To be precise, the details don’t matter: On predictive processing, precision, and level of detail of predictions

Kwisthout, Johan; Bekkering, Harold; Rooij, Iris van

doi:10.1016/j.bandc.2016.02.008

Cited by 68 publications

(62 citation statements)

References 43 publications

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“…If we think of error signals as broadcasting newsworthy information then we must also explain how the brain decides which channels it should "listen to" (Kanai et al 2015; c.f. Kwisthout et al 2017). Estimating the precision of a policy is a special case of a more general phenomenon in which the agent is continuously engaged in monitoring its own level of confidence in its predictions about the world.…”

Section: The Exploit/explore Dilemmamentioning

confidence: 99%

The feeling of grip: novelty, error dynamics, and the predictive brain

2017

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According to the free energy principle biological agents resist a tendency to disorder in their interactions with a dynamically changing environment by keeping themselves in sensory and physiological states that are expected given their embodiment and the niche they inhabit (Friston in Nat Rev Neurosci 11(2):127-138, 2010. doi:10.1038/nrn2787). Why would a biological agent that aims at minimising uncertainty in its encounters with the world ever be motivated to seek out novelty? Novelty for such an agent would arrive in the form of sensory and physiological states that are unexpected. Such an agent ought therefore to avoid novel and surprising interactions with the world one might think. Yet humans and many other animals find play and other forms of novelty-seeking and exploration hugely rewarding. How can this be understood in frameworks for studying the mind that emphasise prediction error minMany thanks to Andy Clark for excellent feedback and suggestions on earlier drafts of this paper, and to Jelle Bruineberg for helping us to understand the recent literature in cognitive neuroscience on epistemic value. We are extremely grateful for detailed reviews we received from two anonymous reviewers that helped us to significantly improve and sharpen our arguments. Erik Rietveld would like to acknowledge the research funding awarded by the Netherlands Organisation for Scientific Research (NWO) 123Synthese imisation? This problem has been taken up in recent research concerned with epistemic action-actions an agent engages in to reduce uncertainty. However that work leaves two questions unanswered, which it is the aim of our paper to address. First, no account has been given yet of why it should feel good to the agent to engage the world playfully and with curiosity. Second an appeal is made to precision-estimation to explain epistemic action, yet it remains unclear how precision-weighting works in action more generally, or active inference. We argue that an answer to both questions may lie in the bodily states of an agent that track the rate at which free energy is being reduced. The recent literature on the predictive brain has connected the valence of emotional experiences to the rate of change in the reduction of prediction error (Joffily and Coricelli in PLoS Comput Biol 9(6):e1003094, 2013. doi:10.1371/journal.pcbi.1003094; Van de Cruys, in Metzinger and Wiese (eds) Philosophy and predictive processing, vol 24, MIND Group, Frankfurt am Main, 2017. doi:10.15502/9783958573253). In this literature valenced emotional experiences are hypothesised to be identical with changes in the rate at which prediction error is reduced. Experiences are negatively valenced when overall prediction error increases and are positively valenced when the sum of prediction errors decrease. We offer an ecological-enactive interpretation of the concept of valence and its connection to rate of change of prediction error. We show how rate of change should be understood in terms of embodied states of affordance-related action read...

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Section: The Exploit/explore Dilemmamentioning

confidence: 99%

The feeling of grip: novelty, error dynamics, and the predictive brain

2017

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“…We formalized the three hypotheses described in the introduction of this paper in a set of computational models. These computational models, based on the causal Bayesian network formalization of predictive processing as proposed in Kwisthout, Bekkering, and van Rooij (2017), compute posterior probability distributions that represent the expectation or prediction of the infant prior to each ball drawn. This prediction is compared to the actually observed event, yielding the prediction error.…”

Section: Computational Modelmentioning

confidence: 99%

Young Children Integrate Current Observations, Priors and Agent Information to Build Predictive Models of Others’ Actions

Kayhan

Heil

Kwisthout

et al. 2018

Preprint

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Highlights We present three formalized hypotheses on how young children generate predictive models of others' sampling actions. We measured pupillary responses of children as a behavioral marker of prediction errors as described in the predictive processing framework. Results showed that young children integrated information about current observations, prior probabilities and agents to generate predictive models about others' actions.  A computational model based on the causal Bayesian network formalization of predictive processing can explain this process.. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/365361 doi: bioRxiv preprint first posted online Jul. 9, 2018; RUNNING HEAD: PREDICTIVE PROCESSING IN DEVELOPMENT 3 AbstractFrom early on in life, children are able to use information from their environment to form predictions about events. For instance, they can use statistical information about a population to predict the sample drawn from that population and infer an agent's preferences from systematic violations of random sampling. We investigated how young children build and update models of an agent's sampling actions over time, and whether a computational model based on the causal Bayesian network formalization of predictive processing can explain this process.We formalized three hypotheses about how different explanatory variables (i.e., prior probabilities, current observations, and agent characteristics) are used to build predictive models of others' actions. We measured pupillary responses as a behavioral marker of 'prediction errors' (i.e., the perceived mismatch between what one's model of an agent predicts and what the agent actually does), as described in the predictive processing framework. Pupillary responses of 24-month-olds, but not 18-month-olds, showed that young children integrated information about current observations, priors and agents to generate predictive models of agents and their actions.These findings shed light on the mechanisms behind toddlers' inferences about agentcaused events. To our knowledge, this is the first study in which young children's pupillary responses are used as markers of prediction errors, and explained by a computational model based on the causal Bayesian network formalization of predictive processing. We argue that the predictive processing framework provides a promising explanation of the way in which young children process other persons' actions.

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“…The field still does not know exactly what is happening during agentive action, but a "feedback mode" model suggests that all depend on how priors are selected/learned. It is possible that the phrase "to be precise, the details don't matter" is applicable when encountering the trade-off between prediction precision and information gain (Kwisthout, Bekkering, & van Rooij, 2017), where the "categorical" probability or inference might be prioritized, as can be seen by the current results (e.g., self or other in Figure 5). This subjective categorical attribution observed in a bidirectional loop between teleology and generativity should, in turn, affect our perception, including delusions/hallucinations (hierarchical top-down modulation, e.g., Powers et al, 2017).…”

Section: Bayesian "Feedback Mode" (Model 4)mentioning

confidence: 99%

The self as a generative, teleological, and subjective prior: Mutually-modulated temporal agency

Asai

Imaizumi

Imamizu

2019

Preprint

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How can we certify our existence? Recent theoretical studies have suggested that, in the brain, the effect inversely infers the possibility of the existence of the self as the cause. While this Bayesian view of the sense of agency is widely accepted, empirical evidence in support of this theory is still lacking. The current study examined outcome-modulated agency in terms of time perception in seven experiments, with a total of 90 participants. It was hypothesized that perceptual generation, not termination, should subjectively infer the existence of the self, even though both include the same stimuli and are driven by the same teleological action.Results suggest support for the hypothesis (Experiments 1 and 2). Participants judged stronger self-agency, detected less delay, or felt shorter duration for auditory generation, compared with termination, which was driven by the same volitional key-press. Furthermore, the main experiment, Experiment 3, focused on temporal probability distributions both for action and outcome (e.g., standard deviations or relative entropy), and concluded that the observed contrast in onset/offset agency indicates a mutual or bidirectional relationship between cause and effect only during agentive action, characterized by active (i.e., teleological) generation.Finally, the concurrent theoretical models for volitional action-modulated time perception are discussed on the basis of the suggested triplet (generativity, teleology, and subjectivity) dimensions of agency. Extended abstractThe sense of agency refers to the subjective experience of generating one's own actions (Gallagher, 2000).Within the past decade, research on this subject has increased, as increased understanding of human agency could be a window into knowing how we are aware of ourselves, and possibly allowing for a better understanding of mental illnesses, like schizophrenia. However, "free will," "self-consciousness," and/or "volition" are concepts that are always difficult to empirically investigate (Frith & Haggard, 2018).Researchers do not know exactly what occurs in our sensorium during "agentive action." However, three conceptions could represent genuine dimensions of agency: generativity, teleology, and subjectivity (Haggard, 2019).Recent theoretical studies have suggested that, in the brain, the effect inversely infers the possibility of the existence of the self as the cause. This Bayesian view of the sense of agency may provide an explanation for classical statements, such as "ego cogito, ergo sum (I think, therefore I am)," "cogito cogito, ergo cogito sum (I think that I think, therefore I think that I am)," "I move, therefore I am," or "I predict, therefore I am." (e.g., Corlett, 2017). All of these theoretical claims implicitly refer to the Bayesian "inverse" probability or inference (e.g., "thought as action"), where the effect (perception) retrospectively postdicts the cause (action), as well as the action predicting the following perception (Figure 1, including generativity, teleology, and subjectivity as ...

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To be precise, the details don’t matter: On predictive processing, precision, and level of detail of predictions

Cited by 68 publications

References 43 publications

The feeling of grip: novelty, error dynamics, and the predictive brain

The feeling of grip: novelty, error dynamics, and the predictive brain

Young Children Integrate Current Observations, Priors and Agent Information to Build Predictive Models of Others’ Actions

The self as a generative, teleological, and subjective prior: Mutually-modulated temporal agency

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