Differential magnocellular versus parvocellular pathway contributions to the combinatorial processing of facial threat

Adams, Reginald B.; Im, Hee Yeon; Cushing, Cody; Boshyan, Jasmine; Ward, Noreen; Albohn, Daniel N.

doi:10.1016/bs.pbr.2019.03.006

Cited by 6 publications

(7 citation statements)

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“…Rapid threat evaluation in the amygdala is thought to be driven by M inputs ( 52 , 53 ). The M pathway drives transient visual attention ( 54 ) and has shown a bias for exogenous attention captured by biologically salient threats (e.g., spiders, ( 55 )).…”

Section: Threat Perception: Fast and Slowmentioning

confidence: 99%

“…Rapid response to threat cues by the M pathway is sensitive to social stimuli like faces over scenes ( 53 ). Amongst social threat cues (e.g., fearful faces), the M pathway is tuned to clear, unambiguous signals of threat ( 52 , 56 ). M response to these clear threat cues is heightened by observer trait anxiety ( 57 ).…”

Section: Threat Perception: Fast and Slowmentioning

confidence: 99%

“…Concurrently, the P pathway processes incoming cues at a slower, more reflective pace, generating a more finely detailed representation. The P pathway has been shown to be sensitive to ambiguous threat cues ( 52 , 56 ). P response to these ambiguous cues is impaired in people with high anxiety ( 57 ).…”

Section: Threat Perception: Fast and Slowmentioning

confidence: 99%

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A generative adversarial model of intrusive imagery in the human brain

et al. 2023

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The mechanisms underlying the subjective experiences of mental disorders remain poorly understood. This is partly due to long-standing over-emphasis on behavioral and physiological symptoms and a de-emphasis of the patient's subjective experiences when searching for treatments. Here we provide a new perspective on the subjective experience of mental disorders based on findings in neuroscience and artificial intelligence (AI). Specifically, we propose the subjective experience that occurs in visual imagination depends on mechanisms similar to generative adversarial networks that have recently been developed in AI. The basic idea is that a generator network fabricates a prediction of the world, and a discriminator network determines whether it is likely real or not. Given that similar adversarial interactions occur in the two major visual pathways of perception in people, we explored whether we could leverage this AI-inspired approach to better understand the intrusive imagery experiences of patients suffering from mental illnesses such as post-traumatic stress disorder (PTSD) and acute stress disorder. In our model, a nonconscious visual pathway generates predictions of the environment that influence the parallel but interacting conscious pathway. We propose that in some patients, an imbalance in these adversarial interactions leads to an overrepresentation of disturbing content relative to current reality, and results in debilitating flashbacks. By situating the subjective experience of intrusive visual imagery in the adversarial interaction of these visual pathways, we propose testable hypotheses on novel mechanisms and clinical applications for controlling and possibly preventing symptoms resulting from intrusive imagery.

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Section: Threat Perception: Fast and Slowmentioning

confidence: 99%

Section: Threat Perception: Fast and Slowmentioning

confidence: 99%

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A generative adversarial model of intrusive imagery in the human brain

et al. 2023

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“…Recently, we have demonstrated that two of the major visual pathways, the M and P pathways, are attuned to somewhat different aspects of face perception ( Adams et al., 2019 ; Cushing et al., 2019 ; Im et al., 2018 ; Im et al., 2017 ). The M pathway, which is formed from the large retinal ganglion cells that combine the inputs of long ( L ) and medium ( M ) wavelength cones (i.e., L + M ) and some rod contributions, is largely insensitive to color but has high gain and sensitivity to small luminance differences ( Merigan & Maunsell, 1990 ).…”

Section: Introductionmentioning

confidence: 99%

Fast saccadic and manual responses to faces presented to the koniocellular visual pathway

Kveraga

Ward

et al. 2020

Journal of Vision

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The parallel pathways of the human visual system differ in their tuning to luminance, color, and spatial frequency. These attunements recently have been shown to propagate to differential processing of higher-order stimuli, facial threat cues, in the magnocellular (M) and parvocellular (P) pathways, with greater sensitivity to clear and ambiguous threat, respectively. The role of the third, koniocellular (K) pathway in facial threat processing, however, remains unknown. To address this gap in knowledge, we briefly presented peripheral face stimuli psychophysically biased towards M, P, or K pathways. Observers were instructed to report via a key-press whether the face was angry or neutral while their eye movements and manual responses were recorded. We found that short-latency saccades were made more frequently to faces presented in the K channel than to P or M channels. Saccade latencies were not significantly modulated by expressive and identity cues. In contrast, manual response latencies and accuracy were modulated by both pathway biasing and by interactions of facial expression with facial masculinity, such that angry male faces elicited the fastest, and angry female faces, the least accurate, responses. We conclude that face stimuli can evoke fast saccadic and manual responses when projected to the K pathway.

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“…By contrast, the P pathway is attuned to colour in the green-to-red range and can resolve higher spatial frequencies [37]. Accordingly, neuroimaging evidence indicates that activity in the SC and structures receiving its ascending projections is constrained when faces are psychophysically biased towards low-level properties engaging the M pathway, such as small luminance variations, medium or long wavelengths and LSFs [38][39][40][41][42].…”

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confidence: 99%

A deep neural network model of the primate superior colliculus for emotion recognition

Méndez

Celeghin

Diano

et al. 2022

Phil. Trans. R. Soc. B

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Although sensory processing is pivotal to nearly every theory of emotion, the evaluation of the visual input as ‘emotional’ (e.g. a smile as signalling happiness) has been traditionally assumed to take place in supramodal ‘limbic’ brain regions. Accordingly, subcortical structures of ancient evolutionary origin that receive direct input from the retina, such as the superior colliculus (SC), are traditionally conceptualized as passive relay centres. However, mounting evidence suggests that the SC is endowed with the necessary infrastructure and computational capabilities for the innate recognition and initial categorization of emotionally salient features from retinal information. Here, we built a neurobiologically inspired convolutional deep neural network (DNN) model that approximates physiological, anatomical and connectional properties of the retino-collicular circuit. This enabled us to characterize and isolate the initial computations and discriminations that the DNN model of the SC can perform on facial expressions, based uniquely on the information it directly receives from the virtual retina. Trained to discriminate facial expressions of basic emotions, our model matches human error patterns and above chance, yet suboptimal, classification accuracy analogous to that reported in patients with V1 damage, who rely on retino-collicular pathways for non-conscious vision of emotional attributes. When presented with gratings of different spatial frequencies and orientations never ‘seen’ before, the SC model exhibits spontaneous tuning to low spatial frequencies and reduced orientation discrimination, as can be expected from the prevalence of the magnocellular (M) over parvocellular (P) projections. Likewise, face manipulation that biases processing towards the M or P pathway affects expression recognition in the SC model accordingly, an effect that dovetails with variations of activity in the human SC purposely measured with ultra-high field functional magnetic resonance imaging. Lastly, the DNN generates saliency maps and extracts visual features, demonstrating that certain face parts, like the mouth or the eyes, provide higher discriminative information than other parts as a function of emotional expressions like happiness and sadness. The present findings support the contention that the SC possesses the necessary infrastructure to analyse the visual features that define facial emotional stimuli also without additional processing stages in the visual cortex or in ‘limbic’ areas. This article is part of the theme issue ‘Cracking the laugh code: laughter through the lens of biology, psychology and neuroscience’.

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Differential magnocellular versus parvocellular pathway contributions to the combinatorial processing of facial threat

Cited by 6 publications

References 50 publications

A generative adversarial model of intrusive imagery in the human brain

A generative adversarial model of intrusive imagery in the human brain

Fast saccadic and manual responses to faces presented to the koniocellular visual pathway

A deep neural network model of the primate superior colliculus for emotion recognition

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