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Menu Design

Menu Design

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Menu Design

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  1. MenuDriven Interface A set of options, displayed on the screen, where the selection and execution of one (or more) of the options results in a change in the state of the interface Designing a Single Menu Panel Comparison Operations Identity matching – the fastest and simple type of search Alphabetical order Class-inclusion matching at the root orother top-level panels Equivalence search at the leaves and bottom levels uncertainty about the precise form of the entry Categorical order better than alphabetical order MenuDesign

  2. Identity matching Conventional names and extensive use for the menu panel Card (1982) – search times for 18 menu items Alphabetical < categorical< random order fixations showed the same trend but the organization effect disappeared after 20 blocks of practice Equivalence Matching Pure semantic search and no specific target in mind – no difference between alphabetical and random orders McDonald et al. (1983) – specific and fuzzy targets for 64 items arranged in four columns of 16 Single list (alphabetical), single list (randomized), four columns (randomized, alphabetical, categorical) MenuDesign

  3. No difference between single lists due to practice effect Specific target  categorical and alphabetical better than randomized Fuzzy target  categorical significantly better Class-Inclusion Matching Faulty with conceptual overlap and belong to more than two categories or fit into no categories Somberg and Picardi (1983) Longer time with less familiar exemplar MenuDesign

  4. Adding the Comparison Operator Adding Descriptors Errors from unclear meaning of the options Lee et al. (1984) vs. Dumais and Landauer (1983) Menus with descriptors much preferred and had fewer errors vs. examples provide little info Two possible reasons descriptors for the next level (Lee et al.) vs. leaves, rather than a middle level 3 examples not enough to understand The second experiment for Dumais and Landauer without miscellaneous category MenuDesign

  5. Using Icons Thee possible advantages over verbal options Targets can be searched in parallel Arend et al. (1987) – independent of the menu size because of a global cue of distinctive icons Distinctiveness enhanced by simple figures  simplification  icons more abstract, thus, error prone More representational icons scanned sequentiallay A speed advantage of icons with no loss in accuracy Icons or pictures should be used selecively Categorizations of pictures can be faster Additional info that increases the accuracy of selections MenuDesign

  6. Search Strategies Logarithmic Models Landauerand Nachbar (1985) predicted the logarithmic ST based on Hick-Hyman Law (RT = c + klog2b) – Figure 1 Hick-Hyman law fails when letters, numbers, or words are the stimuli, and their names are the responses (Neisser, 1967) visual search not memory search Linear Search: When to Stop? Exhaustive vs. self terminating (one vs. redundant evaulations) ST = (bt + k) vs. ST = [(b+1)/2]t + k b: the number of options t: processing time per option k: human response time Figure 2 MenuDesign

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  8. MenuDesign • After all options searched, only one candidate, exhaustive search • Multiple candidates, select the most likely candidate – exhaustive search and reduces the proportion of redundant search • An option immediately chosen and produce self-terminating search • Permanent rejection of that alternative and a continuation of search

  9. Guidelines Organization Alphabetical, categorical, conventional order Frequency of use – Zipf’s law (1949) Frequency is a negative power function of their rank Guideline (Figure 3) Organization and Navigation Depth vs. Breadth in a Hierarchical Menu Structure N = bd d = log2N/log2b MenuDesign

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  11. Factors Favoring More Breadth Snowberry, Parkinson, and Sisson (1983) – Error rates increased from 4% to 34% as depth increased from a single level to six levels Three reasons with greater depth Crowding, insulation, and funneling A General Framework for the Depth-Breadth Tradeoff TST = Sd{u(bi) + c(ri)} Norman (1991) u(bi) is the user response time to select from among b items at level i Lee and MacGregor’s Linear Model Assume a linear and exhaustive search TST = (bt + k + c)d = (bt + k + c)[log2N/log2b] MenuDesign

  12. b(ln b -1) = (k + c) / t Optimal breadth in the range of 4 to 13 if a self-terminating E(O) = (b+1)/2  b(ln b-1) = 1 + 2(k + c) / t Paaap and Roske-Hofstrand’s Linear Model E(O) = (b + 1)/f b(lnb -1) = 1 + f(k + c)/t Optimal number of groups g = sqrt (b)  E(I) = (g+1)/2 + ((b/g)+1)/2 TST = [(E(I)t + k + c)/(ln b)]/(ln n) Varying Depth Miller (1981) with four organizations of 64 items 641, 82, 43, 26 Performance is best at the intermediate levels of depth Consistent with Lee and MacGregor for self-terminating MenuDesign

  13. Kigler (1981) –82, 43, 26, 16 x 4, 4 x 16 Performance (time & accuracy) decrease as depth increase Varying Breadth Across Levels Norman & Chin (1988) – 256 options, 4 levels Constant 4x4x4x4 6 Decreasing 8x8x2x2 7 Increasing 2x2x8x8 5 Convex 2x8x8x2 7 Concave 8x2x2x8 5 Prediction: performance inversely related to the uncertainty Specific target – increasing menu slightly superior Fuzzy target Concave<increasing<constant<decreasing<concave MenuDesign

  14. Performance Change Across Levels Snowberry et al (1983) – a higher proportion of errors occur at the top two levels than the bottom two levels– more abstract and ambiguous Kigler (1981) – time gets faster as user closer to the goal Allen (1983) – retrieval time 1st level 4th level < 3rd < 2nd more practice at the top than any place in the hierarchy Semantics and Syntax Errors by labels that are not natural or precise The semantics are more important than the syntax MenuDesign