Sofiia Rappe

Recent literature often presents memory as ultimately dealing with the future-helping the organism to anticipate events and increase its adaptive success. Yet, the distinct contribution of episodic (as opposed to semantic) memory to future-oriented simulations remains unclear. We claim that episodic memory yields adaptive success because of its crucial role in singular counterfactual causal reasoning, which thus far has been mostly ignored in the literature. Our paper presents a causal inference model based on the predictive processing framework and the minimal trace account of episodic memory. According to our model, evaluating the cause of an event involves (i) generating an episodic memory related to the said potential cause, (ii) constructing a counterfactual scenario through inhibition of the relevant part of the past episode, and (iii) temporal evolution followed by alternative model evaluation.

Over the past several years, there has been a shift in the researchers' thinking about the functional role of episodic memory. Rather than focusing on how memory represents the past, recent literature often presents memory as ultimately dealing with the future-helping the organism to anticipate events and increase its adaptive success. However, the distinct contribution of episodic (as opposed to semantic) memory to future-oriented simulations remains unclear. We claim that episodic memory yields adaptive success because of its crucial role in singular counterfactual causal reasoning, which thus far has been mostly ignored in causal reasoning literature. Our paper presents a causal inference model based on the predictive processing framework and the minimal trace account of episodic memory. According to our model, evaluating the cause of an event involves (i) generating an episodic memory related to the said potential cause, (ii) constructing a counterfactual scenario through inhibition of the relevant part of the past episode, and (iii) temporal evolution followed by alternative model evaluation. We further discuss our approach in the developmental context.

In a Bayesian brain, every perceptual decision will take into account internal priors as well as new incoming evidence. A reality monitoring system—eventually providing the agent us with a subjective sense of reality avoids us them being confused about whether our experience is perceptual or imagined. Yet not all confusions we experience mean that we wonder wonder whether we may be imagining: some confused experiences feel clearly perceptual but still feel not right. What happens in such confused perceptions, and can the Bayesian brain explain this kind of confusion? In this paper, we offer a characterisation of perceptual confusion and argue that it requires our subjective sense of reality to be a composite of several subjective markers, including a categorical one that can clearly identify an experience as perceptual and connecting us to reality. Our composite account makes new predictions regarding the robustness, the non-linear development, and the possible breakdowns of the sense of reality in perception.

The general principles of perceptuo-motor processing and memory give rise to the Now-or-Never bottleneck constraint imposed on the organization of the language processing system. In particular, the Now-or-Never bottleneck demands an appropriate structure of linguistic input and rapid incorporation of both linguistic and multisensory contextual information in a progressive, integrative manner. I argue that the emerging predictive processing framework is well suited for the task of providing a comprehensive account of language processing under the Now-or-Never constraint. Moreover, this framework presents a stronger alternative to the Chunk-and-Pass account proposed by Christiansen and Chater (2016), as it better accommodates the available evidence concerning the role of context (in both the narrow and wider senses) in language comprehension at various levels of linguistic representation. Furthermore, the predictive processing approach allows for treating language as a special case of...

Predictive processing framework (PP) has found wide applications in cognitive science and philosophy. It is an attractive candidate for a unified account of the mind in which perception, action, and cognition fit together in a single model. However, PP cannot claim this role if it fails to accommodate an essential part of cognition—conceptual thought. Recently, Williams (Synthese 1–27, 2018) argued that PP struggles to address at least two of thought’s core properties—generality and rich compositionality. In this paper, I show that neither necessarily presents a problem for PP. In particular, I argue that because we do not have access to cognitive processes but only to their conscious manifestations, compositionality may be a manifest property of thought, rather than a feature of the thinking process, and result from the interplay of thinking and language. Pace Williams, both of these capacities, constituting parts of a complex and multifarious cognitive system, may be fully based o...

Over the last decade or so, several researchers have considered the predictive processing framework (PPF) to be a useful perspective from which to shed some much-needed light on the mechanisms behind psychosis. Most approaches to psychosis within PPF come down to the idea of the “atypical” brain generating inaccurate hypotheses that the “typical” brain does not generate, either due to a systematic topdown processing bias or more general precision weighting breakdown. Strong at explaining common individual symptoms of psychosis, such approaches face some issues when we look at a more general clinical picture. In this paper, we propose an update on the current accounts of psychosis based on the realization that a neurotypical brain constantly generates non-actual, de-coupled, counterfactual hypotheses as part of healthy cognition. We suggest that what is going on in psychosis, at least in some cases, is not so much a generation of erroneous hypotheses, but rather an inability to correctly use the counterfactual ones. This updated view casts “accurate” cognition as more fragile and delicate, but also closes the gap between psychosis and typical cognition.

Predictive processing framework (PP) has found wide applications in cognitive science
and philosophy. It is an attractive candidate for a unified account of the mind in which
perception, action, and cognition fit together in a single model. However, PP cannot
claim this role if it fails to accommodate an essential part of cognition—conceptual
thought. Recently, Williams (Synthese 1–27, 2018) argued that PP struggles to address
at least two of thought’s core properties—generality and rich compositionality. In
this paper, I show that neither necessarily presents a problem for PP. In particular,
I argue that because we do not have access to cognitive processes but only to their
conscious manifestations, compositionality may be a manifest property of thought,
rather than a feature of the thinking process, and result from the interplay of thinking
and language. Pace Williams, both of these capacities, constituting parts of a complex
and multifarious cognitive system, may be fully based on the architectural principles of
PP. Under the assumption that language presents a subsystem separate from conceptual
thought, I sketch out one possible way for PP to accommodate both generality and
rich compositionality.

The general principles of perceptuo-motor processing and memory give rise to the Now-or-Never bottleneck constraint imposed on the organization of the language processing system. In particular, the Now-or-Never bottleneck demands for an appropriate structure of linguistic input and rapid incorporation of both linguistic and multisensory contextual information in a progressive, integrative manner. I argue that the emerging predictive processing framework is well-suited for the task of providing a comprehensive account of language processing under the Now-or-Never constraint. Moreover, this framework presents a stronger alternative to the Chunk-and-Pass account proposed by Christiansen and Chater (2016) as it better accommodates the available evidence concerning the role of context (both in the narrow and wider senses) in language comprehension at various levels of linguistic representation. Furthermore, the predictive processing approach allows for treating language as a special case of domain-general processing strategies, suggesting deep parallels with other cognitive processes such as vision.