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

Abstract

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.

Serial dependence bias can predict the overall estimation error in visual perception

“…They named the bias as attractive serial dependence, which could help observers keep the temporal continuity of the physical environment. Such attractive serial dependence was also observed in other visual features, such as orientation ( Ceylan, Herzog, & Pascucci, 2021 ; Cicchini et al., 2017 ; Cicchini, Mikellidou, & Burr, 2018 ; Fischer & Whitney, 2014 ; Fritsche, Mostert, & de Lange, 2017 ; Pascucci et al., 2019 ; Samaha, Switzky, & Postle, 2019 ), spatial position ( Bliss, Sun, & D'Esposito, 2017 ; Manassi, Liberman, Kosovicheva, Zhang, & Whitney, 2018 ), expression, identity, and attractiveness of faces ( Kim, 2021 ; Liberman, Fischer, & Whitney, 2014 ; Taubert, Van der Berg, & Alais, 2016 ; Xia, Leib, & Whitney, 2016 ; Yu, Yang, & Ying, 2023 ; Yu & Ying, 2021 ), numerosity ( Kim, Burr, Cicchini, & Alais, 2020 ; Fornaciai & Park, 2018 ), and self-motion direction ( Wang et al., 2022 ; Xu et al., 2022 ). In contrast, some studies have found the opposite trend (i.e., repulsive serial dependence meaning that the perceived features of current trials are repelled away from the previously seen features, such as orientation) ( Alais, Leung, & Van der Burg, 2017 ; Kang & Choi, 2015 ; Bae & Luck, 2017 ).…”

The background assimilation effect: Facial emotional perception is affected by surrounding stimuli

“…For instance, many researchers tested the influence of the previous perception on the current perception: the serial dependence (e.g., Taubert et al, 2016). This effect has been found in many facial perception tasks (e.g., Yu & Ying, 2021). Although it sounds unlikely, Yu et al (2023) using hidden Markov models suggested that the spatial and temporal influence the face perception in a complicated way.…”

Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017–2020

“… 31 , 32 , 33 The models allowed precise projections into the future (Figure 3 ). 34 , 35 , 36 Additionally, the novel use of bootstrap simulations allowed for accurate quantification of the variance within these Markov chain projections (Figures 3 , 4 , 5 ). 37 , 38 , 39 , 40 Other studies that have attempted to quantify the variability present in Markov chain predictions focus on the transition matrix, with each transition probability having a confidence interval.…”

“…[31][32][33] The models allowed precise projections into the future (Figure 3). [34][35][36] Additionally, the novel use of bootstrap simulations allowed for accurate quantification of the variance within these Markov chain projections T A B L E 1 Summary of patient characteristics (demographic, socioeconomic, and laboratory measurements), stratified by individuals with weight loss versus no weight loss.…”