Visual Number Beats Abstract Numerical Magnitude: Format-dependent Representation of Arabic Digits and Dot Patterns in Human Parietal Cortex

In numerical cognition, there is a well-known but contested hypothesis that proposes an abstract representation of numerical magnitude in human intraparietal sulcus (IPS). On the other hand, researchers of object cognition have suggested another hypothesis for brain activity in IPS during the processing of number, namely that this activity simply correlates with the number of visual objects or units that are perceived. We contrasted these two accounts by analyzing multivoxel activity patterns elicited by dot patterns and Arabic digits of different magnitudes while participants were explicitly processing the represented numerical magnitude. The activity pattern elicited by the digit “8” was more similar to the activity pattern elicited by one dot (with which the digit shares the number of visual units but not the magnitude) compared to the activity pattern elicited by eight dots, with which the digit shares the represented abstract numerical magnitude. A multivoxel pattern classifier trained to differentiate one dot from eight dots classified all Arabic digits in the one-dot pattern category, irrespective of the numerical magnitude symbolized by the digit. These results were consistently obtained for different digits in IPS, its subregions, and many other brain regions. As predicted from object cognition theories, the number of presented visual units forms the link between the parietal activation elicited by symbolic and nonsymbolic numbers. The current study is difficult to reconcile with the hypothesis that parietal activation elicited by numbers would reflect a format-independent representation of number.

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Development of the numerical brain

10.1093/oxfordhb/9780199642342.013.008 ◽

2014 ◽

Cited By ~ 2

Author(s):

Liane Kaufmann ◽

Karin Kucian ◽

Michael von Aster

Keyword(s):

Numerical Cognition ◽

Temporal Dynamics ◽

Brain Activity ◽

Brain Regions ◽

Future Research ◽

Fine Grained ◽

Numerical Development ◽

Technological Advances ◽

The One ◽

Neural Signatures

This article focuses on typical trajectories of numerical cognition from infancy all the way through to adulthood (please note that atypical pathways of numerical cognition will be dealt in‘Brain Correlates of Numerical Disabilities’). Despite the fact that developmental imaging studies are still scarce to date there is converging evidence that (1) neural signatures of non-verbal number processing may be observed already in infants; and (2) developmental changes in neural responsivity are characterized by increasing functional specialization of number-relevant frontoparietal brain regions. It has been suggested that age and competence-related modulations of brain activity manifest as an anterior-posterior shift. On the one hand, the recruitment of supporting frontal brain regions decreases, while on the other hand, reliance on number-relevant (fronto-)parietal neural networks increases. Overall, our understanding of the neurocognitive underpinnings of numerical development grew considerably during the last decade. Future research is expected to benefit substantially from the fast technological advances enabling researchers to gain more fine-grained insights into the spatial and temporal dynamics of the neural signatures underlying numerical development.

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FFA and OFA encode distinct types of face identity information

10.1101/2020.05.12.090878 ◽

2020 ◽

Cited By ~ 1

Author(s):

Maria Tsantani ◽

Nikolaus Kriegeskorte ◽

Katherine Storrs ◽

Adrian Lloyd Williams ◽

Carolyn McGettigan ◽

...

Keyword(s):

Brain Activity ◽

Activity Patterns ◽

Brain Regions ◽

Image Encoding ◽

Low Level ◽

Social Traits ◽

Face Area ◽

Face Similarity ◽

And Gender ◽

High Level

AbstractFaces of different people elicit distinct functional MRI (fMRI) patterns in several face-selective brain regions. Here we used representational similarity analysis to investigate what type of identity-distinguishing information is encoded in three face-selective regions: fusiform face area (FFA), occipital face area (OFA), and posterior superior temporal sulcus (pSTS). We used fMRI to measure brain activity patterns elicited by naturalistic videos of famous face identities, and compared their representational distances in each region with models of the differences between identities. Models included low-level to high-level image-computable properties and complex human-rated properties. We found that the FFA representation reflected perceived face similarity, social traits, and gender, and was well accounted for by the OpenFace model (deep neural network, trained to cluster faces by identity). The OFA encoded low-level image-based properties (pixel-wise and Gabor-jet dissimilarities). Our results suggest that, although FFA and OFA can both discriminate between identities, the FFA representation is further removed from the image, encoding higher-level perceptual and social face information.

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Discriminative Learning of Propagation and Spatial Pattern for Motor Imagery EEG Analysis

Neural Computation ◽

10.1162/neco_a_00500 ◽

2013 ◽

Vol 25 (10) ◽

pp. 2709-2733 ◽

Cited By ~ 10

Author(s):

Xinyang Li ◽

Haihong Zhang ◽

Cuntai Guan ◽

Sim Heng Ong ◽

Kai Keng Ang ◽

...

Keyword(s):

Spatial Pattern ◽

Motor Imagery ◽

Brain Activity ◽

Brain Computer Interface ◽

Statistical Significance ◽

Activity Patterns ◽

Signal Propagation ◽

Brain Regions ◽

Computer Interface ◽

Scalp Eeg

Effective learning and recovery of relevant source brain activity patterns is a major challenge to brain-computer interface using scalp EEG. Various spatial filtering solutions have been developed. Most current methods estimate an instantaneous demixing with the assumption of uncorrelatedness of the source signals. However, recent evidence in neuroscience suggests that multiple brain regions cooperate, especially during motor imagery, a major modality of brain activity for brain-computer interface. In this sense, methods that assume uncorrelatedness of the sources become inaccurate. Therefore, we are promoting a new methodology that considers both volume conduction effect and signal propagation between multiple brain regions. Specifically, we propose a novel discriminative algorithm for joint learning of propagation and spatial pattern with an iterative optimization solution. To validate the new methodology, we conduct experiments involving 16 healthy subjects and perform numerical analysis of the proposed algorithm for EEG classification in motor imagery brain-computer interface. Results from extensive analysis validate the effectiveness of the new methodology with high statistical significance.

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Computational mechanisms for neural representation of words

10.1101/2021.06.27.450093 ◽

2021 ◽

Author(s):

Ze Fu ◽

Xiaosha Wang ◽

Xiaoying Wang ◽

Huichao Yang ◽

Jiahuan Wang ◽

...

Keyword(s):

Neural Network ◽

Brain Activity ◽

Inferior Frontal Gyrus ◽

Activity Patterns ◽

Explanatory Power ◽

Word Meaning ◽

Brain Regions ◽

Neural Representation ◽

Space Representation ◽

Neural Representations

A critical way for humans to acquire, represent and communicate information is through language, yet the underlying computation mechanisms through which language contributes to our word meaning representations are poorly understood. We compared three major types of word computation mechanisms from large language corpus (simple co-occurrence, graph-space relations and neural-network-vector-embedding relations) in terms of the association of words’ brain activity patterns, measured by two functional magnetic resonance imaging (fMRI) experiments. Word relations derived from a graph-space representation, and not neural-network-vector-embedding, had unique explanatory power for the neural activity patterns in brain regions that have been shown to be particularly sensitive to language processes, including the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were robust across different window sizes and graph sizes and were relatively specific to language inputs. These findings highlight the role of cumulative language inputs in organizing word meaning neural representations and provide a mathematical model to explain how different brain regions capture different types of language-derived information.

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Decoding Covert Speech From EEG-A Comprehensive Review

Frontiers in Neuroscience ◽

10.3389/fnins.2021.642251 ◽

2021 ◽

Vol 15 ◽

Author(s):

Jerrin Thomas Panachakel ◽

Angarai Ganesan Ramakrishnan

Keyword(s):

Formal Education ◽

Vocal Communication ◽

Brain Activity ◽

Activity Patterns ◽

Machine Learning Algorithms ◽

Human Beings ◽

Natural Form ◽

Practical Applications ◽

The One

Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.

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Stimulation-specific information is represented as local activity patterns across the brain

10.1101/726414 ◽

2019 ◽

Author(s):

Amirouche Sadoun ◽

Tushar Chauhan ◽

Samir Mameri ◽

Yifan Zhang ◽

Pascal Barone ◽

...

Keyword(s):

Brain Activity ◽

Activity Patterns ◽

Three Dimensional ◽

Correlation Method ◽

Brain Regions ◽

Specific Information ◽

Spatially Correlated ◽

Face Area ◽

Spatial Configurations ◽

The Brain

AbstractModern neuroimaging represents three-dimensional brain activity, which varies across brain regions. It remains unknown whether activity within brain regions is organized in spatial configurations to reflect perceptual and cognitive processes. We developed a rotational cross-correlation method allowing a straightforward analysis of spatial activity patterns for the precise detection of the spatially correlated distributions of brain activity. Using several statistical approaches, we found that the seed patterns in the fusiform face area were robustly correlated to brain regions involved in face-specific representations. These regions differed from the non-specific visual network meaning that activity structure in the brain is locally preserved in stimulation-specific regions. Our findings indicate spatially correlated perceptual representations in cerebral activity and suggest that the 3D coding of the processed information is organized in locally preserved activity patterns. More generally, our results provide the first demonstration that information is represented and transmitted as local spatial configurations of brain activity.

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Enhanced reinstatement of naturalistic event memories due to hippocampal-network-targeted stimulation

10.1101/2020.08.18.256008 ◽

2020 ◽

Author(s):

Melissa Hebscher ◽

James E. Kragel ◽

Thorsten Kahnt ◽

Joel L. Voss

Keyword(s):

Specific Activity ◽

Brain Activity ◽

Activity Patterns ◽

Occipital Cortex ◽

Brain Regions ◽

Network Location ◽

Hippocampal Activity ◽

Spatially Distributed ◽

Posterior Parietal ◽

Hippocampal Network

AbstractEpisodic memory involves the reinstatement of distributed patterns of brain activity present when events were initially experienced. The hippocampus is thought to coordinate reinstatement via its interactions with a network of brain regions, but this hypothesis has not been causally tested in humans. The current study directly tested the involvement of the hippocampal network in reinstatement using network-targeted noninvasive stimulation. We measured reinstatement of multi-voxel patterns of fMRI activity during encoding and retrieval of naturalistic video clips depicting everyday activities. Reinstatement of video-specific activity patterns was robust in posterior-parietal and occipital areas previously implicated in event reinstatement. Theta-burst stimulation targeting the hippocampal network increased videospecific reinstatement of fMRI activity patterns in occipital cortex and improved memory accuracy relative to stimulation of a control out-of-network location. Furthermore, stimulation targeting the hippocampal network influenced the trial-by-trial relationship between hippocampal activity during encoding and later reinstatement in occipital cortex. These findings implicate the hippocampal network in the reinstatement of spatially distributed patterns of event-specific activity, and identify a role for the hippocampus in encoding complex naturalistic events that later undergo cortical reinstatement.

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Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice

10.1101/2020.05.22.111021 ◽

2020 ◽

Cited By ~ 1

Author(s):

Navvab Afrashteh ◽

Samsoon Inayat ◽

Edgar Bermudez Contreras ◽

Artur Luczak ◽

Bruce L. McNaughton ◽

...

Keyword(s):

Spontaneous Activity ◽

Brain Activity ◽

Activity Patterns ◽

Sensory Stimulation ◽

Wide Field ◽

Evoked Responses ◽

Evoked Activity ◽

Cortical Regions ◽

The One ◽

Sensory Evoked

AbstractBrain activity propagates across the cortex in diverse spatiotemporal patterns, both as a response to sensory stimulation and during spontaneous activity. Despite been extensively studied, the relationship between the characteristics of such patterns during spontaneous and evoked activity is not completely understood. To investigate this relationship, we compared visual, auditory, and tactile evoked activity patterns elicited with different stimulus strengths and spontaneous activity motifs in lightly anesthetized and awake mice using mesoscale wide-field voltage-sensitive dye and glutamate imaging respectively. The characteristics of cortical activity that we compared include amplitude, speed, direction, and complexity of propagation trajectories in spontaneous and evoked activity patterns. We found that the complexity of the propagation trajectories of spontaneous activity, quantified as their fractal dimension, is higher than the one from sensory evoked responses. Moreover, the speed and direction of propagation, are modulated by the amplitude during both, spontaneous and evoked activity. Finally, we found that spontaneous activity had similar amplitude and speed when compared to evoked activity elicited with low stimulus strengths. However, this similarity gradually decreased when the strength of stimuli eliciting evoked responses increased. Altogether, these findings are consistent with the fact that even primary sensory areas receive widespread inputs from other cortical regions, and that, during rest, the cortex tends to reactivate traces of complex, multi-sensory experiences that may have occurred in a range of different behavioural contexts.

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Discriminability of numerosity-evoked fMRI activity patterns in human intra-parietal cortex reflects behavioral numerical acuity

10.1101/198002 ◽

2017 ◽

Author(s):

G. Lasne ◽

M. Piazza ◽

S. Dehaene ◽

A. Kleinschmidt ◽

E. Eger

Keyword(s):

Parietal Cortex ◽

Numerical Cognition ◽

Brain Activity ◽

Activity Patterns ◽

Weber Fraction ◽

Numerical Representation ◽

Comparison Task ◽

Numerosity Discrimination ◽

Number Discrimination ◽

Early Visual Cortex

AbstractAreas of the primate intraparietal cortex have been identified as an important substrate of numerical cognition. In human fMRI studies, activity patterns in these and other areas have allowed researchers to read out the numerosity a subject is viewing, but the relation of such decodable information with behavioral numerical proficiency remains unknown.Here, we estimated the precision of behavioral numerosity discrimination (internal Weber fraction) in twelve adult subjects based on psychophysical testing in a delayed numerosity comparison task outside the scanner. FMRI data were then recorded during a similar task, to obtain the accuracy with which the same sample numerosities could be read out from evoked brain activity patterns, as a measure of the precision of the neuronal representation. Sample numerosities were decodable in both early visual and intra-parietal cortex with approximately equal accuracy on average. In parietal cortex, smaller numerosities were better discriminated than larger numerosities of the same ratio, paralleling smaller behavioral Weber fractions for smaller numerosities. Furthermore, in parietal but not early visual cortex, fMRI decoding performance was correlated with behavioral number discrimination acuity across subjects (subjects with a more precise behavioral Weber fraction measured prior to scanning showed greater discriminability of fMRI activity patterns in intraparietal cortex, and more specifically, the right LIP region).These results suggest a crucial role for intra-parietal cortex in supporting a numerical representation which is explicitly read out for numerical decisions and behavior.

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Trust me if you can – neurophysiological insights on the influence of consumer impulsiveness on trustworthiness evaluations in online settings

European Journal of Marketing ◽

10.1108/ejm-12-2016-0870 ◽

2018 ◽

Vol 52 (1/2) ◽

pp. 118-146 ◽

Cited By ~ 13

Author(s):

Marco Hubert ◽

Mirja Hubert ◽

Marc Linzmajer ◽

René Riedl ◽

Peter Kenning

Keyword(s):

Decision Making ◽

Brain Activity ◽

Activity Patterns ◽

Dorsal Striatum ◽

Region Of Interest ◽

Brain Regions ◽

Anterior Cingulate ◽

Neural Activation ◽

Content Type ◽

Brain Functions

Purpose The purpose of this study is to examine how consumer personality trait impulsiveness influences trustworthiness evaluations of online-offers with different trust-assuring and trust-reducing elements by measuring the brain activity of consumers. Shoppers with high degrees of impulsiveness are referred to as hedonic shoppers, and those with low degrees are referred to as prudent consumers. Design/methodology/approach To investigate the differences between neural processes in the brains of hedonic and prudent shoppers during the trustworthiness evaluation of online-offers, the present study used functional magnetic resonance imaging (fMRI) and region-of-interest analysis to correlate neural activity patterns with behavioral measures of the study participants. Findings Drawing upon literature reviews on the neural correlates of both trust in online settings and consumer impulsiveness and using an experimental design that links behavioral and fMRI data, the study shows that consumer impulsiveness can exert a significant influence on the evaluation of online-offers. With regard to brain activation, both groups (hedonic and prudent shoppers) exhibit similar neural activation tendencies, but differences exist in the magnitude of activation patterns in brain regions that are closely related to trust and impulsiveness such as the dorsal striatum, anterior cingulate, the dorsolateral prefrontal cortex and the insula cortex. Research limitations/implications The data provide evidence that consumers within the hedonic group evaluate online-offers differently with regard to their trustworthiness compared to the prudent group, and that these differences in evaluation are rooted in neural activation differences in the shoppers’ brains. Practical implications Marketers need to be made aware of the fact that neurological insights can be used for market segmentation, because consumers’ decision-making processes help explain behavioral outcomes (here, trustworthiness evaluations of online-offers). In addition, consumers can learn from an advanced understanding of their brain functions during decision-making and their relation to personal traits such as impulsiveness. Originality/value Considering the importance of trust in online shopping, as well as the fact that personality traits such as impulsiveness influence the purchase process to a high degree, this study is the first to systematically investigate the interplay of online trustworthiness perceptions and differences in consumer impulsiveness with neuroscientific methods.

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