Foundations; semiotics, library, cognitive and social science

1. Foundations; semiotics, library, cognitive and social science Peter Fox Xinformatics – ITEC, CSCI, ERTH 4400/6400 Module 4, February 23, 2016

2. Contents • Review of last class, reading • Foundations; semiotics, cognitive science • Assignment 2 • Next classes

3. Reading Review • Information entropy • Information Is Not Entropy, Information Is Not Uncertainty! • More on entropy • Context

4. Semiotics • Also called semiotic studies or semiology, is the study of sign processes (semiosis), or signification and communication, signs and symbols

5. A sign (Peirce and Eco 1979) • “A sign stands for something to the idea which it produces or modifies.... • That for which it stands is called its object, that which it conveys, its meaning; and the idea which it gives rise, its interpretant • ....[the sign creates in the mind] an equivalent sign, or perhaps a more developed sign.” (Peirce) “That sign which it creates I call the interpretant of the first sign. This sign stands for something, its object. It stands for that object, not in all respects, but in reference to a sort of idea which I have sometimes called the ground of that representation.” (Eco ;-( )

6. Examples

7. Extended semiotic ‘triangle’ Of a Person?

8. Icons (Meaning based on similarity of appearance)

9. Index • A sign related to an object • Signifier <-> Signified • Meaning based on cause and effect relationships • E.g. in a particular configuration, the letters "E", "D" and "R" will form the sequence "R", "E", "D". • RED denotes a certain color, but neither the letters individually nor their formal combination into a word have anything to do with redness.

10. Symbol (meaning based on convention)

11. Semiotic model

14. Syntax • Relation of signs to each other in formal structures • … the term syntax is also used to refer directly to the rules and principles that govern the … • But not the meaning or the use!

15. Semantics • Relation between signs and the things to which they refer; their denotata • Study of meaning of … (anything?) • Mainly need to worry about failures

16. Pragmatics • Relation of signs to their impacts on those who use them • the ways in which context contributes to meaning, conveying and use

17. But in a digital world? • Oh, and you thought I would answer all your questions and doubts ;-)

19. Cognitive Science • Cognitive science is the interdisciplinary study of the mind and intelligence • It operates at the intersection of psychology, philosophy, computer science, linguistics, anthropology, and neuroscience.

20. Mental Representation • Thinking = representational structures + procedures that operate on those structures. • Data structures + mental representations+ algorithms +procedures= running programs =thinking • Methodological consequence: study the mind by developing computer simulations of thinking.

21. What is an explanation of behavior? • Programs that simulate cognitive processes explain intelligent behavior by performing the tasks whose performance they explain. • Neurophysiological explanation is compatible with computational explanation, but operates at a different level. • At the neural level, cognitive processes are parallel, but at the symbolic level, the brain behaves like a serial system. • The human mind is an adaptive system, learning to improve its performance in accomplishing its goals.

22. Nature of Expertise • Manifests as cognition • refers to an information processing view of an individual's psychological functions • Process of thought as ‘knowing’ • Indicates a level of knowing and action that is above the non-expert • Characterizing the expert versus the non-expert (or specialist vs. non-) is very important in information systems • E.g. can a non-expert system be just as easily used and exploited by an expert?

23. Epistemology • Theory of knowledge – and to do this effectively you need to be concerned with: • Truth, belief, and justification • Means of production of knowledge • Skepticism about different knowledge claims • Recall the data-information-knowledge ecosystem? • Understanding what part this plays in your modeling and architecture can be critical

24. Classical view of knowledge

25. Intuition • This returns us to semiotics and to some extent heuristics and abduction - understanding without apparent effort • Heuristics - experience-based techniques that help in problem solving, learning and discovery • Abduction we’ve covered … • So how do you eek out (technical term) intuition? • Use the cognitive process – drawing or mapping!

26. Humans in the loop

27. In Information Systems: • An example of inductive research: • Gather data • Analyze and reanalyze the data • Organize the data within broad topics • Create categories within the topics • Identify relationships among the categories • Synthesize the patterns into conclusions

28. Must be inductive? (Haverty) • It does not have an existing body of theory which typically guides the work of a field • Theory constrains acceptable solutions through formal validation • Without it, IAs – Information Architectures tend to treat each problem as novel • Also, it supports emergent phenomena • The IA domain has a small set of initial components and a relatively simple set of rules • These lead to a large number of complex patterns

29. content+structure+navigation=interaction • in any given information system, there are many interactions that can emerge when people use it, influenced by the IA of the site • IAs use combinations of these components to define the framework that constrains user interactions • Problem: we don’t understand well how to study and design for emerging user experiences • We don’t know how each contributes to the user experience • This is why we need inductive analysis

30. Constructive induction (ci) • IA as constructive induction • This is a process for generating a design solution using two intertwined searches • First: identify the most adequate representational framework for the problem • Second: locate the best design solution within the framework and translating it to the problem at hand • ci is useful when existing theory cannot adequately explain the object of study

31. What are the steps for applying ci? • Well, actually, the steps are exactly those for a use case development, modeling, design and implementation • Thus the need for experience in preparing a use case.

32. Interaction theory • We can come to a system with an “information task” • Problem-solving: we go through a patterned process and end with a relevance judgment • We can also have chance encounters, encounters with information, scanning activities • These are less patterned but still end with some type of judgment • Then we browse, navigate, search, evaluate… • Information interaction is the basis of the person’s use experience

33. But wait! • We develop and implement means (designs, architectures, systems, etc.) that perpetuate these two modes of investigation • That’s a good thing? Right? • Well, sometimes…

34. So what about an abductive IS? • Abductive reasoning starts when an inquirer considers of a set of seemingly unrelated facts, armed with an intuition that they are somehow connected. • The term abduction is commonly presumed to mean the same thing as hypothesis; however, an abduction is actually the process of inference that produces a hypothesis as its end result

35. Huh abduction? Is a method of logical inference introduced by C. S. Peirce** which comes prior to induction and deduction for which the colloquial name is to have a "hunch”

36. Is abductive reasoning new? • NO – but we’ve beaten it out of modern information systems….. • Why? • Closed world approaches – huh? • We’ve programmed “systems” • Too much data/ information • We lost sight of other options

37. Abductive Information System? • What would this look like? • If you consent that induction is fundamentally part of how most (all) information system are developed, then how would you allow for abduction before induction may be possible?

38. Abductive Information System? • Choices? • More or less • Presentation? • How would that look different? • Design factors? • TO invoke the human side • Architecture factors? • Hide what’s not needed, but expose what is • Cognitive factors?

39. Quality & Bias FreeMind allows capturing various relations between various aspects of aerosol measurements, algorithms, conditions, validation, etc. The “traditional” worksheets do not support complex multi-dimensional nature of the task from the Aerosol Parameter Ontology

40. Metamodeling and Mindmaps

41. Some tools • For use case development – simple graphics tools, e.g. graffle • Mindmaps, e.g. Freemind • For modeling (esp. UML): • http://en.wikipedia.org/wiki/List_of_Unified_Modeling_Language_tools • For estimating information uncertainty, yes some algorithms and software exist • Concept, topic, subject maps!! (try searching) • http://cmap.ihmc.us

42. Questions? • About semiotics • Cognitive science

43. Reading for this week • Is retrospective but … relates to coming assignment

44. Assignment 2 • Assessing information uncertainty in different aspects of the use case and determine possible ways to condition the system to reduce uncertainty in achieving the goals of <your> use case from Assignment 1. • Due on Mar 8th • Assignment 3 available Mar 1st due Mar 22nd. • Assignment 4 available Mar 8th due Mar 29th.