The Hippocampus May Support Context Retrieval in One-Shot Learning about Pain

Turner, Georgia; Onysk, Jakub

doi:10.1523/jneurosci.2250-21.2022

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…Secondly, we have required the model to store pattern sequences based on only a single exposure, thereby performing one-shot learning. This is more realistic, in particular in dynamic environments [ 50 ] where pattern sequences are usually available only once but might still be relevant, for example to avoid negative experiences after a single exposure, like pain [ 51 ], even if the avoidance behavior is not necessarily an optimal one. The benefit of storing single experiences for learning a behavior has been demonstrated with functional models of episodic memory in reinforcement tasks [ 52 , 53 ].…”

Section: Discussionmentioning

confidence: 99%

A neural network model for online one-shot storage of pattern sequences

Melchior,

Altamimi,

Bayati

et al. 2024

PLoS ONE

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Based on the CRISP theory (Content Representation, Intrinsic Sequences, and Pattern completion), we present a computational model of the hippocampus that allows for online one-shot storage of pattern sequences without the need for a consolidation process. In our model, CA3 provides a pre-trained sequence that is hetero-associated with the input sequence, rather than storing a sequence in CA3. That is, plasticity on a short timescale only occurs in the incoming and outgoing connections of CA3, not in its recurrent connections. We use a single learning rule named Hebbian descent to train all plastic synapses in the network. A forgetting mechanism in the learning rule allows the network to continuously store new patterns while forgetting those stored earlier. We find that a single cue pattern can reliably trigger the retrieval of sequences, even when cues are noisy or missing information. Furthermore, pattern separation in subregion DG is necessary when sequences contain correlated patterns. Besides artificially generated input sequences, the model works with sequences of handwritten digits and natural images. Notably, our model is capable of improving itself without external input, in a process that can be referred to as ‘replay’ or ‘offline-learning’, which helps in improving the associations and consolidating the learned patterns.

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Section: Discussionmentioning

confidence: 99%

A neural network model for online one-shot storage of pattern sequences

Melchior,

Altamimi,

Bayati

et al. 2024

PLoS ONE

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show abstract

“…Such stimuli are found in abundance in the lives of individuals with headache disorders, so it is difficult to apply the results from a focused computer task to a classical or operant conditioning model of these stimuli in real‐world contexts. Relatedly, unlike in the computer task, the actual experience of headache attacks is aversive and multisensory, so models of learning from experience in real‐world contexts are expected to be assisted by enhanced attentional processes 34 and may occur in “one‐shot” exposures 35 . Other notable limitations of the study include the unknown reliability of the association strength ratings and prior belief ratings, which complicates the interpretation of the variance in responses.…”

Section: Discussionmentioning

confidence: 98%

“…Relatedly, unlike in the computer task, the actual experience of headache attacks is aversive and multisensory, so models of learning from experience in real-world contexts are expected to be assisted by enhanced attentional processes 34 and may occur in "one-shot" exposures. 35 Other notable limitations of the study include the unknown reliability of the association strength ratings and prior belief ratings, which complicates the interpretation of the variance in responses. The scales used to measure these constructs were devised for the present study, have unknown reliability, and may be subject to measurement error.…”

Section: Discussionmentioning

confidence: 99%

Learning headache triggers through experience: A laboratory study

Turner

Houle

2023

Headache

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Objective To examine how individuals may learn headache trigger beliefs through sequential symbolic pairings of trigger candidates and headache attacks. Background Learning from experience may be a major source of information about headache triggers. Little is known about learning‐based influences on the establishment of trigger beliefs. Methods This cross‐sectional, observational study included N = 300 adults with headache who participated in a laboratory computer task. First, participants rated the chances (0%–100%) that encountering specific triggers would lead to experiencing a headache. Then, 30 sequential images with the presence or absence of a common headache trigger were presented alongside images representing the presence or absence of a headache attack. The primary outcome measure was the cumulative association strength rating (0 = no relationship to 10 = perfect relationship) between the trigger and headache using all previous trials. Results A total of N = 296 individuals completed 30 trials for each of three triggers, yielding 26,640 total trials for analysis. The median [25th, 75th] association strength ratings for each of the randomly presented headache triggers were 2.2 [0, 3] for the Color Green, 2.7 [0, 5] for Nuts, and 3.9 [0, 8] for Weather Changes. There was a strong association between the “true” cumulative association strength and corresponding ratings. A 1‐point increase on the phi scale (i.e., no relationship to perfect relationship) was associated with a 1.20 (95% CI: 0.81 to 1.49, p < 0.0001) point increase in association strength rating. A participant's prior belief about the potency of a trigger affected their perceived rating of the accumulating evidence, accounting for 17% of the total variation. Conclusion In this laboratory task, individuals appeared to learn trigger–headache associations through repeated exposures to accumulating symbolic evidence. Prior beliefs about the triggers appeared to influence ratings of the strength of relationships between triggers and headache attacks.

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Independent replications reveal anterior and posterior cingulate cortex activation underlying state anxiety-attenuated face encoding

Buehler,

Lowther,

Lukow

et al. 2024

Commun Psychol

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Anxiety involves the anticipation of aversive outcomes and can impair neurocognitive processes, such as the ability to recall faces encoded during the anxious state. It is important to precisely delineate and determine the replicability of these effects using causal state anxiety inductions in the general population. This study therefore aimed to replicate prior research on the distinct impacts of threat-of-shock-induced anxiety on the encoding and recognition stage of emotional face processing, in a large asymptomatic sample (n = 92). We successfully replicated previous results demonstrating impaired recognition of faces encoded under threat-of-shock. This was supported by a mega-analysis across three independent studies using the same paradigm (n = 211). Underlying this, a whole-brain fMRI analysis revealed enhanced activation in the posterior cingulate cortex (PCC), alongside previously seen activity in the anterior cingulate cortex (ACC) when combined in a mega-analysis with the fMRI findings we aimed to replicate. We further found replications of hippocampus activation when the retrieval and encoding states were congruent. Our results support the notion that state anxiety disrupts face recognition, potentially due to attentional demands of anxious arousal competing with affective stimuli processing during encoding and suggest that regions of the cingulate cortex play pivotal roles in this.

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The Hippocampus May Support Context Retrieval in One-Shot Learning about Pain

Cited by 5 publications

References 27 publications

A neural network model for online one-shot storage of pattern sequences

A neural network model for online one-shot storage of pattern sequences

Learning headache triggers through experience: A laboratory study

Independent replications reveal anterior and posterior cingulate cortex activation underlying state anxiety-attenuated face encoding

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