scholarly journals Foundations of human problem solving

2019 ◽  
Author(s):  
Noah Zarr ◽  
Joshua W. Brown
Keyword(s):  
Orbitofrontal Cortex ◽  
Neural Model ◽  
Brain Regions ◽  
Neural Modeling ◽  
General Intelligence ◽  
General Sense ◽  
Know How ◽  
Engineering Control ◽  
Lobe Latéral ◽  
Human Problem

AbstractDespite great strides in both machine learning and neuroscience, we do not know how the human brain solves problems in the general sense. We approach this question by drawing on the framework of engineering control theory. We demonstrate a computational neural model with only localist learning laws that is able to find solutions to arbitrary problems. Using a combination of computational neural modeling, human fMRI, and representational similarity analysis, we show here that the roles of a number of brain regions can be reinterpreted as interacting mechanisms of a control theoretic system. The results suggest a new set of functional perspectives on the orbitofrontal cortex, hippocampus, basal ganglia, anterior temporal lobe, lateral prefrontal cortex, and visual cortex, as well as a new path toward artificial general intelligence.

Growth of prefrontal and limbic brain regions and anxiety disorders in children born very preterm

2021 ◽  
pp. 1-12
Author(s):  
Courtney P. Gilchrist ◽  
Deanne K. Thompson ◽  
Bonnie Alexander ◽  
Claire E. Kelly ◽  
Karli Treyvaud ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Anxiety Disorders ◽  
Orbitofrontal Cortex ◽  
Developmental Trajectories ◽  
Brain Regions ◽  
Social Risk ◽  
Intracranial Volume ◽  
Total Brain Volume ◽  
Very Preterm ◽  
The Right

Abstract Background Children born very preterm (VP) display altered growth in corticolimbic structures compared with full-term peers. Given the association between the cortiocolimbic system and anxiety, this study aimed to compare developmental trajectories of corticolimbic regions in VP children with and without anxiety diagnosis at 13 years. Methods MRI data from 124 VP children were used to calculate whole brain and corticolimbic region volumes at term-equivalent age (TEA), 7 and 13 years. The presence of an anxiety disorder was assessed at 13 years using a structured clinical interview. Results VP children who met criteria for an anxiety disorder at 13 years (n = 16) displayed altered trajectories for intracranial volume (ICV, p < 0.0001), total brain volume (TBV, p = 0.029), the right amygdala (p = 0.0009) and left hippocampus (p = 0.029) compared with VP children without anxiety (n = 108), with trends in the right hippocampus (p = 0.062) and left medial orbitofrontal cortex (p = 0.079). Altered trajectories predominantly reflected slower growth in early childhood (0–7 years) for ICV (β = −0.461, p = 0.020), TBV (β = −0.503, p = 0.021), left (β = −0.518, p = 0.020) and right hippocampi (β = −0.469, p = 0.020) and left medial orbitofrontal cortex (β = −0.761, p = 0.020) and did not persist after adjusting for TBV and social risk. Conclusions Region- and time-specific alterations in the development of the corticolimbic system in children born VP may help to explain an increase in anxiety disorders observed in this population.

scholarly journals Dissociable oscillatory networks support gain and loss processing in human orbitofrontal cortex

2021 ◽  
Author(s):  
Ignacio Saez ◽  
Jack Lin ◽  
Edward Chang ◽  
Josef Parvizi ◽  
Robert T. Knight ◽  
...  
Keyword(s):  
Neural Activity ◽  
Orbitofrontal Cortex ◽  
Low Frequency ◽  
Brain Regions ◽  
Reward Processing ◽  
Neural Oscillations ◽  
Beta Band ◽  
Neuronal Ensembles ◽  
Gain And Loss

AbstractHuman neuroimaging and animal studies have linked neural activity in orbitofrontal cortex (OFC) to valuation of positive and negative outcomes. Additional evidence shows that neural oscillations, representing the coordinated activity of neuronal ensembles, support information processing in both animal and human prefrontal regions. However, the role of OFC neural oscillations in reward-processing in humans remains unknown, partly due to the difficulty of recording oscillatory neural activity from deep brain regions. Here, we examined the role of OFC neural oscillations (<30Hz) in reward processing by combining intracranial OFC recordings with a gambling task in which patients made economic decisions under uncertainty. Our results show that power in different oscillatory bands are associated with distinct components of reward evaluation. Specifically, we observed a double dissociation, with a selective theta band oscillation increase in response to monetary gains and a beta band increase in response to losses. These effects were interleaved across OFC in overlapping networks and were accompanied by increases in oscillatory coherence between OFC electrode sites in theta and beta band during gain and loss processing, respectively. These results provide evidence that gain and loss processing in human OFC are supported by distinct low-frequency oscillations in networks, and provide evidence that participating neuronal ensembles are organized functionally through oscillatory coherence, rather than local anatomical segregation.

scholarly journals A Neural Model of Intrinsic and Extrinsic Hippocampal Theta Rhythms: Anatomy, Neurophysiology, and Function

2021 ◽  
Vol 15 ◽  
Author(s):  
Stephen Grossberg
Keyword(s):  
Entorhinal Cortex ◽  
Category Learning ◽  
Neural Model ◽  
Brain Regions ◽  
Beta Oscillations ◽  
Emotional Processes ◽  
Theoretical Synthesis ◽  
Multiple Species ◽  
And Function ◽  
Hippocampal Theta

This article describes a neural model of the anatomy, neurophysiology, and functions of intrinsic and extrinsic theta rhythms in the brains of multiple species. Topics include how theta rhythms were discovered; how theta rhythms organize brain information processing into temporal series of spatial patterns; how distinct theta rhythms occur within area CA1 of the hippocampus and between the septum and area CA3 of the hippocampus; what functions theta rhythms carry out in different brain regions, notably CA1-supported functions like learning, recognition, and memory that involve visual, cognitive, and emotional processes; how spatial navigation, adaptively timed learning, and category learning interact with hippocampal theta rhythms; how parallel cortical streams through the lateral entorhinal cortex (LEC) and the medial entorhinal cortex (MEC) represent the end-points of the What cortical stream for perception and cognition and the Where cortical stream for spatial representation and action; how the neuromodulator acetylcholine interacts with the septo-hippocampal theta rhythm and modulates category learning; what functions are carried out by other brain rhythms, such as gamma and beta oscillations; and how gamma and beta oscillations interact with theta rhythms. Multiple experimental facts about theta rhythms are unified and functionally explained by this theoretical synthesis.

scholarly journals A Goal-Driven Tree-Structured Neural Model for Math Word Problems

2019 ◽  
Author(s):  
Zhipeng Xie ◽  
Shichao Sun
Keyword(s):  
Word Problems ◽  
State Of The Art ◽  
Neural Model ◽  
Neural Models ◽  
Top Down ◽  
Feedforward Networks ◽  
Whole Process ◽  
Expression Tree ◽  
Generate Solution ◽  
Human Problem

Most existing neural models for math word problems exploit Seq2Seq model to generate solution expressions sequentially from left to right, whose results are far from satisfactory due to the lack of goal-driven mechanism commonly seen in human problem solving. This paper proposes a tree-structured neural model to generate expression tree in a goal-driven manner. Given a math word problem, the model first identifies and encodes its goal to achieve, and then the goal gets decomposed into sub-goals combined by an operator in a top-down recursive way. The whole process is repeated until the goal is simple enough to be realized by a known quantity as leaf node. During the process, two-layer gated-feedforward networks are designed to implement each step of goal decomposition, and a recursive neural network is used to encode fulfilled subtrees into subtree embeddings, which provides a better representation of subtrees than the simple goals of subtrees. Experimental results on the dataset Math23K have shown that our tree-structured model outperforms significantly several state-of-the-art models.

scholarly journals Medial Orbitofrontal Cortex Is Associated with Fatigue Sensation

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
Seiki Tajima ◽  
Shigeyuki Yamamoto ◽  
Masaaki Tanaka ◽  
Yosky Kataoka ◽  
Masao Iwase ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Orbitofrontal Cortex ◽  
Brain Region ◽  
Statistical Parametric Mapping ◽  
Brain Regions ◽  
Mapping Method ◽  
Emission Tomography ◽  
Neural Basis ◽  
Positron Emission ◽  
The Brain

Fatigue is an indispensable bioalarm to avoid exhaustive state caused by overwork or stresses. It is necessary to elucidate the neural mechanism of fatigue sensation for managing fatigue properly. We performedH2O  15positron emission tomography scans to indicate neural activations while subjects were performing 35-min fatigue-inducing task trials twice. During the positron emission tomography experiment, subjects performed advanced trail-making tests, touching the target circles in sequence located on the display of a touch-panel screen. In order to identify the brain regions associated with fatigue sensation, correlation analysis was performed using statistical parametric mapping method. The brain region exhibiting a positive correlation in activity with subjective sensation of fatigue, measured immediately after each positron emission tomography scan, was located in medial orbitofrontal cortex (Brodmann's area 10/11). Hence, the medial orbitofrontal cortex is a brain region associated with mental fatigue sensation. Our findings provide a new perspective on the neural basis of fatigue.

Human Medial Orbitofrontal Cortex Is Recruited During Experience of Imagined and Real Rewards

2010 ◽  
Vol 103 (5) ◽  
pp. 2506-2512 ◽  
Author(s):  
Signe Bray ◽  
Shinsuke Shimojo ◽  
John P. O'Doherty
Keyword(s):  
Decision Making ◽  
Orbitofrontal Cortex ◽  
Critical Role ◽  
Brain Regions ◽  
Mental Simulation ◽  
Human Decision ◽  
Tangible Rewards ◽  
Action Choice

Human decision-making frequently relies on mental simulation of future rewards to guide action choice. In this study, we sought to uncover brain regions engaged during reward imagery and to address whether these regions functionally overlap with regions activated by tangible rewards. We found that medial orbitofrontal cortex (mOFC) is engaged both for real and imagined rewards and is preferentially engaged for imagery with rewarding content compared with other nonrewarding imagery. These findings support a critical role for mOFC in the representation of rewarding goal states, even if hypothetical.

The Neural Substrates of In-Group Bias

2008 ◽  
Vol 19 (11) ◽  
pp. 1131-1139 ◽  
Author(s):  
Jay J. Van Bavel ◽  
Dominic J. Packer ◽  
William A. Cunningham
Keyword(s):  
Orbitofrontal Cortex ◽  
Dorsal Striatum ◽  
Mixed Race ◽  
Neural Substrates ◽  
Brain Regions ◽  
Functional Magnetic Resonance ◽  
Intergroup Perception ◽  
Amygdala Activity ◽  
Group Members

Classic minimal-group studies found that people arbitrarily assigned to a novel group quickly display a range of perceptual, affective, and behavioral in-group biases. We randomly assigned participants to a mixed-race team and used functional magnetic resonance imaging to identify brain regions involved in processing novel in-group and out-group members independently of preexisting attitudes, stereotypes, or familiarity. Whereas previous research on intergroup perception found amygdala activity—typically interpreted as negativity—in response to stigmatized social groups, we found greater activity in the amygdala, fusiform gyri, orbitofrontal cortex, and dorsal striatum when participants viewed novel in-group faces than when they viewed novel out-group faces. Moreover, activity in orbitofrontal cortex mediated the in-group bias in self-reported liking for the faces. These in-group biases in neural activity were not moderated by race or by whether participants explicitly attended to team membership or race, a finding suggesting that they may occur automatically. This study helps clarify the role of neural substrates involved in perceptual and affective in-group biases.

scholarly journals Contributions of different prefrontal cortical regions to abstract rule acquisition and reversal in monkeys

2017 ◽  
Author(s):  
Giancarlo La Camera ◽  
Sebastien Bouret ◽  
Barry J. Richmond
Keyword(s):  
Orbitofrontal Cortex ◽  
Brain Regions ◽  
Abstract Concept ◽  
Abstract Concepts ◽  
Prefrontal Cortical ◽  
Rule Acquisition ◽  
Learning Rules ◽  
Association Task ◽  
Abstract Rule ◽  
Cortical Regions

AbstractThe ability to learn and follow abstract rules relies on intact prefrontal regions including the lateral prefrontal cortex (LPFC) and the orbitofrontal cortex (OFC). Here, we investigate the specific roles of these brain regions in learning rules that depend critically on the formation of abstract concepts as opposed to simpler input-output associations. To this aim, we tested monkeys with bilateral removals of either LPFC or OFC on a rapidly learned task requiring the formation of the abstract concept of same vs. different. While monkeys with OFC removals were significantly slower than controls at both acquiring and reversing the concept-based rule, monkeys with LPFC removals were not impaired in acquiring the task, but were significantly slower at rule reversal. Neither group was impaired in the acquisition or reversal of a delayed visual cue-outcome association task without a concept-based rule. These results suggest that OFC is essential for the implementation of a concept-based rule, whereas LPFC seems essential for its modification once established.

scholarly journals The unreasonable effectiveness of deep learning in artificial intelligence

2020 ◽  
Vol 117 (48) ◽  
pp. 30033-30038 ◽  
Author(s):  
Terrence J. Sejnowski
Keyword(s):  
Deep Learning ◽  
Optimization Theory ◽  
Brain Regions ◽  
Network Architectures ◽  
General Intelligence ◽  
Sample Complexity ◽  
Learning Networks ◽  
Digital Devices ◽  
Unreasonable Effectiveness ◽  
Real World Problems

Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages at high levels of performance. Although applications of deep learning networks to real-world problems have become ubiquitous, our understanding of why they are so effective is lacking. These empirical results should not be possible according to sample complexity in statistics and nonconvex optimization theory. However, paradoxes in the training and effectiveness of deep learning networks are being investigated and insights are being found in the geometry of high-dimensional spaces. A mathematical theory of deep learning would illuminate how they function, allow us to assess the strengths and weaknesses of different network architectures, and lead to major improvements. Deep learning has provided natural ways for humans to communicate with digital devices and is foundational for building artificial general intelligence. Deep learning was inspired by the architecture of the cerebral cortex and insights into autonomy and general intelligence may be found in other brain regions that are essential for planning and survival, but major breakthroughs will be needed to achieve these goals.

Magnetic Resonance Imaging-Based Neuromorphometry in Single Patients –

2012 ◽  
Vol 23 (2) ◽  
pp. 81-96 ◽  
Author(s):  
Jürgen Hänggi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Cortical Thickness ◽  
Orbitofrontal Cortex ◽  
Social Behaviour ◽  
Brain Regions ◽  
Resonance Imaging ◽  
Control Groups ◽  
And Control ◽  
Lobe Atrophy

The usefulness of MRI-based neuromorphometry for the investigation of single patients is described. A longitudinal cortical thickness analysis in a patient revealed lateral temporal lobe atrophy, suggesting rather semantic than frontotemporal dementia. A boy with the diagnosis of ADHD and control boys were compared and showed cortical thickness increases in the patient in the orbitofrontal cortex contradicting ADHD and suggesting disturbed social behaviour. The multiple synaesthetes E. S. was compared with control groups and showed hyperconnectivity between auditory and gustatory brain regions suggesting that interval-taste synaesthesia is rooted in anatomical alterations. McLeod syndrome patients were investigated and revealed severe atrophy of the caudate nucleus bilateral across time. Additionally, statistical recommendations for the comparison of MRI data of single patients are provided.

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