Open Access Publications

@article{charbonneau_does_2023,

title = {Does Observers’ Ethnicity Influence Visual Strategies for Gender and Expressiveness Judgments ?},

author = {Isabelle Charbonneau and Vicki Ledrou-Paquet and Caroline Blais and Daniel Fiset},

url = {https://doi.org/10.1167/jov.23.9.4976},

doi = {10.1167/jov.23.9.4976},

issn = {1534-7362},

year  = {2023},

date = {2023-08-01},

urldate = {2023-12-21},

journal = {Journal of Vision},

volume = {23},

number = {9},

pages = {4976},

abstract = {Recent advances in cross-cultural studies emphasized the importance of including diversified groups of participants to better understand mechanisms underlying various face processing abilities, whether they reveal a difference or not (e.g. Blais et al., 2021). In visual psychophysics, little is known about visual strategies underlying face perception among Black observers. Therefore, we investigated visual strategies in Black and White participants in a Gender and Expressiveness (ExNex) tasks, using a newly validated platform, Pack & Go by VPIXX, which allows high quality psychophysic testing online. Sixty participants (15 Blacks and 45 Whites) completed both experiments (4000 trials per participant) conducted using the Bubble’s technique (Gosselin & Schyns, 2001) which samples visual information on a trial-by-trial basis using small gaussian windows in order to reveal the most useful information in any visual task. Accuracy was maintained at 75% by adjusting online the number of bubbles using QUEST (Watson & Pelli, 1983). Group performance levels were controlled by matching individual Black participants with White participants according to their final average number of bubbles in both tasks. Classification images were produced by calculating a weighted sum of the bubbles mask, using the trial-by-trial accuracy transformed into z-scores as weights. Pixel tests from the Stat4CI (Chauvin et al., 2005) toolbox revealed significant pixels associated with performance (p< .05; Zcrit = 4.05). Mainly, both groups made use of the same visual information for both race stimuli, that is, reliance on the eye in the Gender task and on the mouth in the ExNex task. Interestingly, Black participants also relied significantly more on the left eye for ExNex judgements, but only with Black stimuli. These differences and similarities in visual strategies for Black and White observers, will be discussed regarding cross-cultural differences in face perception in general.},

keywords = {Pack&Go},

pubstate = {published},

tppubtype = {article}

}

@article{gingras_using_2023,

title = {Using Online Testing to Measure Spatial Frequency and Orientation Tuning in Face Processing},

author = {Francis Gingras and Justin Duncan and Frédéric Gosselin and Daniel Fiset and Caroline Blais},

url = {https://jov.arvojournals.org/article.aspx?articleid=2791488},

doi = {10.1167/jov.23.9.5028},

issn = {1534-7362},

year  = {2023},

date = {2023-08-01},

urldate = {2023-12-21},

journal = {Journal of Vision},

volume = {23},

number = {9},

pages = {5028},

keywords = {Pack&Go},

pubstate = {published},

tppubtype = {article}

}

@article{saumure_differences_2023,

title = {Differences Between East Asians and Westerners in the Mental Representations and Visual Information Extraction Involved in the Decoding of Pain Facial Expression Intensity},

author = {Camille Saumure and Marie-Pier Plouffe-Demers and Daniel Fiset and Stéphanie Cormier and Ye Zhang and Dan Sun and Manni Feng and Feifan Luo and Miriam Kunz and Caroline Blais},

url = {https://doi.org/10.1007/s42761-023-00186-1},

doi = {10.1007/s42761-023-00186-1},

issn = {2662-205X},

year  = {2023},

date = {2023-06-01},

urldate = {2024-01-26},

journal = {Affective Science},

volume = {4},

number = {2},

pages = {332–349},

abstract = {Effectively communicating pain is crucial for human beings. Facial expressions are one of the most specific forms of behavior associated with pain, but the way culture shapes expectations about the intensity with which pain is typically facially conveyed, and the visual strategies deployed to decode pain intensity in facial expressions, is poorly understood. The present study used a data-driven approach to compare two cultures, namely East Asians and Westerners, with respect to their mental representations of pain facial expressions (experiment 1},

keywords = {Pack&Go},

pubstate = {published},

tppubtype = {article}

}

@article{fiset_empirical_2022,

title = {An empirical comparison of online and in-lab data collection using a data-driven method on Pack&Go (VPixx Technologies)},

author = {Daniel Fiset and Caroline Blais},

url = {https://doi.org/10.1167/jov.22.14.4417},

doi = {10.1167/jov.22.14.4417},

issn = {1534-7362},

year  = {2022},

date = {2022-12-01},

urldate = {2023-12-21},

journal = {Journal of Vision},

volume = {22},

number = {14},

pages = {4417},

abstract = {In recent years, new challenges have emerged for vision scientists. Firstly, the growing awareness that most of our theories are based on samples that do not reflect diversity – namely, Western, Educated, Industrialized, Rich, and Democratic (WEIRD) samples – stresses the need to reach individuals as diverse as possible for future studies. Secondly, the COVID-19 has put great constraints on our capacity to bring participants to the lab. In reaction to these challenges, new technologies have been developed to allow researchers to collect data on the internet. These technologies, however, are often ill adapted to the experimental paradigms we have developed in the field. For instance, they are often not designed to allow modifications of stimuli as a function of participants’ responses. Moreover, they are not well adapted to the use of data-driven classification image methods. In the present study, we tested a new platform for online experiments: Pack&Go from VPixx Technologies. This platform runs Matlab/Psychtoolbox3 experiments online. We tested participants on a categorization task using Bubbles, a data-driven method allowing to reveal visual information utilization. In Phase 1, participants were tested in the lab, in three different conditions each comprising 1000 trials: 1) The experimental code was run locally; 2) The experiment was conducted on the same hardware, but the experimental code was run by Pack&Go; 3) The experiment was conducted on a different computer, and the experimental code was run by Pack&Go. In Phase 2, we tested participants that were recruited using a panel provider (Prolifics), and tested from their home using Pack&Go. In all conditions, the exact same experimental code was used, making it easy to compare the results across conditions. The pattern of findings was well replicated across conditions. The pros and cons of testing data-driven methods online will be discussed.},

keywords = {Pack&Go},

pubstate = {published},

tppubtype = {article}

}