ISSN 0439-755X
CN 11-1911/B

Acta Psychologica Sinica ›› 2015, Vol. 47 ›› Issue (7): 837-850.doi: 10.3724/SP.J.1041.2015.00837

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Visual Statistical Learning Based on the Visual Feature and Semantic Information of Famous Faces

TANG Yi1; ZHANG Zhijun1; ZENG Meimei1; HUANG Ke1; LIU Wei2; ZHAO Yajun3   

  1. (1 Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China) (2 College of Education, Yunnan University of Nationalities, Kunming 650504, China) (3 College of Sociology and Psychology, Southwest University for Nationalities, Chengdu 610041, China)
  • Received:2014-07-16 Published:2015-07-25 Online:2015-07-25
  • Contact: ZHANG Zhijun, E-mail:


The environment contains considerable information distributed across time and space, and the operation of visual statistical learning (VSL) allows our visual system to be remarkably sensitive to them. VSL can be defined as the ability of human observers to extract information about the joint and conditional probabilities of stimuli co-occurring. Previous studies have already found two distinctive types of VSL: one that based on visual feature, and the other that based on semantic information. However, people learn to obtain novel statistical regularities based on human faces in the real world, yet mere use of novel shapes or objects was found in those experiments. To our knowledge, none of the existing research has addressed VSL based on human faces. In the present study, combined use of VSL paradigm and backward test technique was thereby employed to investigate the extent to which participants’ computation of statistical regularities was based on the visual features and semantic information of famous faces. We conducted four experiments, and each included a familiarization stage and a familiarity test stage. During the familiarization stage, the participants watched a 20-min sequence of 1,248 face pictures (include 96 cover task pictures), presented one at a time for 300ms and with a 700ms (or 400ms) inter-stimulus interval (ISI). Each face was assigned a unique position within one of four triplets (e.g., A-B-C-G-H-I-D-E-F-J-K-L…). The (cover) task was to detect back-to-back repeats of the same picture (96 trials) and to indicate them as quickly as possible by hitting the space bar. Following this stage, the participants received a surprise two-interval forced-choice (2IFC) familiarity test. On each test trial, they viewed two 3-image test sequences, a familiar triplet (e.g., A-B-C) and a foil triplet (e.g., A-E-I), which were segmented from each other by an additional 1,000-ms pause. The participants were asked to indicate whether the first or second test sequence seemed more familiar based on the familiarization stage. We examined whether VSL can operate on statistical regularities of famous faces with face pictures in the test stage of Experiment 1 and with name stimuli representing each face in the test stage of Experiment 2. In experiment 3, the test stage contained both the forward triplets (e.g., A-B-C) and backward triplets (e.g., C-B-A) to examine whether temporal order information is critical to the operation of VSL based on famous faces. In Experiment 4, the ISI of face pictures in familiarization stage was reduced (from 700ms to 400ms) to investigate the time attributes of VSL based on visual features and semantic information of famous faces. In experiment 5, we manipulated face display orientation (upright vs. inverted) to investigate the specificity of face in VSL. The results of Experiment 1 and 2 showed that in the familiarity task (the measure of VSL), performance was markedly above chance levels with visual feature and name stimuli of famous faces. In Experiment 3, participants showed significant VSL only in the forward triplets test condition with both the visual feature and name stimuli of famous faces. It was noteworthy that a significant learning effect was found in experiment 4A, where the stimuli were famous face pictures. Contrary to results of experiment 4A, VSL was not observed in the name test condition (experiment 4B). In experiment 5, the performance for upright faces was significantly better than inverted faces in VSL. In summary, the present results provide clear evidence that the VSL based on visual features and semantic information of famous face is face specific. Based on the time procedure, it suggests processes of visual features and semantic information of famous face in VSL are separate, and the statistical computation of the temporal order is successively behind the face processing.

Key words: visual statistical learning, visual feature, semantic information, temporal order, famous face.