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Working memory and conscious perception are thought to share similar brain mechanisms, yet recent reports of non-conscious working memory challenge this view. Combining visual masking with magnetoencephalography, we investigate the reality of non-conscious working memory and dissect its neural mechanisms. In a spatial delayed-response task, participants reported the location of a subjectively unseen target above chance-level after several seconds. Conscious perception and conscious working memory were characterized by similar signatures: a sustained desynchronization in the alpha/beta band over frontal cortex, and a decodable representation of target location in posterior sensors. During non-conscious working memory, such activity vanished. Our findings contradict models that identify working memory with sustained neural firing, but are compatible with recent proposals of ‘activity-silent’ working memory. We present a theoretical framework and simulations showing how slowly decaying synaptic changes allow cell assemblies to go dormant during the delay, yet be retrieved above chance-level after several seconds.
Mounting evidence suggests that perception depends on a largely-feedforward brain network. However, the discrepancy between (i) the latency of the corresponding feedforward responses (150-200 ms) and (ii) the time it takes human subjects to recognize brief images (often >500 ms) suggests that recurrent neuronal activity is critical to visual processing. Here, we use magneto-encephalography to localize, track and decode the feedforward and recurrent responses elicited by brief presentations of variably-ambiguous letters and digits. We first confirm that these stimuli trigger, within the first 200 ms, a feedforward response in the ventral and dorsal cortical pathways. The subsequent activity is distributed across temporal, parietal and prefrontal cortices and leads to a slow and incremental cascade of representations culminating in action-specific motor signals. We introduce an analytical framework to show that these brain responses are best accounted for by a hierarchy of recurrent neural assemblies. An accumulation of computational delays across specific processing stages explains subjects’ reaction times. Finally, the slow convergence of neural representations towards perceptual categories is quickly followed by all-or-none motor decision signals. Together, these results show how recurrent processes generate, over extended time periods, a cascade of hierarchical decisions that ultimately predicts subjects’ perceptual reports.
Models of perceptual decision making have historically been designed to maximally explain behaviour and brain activity independently of their ability to actually perform tasks. More recently, performance-optimized models have been shown to correlate with brain responses to images and thus present a complementary approach to understand perceptual processes. In the present study, we compare how these approaches comparatively account for the spatio-temporal organization of neural responses elicited by ambiguous visual stimuli. Forty-six healthy human subjects performed perceptual decisions on briefly flashed stimuli constructed from ambiguous characters. The stimuli were designed to have 7 orthogonal properties, ranging from low-sensory levels (e.g. spatial location of the stimulus) to conceptual (whether stimulus is a letter or a digit) and task levels (i.e. required hand movement). Magneto-encephalography source and decoding analyses revealed that these 7 levels of representations are sequentially encoded by the cortical hierarchy, and actively maintained until the subject responds. This hierarchy appeared poorly correlated to normative, drift-diffusion, and 5-layer convolutional neural networks (CNN) optimized to accurately categorize alpha-numeric characters, but partially matched the sequence of activations of 3/6 state-of-the-art CNNs trained for natural image labeling (VGG-16, VGG-19, MobileNet). Additionally, we identify several systematic discrepancies between these CNNs and brain activity, revealing the importance of single-trial learning and recurrent processing. Overall, our results strengthen the notion that performance-optimized algorithms can converge towards the computational solution implemented by the human visual system, and open possible avenues to improve artificial perceptual decision making.