scholarly journals The neuroscience of advanced scientific concepts

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Robert A. Mason ◽  
Reinhard A. Schumacher ◽  
Marcel Adam Just
Keyword(s):  
Causal Reasoning ◽  
Level Structure ◽  
Mathematical Formulation ◽  
Brain Regions ◽  
Neural Representation ◽  
Scientific Concepts ◽  
Activation Patterns ◽  
Physics Students ◽  
Machine Learning Classification ◽  
Individual Concepts

AbstractCognitive neuroscience methods can identify the fMRI-measured neural representation of familiar individual concepts, such as apple, and decompose them into meaningful neural and semantic components. This approach was applied here to determine the neural representations and underlying dimensions of representation of far more abstract physics concepts related to matter and energy, such as fermion and dark matter, in the brains of 10 Carnegie Mellon physics faculty members who thought about the main properties of each of the concepts. One novel dimension coded the measurability vs. immeasurability of a concept. Another novel dimension of representation evoked particularly by post-classical concepts was associated with four types of cognitive processes, each linked to particular brain regions: (1) Reasoning about intangibles, taking into account their separation from direct experience and observability; (2) Assessing consilience with other, firmer knowledge; (3) Causal reasoning about relations that are not apparent or observable; and (4) Knowledge management of a large knowledge organization consisting of a multi-level structure of other concepts. Two other underlying dimensions, previously found in physics students, periodicity, and mathematical formulation, were also present in this faculty sample. The data were analyzed using factor analysis of stably responding voxels, a Gaussian-naïve Bayes machine-learning classification of the activation patterns associated with each concept, and a regression model that predicted activation patterns associated with each concept based on independent ratings of the dimensions of the concepts. The findings indicate that the human brain systematically organizes novel scientific concepts in terms of new dimensions of neural representation.

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

scholarly journals Mapping the common and distinct neural correlates of visual, rule and motor conflict

2020 ◽  
Author(s):  
Bryony Goulding Mew ◽  
Darije Custovic ◽  
Eyal Soreq ◽  
Romy Lorenz ◽  
Ines Violante ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Neural Correlates ◽  
Brain Regions ◽  
Control Mechanisms ◽  
Activation Patterns ◽  
The Common ◽  
Full Factorial ◽  
The Brain ◽  
Categorical Scale ◽  
Conflict Types

AbstractFlexible behaviour requires cognitive-control mechanisms to efficiently resolve conflict between competing information and alternative actions. Whether a global neural resource mediates all forms of conflict or this is achieved within domainspecific systems remains debated. We use a novel fMRI paradigm to orthogonally manipulate rule, response and stimulus-based conflict within a full-factorial design. Whole-brain voxelwise analyses show that activation patterns associated with these conflict types are distinct but partially overlapping within Multiple Demand Cortex (MDC), the brain regions that are most commonly active during cognitive tasks. Region of interest analysis shows that most MDC sub-regions are activated for all conflict types, but to significantly varying levels. We propose that conflict resolution is an emergent property of distributed brain networks, the functional-anatomical components of which place on a continuous, not categorical, scale from domain-specialised to domain general. MDC brain regions place towards one end of that scale but display considerable functional heterogeneity.

scholarly journals Decoding the Information Structure Underlying the Neural Representation of Concepts

2021 ◽  
Author(s):  
Leonardo Fernandino ◽  
Lisa L. Conant ◽  
Colin J. Humphries ◽  
Jeffrey R. Binder
Keyword(s):  
Information Structure ◽  
Conceptual Knowledge ◽  
Neural Representation ◽  
Neural Activation ◽  
Activation Patterns ◽  
Concept Representation ◽  
Native Space ◽  
Distributional Information ◽  
Similarity Structure ◽  
Types Of Information

The nature of the neural code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. Three main types of information have been proposed as candidates for the neural representations of lexical concepts: taxonomic (i.e., information about category membership and inter-category relations), distributional (i.e., information about patterns of word co-occurrence in natural language use), and experiential (i.e., information about sensory-motor, affective, and other features of phenomenal experience engaged during concept acquisition). In two experiments, we investigated the extent to which these three types of information are encoded in the neural activation patterns associated with hundreds of English nouns from a wide variety of conceptual categories. Participants made familiarity judgments on the meaning of written nouns while undergoing functional MRI. A high-resolution, whole-brain activation map was generated for each noun in each participant′s native space. These word-specific activation maps were used to evaluate different representational spaces corresponding to the three types of information described above. In both studies, we found a striking advantage for experience-based models in most brain areas previously associated with concept representation. Partial correlation analyses revealed that only experiential information successfully predicted concept similarity structure when inter-model correlations were taken into account. This pattern of results was found independently for object concepts and event concepts. Our findings indicate that the neural representation of conceptual knowledge primarily encodes information about features of experience, and that - to the extent that it is represented in the brain - taxonomic and distributional information may rely on such an experience-based code.

scholarly journals Neural tracking of subjective value under riskand ambiguity in adolescence

2019 ◽  
Vol 19 (6) ◽  
pp. 1364-1378 ◽  
Author(s):  
Neeltje E. Blankenstein ◽  
Anna C. K. van Duijvenvoorde
Keyword(s):  
Brain Activation ◽  
Superior Temporal Gyrus ◽  
Building Blocks ◽  
Brain Regions ◽  
Activation Patterns ◽  
Subjective Value ◽  
Adolescent Risk ◽  
Dorsolateral Prefrontal ◽  
Adolescent Sample ◽  
Superior Parietal Cortex

Abstract Although many neuroimaging studies on adolescent risk taking have focused on brain activation during outcome valuation, less attention has been paid to the neural correlates of choice valuation. Subjective choice valuation may be particularly influenced by whether a choice presents risk (known probabilities) or ambiguity (unknown probabilities), which has rarely been studied in developmental samples. Therefore, we examined the neural tracking of subjective value during choice under risk and ambiguity in a large sample of adolescents (N = 188, 12–22 years). Specifically, we investigated which brain regions tracked subjective value coding under risk and ambiguity. A model-based approach to estimate individuals’ risk and ambiguity attitudes showed prominent variation in individuals’ aversions to risk and ambiguity. Furthermore, participants subjectively experienced the ambiguous options as being riskier than the risky options. Subjective value tracking under risk was coded by activation in ventral striatum and superior parietal cortex. Subjective value tracking under ambiguity was coded by dorsolateral prefrontal cortex (PFC) and superior temporal gyrus activation. Finally, overlapping activation in the dorsomedial PFC was observed for subjective value under both conditions. Overall, this is the first study to chart brain activation patterns for subjective choice valuation under risk and ambiguity in an adolescent sample, which shows that the building blocks for risk and ambiguity processing are already present in early adolescence. Finally, we highlight the potential of combining behavioral modeling with fMRI for investigating choice valuation in adolescence, which may ultimately aid in understanding who takes risks and why.

scholarly journals Functional brain organization for visual search in ASD

2008 ◽  
Vol 14 (6) ◽  
pp. 990-1003 ◽  
Author(s):  
BRANDON KEEHN ◽  
LAURIE BRENNER ◽  
ERICA PALMER ◽  
ALAN J. LINCOLN ◽  
RALPH-AXEL MÜLLER
Keyword(s):  
Visual Search ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Neural Mechanisms ◽  
Search Efficiency ◽  
Typically Developing ◽  
Brain Organization ◽  
Activation Patterns ◽  
Set Size ◽  
Functional Brain Organization

AbstractAlthough previous studies have shown that individuals with autism spectrum disorder (ASD) excel at visual search, underlying neural mechanisms remain unknown. This study investigated the neurofunctional correlates of visual search in children with ASD and matched typically developing (TD) children, using an event-related functional magnetic resonance imaging design. We used a visual search paradigm, manipulating search difficulty by varying set size (6, 12, or 24 items), distractor composition (heterogeneous or homogeneous) and target presence to identify brain regions associated with efficient and inefficient search. While the ASD group did not evidence accelerated response time (RT) compared with the TD group, they did demonstrate increased search efficiency, as measured by RT by set size slopes. Activation patterns also showed differences between ASD group, which recruited a network including frontal, parietal, and occipital cortices, and the TD group, which showed less extensive activation mostly limited to occipito-temporal regions. Direct comparisons (for both homogeneous and heterogeneous search conditions) revealed greater activation in occipital and frontoparietal regions in ASD than in TD participants. These results suggest that search efficiency in ASD may be related to enhanced discrimination (reflected in occipital activation) and increased top-down modulation of visual attention (associated with frontoparietal activation). (JINS, 2008, 14, 990–1003.)

Imaging Cognition: An Empirical Review of PET Studies with Normal Subjects

1997 ◽  
Vol 9 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Roberto Cabeza ◽  
Lars Nyberg
Keyword(s):  
Working Memory ◽  
Episodic Memory ◽  
Cognitive Processes ◽  
Memory Retrieval ◽  
Normal Subjects ◽  
Brain Regions ◽  
Procedural Memory ◽  
Skill Learning ◽  
Activation Patterns ◽  
Selective Perception

We review PET studies of higher-order cognitive processes, including attention (sustained and selective), perception (of objects, faces, and locations), language (word listening, reading, and production), working memory (phonological and visuo-spatial), semantic memory retrieval (intentional and incidental), episodic memory retrieval (verbal and nonverbal), priming, and procedural memory (conditioning and skill learning). For each process, we identify activation patterns including the most consistently involved regions. These regions constitute important components of the network of brain regions that underlie each function.

scholarly journals Color signals through dorsal and ventral visual pathways

2013 ◽  
Vol 31 (2) ◽  
pp. 197-209 ◽  
Author(s):  
BEVIL R. CONWAY
Keyword(s):  
Visual Processing ◽  
Color Space ◽  
Temporal Cortex ◽  
Color Perception ◽  
Original Data ◽  
Brain Regions ◽  
Neural Representation ◽  
Inferior Temporal Cortex ◽  
Extrastriate Cortex ◽  
Chromatic Contrast

AbstractExplanations for color phenomena are often sought in the retina, lateral geniculate nucleus, and V1, yet it is becoming increasingly clear that a complete account will take us further along the visual-processing pathway. Working out which areas are involved is not trivial. Responses to S-cone activation are often assumed to indicate that an area or neuron is involved in color perception. However, work tracing S-cone signals into extrastriate cortex has challenged this assumption: S-cone responses have been found in brain regions, such as the middle temporal (MT) motion area, not thought to play a major role in color perception. Here, we review the processing of S-cone signals across cortex and present original data on S-cone responses measured with fMRI in alert macaque, focusing on one area in which S-cone signals seem likely to contribute to color (V4/posterior inferior temporal cortex) and on one area in which S signals are unlikely to play a role in color (MT). We advance a hypothesis that the S-cone signals in color-computing areas are required to achieve a balanced neural representation of perceptual color space, whereas those in noncolor-areas provide a cue to illumination (not luminance) and confer sensitivity to the chromatic contrast generated by natural daylight (shadows, illuminated by ambient sky, surrounded by direct sunlight). This sensitivity would facilitate the extraction of shape-from-shadow signals to benefit global scene analysis and motion perception.

scholarly journals A geometric representation unveils learning dynamics in primate neurons

2019 ◽  
Author(s):  
Yarden Cohen ◽  
Elad Schneidman ◽  
Rony Paz
Keyword(s):  
De Novo ◽  
Large Fraction ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Neural Representation ◽  
Neural Dynamics ◽  
Geometric Representation ◽  
Dorsal Anterior Cingulate Cortex ◽  
Magnitude Increase ◽  
Vector Magnitude

AbstractPrimates can quickly and advantageously adopt new behaviors based on changing stimuli relationships. We studied acquisition of a classification task while recording single neurons in the dorsal-anterior-cingulate-cortex (dACC) and the Striatum. Monkeys performed trial-by-trial classification on a rich set of multi-cue patterns, allowing de-novo learning every few days. To examine neural dynamics during the learning itself, we represent each rule with a spanning set of the space formed by the stimuli features. Because neural preference can be expressed by feature combinations, we can track neural dynamics in geometrical terms in this space, allowing a compact description of neural trajectories by observing changes in either vector-magnitude and/or angle-to- rule. We find that a large fraction of cells in both regions follow the behavior during learning. Neurons in the dACC mainly rotate towards the policy, suggesting an increase in selectivity that approximates the rule; whereas in the Putamen we also find a prominent magnitude increase, suggesting strengthening of confidence. Additionally, magnitude increases in the striatum followed rotation in the dACC. Finally, the neural representation at the end of the session predicted next-day behavior. The use of this novel framework enables tracking of neural dynamics during learning and suggests differential yet complementing roles for these brain regions.

scholarly journals Neural codes of seeing architectural styles

2016 ◽  
Author(s):  
Heeyoung Choo ◽  
Jack Nasar ◽  
Bardia Nikrahei ◽  
Dirk B. Walther
Keyword(s):  
Architectural Design ◽  
Brain Regions ◽  
Neural Representation ◽  
Neural Encoding ◽  
Substantial Impact ◽  
Fine Grained ◽  
Face Area ◽  
Architectural Styles ◽  
Subordinate Level ◽  
High Level

AbstractImages of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people’s visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

scholarly journals A "global" network dysfunction in the mirror neuron system in autism is modulated by task complexity and age: A meta-analysis of neuroimaging studies

2020 ◽  
Author(s):  
Mei Yan Melody Chan ◽  
Yvonne M.Y. Han
Keyword(s):  
Mirror Neurons ◽  
Task Complexity ◽  
Mirror Neuron System ◽  
Meta Analysis ◽  
Mirror Neuron ◽  
Brain Regions ◽  
Local Network ◽  
Activation Patterns ◽  
Neuron System ◽  
The Brain

Abstract Background Impaired imitation has been found to be an important factor contributing to social communication deficits in individuals with autism spectrum disorder (ASD). It has been hypothesized that the neural correlates of imitation, the mirror neuron system (MNS), are dysfunctional in ASD, resulting in imitation impairment as one of the key behavioral manifestations in ASD. Previous MNS studies produced inconsistent results, leaving the debate of whether mirror neurons are “broken” in ASD unresolved.Methods This meta-analysis aimed to explore the differences in MNS activation patterns between typically developing (TD) and ASD individuals when they observe/imitate biological motions with/without emotional components. Effect-size signed differential mapping (ES-SDM) was adopted to synthesize the available fMRI data. Results The MNS is dysfunctional in ASD; not only the brain regions containing mirror neurons were affected, the brain regions supporting MNS functioning were also impaired. Second, MNS dysfunction in ASD is modulated by task complexity; differential activation patterns during the presentation of “cold” and “hot” stimuli might be a result of atypical functional connectivity in ASD. Third, MNS dysfunction in ASD individuals is modulated by age. MNS regions were found to show delayed maturation; abnormal lateralization development in some of the brain regions also contributed to the atypical development of the MNS in ASD. Limitations We have attempted to include a comprehensive set of original data for this analysis. However, whole brain analysis data were not obtainable from some of the published papers, these studies could not be included as a result. Moreover, the results indicating the age effect on MNS in ASD could only be generalized to individuals aged 11-37, as MNS activation remains unstudied for populations beyond this age range. Also, the ES-SDM linear regression modelling might not be ideal to illustrate the associations between age and MNS activation; the meta-regression results should be treated with caution. Conclusion There is a “global” rather than a “local” network dysfunction, which may underlie the imitation impairments in individuals with ASD. Task complexity and age modulate the functioning of the MNS, which may explain the previous peculiar results contributing to the unresolved “broken mirror neuron” debate.

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