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

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.

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

2015 ◽  
Vol 27 (7) ◽  
pp. 1376-1387 ◽  
Author(s):  
Jessica Bulthé ◽  
Bert De Smedt ◽  
Hans P. Op de Beeck
Keyword(s):  
Activity Pattern ◽  
Numerical Cognition ◽  
Brain Activity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Numerical Magnitude ◽  
Visual Objects ◽  
Arabic Digits ◽  
The One ◽  
Object Cognition

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.

scholarly journals Population Dynamics of Early Visual Cortex during Working Memory

2018 ◽  
Vol 30 (2) ◽  
pp. 219-233 ◽  
Author(s):  
Masih Rahmati ◽  
Golbarg T. Saber ◽  
Clayton E. Curtis
Keyword(s):  
Working Memory ◽  
Population Dynamics ◽  
Visual Cortex ◽  
Parietal Cortex ◽  
Visual Stimulus ◽  
Visual Stimulation ◽  
Brain Activity ◽  
Top Down ◽  
Population Activity ◽  
Early Visual Cortex

Although the content of working memory (WM) can be decoded from the spatial patterns of brain activity in early visual cortex, how populations encode WM representations remains unclear. Here, we address this limitation by using a model-based approach that reconstructs the feature encoded by population activity measured with fMRI. Using this approach, we could successfully reconstruct the locations of memory-guided saccade goals based on the pattern of activity in visual cortex during a memory delay. We could reconstruct the saccade goal even when we dissociated the visual stimulus from the saccade goal using a memory-guided antisaccade procedure. By comparing the spatiotemporal population dynamics, we find that the representations in visual cortex are stable but can also evolve from a representation of a remembered visual stimulus to a prospective goal. Moreover, because the representation of the antisaccade goal cannot be the result of bottom–up visual stimulation, it must be evoked by top–down signals presumably originating from frontal and/or parietal cortex. Indeed, we find that trial-by-trial fluctuations in delay period activity in frontal and parietal cortex correlate with the precision with which our model reconstructed the maintained saccade goal based on the pattern of activity in visual cortex. Therefore, the population dynamics in visual cortex encode WM representations, and these representations can be sculpted by top–down signals from frontal and parietal cortex.

scholarly journals Distinct Frontoparietal Networks Underlying Attentional Effort and Cognitive Control

2017 ◽  
Vol 29 (7) ◽  
pp. 1212-1225 ◽  
Author(s):  
Anne S. Berry ◽  
Martin Sarter ◽  
Cindy Lustig
Keyword(s):  
Functional Connectivity ◽  
Cognitive Control ◽  
Parietal Cortex ◽  
Brain Activity ◽  
Activity Patterns ◽  
Visual Signal ◽  
Subsequent Performance ◽  
Performance Functional ◽  
Superior Parietal Cortex ◽  
Control Regions

We investigated the brain activity patterns associated with stabilizing performance during challenges to attention. Our findings revealed distinct patterns of frontoparietal activity and functional connectivity associated with increased attentional effort versus preserved performance during challenged attention. Participants performed a visual signal detection task with and without presentation of a perceptual-attention challenge (changing background). The challenge condition increased activation in frontoparietal regions including right mid-dorsal/dorsolateral PFC (RPFC), approximating Brodmann's area 9, and superior parietal cortex. We found that greater behavioral impact of the challenge condition was correlated with greater RPFC activation, suggesting that increased engagement of cognitive control regions is not always sufficient to maintain high levels of performance. Functional connectivity between RPFC and ACC increased during the challenge condition and was also associated with performance declines, suggesting that the level of synchronized engagement of these regions reflects individual differences in attentional effort. Pretask, resting-state RPFC–ACC connectivity did not predict subsequent performance, suggesting that RPFC–ACC connectivity increased dynamically during task performance in response to performance decrement and error feedback. In contrast, functional connectivity between RPFC and superior parietal cortex not only during the task but also during pretask rest was associated with preserved performance in the challenge condition. Together, these data suggest that resting frontoparietal connectivity predicts performance on attention tasks that rely on those same cognitive control networks and that, under challenging conditions, other control regions dynamically couple with this network to initiate the engagement of cognitive control.

scholarly journals Numerical representation in the parietal lobes: Abstract or not abstract?

2009 ◽  
Vol 32 (3-4) ◽  
pp. 313-328 ◽  
Author(s):  
Roi Cohen Kadosh ◽  
Vincent Walsh
Keyword(s):  
Human Brain ◽  
Special Reference ◽  
Parietal Cortex ◽  
Cognitive Neuroscience ◽  
Numerical Cognition ◽  
Numerical Representation ◽  
Neuronal Populations ◽  
Parietal Lobes ◽  
Atypical Development

AbstractThe study of neuronal specialisation in different cognitive and perceptual domains is important for our understanding of the human brain, its typical and atypical development, and the evolutionary precursors of cognition. Central to this understanding is the issue of numerical representation, and the question of whether numbers are represented in an abstract fashion. Here we discuss and challenge the claim that numerical representation is abstract. We discuss the principles of cortical organisation with special reference to number and also discuss methodological and theoretical limitations that apply to numerical cognition and also to the field of cognitive neuroscience in general. We argue that numerical representation is primarily non-abstract and is supported by different neuronal populations residing in the parietal cortex.

scholarly journals Neural alignment predicts learning outcomes in students taking an introduction to computer science course

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Meir Meshulam ◽  
Liat Hasenfratz ◽  
Hanna Hillman ◽  
Yun-Fei Liu ◽  
Mai Nguyen ◽  
...  
Keyword(s):  
Computer Science ◽  
Learning Outcomes ◽  
Brain Activity ◽  
Activity Patterns ◽  
Real Life ◽  
Final Exam ◽  
Neural Representations ◽  
Real Life Setting ◽  
The Right ◽  
Mri Study

AbstractDespite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.

scholarly journals Using Brain Activity Patterns to Differentiate Real and Virtual Attended Targets during Augmented Reality Scenarios

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 226
Author(s):  
Lisa-Marie Vortmann ◽  
Leonid Schwenke ◽  
Felix Putze
Keyword(s):  
Augmented Reality ◽  
Eye Tracking ◽  
Brain Activity ◽  
Activity Patterns ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Neural Net ◽  
Dependent Manner ◽  
Tracking Data ◽  
Average Accuracy

Augmented reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target that is either real or virtual. In this study, we investigated whether this target is real or virtual by using machine learning techniques to classify electroencephalographic (EEG) and eye tracking data collected in augmented reality scenarios. A shallow convolutional neural net classified 3 second EEG data windows from 20 participants in a person-dependent manner with an average accuracy above 70% if the testing data and training data came from different trials. This accuracy could be significantly increased to 77% using a multimodal late fusion approach that included the recorded eye tracking data. Person-independent EEG classification was possible above chance level for 6 out of 20 participants. Thus, the reliability of such a brain–computer interface is high enough for it to be treated as a useful input mechanism for augmented reality applications.

scholarly journals Correlation between fetal brain activity patterns and behavioral states: An exploratory fetal magnetoencephalography study

2011 ◽  
Vol 228 (2) ◽  
pp. 200-205 ◽  
Author(s):  
Naim Haddad ◽  
Rathinaswamy B. Govindan ◽  
Srinivasan Vairavan ◽  
Eric Siegel ◽  
Jessica Temple ◽  
...  
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
Fetal Brain ◽  
Behavioral States

Analog Numerical Representations in Rhesus Monkeys: Evidence for Parallel Processing

2004 ◽  
Vol 16 (5) ◽  
pp. 889-901 ◽  
Author(s):  
Andreas Nieder ◽  
Earl K. Miller
Keyword(s):  
Rhesus Monkeys ◽  
Neuronal Response ◽  
Weber Fraction ◽  
Discrimination Performance ◽  
Model Organisms ◽  
Response Latencies ◽  
Numerosity Discrimination ◽  
Numerical Information ◽  
Match To Sample ◽  
Numerical Representations

Monkeys have been introduced as model organisms to study neural correlates of numerical competence, but many of the behavioral characteristics of numerical judgments remain speculative. Thus, we analyzed the behavioral performance of two rhesus monkeys judging the numerosities 1 to 7 during a delayed match-to-sample task. The monkeys showed similar discrimination performance irrespective of the exact physical appearance of the stimuli, confirming that performance was based on numerical information. Performance declined smoothly with larger numerosities, and reached discrimination threshold at numerosity “4.” The nonverbal numerical representations in monkeys were based on analog magnitudes, object tracking process (“subitizing”) could not account for the findings because the continuum of small and large numbers shows a clear Weber fraction signature. The lack of additional scanning eye movements with increasing set sizes, together with indistinguishable neuronal response latencies for neurons with different preferred numerosities, argues for parallel encoding of numerical information. The slight but significant increase in reaction time with increasing numerosities can be explained by task difficulty and consequently time-consuming decision processes. The behavioral results are compared to single-cell recordings from the prefrontal cortex in the same subjects. Models for numerosity discrimination that may account for these results are discussed.

scholarly journals Preparatory Activity in Posterior Temporal Cortex Causally Contributes to Object Detection in Scenes

2015 ◽  
Vol 27 (11) ◽  
pp. 2117-2125 ◽  
Author(s):  
Reshanne R. Reeder ◽  
Francesca Perini ◽  
Marius V. Peelen
Keyword(s):  
Visual Cortex ◽  
Object Detection ◽  
Activity Patterns ◽  
Temporal Cortex ◽  
Relevant Information ◽  
Visual Selective Attention ◽  
Object Categories ◽  
Preparatory Attention ◽  
Preparatory Activity ◽  
Early Visual Cortex

Theories of visual selective attention propose that top–down preparatory attention signals mediate the selection of task-relevant information in cluttered scenes. Neuroimaging and electrophysiology studies have provided correlative evidence for this hypothesis, finding increased activity in target-selective neural populations in visual cortex in the period between a search cue and target onset. In this study, we used online TMS to test whether preparatory neural activity in visual cortex is causally involved in naturalistic object detection. In two experiments, participants detected the presence of object categories (cars, people) in a diverse set of photographs of real-world scenes. TMS was applied over a region in posterior temporal cortex identified by fMRI as carrying category-specific preparatory activity patterns. Results showed that TMS applied over posterior temporal cortex before scene onset (−200 and −100 msec) impaired the detection of object categories in subsequently presented scenes, relative to vertex and early visual cortex stimulation. This effect was specific to category level detection and was related to the type of attentional template participants adopted, with the strongest effects observed in participants adopting category level templates. These results provide evidence for a causal role of preparatory attention in mediating the detection of objects in cluttered daily-life environments.

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