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arxiv: 2606.28470 · v1 · pith:FJHRG5ZVnew · submitted 2026-06-26 · 💻 cs.LG · q-bio.NC· quant-ph

Modelling Emotional Memory in Children with Tensor Networks

Henry Groves , Lucia F. Jackson , Barbara-Anne Robertson , Jonte R. Hance This is my paper

Pith reviewed 2026-06-30 01:02 UTC · model grok-4.3

classification 💻 cs.LG q-bio.NCquant-ph
keywords emotional memoryrecognition memoryvalenceorder dependencetensor networkschildrencognitive modelingquantum-inspired methods
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The pith

A tensor network that factors emotional valence models children's recognition memory at 77.98 percent accuracy by capturing order effects.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that recall accuracy for a sequence of toys depends on the emotional valence of each item and the items shown immediately before and after it. Standard psychological models register this order dependence but achieve only low accuracy and do not represent how memory for one emotional object affects others in the set. A classical tensor network that incorporates valence reaches 77.98 percent accuracy on the experimental outcomes. This result is presented as evidence that quantum-inspired tensor structures can handle order-dependent emotional memory without constituting a full quantum cognition model. The authors also introduce a task protocol for studying emotional temporal memory in children that can be analyzed with either classical or quantum-like methods.

Core claim

Emotional valence influences the order-dependent structure of children's recognition memory: correct recall of a sequence of emotionally-valenced toys depended not just on the valence of a given toy itself, but also on the valence of the toys shown before and after it. A classical tensor network model factoring in valence achieves 77.98 percent accuracy, a substantial improvement over standard psychological models that do not reflect how memory for an emotional object influences others in the set.

What carries the argument

Classical tensor network model that factors valence into the representation of sequential memory to capture order dependence.

If this is right

  • Order dependence in emotional memory can be represented through tensor network factorization of valence.
  • The introduced task protocol supplies a repeatable method for collecting data on how surrounding emotional context affects recall.
  • Quantum-inspired classical models can deliver high accuracy on order-dependent cognitive phenomena without requiring quantum hardware.
  • Memory for an individual emotional item is shaped by the valence pattern of the entire sequence rather than by isolated item properties.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Tensor network representations of valence could be tested on adult emotional memory or on non-emotional but still ordered sequences such as word lists.
  • If the model generalizes beyond the original toy set, it could be used to predict how classroom materials with varying emotional tones affect learning order.
  • The gap between standard models and the tensor network suggests that explicit modeling of cross-item influences is a necessary addition to accounts of recognition memory.

Load-bearing premise

The chosen tensor network structure and the specific way valence is incorporated correctly reflect the psychological mechanisms of order dependence instead of merely matching the collected data.

What would settle it

Apply the same tensor network to a fresh set of children's recall data collected with different sequences or age groups and measure whether accuracy stays near 78 percent or drops sharply.

Figures

Figures reproduced from arXiv: 2606.28470 by Barbara-Anne Robertson, Henry Groves, Jonte R. Hance, Lucia F. Jackson.

Figure 1
Figure 1. Figure 1: FIG. 1: The top row displays objects used in stimulus [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2: Scaling of parameters required to model the [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3: A diagrammatic representation of the MPS from Eq. (12) in Penrose graphical notation. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4: Penrose diagrams visualising the tensor network representation of combined valence-recall pairs. Fig. 4a [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5: Qubit (Fig. 5a) and qutrit (Fig. 5b) probability [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6: Probability of recall of Item 2 in a sequence, based on the recall state and valence of Item 1 in that [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7: Matrix product state cross-validation [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8: Singular value decay for each bond in the qubit [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: a), the valence-primed model is sensitive to the SVD cutoff threshold ϵ (Fig. 9b). Performance is mostly stable until a threshold of 10−2 , after which accuracy degrades, especially in the middle positions. This con￾firms that the higher-order singular values in the valence￾primed MPS are carrying real predictive information, un￾like the earlier models, where truncating those values had no effect due to co… view at source ↗
read the original abstract

We demonstrate how emotional valence influences the order-dependent structure of children's recognition memory: correct recall of a sequence of emotionally-valenced toys depended not just on the valence of a given toy itself, but also on the valence of the toys shown before and after it. Whilst standard psychological models confirm that order-dependence differs across an event (a set of toys shown in sequence), accuracy is low and the model does not reflect how memory for an emotional object influences others in the set. A classical tensor network model factoring in valence is able to achieve a 77.98\% accuracy in modelling the results of the study. While not strictly a ``quantum cognition'' model, this massive increase in accuracy shows the value of quantum-inspired methods for modelling order-dependent phenomena, such as emotional memory. Further, the task protocol we introduce presents a novel, real-world tool for exploring emotional temporal memory in children for analysis using classical and quantum-like models of cognition.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript claims that emotional valence influences the order-dependent structure of children's recognition memory, where correct recall of a toy depends on the valences of preceding and following toys in the sequence. Standard psychological models exhibit low accuracy and fail to capture inter-object memory influences, whereas a classical tensor network model that factors in valence achieves 77.98% accuracy. This is presented as evidence for the value of quantum-inspired methods in modeling order-dependent phenomena, alongside a new experimental protocol for studying emotional temporal memory in children.

Significance. Should the reported accuracy prove robust, the work would demonstrate that tensor network models can substantially outperform traditional approaches in capturing valence-mediated order effects in memory, highlighting the utility of quantum-inspired techniques for cognitive modeling and providing a practical new tool for developmental psychology research.

major comments (2)
  1. [Abstract] Abstract: The 77.98% accuracy figure is stated without accompanying details on the tensor network architecture (e.g., bond dimensions, contraction order), number of parameters, training procedure, cross-validation strategy, or statistical error bars. This information is necessary to determine if the model captures the claimed psychological order-dependence or overfits the experimental data.
  2. [Abstract] Abstract: No quantitative comparison is provided for the 'standard psychological models' mentioned, including their specific accuracies or how they were implemented, which undermines the claim of a 'massive increase in accuracy'.
minor comments (1)
  1. The abstract uses British spellings ('modelling', 'whilst') consistently, but verify uniform usage and expand the methods description in the main text to support reproducibility of the accuracy result.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight opportunities to improve the clarity of the abstract. We address each point below and have revised the manuscript to incorporate the suggested details.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The 77.98% accuracy figure is stated without accompanying details on the tensor network architecture (e.g., bond dimensions, contraction order), number of parameters, training procedure, cross-validation strategy, or statistical error bars. This information is necessary to determine if the model captures the claimed psychological order-dependence or overfits the experimental data.

    Authors: We agree that the abstract would benefit from additional context on the model. The full manuscript details the tensor network architecture (including bond dimensions and contraction order), number of parameters, training procedure, cross-validation strategy, and statistical error bars in the Methods and Results sections. We will revise the abstract to briefly reference these elements and report the accuracy with error bars for improved transparency. revision: yes

  2. Referee: [Abstract] Abstract: No quantitative comparison is provided for the 'standard psychological models' mentioned, including their specific accuracies or how they were implemented, which undermines the claim of a 'massive increase in accuracy'.

    Authors: The manuscript provides quantitative comparisons, including specific accuracies and implementation details for the standard psychological models, in the Results section. To address the concern directly in the abstract, we will revise it to include the accuracy figures for these models alongside the tensor network result. revision: yes

Circularity Check

0 steps flagged

No circularity: reported accuracy is an empirical modelling result with no derivation chain reducing to inputs by construction

full rationale

The paper presents an empirical result: a tensor network model with valence factoring reaches 77.98% accuracy on the collected recognition-memory sequences, contrasted with low accuracy from standard psychological models. No equations, parameter-fitting procedure, or self-citation chain is supplied in the provided text that would make the accuracy a tautological renaming or forced fit of the same data. The central claim is therefore an in-sample performance number rather than a first-principles derivation that collapses to its own inputs. No self-definitional, fitted-input-called-prediction, or uniqueness-imported-from-authors pattern appears. The modelling result stands as an independent empirical observation on the given dataset.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the model is described only at the level of 'factoring in valence'.

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Reference graph

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