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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Inhibition theory ： ウィキペディア英語版 Inhibition theory Inhibition theory is based on the basic assumption that during the performance of any mental task requiring a minimum of mental effort, the subject actually goes through a series of alternating latent states of distraction (non-work ''0'') and attention (work ''1'') which cannot be observed and are completely imperceptible to the subject. Additionally, the concept of inhibition or reactive inhibition which is also latent, is introduced. The assumption is made that during states of attention inhibition linearly increases with a slope ''a''1 and during states of distraction inhibition linearly decreases with a slope ''a''0.According to this view the distraction states can be considered a sort of recovery state. It is further assumed, that when the inhibition increases during a state of attention, depending on the amount of increase, the inclination to switch to a distraction state also increases. When inhibition decreases during a state of distraction, depending on the amount of decrease, the inclination to switch to an attention state increases. The inclination to switch from one state to the other is mathematically described as a transition rate or hazard rate, making the whole process of alternating distraction times and attention times a stochastic process. == Theory == A non-negative continuous random variable ''T'' represents the time until an event will take place. The hazard rate ''λ''(''t'') for that random variable is defined to be the limiting value of the probability that the event will occur in a small interval (); given the event has not occurred before time ''t'', divided by ''Δt''. Formally, the hazard rate is defined by the following limit: :$\backslash lambda(t)\; =\; \backslash lim\_\; \backslash frac$ The hazard rate ''λ''(''t'') can also be written in terms of the density function or probability density function ''f''(''t'') and the distribution function or cumulative distribution function ''F''(''t''): :$\backslash lambda(t)\; =\; \backslash frac$ The transition rates ''λ''1(''t''), from state ''1'' to state ''0'', and ''λ''0(''t''), from state ''0'' to state ''1'', depend on inhibition Y(''t''): ''λ''1(''t'') = ''l''1(Y(''t'')) and ''λ''0(''t'') = ''l''0(Y(''t'')), where ''l''1 is a non-decreasing function and ''l''0 is a non-increasing function. Note, that ''l''1 and ''l''0 are dependent on ''Y'', whereas ''Y'' is dependent on ''T''. Specification of the functions ''l''1 and ''l''0 leads to the various inhibition models. What can be observed in the test are the actual reaction times. A reaction time is the sum of a series of alternating distraction times and attention times, which cannot be observed. It is, nevertheless, possible to estimate from the observable reaction times some properties of the latent process of distraction times and attention times, i.e., the average distraction time, the average attention time, and the ratio a1/a0. In order to be able to simulate the consecutive reaction times, inhibition theory has been specified into various inhibition models. One is the so-called beta inhibition model. In the beta-inhibition model, it is assumed that the inhibition Y(''t'') oscillates between two boundaries which are ''0'' and ''M'' (''M'' for Maximum), where ''M'' is positive. In this model ''l''1 and ''l''0 are as follows: :$l\_1\; (y)\; =\; \backslash frac$ and :$l\_0\; (y)\; =\; \backslash frac$ both with ''c''0 > 0 and ''c''1 > 0. Note that, according to the first assumption, as ''y'' goes to ''M'' (during an interval), ''l''1(''y'') goes to infinity and this forces a transition to a state of rest before the inhibition can reach ''M''. According to the second assumption, as y goes to zero (during a distraction), ''l''0(''y'') goes to infinity and this forces a transition to a state of work before the inhibition can reach zero. For a work interval starting at ''t''0 with inhibition level ''y''0=''Y''(''t''0) the transition rate at time ''t''0+''t'' is given by ''λ''1(''t'') = ''l''1(''y''0+''a''1t). For a non-work interval starting at ''t''0 with inhibition level ''y''0=''Y''(''t''0) the transition rate is given by ''λ''0(''t'') = ''l''0(''y''0-''a''0''t''). Therefore :$\backslash lambda\_1\; (t)\; =\; \backslash frac$ and :$\backslash lambda\_0\; (t)\; =\; \backslash frac$ The model has ''Y'' fluctuating in the interval between ''0'' and ''M''. The stationary distribution of ''Y''/''M'' in this model is a beta distribution (the beta inhibition model). The total real working time until the conclusion of the task (or the task unit in case of a repetition of equivalent unit tasks), for example, in the Attention Concentration Test, is referred to as ''A''. The average stationary response time ''E''(''T'') may written as :$E(T)\; =\; A\; +\; \backslash frac\; A$. For ''M'' goes to infinity ''λ''1(''t'') = ''c''1. This model is known as the gamma - or Poisson inhibition model (see Smit and van der Ven, 1995). 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Inhibition theory」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.