Effects of spatial frequency and location of fearful faces on human amygdala activity

Low-spatial-frequency information facilitates threat detection in a response-specific manner

The role of different spatial frequency bands in threat detection has been explored extensively. However, most studies use manual responses and the results are mixed. Here, we aimed to investigate the contribution of spatial frequency information to threat detection by using three response types, including manual responses, eye movements, and reaching movements, together with a priming paradigm. The results showed that both saccade and reaching responses were significantly faster to threatening stimuli than to nonthreatening stimuli when primed by low-spatial-frequency gratings rather than by high-spatial-frequency gratings. However, the manual response times to threatening stimuli were comparable to nonthreatening stimuli, irrespective of the spatial frequency content of the primes. The findings provide clear evidence that low-spatial-frequency information can facilitate threat detection in a response-specific manner, possibly through the subcortical magnocellular pathway dedicated to processing threat-related signals, which is automatically prioritized in the oculomotor system and biases behavior.

Pinpointing the optimal spatial frequency range for automatic neural facial fear processing

Faces convey an assortment of emotional information via low and high spatial frequencies (LSFs and HSFs). However, there is no consensus on the role of particular spatial frequency (SF) information during facial fear processing. Comparison across studies is hampered by the high variability in cut-off values for demarcating the SF spectrum and by differences in task demands. We investigated which SF information is minimally required to rapidly detect briefly presented fearful faces in an implicit and automatic manner, by sweeping through an entire SF range without constraints of predefined cut-offs for LSFs and HSFs. We combined fast periodic visual stimulation with electroencephalography. We presented neutral faces at 6 ​Hz, periodically interleaved every 5th image with a fearful face, allowing us to quantify an objective neural index of fear discrimination at exactly 1.2 ​Hz. We started from a stimulus containing either only very low or very high SFs and gradually increased the SF content by adding higher or lower SF information, respectively, to reach the full SF spectrum over the course of 70 ​s. We found that faces require at least SF information higher than 5.93 cycles per image (cpi) to implicitly differentiate fearful from neutral faces. However, exclusive HSF faces, even in a restricted SF range between 94.82 and 189.63 cpi already carry the critical information to extract the emotional expression of the faces.

Testing the magnocellular-pathway advantage in facial expressions processing for consistency over time

The ability to identify facial expressions rapidly and accurately is central to human evolution. Previous studies have demonstrated that this ability relies to a large extent on the magnocellular, rather than parvocellular, visual pathway, which is biased toward processing low spatial frequencies. Despite the generally consistent finding, no study to date has investigated the reliability of this effect over time. In the present study, 40 participants completed a facial emotion identification task (fearful, happy, or neutral faces) using facial images presented at three different spatial frequencies (low, high, or broad spatial frequency), at two time points separated by one year. Bayesian statistics revealed an advantage for the magnocellular pathway in processing facial expressions; however, no effect for time was found. Furthermore, participants' RT patterns of results were highly stable over time. Our replication, together with the consistency of our measurements within subjects, underscores the robustness of this effect. This capacity, therefore, may be considered in a trait-like manner, suggesting that individuals may possess various ability levels for processing facial expressions that can be captured in behavioral measurements.

The Role of Low-Spatial Frequency Components in the Processing of Deceptive Faces: A Study Using Artificial Face Models

Interpreting another's true emotion is important for social communication, even in the face of deceptive facial cues. Because spatial frequency components provide important clues for recognizing facial expressions, we investigated how we use spatial frequency information from deceptive faces to interpret true emotion. We conducted two different tasks: a face-generating experiment in which participants were asked to generate deceptive and genuine faces by tuning the intensity of happy and angry expressions (Experiment 1) and a face-classification task in which participants had to classify presented faces as either deceptive or genuine (Experiment 2). Low- and high-spatial frequency (LSF and HSF) components were varied independently. The results showed that deceptive happiness (i.e., anger is the hidden expression) involved different intensities for LSF and HSF. These results suggest that we can identify hidden anger by perceiving unbalanced intensities of emotional expression between LSF and HSF information contained in deceptive faces.

The role of spatial frequency in emotional face classification

Previous studies with emotional face stimuli have revealed that our ability to identify different emotional states is dependent on the faces' spatial frequency content. However, these studies typically only tested a limited number of emotional states. In the present study, we measured the consistency with which 24 different emotional states are classified when the faces are unfiltered, high-, or low-pass filtered, using a novel rating method that simultaneously measures perceived arousal (high to low) and valence (pleasant to unpleasant). The data reveal that consistent ratings are made for every emotional state independent of spatial frequency content. We conclude that emotional faces possess both high- and low-frequency information that can be relied on to facilitate classification.

An Explanation for the Role of the Amygdala in Aesthetic Judgments

It has been proposed that the top-down guidance of feature-based attention is the basis for the involvement of the amygdala in various tasks requiring emotional decision-making (Jacobs et al., 2012a). Aesthetic judgments are correlated with particular visual features and can be considered emotional in nature (Jacobs et al., 2016). Moreover, we have previously shown that various aesthetic judgments result in observers preferentially attending to different visual features (Jacobs et al., 2010). Here, we argue that—together—this explains why the amygdalae become active during aesthetic judgments of visual materials. We discuss potential implications and predictions of this theory that can be tested experimentally.

Recognition memory for low- and high-frequency-filtered emotional faces: Low spatial frequencies drive emotional memory enhancement, whereas high spatial frequencies drive the emotion-induced recognition bias

This article deals with two well-documented phenomena regarding emotional stimuli: emotional memory enhancement-that is, better long-term memory for emotional than for neutral stimuli-and the emotion-induced recognition bias-that is, a more liberal response criterion for emotional than for neutral stimuli. Studies on visual emotion perception and attention suggest that emotion-related processes can be modulated by means of spatial-frequency filtering of the presented emotional stimuli. Specifically, low spatial frequencies are assumed to play a primary role for the influence of emotion on attention and judgment. Given this theoretical background, we investigated whether spatial-frequency filtering also impacts (1) the memory advantage for emotional faces and (2) the emotion-induced recognition bias, in a series of old/new recognition experiments. Participants completed incidental-learning tasks with high- (HSF) and low- (LSF) spatial-frequency-filtered emotional and neutral faces. The results of the surprise recognition tests showed a clear memory advantage for emotional stimuli. Most importantly, the emotional memory enhancement was significantly larger for face images containing only low-frequency information (LSF faces) than for HSF faces across all experiments, suggesting that LSF information plays a critical role in this effect, whereas the emotion-induced recognition bias was found only for HSF stimuli. We discuss our findings in terms of both the traditional account of different processing pathways for HSF and LSF information and a stimulus features account. The double dissociation in the results favors the latter account-that is, an explanation in terms of differences in the characteristics of HSF and LSF stimuli.

Trait Anxiety Is Associated with Negative Interpretations When Resolving Valence Ambiguity of Surprised Faces

The current research examines whether trait anxiety is associated with negative interpretation bias when resolving valence ambiguity of surprised faces. To further isolate the neuro-cognitive mechanism, we presented angry, happy, and surprised faces at broad spatial frequency (BSF), high spatial frequency (HSF), and low spatial frequency (LSF) and asked participants to determine the valence of each face. High trait anxiety was associated with more negative interpretations of BSF (i.e., intact) surprised faces. However, the modulation of trait anxiety on the negative interpretation of surprised faces disappeared at HSF and LSF. The current study provides evidence that trait anxiety modulates negative interpretations of BSF surprised faces. However, the negative interpretation of LSF surprised faces appears to be a robust default response that occurs regardless of individual differences in trait anxiety.

Spatial Frequency Components of Images Modulate Neuronal Activity in Monkey Amygdala

Processing the spatial frequency components of an image is a crucial feature for visual perception, especially in recognition of faces. Here, we study the correlation between spatial frequency components of images of faces and neuronal activity in monkey amygdala while performing a visual recognition task. The frequency components of the images were analyzed using a fast Fourier transform for 40 spatial frequency ranges. We recorded 65 neurons showing statistically significant responses to at least one of the images used as a stimulus. A total of 37 of these neurons (n = 37) showed significant responses to at least three images, and in eight of them (8/37, 22%), we found a statistically significant correlation between neuron response and the modulus amplitude of at least one frequency range present in the images. Our results indicate that high spatial frequency and low spatial frequency components of images influence the activity of amygdala neurons.

Amygdala responses to masked and low spatial frequency fearful faces: a preliminary fMRI study in panic disorder

Previous studies have demonstrated amygdala activation in response to fearful faces even if presented below the threshold of conscious visual perception. It has also been proposed that subcortical regions are selectively sensitive to low spatial frequency (LSF) information. However, chronic hyperarousal may reduce amygdala activation in panic disorder (PD). Our aim was to establish whether the amygdala is engaged by masked and LSF fearful faces in PD as compared to healthy subjects. Neutral faces were used as the mask stimulus. Thirteen PD patients (seven females, six males; mean age=29.1 (S.D: 5.9)) and 15 healthy volunteers (seven females, eight males; mean age=27.9 (S.D. 4.5)) underwent two passive viewing tasks during a 3T functional magnetic resonance imaging (fMRI) as follows: 1) presentation of faces with fearful versus neutral expressions (17ms) using a backward masking procedure and 2) presentation of the same faces whose spatial frequency contents had been manipulated by low-pass filtering. Level of awareness was confirmed by a forced choice fear-detection task. Whereas controls showed bilateral activation to fearful masked faces versus neutral faces, patients failed to show activation within the amygdala. LSF stimuli did not elicit amygdala response in either group, contrary to the view that LSF information plays a crucial role in the processing of facial expressions in the amygdala. Findings suggest maladaptive amygdala responses to potentially threatening visual stimuli in PD patients.

Spatial Frequency Tuning during the Conscious and Non-Conscious Perception of Emotional Facial Expressions - An Intracranial ERP Study

Previous studies have shown that complex visual stimuli, such as emotional facial expressions, can influence brain activity independently of the observers’ awareness. Little is known yet, however, about the “informational correlates” of consciousness – i.e., which low-level information correlates with brain activation during conscious vs. non-conscious perception. Here, we investigated this question in the spatial frequency (SF) domain. We examined which SFs in disgusted and fearful faces modulate activation in the insula and amygdala over time and as a function of awareness, using a combination of intracranial event-related potentials (ERPs), SF Bubbles (Willenbockel et al., 2010a), and Continuous Flash Suppression (CFS; Tsuchiya and Koch, 2005). Patients implanted with electrodes for epilepsy monitoring viewed face photographs (13° × 7°) that were randomly SF filtered on a trial-by-trial basis. In the conscious condition, the faces were visible; in the non-conscious condition, they were rendered invisible using CFS. The data were analyzed by performing multiple linear regressions on the SF filters from each trial and the transformed ERP amplitudes across time. The resulting classification images suggest that many SFs are involved in the conscious and non-conscious perception of emotional expressions, with SFs between 6 and 10 cycles per face width being particularly important early on. The results also revealed qualitative differences between the awareness conditions for both regions. Non-conscious processing relied on low SFs more and was faster than conscious processing. Overall, our findings are consistent with the idea that different pathways are employed for the processing of emotional stimuli under different degrees of awareness. The present study represents a first step to mapping how SF information “flows” through the emotion-processing network with a high temporal resolution and to shedding light on the informational correlates of consciousness in general.