Prediction and memory: A predictive coding account

Barron, Helen C.; Auksztulewicz, Ryszard; Friston, Karl J.

doi:10.1016/j.pneurobio.2020.101821

Cited by 173 publications

(209 citation statements)

References 201 publications

(251 reference statements)

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“…In this model, precision-weighting/gain-amplification takes place via "big loop recurrence" with the frontal lobes [64], with the more specific suggestion that selection/biasing of policies over forward models cause efference copies to be projected to posterior generative models. In line with recent proposals [106], the hippocampus can operate with either "predictive-suppressive" or "fictive prediction error" modes, which are here suggested to correspond to degree of coupling with respective posterior vs. frontal cortices, with the former corresponding to direct suppression of observations, and the latter facilitating the 'reinstatement' of memories, and novel imaginings for the sake of planning and causal reasoning [68,107]. This frontal coupling is hypothesized to be a source of "successor representations" (i.e., population vectors forming predictive anticipatory sweeps of where the organism is likely to go next) via integration of likely policies and action models (via dorsal prefrontal cortex) and evaluations of likely outcomes (via ventral prefrontal cortex).…”

Section: Conclusion: Functions Of Basic Phenomenal Consciousness?supporting

confidence: 83%

Integrated World Modeling Theory (IWMT) Implemented: Towards Reverse Engineering Consciousness with the Free Energy Principle and Active Inference

Safron

2020

Active Inference

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Section: Conclusion: Functions Of Basic Phenomenal Consciousness?supporting

confidence: 83%

Integrated World Modeling Theory (IWMT) Implemented: Towards Reverse Engineering Consciousness with the Free Energy Principle and Active Inference

Safron

2020

Active Inference

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“…However, this stronger representation of B items in the hippocampus did not translate to consistent evidence for enhanced subsequent memory of B items. This raises intriguing questions about potential differences between prediction and retrieval (Barron et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Statistical prediction of the future impairs episodic encoding of the present

Sherman¹,

Turk‐Browne²

2019

Preprint

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Memory enables reminiscence about past experiences and guides processing of future experiences. However, these two functions are inherently at odds: remembering specific past experiences requires storing idiosyncratic properties, but by definition such properties may not be shared with similar situations in the future and thus are not as useful for prediction. We discovered that, when faced with this conflict, the brain prioritizes prediction over encoding. Behavioral tasks showed that pictures allowing for prediction of what will appear next based on learned regularities are poorly encoded into memory. Brain imaging revealed that predictive representations in the hippocampus may be responsible for this worse episodic encoding and suggested that such interference may result from competition between hippocampal pathways. This tradeoff between statistical learning and episodic memory may be adaptive, focusing encoding on experiences for which we do not yet have a predictive model. whereas statistical learning refers to extracting what is common across mul-5 tiple experiences (e.g., what tends to happen at birthday parties). Episodic memory allows for vivid recollection and nostalgia about past events, whereas statistical learning leads to more generalized knowledge and predictions about new situations. Episodic memory occurs rapidly and stores even related expe-10 riences distinctly in order to minimize interference, whereas statistical learning occurs more slowly and overlays memories in order to represent their common elements or regularities. Given these behavioral and computational differences, theories of memory have argued that these two kinds of information must be processed serially and stored separately in the brain (McClelland et al., 1995; 15 Squire, 2004): episodic memories are formed first in the hippocampus and these memories in turn provide the input for later statistical learning in the neocortex as a result of consolidation (Frankland and Bontempi, 2005; Richards et al., 2014;Tompary and Davachi, 2017).Here we reveal a relationship between episodic memory and statistical learn-20 ing in the reverse direction, whereby learned regularities determine which memories are formed in the first place. Specifically, we examine whether the ability to predict what will appear next -a signature of statistical learning -reduces encoding of the current experience into episodic memory. This hypothesis depends on two theoretical commitments: first, that the adaptive function of memory is 25 to guide future behavior by generating expectations based on prior experience ; second, that memory resources are limited, because of attentional bottlenecks that constrain encoding (Aly and Turk-Browne, 2017) and/or because new encoding interferes with the storage or retrieval of existing memories (Shiffrin and Atkinson, 1969). Accordingly, in allocating memory re-30 sources, we propose that it is less important to encode an ongoing experience when it already generates strong expectations about future states of the world...

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“…However, the mechanism that allows the hippocampus to coordinate reinstatement across distributed neocortical circuits remains unclear. In animal models, neural circuit manipulations suggest higher-order brain regions may modulate release of sensory information in neocortex via disinhibitory circuit mechanisms 11,12 . For example, during attentional modulation, projections from the cingulate region of mouse frontal cortex modulate GABAergic circuits in visual cortex to enhance visual discrimination 13 .…”

Section: Introductionmentioning

confidence: 99%

A mechanism for hippocampal memory recall based on excitatory-inhibitory fluctuations in neocortex

Koolschijn

Shpektor

et al. 2020

Preprint

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The brain has a remarkable capacity to acquire and store memories that can later be selectively recalled. These processes are supported by the hippocampus which is thought to index memory recall by reinstating information stored across distributed neocortical circuits. However, the mechanism that supports this interaction remains unclear. Here, in humans, we show that recall of a visual cue from a paired associate is accompanied by a transient increase in the ratio between glutamate and GABA in visual cortex. Moreover, these excitatory-inhibitory fluctuations are predicted by activity in the hippocampus. These data suggest the hippocampus gates memory recall by indexing information stored across neocortical circuits using a disinhibitory mechanism.

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Prediction and memory: A predictive coding account

Cited by 173 publications

References 201 publications

Integrated World Modeling Theory (IWMT) Implemented: Towards Reverse Engineering Consciousness with the Free Energy Principle and Active Inference

Integrated World Modeling Theory (IWMT) Implemented: Towards Reverse Engineering Consciousness with the Free Energy Principle and Active Inference

Statistical prediction of the future impairs episodic encoding of the present

A mechanism for hippocampal memory recall based on excitatory-inhibitory fluctuations in neocortex

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