Causal learning in the Crib: A predictive processing formalization and babybot simulation

2016 ◽  
Author(s):  
Maria Otworowska ◽  
Lorijn Zaadnoordijk ◽  
Erwin de Wolff ◽  
Johan Kwisthout ◽  
Iris van Rooij
Keyword(s):  
Causal Learning ◽  
Predictive Processing

A challenge for predictive coding: Representational or experiential diversity?

2020 ◽  
Vol 43 ◽  
Author(s):  
Martina G. Vilas ◽  
Lucia Melloni
Keyword(s):  
Brain Function ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Process Theory

Abstract To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.

Biological Significance in Human Causal Learning

2015 ◽  
Vol 20 (2) ◽  
pp. 65-72
Author(s):  
Prescilla John ◽  
Oska Pineño
Keyword(s):  
Biological Significance ◽  
Causal Learning ◽  
Human Causal Learning

From many to one: Meditation and the plasticity of the predictive mind

2020 ◽  
Author(s):  
Ruben Laukkonen ◽  
Heleen A Slagter
Keyword(s):  
Cognitive Enhancement ◽  
The Self ◽  
Predictive Processing ◽  
Focused Attention ◽  
Conceptual Processing ◽  
Current State ◽  
Contemplative Science ◽  
Open Monitoring ◽  
Living Organisms ◽  
The Brain

How profoundly can humans change their own minds? In this paper we offer a unifying account of meditation under the predictive processing view of living organisms. We start from relatively simple axioms. First, the brain is an organ that serves to predict based on past experience, both phylogenetic and ontogenetic. Second, meditation serves to bring one closer to the here and now by disengaging from anticipatory processes. We propose that practicing meditation therefore gradually reduces predictive processing, in particular counterfactual cognition—the tendency to construct abstract and temporally deep representations—until all conceptual processing falls away. Our Many- to-One account also places three main styles of meditation (focused attention, open monitoring, and non-dual meditation) on a single continuum, where each technique progressively relinquishes increasingly engrained habits of prediction, including the self. This deconstruction can also make the above processes available to introspection, permitting certain insights into one’s mind. Our review suggests that our framework is consistent with the current state of empirical and (neuro)phenomenological evidence in contemplative science, and is ultimately illuminating about the plasticity of the predictive mind. It also serves to highlight that contemplative science can fruitfully go beyond cognitive enhancement, attention, and emotion regulation, to its more traditional goal of removing past conditioning and creating conditions for potentially profound insights. Experimental rigor, neurophenomenology, and no-report paradigms combined with neuroimaging are needed to further our understanding of how different styles of meditation affect predictive processing and the self, and the plasticity of the predictive mind more generally.

The Influence of Delays in Real-Time Causal Learning~!2009-10-01~!2010-01-07~!2010-07-13~!

2010 ◽  
Vol 3 (2) ◽  
pp. 184-195 ◽  
Author(s):  
David A. Lagnado ◽  
Maarten Speekenbrink
Keyword(s):  
Real Time ◽  
Causal Learning

Activity, Agency, and Inner Speech Pathology

2018 ◽  
Author(s):  
Lauren Swiney
Keyword(s):  
Motor Control ◽  
Sensory Input ◽  
The Other ◽  
Predictive Processing ◽  
Inner Speech ◽  
Speech Pathology ◽  
Active Inference ◽  
Comparator Hypothesis

Over the last thirty years the comparator hypothesis has emerged as a prominent account of inner speech pathology. This chapter discusses a number of cognitive accounts broadly derived from this approach, highlighting the existence of two importantly distinct notions of inner speech in the literature; one as a prediction in the absence of sensory input, the other as an act with sensory consequences that are themselves predicted. Under earlier frameworks in which inner speech is described in the context of classic models of motor control, I argue that these two notions may be compatible, providing two routes to inner speech pathology. Under more recent accounts grounded in the architecture of Bayesian predictive processing, I argue that “active inference” approaches to action generation pose serious challenges to the plausibility of the latter notion of inner speech, while providing the former notion with rich explanatory possibilities for inner speech pathology.

scholarly journals Modelling ourselves: what the free energy principle reveals about our implicit notions of representation

Synthese ◽  
2021 ◽  
Author(s):  
Matt Sims ◽  
Giovanni Pezzulo
Keyword(s):  
Free Energy ◽  
Philosophy Of Mind ◽  
Predictive Processing ◽  
Energy Principle ◽  
Philosophical Debate ◽  
Free Energy Principle ◽  
Self Organized ◽  
Functional Aspects ◽  
Structural Content ◽  
Unified View

AbstractPredictive processing theories are increasingly popular in philosophy of mind; such process theories often gain support from the Free Energy Principle (FEP)—a normative principle for adaptive self-organized systems. Yet there is a current and much discussed debate about conflicting philosophical interpretations of FEP, e.g., representational versus non-representational. Here we argue that these different interpretations depend on implicit assumptions about what qualifies (or fails to qualify) as representational. We deploy the Free Energy Principle (FEP) instrumentally to distinguish four main notions of representation, which focus on organizational, structural, content-related and functional aspects, respectively. The various ways that these different aspects matter in arriving at representational or non-representational interpretations of the Free Energy Principle are discussed. We also discuss how the Free Energy Principle may be seen as a unified view where terms that traditionally belong to different ontologies—e.g., notions of model and expectation versus notions of autopoiesis and synchronization—can be harmonized. However, rather than attempting to settle the representationalist versus non-representationalist debate and reveal something about what representations are simpliciter, this paper demonstrates how the Free Energy Principle may be used to reveal something about those partaking in the debate; namely, what our hidden assumptions about what representations are—assumptions that act as sometimes antithetical starting points in this persistent philosophical debate.

scholarly journals Computational framework for investigating predictive processing in auditory perception

2021 ◽  
pp. 109177
Author(s):  
Benjamin Skerritt-Davis ◽  
Mounya Elhilali
Keyword(s):  
Auditory Perception ◽  
Predictive Processing ◽  
Computational Framework

scholarly journals Cross‐modal predictive processing depends on context rather than local contingencies

2021 ◽  
Author(s):  
Tjerk T. Dercksen ◽  
Maria V. Stuckenberg ◽  
Erich Schröger ◽  
Nicole Wetzel ◽  
Andreas Widmann
Keyword(s):  
Predictive Processing

scholarly journals Psychomotor Predictive Processing

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 806
Author(s):  
Stephen Fox
Keyword(s):  
Subjective Experience ◽  
New Technologies ◽  
Scientific Basis ◽  
Predictive Processing ◽  
Sensory Inputs ◽  
Processing Theory ◽  
New Formulation ◽  
Further Development ◽  
Primary Contribution ◽  
Planning And Operation

Psychomotor experience can be based on what people predict they will experience, rather than on sensory inputs. It has been argued that disconnects between human experience and sensory inputs can be addressed better through further development of predictive processing theory. In this paper, the scope of predictive processing theory is extended through three developments. First, by going beyond previous studies that have encompassed embodied cognition but have not addressed some fundamental aspects of psychomotor functioning. Second, by proposing a scientific basis for explaining predictive processing that spans objective neuroscience and subjective experience. Third, by providing an explanation of predictive processing that can be incorporated into the planning and operation of systems involving robots and other new technologies. This is necessary because such systems are becoming increasingly common and move us farther away from the hunter-gatherer lifestyles within which our psychomotor functioning evolved. For example, beliefs that workplace robots are threatening can generate anxiety, while wearing hardware, such as augmented reality headsets and exoskeletons, can impede the natural functioning of psychomotor systems. The primary contribution of the paper is the introduction of a new formulation of hierarchical predictive processing that is focused on psychomotor functioning.

Causal Learning

2020 ◽  
pp. 169-212
Keyword(s):  
Causal Learning
Sign in / Sign up

Export Citation Format

Share Document