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![Anatomy and connections related to relational reasoning. Areas of the prefrontal cortex (PFC) frequently identified in reasoning studies include the rostrolateral prefrontal cortex (RLPFC; anterior region of the inferior frontal gyrus, approximately Brodmann area 10, sometimes referred to as frontopolar prefrontal cortex), the dorsolateral prefrontal cortex (DLPFC; anterior region of the middle frontal gyrus, approximately Brodmann areas 9/46), and the ventrolateral prefrontal cortex (VLPFC; posterior region of the inferior frontal gyrus, approximately Brodmann areas 47/45/44). The anterior temporal lobe (ATL; located on the anterior lateral surface of the temporal lobe, approximately Brodmann areas 20, 31, 38) is frequently associated with semantic memory (see [72]) and is important for reasoning about semantic relations [24]. The medial temporal lobe (MTL; located on the medial surface of the temporal lobe including the hippocampus and entorhinal cortex, approximately Brodmann areas 27, 28, 34, 35, 36) is critical for episodic memory [73], and thus is important for relational reasoning about specific events. The ATL and MTL are connected to areas in the VLPFC via the uncinate fasiculus (UF). Regions in the parietal lobe, such as areas around and including the precuneus (PC; approximately Brodmann area 7) and the temporal parietal junction (TPJ; approximately Brodmann area 39) have heavy reciprocal connections to the PFC via the superior longitudinal fasciculus (SLF). These areas are frequently associated with tasks requiring relational reasoning about visuospatial entitites. The anterior cingulate cortex (ACC; located on the medial surface of prefrontal cortex approximately, Brodmann areas 24, 32, 33) is frequently active during relational reasoning and has reciprocal connections to the DLPFC.](profile/Robert-Morrison-9/publication/227708787/figure/fig1/AS:640772512702465@1529783480443/Anatomy-and-connections-related-to-relational-reasoning-Areas-of-the-prefrontal-cortex.png)
Anatomy and connections related to relational reasoning. Areas of the prefrontal cortex (PFC) frequently identified in reasoning studies include the rostrolateral prefrontal cortex (RLPFC; anterior region of the inferior frontal gyrus, approximately Brodmann area 10, sometimes referred to as frontopolar prefrontal cortex), the dorsolateral prefrontal cortex (DLPFC; anterior region of the middle frontal gyrus, approximately Brodmann areas 9/46), and the ventrolateral prefrontal cortex (VLPFC; posterior region of the inferior frontal gyrus, approximately Brodmann areas 47/45/44). The anterior temporal lobe (ATL; located on the anterior lateral surface of the temporal lobe, approximately Brodmann areas 20, 31, 38) is frequently associated with semantic memory (see [72]) and is important for reasoning about semantic relations [24]. The medial temporal lobe (MTL; located on the medial surface of the temporal lobe including the hippocampus and entorhinal cortex, approximately Brodmann areas 27, 28, 34, 35, 36) is critical for episodic memory [73], and thus is important for relational reasoning about specific events. The ATL and MTL are connected to areas in the VLPFC via the uncinate fasiculus (UF). Regions in the parietal lobe, such as areas around and including the precuneus (PC; approximately Brodmann area 7) and the temporal parietal junction (TPJ; approximately Brodmann area 39) have heavy reciprocal connections to the PFC via the superior longitudinal fasciculus (SLF). These areas are frequently associated with tasks requiring relational reasoning about visuospatial entitites. The anterior cingulate cortex (ACC; located on the medial surface of prefrontal cortex approximately, Brodmann areas 24, 32, 33) is frequently active during relational reasoning and has reciprocal connections to the DLPFC.
Source publication
The representation and manipulation of structured relations is central to human reasoning. Recent work in computational modeling and neuroscience has set the stage for developing more detailed neurocomputational models of these abilities. Several key neural findings appear to dovetail with computational constraints derived from a model of analogica...
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Citations
... While our model is not construed to be as specific about the implementation of the R module, we assume that -as a standard deep learning component -it is likely to map onto neocortical structures that interact with the entorhinal cortex and, in particular, regions of the prefrontal-posterior parietal network widely believed to be involved in abstract relational processes (Waltz et al., 1999;Christoff et al., 2001;Knowlton et al., 2012;Summerfield et al., 2020). In particular, the role of the prefrontal cortex in the encoding and active maintenance of abstract information needed for task performance (such as rules and relations) has often been modeled using gated recurrent networks, such as LSTMs (Frank et al., 2001;Braver and Cohen, 2000), and the posterior parietal cortex has long been known to support 'maps' that may provide an important substrate for computing complex relations (Summerfield et al., 2020). ...
Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization – successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid cell code (e.g., in the entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over the grid cell code using determinantal point process (DPP), that we call DPP attention (DPP-A) – a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in the grid cell code, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how the grid cell code in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artificial neural networks.
... Tasks capturing relation integration strongly predict reasoning and problem solving and explain more or less the same variance that is measured by more typical WM tasks (Jastrzebski et al., 2020). Processing relations in the brain was also frequently linked to the theta rhythms (e.g., Brzezicka et al., 2011;Knowlton et al., 2012;Zhang et al., 2015), so theta band seemed the most promising target for relational integration stimulation. ...
Introduction
Recent studies have suggested that transcranial alternating current stimulation (tACS), and especially the theta-frequency tACS, can improve human performance on working memory tasks. However, evidence to date is mixed. Moreover, the two WM tasks applied most frequently, namely the n-back and change-detection tasks, might not constitute canonical measures of WM capacity.
Method
In a relatively large sample of young healthy participants (N = 62), we administered a more canonical WM task that required stimuli recall, as well as we applied two WM tasks tapping into other key WM functions: attention control (the antisaccade task) and relational integration (the graph mapping task). The participants performed these three tasks three times: during the left frontal 5.5-Hz and the left parietal 5.5-Hz tACS session as well as during the sham session, with a random order of sessions. Attentional vigilance and subjective experience were monitored.
Results
For each task administered, we observed significant gains in accuracy neither for the frontal tACS session nor for the parietal tACS session, as compared to the sham session. By contrast, the scores on each task positively inter-correlated across the three sessions.
Discussion
The results suggest that canonical measures of WM capacity are strongly stable in time and hardly affected by theta-frequency tACS. Either the tACS effects observed in the n-back and change detection tasks do not generalize onto other WM tasks, or the tACS method has limited effectiveness with regard to WM, and might require further methodological advancements.
... For example, FAs in the bilateral inferior-middle segments and left superior-middle segment significantly contributed to relational task accuracy. By examining the streamlines passing through these FAT segments, we found that the maximal streamlines reached RMF gyrus, overlapped with rostral lateral prefrontal cortex and dlPFC, which are found to be functionally related to the relational reasoning process in previous literature (Knowlton et al., 2012;Vendetti and Bunge, 2014). ...
White matter of the human brain is influenced by common genetic variations and shaped by neural activity-dependent experiences. Variations in microstructure of cerebral white matter across individuals and even across fiber tracts might underlie differences in cognitive capacity and vulnerabilities to mental disorders. The frontoparietal and cingulo-opercular networks of the brain constitute the central system supporting cognitive functions, and functional connectivity of these networks has been used to distinguish individuals known as “functional fingerprinting”. The frontal aslant tract (FAT) that passes through the two networks has been implicated in executive functions. However, whether FAT can be used as a “structural fingerprint” to distinguish individuals and predict individual’s cognitive function and dysfunction is unknown. Here we investigated the fingerprinting property of FAT microstructural profiles using three independent diffusion MRI datasets with repeated scans on human participants including both females and males. We found that diffusion and geometric profiles of FAT can be used to distinguish individuals with a high accuracy. Next, we demonstrated that fractional anisotropy in different FAT segments predicted distinct cognitive functions, including working memory, inhibitory control, and relational reasoning. Finally, we assessed the contribution of altered FAT microstructural profiles to cognitive dysfunction in unmedicated patients with obsessive-compulsive disorders. We found that the altered microstructure in FAT was associated with the severity of obsessive-compulsive symptoms. Collectively, our findings suggest that the microstructural profiles of FAT can identify individuals with a high accuracy and may serve as an imaging marker for predicting individual’s cognitive capacity and disease severity.
Significance Statement
The frontoparietal network and cingulo-opercular network of the brain constitute a dual-network architecture for human cognitive functions, and functional connectivity of these two networks can be used as a “functional fingerprint” to distinguish individuals. However, the structural underpinnings of these networks subserving individual heterogeneities in their functional connectivity and cognitive ability remain unknown. We show here that the frontal aslant tract (FAT) that passes through the two networks distinguishes individuals with a high accuracy. Further, we demonstrate that the diffusion profiles of FAT predict distinct cognitive functions in healthy subjects, and are associated with the clinical symptoms in patients with obsessive-compulsive disorders. Our findings suggest that the FAT may serve as unique structural fingerprint underlying individual cognitive capability.
... Researchers have hypothesized that the theta rhythm generated in the hippocampus is the primary neural unit of information encoding for memory and spatial processing (Hasselmo & Stern, 2014;Lisman & Jensen, 2013;Sauseng, Peylo, Biel, Friedrich, & Romberg-Taylor, 2019), and recent evidence seems to support this conjecture (Bahramisharif, Jensen, Jacobs, & Lisman, 2018;Qasim, Fried, & Jacobs, 2021). The cortical theta rhythm has also been associated with control over cognitive processing, and especially the coupling of multiple neural processes to the theta rhythm phase (Canolty et al., 2006) is interpreted as the brain mechanism of long-range coordination (Colgin, 2013;Knowlton, Morrison, Hummel, & Holyoak, 2012). Interestingly, a reduction in theta phase coupling has been observed in people with schizophrenia (Dickerson, Wolff, & Bilkey, 2010;Meghdadi et al., 2021). ...
... The differentiable neural computer architecture of Graves et al. [65] achieves the same effect by separating operations performed by a 'controller' from the contents being processed (vectors retrieved from memory). The hybrid symbolic-connectionist architecture of LISA has dedicated individual units for each object and each relational role [66]. These models all successfully implement context-invariance of conceptual contents across different modes of combination. ...
Research on concepts has focused on categorization. Categorization starts with a stimulus. Equally important are episodes that start with a thought. We engage in thinking to draw out new consequences from stored information, or to work out how to act. Each of the concepts out of which thought is constructed provides access to a large body of stored information. Access is not always just a matter of retrieving a stored belief (semantic memory). Often it depends on running a simulation. Simulation allows conceptual thought to draw on information in special-purpose systems, information stored in special-purpose computational dispositions and special-purpose representational structures. While the utility of simulation, prospection or imagination is widely appreciated, the role of concepts in the process is not well understood. This paper turns to cognitive and computational neuroscience for a model of how simulations enable thinkers to reach novel conclusions. Carried over to conceptual thought, the model suggests that concepts are ‘plug & play’ devices. The distinctive power of thought-driven simulation derives from the ability of concepts to plug into two kinds of structure at once: the combinatorial structure of a thought at one end and special-purpose structural representations at the other.
This article is part of the theme issue ‘Concepts in interaction: social engagement and inner experiences’.
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Owing to the 4th Industrial Revolution, the amount of unstructured data, such as voice and video data, is rapidly increasing. Brain-inspired neuromorphic computing is a new computing method that can efficiently and parallelly process rapidly increasing data. Among artificial neural networks that mimic the structure of the brain, the spiking neural network (SNN) is a network that imitates the information-processing method of biological neural networks. Recently, memristors have attracted attention as synaptic devices for neuromorphic computing systems. Among them, the ferroelectric doped-HfO2-based ferroelectric tunnel junction (FTJ) is considered as a strong candidate for synaptic devices due to its advantages, such as complementary metal–oxide–semiconductor device/process compatibility, a simple two-terminal structure, and low power consumption. However, research on the spiking operations of FTJ devices for SNN applications is lacking. In this study, the implementation of long-term depression and potentiation as the spike timing-dependent plasticity (STDP) rule in the FTJ device was successful. Based on the measured data, a CrossSim simulator was used to simulate the classification of handwriting images. With a high accuracy of 95.79% for the Mixed National Institute of Standards and Technology (MNIST) dataset, the simulation results demonstrate that our device is capable of differentiating between handwritten images. This suggests that our FTJ device can be used as a synaptic device for implementing an SNN.
... Also, a study of dynamic brain communication proves that visual attention requires coordination of activity between brain regions [4]. The anterior polar region of the PFC, which forms the largest anterior part of the PFC, plays a role in shifting attention between information taken from the environment and information produced spontaneously in the mind [5]. This network is activated in performing many visualspatial tasks, including detection, cleaning and tracking [6]. ...
Objective: Detection of sensitive signs in many work environments with automated systems (aviation industry, flight safety tower, maritime industry, monitoring in the military industry, etc.) is essential and requires constant visual attention. Therefore, the aim of this study was to investigate the effect of forehead cooling on the accuracy of stable visual attention.
Methods: This interventional study was performed on 34 male students. The sampling method was randomized block design. Subjects were assessed by demographic questionnaire, Snelln chart, Spiel Berger State-Trait Anxiety Inventory (STAI) and physiological and cognitive measurements.
Results: Pre-frontal cortex cooling caused significant changes in sublingual temperature during 4 measurements in the intervention group. There were no significant changes in heart rate, diastolic blood pressure and Saturation of Peripheral Oxygen(%SpO2) between the two groups. Critical Flicker Frequency(CFF) as an indicator of cognitive fatigue showed that cognitive improvement after pre-frontal cortex cooling occurred following a reduction in cognitive fatigue.
Conclusions: Considering the importance of choosing non-invasive methods to improve the operator's cognitive skills while performing cognitive tasks in the field of neuroergonomics, it can be concluded that prefrontal cortex cooling is an effective and safe way to improve some cognitive skills such as visual attention.
... In contrast, the relation's predicate oscillated for the total time of oscillation of all its pairs, binding them into the complete relation (see Figure 1). The relation cycle was associated with the theta rhythm, while the cycles for particular pairs reflected gamma oscillations (Knowlton et al. 2012). With more capacious working memory, LISA was able to process more complex analogies. ...
... 1). The relation cycle was associated with the theta rhythm, while the cycles for particular pairs reflected gamma oscillations (Knowlton et al. 2012). With more capacious working memory, LISA was able to process more complex analogies. ...
Based on recent findings in cognitive neuroscience and psychology as well as computational models of working memory and reasoning, I argue that fluid intelligence (fluid reasoning) can amount to representing in the mind the key relation(s) for the task at hand. Effective representation of relations allows for enormous flexibility of thinking but depends on the validity and robustness of the dynamic patterns of argument–object (role–filler) bindings, which encode relations in the brain. Such a reconceptualization of the fluid intelligence construct allows for the simplification and purification of its models, tests, and potential brain mechanisms.
... The medial SFG is located in the ventromedial prefrontal cortex (VMPFC), which is implicated in the processes of risk and fear (Motzkin et al., 2015), the inhibition of emotional responses (Hänsel and von Känel, 2008), decision-making (Bechara et al., 2000) and self-control, and the cognitive evaluation of morality (Hu and Jiang, 2014). Similarly, Brodmann area 10 (BA 10) is the anterior-most portion of the prefrontal cortex in the human brain (Knowlton et al., 2012). It is involved in strategic processes in memory retrieval and executive functioning (Semendeferi et al., 2001). ...
Background
Social cognition is significantly associated with daily functioning in patients with schizophrenia. Its neural basis remains unknown.
Methods
A systematic literature search was performed. Studies using imaging to measure theory of mind (ToM) in schizophrenia were identified. Imaging data were synthesised using the seed-based d mapping approach. Potential neuropathways were hypothesized based on the identified brain regions activated during ToM tasks.
Results
A total of 25 studies were included in the present study. Compared with healthy people, patients with schizophrenia showed hyperactivations in superior longitudinal fasciculus and hypoactivations in superior frontal gyrus, precuneus and cuneus, and precentral gyrus during ToM tasks. The primary brain regions involved in the potential neuropathways in schizophrenia were the medial temporal gyrus, superior and inferior frontal gyrus, and supplementary motor areas.
Conclusion
Deactivated brain regions in schizophrenia overlapped with the default mode and salience networks. Our findings shed light on how to develop a diagnostic tool for deficits in social cognition using neuroimaging techniques and effective therapeutic interventions to rectify dysfunction in schizophrenia.
... The present behavioral findings also converge with neural evidence that relational reasoning is heavily dependent on circuitry (primarily in prefrontal cortex) that supports aspects of human cognitive capacity, particularly working memory and inhibitory control (Bunge et al., 2005;Cho et al., 2010;Knowlton et al., 2012; for a review see . The RPM, a basic measure of cognitive capacity, is a general predictor of relational reasoning (Gray & Holyoak, 2020;Kubricht et al., 2017;Vendetti et al., 2014). ...
Although models of word meanings based on distributional semantics have proved effective in predicting human judgments of similarity among individual concepts, it is less clear whether or how such models might be extended to account for judgments of similarity among relations between concepts. Here we combine an individual-differences approach with computational modeling to predict human judgments of similarity among word pairs instantiating a variety of abstract semantic relations (e.g., contrast, cause-effect, part-whole). A measure of cognitive capacity predicted individual differences in the ability to discriminate among distinct relations. The human pattern of relational similarity judgments, both at the group level and for individual participants, was best predicted by a model that takes representations of word meanings based on distributional semantics as its inputs and uses them to learn an explicit representation of relations. These findings indicate that although the meanings of abstract semantic relations are not directly coded in the meanings of individual words, important aspects of relational similarity can be derived from distributional semantics. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
