Applications of Conceptual Spaces

Zenker, Frank; Gärdenfors, Peter

doi:10.1007/978-3-319-15021-5

Cited by 29 publications

(3 citation statements)

References 201 publications

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“…Specifically, we observed that repetition suppression of signals in the hippocampus scales with semantic distance. This representation aligns with the defining features of a cognitive map: relationships can be quantified in terms of a metric; this metric is symmetric and it adheres to geometric norms ( Gärdenfors 2004 ; Gärdenfors and Zenker 2015 ; Bellmund et al. 2018 ).…”

Section: Discussionmentioning

confidence: 79%

Parallel cognitive maps for multiple knowledge structures in the hippocampal formation

Zheng,

Hebart,

Grill

et al. 2024

Cerebral Cortex

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The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information. However, objects can often be characterized by different types of relations simultaneously. How does the hippocampal formation handle the embedding of stimuli in multiple relational structures that differ vastly in their mode and timescale of acquisition? Does the hippocampal formation integrate different stimulus dimensions into one conjunctive map or is each dimension represented in a parallel map? Here, we reanalyzed human functional magnetic resonance imaging data from Garvert et al. (2017) that had previously revealed a map in the hippocampal formation coding for a newly learnt transition structure. Using functional magnetic resonance imaging adaptation analysis, we found that the degree of representational similarity in the bilateral hippocampus also decreased as a function of the semantic distance between presented objects. Importantly, while both map-like structures localized to the hippocampal formation, the semantic map was located in more posterior regions of the hippocampal formation than the transition structure and thus anatomically distinct. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps that reflect the embedding of objects in diverse relational structures.

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Section: Discussionmentioning

confidence: 79%

Parallel cognitive maps for multiple knowledge structures in the hippocampal formation

Zheng,

Hebart,

Grill

et al. 2024

Cerebral Cortex

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“…Experiments are conducted to test the cognitive abilities of the proposed model. Nina pro dataset is chosen for experimental analysis as a sample of prosthetic arm functionalities [38], [39]. This dataset has EMG signals corresponding to 52 configurations performed by 67 intact subjects and 11 amputated subjects as shown in Fig.…”

Section: Experimental Analysismentioning

confidence: 99%

Modeling Cognitive Functionalities of Prosthetic Arm using Conceptual Spaces

Ishwarya¹,

Cherukuri²

2019

JAMRIS

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Conceptual space framework is used for representing knowledge in cognitive systems. In this paper, we have adapted conceptual space framework for prosthetic arm considering its cognitive abilities such as receiving signals, recognizing and decoding the signal and responding with the corresponding action in order to develop a conceptual space of the prosthetic arm. Cognitive functionalities such as learning, memorizing and distinguishing configurations of prosthetic arm are achieved via its conceptual space. To our knowledge, this work is the first attempt to adapt the conceptual spaces to model cognitive functionalities of prosthetic arm. Adding to this, we have made use of different notion of concept that reflects the topological structure in concepts. To model the actions of the prosthetic arm functionalities, we have made use of force patterns to represent action. Similarly, to model the distinguishing ability, we make use of the relationship between the attributes conveyed by adapted different notion of concept.

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“…The overall conceptual space is structured into different cognitive domains (such as color and shape), which are represented by low-dimensional psychological similarity spaces with cognitively meaningful dimensions. Conceptual spaces have seen a wide variety of applications in artificial intelligence, linguistics, psychology, and philosophy [31,65]. Typically, the structure of a conceptual space is obtained based on dissimilarity ratings from psychological experiments, which are then translated into a spatial representation through multidimensional scaling [13].…”

Section: Introductionmentioning

confidence: 99%

Grounding Psychological Shape Space in Convolutional Neural Networks

Bechberger¹,

Kühnberger²

2021

Preprint

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Shape information is crucial for human perception and cognition, and should therefore also play a role in cognitive AI systems. We employ the interdisciplinary framework of conceptual spaces, which proposes a geometric representation of conceptual knowledge through low-dimensional interpretable similarity spaces. These similarity spaces are often based on psychological dissimilarity ratings for a small set of stimuli, which are then transformed into a spatial representation by a technique called multidimensional scaling. Unfortunately, this approach is incapable of generalizing to novel stimuli. In this paper, we use convolutional neural networks to learn a generalizable mapping between perceptual inputs (pixels of grayscale line drawings) and a recently proposed psychological similarity space for the shape domain. We investigate different network architectures (classification network vs. autoencoder) and different training regimes (transfer learning vs. multi-task learning). Our results indicate that a classification-based multi-task learning scenario yields the best results, but that its performance is relatively sensitive to the dimensionality of the similarity space.

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Applications of Conceptual Spaces

Cited by 29 publications

References 201 publications

Parallel cognitive maps for multiple knowledge structures in the hippocampal formation

Parallel cognitive maps for multiple knowledge structures in the hippocampal formation

Modeling Cognitive Functionalities of Prosthetic Arm using Conceptual Spaces

Grounding Psychological Shape Space in Convolutional Neural Networks

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