Katkov Mikhail, Tsodyks Misha, Sagi Dov
Department of Neurobiology/Brain Research, The Weizmann Institute of Science, Rehovot 76100, Israel.
Vision Res. 2007 Oct;47(22):2855-67. doi: 10.1016/j.visres.2007.06.024. Epub 2007 Sep 14.
Mathematical singularities found in the Signal Detection Theory (SDT) based analysis of the 2-Alternative-Forced-Choice (2AFC) method [Katkov, M., Tsodyks, M., & Sagi, D. (2006a). Analysis of two-alternative force-choice Signal Detection Theory model. Journal of Mathematical Psychology, 50, 411-420; Katkov, M., Tsodyks, M., & Sagi, D. (2006b). Singularities in the inverse modeling of 2AFC contrast discrimination data. Vision Research, 46, 256-266; Katkov, M., Tsodyks, M., & Sagi, D. (2007). Singularities explained: Response to Klein. Vision Research, doi:10.1016/j.visres.2006.10.030] imply that contrast discrimination data obtained with the 2AFC method cannot always be used to reliably estimate the parameters of the underlying model (internal response and noise functions) with a reasonable number of trials. Here we bypass this problem with the Identification Task (IT) where observers identify one of N contrasts. We have found that identification data varies significantly between experimental sessions. Stable estimates using individual session data showed Contrast Response Functions (CRF) with high gain in the low contrast regime and low gain in the high contrast regime. Noise Amplitudes (NA) followed a decreasing function of contrast at low contrast levels, and were practically constant above some contrast level. The transition between these two regimes corresponded approximately to the position of the dipper in the Threshold versus Contrast (TvC) curves that were computed using the estimated parameters and independently measured using 2AFC.
在基于信号检测理论(SDT)对二项迫选(2AFC)方法的分析中发现的数学奇点[卡特科夫,M.,措迪克斯,M.,& 萨吉,D.(2006a)。二项迫选信号检测理论模型分析。《数学心理学杂志》,50,411 - 420;卡特科夫,M.,措迪克斯,M.,& 萨吉,D.(2006b)。2AFC对比度辨别数据逆模型中的奇点。《视觉研究》,46,256 - 266;卡特科夫,M.,措迪克斯,M.,& 萨吉,D.(2007)。奇点解释:对克莱因的回应。《视觉研究》,doi:10.1016/j.visres.2006.10.030]表明,用2AFC方法获得的对比度辨别数据并非总能在合理数量的试验中可靠地用于估计基础模型(内部响应和噪声函数)的参数。在此,我们通过识别任务(IT)绕过了这个问题,在该任务中观察者识别N个对比度中的一个。我们发现识别数据在不同实验环节之间有显著差异。使用单个环节数据的稳定估计显示,对比度响应函数(CRF)在低对比度区域具有高增益,在高对比度区域具有低增益。噪声幅度(NA)在低对比度水平下随对比度呈递减函数,并且在高于某个对比度水平时基本恒定。这两种状态之间的转变大致对应于使用估计参数计算并通过2AFC独立测量的阈值与对比度(TvC)曲线中拐点的位置。