Mental models use common neural spatial structure for spatial and abstract content

Alfred, Katherine L.; Connolly, Andrew C.; Cetron, Joshua S.; Kraemer, David J. M.

doi:10.1038/s42003-019-0740-8

Cited by 18 publications

(22 citation statements)

References 40 publications

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“…Parietal activation is typically prominent in transitive reasoning tasks, which require integration of comparative relations (e.g., Tom is taller than Bill, Harry is taller than Tom) to infer a further relation (Harry is taller than Tom). Furthermore, the pattern of parietal involvement (in conjunction with anterior frontal activation) is similar for transitive inferences based on physical dimensions such as height, more abstract dimensions such as monetary expensiveness, and even nonsensical dimensions (introduced as orderings) such as "vilchiness" (Alfred, Connolly, Cetron, & Kraemer, 2020; for a review of parietal involvement in reasoning, see Wendelken, 2015).…”

Section: Relation Representation: Parietal Cortexmentioning

confidence: 97%

Relational Integration in the Human Brain: A Review and Synthesis

Holyoak

Monti

2021

Journal of Cognitive Neuroscience

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Relational integration is required when multiple explicit representations of relations between entities must be jointly considered to make inferences. We provide an overview of the neural substrate of relational integration in humans and the processes that support it, focusing on work on analogical and deductive reasoning. In addition to neural evidence, we consider behavioral and computational work that has informed neural investigations of the representations of individual relations and of relational integration. In very general terms, evidence from neuroimaging, neuropsychological, and neuromodulatory studies points to a small set of regions (generally left lateralized) that appear to constitute key substrates for component processes of relational integration. These include posterior parietal cortex, implicated in the representation of first-order relations (e.g., A: B); rostrolateral pFC, apparently central in integrating first-order relations so as to generate and/or evaluate higher-order relations (e.g., A: B:: C: D); dorsolateral pFC, involved in maintaining relations in working memory; and ventrolateral pFC, implicated in interference control (e.g., inhibiting salient information that competes with relevant relations). Recent work has begun to link computational models of relational representation and reasoning with patterns of neural activity within these brain areas.

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Section: Relation Representation: Parietal Cortexmentioning

confidence: 97%

Relational Integration in the Human Brain: A Review and Synthesis

Holyoak

Monti

2021

Journal of Cognitive Neuroscience

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“…For instance, a complex relational structure (ANBNCNDNE; where 'N' indicates 'bigger than') may be constructed from the experience of single comparison pairs (ANB; CND; BNC). Although there is evidence that both the hippocampal formation and the parietal cortex play a crucial role in this process [91][92][93][94], their relative functions are still not clearly understood. Different frames of reference may emerge in the same conceptual space.…”

Section: Unidimensionality and The Parietal Cortexmentioning

confidence: 99%

Knowledge Across Reference Frames: Cognitive Maps and Image Spaces

Bottini

Doeller

2020

Trends in Cognitive Sciences

102

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In human and non-human animals, conceptual knowledge is partially organized according to low-dimensional geometries that rely on brain structures and computations involved in spatial representations. Recently, two separate lines of research have investigated cognitive maps, that are associated with the hippocampal formation and are similar to world-centered representations of the environment, and image spaces, that are associated with the parietal cortex and are similar to self-centered spatial relationships. We review evidence supporting cognitive maps and image spaces, and we propose a hippocampal-parietal network that can account for the organization and retrieval of knowledge across multiple reference frames. We also suggest that cognitive maps and image spaces may be two manifestations of a more general propensity of the mind to create low-dimensional internal models. Organizing Knowledge in Low-Dimensional SpaceEvery second, our brains process an amazing amount of information, perceive a dynamic and complex sensory environment, and spontaneously generate countless thoughts. Making sense of this vast amount of data must rely on some structure and organizational principles [1,2]. Determining exactly what these organizational principles are has proved to be a formidable challenge [3][4][5]. However, convergent evidence from neural, cognitive, and information sciences is pointing toward a fascinating hypothesis: that the human brain may organize knowledge into low-dimensional spaces (see Glossary) that we can easily navigate, explore, and manipulate as we, for example, navigate a familiar environment, explore a picture in a frame, or manipulate an object in our hands [1,2,6,7]. In other words, the neural machinery that evolved to map objects and structure events in the physical world may have been recycled to map and structure knowledge within our minds [6].Although this idea, broadly taken, has a venerable tradition [8][9][10][11], research in cognitive science and neuroscience has only recently provided solid empirical ground for this view. We review here evidence suggesting that the neurocognitive structures and algorithms that are recruited to represent and navigate space are also recruited to represent and navigate (nonspatial) conceptual knowledge. In particular, we focus on, and contrast, world-centered cognitive maps (that are usually associated with the hippocampal formation) and self-centered image spaces (usually associated with the parietal cortex). We then attempt to integrate cognitive maps and image spaces with the mechanisms of a hippocampal-parietal network, inspired by current models of spatial navigation and based on complementary reference frames (allocentric and egocentric). Finally, we discuss the role of low-dimensional conceptual spaces in cognition. World-Centered Cognitive Maps and the Hippocampal FormationA long history of neuropsychological studies with amnesic patients [12,13] and more recent neuroimaging experiments [14] have shown that the hippocampal formation (i.e., the hippoca...

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“…Cognitive scientists have long built theories about how humans learn concepts and reason abstractly but much less is known about their neural representation 3,4 . One view is that conceptual knowledge relies on neural ensembles that code for relations among stimuli but are invariant to their physical properties [5][6][7][8][9][10][11][12][13] . Recent evidence hints that when sets of stimuli share relational structure across contexts, they are embedded on parallel low-dimensional neural manifolds, so that a linear decoder learned in one context can be readily repurposed for another [14][15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

Neural state space alignment for magnitude generalization in humans and recurrent networks

Sheahan

Luyckx

Nelli

et al. 2021

Neuron

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A prerequisite for intelligent behaviour is to understand how stimuli are related and to generalise this knowledge across contexts. Generalisation can be challenging when relational patterns are shared across contexts but exist on different physical scales. Here, we studied neural representations in humans and recurrent neural networks performing a magnitude comparison task, for which it was advantageous to generalise concepts of "more" or "less" between contexts. Using multivariate analysis of human brain signals and of neural network hidden unit activity, we observed that both systems developed parallel neural "number lines" for each context. In both model systems, these number state spaces were aligned in a way that explicitly facilitated generalisation of relational concepts (more and less). These findings suggest a previously overlooked role for neural normalisation in supporting transfer of a simple form of abstract relational knowledge (magnitude) in humans and machine learning systems..

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Mental models use common neural spatial structure for spatial and abstract content

Cited by 18 publications

References 40 publications

Relational Integration in the Human Brain: A Review and Synthesis

Relational Integration in the Human Brain: A Review and Synthesis

Knowledge Across Reference Frames: Cognitive Maps and Image Spaces

Neural state space alignment for magnitude generalization in humans and recurrent networks

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