Minimizing prediction errors in predictive processing: from inconsistency to non-representationalism

Es, Thomas van

doi:10.1007/s11097-019-09649-y

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…They are usually explained in expressiveness and reasoning, allowing the brain to make content-rich reasoning based on representative models. According to the article by van et al [18], the predictive processing framework does not fit into these cognitive situations. In particular, the author believes that the combination of hierarchical modeling, content logic, and expressiveness leads to internal contradictions.…”

Section: Related Workmentioning

confidence: 99%

Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction

2021

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Modern computing resources, including machine learning-based techniques, are used to maintain stability between the demand and supply of electricity. Machine learning is widely used for the prediction of energy consumption. The researchers present several artificial intelligence and machine learning-based methods to improve the prediction accuracy of energy consumption. However, the discrepancy between actual energy consumption and predicted energy consumption is still challenging. Various factors, including changes in weather, holidays, and weekends, affect prediction accuracy. This article analyses the overall prediction using error curve learning and a hybrid model. Actual energy consumption data of Jeju island, South Korea, has been used for experimental purposes. We have used a hybrid ML model consisting of Catboost, Xgboost, and Multi-layer perceptron for the prediction. Then we analyze the factors that affect the week-ahead (WA) and 48 h prediction results. Mean error on weekdays is recorded as 2.78%, for weekends 2.79%, and for special days it is recorded as 4.28%. We took into consideration significant predicting errors and looked into the reasons behind those errors. Furthermore, we analyzed whether factors, such as a sudden change in temperature and typhoons, had an effect on energy consumption. Finally, the authors have considered the other factors, such as public holidays and weekends, to analyze the significant errors in the prediction. This study can be helpful for policymakers to make policies according to the error-causing factors.

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Section: Related Workmentioning

confidence: 99%

Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction

2021

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“…Some supporters of the radical approach (see, among others, Bruineberg, Kiverstein & Rietveld, 2018;Orlandi, 2016;van Es, 2020a) criticize the conservative approach because of its internalism and representationalism. They claim that internalism means a position that-in contrast to extended and embodied approaches to cognition-emphasizes that the limit within which one should think and study cognition is the limit of the nervous system.…”

Section: Radical Predictive Processingmentioning

confidence: 99%

“…The point is that the brain abductively "infers" about the causes of sensory stimulation (input sensors) in such a way that it presents hypotheses that best explain information coming from the environment(Kiefer 2017; see alsoGregory 1980).44 Some researchers belonging to radical PP reject the existence of representation at all (cf Kirchhoff & Robertson, 2018;Orlandi, 2016;van Es, 2020a)…”

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confidence: 99%

Understanding Predictive Processing. A Review

2021

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The purpose of this paper is to provide a systematic review of the Predictive Processing framework (hereinafter PP) and to identify its basic theoretical difficulties. For this reason, it is, primarily, polemic-critical and, secondarily, historical. I discuss the main concepts, positions and research issues present within this framework ( §1-2). Next, I present the Bayesian-brain thesis ( §3) and the difficulty associated with it ( §4). In §5, I compare the conservative and radical approach to PP and discuss the internalist nature of the generative model in the context of Markov blankets. The possibility of linking PP with the free energy principle (hereinafter FEP) and the homeostatic nature of predictive mechanisms is discussed in §6. This is followed by the presentation of PP's difficulties with solving the dark room problem and the exploration-exploitation trade-off ( §7). I emphasize the need to integrate PP with other models and research frameworks within cognitive science. Thus, this review not only discusses PP, but also provides an assessment of the condition of this research framework in the light of the hopes placed on it by many researchers. The Conclusions section discuss further research challenges and the epistemological significance of PP.

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“…This is to say that it is not necessarily obvious to associate the boundaries induced by the Markov blanket with physical boundaries, though it does lend itself well to this particular application. We should thus be wary about overstating the implications of this statistical partitioning of states when considering its application onto other systems (van Es 2019(van Es , 2020.…”

Section: Introductionmentioning

confidence: 99%

Between pebbles and organisms: weaving autonomy into the Markov blanket

Kirchhoff

2021

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The free energy principle (FEP) purports to provide a single principle for the organizational dynamics of living systems, including their cognitive profiles. It states that for a system to maintain non-equilibrium steady-state with its environment it must minimise its free energy. It is said to be entirely scale-free, applying to anything from particles to organisms, and interactive machines, spanning from the abiotic to the biotic. Because the FEP is so general in its application, it is for this reason that one might wonder in what sense this framework captures anything specific to biological characteristics, if details at all. We take steps to correct for this here. We do so by taking up a distinct challenge that the FEP must overcome if it is to be of interest to those working in the biological sciences. We call this the pebble challenge : it states that the FEP cannot capture the organisational principles specific to biology, for its formalisms apply equally well to pebbles. We progress in solving the pebble challenge by articulating how the notion of 'autonomy as precarious operational closure' from the enactive literature can be unpacked within the FEP. This enables the FEP to delineate between the abiotic and the biotic; avoiding the pebble challenge that keeps it out of touch with the living systems we encounter in the world and is of interest to the sciences of life and mind. This is a pre-print. This paper is currently under review.

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Minimizing prediction errors in predictive processing: from inconsistency to non-representationalism

Abstract: Institutional repository IRUAThomas van es. Post-peer review, pre-print. Forthcoming in Phenomenology and the Cognitive sciences.

Cited by 8 publications

References 32 publications

Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction

Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction

Understanding Predictive Processing. A Review

Between pebbles and organisms: weaving autonomy into the Markov blanket

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