There is ample evidence to show that the processing of face information involves a distributed neural network of face-sensitive areas in the occipitotemporal cortex and beyond (Duchaine and Yovel, 2015; Haxby et al., 2000). Three bilateral face-selective areas are considered as the core face-processing system, defined in functional Magnetic Resonance Imaging (fMRI) studies as regions showing significantly higher response to faces than objects, which are Occipital Face Area (OFA) in the inferior occipital gyrus (Gauthier et al., 2000; Haxby et al., 1999), Fusiform Face Area (FFA) in the fusiform gyrus (Kanwisher et al., 1997; Grill-Spector et al., 2004) and a face-sensitive area in the posterior superior temporal sulcus (pSTS) (Hoffman and Haxby, 2000; Puce et al., 1998). Similarly, a number of so-called face patches have been identified in macaque monkeys along the superior temporal sulcus (Tsao et al., 2003; Tsao et al., 2006; Tsao et al., 2008). Although the functional properties of these areas have been studied extensively, we do not yet have a comprehensive understanding of how the face-processing network functions in a dynamic manner. Hierarchical models postulate that face specific processes are initiated in the OFA based on local facial features, then the information is forwarded to higher level regions, such as FFA, for holistic processing (Haxby et al., 2000; Fairhall and Ishai, 2007; Liu et al., 2002). This model is supported by neuroimaging studies showing functional properties of face-selective areas and is consistent with generic local-to-global views of object processing. However, it has been challenged by results from studies in which patients with damaged OFA can still showed FFA activation to faces (Rossion et al., 2003; Steeves et al., 2006). Further, it was reported that during the perception of faces with minimal local facial features, FFA could still show face-preferential activation without face-selective inputs from OFA (Rossion et al., 2011). Thus a non-hierarchical model was proposed postulating that face detection is initiated at the FFA followed by a fine analysis in the OFA (Rossion et al., 2011; Gentile et al., 2017). These competing models may reflect different modes of operation of the face network under different demands. To reconcile these models, a comprehensive dynamic picture of face processing under different conditions with more detailed temporal information is needed.

In the current study, we investigated the dynamics of face processing in the ‘core face processing system’ using Magnetoencephalography (MEG) and fMRI. We designed the face-related stimuli specifically to reveal mechanisms for processing 1) normal faces, 2) Mooney faces with very little explicit facial features, 3) distorted faces with internal facial features spatially misarranged, and 4) contextually induced face representations with internal facial features completely missing. During the experiment, subjects were presented with various types of face pictures while MEG signals were recorded. The key effort in this study was in reconstructing the source signals from the MEG sensor data, to obtain a dynamic depiction of cortical responses to faces and other types of stimuli. With the timing of activation revealed in each face-selective area in the ‘core face processing system’, we could uncover when and where face information is processed in the human brain.

The main findings are briefly summarized here. First, we revealed the basic, mainly bottom-up, processing sequence along ventral temporal cortex by presenting face pictures of famous individuals to subjects. Face processing was initiated in the posterior areas and then proceeded forward to anterior regions. Right OFA (rOFA) and right posterior FFA (rpFFA) were activated very close in time, peaking around 120 ms, while right anterior FFA (raFFA) reached its peak at about 150 ms. The right pSTS (rpSTS) in the dorsal pathway showed a weaker and temporally more variable response, participating in face processing within a time window from 130 to 180 ms. Then, we highlighted the top-down operation in face processing by using two-tone Mooney face images (Mooney, 1957) lacking prototypical local facial features. According to the predictive coding theory (Rao and Ballard, 1999; Murray et al., 2004; Mumford, 1992), face prediction created at FFA based on impoverished information of Mooney faces and prior knowledge is poorly matched with the input representation at OFA due to the lack of explicit local facial features. The activity in OFA, representing ‘residual error’ between top-down prediction and bottom-up input, is then expected to increase subsequently. Consistent with this model, rOFA was activated later than rpFFA, and rpFFA exerted extensive directional influence onto rOFA when processing Mooney faces, suggesting a cortical analysis dominated by rpFFA to rOFA projection. However, when explicit internal facial features were available but misarranged within a normal face contour, a temporal pattern similar to that of normal faces was observed. Finally, we further investigated the temporal dynamics when face-specific responses were driven by contextual cues alone with the internal face features entirely missing (Cox et al., 2004). In this case, rOFA, rpFFA and raFFA were activated somewhat late and almost simultaneously, corresponding to contextual modulation that parallelly facilitated the processing of the core face-processing network.

Using a combined fMRI and MEG source localization approach, our results systematically revealed an intricately detailed dynamic picture of face information processing. Within the ventral occipitotemporal face processing network, normal faces were processed mainly in a bottom-up manner through the hierarchical pathway where input information was processed sequentially from posterior to anterior ventral temporal cortex. This temporal order was also observed when processing face-like stimuli with misarranged internal facial features. In contrast, during the processing of Mooney faces in the absence of prototypical facial features, top-down modulation was more prominent in which the dominant information flow was from the rpFFA to rOFA. Moreover, face-specific responses from contextual cues alone were evoked late and simultaneously across the rOFA, rpFFA and raFFA, suggesting that contextual facilitation acted parallelly on the core face-processing network. These results advance our understanding of the hierarchical and non-hierarchical models of face perception, especially underscoring the stimulus- and context-dependent nature of the processing sequences.

During the perception of 2-tone Mooney faces, it is necessary to discount shadows and recover 3D surface structure from 2D images (Grützner et al., 2010). Interestingly, only familiar objects, like faces, can be interpreted to be volumetric easily from 2-tone representations (Moore and Cavanagh, 1998; Hegde et al., 2007). Thus it is supposed that prior knowledge should play an important role in the recovery of 3D shape from Mooney images (Braje et al., 1998; Gerardin et al., 2010). A top-down model emphasized the guidance of prior experience at higher levels (Cavanagh, 1991). This model is supported by evidence from experiments showing that early visual processing is affected by high-level attributes in both human and monkey (Lee et al., 2002; Humphrey et al., 1997; Issa et al., 2018). As briefly mentioned in the results section, the dynamics of MEG signals associated with processing Mooney faces, which highlights the top-down modulation, is consistent with the explanation based on predictive coding model. It proposed that hypotheses or predictions made at higher cortical areas are compared with, through feedback, representations at lower areas to generate residual error, which is then forwarded to higher stages as ‘neural activity’ (Rao and Ballard, 1999; Murray et al., 2004; Friston, 2005; Friston, 2010). Specifically, the face model/prediction is generated at the rpFFA based on the global configuration of Mooney faces using prior knowledge about 3D faces, illumination, and cast shadows. This prediction of expected facial features is then poorly matched with the input representation at the rOFA which lacks the explicit prototypical facial features due to the mixed illumination-invariant and illumination-dependent features, generating an increased signal at rOFA. Thus, the dominant signal at the rOFA (residual) necessarily lags behind the signal at the rpFFA (hypothesis). However, when processing normal faces or face with misarranged facial features, the prominent signal in the early stage of rOFA is mainly due to the strong feedforward input from early visual cortex as rOFA is robustly responsive to the clear facial components. The prediction feedback from rpFFA would be consistent with representation at the rOFA in the case of the normal faces, resulting in little error signal; with the misarranged facial features, there was a hint of a late increase of rOFA signal, possibly indicating that the feedback signal could contain some spatial information as well.

The timing of face induced neural activation has been studied for a long time with various techniques, such as the combination of MEG and fMRI using representational similarities (Cichy et al., 2014; Cichy et al., 2016), MEG source localization and intracranial EEG (Kadipasaoglu et al., 2017; Keller et al., 2017; Ghuman et al., 2014). An early MEG study suggested two stages (early categorization and late identification) were involved in face processing (Liu et al., 2002). Combined with the fMRI observation that OFA is responsible for identifying facial parts while FFA for holistic configuration (Rotshtein et al., 2005; Liu et al., 2010; Pitcher et al., 2011b; Arcurio et al., 2012; Pitcher et al., 2007; Schiltz, 2010), OFA is expected to respond earlier than FFA. An simultaneous electroencephalogram (EEG)-fMRI study also showed that OFA responded to faces earlier than FFA (OFA: 110 ms; FFA: 170 ms) (Sadeh et al., 2010). Using transient stimulation to temporally disrupt local neural processing, Transcranial Magnetic Stimulation (TMS) experiments suggested that OFA processes facial information at about 100/110 ms, while pSTS begins processing face at about 100/140 ms (Pitcher et al., 2012; Pitcher et al., 2014). However, the sources of N/M170 face selective component remain controversial, it is suggested to come from fusiform gyrus in some studies (Deffke et al., 2007; Kume et al., 2016; Perry and Singh, 2014). While some other studies emphasized the contribution of inferior occipital gyrus besides fusiform gyrus (Itier et al., 2006; Gao et al., 2013) or even of pSTS (Nguyen and Cunnington, 2014). Our results provide more precise and detailed timing information of the core face network under various stimulus and contextual conditions, especially the temporal relationship between rpFFA and raFFA. raFFA is engaged significantly later, about 20 ms after the rpFFA, suggesting that the raFFA likely plays a different functional role from rpFFA. This idea is supported by previous anatomical evidence showing that pFFA and aFFA have different cellular architectures (Weiner et al., 2017).

Our results also shed light on the role of internal and external features in face perception. Although when assembling into a whole face, facial features are processed holistically and the representation of internal features are influenced by external features (Andrews et al., 2010), eyes in isolation elicit a later but larger N170 (Bentin et al., 1996; Rossion and Jacques, 2011) and can drive face-selective neurons as well as full-face images (Issa and DiCarlo, 2012) in monkeys. In our results, the somewhat slower but still sequential progression of face responses elicited by face-like stimuli with clear but misarranged internal features in face outline further supports that facial features are sufficient to trigger the bottom-up face processing sequence. In addition, certain stimulus manipulations, such as face inversion (Bentin et al., 1996), contrast reversal, Mooney transformation or removal of facial features produced comparable (or even increased amplitude) but delayed N170 responses (Rossion and Jacques, 2011). Thus it is suggested that as long as the impoverished stimuli is perceived as a face, inferior temporal cortex areas would be activated (McKeeff and Tong, 2007; Grützner et al., 2010). Our results provide further more details for this explanation by showing the top-down rpFFA to rOFA projection when the prototypical facial features are lack.

Besides facial features, contextual information is also important for face interpretation (Chen and Whitney, 2019; Martinez, 2019). Interestingly, FFA can be activated by the perceived presence of faces from contextual body cues alone (Cox et al., 2004). Here our MEG data showed that the face-selective areas in ventral core face network were indeed activated by the contextual cues for faces, but they were not activated in any order, instead, they became active together at a late stage. This is similar to the temporal dynamics observed in visual imagery, a top-down process given the absence of visual inputs (Dijkstra et al., 2018). Future studies are needed to elucidate how core face network interacts with other brain regions to trigger the face perception. For example, according to a MEG study using fast periodic visual stimulation approach (Rossion et al., 2012; Rossion et al., 2015; de Heering and Rossion, 2015), top-down attention increase the response in FFA by gamma synchrony between the inferior frontal junction and FFA (Baldauf and Desimone, 2014).

Face perception is shaped by long-term visual experience, for example, familiar faces are processed more efficiently than unfamiliar ones (Landi and Freiwald, 2017; Schwartz and Yovel, 2016; Dobs et al., 2019; Gobbini and Haxby, 2006). In terms of the dynamics in the ventral occipitotemporal areas, the present results showed little differences between processing famous and unfamiliar faces. This could be due to several reasons. First, many studies suggested that regions in the anterior temporal lobe rather than OFA and FFA represent face familiarity (Gobbini and Haxby, 2007; Pourtois et al., 2005; Sugiura et al., 2011). However, extended face system is beyond the scope of our current study because some areas in the extended system are too deep to obtain a good MEG source signal. Second, some subjects might not be familiar with all the famous faces we used. Third, familiarity may affect face recognition via high gamma frequency band activity (Anaki et al., 2007), which is not included in our data analysis.

Bilateral pSTS showed weak and multi-peaked responses during both famous and unfamiliar face processing despite the task differences. One possible reason for the multiple peaks of responses is that as a hub for integrating information from multiple sources (e.g., face, body, and voice), STS contains regions that respond to different types of information (Grossman et al., 2005; Bernstein and Yovel, 2015). A lot of studies have suggested diverse functional role of pSTS in representing changeable aspects of faces, such as expression, lip movement and eye-gaze (Baseler et al., 2014; Engell and Haxby, 2007). Specifically, pSTS is involved in the analysis of facial muscle articulations which are combined to produce facial expressions (Srinivasan et al., 2016; Martinez, 2017). In addition, pSTS may respond to dynamic motion information conveyed through faces (O'Toole et al., 2002).

Previous studies showed that left and right fusiform gyrus are differentially involved in face/non-face judgements (Meng et al., 2012; Goold and Meng, 2017), ‘low-level’ face semblance and perceptual learning of face (Bi et al., 2014; Feng et al., 2011; McGugin et al., 2018). Interestingly, in our results, the peak latency of the left pFFA was later than that of the right pFFA in all conditions except famous face. Responses evoked from distorted faces with misarranged features had the largest lateral difference (20 ms). One possible reason is that the signal attributed to the left pFFA is in fact a mixture of signals from pFFA and aFFA.

Although the exact correspondence between human and macaque face-selective areas are still unclear (Tsao et al., 2003; Tsao et al., 2006; Tsao et al., 2008), the dynamic picture of normal face processing revealed in our study is generally similar to that in macaques. Single-unit recording studies showed that activity begins slightly earlier in posterior face patches than anterior ones, reaching peak levels around 126, 133, and 145 ms for middle lateral (ML)/middle fundus (MF), anterior lateral (AL), and anterior medial (AM) (Freiwald and Tsao, 2010) , respectively. Interestingly, there is a discrepancy in response to Mooney faces in high level face patch AM between two monkeys. One of them showed nearly the same peak latency as normal faces but with more sustained activation, while the other did not response to Mooney faces (Moeller et al., 2017). This may imply that the processing of Mooney faces is related to individual face detection ability or life experience and face processing is not a simple feedforward process from low level to high level areas. Consistent with that, a more recent study showed a rapid and more sustained response in high level face area (aIT) and an early rising then quickly decreased activity in low level areas in monkeys, a signature of predictive coding model (Issa et al., 2018).

Our study is obviously limited in scope. There are many types of cues and tasks relevant for face perception that could be investigated. In addition to facial features and context, many low level cues contribute to face recognition, such as illumination direction, pigmentation (surface appearance) and contrast polarity (one region brighter than another) (Russell et al., 2007; Sinha et al., 2006). In particular, neurons tuned for contrast polarity were found in macaque inferotemporal cortex, supporting the notion that low-level image properties are encoded in face regions (Ohayon et al., 2012; Weibert et al., 2018). We purposely avoided the complication of color cues in this study by using gray-scale images, but we are aware the importance of color in face perception (Yip and Sinha, 2002; Benitez-Quiroz et al., 2018). Moreover, the temporal dynamics of face processing could very well be influenced by different tasks. In our results, there is little difference between the temporal patterns in response to unfamiliar faces under face category task (Figure 2—figure supplement 1) and image identity one-back task (Figure 3). Future studies are needed to more comprehensively investigate the role of behavioral tasks, especially during the relatively late stages of face processing.

In summary, our study delineated the precise timing of bottom-up, top-down, as well as context-facilitated processing sequences in the occipital-temporal face network. These results provide a way to understand and reconcile previous discrepant findings, revealing the dominant bottom-up processing when explicit facial features were present, and highlighting the importance of the top-down feedback operations when faced with impoverished inputs with unclear or ambiguous facial features.

The source data files have been provided for Figures 2, 3, 4, 5 and Figure 2—figure supplement 1. MEG source activation data (processed based on original fMRI and MEG datasets ) have been deposited in Open Science Framework and can be accessed at https://osf.io/vhefz/.

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Author details

1. Xiaoxu Fan

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization,Methodology, Writing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8115-8621

2. Fan Wang

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Methodology, Data acquisition, Data analysis, Funding acquisition

   Competing interests

   No competing interests declared

3. Hanyu Shao

   State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

   Contribution

   Methodology, Data acquisition

   Competing interests

   No competing interests declared

4. Peng Zhang

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Methodology, Data analysis, Funding acquisition

   Competing interests

   No competing interests declared

5. Sheng He

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China
   3. Department of Psychology, University of Minnesota, Minneapolis, United States

   Contribution

   Conceptualization, Investigation, Methodology, Writing, Funding acquisition, Project administration

   For correspondence

   sheng@umn.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5547-923X

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