Dynamic semantic networks for exploration of creative thinking

• URL: http://arxiv.org/abs/2501.11090v1
• Date: Sun, 19 Jan 2025 15:59:07 GMT
• Title: Dynamic semantic networks for exploration of creative thinking
• Authors: Danko D. Georgiev, Georgi V. Georgiev,
• Abstract summary: Human creativity originates from brain cortical networks specialized in idea generation, processing, and evaluation. The concurrent verbalization of our inner thoughts during the execution of a design task enables the use of dynamic semantic networks as a tool for investigating, evaluating, and monitoring creative thought.
• Score: 0.0
• License:
• Abstract: Human creativity originates from brain cortical networks that are specialized in idea generation, processing, and evaluation. The concurrent verbalization of our inner thoughts during the execution of a design task enables the use of dynamic semantic networks as a tool for investigating, evaluating, and monitoring creative thought. The primary advantage of using lexical databases such as WordNet for reproducible information-theoretic quantification of convergence or divergence of design ideas in creative problem solving is the simultaneous handling of both words and meanings, which enables interpretation of the constructed dynamic semantic networks in terms of underlying functionally active brain cortical regions involved in concept comprehension and production. In this study, the quantitative dynamics of semantic measures computed with a moving time window is investigated empirically in the DTRS10 dataset with design review conversations and detected divergent thinking is shown to predict success of design ideas. Thus, dynamic semantic networks present an opportunity for real-time computer-assisted detection of critical events during creative problem solving, with the goal of employing this knowledge to artificially augment human creativity.

Related papers

• Discovering Chunks in Neural Embeddings for Interpretability [53.80157905839065]
  We propose leveraging the principle of chunking to interpret artificial neural population activities. We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities. We identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts.
  arXiv  Detail & Related papers  (2025-02-03T20:30:46Z)
• Identifying Sub-networks in Neural Networks via Functionally Similar Representations [41.028797971427124]
  We take a step toward automating the understanding of the network by investigating the existence of distinct sub-networks. Specifically, we explore a novel automated and task-agnostic approach based on the notion of functionally similar representations within neural networks. We show the proposed approach offers meaningful insights into the behavior of neural networks with minimal human and computational cost.
  arXiv  Detail & Related papers  (2024-10-21T20:19:00Z)
• A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts [2.709166684084394]
  This study proposes a framework called GPS, which employs goals, prompts, and strategies to guide designers to systematically work with an LLM tool for improving the creativity of ideas generated during brainstorming. Our framework, tested through a design example and a case study, demonstrates its effectiveness in stimulating creativity and its seamless LLM tool integration into design practices.
  arXiv  Detail & Related papers  (2024-10-10T13:39:27Z)
• An Artificial Intelligence Approach for Interpreting Creative Combinational Designs [1.3948357001626264]
  Combinational creativity is a form of creativity involving the blending of familiar ideas. This study focuses on the computational interpretation, specifically identifying the 'base' and 'additive' components that constitute a creative design.
  arXiv  Detail & Related papers  (2024-05-08T11:47:32Z)
• OC-NMN: Object-centric Compositional Neural Module Network for Generative Visual Analogical Reasoning [49.12350554270196]
  We show how modularity can be leveraged to derive a compositional data augmentation framework inspired by imagination. Our method, denoted Object-centric Compositional Neural Module Network (OC-NMN), decomposes visual generative reasoning tasks into a series of primitives applied to objects without using a domain-specific language.
  arXiv  Detail & Related papers  (2023-10-28T20:12:58Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning [68.63380306259742]
  Despite its promising potential, semantic communications and semantic-aware networking are still at their infancy. We propose a novel reasoning-based implicit semantic-aware communication network architecture that allows multiple tiers of CDC and edge servers to collaborate. We introduce a new multi-layer representation of semantic information taking into consideration both the hierarchical structure of implicit semantics as well as the personalized inference preference of individual users.
  arXiv  Detail & Related papers  (2022-10-28T13:26:08Z)
• The Neural Race Reduction: Dynamics of Abstraction in Gated Networks [12.130628846129973]
  We introduce the Gated Deep Linear Network framework that schematizes how pathways of information flow impact learning dynamics. We derive an exact reduction and, for certain cases, exact solutions to the dynamics of learning. Our work gives rise to general hypotheses relating neural architecture to learning and provides a mathematical approach towards understanding the design of more complex architectures.
  arXiv  Detail & Related papers  (2022-07-21T12:01:03Z)
• Backprop-Free Reinforcement Learning with Active Neural Generative Coding [84.11376568625353]
  We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
  arXiv  Detail & Related papers  (2021-07-10T19:02:27Z)
• Creativity of Deep Learning: Conceptualization and Assessment [1.5738019181349994]
  We use insights from computational creativity to conceptualize and assess current applications of generative deep learning in creative domains. We highlight parallels between current systems and different models of human creativity as well as their shortcomings.
  arXiv  Detail & Related papers  (2020-12-03T21:44:07Z)
• Explaining Creative Artifacts [69.86890599471202]
  We develop an inverse problem formulation to deconstruct the products of and compositional creativity into associative chains. In particular, our formulation is structured as solving a traveling salesman problem through a knowledge graph of associative elements.
  arXiv  Detail & Related papers  (2020-10-14T14:32:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.