Version 1
: Received: 18 September 2022 / Approved: 19 September 2022 / Online: 19 September 2022 (04:49:39 CEST)
How to cite:
Ciria, A.; Albarracin, M.; Miller, M.; Lara, B. Social Media Platforms: Trading with Prediction Error Minimization for Your Attention. Preprints2022, 2022090259. https://doi.org/10.20944/preprints202209.0259.v1
Ciria, A.; Albarracin, M.; Miller, M.; Lara, B. Social Media Platforms: Trading with Prediction Error Minimization for Your Attention. Preprints 2022, 2022090259. https://doi.org/10.20944/preprints202209.0259.v1
Ciria, A.; Albarracin, M.; Miller, M.; Lara, B. Social Media Platforms: Trading with Prediction Error Minimization for Your Attention. Preprints2022, 2022090259. https://doi.org/10.20944/preprints202209.0259.v1
APA Style
Ciria, A., Albarracin, M., Miller, M., & Lara, B. (2022). Social Media Platforms: Trading with Prediction Error Minimization for Your Attention. Preprints. https://doi.org/10.20944/preprints202209.0259.v1
Chicago/Turabian Style
Ciria, A., Mark Miller and Bruno Lara. 2022 "Social Media Platforms: Trading with Prediction Error Minimization for Your Attention" Preprints. https://doi.org/10.20944/preprints202209.0259.v1
Abstract
Culture exploits the acquisition of meaningful content by crafting regimes of shared attention, determining what is relevant, valuable, and salient. Culture changes the field of relevant social affordances worthy of being acted upon in a context-sensitive manner. When relevant affordances are highly weighted, their attentional capture and their salience increase the probability of them being enacted due to the associated expectation for minimizing prediction error. This process is known as active inference. In the digital era, individuals need to infer the action-related attributes of digital cues, here characterized as digital affordances. The digital affordances of digital social platforms are of particular interest here. Digital social affordances are defined as online possibilities of social interactions. By their own nature, these are salient because they are related to social interactions and relevant social cues. However, the problem of digital social platforms is that they are not equivalent to situated social interactions because their structure is built, mediated, and defined by third-parties with diverse interests. The third-parties behind the digital social platforms are using the same mechanism exploited by culture to manipulate the shared patterns of attention. Moreover, digital social platforms are deliberately designed to be hyper-stimulating, making digital social affordances highly rewarding and increasingly salient. This appropriation, for economic purposes, is an issue of great importance, especially as the COVID-19 pandemic brought deep global changes, pushing societies to an online digital way of life. Here, we examined different types of digital social affordances under an active inference view, placing them into two categories, those for self-identity formation, and those for belief-updating. This paper aims to analyze digital social affordances in light of the prediction error dynamics they might elicit to their users. Although each of the analyzed digital social affordances allows different epistemic and instrumental digital actions, they all share the characteristic of having an "easy" and a fast expected rate of error reduction. Here, we aim to provide a new hypothesis about how the design behind digital social affordances is built on our natural attractiveness to minimize prediction error and the resulting positive embodied feelings when doing so. Finally, it is suggested that because digital social affordances are becoming highly weighted in the field of affordances, this might be putting at risk our context-sensitive grip on a rich, dynamic and varied field of relevant affordances.
Keywords
active inference; digital affordances; patterns of attention; prediction error minimization; prediction error dynamics
Subject
Social Sciences, Psychology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
