Hick-Hyman Law INFORMATION THEORY 1. THE COMMUNICAION SYSTEMS β Channel capacity (C) – the amount of info transmitted per time through a channel Division of Information Management Engineering Hick-Hyman Law β the rate of gain of information (Hick, 1952) and index of performance (IP) in Fitts (1948) Division of Information Management Engineering Hick-Hyman Law 2. QUANTIFYING INFORMATION β β Information – reduction in uncertainty (bit) Shannon-Weiner measure of information π»= β β β π π=1 ππ log 2 1 ππ or π» = π π=1 ππ log 2 ππ 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 Division of Information Management Engineering Hick-Hyman Law THE HICK-HYMAN LAW 1. Hick (1952) Original Experiments ο± choice RT vs. stimulus info content ο± errorless responses Division of Information Management Engineering Hick-Hyman Law β Experiment II β 3 phases – as fast as possible, then as accurately as possible, finally as fast as possible again training (accurate) diamonds for fast RT Division of Information Management Engineering Hick-Hyman Law 2. Hyman (1952) Original Experiments Division of Information Management Engineering Hick-Hyman Law 2. Hyman (1952) Original Experiments β The first to articulate the linearity between RT and HT β Altered the probabilities of the stimuli to assess RT as a function of HT β RT was linear as s function of bits of the alternatives with unequal probabilities β RT = a + b HT β 1/b: the rate of gain of information (information capacity) Division of Information Management Engineering Hick-Hyman Law 4. Research and Applications Speed-Accuracy Tradeoff Stimulus-Response Compatibility (SRC) β Compatible S-R pairs facilitate the responding of a stimulus, thus yielding a higher rate of information transfer Psychometrics β investigate RT-IQ relationship HCI Applications Division of Information Management Engineering Fitts’ Law 5. FITTS’ LAW β a linear relationship between task difficulty (ID) and RT πΌπ· = log 2 2π΄ π β human motor system as a communication channel, movement amplitude as the signal, target width as the noise 1. Fitts (1954) Original Experiemtns β β 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 Division of Information Management Engineering Fitts’ Law β IP (index of performance or throughput) = ID/MT ο the capacity of the human motor system ππ = π + π log 2 2π΄ π Division of Information Management Engineering β 2. channel capacity (Shannon’s Theorem 17) π+π πΆ = π log 2 π β W is the bandwidth, P is the signal power and N is the noise power Theoretical Development β π΄+0.5π π π΄+π π log 2 π Welford (1960) ο ππ = π + π log 2 β MacKenzie (1992) ο ππ = π + β Meyer et al. (1988) ο ππ = π + π π΄ π β deterministic iterative-correction model (Crossman and Goodeve, 1983), stochastic optimized-submovement model (Meyer and colleagues, 1990) Division of Information Management Engineering β Meyer et al. (1998) ο πΌπ· = submovements 3. π΄ π 1 π where n is number of Research and Applications β kinematics and neurocognitive focus Speed-Accuracy Tradeoff Psychometrics HCI Applications Pointing. Angle of Approach. β the original Fitts’ paradigm – 1D task Division of Information Management Engineering β Accot and Zhai (2003) – classical paradigm as AP (pointing with amplitude constraints); paradigm with height constraints as DP (pointing with direction constraints) Division of Information Management Engineering Semantic Pointing. β both decreasing A and increasing W Text Entry on Soft Keyboards. β text entry on GUI Navigation. Division of Information Management Engineering Hick-Hyman Law 6. INTEGRATION OF THE LAWS β Combine the Hick-Hyman Law and Fitts’ Law β Beggs et al. (1972) β Fitts’ Law did not hold in the fusion β Hoffman and Lim (1997) β Home-to-target paradigm with both sequential and concurrent tasks β The sum of the decision and movement time (sequential) β Substantial interference (concurrent) β Soukoreff and MacKenzie (1995) β Unable to fit the data to the model Division of Information Management Engineering Hick-Hyman Law 7. THE HICK-HYMAN LAW AND HCI β Common characteristics in both Laws a. Same analogies based on Shannon and Weaver’s (1949) b. Same measures such as performance rate and information capacity c. Substantial support in research β Possible reasons for the lack of momentum in HCI (Laming, 1966) 1. discrepancies between Shannon’s theory and Hick’s analogy 2. Victim for the eviction of the soft sciences by hard sciences I. Fitts’ Law has also comparable quantitative components II. HCI has shifted its focus to include some soft sciences such as sociology Division of Information Management Engineering Hick-Hyman Law 1. Difficulty in Application β 2. Complexity of Stimuli β 3. No need to engage in the complexity of the information theoretic measures Multidimensional stimuli for the highly complex interfaces needed with simple unidimensional stimuli to reduce confounding Levels and Types of Performance β Fitts’ for somewhat monotonous tasks Division of Information Management Engineering