Visuocortical tuning to a threat-related feature persists after extinction and consolidation of conditioned fear

Abstract

neurons in the visual cortex sharpen their orientation tuning as humans learn aversive contingencies.A stimulus orientation (cS+) that reliably predicts an aversive noise (unconditioned stimulus: US) is selectively enhanced in lower-tier visual cortex, while similar unpaired orientations (cS−) are inhibited. Here, we examine in male volunteers how sharpened visual processing is affected by fear extinction learning (where no US is presented), and how fear and extinction memory undergo consolidation one day after the original learning episode. Using steady-state visually evoked potentials from electroencephalography in a fear generalization task, we found that extinction learning prompted rapid changes in orientation tuning: Both conditioned visuocortical and skin conductance responses to the cS+ were strongly reduced. next-day re-testing (delayed recall) revealed a brief but precise return-of-tuning to the cS+ in visual cortex accompanied by a brief, more generalized return-of-fear in skin conductance. explorative analyses also showed persistent tuning to the threat cue in higher visual areas, 24 h after successful extinction, outlasting peripheral responding. Together, experiencebased changes in the sensitivity of visual neurons show response patterns consistent with memory consolidation and spontaneous recovery, the hallmarks of long-term neural plasticity.

Attentional threat biases and their role in anxiety: A neurophysiological perspective

“…Findings overall suggested that the short-term retuning of human visual cortical neurons involves distal top-down projections as well as local inhibitory interactions. These results, recently replicated with the same (Antov et al, 2020) and different face stimuli (Stegmann et al, 2020), illustrate the potential of testing mechanistic neurophysiological hypotheses in healthy and patient samples. Specifically, they have promise for identifying novel treatment targets.…”

“…Suppression of neural responses that do not match the behaviorally relevant stimulus. May occur subsequent to and as a result of facilitated cue perception, or may result from inhibitory interactions Inhibitory interactions between similar feature representations along the visual hierarchy; Divisive normalization (Reynolds and Heeger, 2009); push-pull mechanisms; distractor positivity (Pd); ssVEP (Antov et al, 2020;McTeague et al, 2015;Stegmann, et al, 2020); fMRI (MVPA) Oscillatory sampling (expectancy/ interpretation biases) Oscillatory activity in sensory or extra-sensory areas is temporally aligned to facilitate the processing of (and motor responding to) an attended/expected stimulus, compared to unattended/unexpected stimuli.…”

Associative learning shapes visual discrimination in a web-based classical conditioning task

“…First, the JND is only a point measure of discrimination acuity, thus obscuring potential nongradual effects on visual discrimination along the perceptual continuum. These nongradual effects have already been identified, for example, for visuocortical activation patterns during aversive generalization learning paradigms using grating stimuli 10 , 25 or faces 18 . In this line of studies, visuocortical activity as an index of sensory engagement increased with increasing similarity to the threat-related stimulus for all except for the most similar generalization stimuli, which elicited decreased activity.…”

Social aversive generalization learning sharpens the tuning of visuocortical neurons to facial identity cues

“…This approach allowed us to leverage the specific model predictions under the lateral inhibition, generalization, and linear hypotheses, and to quantify the fit between the empirical data and model predictions in one test across all conditions. The lateral inhibition pattern was expressed as the difference of two Gaussians (weights: +2,-2, +0.5, +1, +0.5,-2 for CS+, GS1, GS2, GS3, GS4, CS-), paralleling previous studies on visuocortical tuning (Antov et al, 2020;McTeague et al, 2015). The quadratic (weights: +2.5334, +1.0934,-0.0267, À0.8267,-1.3067, À1.4667) and linear (weights: +2.5, +1.5, +0.5,-0.5, À1.5,-2.5) trend were modeled after the analyses of the linear and quadratic component of the generalization gradient, which are commonly employed in the fear generalization literature (Ahrens et al, 2016;Lissek et al, 2014a;Lissek et al, 2014b).…”