May 2016, Dublin - Early morning and the hotel lobby is already buzzing. Researchers from all over the world, members of the organizing committee, student volunteers, sales staff - all are gathering for the same purpose: the first day of Measuring Behavior 2016. At ten minutes to 9, the chatter slowly stops when Cathal Gurrin takes center stage. "Fáilte roimh a baile átha Cliath, welcome to Dublin!"
Every two years, the international multi-disciplinary conference Measuring Behavior is organized and held in Europe. If you are a behavioral researcher, you really shouldn’t miss it. Why?
Here are 10 reasons why you should attend Measuring Behavior 2016!
Measuring Behavior is an international multidisciplinary conference which takes place every two years. This August it is in Wageningen, in the Netherlands. If you are a behavioral researcher, you really ought to attend. Why?
1. The diverse, multidisciplinary program. The scientific program contains contributions focusing on purely scientific aspects (issues of replicability, dynamic aspects of behavior) and applied research (animal welfare), human behavior (eye trackers in consumer research) and animal (rodent behavior), technical sessions (video tracking of social animals and recognition of human behaviors from video), sessions presenting the latest technology (3D simulators) and topics that are of relevance to everyone (eating behavior of people). The above list just scratches the surface of what promises to be a very diverse and interesting three days.
Topics: animal behavior research, emotion recognition, animal welfare, methods and techniques, Automating behavioral observations, human behavior research, consumer behavior, behavioral research, measuring behavior, conferences
1 Record video
It may sound very simple, but recording video and playing it back enables more detailed analysis of facial expressions. When only annotating events and facial expressions live without recording it on video, it is likely the annotator may miss essential information. When making video recordings, it can still be difficult to record the face. For example, Forestell and Mennella (2012) recorded infants as their mothers fed them green beans in order to objectively quantify infants’ facial expressions by manually coding Action Units. In this study 16 mother-infant dyads had to be excluded because the infant’s face was partially or fully occluded during the feeding session. Fortunately, the researchers had a final sample of 92 dyads, which was sufficient to finish their study.
2 Use a stationary microphone and chair
Platt et al. (2013) gave a great tip on how to keep a participant from moving/ shaking/ turning his or her head. They placed a voice-recording instrument in front of the participant and asked the participant to speak in the direction of the device. The researchers explained that this limited the participant from turning to face the interviewer. He/she only turned to the interviewer to exchange information and these parts were irrelevant for the study and therefore later excluded. Furthermore, one can easily imagine what happens when a participant sits on an office chair. Therefore, Platt et al. fixed the chair and table so that no turning was possible. In short, this study was aimed at identifying whether individuals with a fear of being laughed at (gelotophobia) respond with less facially displayed joy (Duchenne display) generally towards enjoyable emotions or only those eliciting laughter.
Medical doctors receive extensive education and training before and during their time spent working at a hospital or clinic. There are many training sessions organized by groups of medical specialists, hospitals, and professional societies worldwide that doctors can choose from to continue improving their communication and technical skills.
Airport design - passenger retail experience
To learn more about airport design and to investigate how to make time at an airport more enjoyable, Livingstone et al. undertook a study into passenger experience.
Computerized learning tools have already become a standard educational tool in many institutions. The break-through point was when many leading universities joined up on common platforms and offered so-called massive open online course (MOOC) such as https://www.coursera.org/universities. The courses - open for everyone - have thousands of participants. In e-learning courses, participants receive the semi-individualized feedback only thanks to the sophisticated computer algorithms. The algorithms employ the patterns of characteristics of correct and incorrect answers. Additionally, keyboard- and mouse- movements are measured to provide feedback. This entire instantaneous assessment relies on the logical and rational input while unintentionally omitting the affective and emotional factors. However, computer scientists and psychologists have recently developed tools to automatically assess and analyze patterns of emotions in the participants of the computerized courses. A computer – equipped with a standard webcam - may analyze in-real time an emotional state of the participant who is taking an online course. Research proves that such automated affective assessment improves learning outcomes due to the enriched feedback it can provide.
The scientists from University of Macedonia, Greece  found that empathetic and emotional feedback facilitates computerized learning and assessment (e-learning). A presence of digital avatar that responds to a student’s emotional state increases the perceived: usefulness, ease of use and playfulness of the studied material. The researchers used FaceReader  - software that automatically recognize facial expressions of emotions - to prove that instantaneously assessing participants’ emotions helps in the learning process. In the experiment, the digital female avatar showed corresponding empathetic feedback, only if FaceReader and the independent human judges agreed with each other. If they both agreed that participant looked “sad” then the female digital avatar showed sad face –empathizing with the student– and then she smiled and said encouragingly “cheer up, continue trying and you will succeed.” Students that learnt the material with empathetic and emotional avatar perceived the material as more easy to learn, enjoyable and useful than the control group.
A quick search on the internet reveals that no one seems to know how many scientific conferences take place in a year. The best I could find was “it’s got to be huge”, and when it comes to it, that is as a good a quantification as you need. With so many meetings, in so many fields of behavioural research, what makes Measuring Behavior stand out? Two very important things.
First it is not about results, which is very unusual. Virtually every conference focuses on scientific results. However, in practice, it is often the developments in methodology that have lead to the real advances in science. Without the microscope there would have been no microbiology. Without the telescope, there would be no astronomy. And without x-ray crystallography, Watson and Crick would never have discovered the structure of DNA. New methods and techniques are vital for the advancement of science.
Gait parameters as behavioral endpoints – parameters from a footprint
So here it is – the first blog in a series of three, about rodent gait analysis and what a single footprint can tell us.
Modern systems – better than ink
So what can one footprint tell you? Well, it could tell you a lot. Simply putting the paw in ink and studying the print left behind is one way to go about it, but there are far more sophisticated ways of footprint analysis. While an ink-print can give you an idea of the print area of a foot, you cannot tell how the animal is distributing his weight across its feet. It also cannot tell you the maximum surface area of a foot touching the ground during the duration of the entire footfall. Modern systems can.
Modern systems that use light to detect a footfall can indicate the intensity of a print, which in turn can correlate with how the animal is bearing its weight. In models of conditions that affect a single limb, the animal often shows less use of the affected paw. This is reflected in a relatively lower intensity of that footprint, which is found in models of arthritis [8,9] and sciatic nerve injury [1,2,4,10].
As a consumer, you have to make many different choices. Which peanut butter do you want? Which potato chips are the healthy choice? Some consumer groups are more vulnerable than others. For example, food and drinks meant for children, elderly, and consumers with poor health should receive extra consideration.
In the past, research in this area had mostly been done using questionnaires. You often saw students in the supermarket carrying clipboards with 5 to 10 questions per product. Supermarket visitors then provided researchers with answers about preference. However, these might have been socially desirable or so called ‘conscious’ answers. They might not have reflected the initial response of this particular person. This initial response or so called unbiased reaction is what researchers are after these days. Observing behavior is therefore becoming more and more popular.
In a recent publication in Food quality and preference De Wijk et al. 2012 explain that consumer acceptance may be based on unconscious processes. Therefore, they explored several behavioral and physiological measures (learn more about psychophysiology) to see if they could gain more insight into consumer behavior. They investigated which techniques were most suitable for future consumer research. They want to make a contribution and expect that research will make a difference in facilitating the development of (healthy) food for various consumer groups, such as the elderly.
The test took place in the sensory laboratory of the Restaurant of the Future. This restaurant is a unique environment where scientists can observe restaurant visitors. It comprises of two parts: a company restaurant (living lab) and a sensory consumer research lab. A total of 16 children and 15 young adults participated. In short, three liked and three disliked food items were selected for each participant and their responses were measured at first sight and after the test leader asked them to inspect, smell or taste the food item.
In the experiment De Wijk et al. carried out, facial expressions were analyzed using FaceReader software. This software automatically analyzes six basic emotions: sad, scared, happy, disgusted, angry, and surprised (learn more about FaceReader). One camera was used to record the facial expressions and afterwards FaceReader analyzed the videos. To measure the heart rate, skin conductance and finger temperature, electrodes and ear clips were placed on the test participants. The physiological measures were recorded continuously and analyzed afterwards per experimental session.
Using these methods, De Wijk et al. were able to obtain detailed information on food preferences in relation to specific food properties. For example, they conclude that facial expressions successfully reflect negative but not positive food preferences. And they found much more. For example that finger temperature was higher for liked foods than for disliked foods. This means facial expression analysis and physiological measurements can add real value to the understanding of behavior.