A Drop Below Baseline: Can a Romantic Partner's Predictive Model of Chat Style Detect Account Replacement by a Bot or Impostor?

This paper proposes, rather than claims to demonstrate, that romantic partners form a usable internal predictive model of each other's written replies in computer-mediated communication (CMC) [1–4, 9]. Existing behavioral and psycholinguistic studies indicate that individuals forecast upcoming utterances above chance [5, 9], that this predictive advantage is significantly larger for an intimate partner than for a stranger [3, 4], and that romantic couples' texting syntax, lexicon, and timing converge systematically over time [2, 6, 8, 10]. The hypothesis advanced here is narrower and operational: if one partner's messaging account is surreptitiously or experimentally replaced by an automated dialogue model (a bot) or by a human impostor writing through that account, the remaining partner's accuracy at anticipating the next reply should fall significantly below that individual's own established baseline. That within-person drop below baseline—rather than an isolated, uncalibrated impression that *"this feels off"*—is formalized as a testable behavioral signal of account replacement. A prospective, blinded, within-person experimental protocol is outlined. Following sober baseline calibration, each participant completes 20 randomized conversation blocks disguised under their partner's messaging interface: 10 blocks in which replies originate from their actual partner, and 10 blocks in which replies originate from a style-prompted bot or a trained human impostor. A pre-registered **8-of-10 pass bar** ($\ge 8/10$ replacement blocks dropping below personal baseline or explicitly classified as non-partner, paired with $\le 2/10$ false alarms on real-partner blocks) serves as the practical binary classification rule alongside continuous within-person accuracy contrasts. Furthermore, a between-subjects pharmacological factor compares a caffeine-abstinent control arm against an acute caffeine/pre-workout arm targeting approximately $200\text{ mg}$ of caffeine (corresponding to a standard half-scoop of a commercial pre-workout blend labeled at $400\text{ mg}$ caffeine per full scoop, matching the author's habitual intake). Because multi-ingredient pre-workout supplements (MIPS) contain co-active compounds that confound pure adenosinergic interpretations [11], full quantitative ingredient disclosure—or a parallel USP caffeine-only capsule arm—is mandated in the protocol. Methodological confounds, expectation biases, assumed similarity [7], and ethical safeguards for relationship trust and data privacy are critically examined. No causal or empirical conclusion is claimed prior to execution of the trial.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-07
DOI
https://doi.org/10.5281/zenodo.24043465
Primary Topic
User Authentication and Security Systems
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
preprint

A Drop Below Baseline: Can a Romantic Partner's Predictive Model of Chat Style Detect Account Replacement by a Bot or Impostor?

Dr Akshay Honrao, BDS,PGDIP(Orthodontics)
2 links
Zenodo (CERN European Organization for Nuclear Research)
User Authentication and Security Systems
preprint

A Drop Below Baseline: Can a Romantic Partner's Predictive Model of Chat Style Detect Account Replacement by a Bot or Impostor?

Dr Akshay Honrao, BDS,PGDIP(Orthodontics)
preprint en

Abstract

This paper proposes, rather than claims to demonstrate, that romantic partners form a usable internal predictive model of each other's written replies in computer-mediated communication (CMC) [1–4, 9]. Existing behavioral and psycholinguistic studies indicate that individuals forecast upcoming utterances above chance [5, 9], that this predictive advantage is significantly larger for an intimate partner than for a stranger [3, 4], and that romantic couples' texting syntax, lexicon, and timing converge systematically over time [2, 6, 8, 10]. The hypothesis advanced here is narrower and operational: if one partner's messaging account is surreptitiously or experimentally replaced by an automated dialogue model (a bot) or by a human impostor writing through that account, the remaining partner's accuracy at anticipating the next reply should fall significantly below that individual's own established baseline. That within-person drop below baseline—rather than an isolated, uncalibrated impression that *"this feels off"*—is formalized as a testable behavioral signal of account replacement. A prospective, blinded, within-person experimental protocol is outlined. Following sober baseline calibration, each participant completes 20 randomized conversation blocks disguised under their partner's messaging interface: 10 blocks in which replies originate from their actual partner, and 10 blocks in which replies originate from a style-prompted bot or a trained human impostor. A pre-registered **8-of-10 pass bar** ($\ge 8/10$ replacement blocks dropping below personal baseline or explicitly classified as non-partner, paired with $\le 2/10$ false alarms on real-partner blocks) serves as the practical binary classification rule alongside continuous within-person accuracy contrasts. Furthermore, a between-subjects pharmacological factor compares a caffeine-abstinent control arm against an acute caffeine/pre-workout arm targeting approximately $200\text{ mg}$ of caffeine (corresponding to a standard half-scoop of a commercial pre-workout blend labeled at $400\text{ mg}$ caffeine per full scoop, matching the author's habitual intake). Because multi-ingredient pre-workout supplements (MIPS) contain co-active compounds that confound pure adenosinergic interpretations [11], full quantitative ingredient disclosure—or a parallel USP caffeine-only capsule arm—is mandated in the protocol. Methodological confounds, expectation biases, assumed similarity [7], and ethical safeguards for relationship trust and data privacy are critically examined. No causal or empirical conclusion is claimed prior to execution of the trial.

Zenodo (CERN European Organization for Nuclear Research)
User Authentication and Security Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon