我们研究一类一维二值概率元胞自动机（PCA），其在Wolfram经典规则23、77、178和232之间进行插值。这些规则是唯一满足以下两个条件的规则：（i）当邻域状态存在多数时，中心格点取多数状态或其相反状态；（ii）当邻域状态平局时，中心格点改变其当前状态或保持不变。该PCA由两个参数为 $p,r \in [0,1]$ 的Bernoulli随机变量定义；我们通过马尔可夫过程建模，解析求解小规模情形。我们推导出所有初始状态下，系统渐近达到每一可能全局构型的概率关于 $p$ 和 $r$ 的解析表达式。我们证明：当 $0 < p,r < 1$ 时，该PCA达到任一状态的渐近概率分布与初始条件无关。这与确定性Wolfram规则23（$p=0,r=0$）、77（$p=1,r=0$）、178（$p=0,r=1$）和232（$p=1,r=1$）的行为形成对比——后者的渐近行为中可能出现额外状态，特别是周期性构型。最后，我们讨论将此类PCA应用于描述含犹豫代理者的意见动力学。

We study one dimensional binary Probabilistic Cellular Automaton (PCA) that interpolate between Wolfram's classical rules 23, 77, 178 and 232. These rules are the only ones that satisfy two criteria: (i) in the case of a majority in the neighborhood states, the central site takes either the majority state or the opposite and (ii) if the neighborhood states are tied, the central site either changes its current state or keeps it. The PCA is defined by two Bernoulli random variables with parameters $p,r \in [0,1]$, and we analytically solve small size cases by using a Markov process formulation. We derive analytical expressions for the probability of asymptotically reaching each possible global configuration as a function of $p$ and $r$, for all initial states. We show that for $0 < p,r < 1$, the asymptotic probability distributions of achieving any of the states for the PCA are independent of the initial conditions. This contrasts with the behavior of the deterministic Wolfram's rules 23 ($p=0,r=0$), 77 ($p=1,r=0$), 178 ($p=0,r=1$) and 232 ($p=1,r=1$), for which additional asymptotic states can occur, in particular periodic configurations Finally, we discuss applying this kind of PCA to describe opinion dynamics involving hesitant agents.