Hick-Hyman Law

1. 2. Psychology and HCI Hard science (ComSci) would drive out soft science (Psy)  “harden Psy” to improve scientific caliber Evaluation tool rather than design tool 3 possible roles in Psy Primary professionals like in mental health and counseling Working with primary professionals, the system engineers The primary professionals could apply psychology themselves Hick-Hyman Law

2. 3. INFORMATION THEORY THE COMMUNICAION SYSTEMS Hick-Hyman assessed the cognitive info capacity in choice reaction experiments. Fitts’ law for an empirical determination of the info capacity of the human motor system Hick-Hyman Law

3. Channel capacity (C) – the amount of info transmitted per time through a channel 1/b (bits/sec) = the rate of gain of information (Hick, 1952) and index of performance (IP) in Fitts (1948) Hick-Hyman Law

4. QUANTIFYING INFORMATION Information (bit) – reduction in uncertainty (Shannon & Weaver, 1949) Shannon-Weiner measure of information or Have: the entropy of a stimulus or a set of stimuli when the alternatives are not equiprobable Hmax: the alternatives are equiprobable HT = H(x) – Hy(x) where H(x): the expected information of the source Hy(x): the received information at the destination Hick-Hyman Law

5. 4. THE HICK-HYMAN LAW Hick (1952) Original Experiments Hick-Hyman Law • choice RT vs. stimulus info content • Trained himself until attaining errorless responses (over 2,400)

6. Experiment II 3 phases – as fast as possible, then as accurately as possible, finally as fast as possible again Hick-Hyman Law training (accurate) diamonds for fast RT antilogarithm of the info gained

7. Hyman (1952) Original Experiments Hick-Hyman Law

8. Hyman (1953) Original Experiments The amount of info extracted is proportional to the time taken to extract it, on the average (1952) Not postulate a linear relationship between RT & Ht The first to articulate the linearity between RT and HT Altered the probabilities of the stimuli to assess RT as a function of HT RT was linear as a function of bits of the alternatives with unequal probabilities RT = a + b HT 1/b: the rate of gain of information (information capacity) Hick-Hyman Law

9. Theoretical Developments Longstreth et al. (1985) – the law is false RT = a + b (1 – N-1) Welford (1987) against Longstreth Negative intercept Decreasing RT variability as function of the number of alternatives Effective for a sequential and hierarchical process Christie and Luce (1956), Laming (1968) Parallel exhausted process model instead of serial process Hick-Hyman Law

10. Research and Applications Speed-Accuracy Tradeoff Stimulus-Response Compatibility (SRC) Compatible S-R pairs facilitate the responding of a stimulus, thus yielding a higher rate of information transfer Psychometrics investigate RT-IQ relationship HCI Applications Hick-Hyman Law

11. FITTS’ LAW a linear relationship between task difficulty (ID) and RT Adapting Shannon’s (1948) Theorem 17 -- human motor system as a communication channel, movement amplitude as the signal, target width as the noise Fitts (1954) Original Experiemtns the reciprocal tapping task Experiment I – metal-tipped stylus (1 oz vs. 1 lb); W from 0.25” to 2”; D from 2 to 16 ”; accuracy was encouraged Fitts’ Law

12. Fitts’ Law • IP (index of performance or throughput) = ID/MT the rate of gain of info (Hick, 1952), the capacity of the human motor system

13. channel capacity (Shannon’s Theorem 17) B is the bandwidth, S is the signal power and N is the noise power Theoretical Development Welford (1960)  MacKenzie (1992)  Meyer et al. (1988)  deterministic iterative-correction model (Crossman and Goodeve, 1983), stochastic optimized-submovement model (Meyer and colleagues, 1990)

14. Meyer et al. (1998)  where n is number of submovements Research and Applications kinematics and neurocognitive focus Speed-Accuracy Tradeoff Psychometrics No correlation between IQ and MT HCI Applications Pointing. Angle of Approach. the original Fitts’ paradigm – 1D task

15. Accot and Zhai (2003) – classical paradigm as AP (pointing with amplitude constraints); paradigm with height constraints as DP (pointing with direction constraints)

16. Semantic Pointing. both decreasing A and increasing W Text Entry on Soft Keyboards. text entry on GUI Navigation.

17. INTEGRATION OF THE LAWS Combine the Hick-Hyman Law and Fitts’ Law Beggs et al. (1972) Fitts’ Law did not hold in the fusion Hoffman and Lim (1997) Home-to-target paradigm with both sequential and concurrent tasks The sum of the decision and movement time (sequential) Substantial interference (concurrent) Soukoreff and MacKenzie (1995) Unable to fit the data to the model Hick-Hyman Law

18. THE HICK-HYMAN LAW AND HCI Common characteristics in both Laws Same analogies based on Shannon and Weaver’s (1949) IT Same temporal dependent measures and accuracy to address performance rates & limits of a human system Substantial support in research Possible reasons for the lack of momentum in HCI (Laming, 1966) The law’s analogy to the classic IT cannot be maintained Victim for the eviction of the soft sciences by hard sciences Fitts’ Law has also comparable quantitative components HCI has shifted its focus to include some soft sciences such as sociology Hick-Hyman Law

19. Difficulty in Application No need to engage in the complexity of the information theoretic measures Complexity of Stimuli Multidimensional stimuli for the highly complex interfaces needed with simple unidimensional stimuli to reduce confounding Levels and Types of Performance Fitts’ for somewhat monotonous tasks Hick-Hyman Law