Abstract:Rational analyses of memory suggest that retrievability of past experience depends on its usefulness for predicting the future: memory is adapted to the temporal structure of the environment. Recent research has enriched this view by applying it to semantic memory and reinforcement learning. This paper describes how multiple forms of memory can be linked via common predictive principles, possibly subserved by a shared neural substrate in the hippocampus. Predictive principles offer an explanation for a wide ra… Show more
“…For example, there are elements of hierarchal decision-making and successor representations implicit in the deep temporal (generative) model that underlies inferences about policies and subsequent policy selection. Furthermore, bidirectional planning is, in a loose sense, implicit in the bidirectional message passing between active inference representations of the past and future: see also Gershman (2017). As in pruning approaches, this bidirectional aspect is kept to a manageable size by the induction of subgoals that allow for a chunking or decomposition of the tree search.…”
This paper introduces an active inference formulation of planning and navigation. It illustrates how the exploitation–exploration dilemma is dissolved by acting to minimise uncertainty (i.e. expected surprise or free energy). We use simulations of a maze problem to illustrate how agents can solve quite complicated problems using context sensitive prior preferences to form subgoals. Our focus is on how epistemic behaviour—driven by novelty and the imperative to reduce uncertainty about the world—contextualises pragmatic or goal-directed behaviour. Using simulations, we illustrate the underlying process theory with synthetic behavioural and electrophysiological responses during exploration of a maze and subsequent navigation to a target location. An interesting phenomenon that emerged from the simulations was a putative distinction between ‘place cells’—that fire when a subgoal is reached—and ‘path cells’—that fire until a subgoal is reached.
“…For example, there are elements of hierarchal decision-making and successor representations implicit in the deep temporal (generative) model that underlies inferences about policies and subsequent policy selection. Furthermore, bidirectional planning is, in a loose sense, implicit in the bidirectional message passing between active inference representations of the past and future: see also Gershman (2017). As in pruning approaches, this bidirectional aspect is kept to a manageable size by the induction of subgoals that allow for a chunking or decomposition of the tree search.…”
This paper introduces an active inference formulation of planning and navigation. It illustrates how the exploitation–exploration dilemma is dissolved by acting to minimise uncertainty (i.e. expected surprise or free energy). We use simulations of a maze problem to illustrate how agents can solve quite complicated problems using context sensitive prior preferences to form subgoals. Our focus is on how epistemic behaviour—driven by novelty and the imperative to reduce uncertainty about the world—contextualises pragmatic or goal-directed behaviour. Using simulations, we illustrate the underlying process theory with synthetic behavioural and electrophysiological responses during exploration of a maze and subsequent navigation to a target location. An interesting phenomenon that emerged from the simulations was a putative distinction between ‘place cells’—that fire when a subgoal is reached—and ‘path cells’—that fire until a subgoal is reached.
“…In summary, the generation of fictive (offline) prediction errors is an essential part of machine learning schemes ( Hinton et al, 1995 ) and has been proposed as the basis of synaptic homoeostasis (Gilestro et al, 2009; Tononi and Cirelli, 2006) These purely theoretical considerations seem to be particularly prescient for the role of the hippocampus in sleep ( Buckner, 2010 ; Buzsaki, 1998 ). Furthermore, they speak to the mechanisms that may underwrite more general structure learning in finessing our generative models of the world ( Gershman, 2017 ; Tenenbaum et al, 2011 ; Tervo et al, 2016 ). Glossary of terms Memory index: During memory recall, the hippocampal index facilitates neocortical reinstatement of selective activity patterns to recapitulate previous experience.…”
Highlights
The hippocampus is involved in both memory recall and online prediction.
Within a predictive coding framework, opposing hippocampal-neocortical interactions accompany recall and prediction.
Here, we propose recall and prediction differentially route information within the cortical microcircuit.
By weighting prediction errors, neuromodulation sets the dominant processing mode.
In this framework, memory recall is cast as arising from fictive prediction errors.
“…How this plays out in psychopathology are main themes of this article. Much of our focus will be on what Friston and collaborators call “structure learning” (Tervo et al., 2016; Friston et al., 2017; Gershman, 2017; Isomura and Friston, 2018), namely, learning the repertoire or narratives that constitute our prior beliefs–or hypotheses–about how our world works, and how these might be influenced therapeutically. Although the FEP applies to these structural priors, getting them right can be a tricky business.…”
The free energy principle (FEP) has gained widespread interest and growing acceptance as a new paradigm of brain function, but has had little impact on the theory and practice of psychotherapy. The aim of this paper is to redress this. Brains rely on Bayesian inference during which “bottom-up” sensations are matched with “top-down” predictions. Discrepancies result in “prediction error.” The brain abhors informational “surprise,” which is minimized by (1) action enhancing the statistical likelihood of sensory samples, (2) revising inferences in the light of experience, updating “priors” to reality-aligned “posteriors,” and (3) optimizing the complexity of our generative models of a capricious world. In all three, free energy is converted to bound energy. In psychopathology energy either remains unbound, as in trauma and inhibition of agency, or manifests restricted, anachronistic “top-down” narratives. Psychotherapy fosters client agency, linguistic and practical. Temporary uncoupling bottom-up from top-down automatism and fostering scrutinized simulations sets a number of salutary processes in train.
Mentalising
enriches Bayesian inference, enabling experience and feeling states to be “metabolized” and assimilated. “Free association” enhances more inclusive sensory sampling, while dream analysis foregrounds salient emotional themes as “attractors.” FEP parallels with psychoanalytic theory are outlined, including Freud’s unpublished project, Bion’s “contact barrier” concept, the Fonagy/Target model of sexuality, Laplanche’s therapist as “enigmatic signifier,” and the role of projective identification. The therapy stimulates patients to become aware of and revise the priors’ they bring to interpersonal experience. In the therapeutic “duet for one,” the energy binding skills and non-partisan stance of the analyst help sufferers face trauma without being overwhelmed by psychic entropy. Overall, the FEP provides a sound theoretical basis for psychotherapy practice, training, and research.
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