Attention is all they need: Cognitive science and the (techno)political economy of attention in humans and machines
- URL: http://arxiv.org/abs/2405.06478v1
- Date: Fri, 10 May 2024 13:53:46 GMT
- Title: Attention is all they need: Cognitive science and the (techno)political economy of attention in humans and machines
- Authors: Pablo González de la Torre, Marta Pérez-Verdugo, Xabier E. Barandiaran,
- Abstract summary: This paper critically analyses the "attention economy" within the framework of cognitive science and techno-political economics.
We explore how current business models, particularly in digital platform capitalism, harness user engagement by strategically shaping attentional patterns.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: This paper critically analyses the "attention economy" within the framework of cognitive science and techno-political economics, as applied to both human and machine interactions. We explore how current business models, particularly in digital platform capitalism, harness user engagement by strategically shaping attentional patterns. These platforms utilize advanced AI and massive data analytics to enhance user engagement, creating a cycle of attention capture and data extraction. We review contemporary (neuro)cognitive theories of attention and platform engagement design techniques and criticize classical cognitivist and behaviourist theories for their inadequacies in addressing the potential harms of such engagement on user autonomy and wellbeing. 4E approaches to cognitive science, instead, emphasizing the embodied, extended, enactive, and ecological aspects of cognition, offer us an intrinsic normative standpoint and a more integrated understanding of how attentional patterns are actively constituted by adaptive digital environments. By examining the precarious nature of habit formation in digital contexts, we reveal the techno-economic underpinnings that threaten personal autonomy by disaggregating habits away from the individual, into an AI managed collection of behavioural patterns. Our current predicament suggests the necessity of a paradigm shift towards an ecology of attention. This shift aims to foster environments that respect and preserve human cognitive and social capacities, countering the exploitative tendencies of cognitive capitalism.
Related papers
- Productive self/vulnerable body: self-tracking, overworking culture, and conflicted data practices [0.0]
This paper situates self-tracking in an overworking culture in China and draws on semi structured and in depth interviews with overworking individuals.
It builds on the current literature of self-tracking and engages with theories from Science and Technology Studies.
The paper argues that the productivity and value oriented assumptions and workplace culture shape the imaginary of intensive (and sometimes impossible) self-care and health.
arXiv Detail & Related papers (2024-07-24T20:11:26Z) - Towards a Psychological Generalist AI: A Survey of Current Applications
of Large Language Models and Future Prospects [19.46832545633166]
The paper emphasizes the importance of performance validation for large-scale AI models.
We review the cutting-edge advancements and practical implementations of these expansive models in psychology.
These future generalist AI models harbor the potential to substantially curtail labor costs and alleviate social stress.
arXiv Detail & Related papers (2023-12-01T08:35:18Z) - Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components.
We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z) - A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian
Learning and Free Energy Minimization [55.11642177631929]
Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images.
We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
arXiv Detail & Related papers (2023-10-14T23:28:48Z) - Exploring the Cognitive Dynamics of Artificial Intelligence in the
Post-COVID-19 and Learning 3.0 Era: A Case Study of ChatGPT [1.933681537640272]
ChatGPT serves as a catalyst for the transformation of various established domains, including but not limited to education, journalism, security, and ethics.
This paper is to scrutinize the underlying psychological principles of ChatGPT, delve into the factors that captivate user attention, and implicate its ramifications on the future of learning.
arXiv Detail & Related papers (2023-02-03T19:25:13Z) - World Models and Predictive Coding for Cognitive and Developmental
Robotics: Frontiers and Challenges [51.92834011423463]
We focus on the two concepts of world models and predictive coding.
In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
arXiv Detail & Related papers (2023-01-14T06:38:14Z) - Adaptive cognitive fit: Artificial intelligence augmented management of
information facets and representations [62.997667081978825]
Explosive growth in big data technologies and artificial intelligence [AI] applications have led to increasing pervasiveness of information facets.
Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions of information.
We suggest that artificially intelligent technologies that can adapt information representations to overcome cognitive limitations are necessary.
arXiv Detail & Related papers (2022-04-25T02:47:25Z) - The world seems different in a social context: a neural network analysis
of human experimental data [57.729312306803955]
We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals.
An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
arXiv Detail & Related papers (2022-03-03T17:19:12Z) - From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven
Learning in Artificial Intelligence Tasks [56.20123080771364]
Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition.
In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning.
CDL has become increasingly popular, where agents are self-motivated to learn novel knowledge.
arXiv Detail & Related papers (2022-01-20T17:07:03Z) - Cognitive network science for understanding online social cognitions: A
brief review [0.0]
Social media are digitalising massive amounts of users' cognitions in terms of timelines and emotional content.
This work outlines how cognitive network science can open new, quantitative ways for understanding cognition through online media.
arXiv Detail & Related papers (2021-02-25T11:53:28Z)
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.