HCI: Mental Models

1. HCI: Mental Models Sang Won Lee Jessica SheffieldMichael Stryker October 5, 2006

2. Chapters 5 and 6 • Notational Systems--The Cognitive Dimensions of Notations Framework, by Alan Blackwell and Thomas Green • Users’ Mental Models: The Very Ideas, by Stephen J. Payne

3. Notational Systems • Support the activity of designers based on an understanding of the process of design • Cognitive Dimensions notations framework • Describes necessary (though not sufficient) conditions for usability • Derives usability predictions from the structural properties of a notation, the properties and resources of an environment, and the type of activity

4. Motivations • To offer a comprehensible, broad-brush evaluation • To use terms that were readily comprehended by nonspecialists • To be applicable not just to interactive devices, but also to paper-based notations and other noninteractive information systems • To be theoretically coherent • To distinguish between different types of user needs

5. Cognitive Dimensions framework • Set of discussion tools for use by designers and people evaluating designs to improve the quality of discussion • Better terms with which to think about issues and discuss them • Reminder of issues to be discussed • Effective discussion tools include: • Shared vocabulary • Standard examples • Trade-offs

6. Components of Notational Systems • Interaction language or notation • Environment for editing the notation • Medium of interaction • Subdevices • Helper devices (offer a new notational view) • Redefinition devices (allow the main notation to be changed)

7. CDs framework • Are the users’ intended activities adequately supported by the structure of the information artifact? • If not, what design maneuver would fix it, and what trade-offs would be entailed? • Evaluation: • Classify intended activities • Analyze cognitive dimensions • Decide whether the requirements of the activities are met

8. Notation-use activity • Incrementation • Transcription • Modification • Exploratory design • Searching • Exploratory understanding

9. Notational Dimensions • Viscosity: resistance to change • Visibility: ability to view components easily • Premature commitment: constraints on the order of doing things • Hidden dependencies: important links between entities are not visible • Role-expressiveness: the purpose of an entity is readily inferred

10. Notational Dimensions • Error-proneness: the notation invites mistakes and the system gives little protection • Abstraction: types and availability of abstraction mechanisms • Secondary notation: extra information in means other than formal syntax • Closeness of mapping: closeness of representation to domain

11. Notational Dimensions • Consistency: similar semantics are expressed in similar syntactic forms • Diffuseness: verbosity of language • Hard mental operations: high demand on cognitive resources • Provisionality: degree of commitment to actions or marks • Progressive evaluation: work-to-date can be checked at any time

12. Case Study • Compared textual (BASIC) and box-and-wire (LabVIEW) rocket trajectory programs • Viscosity • Hidden dependencies • Premature commitment • Commitment to layout • Commitment to connections • Commitment to choice of construct • Abstraction • Secondary notation • Visibility and juxtaposability

13. Current Status • Dissemination • Clarification and formalization • Coverage • Analysis tools • Beyond CDs: misfit analysis

15. Users’ Mental Models • Mental models as: • Topic: important and still under-researched aspect of the cognitive program • Theory: offers important insights into discourse comprehension and inference

16. Scientific foundations Idea 1: Mental content vs. cognitive architecture (mental models as theories) • It is important to try to systematically investigate what people believe to be true about particular domains (such as interactive devices). • Emphasis on mental content over mental structure • Example: students’ models of ATMs

17. Scientific foundations Idea 2: Models vs. methods (mental models as problem spaces) • Mental models can furnish a problem space - a mental structure of possible states of the world that the user can search in order to plan their behavior • Automatic (rule-based) versus controlled (skill-based) behavior • Examples: Reverse Polish Notation (RPN)

18. Scientific foundations Idea 3: Models vs. descriptions (mental models as homomorphisms) • Mental models might in some sense share the structure of the physical world that they represent (analog representation) • Example: “The spoon is to the left of the fork” and “The knife is to the left of the spoon = knife spoon fork

19. Scientific foundations Idea 4: Models of representations (mental models can be derived from language, perception, or imagination • Models can be derived from perception, or from language or imagination. • Text comprehension: readers (or listeners) first form representations of the text itself and then compute a mental model of the meaning of the text • The model represents the situation described by the language, rather than the language itself

20. Detailed description Idea 5: Mental representations of representational artifacts • Yoked state space (YSS): text is a representational artifact, and to use it one needs a mental representation of the structure of the text, the “situation” described by the text, and the mapping between the two • Goal space • Device space • Example: Text-editing on a word processor

21. Detailed description Idea 6: Mental models as computationally equivalent to external representations • Task-relative versions of informational and computational equivalence

22. Case study • Yoked state spaces analysis of calendar design