Content–state dimensions characterize different types of neuronal markers of consciousness Identifying the neuronal markers of consciousness is key to supporting the different scientific theories of consciousness. Neuronal markers of consciousness can be defined to reflect either the brain signatures underlying specific conscious content or those supporting different states of consciousness, two aspects traditionally studied separately. In this paper, we introduce a framework to characterize markers according to their dynamics in both the “state” and “content” dimensions. The 2D space is defined by the marker’s capacity to distinguish the conscious states from non-conscious states (on the x-axis) and the content (e.g. perceived versus unperceived or different levels of cognitive processing on the y-axis). According to the sign of the x– and y-axis, markers are separated into four quadrants in terms of how they distinguish the state and content dimensions. We implement the framework using three types of electroencephalography markers: markers of connectivity, markers of complexity, and spectral summaries. The neuronal markers of state are represented by the level of consciousness in (i) healthy participants during a nap and (ii) patients with disorders of consciousness. On the other hand, the neuronal markers of content are represented by (i) the conscious content in healthy participants’ perception task using a visual awareness paradigm and (ii) conscious processing of hierarchical regularities using an auditory local–global paradigm. In both cases, we see separate clusters of markers with correlated and anticorrelated dynamics, shedding light on the complex relationship between the state and content of consciousness and emphasizing the importance of considering them simultaneously. This work presents an innovative framework for studying consciousness by examining neuronal markers in a 2D space, providing a valuable resource for future research, with potential applications using diverse experimental paradigms, neural recording techniques, and modeling investigations.
Teleosemantics, Structural Resemblance and Predictive Processing We propose a pluralist account of content for predictive processing systems. Our pluralism combines Millikan’s teleosemantics with existing structural resemblance accounts. The paper has two goals. First, we outline how a teleosemantic treatment of signal passing in predictive processing systems would work, and how it integrates with structural resemblance accounts. We show that the core explanatory motivations and conceptual machinery of teleosemantics and predictive processing mesh together well. Second, we argue this pluralist approach expands the range of empirical cases to which the predictive processing framework might be successfully applied. This is because our pluralism is practice-oriented. A range of different notions of content are used in the cognitive sciences to explain behaviour, and some of these cases look to employ teleosemantic notions. As a result, our pluralism gives predictive processing the scope to cover these cases.
Neural activity ramps in frontal cortex signal extended motivation during learning Learning requires the ability to link actions to outcomes. How motivation facilitates learning is not well understood. We designed a behavioral task in which mice self-initiate trials to learn cue-reward contingencies and found that the anterior cingulate region of the prefrontal cortex (ACC) contains motivation-related signals to maximize rewards. In particular, we found that ACC neural activity was consistently tied to trial initiations where mice seek to leave unrewarded cues to reach reward-associated cues. Notably, this neural signal persisted over consecutive unrewarded cues until reward-associated cues were reached, and was required for learning. To determine how ACC inherits this motivational signal we performed projection-specific photometry recordings from several inputs to ACC during learning. In doing so, we identified a ramp in bulk neural activity in orbitofrontal cortex (OFC)-to-ACC projections as mice received unrewarded cues, which continued ramping across consecutive unrewarded cues, and finally peaked upon reaching a reward-associated cue, thus maintaining an extended motivational state. Cellular resolution imaging of OFC confirmed these neural correlates of motivation, and further delineated separate ensembles of neurons that sequentially tiled the ramp. Together, these results identify a mechanism by which OFC maps out task structure to convey an extended motivational state to ACC to facilitate goal-directed learning.
The blushing brain: neural substrates of cheek temperature increase in response to self-observation Darwin proposed that blushing—the reddening of the face owing to heightened self-awareness—is ‘the most human of all expressions’. Yet, relatively little is known about the underlying mechanisms of blushing. Theories diverge on whether it is a rapid, spontaneous emotional response that does not involve reflection upon the self or whether it results from higher-order socio-cognitive processes. Investigating the neural substrates of blushing can shed light on the mental processes underlying blushing and the mechanisms involved in self-awareness. To reveal neural activity associated with blushing, 16–20 year-old participants (n = 40) watched pre-recorded videos of themselves (versus other people as a control condition) singing karaoke in a magnetic resonance imaging scanner. We measured participants’ cheek temperature increase—an indicator of blushing—and their brain activity. The results showed that blushing is higher when watching oneself versus others sing. Those who blushed more while watching themselves sing had, on average, higher activation in the cerebellum (lobule V) and the left paracentral lobe and exhibited more time-locked processing of the videos in early visual cortices. These findings show that blushing is associated with the activation of brain areas involved in emotional arousal, suggesting that it may occur independently of higher-order socio-cognitive processes. Our results provide new avenues for future research on self-awareness in infants and non-human animals.
Vigilant and Prepared: Working Memory-Driven Attentional Capture by Task-Irrelevant Threat Is Contingent Upon Action Preparation Threat-associated stimuli can capture our attention even when they are task-irrelevant. It has, however, not been determined whether this interference can be caused by background threat-detection goals active in visual working memory (VWM). To test this, five dual-task combined visual search and VWM change detection task experiments were run (4/5 pre-registered; total N = 119), in which participants had to detect the change in either positive (kitten) or threat-related (spider) animal exemplars across a trial, whilst performing an intervening visual search task with peripheral distractors from these affective categories. It was hypothesised that threat-related spider and positive kitten distractors would disrupt search efficiency more, versus a neutral (bird or no distractor) baseline, when congruent with the contents of VWM. Experiments 1a, 2, and 3, however, found no evidence of increased capture by VWM-matching affective stimuli, despite cumulative evidence across all experiments of goal-independent value-driven interference by spiders, and a separate self-report rating study (Experiment 1b; n = 82) confirming the distractors’ affective associations. When, however, the trial structure became unpredictable, requiring constant preparation for the VWM task response (Experiment 4), or advanced action preparation to the VWM task was enabled (Experiment 5), then VWM-matching threat-related distractors caused greater interference – though these results were absent for positive distractors. The results provide evidence for distinct goal-driven and value-driven attentional capture by threat; and suggests that a background goal-driven mechanism may operate depending on varying states of action preparation and prioritisation in VWM, rather than task-relevance amplifying affective perceptual inputs.
Inductive biases of neural network modularity in spatial navigation The brain may have evolved a modular architecture for daily tasks, with circuits featuring functionally specialized modules that match the task structure. We hypothesize that this architecture enables better learning and generalization than architectures with less specialized modules. To test this, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the modular agent, with an architecture that segregates computations of state representation, value, and action into specialized modules, achieved better learning and generalization. Its learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to recursive Bayesian estimation. This agent’s behavior also resembles macaques’ behavior more closely. Our results shed light on the possible rationale for the brain’s modularity and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.
Fixating targets in visual search: The role of dorsal and ventral attention networks in the processing of relevance and rarity The dorsal attention network, often observed to be activated in serial visual search tasks, has been associated with goal-directed attention, responsible for the processing of task relevance. In serial visual search, the moment of target detection constitutes not only a task-relevant event, but also a rare event. In the present fMRI experiment, we disentangled task relevance from item rarity using a fixation-based analysis approach. We used a multiple target search task, and participants had to report the number of targets among distractors in the display. We had also added rare distractors to the displays. We found that rare events (targets and rare distractors) activated the dorsal attention network more strongly than common distractors. More importantly, we observed that the left IPS and the left insula, belonging to the dorsal and ventral attention system, respectively, were more strongly activated for targets compared to rare distractors. Using multi-voxel pattern analysis, we found that activation in the TPJ, bilaterally, an area also associated with the ventral attention system, distinguished between target and rare distractor fixations. These results point to an expanded role of the TPJ that seems to process post-perceptual information which is linked to task relevance.
Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.