Human walking behavior: the effect of density on walking speed and direction

Humans have a natural desire to keep a certain physical distance from other humans. This distance is called personal space (or personal distance). Edward T. Hall (1966) describes it as a distance of 45 to 120cm kept from each other, a which people can touch if they extend their arms and see each other clearly, but not asa whole. Humans always try to keep this minimum distance to strangers, but might let familiar people closer, depending on their relationship. If personal space isinvaded without consent it comes to physical reactions such as increased heart rate, sweating and increased blood pressure (Middlemist and Knowles, 1976). We simply feel uncomfortable when others come too close. Personal space is well described for standing and seated test subjects (e.g. Newman and Pollack, 1973; Thompson et al., 1979;Hayduk and Mainprize, 1980; Hayduk, 1981; Strube and Werner, 1984; Evans and Wener, 2007; Robson, 2008), but not for walking people. Gérin-Lajoie and his colleagues (Gérin-Lajoie et al., 2006; Gérin-Lajoie et al., 2008) described minimum distances that pedestrians keep from stationary and moving obstacles, which were used as a basis for this study. Using a newly developed system called CCB Analyser the walking patterns of pedestrians in an Austrian shopping center were recorded. Data included number and frequency of people,average speed, speed changes and number of speed changes, direction changes and number of direction changes, and two different measures for personal space, one being personal space in circles around stationary recording frames and the other being personal space for pedestrians that plan their paths ahead. The tested hypothesis was that high density and low interpersonal distance leads to a change of walking behavior – increasing walking speed because of stress (Kone?ni et al., 1975) and making people change their speed and directions whenwalking. The results of the present study show that all measured variables seem to highly depend on each other. We could at least partly confirm the hypothesis ofpeople walking faster when personal space is 51invaded. People changed their walking speed and direction to a higher degree at high densities, however the percentage of people changing their walking behavior was the same or even smaller. These results offer a first insight into the relationship of human walking behavior and personal space, but much more research needs to be done on this topic.</p

Picture-object recognition in a comparative approach: performance of humans (Homo sapiens) and pigeons (Columba livia) in a rotational invariance and a complementary information task

Pigeons and humans are two highly visual species that have evolved separately for about 310 million years (Kumar and Hedges, 1998) and developed largely convergent visual systems due to similar visual needs. To investigatepigeon vision and cognitive abilities twodimensional pictorial stimuli are often used. However, it is not entirely clear, how pigeons perceive such stimuli and whether or not they can associate photographs with real objects. In the present study nine pigeons and eleven humans were trained to discriminate between photographs of two biologically irrelevant objects (“Greebles”). The pigeons were housed in an aviary containing the real Greebles and were trained in wooden chambers where they had to peck on a Plexiglas disk when positive stimuli were presented, thus obtaining food. Humans were trained with the same stimuli presented on a computer screen and had to click with a computer mouse on positive stimuli. Results showed that humans were much faster at learning to discriminate the two Greebles. In the first test, pigeons and humans had to discriminate new rotational views of the Greebles. Humans performed equally well on interpolated test views (i.e. views that lay between the training views) and extrapolated views (i.e. views outside of training range), while pigeons performed better on interpolated than on extrapolated test views. Therefore, it can be concluded that object recognition was viewpointindependent for humans and viewpoint-dependent for pigeons. In the second test, following a procedure by Aust and Huber (2006), pigeons were presented with parts of the Greebles that were not included in training and the first test to see whether they formed associations between the 2D images and the 3D objects in their aviary. They did not discriminate these parts correctly. The test was repeated with three of the test views already used in the second test but presented in different sizes. Discrimination seemed to depend on the visibility of the appendages and might have been based on visual features of the pictures themselves without 71 recognition of what they portrayed. The results of this study were compared to a previous study in which pigeons were trained to discriminate either real Greebles, holograms, or computer images of them. There, too, the real Greebles were installed in the pigeons’ aviary; however, the pigeons trained and tested on computerimages lived in the adjacent aviary and thus only had limited visual contact to them. We wanted to find out whether the more extensive visual contact to the Greebles had any influence on the pigeons’ performance. However, there was no difference in performance between the two groups. This is evidence that the result of the previous study — better performance with real objects and holograms than with computer images — was not based on the fact that pigeons trained with the latter stimulus type had only limited visual access to thereal 3D objects.</p

The quality and quantity condition test trials in Experiment 2.

The least preferred (LP) in location 1 would come within participants’ reach after 5 seconds, and location 2 with the most preferred (MP) reward after 15 seconds.</p

Rotating tray with two containers.

Rotating tray with two containers.</p

Generalized linear mixed models for Experiment 2.

Generalized linear mixed models for Experiment 2.</p

Mean percentage of correct test trials across conditions by age groups in Experiment 1.

* indicates performance above chance level (p 52].</p

Generalized linear mixed models for Experiment 1.

Generalized linear mixed models (final model) on factors affecting the number of correct test and control trials in children. N = Group 1: China 75; Group 2: UK 61. P-values 52]. (DOCX)</p

Correlations and descriptive statistics for inhibition control and delay choice tasks.

Correlations and descriptive statistics for inhibition control and delay choice tasks.</p

Age effect on inhibitory control and delay choice tasks.

Age effect on inhibitory control and delay choice tasks.</p

S1 Video -

(MP4)</p