Cited by 340Embodied and Extended CognitionFree Will and AgencyDecision-Making and Behavioral Economics

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The anatomy of choice: active inference and agency

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Abstract

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This theoretical paper recasts agency and choice as active inference, where prior beliefs about control minimize the KL divergence between attainable and desired future states. It shows that expected utility, exploration bonuses, softmax choice rules, optimism bias, temporal discounting, and marginal utility emerge from variational free-energy minimization.

Key findings

  • Agency is modeled as prior beliefs that action minimizes the divergence between distributions over final states and goal states; value decomposes into an entropy/exploration bonus plus expected utility (Equation 4).
  • Precision or inverse temperature is not a free parameter but is optimized as a sufficient statistic of beliefs about policies; this yields softmax choice rules and quantal response equilibria as consequences of free-energy minimization.
  • In a simulated 16-trial “limited offer” game with two control states (reject/accept) and five hidden states, the agent accepted a high offer after it replaced a low offer on the 11th trial; expected precision rose markedly after the high offer and remained at its maximum value of eight.
  • Temporal discounting and a convex marginal utility relationship emerge from Bayes-optimal inference under time-sensitive contingencies; the paper reports prior utility vector σ([1,1,1,2,4]ᵀ), meaning the high offer is believed accepted exp(4−2)=7.39 times more than the low offer, and the low offer exp(2−1)=2.718 times more likely than neither.

Methods

  • Theoretical/computational modeling: active inference is formalized with a Markovian generative model over hidden states, control states, and precision, inverted using variational Bayes under a mean-field assumption.
  • Simulation: a limited-offer game with T=16 trials, two control states (reject/stay or accept/shift), five hidden states (low offer, high offer, no offer, accepted low offer, accepted high offer), identity sensory mapping A=I, and gamma prior over precision with α=8 and β=1.
  • Main analysis: iterated variational updates for expected hidden states, policies, and precision; simulated choice behavior and precision dynamics are compared with softmax/QRE formulations and dopaminergic responses.

Limitations

  • The paper states that using discrete-time variational Bayes instead of continuous-time predictive coding “necessarily implies a loss of biological realism,” although it provides an explicit model of discrete choices.
  • The paper states that the mean-field factorization with respect to precision is an approximation because the true posterior will show mild conditional dependencies between precision and hidden states.
  • The paper states that neurobiological plausibility of variational message passing remains an open question and that proper validation of active inference will require careful model comparison using empirical choice behaviors and detailed mapping between model variables and neuronal correlates; the functional anatomy in Figure 4 is described as “purely iconic” and “should not be taken too seriously.”

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Registry

Crossref · 10.3389/fnhum.2013.00598

Cites

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Figures

Figure 1 Left panel: this is a schematic of the dependencies among variables underlying active inference. Here, a generative process representing state transitions in the real world generates observations or outcomes that are used to update the internal states of an agent. These internal states encode the sufficient statistics of an approximate posterior distribution over variables defined by a generative model (right panel). Particular sufficient statistics, encoding beliefs about choices or control states are reflexively transcribed into action, which affects real state transitions—thereby closing the action–perception cycle. Right panel: notice that the generative model, which defines free energy has a much simpler form. It simply supposes that there are mutually dependent hidden and control states that conspire to produce observations.
Figure 1 Left panel: this is a schematic of the dependencies among variables underlying active inference. Here, a generative process representing state transitions in the real world generates observations or outcomes that are used to update the internal states of an agent. These internal states encode the sufficient statistics of an approximate posterior distribution over variables defined by a generative model (right panel). Particular sufficient statistics, encoding beliefs about choices or control states are reflexively transcribed into action, which affects real state transitions—thereby closing the action–perception cycle. Right panel: notice that the generative model, which defines free energy has a much simpler form. It simply supposes that there are mutually dependent hidden and control states that conspire to produce observations.CC BY · PubMed Central
Figure 2 Upper panel: this is an example of a generative model, based on a hierarchical hidden Markov model. The key feature of this model is that there are two sets of states; hidden states and control states. The transitions among one set of states depend upon the state occupied in the other set. Lower panels: this provides an example of a particular generative model in which there are two control states; reject (stay) or accept (shift). The control state determines the transitions amongst the hidden states which, in this example, comprise a low offer (first state), a high offer (second state), a no-offer state (third state), and absorbing states that are entered whenever a low (fourth state) or high (fifth state) offer is accepted. The probability of moving from one state to another is one, unless specified by the values of the (control dependent) transition probabilities shown in the middle row. For example, the parameter r controls the rate of offer withdrawal (cf. a hazard rate). Note that absorbing states—that re-enter themselves with unit probability—render this Markovian process irreversible. We will use this example in simulations of choice behavior.
Figure 2 Upper panel: this is an example of a generative model, based on a hierarchical hidden Markov model. The key feature of this model is that there are two sets of states; hidden states and control states. The transitions among one set of states depend upon the state occupied in the other set. Lower panels: this provides an example of a particular generative model in which there are two control states; reject (stay) or accept (shift). The control state determines the transitions amongst the hidden states which, in this example, comprise a low offer (first state), a high offer (second state), a no-offer state (third state), and absorbing states that are entered whenever a low (fourth state) or high (fifth state) offer is accepted. The probability of moving from one state to another is one, unless specified by the values of the (control dependent) transition probabilities shown in the middle row. For example, the parameter r controls the rate of offer withdrawal (cf. a hazard rate). Note that absorbing states—that re-enter themselves with unit probability—render this Markovian process irreversible. We will use this example in simulations of choice behavior.CC BY · PubMed Central
Figure 3 This figure illustrates the temporal dependencies among hidden states and control states in the generative model considered in this paper. This Bayesian graph illustrates the dependencies among successive hidden states and how they depend upon action in the past and control states in the future. Note that future control states depend upon the current state because it depends upon the relative entropy or divergence between distributions over the final state that are, and are not, conditioned on the current state. The resulting choices depend upon the precision of beliefs about control states, which, in turn depend upon the parameters of the model. Observed outcomes depend on, and only on, the hidden states at any given time.
Figure 3 This figure illustrates the temporal dependencies among hidden states and control states in the generative model considered in this paper. This Bayesian graph illustrates the dependencies among successive hidden states and how they depend upon action in the past and control states in the future. Note that future control states depend upon the current state because it depends upon the relative entropy or divergence between distributions over the final state that are, and are not, conditioned on the current state. The resulting choices depend upon the precision of beliefs about control states, which, in turn depend upon the parameters of the model. Observed outcomes depend on, and only on, the hidden states at any given time.CC BY · PubMed Central
Figure 4 This figure illustrates the cognitive and functional anatomy implied by the variational scheme—or more precisely, the mean field assumption implicit in variational updates. Here, we have associated the variational updates of expected states with perception, of future control states (policies) within action selection and, finally, expected precision with evaluation. The forms of these updates suggest the sufficient statistics from each subset are passed among each other until convergence to an internally consistent (Bayes optimal) solution. In terms of neuronal implementation, this might be likened to the exchange of neuronal signals via extrinsic connections among functionally specialized brain systems. In this (purely iconic) schematic, we have associated perception (inference about the current state of the world) with the prefrontal cortex, while assigning action selection to the basal ganglia. Crucially, precision has been associated with dopaminergic projections from the ventral tegmental area and substantia nigra that, necessarily, project to both cortical (perceptual) and subcortical (action selection) systems. See main text for a full description of the equations.
Figure 4 This figure illustrates the cognitive and functional anatomy implied by the variational scheme—or more precisely, the mean field assumption implicit in variational updates. Here, we have associated the variational updates of expected states with perception, of future control states (policies) within action selection and, finally, expected precision with evaluation. The forms of these updates suggest the sufficient statistics from each subset are passed among each other until convergence to an internally consistent (Bayes optimal) solution. In terms of neuronal implementation, this might be likened to the exchange of neuronal signals via extrinsic connections among functionally specialized brain systems. In this (purely iconic) schematic, we have associated perception (inference about the current state of the world) with the prefrontal cortex, while assigning action selection to the basal ganglia. Crucially, precision has been associated with dopaminergic projections from the ventral tegmental area and substantia nigra that, necessarily, project to both cortical (perceptual) and subcortical (action selection) systems. See main text for a full description of the equations.CC BY · PubMed Central
Figure 5 The strictly increasing, monotonic relationship between expected precision and expected value. Note that value never exceeds zero. This is because a Kullback–Leibler divergence can never be less than zero; by Gibbs inequality.
Figure 5 The strictly increasing, monotonic relationship between expected precision and expected value. Note that value never exceeds zero. This is because a Kullback–Leibler divergence can never be less than zero; by Gibbs inequality.CC BY · PubMed Central
Figure 6 This figure shows the results of a simulation of 16 trials, where a low offer was replaced by high offer on the 11th trial, which was accepted on the subsequent trial. The upper left panel shows the expected states as a function of trials or time, where the states are defined in Figure 2. The upper right panel shows the corresponding expectations about control in the future, where the dotted lines are expectations during earlier trials and the full lines correspond to expectations during the final trial. Blue corresponds to reject (stay) and green to accept (shift). The lower panels show the time-dependent changes in expected precision, after convergence on each trial (lower left) and deconvolved updates after each iteration of the variational updates (lower right).
Figure 6 This figure shows the results of a simulation of 16 trials, where a low offer was replaced by high offer on the 11th trial, which was accepted on the subsequent trial. The upper left panel shows the expected states as a function of trials or time, where the states are defined in Figure 2. The upper right panel shows the corresponding expectations about control in the future, where the dotted lines are expectations during earlier trials and the full lines correspond to expectations during the final trial. Blue corresponds to reject (stay) and green to accept (shift). The lower panels show the time-dependent changes in expected precision, after convergence on each trial (lower left) and deconvolved updates after each iteration of the variational updates (lower right).CC BY · PubMed Central
Figure 7 This figure uses the same format as the previous figure; however, here, the low offer was withdrawn on the fifth trial, leading to a decrease in expected precision. Note the difference (divergence) between the expected states on the 15th (penultimate) and 16 (final) trial. It is this large divergence (or more exactly the divergence between distributions over the final state) that leads to a small value and associated precision.
Figure 7 This figure uses the same format as the previous figure; however, here, the low offer was withdrawn on the fifth trial, leading to a decrease in expected precision. Note the difference (divergence) between the expected states on the 15th (penultimate) and 16 (final) trial. It is this large divergence (or more exactly the divergence between distributions over the final state) that leads to a small value and associated precision.CC BY · PubMed Central
Figure 8 The upper panels show the probability of accepting with (left) and without (right) the entropy or novelty part of value, where the low offer remained available and action was precluded. These probabilities are shown as a function of trial number and the relative utility of the low offer (white corresponds to high probabilities). The lower panels show the same results but in terms of the probability distribution over the latency or time to choice. Note that including the entropy in value slightly delays the time to choice—to ensure a greater latitude of options. This is particularly noticeable in the ambiguous situation when the low offer has the same utility as the high offer (of four).
Figure 8 The upper panels show the probability of accepting with (left) and without (right) the entropy or novelty part of value, where the low offer remained available and action was precluded. These probabilities are shown as a function of trial number and the relative utility of the low offer (white corresponds to high probabilities). The lower panels show the same results but in terms of the probability distribution over the latency or time to choice. Note that including the entropy in value slightly delays the time to choice—to ensure a greater latitude of options. This is particularly noticeable in the ambiguous situation when the low offer has the same utility as the high offer (of four).CC BY · PubMed Central
Figure 9 Upper left panel: the probability of accepting an offer as a function of time or trials. Note that the probability of accepting (green) increases over time to approach and surpass the probability of rejection. This produces an increase in the uncertainty about action—shown in red. Upper right panel: these are the expected utility and entropy components of expected value as a function of trial number. The key result here is the time-dependent change in expected utility, which corresponds to temporal discounting of the expected utility: i.e., the expected utility of the final state is greater when there are fewer intervening trials. Lower panel: the marginal utility of the high offer (green) and low offer (blue) as a function of the relative utility of the high offer. Marginal utility is defined here as expected utility times expected precision. The multiple curves correspond to the marginal utilities as a function of trial number (and do not differ greatly because expected precision changes more slowly over time—for a given utility—than it changes over utility—for a given time).
Figure 9 Upper left panel: the probability of accepting an offer as a function of time or trials. Note that the probability of accepting (green) increases over time to approach and surpass the probability of rejection. This produces an increase in the uncertainty about action—shown in red. Upper right panel: these are the expected utility and entropy components of expected value as a function of trial number. The key result here is the time-dependent change in expected utility, which corresponds to temporal discounting of the expected utility: i.e., the expected utility of the final state is greater when there are fewer intervening trials. Lower panel: the marginal utility of the high offer (green) and low offer (blue) as a function of the relative utility of the high offer. Marginal utility is defined here as expected utility times expected precision. The multiple curves correspond to the marginal utilities as a function of trial number (and do not differ greatly because expected precision changes more slowly over time—for a given utility—than it changes over utility—for a given time).CC BY · PubMed Central

Verification

Claims the paper makes

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  • Probably not a substantial claim. 47
  • It may state two separate claims as one. 47
  • The quote only partly states this claim. 1
85 of 85
  • The authors state this as an assumption.

    Policies are selected under the prior belief that they minimize the divergence, or relative entropy, between a probability distribution over states that can be reached and states agents believe they should occupy, which are states or goals believed a priori to have high utility.

    “We assume that policies are selected under the prior belief1 they minimize the divergence (relative entropy) between a probability distribution over states that can be reached and states agents believe they should occupy—states or goals that agents believe, a priori, have high utility.”
  • The authors state this as an assumption.

    Action is sampled from posterior beliefs about control states.

    “In other words, under the assumption that action is sampled from posterior beliefs about control, we can treat decision-making and action selection as a pure inference problem that necessarily entails optimizing beliefs about behavior and its consequences.”
  • The authors state this as an assumption.

    The agent knows its past actions by observing them.

    “For simplicity, we have assumed that the agent knows its past actions by observing them.”
  • The authors state this as an assumption.

    Inference underlies a sense of agency.

    “In the context of behavior, we suppose that inference underlies a sense of agency.”
  • The authors state this as an assumption.

    The true generative process is Markovian.

    “These particular mean field assumptions are not approximations, because the true generative process is Markovian.”
  • The authors state this as an assumption.

    Neuronal activity encodes sufficient statistics.

    “If we assume that neuronal activity encodes sufficient statistics, then the variational update scheme could provide a metaphor for functional segregation—the segregation of representations corresponding to the mean field assumption, and functional integration—the recursive (reciprocal) exchange of sufficient statistics during approximate Bayesian inference.”
  • The authors state this as an assumption.

    The approximate posterior factorizes over past hidden states, future control states, and precision, and factorizes successive states over time.

    “Here, we have assumed a factorization over (past) hidden states, (future) control states and precision. Furthermore, we have factorized successive states over time, which means we only have to represent the current state explicitly.”
  • The authors state this as an assumption.

    The approximate marginal over precision is a gamma distribution with shape parameter same as the prior α = 8 and rate parameter optimized.

    “Equation (6) assumes the approximate marginal over precision is, like its conjugate prior, a gamma distribution; where the shape parameter is the same as the prior α = 8 and the rate parameter is optimized.”
  • The authors state this as an assumption.

    The entropy term in Equation (4) is small enough to be ignored.

    “Assuming the entropy term in Equation (4) small enough to be ignored, it is easy to see that expected marginal utility directly informs choices: (10)”
  • The paper relies on this without support.

    The offer will be withdrawn before the final outcome is reached (prior belief).

    “can be explained simply by prior beliefs that the offer will be withdrawn before the final outcome is reached.”
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Metadata

Authors
Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J.
Affiliations
  • Wellcome Centre for Human Neuroimaging (GB)
  • University College London (GB)
  • University of Oxford (GB)
Corresponding author
Friston, Karl
Venue
Frontiers in Human Neuroscience
Published
2013
Volume
7
Pages
598-598
ISSN
1662-5161 (ISSN-L)
OpenAlex
W2093466600
Cited by
340
References
97
Citations by year
2026: 15 · 2025: 23 · 2024: 28 · 2023: 22 · 2022: 27 · 2021: 37 · 2020: 32 · 2019: 23 · 2018: 24 · 2017: 32 · 2016: 25 · 2015: 24 · 2014: 27 · 2013: 1
FWCI
14.08
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Human Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UCL Discovery (University College London) (submittedVersion)
  • Max Planck Digital Library (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
  • UEA Digital Repository (University of East Anglia) (submittedVersion)
Funders
  • Wellcome Trust (091593, 098362, 098362/Z/12/Z, 088130)
  • National Institute for Health and Care Research
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
  • Social Sciences › Arts and Humanities › Philosophy › Free Will and Agency
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
Keywords
active inference, sense of agency, free energy principle, variational inference, predictive coding, bounded rationality, expected utility, exploration bonus, optimism bias, Kullback-Leibler divergence, decision-making, inverse temperature, optimal control

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