Self-similarity and recursion as default modes in human cognition

Multimodal neurophenomenology of advanced concentration absorption meditation: An intensively sampled case study of Jhana

Using a combination of fMRI, EEG, and phenomenology ratings, we examined the neurophenomenology of advanced concentrative absorption meditation, namely jhanas (ACAM-J), in a practitioner with over 23,000 h of meditation practice. Our study shows that ACAM-J states induce reliable changes in conscious experience and that these experiences are related to neural activity. Using resting-state fMRI functional connectivity, we found that ACAM-J is associated with decreased within-network modularity, increased global functional connectivity (GFC), and desegregation of the default mode and visual networks. Compared to control tasks, the ACAM-J were also related to widespread decreases in broadband EEG oscillatory power and increases in Lempel-Ziv complexity (LZ, a measure of brain entropy). Some fMRI findings varied by the control task used, while EEG results remained consistent, emphasizing both shared and unique neural features of ACAM-J. These differences in fMRI and EEG-measured neurophysiological properties correlated with specific changes in phenomenology - and especially with ACAM-J-induced states of bliss - enriching our understanding of these advanced meditative states. Our results show that advanced meditation practices markedly dysregulate high-level brain systems via practices of enhanced attention to sensations, corroborating recent neurocognitive theories of meditation as the deconstruction of the brain's cortical hierarchy. Overall, our results suggest that ACAM-J is associated with the modulation of large-scale brain networks in both fMRI and EEG, with potential implications for understanding the mechanisms of deep concentration practices and their effects on subjective experience.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Cognitive and Neural Representations of Fractals in Vision, Music, and Action

The concept of fractal was popularized by Mandelbrot as a tool to tame the geometrical structure of objects with infinite hierarchical depth. The key aspect of fractals is the use of simple parsimonious rules and initial conditions, which when applied recursively can generate unbounded complexity. Fractals are structures ubiquitous in nature, being present in coast lines, bacteria colonies, trees, and physiological time series. However, within the field of cognitive science, the core question is not which phenomena can generate fractal structures, but whether human or animal minds can represent recursive processes, and if so in which domains. In this chapter, we will explore the cognitive and neural mechanisms underlying the representation of recursive hierarchical embedding. Language is the domain in which this capacity is best studied. Humans can generate an infinite array of hierarchically structured sentences, and this capacity distinguishes us from other species. However, recent research suggests that humans can represent similar structures in the domains of music, vision, and action and has provided additional cues as to how these capacities are cognitively implemented. Using a comparative approach, we will map the commonalities and differences across domains and offer a roadmap to understand the neurobiological implementation of fractal cognition.

Electrophysiological connectivity of logical deduction: Early cortical MEG study

Complex human reasoning involves minimal abilities to extract conclusions implied in the available information. These abilities are considered "deductive" because they exemplify certain abstract relations among propositions or probabilities called deductive arguments. However, the electrophysiological dynamics which supports such complex cognitive processes has not been addressed yet. In this work we consider typically deductive logico-probabilistically valid inferences and aim to verify or refute their electrophysiological functional connectivity differences from invalid inferences with the same content (same relational variables, same stimuli, same relevant and salient features). We recorded the brain electrophysiological activity of 20 participants (age = 20.35 ± 3.23) by means of an MEG system during two consecutive reasoning tasks: a search task (invalid condition) without any specific deductive rules to follow, and a logically valid deductive task (valid condition) with explicit deductive rules as instructions. We calculated the functional connectivity (FC) for each condition and conducted a seed-based analysis in a set of cortical regions of interest. Finally, we used a cluster-based permutation test to compare the differences between logically valid and invalid conditions in terms of FC. As a first novel result we found higher FC for valid condition in beta band between regions of interest and left prefrontal, temporal, parietal, and cingulate structures. FC analysis allows a second novel result which is the definition of a propositional network with operculo-cingular, parietal and medial nodes, specifically including disputed medial deductive "core" areas. The experiment discloses measurable cortical processes which do not depend on content but on truth-functional propositional operators. These experimental novelties may contribute to understand the cortical bases of deductive processes.

Assessing landscape aesthetic values: Do clouds in photographs influence people's preferences?

Photo-based surveys are widely applied to elicit landscape preferences and to assess cultural ecosystem services. Variations in weather and light conditions can potentially alter people's preferences, as sunny landscapes are more positively perceived than those under inclement weather conditions. To assure comparability across pictures, studies usually include photographs taken at sunny days (i.e., blue sky). However, the influence of clouds in sunny landscapes on people's preferences has been rarely considered, although color contrasts between clouds and the blue sky may attract people's attention. This study therefore aimed to assess the effects of clouds in landscape photographs on people's preferences by (1) examining differences in preference between pairs of landscape photographs (i.e., with clouds and without clouds), and (2) explaining variations through variables from eye-tracking simulation, photo content analysis, and Geographic Information System (GIS)-based analysis. Our results indicate no significant differences in preferences between pictures with and without clouds when the pictures with clouds contained a proportion of sky around 22% and a cloud cover of about 39%. However, a higher proportion of sky positively influenced landscape preferences, while a higher proportion of clouds, especially in combination with a lower proportion of sky, had negative effects. These findings suggest that landscape preference studies should pay attention not only to the appearance of the sky in terms of cloudiness, but they also should control the proportion of sky across different pictures to obtain comparable results. Future research should address limitations regarding the transferability of our findings to other types of landscapes and regarding potential differences in perceptions between respondents with different socio-cultural characteristics. Moreover, landscape preferences under changing weather conditions or different cloud types as well as diurnal and seasonal changes should be further explored.

Cognitive Mechanisms Underlying Recursive Pattern Processing in Human Adults

The capacity to generate recursive sequences is a marker of rich, algorithmic cognition, and perhaps unique to humans. Yet, the precise processes driving recursive sequence generation remain mysterious. We investigated three potential cognitive mechanisms underlying recursive pattern processing: hierarchical reasoning, ordinal reasoning, and associative chaining. We developed a Bayesian mixture model to quantify the extent to which these three cognitive mechanisms contribute to adult humans' performance in a sequence generation task. We further tested whether recursive rule discovery depends upon relational information, either perceptual or semantic. We found that the presence of relational information facilitates hierarchical reasoning and drives the generation of recursive sequences across novel depths of center embedding. In the absence of relational information, the use of ordinal reasoning predominates. Our results suggest that hierarchical reasoning is an important cognitive mechanism underlying recursive pattern processing and can be deployed across embedding depths and relational domains.

Origins of Hierarchical Logical Reasoning

Hierarchical cognitive mechanisms underlie sophisticated behaviors, including language, music, mathematics, tool-use, and theory of mind. The origins of hierarchical logical reasoning have long been, and continue to be, an important puzzle for cognitive science. Prior approaches to hierarchical logical reasoning have often failed to distinguish between observable hierarchical behavior and unobservable hierarchical cognitive mechanisms. Furthermore, past research has been largely methodologically restricted to passive recognition tasks as compared to active generation tasks that are stronger tests of hierarchical rules. We argue that it is necessary to implement learning studies in humans, non-human species, and machines that are analyzed with formal models comparing the contribution of different cognitive mechanisms implicated in the generation of hierarchical behavior. These studies are critical to advance theories in the domains of recursion, rule-learning, symbolic reasoning, and the potentially uniquely human cognitive origins of hierarchical logical reasoning.

Potential of eye-tracking simulation software for analyzing landscape preferences

Profound knowledge about landscape preferences is of high importance to support decision-making, in particular, in the context of emerging socio-economic developments to foster a sustainable spatial development and the maintenance of attractive landscapes. Eye-tracking experiments are increasingly used to examine how respondents observe landscapes, but such studies are very time-consuming and costly. For the first time, this study explored the potential of using eye-tracking simulation software in a mountain landscape by (1) identifying the type of information that can be obtained through eye-tracking simulation and (2) examining how this information contributes to the explanation of landscape preferences. Based on 78 panoramic landscape photographs, representing major landscape types of the Central European Alps, this study collected 19 indicators describing the characteristics of the hotspots that were identified by the Visual Attention Software by 3M (3M-VAS). Indicators included quantitative and spatial information (e.g., number of hotspots, probabilities of initially viewing the hotspots) as well variables indicating natural and artificial features within the hotspots (e.g., clouds, lighting conditions, natural and anthropogenic features). In addition, we estimated 18 variables describing the photo content and calculated 12 landscape metrics to quantify spatial patterns. Our results indicate that on average 3.3 hotspots were identified per photograph, mostly containing single trees and tree trunks, buildings and horizon transitions. Using backward stepwise linear regression models, the hotspot indicators increased the model explanatory power by 24%. Thus, our findings indicate that the analysis of eye-tracking hotspots can support the identification of important elements and areas of a landscape, but it is limited in explaining preferences across different landscape types. Future research should therefore focus on specific landscape characteristics such as complexity, structure or visual appearance of specific elements to increase the depth of information obtained from eye-tracking simulation software.

Electrocorticographic evidence of a common neurocognitive sequence for mentalizing about the self and others

Neuroimaging studies of mentalizing (i.e., theory of mind) consistently implicate the default mode network (DMN). Nevertheless, the social cognitive functions of individual DMN regions remain unclear, perhaps due to limited spatiotemporal resolution in neuroimaging. Here we use electrocorticography (ECoG) to directly record neuronal population activity while 16 human participants judge the psychological traits of themselves and others. Self- and other-mentalizing recruit near-identical cortical sites in a common spatiotemporal sequence. Activations begin in the visual cortex, followed by temporoparietal DMN regions, then finally in medial prefrontal regions. Moreover, regions with later activations exhibit stronger functional specificity for mentalizing, stronger associations with behavioral responses, and stronger self/other differentiation. Specifically, other-mentalizing evokes slower and longer activations than self-mentalizing across successive DMN regions, implying lengthier processing at higher levels of representation. Our results suggest a common neurocognitive pathway for self- and other-mentalizing that follows a complex spatiotemporal gradient of functional specialization across DMN and beyond.

What Happens in Your Brain When You Walk Down the Street? Implications of Architectural Proportions, Biophilia, and Fractal Geometry for Urban Science

This article reviews current research in visual urban perception. The temporal sequence of the first few milliseconds of visual stimulus processing sheds light on the historically ambiguous topic of aesthetic experience. Automatic fractal processing triggers initial attraction/avoidance evaluations of an environment’s salubriousness, and its potentially positive or negative impacts upon an individual. As repeated cycles of visual perception occur, the attractiveness of urban form affects the user experience much more than had been previously suspected. These perceptual mechanisms promote walkability and intuitive navigation, and so they support the urban and civic interactions for which we establish communities and cities in the first place. Therefore, the use of multiple fractals needs to reintegrate with biophilic and traditional architecture in urban design for their proven positive effects on health and well-being. Such benefits include striking reductions in observers’ stress and mental fatigue. Due to their costs to individual well-being, urban performance, environmental quality, and climatic adaptation, this paper recommends that nontraditional styles should be hereafter applied judiciously to the built environment.

White matter correlates of scam susceptibility in community-dwelling older adults

Scam susceptibility places older adults - even those with intact cognition - at great risk. Lower grey matter volumes, particularly within right medial temporal regions, are associated with higher scam susceptibility; however, very little is known about white matter associates. We investigated associations between white matter integrity measured using diffusion tensor imaging (DTI) and scam susceptibility in 302 non-demented older adults (75% female; mean years: age = 81.3 + 7.5, education = 15.7 + 2.9). Participants completed comprehensive neuroimaging (including DTI, T1- and T2-weighted imaging), a self-report measure of scam susceptibility, and neuropsychological testing. Tract-Based Spatial Statistics (TBSS) investigated associations of DTI-derived measures of fractional anisotropy (FA), trace of the diffusion tensor, axial and radial diffusivity (separately) with scam susceptibility adjusting for age, sex, education, and white matter hyperintensities (WMH; total volume and voxelwise separately). Statistical significance was determined at p < 0.05, Family Wise Error corrected. TBSS revealed significant negative associations between FA in tracts connecting a number of right hemisphere white matter regions and scam susceptibility, particularly after additional adjustment for global cognitive functioning. The pathways implicated were mainly in right temporal-parietal and temporal-occipital regions. Association of trace, axial, and radial diffusivity with scam susceptibility were not significant in fully-adjusted models. Lower white matter integrity within right hemisphere tracts was associated with higher scam susceptibility independent of relevant confounds including global cognition. Thus, a right hemisphere brain network that includes key structures implicated in multi-sensory processing of immediate and future consequences may serve as a neurobiologic substrate of scam susceptibility in vulnerable older adults.

Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning

In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language processing, have witnessed remarkable improvements in state of the art models. One advantage of this technological boost is to facilitate comparison between different neural networks and human performance, in order to deepen our understanding of human cognition. Here, we investigate which neural network architecture (feedforward vs. recurrent) matches human behavior in artificial grammar learning, a crucial aspect of language acquisition. Prior experimental studies proved that artificial grammars can be learnt by human subjects after little exposure and often without explicit knowledge of the underlying rules. We tested four grammars with different complexity levels both in humans and in feedforward and recurrent networks. Our results show that both architectures can "learn" (via error back-propagation) the grammars after the same number of training sequences as humans do, but recurrent networks perform closer to humans than feedforward ones, irrespective of the grammar complexity level. Moreover, similar to visual processing, in which feedforward and recurrent architectures have been related to unconscious and conscious processes, the difference in performance between architectures over ten regular grammars shows that simpler and more explicit grammars are better learnt by recurrent architectures, supporting the hypothesis that explicit learning is best modeled by recurrent networks, whereas feedforward networks supposedly capture the dynamics involved in implicit learning.

Recursive hierarchical embedding in vision is impaired by posterior middle temporal gyrus lesions

The generation of hierarchical structures is central to language, music and complex action. Understanding this capacity and its potential impairments requires mapping its underlying cognitive processes to the respective neuronal underpinnings. In language, left inferior frontal gyrus and left posterior temporal cortex (superior temporal sulcus/middle temporal gyrus) are considered hubs for syntactic processing. However, it is unclear whether these regions support computations specific to language or more generally support analyses of hierarchical structure. Here, we address this issue by investigating hierarchical processing in a non-linguistic task. We test the ability to represent recursive hierarchical embedding in the visual domain by contrasting a recursion task with an iteration task. The recursion task requires participants to correctly identify continuations of a hierarchy generating procedure, while the iteration task applies a serial procedure that does not generate new hierarchical levels. In a lesion-based approach, we asked 44 patients with left hemispheric chronic brain lesion to perform recursion and iteration tasks. We modelled accuracies and response times with a drift diffusion model and for each participant obtained parametric estimates for the velocity of information accumulation (drift rates) and for the amount of information accumulated before a decision (boundary separation). We then used these estimates in lesion-behaviour analyses to investigate how brain lesions affect specific aspects of recursive hierarchical embedding. We found that lesions in the posterior temporal cortex decreased drift rate in recursive hierarchical embedding, suggesting an impaired process of rule extraction from recursive structures. Moreover, lesions in inferior temporal gyrus decreased boundary separation. The latter finding does not survive conservative correction but suggests a shift in the decision criterion. As patients also participated in a grammar comprehension experiment, we performed explorative correlation-analyses and found that visual and linguistic recursive hierarchical embedding accuracies are correlated when the latter is instantiated as sentences with two nested embedding levels. While the roles of the inferior temporal gyrus and posterior temporal cortex in linguistic processes are well established, here we show that posterior temporal cortex lesions slow information accumulation (drift rate) in the visual domain. This suggests that posterior temporal cortex is essential to acquire the (knowledge) representations necessary to parse recursive hierarchical embedding in visual structures, a finding mimicking language acquisition in young children. On the contrary, inferior frontal gyrus lesions seem to affect recursive hierarchical embedding processing by interfering with more general cognitive control (boundary separation). This interesting separation of roles, rooted on a domain-general taxonomy, raises the question of whether such cognitive framing is also applicable to other domains.

Recursion in action: An fMRI study on the generation of new hierarchical levels in motor sequences

Generation of hierarchical structures, such as the embedding of subordinate elements into larger structures, is a core feature of human cognition. Processing of hierarchies is thought to rely on lateral prefrontal cortex (PFC). However, the neural underpinnings supporting active generation of new hierarchical levels remain poorly understood. Here, we created a new motor paradigm to isolate this active generative process by means of fMRI. Participants planned and executed identical movement sequences by using different rules: a Recursive hierarchical embedding rule, generating new hierarchical levels; an Iterative rule linearly adding items to existing hierarchical levels, without generating new levels; and a Repetition condition tapping into short term memory, without a transformation rule. We found that planning involving generation of new hierarchical levels (Recursive condition vs. both Iterative and Repetition) activated a bilateral motor imagery network, including cortical and subcortical structures. No evidence was found for lateral PFC involvement in the generation of new hierarchical levels. Activity in basal ganglia persisted through execution of the motor sequences in the contrast Recursive versus Iteration, but also Repetition versus Iteration, suggesting a role of these structures in motor short term memory. These results showed that the motor network is involved in the generation of new hierarchical levels during motor sequence planning, while lateral PFC activity was neither robust nor specific. We hypothesize that lateral PFC might be important to parse hierarchical sequences in a multi‐domain fashion but not to generate new hierarchical levels.

Short parietal lobe connections of the human and monkey brain

The parietal lobe has a unique place in the human brain. Anatomically, it is at the crossroad between the frontal, occipital, and temporal lobes, thus providing a middle ground for multimodal sensory integration. Functionally, it supports higher cognitive functions that are characteristic of the human species, such as mathematical cognition, semantic and pragmatic aspects of language, and abstract thinking. Despite its importance, a comprehensive comparison of human and simian intraparietal networks is missing. In this study, we used diffusion imaging tractography to reconstruct the major intralobar parietal tracts in twenty-one datasets acquired in vivo from healthy human subjects and eleven ex vivo datasets from five vervet and six macaque monkeys. Three regions of interest (postcentral gyrus, superior parietal lobule and inferior parietal lobule) were used to identify the tracts. Surface projections were reconstructed for both species and results compared to identify similarities or differences in tract anatomy (i.e., trajectories and cortical projections). In addition, post-mortem dissections were performed in a human brain. The largest tract identified in both human and monkey brains is a vertical pathway between the superior and inferior parietal lobules. This tract can be divided into an anterior (supramarginal gyrus) and a posterior (angular gyrus) component in both humans and monkey brains. The second prominent intraparietal tract connects the postcentral gyrus to both supramarginal and angular gyri of the inferior parietal lobule in humans but only to the supramarginal gyrus in the monkey brain. The third tract connects the postcentral gyrus to the anterior region of the superior parietal lobule and is more prominent in monkeys compared to humans. Finally, short U-shaped fibres in the medial and lateral aspects of the parietal lobe were identified in both species. A tract connecting the medial parietal cortex to the lateral inferior parietal cortex was observed in the monkey brain only. Our findings suggest a consistent pattern of intralobar parietal connections between humans and monkeys with some differences for those areas that have cytoarchitectonically distinct features in humans. The overall pattern of intraparietal connectivity supports the special role of the inferior parietal lobule in cognitive functions characteristic of humans.