The Aha! Moment: The Science Behind Creative Insights

Carpenter, Wesley

doi:10.5772/intechopen.84973

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…This list does not exhaustively describe each of the properties of natural understanding, nor does it collectively provide a precise definition. And it is worth noting that some forms of understanding are arrived at by a process of logical analysis rather than sudden insight ( Jung-Beeman et al, 2004 ; Carpenter, 2020 ). But, at least with respect to the cases discussed here, this list is indicative of the range of properties that natural understanding entails.…”

Section: Natural Understandingmentioning

confidence: 99%

Does Machine Understanding Require Consciousness?

Pepperell

2022

Front. Syst. Neurosci.

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This article addresses the question of whether machine understanding requires consciousness. Some researchers in the field of machine understanding have argued that it is not necessary for computers to be conscious as long as they can match or exceed human performance in certain tasks. But despite the remarkable recent success of machine learning systems in areas such as natural language processing and image classification, important questions remain about their limited performance and about whether their cognitive abilities entail genuine understanding or are the product of spurious correlations. Here I draw a distinction between natural, artificial, and machine understanding. I analyse some concrete examples of natural understanding and show that although it shares properties with the artificial understanding implemented in current machine learning systems it also has some essential differences, the main one being that natural understanding in humans entails consciousness. Moreover, evidence from psychology and neurobiology suggests that it is this capacity for consciousness that, in part at least, explains for the superior performance of humans in some cognitive tasks and may also account for the authenticity of semantic processing that seems to be the hallmark of natural understanding. I propose a hypothesis that might help to explain why consciousness is important to understanding. In closing, I suggest that progress toward implementing human-like understanding in machines—machine understanding—may benefit from a naturalistic approach in which natural processes are modelled as closely as possible in mechanical substrates.

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Section: Natural Understandingmentioning

confidence: 99%

Does Machine Understanding Require Consciousness?

Pepperell

2022

Front. Syst. Neurosci.

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“…Insight can also be viewed as a psychological state. Laypeople often describe an insight as a eureka moment [7], a light bulb moment [1], and an aha moment [10]. In cognitive science, a prevailing view is that an insight occurs when people transition from a state of not knowing how to solve a problem to a state of knowing how to solve it [26].…”

Section: Prevailing Perspectives On Data Insightsmentioning

confidence: 99%

What are Data Insights to Professional Visualization Users?

Law

Endert

Stasko

2020

2020 IEEE Visualization Conference (VIS)

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While many visualization researchers have attempted to define data insights, little is known about how visualization users perceive them. We interviewed 23 professional users of end-user visualization platforms (e.g., Tableau and Power BI) about their experiences with data insights. We report on seven characteristics of data insights based on interviewees descriptions. Grounded in these characteristics, we propose practical implications for creating tools that aim to automatically communicate data insights to users.

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“How to meme it”: reverse engineering the creative process of mathematical Internet memes

2022

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Mathematical Internet memes are examples of how the creative thrust characterising the Web 2.0 environment reaches the field of mathematics, translating mathematical statements into a new digital form endowed with an epistemic potential that is capable of initiating a process of mathematical argumentation. The research presented in this paper aims to shed light on the creative process of mathematical memes, contributing to building a body of knowledge on mathematical memes that, prospectively, could enable educators to profit from these objects in their teaching. Theoretically, this is based on a widened concept of creativity that focuses on the connection linking digital culture with mathematics, and on distinguishing and merging three perspectives to disclose the meanings of mathematical memes. Methodologically, the process of mathematical memes’ creation is investigated through a reverse engineering approach on a dataset of about 2100 items collected in a 3-year-long ethnographic observation within online communities. The result is a heuristic action model of the creation process, that is validated by creating two new mathematical Internet memes that are shared online within the observed communities to explore if they retain the mathematical and epistemic characteristics of Web-found ones.

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The Aha! Moment: The Science Behind Creative Insights

Cited by 6 publications

References 34 publications

Does Machine Understanding Require Consciousness?

Does Machine Understanding Require Consciousness?

What are Data Insights to Professional Visualization Users?

“How to meme it”: reverse engineering the creative process of mathematical Internet memes

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