Research over the past decade has shown that various personality traits are communicated through musical preferences. One limitation of that research is external validity, as most studies have assessed individual differences in musical preferences using self-reports of music-genre preferences. Are personality traits communicated through behavioral manifestations of musical preferences? We addressed this question in two large-scale online studies with demographically diverse populations. Study 1 ( N = 22,252) shows that reactions to unfamiliar musical excerpts predicted individual differences in personality-most notably, openness and extraversion-above and beyond demographic characteristics. Moreover, these personality traits were differentially associated with particular music-preference dimensions. The results from Study 2 ( N = 21,929) replicated and extended these findings by showing that an active measure of naturally occurring behavior, Facebook Likes for musical artists, also predicted individual differences in personality. In general, our findings establish the robustness and external validity of the links between musical preferences and personality.

Decision-making in complex environments relies on flexibly using prior experience. This process depends on the medial frontal cortex (MFC) and the medial temporal lobe, but it remains unknown how these structures implement selective memory retrieval. We recorded single neurons in the MFC, amygdala, and hippocampus while human subjects switched between making recognition memory–based and categorization-based decisions. The MFC rapidly implemented changing task demands by using different subspaces of neural activity and by representing the currently relevant task goal. Choices requiring memory retrieval selectively engaged phase-locking of MFC neurons to amygdala and hippocampus field potentials, thereby enabling the routing of memories. These findings reveal a mechanism for flexibly and selectively engaging memory retrieval and show that memory-based choices are preferentially represented in the frontal cortex when required.

Neurons in the primate amygdala respond prominently to faces. This implicates the amygdala in the processing of socially significant stimuli, yet its contribution to social perception remains poorly understood. We evaluated the representation of faces in the primate amygdala during naturalistic conditions by recording from both human-and macaque amygdala neurons during free viewing of identical arrays of images with concurrent eye tracking. Neurons responded to faces only when they were fixated, suggesting that neuronal activity was gated by visual attention. Further experiments in humans utilizing covert attention confirmed this hypothesis. In both species, the majority of face-selective neurons preferred faces of conspecifics, a bias also seen behaviorally in first fixation preferences. Response latencies, relative to fixation onset, were shortest for conspecific-selective neurons, and were ~100ms shorter in monkeys compared to humans. This argues that attention to faces gates amygdala responses, which in turn prioritize species-typical information for further processing.

Present day cortical brain machine interfaces (BMI) have made impressive advances using decoded brain signals to control extracorporeal devices. Although BMIs are used in a closed-loop fashion, sensory feedback typically is visual only. However medical case studies have shown that the loss of somesthesis in a limb greatly reduces the agility of the limb even when visual feedback is available (for review see Robles-De-La-Torre, 2006). To overcome this limitation, this study tested a closed-loop BMI that utilizes intracortical microstimulation (ICMS) to provide ‘tactile’ sensation to a non-human primate (NHP). Using stimulation electrodes in Brodmann area 1 of somatosensory cortex (BA1) and recording electrodes in the anterior intraparietal area (AIP), the parietal reach region (PRR) and dorsal area 5 (area 5d), it was found that this form of feedback can be used in BMI tasks.

Behavior arises from the coordinated activity of numerous anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates both scalable computational models to extract meaningful features of interregion communication and principled theories to interpret those features. Here, we introduce Current-Based Decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that directly reproduce experimentally-obtained neural data. CURBD leverages the functional interactions inferred by such models to reveal directional currents between multiple brain regions. We first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. We then apply CURBD to multi-region neural recordings obtained from mice during running, macaques during Pavlovian conditioning, and humans during memory retrieval to demonstrate the widespread applicability of CURBD to untangle brain-wide interactions underlying behavior from a variety of neural datasets.

The amygdala plays an important role in many aspects of social-cognition and reward-learning. Here we aimed to determine whether human amygdala neurons are involved in the computations necessary to implement learning through observation. We performed single-neuron recordings from the amygdalae of human neurosurgical patients (male and female) while they learned about the value of stimuli through observing the outcomes experienced by another agent interacting with those stimuli. We used a detailed computational modeling approach to describe patients' behavior in the task. Then, using both population-level decoding and single neuron analyses we found evidence to implicate amygdala neurons in two key computations relevant for observational-learning: tracking the expected future reward associated with a given stimulus, and in tracking outcome values received by oneself or other agents. Encoding and decoding analyses suggested observational value coding in amygdala neurons occurred in a different subset of neurons than experiential value coding. Collectively, these findings support a key role for the human amygdala in the computations underlying the capacity for learning through observation. Significance statement Single neuron studies of the human brain provide a unique window into the computational mechanisms of cognition. In this study, epilepsy patients implanted intracranially with depth microelectrodes performed an observational learning task. We measured activity bilaterally in the amygdala and found a representation for observational rewards as well as observational expected reward values. Additionally, the representation of self-experienced and observational values was performed by distinct subsets of amygdala neurons. This study thus provides a rare glimpse into the role of human amygdala neurons in social cognition.

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Decisions in complex environments rely on flexibly utilizing past experience as required by context and instructions. This process depends on medial frontal cortex (MFC) and the medial temporal lobe (MTL), but it remains unknown how these structures interact during memory retrieval. We recorded single neurons in MFC and MTL while human subjects switched between making memory and categorization-based decisions. Here we show that MFC rapidly implements changing task demands by utilizing different subspaces of neural activity during different types of decisions. Choices requiring memory retrieval selectively engaged phase-locking between thetafrequency band oscillations in MTL and MFC neurons. Choice neurons signaled decisions independent of output modality. In contrast, no effect of task demands was seen locally in the MTL. This work reveals a mechanism for selectively engaging memory retrieval and shows that unlike perceptual decision-making, memory-related information is only represented in frontal cortex when choices require it.