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The predictive processing account aspires to explain all of cognition using a single, unifying principle. Among the major challenges is to explain how brains are able to infer the structure of their generative models. Recent attempts to further this goal build on existing ideas and techniques from engineering fields, like Bayesian statistics and machine learning. While apparently promising, these approaches make specious assumptions that effectively confuse structure learning with Bayesian parameter estimation in a fixed state space. We illustrate how this leads to a set of theoretical problems for the predictive processing account. These problems highlight a need for developing new formalisms specifically tailored to the theoretical aims of scientific explanation. We lay the groundwork for a possible way forward.
The German word kitsch has been internationally successful. Today, it is commonly used in many modern languages including Serbian and Slovenian (kič)-but does it mean the same? In a pilot study, thirty-six volunteers from Bavaria, Serbia and Slovenia rated two hundred images of kitsch objects in terms of liking, familiarity, determinacy, arousal, perceived threat, and kitschiness. Additionally, art expertise, ambiguity tolerance, and value orientations were assessed. Multilevel regression analysis with crossed random effects was used to explore crosscultural differences: Regardless of cultural background, liking of kitsch objects was positively linked to emotionally arousing items with non-threatening content. Self-transcendence was positively linked to liking, while ambiguity of the parental image was concordantly associated with kitschiness. For participants from Serbia and Slovenia, threatening content was correlated with kitschiness, while participants from Bavaria rated determinate items as kitschier. Results are discussed with regard to literature on kitsch and implications for future research.
Whilst the topic of representations is one of the key topics in philosophy of mind, it has only occasionally been noted that representations and representational features may be gradual. Apart from vague allusions, little has been said on what representational gradation amounts to and why it could be explanatorily useful. The aim of this paper is to provide a novel take on gradation of representational features within the neuroscientific framework of predictive processing. More specifically, we provide a gradual account of two features of structural representations: structural similarity and decoupling. We argue that structural similarity can be analysed in terms of two dimensions: number of preserved relations and state space granularity. Both dimensions can take on different values and hence render structural similarity gradual. We further argue that decoupling is gradual in two ways. First, we show that different brain areas are involved in decoupled cognitive processes to a greater or lesser degree depending on the cause (internal or external) of their activity. Second, and more importantly, we show that the degree of decoupling can be further regulated in some brain areas through precision weighting of prediction error. We lastly argue that gradation of decoupling (via precision weighting) and gradation of structural similarity (via state space granularity) are conducive to behavioural success.
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