Attention is all they need: Cognitive science and the (techno)political economy of attention in humans and machines
<|reference_start|>Attention is all they need: Cognitive science and the (techno)political economy of attention in humans and machines: 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.<|reference_end|>
@article{de la torre2024attention,
    title={Attention is all they need: Cognitive science and the (techno)political
  economy of attention in humans and machines},
    author={Pablo Gonz'alez de la Torre, Marta P'erez-Verdugo, and Xabier E.
  Barandiaran},
    journal={arXiv preprint arXiv:2405.06478},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06478},
    primaryClass={cs.CY cs.AI cs.SI}
}