Human Computer Interaction Lecture 24 Cognitive Models Linguistic

Human Computer Interaction Lecture 24 Cognitive Models

Linguistic notations • Understanding the user's behaviour and cognitive difficulty based on analysis of language between user and system. • Backus–Naur Form (BNF) • Task–Action Grammar (TAG)

Backus-Naur Form (BNF) • Very common notation from computer science • A purely syntactic view of the dialogue • Terminals – lowest level of user behaviour – e. g. CLICK-MOUSE, MOVE-MOUSE • Nonterminals – ordering of terminals – higher level of abstraction – e. g. select-menu, position-mouse

Example of BNF • Basic syntax: – nonterminal : : = expression • An expression – contains terminals and nonterminals – combined in sequence (+) or as alternatives (|) draw line : : = select line + choose points + last point select line : : = pos mouse + CLICK MOUSE choose points : : = choose one | choose one + choose points

Measurements with BNF • Number of rules (not so good) • Number of + and | operators • Complications – same syntax for different semantics – no reflection of user's perception

Task Action Grammar (TAG) • Making consistency more explicit • Encoding user's world knowledge • Parameterised grammar rules • Nonterminals are modified to include additional semantic features

Consistency in TAG • In BNF, three UNIX commands would be described as: copy : : = cp + filename | cp + filenames + directory move: : = mv + filename | mv + filenames + directory link : : = ln + filename | ln + filenames + directory • No BNF measure could distinguish between this and a less consistent grammar in which link: : = ln + filename | ln + directory + filenames

Consistency in TAG (cont'd) • consistency of argument order made explicit using a parameter, or semantic feature for file operations • Feature Possible values Op = copy; move; link • Rules file-op[Op] : : = command[Op] + filename | command[Op] + filenames + directory command[Op = copy] : : = cp command[Op = move] : : = mv command[Op = link] : : = ln

Physical and device models • The Keystroke Level Model (KLM) • Buxton's 3 -state model • Based on experimental knowledge of human mental system

Keystroke Level Model (KLM) • Lowest level of (original) GOMS • Six execution phase operators – Physical motor: K - keystroking P - pointing H - homing D – drawing – Mental M - mental preparation – System R – response • Times are empirically determined. Texecute = TK + TP + TH + TD + TM + TR

Keystroke Level Model (KLM) Operator Description Time (Sec) K Pressing a single key or button Skilled typist (55 wps) Average typist (40 wps) User unfamiliar with the keyboard Pressing shift or control key 0. 35(average) 0. 22 0. 28 1. 20 0. 80 P Pointing with a mouse or other device to a Target on a display 1. 10 P 1 Clicking the mouse or similar device 0. 20 H Homing hands on the keyboard or other device 0. 40 D Draw a line using mouse Variable depending the length of line

Keystroke Level Model (KLM) Operator Description Time (Sec) M Mentally prepare to do something (e. g. , make a Decision) 1. 35 R(t) System response time—countered only if it Causes the user to wait when carrying out their Task t Texecute= TK+TP+TH+TD+TM+TR

KLM - Example For example, consider how long it would take to insert the word not into the following sentence, using a word processor like Microsoft Word: Running through the streets is normal. So it becomes: Running through the streets is not normal.

KLM - Example The times for each of these operators can be: Mentally prepare(M) Reach for the mouse(H) Position mouse before the word “normal”(P) Click mouse(P 1) Move hands to home position on keys(H) Mentally prepare(M) Type “n” (good typist) (K) Type “ 0” (K) Type “t” (K) Type “space” (K) Total predicted time: 1. 35 0. 40 1. 10 0. 20 0. 40 1. 35 0. 22 5. 68 Sec

Architectural models • All of these cognitive models make assumptions about the architecture of the human mind. • Long-term/Short-term memory • Problem spaces • Interacting Cognitive Subsystems