neither was there a significant interaction of target image type and order  = 3.62, p = .07, η2 = .13) for the males. Conversely if only the females (N = 59) were examined, then a significant main effect of target image type was found  = 4.51, p<.05, η2 = .07) along with a significant interaction of target image type and order  = 27.33, p<.001, η2 = .32). Therefore in the case of the females it seems that, on average, the trials with the negative images resulted in lower driving speed (94.96 km/h on average) than trials with the neutral images (97.72 km/h on average). Furthermore, as with the total sample, it appears that females tended to increase their speed from the first to second drive, but did so to a lesser extent if the negative target image trial was experienced second. In the females, this resulted in an increase of 7.91 km/h when the neutral target images were second, compared with an increase of 3.34 km/h when the negative images were second. As shown in As shown in All recorded psychophysiological measures were compared between the two target image trials, but also to the baseline measurement as a third variable. Furthermore, as with the above analyses, condition order and gender were included as between subject factors. It should be noted that, as explained in section 2.2.3, the analysis of the psychophysiological data was carried out on only 150 seconds of data and with a smaller sample size of 65 participants.2 = .39 to .63) main effects were found on average heart rate, as well as mid and high band heart rate variability. However post hoc tests with a Bonferroni correction revealed this was only due to significant differences between the baseline measurement and the measurements collected during the target image trials (p<.001). This means that no significant differences in these measurements between the target image trials was found (p = 1.00).Significant  = 19.16 to 50.02, p<.001, η2 = .09) main effect of gender on mid band heart rate variability, with males having a higher variability than females on average. However, there were no interactions of the target image type with gender  = .07, p = .92, η2 = .00) or condition order  = .35, p = .71, η2 = .01) for mid band heart rate variability.There was also a significant  = 5.85, p<.01, η2 = .15) main effect of target image type on skin conductance was found with the experimental trials seeming to produce a higher average skin conductance response than the baseline. However, subsequent post hoc tests with a Bonferroni correction only found a significant difference between the baseline measurement and the negative target image trial (p<.05) but no significant difference between the baseline measurements and the neutral target image trial (p = .07) nor between the target image trials themselves (p = 1.00). No significant effects for any of the variables or conditions were found for the respiration measurements.A significant  = 5.12, p<.01, η2 = .58) and high band heart rate variability  = 5.02, p<.01, η2 = .14). Meaning that the average heart rate decreased and the average high band heart rate variability tended to increase in the second trial. As can be seen in There were also significant interaction effects between image type and condition order for average heart rate  = 42.14, p<.001, η2 = .11).When the male (N = 22) and female (N = 43) participants were examined separately then similar results to those mentioned above were found, with two exceptions. The first being a significant (p<.05) difference for the males between their average baseline respiration measure and the average respiration measure during the negative target image trial. There was however no significant difference in respiration for the males between the baseline measures and the measures during the neutral target image trial (p = .20), nor any significant difference between the average respiration in the neutral and negative image trials (p = .69). The second difference is that for the females there was no main effect of condition  on skin conductance response at all  = 2.47, p = .10, ηWhile the memory task was simply an excuse to have images presented on the screen, it is worth noting that there was no significant effect of the type of target image  on the number of pairs reported. Rather the participants performed this task well in both conditions with an average reported number of image pairs of 11.67 (SD = 3.15) during the negative target image trials and 11.49 (SD = 3.81) during the neutral target image trials. The actual number of image pairs was 12.06 (SD = 2.84) and 11.33 (SD = 2.79) for the negative and neutral target image trials respectively. This indicates that the ‘memory task’ was equally demanding during both conditions, and that the participants were paying attention to the images. If the male and female participants are examined separately then their performance on this task and the average number of images presented to them is similar to the combined dataset.At the end of the experiment participants were asked ‘What was/were the difference(s), if any, that you noticed between the first and second roads you drove?’ This question, along with a question about the images, was primarily to check if the participants had seen any of the target images as the road did not differ between the two drives. However, only 43.53%  of the participants correctly reported that there was no difference between the roads. A further 38.82%  reported that something about the construction of the road had differed. Out of these people, 19  stated that either the road in the negative image trial was more curvy  or that the road in the neutral image trial was less curvy  . Conversely, 14  participants said that either the road in the neutral image trial was more curvy  or had more hills , or that the road in the negative image trial was less curvy . In nearly all of the cases , if a road was labelled as more curvy it was the road that was driven second. Therefore it is likely that this impression of more or sharper curves was created by the fact that participants tended to drive faster during the second drive. In answer to this question, a further 9  participants commented that they had detected more image pairs during the ‘memory task’, or that the images were placed in a different position on the second drive . Of the remaining 4 participants, 2 did not answer this question, and the other 2 just mentioned being more used to the road on the second drive.According to many models of driver behaviour The primary behavioural variable examined was driving speed, and if the total dataset of males and females is examined then there was no significant main effect of the target image type on driven speed. However, there was a significant interaction effect of type of target image, negative or neutral, and the order in which the images were presented to the participants. This showed that there was a general tendency for participants to drive faster on the second trial, most likely due to familiarity and learning effects. However this general effect also interacted with the type of target image being used, with the increase in speed being smaller if the second trial contained the negative target images (an increase of 3.43 km/h on average) than if the neutral target images were presented second (an increase of 7.08 km/h). This suggests that the negative target image could have had a suppressing effect on speed in terms of reducing the normal increase associated with familiarity or learning effects.Gender also played a role in this effect, and when the male and female datasets were examined independently then was no effect, interaction or otherwise, of the target images on the males speeds. However for the females there was the above interaction effect mentioned for the combined dataset, and also a further significant main effect of image type on driving speed. This resulted in significantly lower average speed overall for the female drivers during the negative target image trials (94.96 km/h) than during the neutral target image trials (97.72 km/h). This gender difference is apparent in the statistics and also easy to see from the graph of speed over time shown in The difference between the male and female participants could be explained in several ways. Firstly it is possible that the negative target images were more emotionally impactful for the female participants than the males. Certainly there is a difference in both valence and arousal ratings by gender for many images in IAPS Another possible explanation for the gender difference is that given the effect sizes observed that there may not been enough males to detect any consistent effect of the images on behaviour. This study did set out to recruit a large number of both male and female participants to take part; however we did not succeed in the case of the male subjects.Interestingly, at least in the case of the females, the effect of the negative target image on driving speed seems to be mainly on the initial setting of the speed, which is then generally maintained throughout the drive see . This paMoving away from speed to the recorded psychophysiology there is a significant target image type and order interaction effect in both heart rate and high band heart rate variability that is potentially interesting. This interaction effect is found in the combined dataset, in the males and, most importantly in light of the above speed effects, in the female dataset. The interaction indicates decreases in heart rate, and increases in high band heart rate variability between the first and second drive. This is consistent with a familiarity or relaxation effect with the second drive becoming less stressful or effortful for the participants. Interestingly however, the decrease in heart rate and increase in high band variability was lessoned if the negative target images were second. This, when combined with the above speed data where the increase in speed was also less when the negative images were second, is suggestive of a physiological effect of the negative target images. Specifically it appears that the usual familiarity effect in terms of increasing speed, but also in terms of decreased physiological load for the second trial was lessoned if the negative images were presented second. That this effect is seen in the cardiovascular measures but not in skin conductance, which does not seem to meaningfully differ between the trials, or in the case of the female participants even between the baseline and the driving task, is interesting. While both cardiovascular and skin conductance measures are sensitive to changes in emotional arousal, heart rate and high band heart rate variability are generally considered to be more reflective of changes in mental workload or effort Care should be taken with this interpretation however. Another way to interpret the lack of an impact on skin conductance response would be to say that because the participants  drove, on average, slower when influenced by the negative image they were bringing their body state back into its normal range. This process of returning to a normal, comfortable, or set body state may occur quite quickly, during the first few seconds of driving, and therefore may not show up in the averaged psychophysiological data presented here. Especially since the first 30 seconds of psychophysiological data had to be excluded from analysis. This data was excluded because there was an immediate large impact of beginning to drive and starting to perform the memory task on the cardiovascular and skin conductance measures. The above mentioned physiological consistency or homeostasis is what would be predicted by models of driver behaviour such as RAT A related explanation for the lack of a meaningful response to the target images in skin conductance could be due to ceiling effects related to performing the driving task. Previous studies which have shown an effect of masked images on psychophysiology have tended to involve simple experimental tasks carried out in a standard lab environment It was also thought that the negative image may have primed the participants to be aware of any possible threats and made them ready for action, resulting in a faster reaction time in response to an image of a stop sign being presented on the screen. However, no such effect was found in the combined, male, or female datasets. This could again be explained through the above mentioned return to a normal body state at the start of the drive. If participants had indeed quickly eliminated or balanced out the effect of the negative image on their body state by lowering their speed, then the negative image would possibly have no further impact, and could not help in raising their alertness to the onset of the stop sign. Another explanation could be that the stop sign in itself, while a road safety related stimulus, is not threatening enough when it occurs in an open road situation with no other traffic. Therefore perhaps future experiments investigating the influence of masked negative images on threat detection could use a more relevant stimulus such as a car unexpectedly pulling out from a parking spot.There was also a significant interaction effect of target image and order on participant's perceived speed in the combined, male, and female datasets. This reflects that actual travel speeds increased between the two trials. However, in this case the perceived increase in speed was larger if the negative images were presented during the second trial . At least for the female participants this is the reverse of the effect on actual driven speed, with the speed increase in this case being smaller when the negative target images were presented second. This could be interpreted as exposure to the negative target images creating an impression of faster driving speeds and therefore influencing the speed actually driven. However, it is clear from the data that this difference in speed perception, while present in the female participants, is mostly contributed to by the male participants who did not seem to be significantly affected by the target images in terms of their actual driving speed.The results for the subjective ratings of feeling of risk and effort are somewhat difficult to interpret for the combined dataset. In the case of ratings of risk there is not only a significant interaction of target image type and order, but also a significant interaction with gender. This means that in the case of the male participants they decreased their ratings of feeling of risk from the first to second trial if the negative target images were presented first (a decrease of 6.64 on average), but increased their ratings if the negative target images were second (an increase of 3.86 on average). However, if the male dataset is analysed separately there are no significant target image or order effects or interaction on ratings of feeling of risk, which makes it difficult to draw any conclusions from this finding. Conversely if only the female participants are examined there is a significant target image type and order effect with a tendency to increase ratings of risk between the first and second drives, with larger increases occurring if the neutral target images were second (an average increase of 6.59 compared with an increase of 3.86 if the negative target images were second). This again is difficult to explain, but perhaps could be related to the participants, correct, perception that speeds were increasing from the first trial to the second. It is therefore possible that participants rationalised that since they were driving faster, then the risk must also be greater. Although why this effect occurs significantly more in the presence of the neutral image for the females is not clear. However, it does at least in the case of the female participants suggest that the emotionally negative images were not having any significant increasing effect on the female participant's feelings of risk.The situation in the case of ratings of effort differs from that for ratings of feeling of risk. In in the combined dataset there is a significant order and target image type interaction, with ratings of effort generally decreasing between the first and second trials, and this effect seems to be larger if the negative target images were second . However, if the male and female datasets are examined separately it appears the majority of this effect comes from the male participants, who in contrast to the combined dataset and the female participants, decrease their ratings of effort more if the neutral target images were second. In the case of the female participants their data appears similar to that for the combined dataset, although there were no significant main effects or interactions.The above findings for ratings of effort are particularly interesting in light of the previously discussed impacts of the target images on perceived speed, actual speed driven and on the psychophysiological measures of heart rate and high heart rate variability. In this case the target image type and order interaction effect observed in the other variables is reversed in the combined dataset for ratings of effort, or in the case of the females is non-significant. This, when combined with no consistent effect on ratings of subjective feelings of risk do seem to suggest that the participants subjective feelings were not altered by the images, despite producing the observed effects on speed, perceived speed and psychophysiology.Finally, when asked about the differences between the roads they had driven only 43.53% of the participants correctly identified that the roads had not differed. If the remaining participants are examined then 38.82% reported something had changed about how the road was constructed. Typically participants mentioned that the second road they drove had more curves. The rest of the participants either commented on the memory task, suggesting that they had seen more pairs on one of the trials, or did not answer this question. That some participants did report a difference in the road is likely due to the demand characteristics of being in an experiment and being asked if there were differences. Having been asked, they perhaps felt pressured to say that there were. It is interesting however that this resulted in the second drive being generally attributed the characteristic of being more curvy, and this is likely to do with the fact that the second road tended to be driven at a higher speed. A similar finding was made by Lewis-Evans and Charlton Apart from the points already made above there are several other potential issues that can be raised with this study. One obvious issue is the question of whether the participants really were unaware of the images. As mentioned in the introduction, this is a controversial issue A greater proportion of males (15.38%) explicitly saw the target images compared to the female participants (2.78%). Although a similar proportion of males (17.95%) and females (16.67%) were in the group that was removed for suspecting that masking was occurring. This gender difference could be related to the generally better visuospatial abilities typically attributed to males Another issue is with the order effect that was encountered. The general tendency for participants to increase their speed from the first to second trial is not uncommon in simulator research In addition to a longer practice period, longer experimental driving trials could also be investigated. These could perhaps include changes from emotionally neutral to emotionally negative image targets occurring on several occasions during the drive itself. This would also allow for the psychophysiological data to be collected for longer periods, and reduce the problem of having to remove the initial increase in these variables caused by simply starting to drive. All this said, at least in the case of the female participants, the effect of the negative target images on speed behaviour appears to occur within the first 20 seconds or so after the images have been presented and then maintained overtime. Therefore the time periods used in this experiment do appear sufficient to catch at least this critical moment in terms of the impact of the target images on driving speed.Another potential issue is the high level of variability in the speed data. This was likely caused by the fact that participants were denied the use of a speedometer and told to drive at whatever speed they were most comfortable at, resulting in large between subjects differences in speed. However, due to the nature of the within-subjects repeated measures analyses used in this study the large variability between the subjects should not have had a negative impact on the results.The fact that the participants were denied the use of the speedometer could also challenge the validity of this experiment. The speedometer was removed in an attempt to force participants to rely on their own perception of speed, and therefore hopefully encourage them to use the automatic or implicit control processes that are most likely to be affected by the masked target images. Furthermore it allowed for the question about participants perceived speed of travel to be asked. Still, the lack of a speedometer is not a common occurrence in every day driving. However, evidence from studies of drivers gaze patterns show that they typically devote very little time, as low as 0.6% of total gaze time during a drive, glancing at their speedometers The type of target images used could also be questioned, especially in terms of the negative target images. In the case of this study the images were selected as being some of the lowest valence and highest arousal negative images in IAPS, along with four motor vehicle accident related images. This means that the majority of the negative target images were of mutilated and deceased humans removed from the typical driving context. These IAPS images were used as they were of a known quality, taken from an internationally recognised sample, and had been previously used in masking studies The validity of the “memory task” that the participants were performing in terms of normal driving could also be challenged. It is indeed a somewhat unusual task to be paying attention to an additional visual element and trying to detect repeated patterns in it while driving. However driving is a visual task, and often does require that visual attention be split, and that changes or the lack of change in the visual environment be detected and remembered. For example it may be important to recognise landmarks and know if you have seen them before when trying to navigate from A to B. Also given that the same task was required of all participants, and seems to have been consistently well performed, it is unlikely that it impacted on the results of the experiment in any meaningful way.Ultimately it does appear that the masked negative target images did have an impact on the behaviour of the participants in this experiment, at least in the case of the females. This seems to primarily have occurred via suppression or a lessening of the normal familiarity effect between the first and second experimental drives when the negative target image was shown in the second drive. The data from the psychophysiological measures in terms of heart rate and high band heart rate variability support this interpretation and when coupled with the lack of any meaningful effect on subjective ratings of effort or risk suggests that the impact of the negative target images was not explicit. Interestingly there does seem to be one clear subjective effect, with the participants perceiving the second drive as faster in general, but more so when the negative target image was second. This therefore suggests an impact of the negative target image on speed judgement, although care should be taken with this interpretation as the majority of this effect seems to be amongst the male, rather than the female, participants.The explanations given above for the results of this study could be labelled as speculative. However, this study does represent the first attempt to use masked images as part of a driving task, which in itself is noteworthy. We are unaware of any other studies examining the effect of masked images on a behaviour as complex as driving, which just by itself can have a large impact on psychophysiology