The MR2: A multi-racial, mega-resolution database of facial stimuli

Strohminger, Nina; Gray, Kurt; Chituc, Vladimir; Heffner, Joseph; Schein, Chelsea; Brooks, Heagins, Titus

doi:10.3758/s13428-015-0641-9

Cited by 109 publications

(89 citation statements)

References 52 publications

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“…In this study, two different stimulus sets were used: a) A set of 16 real face images, and 80 images of objects from nonface categories (fruits, bodies, gadgets, hands, and scrambled images) (Freiwald and Tsao, 2010;Ohayon et al, 2012;Tsao et al, 2006). b) A set of 2100 images of real faces from multiple face databases, FERET face database (Phillips et al, 2000;Phillips et al, 1998b), CVL face database (Solina et al, 2003), MR2 face database (Strohminger et al, 2016), PEAL face database (Gao et al, 2008), AR face database (Martinez and Benavente, 1998), Chicago face database (Ma et al, 2015) and CelebA database (Yang et al, 2015). 17 online photos of celebrities were also included.…”

Section: Behavioral Task and Visual Stimulimentioning

confidence: 99%

Explaining face representation in the primate brain using different computational models

Chang

Egger

Vetter

et al. 2020

Preprint

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Understanding how the brain represents the identity of complex objects is a central challenge of visual neuroscience. The principles governing object processing have been extensively studied in the macaque face patch system, a sub-network of inferotemporal (IT) cortex specialized for face processing (Tsao et al., 2006). A previous study reported that single face patch neurons encode axes of a generative model called the "active appearance" model (Chang and Tsao, 2017), which transforms 50-d feature vectors separately representing facial shape and facial texture into facial images (Cootes et al., 2001;Edwards et al., 1998). However, it remains unclear whether this model constitutes the best model for explaining face cell responses. Here, we recorded responses of cells in the most anterior face patch AM to a large set of real face images, and compared a large number of models for explaining neural responses. We found that the active appearance model better explained responses than any other model except CORnet-Z, a feedforward deep neural network trained on general object classification to classify non-face images, whose performance it tied on some face image sets and exceeded on others. Surprisingly, deep neural networks trained specifically on facial identification did not explain neural responses well. A major reason is that units in the network, unlike neurons, are less modulated by face-related factors unrelated to facial identification such as illumination.

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Section: Behavioral Task and Visual Stimulimentioning

confidence: 99%

Explaining face representation in the primate brain using different computational models

Chang

Egger

Vetter

et al. 2020

Preprint

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“…The stimuli consisted of 16 grayscale photographic images of faces (Strohminger et al, 2016) and two colored cues (red and blue squares). Each of the colored cues indicates the task-relevant dimension of social hierarchy for the current trial.…”

Section: Stimulusmentioning

confidence: 99%

Map making: Constructing, combining, and inferring on abstract cognitive maps

Park

Miller

Nili

et al. 2019

Preprint

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Cognitive maps are thought to enable model-based inferences from limited experience that can guide novel decisions-a hallmark of goal-directed behavior. We tested whether the hippocampus (HC), entorhinal cortex (EC), and ventromedial prefrontal cortex (vmPFC)/medial orbitofrontal cortex (mOFC) organize abstract and discrete relational information into a cognitive map to guide novel choices. Subjects learned the status of people in two separate unseen 2-D social hierarchies defined by competence and popularity piecemeal from binary comparisons. Although only one dimension was ever behaviorally relevant, multivariate activity patterns in HC, EC and mOFC were linearly related to the Euclidian distance between people in the mentally reconstructed 2-D space. Hubs created unique comparisons between the two hierarchies, enabling inferences between novel pairs of people. We found that both behavior and neural activity in EC and vmPFC/mOFC reflected the Euclidian distance to the retrieved hub, which was reinstated in HC. These findings reveal how abstract and discrete relational structures are represented, combined, and navigated in the human brain.Kriete, T., Noelle, D. C., Cohen, J. D., & O'Reilly, R. C. (2013). Indirection and symbol-like processing in the prefrontal cortex and basal ganglia.

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“…Study 1 was composed of two conditions, neuFace, in which objects were displayed as the 2 frequent stimuli and neutral faces as the oddball stimuli, presented either with or without 3 continuous flash suppression (e.g., "neuFace_noCFS"). Object and face images came from the 4 set made available by Brady, Konkle, Alvarez, and Oliva (2008) and the "MR2 Face Database" 5 (Strohminger et al, 2016), respectively. We selected 200 object images, excluding those that 6 suggested animacy (e.g., dolls, toy animals) or any with a face-like appearance.…”

Section: Experimental Procedure: Study 1mentioning

confidence: 99%

Faces under continuous flash suppression capture attention faster than objects, but without a face evoked steady-state visual potential: Is curvilinearity responsible for the behavioral effect?

Engell

Quillian

2019

Preprint

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Highlights 1• Faces presented outside of awareness do not evoke a steady-state visually evoked 2 potential. 3• This is true for both neutral and fearful faces. 4• However, faces do breakthrough interocular suppression faster than objects. 5• Curvilinear objects breakthrough interocular suppression faster than rectilinear objects. 6• The breakthrough time advantage for faces over objects is due to their curvilinearity. 7 8 Nonconscious face perception and curvilinearity 3 Abstract 1 Face perception is a vital part of human social interactions. The social value of faces makes their 2 efficient detection evolutionarily advantageous. It has been suggested that this might occur 3 nonconsciously, but experimental results are equivocal thus far. Here, we probe nonconscious 4 face perception using a novel combination of binocular rivalry with continuous flash 5 suppression, and steady-state visually evoked potentials. In the first two experiments, 6 participants viewed either non-face objects, neutral faces (Study 1), or fearful faces (Study 2). 7Consistent with the hypothesis that faces are processed nonconsciously, we found that faces 8 broke through suppression faster than objects. We did not, however, observe a concomitant face-9 selective SSVEP. Study 3 was run to reconcile this paradox. We hypothesized that the faster 10 breakthrough time was due to a mid-level visual feature, curvilinearity, rather than high-level 11 category membership, which would explain the behavioral difference without neural evidence of 12 face-selective processing. We tested this hypothesis by presenting participants with four different 13 groups of stimuli outside of conscious awareness: rectilinear objects (e.g., chessboard), 14 curvilinear objects (e.g., dartboard), faces, and objects that were not dominantly curvilinear or 15 rectilinear. We found that faces and curvilinear objects broke through suppression faster than 16 objects and rectilinear objects. Moreover, there was no difference between faces and curvilinear 17 objects. These results support our hypothesis that the observed behavioral advantage for faces is 18 due to their curvilinearity, rather than category membership. 19 20 Nonconscious face perception and curvilinearity 4

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The MR2: A multi-racial, mega-resolution database of facial stimuli

Cited by 109 publications

References 52 publications

Explaining face representation in the primate brain using different computational models

Explaining face representation in the primate brain using different computational models

Map making: Constructing, combining, and inferring on abstract cognitive maps

Faces under continuous flash suppression capture attention faster than objects, but without a face evoked steady-state visual potential: Is curvilinearity responsible for the behavioral effect?

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