Network Configurations in the Human Brain Reflect Choice Bias during Rapid Face Processing

Abstract

Network interactions are likely to be instrumental in processes underlying rapid perception and cognition. Specifically, high-level and perceptual regions must interact to balance pre-existing models of the environment with new incoming stimuli. Simultaneous electroencephalography (EEG) and fMRI (EEG/fMRI) enables temporal characterization of brain-network interactions combined with improved anatomical localization of regional activity. In this paper, we use simultaneous EEG/fMRI and multivariate dynamical systems (MDS) analysis to characterize network relationships between constitute brain areas that reflect a subject's choice for a face versus nonface categorization task. Our simultaneous EEG and fMRI analysis on 21 human subjects (12 males, 9 females) identifies early perceptual and late frontal subsystems that are selective to the categorical choice of faces versus nonfaces. We analyze the interactions between these subsystems using an MDS in the space of the BOLD signal. Our main findings show that differences between face-choice and house-choice networks are seen in the network interactions between the early and late subsystems, and that the magnitude of the difference in network interaction positively correlates with the behavioral false-positive rate of face choices. We interpret this to reflect the role of saliency and expectations likely encoded in frontal "late" regions on perceptual processes occurring in "early" perceptual regions. Our choices are affected by our biases. In visual perception and cognition such biases can be commonplace and quite curious-e.g., we see a human face when staring up at a cloud formation or down at a piece of toast at the breakfast table. Here we use multimodal neuroimaging and dynamical systems analysis to measure whole-brain spatiotemporal dynamics while subjects make decisions regarding the type of object they see in rapidly flashed images. We find that the degree of interaction in these networks accounts for a substantial fraction of our bias to see faces. In general, our findings illustrate how the properties of spatiotemporal networks yield insight into the mechanisms of how we form decisions.

The perceptual nature of illusory object recognition

“…2A , inset 3), with a central-parietal topography (late-stage topography). This decoding profile has previously been suggested to reflect global ignition, large-scale feedback processes allowing information to be broadcasted throughout the entire brain, making information explicit for report and decision making (8, 34, 37, 50).…”

“…Yet, even in conditions without strong perceptual priors, perception can spontaneously differ from the veridical sensory input. In particular when perceptual information is sparse or ambiguous, interpretation of the visual input is challenged, resulting occasionally in incorrect perceptual decisions (7,8).The experiential aspect of such incorrect decisions is often inferred based on participants' behavioral report. However, even when one trust such introspective reports, the source of these decision errors remains unclear.…”

Prioritized neural processing of social threats during perceptual decision-making

“…Indeed, although previous decision-making experiments that used visual stimuli revealed an encoding of the stimulus sensory properties (i.e. face vs. house) in the occipital and parietal cortices, around 150ms post-stimulus (Delis et al, 2016; Tu et al, 2017), most of them failed to find sensory-related activity in the premotor cortex (Boussaoud & Wise, 1993; Coallier, Michelet, & Kalaska, 2015; di Pellegrino & Wise, 1991; Nakayama, Yamagata, Tanji, & Hoshi, 2008; Wang et al, 2019). Rather, they showed that the premotor plays a key role in the conversion of sensory information into a choice-related signal (Cisek & Kalaska, 2002; Donner et al, 2009; Wang et al, 2019).…”

“…Indeed, past studies used either only social stimuli – i.e. emotional displays (El Zein et al, 2015b; Pessoa & Padmala, 2005, 2007; Thielscher & Pessoa, 2007), or asked participants to categorize ambiguous stimuli as either social or non-social (Delis, Onken, Schyns, Panzeri, & Philiastides, 2016; Heekeren, Marrett, Bandettini, & Ungerleider, 2004; Philiastides, Ratcliff, & Sajda, 2006; Philiastides & Sajda, 2006, 2007; Tu, Schneck, Muraskin, & Sajda, 2017). To our knowledge, only one experiment has directly assessed the neural overlap between social and non-social decisions (Sun, Yu, & Wang, 2017).…”

Large-scale network dynamics in neural response to emotionally negative stimuli linked to serotonin 1A binding in major depressive disorder

“…When a network incorporates regions that serve functions as diverse as memory, attention, cognitive control, emotion and perception, the pattern of causal connectivity across the network may be indicative of the type of mental process occurring in response to a given stimulus, e.g. top-down control processes vs. bottom-up perceptually driven processes 33,34 . For this reason, we examined the relationship between 5-HT 1A autoreceptor binding and the pattern of causal connectivity within a large-scale brain network involved in responding to negative emotional stimuli.…”