Help! What the f*** is this thing?
Quick description and rationale
The Chain-Saw-Processor is inspired by artificial intelligence researches and various psychological theories such as :
- S. Freud’s distinction between dream-like thinking mode (primitive, unstructured and affect-driven thinking) and realistic-like thinking mode (concrete and reality oriented thinking) ;
- H. Eysenck’s theory of creativity based on remote associations (loose categorization of things, psychotic-like thinking), and classical, analytical intelligence ;
- H. Rorschach’s work on apprehension of reality (for example, how people perceive and interpret specific images...) and its relation with personality : an apprehension based on formalism and dark/cold colors is related to calm and intelligence, and an apprehension based on hot color and visualization of motion is related to dynamism and excitation.
Visitors are invited to explore the interplay of about 300 images and 100 key-words, and to build chains of thought with them, by being more or less constrained by these theoretical principles.
All right, but how does it work then?
Main page & processing
The main page is of course the index page, where the main image is displayed.
The PROCESS key allows you to ask the processor to change that image.
This processing function compares the properties of the initial image with properties the other images of the database.
The next image is chosen as it matches some properties of the initial images.
When the next image is then displayed, the link between this image and the previous is represented by the underlined word
(and the previous image is displayed at the bottom of the page, in smaller size).
And so on...
The EXAMPLE 1 below shows explicitly how images are linked by this process with matching keywords.
The keywords' disposition and selection are described in more detail in the next sections.
EXAMPLE 1. Connection principle
More about tags...
Images are tagged on multiple dimensions, so it can be disappointing at first glance. However, the structure of the display is quite simple :
- The keywords displayed at the top of the image are remote descriptors, that describe images in a hot, funny, fuzzy, messy way (e.g., image's hot colors, or interplay between keyword's structure and image's content) ;
- The keywords displayed at the bottom of the image are formal descriptors, that describe images in a cold, formal, serious, structured way (e.g., image's cold colors, shape, or number of main elements) ;
- The keywords displayed at the left of the image are specific semantic descriptors (precise or concrete categories, such as "stair" or "reflect") ;
- The keywords displayed at the right of the image are loose semantic descriptors (vague or loose categories, such as "thing" or "landscape").
EXAMPLE 2. Image's tags
The Settings page
The image selection process is only partially random, and
the SETTINGS (or control) page is designed to impose some constraints to this selection process.
The quadrant proposed in this settings page allows you to choose a degree of constraint in a 2-dimensional associative space :
- The horizontal high ←→ low semantic proximity dimension, that respectively set constraint based on a match between images' specific or vague semantic descriptors ;
- The vertical psychotic ←→ seriousness dimension,
that respectively set constraint based on a match between images' remote or formal descriptors
– See previous section for more details about descriptors.
Note that with default settings (as in the PROCESS' example above), no specific degree is selected (every types of connectors are equally probable).
The EXAMPLE 3 shows how the selection process is constrained, limited to certain type of keywords (connectors), and how the display in the main page will change as a function of these settings.
EXAMPLE 3. Settings' impact on selection constraints and display
The Chain-Seen page
Last but not least, the CHAIN-SEEN (or memory) page
helps you to better apprehend the chain of words and images that you have processed.
This chain is made of the images you have seen, linked by the word that has connected them during the process.
The EXAMPLE 4 below represent a possible Chain-Seen,
corresponding here to images and underlined words (connectors) presented in example 1.
EXAMPLE 4. A possible Chain-Seen
