Direct brain recordings reveal continuous encoding of structure in random stimuli

Abstract

AbstractThe brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to the creation of sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where this modeling takes place is a core question in statistical learning. It is unknown how this modeling applies to random sensory signals. Here, we identify conditional relations, through transitional probabilities, as an implicit structure supporting the encoding of a random auditory stream. We evaluate this representation using intracranial electroencephalography recordings by applying information-theoretical principles to high-frequency activity (75 to 145 Hz). We demonstrate how the brain continuously encodes conditional relations between random stimuli in a network outside of the auditory system following a hierarchical organization including temporal, frontal and hippocampal regions. Our results highlight that hierarchically organized brain areas continuously attempt to order incoming information by maintaining a probabilistic representation of the sensory input, even under random stimuli presentation.Statement of SignificanceHumans are biased to perceive patterns in random sensory signals. However, the underlying neurophysiological mechanisms are unknown. Utilizing the high temporal and spatial precision of intracranial electroencephalography, we found that the brain automatically encodes temporal relationships between events when exposed to random acoustic stimuli. We also revealed a hierarchical structure of brain areas supporting this mechanism. These results suggest that the brain continuously attempts to predict and provide structure from events in the environment, even when they are not behaviorally relevant and have no evident relation between them. Linking the frameworks of statistical learning and predictive coding, our work illuminates an implicit process that might be crucial for the swift detection of patterns and unexpected events in the environment.