Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains

Stocco, Andrea; Sibert, Catherine; Steine-Hanson, Zoe; Koh, Natalie; Laird, John E.; Lebière, Christian; Rosenbloom, Paul S.

doi:10.1016/j.neuroimage.2021.118035

Cited by 28 publications

(81 citation statements)

References 58 publications

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“…Common models of human cognition have been proposed as candidates for the large-scale brain functional architecture. These models can be used for reproducing human-like artificial intelligence for research and clinical purposes ( 97 ). By identifying the cognitive primary domains that subserve the functionality of higher cognition, it is possible to refine neuroarchitecture models and establish a framework to further understand cognitive impairment in several psychopathologies.…”

Section: Discussionmentioning

confidence: 99%

An fMRI Study Using a Combined Task of Interval Discrimination and Oddball Could Reveal Common Brain Circuits of Cognitive Change

et al. 2021

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Recent functional neuroimaging studies suggest that the brain networks responsible for time processing are involved during other cognitive processes, leading to a hypothesis that time-related processing is needed to perform a range of tasks across various cognitive functions. To examine this hypothesis, we analyze whether, in healthy subjects, the brain structures activated or deactivated during performance of timing and oddball-detection type tasks coincide. To this end, we conducted two independent signed differential mapping (SDM) meta-analyses of functional magnetic resonance imaging (fMRI) studies assessing the cerebral generators of the responses elicited by tasks based on timing and oddball-detection paradigms. Finally, we undertook a multimodal meta-analysis to detect brain regions common to the findings of the two previous meta-analyses. We found that healthy subjects showed significant activation in cortical areas related to timing and salience networks. The patterns of activation and deactivation corresponding to each task type partially coincided. We hypothesize that there exists a time and change-detection network that serves as a common underlying resource used in a broad range of cognitive processes.

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Section: Discussionmentioning

confidence: 99%

An fMRI Study Using a Combined Task of Interval Discrimination and Oddball Could Reveal Common Brain Circuits of Cognitive Change

et al. 2021

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“…Indeed, recently, we have been looking precisely at whether this common organization of pathways can be identified in the human brain, using neuroimaging data collected in the Human Connectome Project. The results show that such a high-level, common organization can be identified (Stocco et al, 2021) and that it closely reflects the main tenet that most cognitive architectures have converged upon (Laird et al, 2017). Such a common organization can be interpreted as the source of invariant cognitive effects across individuals.…”

Section: Qualitatively Invariant Effects As Brain Constraintsmentioning

confidence: 55%

Qualitative Invariant Effects Arise from Neural Constraints: Common Architecture and Sources of Individual Differences

Stocco

2021

Journal of Cognition

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The distinction between qualitative and quantitative effects can be meaningfully related to the distinction between "architecture" and "model" in computational cognitive science. In turn, this distinction can be related to differences between the invariant systems-level organization of the human brain and individual differences in structural and functional activity. Taken together, this presents an iterative new way to answer Newell's "20 Questions" problem and to systematize psychological effects as belonging to either architecture or the individual variations within it. Although some limits to this approach exist (for example, Individual differences in strategy might affect whether an effect is qualitative or quantitative), the approach might also shed light on how to account for special populations (neurological or psychological patients) within the same framework.

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Section: Introductionmentioning

confidence: 87%

“…Resting-state activity is also reliable within participants (Braun et al, 2012). It has been speculated that it reflects the "architecture" of the brain (Stocco et al, 2021); small variations within this architecture are characteristic to every individual, to the point that it can be used to "fingerprint" individual participants (Finn et al, 2015;Waller et al, 2017). Consistent with this finding, individual differences in the resting-state functional connectivity between different regions have been shown to be predictive of performance differences in a variety of cognitive BRAIN CONNECTIVITY PREDICTS IDIOGRAPHIC FORGETTING RATE 5 abilities, including working memory (Avery et al, 2020), executive functions (Reineberg et al, 2015;Reineberg & Banich, 2016), motor learning (McGregor & Gribble, 2017), perceptual discrimination (Baldassarre et al, 2012), and most importantly, recognition memory (Persson et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Distributed Patterns of Functional Connectivity Underlie Individual Differences in Long-Term Memory Forgetting

Prat

Sense

et al. 2021

Preprint

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Despite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade and, without such a measure, it is hard to identify the corresponding neural correlates. This study addresses this problem using a combination of individual differences, model-based inferences, and resting-state functional connectivity. The individual-specific values of rate of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to data from an adaptive fact learning task. Individual rates of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine-learning techniques to identify the most predictive and generalizable features. Consistent with the existing literature, our results identified a sparse, distributed network of cortical and subcortical regions that underlies forgetting in LTM. Cross-validation showed that individual rates of forgetting were predicted with high accuracy (r = .96) from this connectivity pattern alone. These results open up new opportunities for the study of individual differences in LTM function and dysfunction.

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Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains

Cited by 28 publications

References 58 publications

An fMRI Study Using a Combined Task of Interval Discrimination and Oddball Could Reveal Common Brain Circuits of Cognitive Change

An fMRI Study Using a Combined Task of Interval Discrimination and Oddball Could Reveal Common Brain Circuits of Cognitive Change

Qualitative Invariant Effects Arise from Neural Constraints: Common Architecture and Sources of Individual Differences

Distributed Patterns of Functional Connectivity Underlie Individual Differences in Long-Term Memory Forgetting

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