Peter Clutton

Phenomenal intentionality theories have recently enjoyed significant attention. According to these theories, the intentionality of a mental representation (what it is about) crucially depends on its phenomenal features. We present a new puzzle for these theories, involving a phenomenon called ‘intentional identity’, or ‘co-intentionality’. Co-intentionality is a ubiquitous intentional phenomenon that involves tracking things even when there is no concrete thing being tracked. We suggest that phenomenal intentionality theories need to either develop new uniquely phenomenal resources for handling the puzzle, or restrict their explanatory ambitions.

Conspiracy theories play a troubling role in political discourse. Online forums provide a valuable window into everyday conspiracy theorizing, and can give a clue to the motivations and interests of those who post in such forums. Yet this online activity can be difficult to quantify and study. We describe a unique approach to studying online conspiracy theorists which used non-negative matrix factorization to create a topic model of authors' contributions to the main conspiracy forum on Reddit. This subreddit provides a large corpus of comments which spans many years and numerous authors. We show that within the forum, there are multiple sub-populations distinguishable by their loadings on different topics in the model. Further, we argue, these differences are interpretable as differences in background beliefs and motivations. The diversity of the distinct subgroups places constraints on theories of what generates conspiracy theorizing. We argue that traditional “monological” believers are only the tip of an iceberg of commenters. Neither simple irrationality nor common preoccupations can account for the observed diversity. Instead, we suggest, those who endorse conspiracies seem to be primarily brought together by epistemological concerns, and that these central concerns link an otherwise heterogenous group of individuals.

It has recently been suggested that delusions be conceived of as symptoms on the harmful dysfunction account of disorder: delusions sometimes arise from dysfunction, but can also arise through normal cognition. Much attention has thus been payed to the question of how we can determine whether a delusion arises from dysfunction as opposed to normal cognition. In this paper, we consider another question, one that remains under-explored: which delusions warrant treatment? On the harmful dysfunction account, this question dissociates from the question about dysfunction—there are a broad range of “treatable conditions” beyond mere harmful dysfunctions. As such, many conditions that arise from normal cognition are also eligible for medical intervention. We argue that some delusions that arise from normal cognition may well fall under the banner of treatable conditions. We examine the practical and ethical questions surrounding such treatment, including the issue of coercive and deceptive treatment options.

YouTube has been implicated in the transformation of users into extremists and conspiracy theorists. The alleged mechanism for this radicalizing process is YouTube’s recommender system, which is optimized to amplify and promote clips that users are likely to watch through to the end. YouTube optimizes for watch-through for economic reasons: people who watch a video through to the end are likely to then watch the next recommended video as well, which means that more advertisements can be served to them. This is a seemingly innocuous design choice, but it has a troubling side-effect. Critics of YouTube have alleged that the recommender system tends to recommend extremist content and conspiracy theories, as such videos are especially likely to capture and keep users’ attention. To date, the problem of radicalization via the YouTube recommender system has been a matter of speculation. The current study represents the first systematic, pre-registered attempt to establish whether and to what extent the recommender system tends to promote such content. We begin by contextualizing our study in the framework of technological seduction. Next, we explain our methodology. After that, we present our results, which are consistent with the radicalization hypothesis. Finally, we discuss our findings, as well as directions for future research and recommendations for users, industry, and policy-makers.