Review Papers Claim by Claim

Methodological strengths

• Careful within-subject counterbalanced design equating total elapsed seconds (64.8 s) while manipulating items-per-event—isolates information-density effects rather than elapsed time (paper methods & OSF dataset). Design details (Logie et al. 2026)
• Open data link provided (OSF) increases reproducibility potential. OSF data availability

Key limitations & blind spots

• Sample size modest (N=26 analysed); authors claim power based on previous studies but Bayesian evidence is often "anecdotal" in some comparisons—interpret effect sizes with caution. Sample & Bayesian notes
• Boundaries implemented via predictable spatial transitions (room color/floor changes) — predictable boundaries may produce different effects than surprising or narrative boundaries studied elsewhere; generalization to naturalistic, unpredictable boundaries remains open. Boundary predictability (Logie et al. 2026)
• No neurophysiological measures—behavioural inference about contextual drift vs reset models remains indirect; bridging to neural replay/hippocampal context models requires additional data (e.g., intracranial/MEG/fMRI or animal physiology).

What the evidence supports (balanced)

1. Robust temporal compression: participants' placed early items later and late items earlier on a timeline—observed at nested event/item levels and stronger when events contain fewer items (6×4 > 3×8). Temporal compression evidence (Logie et al. 2026)
2. Memory for 'what' (recognition d') and 'where' (grid placement) remained above chance and largely stable across conditions—compression selectively affected 'when'. What/Where results (Logie et al. 2026)
3. Response times suggest backward scanning / reconstruction from event ends rather than strictly forward scanning—compatible with reconstructive memory models and boundary-anchored retrieval. RT evidence (Logie et al. 2026)

Concrete follow-up experiments to disambiguate mechanisms

1. Introduce unpredictable (surprising) boundaries vs predictable spatial boundaries within-subject to test if prediction error modulates compression amplitude (prediction-errors should increase temporal expansion, per prior literature).
2. Pair behavioural VR with concurrent MEG (or scalp EEG) to detect boundary-locked neural signatures (context-reset indices, hippocampal theta/ripple proxies) and test whether neural drift rates predict individual compression factors.
3. Manipulate event duration vs information density orthogonally (vary seconds per item while holding items constant) to confirm compression depends on information, not elapsed seconds.