Localized Task-Invariant Emotional Valence Encoding Revealed by Intracranial Recordings

The ability to distinguish between negative, positive and neutral valence is a key part of emotion perception. Emotional valence has conceptual meaning that supersedes any particular type of stimulus, although it is typically captured experimentally in association with particular tasks. We sought to identify neural encoding for task-invariant emotional valence. We evaluated whether high gamma responses (HGRs) to visually displayed words conveying emotions could be used to decode emotional valence from HGRs to facial expressions. Intracranial electroencephalography (iEEG) was recorded from fourteen individuals while they participated in two tasks, one involving reading words with positive, negative, and neutral valence, and the other involving viewing faces with positive, negative, and neutral facial expressions. Quadratic discriminant analysis was used to identify information in the HGR that differentiates the three emotion conditions. A classifier was trained on the emotional valence labels from one task and was cross-validated on data from the same task (within-task classifier) as well as the other task (between-task classifier). Emotional valence could be decoded in the left medial orbitofrontal cortex and middle temporal gyrus, both using within-task classifiers as well as between-task classifiers. These observations suggest the presence of task-independent emotional valence information in the signals from these regions.

Tunable Neural Encoding of a Symbolic Robotic Manipulation Algorithm

We present a neurocomputational controller for robotic manipulation based on the recently developed “neural virtual machine” (NVM). The NVM is a purely neural recurrent architecture that emulates a Turing-complete, purely symbolic virtual machine. We program the NVM with a symbolic algorithm that solves blocks-world restacking problems, and execute it in a robotic simulation environment. Our results show that the NVM-based controller can faithfully replicate the execution traces and performance levels of a traditional non-neural program executing the same restacking procedure. Moreover, after programming the NVM, the neurocomputational encodings of symbolic block stacking knowledge can be fine-tuned to further improve performance, by applying reinforcement learning to the underlying neural architecture.

Different brain systems support the aversive and appetitive sides of human pain-avoidance learning

Both unexpected pain and unexpected pain absence can drive avoidance learning, but whether they do so via shared or separate neural and neurochemical systems is largely unknown. To address this issue, we combined an instrumental pain-avoidance learning task with computational modeling, functional magnetic resonance imaging (fMRI) and pharmacological manipulations of the dopaminergic (100 mg levodopa) and opioidergic (50 mg naltrexone) systems (N=83). Computational modeling provided evidence that untreated participants learned more from received than avoided pain. Our dopamine and opioid manipulations negated this learning asymmetry by selectively increasing learning rates for avoided pain. Furthermore, our fMRI analyses revealed that pain prediction errors were encoded in subcortical and limbic brain regions, whereas no-pain prediction errors were encoded in frontal and parietal cortical regions. However, we found no effects of our pharmacological manipulations on the neural encoding of prediction errors. Together, our results suggest that human pain-avoidance learning is supported by separate threat- and safety-learning systems, and that dopamine and endogenous opioids specifically regulate learning from successfully avoided pain.

Informational Masking Effects of Speech Versus Nonspeech Noise on Cortical Auditory Evoked Potentials

Purpose Background noise has been categorized as energetic masking due to spectrotemporal overlap of the target and masker on the auditory periphery or informational masking due to cognitive-level interference from relevant content such as speech. The effects of masking on cortical and sensory auditory processing can be objectively studied with the cortical auditory evoked potential (CAEP). However, whether effects on neural response morphology are due to energetic spectrotemporal differences or informational content is not fully understood. The current multi-experiment series was designed to assess the effects of speech versus nonspeech maskers on the neural encoding of speech information in the central auditory system, specifically in terms of the effects of speech babble noise maskers varying by talker number. Method CAEPs were recorded from normal-hearing young adults in response to speech syllables in the presence of energetic maskers (white or speech-shaped noise) and varying amounts of informational maskers (speech babble maskers). The primary manipulation of informational masking was the number of talkers in speech babble, and results on CAEPs were compared to those of nonspeech maskers with different temporal and spectral characteristics. Results Even when nonspeech noise maskers were spectrally shaped and temporally modulated to speech babble maskers, notable changes in the typical morphology of the CAEP in response to speech stimuli were identified in the presence of primarily energetic maskers and speech babble maskers with varying numbers of talkers. Conclusions While differences in CAEP outcomes did not reach significance by number of talkers, neural components were significantly affected by speech babble maskers compared to nonspeech maskers. These results suggest an informational masking influence on neural encoding of speech information at the sensory cortical level of auditory processing, even without active participation on the part of the listener.

Neural encoding of perceived patch value during competitive and hazardous virtual foraging

Natural observations suggest that in safe environments, organisms avoid competition to maximize gain, while in hazardous environments the most effective survival strategy is to congregate with competition to reduce the likelihood of predatory attack. We probed the extent to which survival decisions in humans follow these patterns, and examined the factors that determined individual-level decision-making. In a virtual foraging task containing changing levels of competition in safe and hazardous patches with virtual predators, we demonstrate that human participants inversely select competition avoidant and risk diluting strategies depending on perceived patch value (PPV), a computation dependent on reward, threat, and competition. We formulate a mathematically grounded quantification of PPV in social foraging environments and show using multivariate fMRI analyses that PPV is encoded by mid-cingulate cortex (MCC) and ventromedial prefrontal cortices (vMPFC), regions that integrate action and value signals. Together, these results suggest humans utilize and integrate multidimensional information to adaptively select patches highest in PPV, and that MCC and vMPFC play a role in adapting to both competitive and predatory threats in a virtual foraging setting.

Neural Experience of Conscious Time

“Pure perception and pure memory constantly intermingle” Henri Bergson, 1908. One can consider that “Time” and “memory” are related experiential facets of mentality. Without memory, there is no Time. To clarify, we distinguish between the physisist’s objective time (pTime), which has no emotive quality or memory component, and the subjective conscious time (cTime), which engages both emotions and memory. Our tripartite mechanism of a neural memory involves neurons interacting with their surrounding extracellular matrix (nECM). Incoming perceptions are chemically encoded in the nECM as metal-centered cognitive units of information (cuinfo), wherein NTs serve as molecular encoders of emotive states In the context of the tripartite mechanism (Marx & Gilon, 2012-2020), we consider two possible modes whereby the temporal sequence of events (i.e. cTime) could be recalled by the sensing neural net. Chemical (allosteric) sensing of cuinfo in the nECM by neural receptors (i.e. GPCR, integrins, etc.) which establish fleeting contact with the nECM as they diffuse along the neural membrane. Effectively, this is a lateral decoding process. Electrodynamic sensing of cuinfo vertically displaced from the neural surface. New nECM components and cuinfo are constantly being formed, like coral growths, extending from the neural surface. The individual neuron senses and decodes the distal cuinfo in the surrounding nECM (like long-distance radar detection). Neural sensing is consolidated and transformed by the net into comprehensive memory. These speculations suggest experimental tests to measure the interactions of the tripartite components, to examine the electro-chemical aspects of neural encoding of memory perceived as cTime.