Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

Cognitive engineering

THIS is a single volume, easy to handle and read, containing explanations of philosophical terms and outline accounts of schools of thought, special subjects and individual thinkers. It looks as though the efforts of a number of specialists had been put together in alphabetical order with little editing. Many of the articles are just right; for example, that on ‘Hegelianism’, and most of the definitions of Aristotelian terms. A few articles are too brief for clarity; many are too long and try to do too much. The Dictionary of Philosophy Edited by Dagobert D. Runes. Pp. viii + 343. (London: George Routledge and Sons, Ltd., 1944.) 27s. 6d. net.

The Dictionary of Philosophy

88 remarks that each researcher is a “detective at the scene of a crime looking for the clues” that nature freely—but maybe not so readily— provides when the correct questions are asked and the appropriate facilities are available. Sometimes previous experiments by other scientists may have already provided the sought-for answers, but a researcher must be aware of these results and trust them. If the “alleged facts ... seem to be nonsense,” they may be, notes Webb; alternatively, another researcher may not understand the results. In some subjects, like high-energy physics, researchers watch a game played before them, and their task as researchers is to deduce the contest’s rules, learning how things operate so that the principles can be extrapolated to new situations. By contrast, in observational sciences like astronomy or geology, researchers are presented with the contest’s outcome, from which they must try to derive the rules (or break the code). Knowing how the universe has evolved, these scientists seek ultimately to infer the starting conditions that lead to the world in which we live today. Over the last half century, the science of biology has, at an accelerating pace, moved away from mere cataloging to studying processes with the most modern tools of the physical sciences and technology. Current wisdom is that the majority of this generation’s scientific breakthroughs will happen in the life sciences.

The nature of research.

Modeling occupant behavior in buildings

New lighting technologies create new opportunities that may contribute to people's experience of light. These opportunities are a result of the increased variety and freedom in terms of colour, form factor and connectivity of the lights. To allow people to fully benefit from the potential of such novel lighting systems, there is a need for a new user interaction paradigm. To develop this paradigm, we have to better understand the aspects that play a part in the interaction with lighting, paying special attention to people's motivation for interaction. This paper reports on a context-mapping study that was performed to gain insight in these aspects. As result, we present a set of seven themes that regard the interaction with lighting in the current situation and in the future. These themes provide an overview of the relevant aspects in this domain and contain considerations and opportunities for the design of new interfaces for novel lighting systems. We conclude that people have different levels of lighting needs that are highly dependent on context and that also require control at different levels. The context and lighting needs have a large influence on the extent to which people are motivated to adjust their lighting. Moreover, the lighting interface itself has a large effect on this motivation, mainly influenced by the degrees of freedom, the control location and availability, the degree of automation and general interaction qualities.

/pdf/user-interaction-with-everyday-lighting-systems-5d83y9urft.pdf

User interaction with everyday lighting systems

Understanding tangible interaction's foundational concepts can lead to systems with direct, integrated, and meaningful data control and representation

Moving Tangible Interaction Systems to the Next Level

This article focuses on the conceptual relation between the user's input and a system's output in interaction with smart tangible objects. Understanding this input-output relation (IO relation) is a prerequisite for the design of meaningful interaction. A meaningful IO relation allows the user to know what to do with a system to achieve a certain goal and to evaluate the outcome. The work discussed in this article followed a design research process in which four concepts were developed and prototyped. An evaluation was performed using these prototypes to investigate the effect of highly different IO relations on the user's understanding of the interaction. The evaluation revealed two types of IO relations differing in functionality and the number of mappings between the user and system actions. These two types of relations are described by two IO models that provide an overview of these mappings. Furthermore, they illustrate the role of the user and the influence of the system in the process of understanding the interaction. The analysis of the two types of IO models illustrates the value of understanding IO relations for the design of smart tangible objects.

An analysis of input-output relations in interaction with smart tangible objects

LED-based lighting systems have introduced radically new possibilities in the area of artificial lighting. Being physically small the LED can be positioned or embedded into luminaires, materials and even the very fabric of a building or environment. Hundreds of LEDs can be used in a single luminaire or space, of which each could have different light output properties. The light switch therefore in many situations will need to be enhanced or fully replaced by intelligent controls and smart environments that are sensitive to the context and responsive to the people in the environment. The focus of this workshop is to explore new ways of interacting with light where lighting is no longer simply an on or off system, but a flexible system capable of creating a large range of functional, decorative and ambient light effects.

/pdf/designing-interactive-lighting-2ob60c2yhs.pdf

Designing interactive lighting

Modern lighting systems allow for light settings that are more in tune with users' activities, by going beyond mere functional illumination. These systems have a large amount of controllable parameters such as intensity and colour of individual light sources. Using an autonomous control system is therefore an attractive option, especially since such control systems may also lead to reduced energy consumption. From a user experience point of view however, there are certain drawbacks to this automation.This paper proposes a hybrid approach towards lighting control to create a dynamic balance between user control and system automation. Such a hybrid system has the ability to autonomously set the lighting according to its knowledge about the current context, while offering users the possibility to manually adapt the light settings. These manual adaptations can in turn be used by the system to learn about user preferences in various situations, and thereby to improve its future lighting suggestions.To explore and evaluate this approach, a smart lighting system was developed as an initial implementation, and installed in a real office environment. The system employs a machine learning algorithm to achieve intelligent behaviour and provides users with an interface to control the lights and give feedback to the system. In a six-week study, the user experience of this initial implementation is evaluated. The results provide an insight in design considerations when adopting this approach for the design of smart lighting control systems. The considerations regard the type of machine learning, the degrees of freedom offered to the user, the insight in the system's decision making process, and the user interface.

Serge Offermans

Papers

User interaction with everyday lighting systems

Moving Tangible Interaction Systems to the Next Level

An analysis of input-output relations in interaction with smart tangible objects

Designing interactive lighting

Exploring a hybrid control approach for enhanced user experience of interactive lighting