A general serial dependence among various facial traits: Evidence from Markov Chain and derivative of Gaussian.

The human vision system can extract a stable representation of the always-changing visual world. However, the mechanism underlying such perceptual continuity remains unclear. A possible candidate is the serial dependence: visual perception of an object is positively biased toward the visual input from the recent past. Does the visual system use one pattern of serial dependence for general purposes? Or different patterns of serial dependence for different visual tasks? Because different social facial traits (e.g., trustworthiness and dominance) are dissociable, it is reasonable to assume that the perception of different facial characteristics would require different patterns of serial dependences. In this study, we examine the existence and the similarities of the serial dependence(s) in the evaluation of seven facial characteristics (i.e., attractiveness, trustworthiness, confidence, dominance, intelligence, age, and aggressiveness). The convergent evidence from conventional Derivative of Gaussian fitting and Markov Chain modeling demonstrated that (1) serial dependence exists in judgments of all seven social facial characteristic, (2) the serial dependences of them are highly similar, and (3) the serial dependence follows the efficient coding. Thus it is highly possible that there exists a general serial dependence mechanism for (at least high-level) vision processing. Moreover, we used the Markov Chain modeling to better describe the transitional pattern of serial dependence, which is a kind of Markov process. These findings may shed light on future works regarding serial dependence, as well as face perception.