Multiperspective understanding of cognitive behavior and its applications
Predicting human behaviour
Understanding the mind’s influence on what we perceive may help researchers model human behaviour to create better smart devices.
As computers become more powerful, nuanced modelling of human decisions is becoming possible. At the same time, new technologies are also bringing fresh tools and disciplines to researchers at Chiba University studying the hidden relationships between behaviour and phenomena, ranging from the physiological to the physical. “With the help of environmental engineers and using deep learning and virtual reality techniques we can expect big developments in the cognitive sciences,” says Professor Makoto Ichikawa, part a diverse group known as the Multi-perspective Understanding Cognitive Behaviour Group.
Ichikawa and his colleagues have been examining the reactions of individuals during a digitally created fire event in a virtual reality environment. While psychologists often use methods such as questionnaires to understand reactions and decisions, Ichikawa’s colleagues are also able to use virtual reality scenarios to study biological parameters – such as blood pressure, heart rate and pupil dilation – in real-time. At the same time, they can also record reactions related to changes in room temperature and humidity. Virtual reality, says Ichikawa, can reveal the effects of situational elements such as illumination, luminance, noise and space on physiological reactions, ranging from metabolism to circadian rhythm. These developments, he says, are not only exciting to psychologists, but will also provide the basis for creating more believable digital worlds.
Ichikawa's work is being complemented by mathematicians, such as findings published in 2016 by Chiba University computer scientists. In their paper, they proposed a new method for predicting pedestrian dynamics. Using a technique from geostatistics called ‘kriging’ the researchers were able to predict trajectories up to 10 steps ahead more than 80 per cent of the time. This, they say, could lead to exciting developments in navigation, group-behaviour analysis and abnormal-behaviour detection.
The common fate influence
To accurately model human behaviour, it's also important to also understand the mind's effect on perception. For example, while sitting in a stationary car, some may have felt like they have moved backwards if a neighbouring vehicle creeps forward. This sense of ‘induced motion’ is an optical illusion created in the mind.
In a recent paper published in the journal Perception, Ichikawa and his colleagues examined why one part of a visual illusion (concentric circles) appeared to be moving with or against another part of the illusion. They found that whether the parts of the image appeared to moved together or in opposition is linked to a person's thoughts on the connection between the parts.
In some configurations, the two parts appeared to be moving in opposite directions. As in the car example, if one object appears to be accelerating away from another, the stationary or slower parts may seem to be moving in the opposite direction.
However, a recognition of a common fate, which is an appreciation for the linked nature of the parts, may encourage participants to see the elements move together. The researchers suggest this stems from a principle from the German Gestalt school of psychology that seeks to explain how our brains create order in the world. The common fate principle suggests that because logical patterns tend to take precedence over individual elements in how we see the world, a flock of birds, for example, may seem to be moving as one.
All of these findings, Ichikawa says, will have important ramifications for how we display motion, and group and individual dynamics, on digital devices, in cinematography and in virtual reality.
|Name||Title, Affiliation||Research Themes|
|Makoto ICHIKAWA||Professor, Graduate School of Humanities and Studies on Public Affairs||Cognitive Psychology|
|Name||Title, Affiliation||Research Themes|
|Tomokazu USHITANI||Associate Professor, Graduate School of Humanities and Studies on Public Affairs||Comparative Cognition|
|Eiji KIMURA||Professor, Graduate School of Humanities and Studies on Public Affairs||Perceptual Psycology|
|Rumi TOKUNAGA||Assistant Professor, College of Liberal Arts and Sciences||Visual Information Processing, Color Dynamics|
|Midori TANAKA||Assistant Professor, College of Liberal Arts and Sciences||Imaging Science|
|Yasuharu DEN||Professor, Graduate School of Humanities and Studies on Public Affairs||Corpus Linguistics, Interaction Analysis|
|Toshihiko MATSUKA||Professor, Graduate School of Humanities and Studies on Public Affairs||Cognitive Modeling|
|Kazuhiko KAWAMOTO||Professor, Graduate School of Engineering||Behavior Analysis, Machine Learning|
|Yoko MIZOKAMI||Professor, Graduate School of Engineering||Visual Information Processing|
|Noriko YATA||Assistant Professor, Graduate School of Engineering||Evolutionary Computation Neural Network|
|Akinori ABE||Professor, Graduate School of Humanities and Studies on Public Affairs||Artificial Intelligence|
|Sachiyo ARAI||Professor, Graduate School of Engineering||Artificial Intelligence|
|Hiroo SEKIYA||Professor, Graduate School of Engineering||Wireless Communication Technology|
|Yoshitsugu MANABE||Professor, Graduate School of Engineering||Mixed Reality Image Instrumentation|
|Takahiko HORIUCHI||Professor, Graduate School of Engineering||Color Dynamics, Pattern Recognition|
|Keita HIRAI||Associate Professor, Graduate School of Engineering||Color Information Processing|
|Nobuyoshi KOMURO||Associate Professor, IMIT||Sensor Network|
|Shoko IMAIZUMI||Associate Professor, Graduate School of Engineering||Information Security|
|Satoru SHIMIZU||Visiting Associate Professor, Graduate School of Engineering||Wireless Communication|