This protocol establishes an empirical framework for evaluating the expectancy-mediated mechanisms of algorithmic validation in digital courtship. While young adults increasingly encounter algorithmically mediated compatibility systems, research has largely emphasized subjective reporting, with comparatively limited multimodal psychophysiological validation. We propose a 3×2 factorial experimental design to quantify the causal influence of perceived algorithmic authority and compatibility feedback on validation. Crucially, physiological measures are treated as complementary indicators of autonomic and endocrine response rather than as a ground-truth representation of psychological validation. By defining a strict endpoint hierarchy and utilizing sequential causal mediation, this design enables the explicit statistical modeling of convergent or divergent responses across subjective, autonomic, endocrine, facial, and behavioral modalities.
The interaction between human psychological needs and arbitrary digital algorithms sits at a critical intersection of Human-Computer Interaction (HCI) and social psychology. This protocol transitions the study of digital compatibility from descriptive observation to a multimethod causal experiment, testing whether algorithmic authority changes susceptibility to compatibility feedback.
We define the following testable hypotheses, explicitly separating confirmatory estimates from exploratory mappings:
Existing literature documents the Barnum effect, confirmation bias, and the broader heuristics and biases framework (Tversky & Kahneman, 1974) in decision-making. In HCI, research on blended dating systems demonstrates how users evaluate algorithmic involvement (Tong et al., 2016). However, the psychophysiological response to pseudo-deterministic algorithmic authority in romantic contexts remains under-examined, requiring a framework that integrates subjective expectancy with validated autonomic measurement standards (Boucsein et al., 2012).
The functional unit is a controlled user session with an independently generated compatibility interface. The causal pathway is conceptualized as a moderated sequential mediation model:
| Category | Endpoint | Construct Role & Limitation |
|---|---|---|
| Primary Subjective | Validated Subjective Validation Scale | Anticipated emotional reassurance (Reporting bias) |
| Primary Physiological | Event-related Phasic EDA (Amplitude/AUC) | Autonomic arousal (Valence ambiguity) |
| Secondary Subjective | PANAS (Positive/Negative Affect) | General affect (Reporting bias) |
| Secondary Physiological | Heart Rate, Salivary Cortisol (Trajectory) | Autonomic/Endocrine (Diurnal variation, slow kinetics) |
| Exploratory | FACS (Action Units), Web Telemetry | Nonverbal/Behavioral (Inference uncertainty) |
To ensure reproducible statistical power, sample size N is calculated a priori: N=f(α,β,d,ρ,σ,allocation,attrition,primary endpoint). The target cohort is adults aged 18–24 to simplify ethical clearance regarding romantic content.
Participants are tested in climate-controlled kiosks.
The protocol utilizes a 3×2 factorial experimental design manipulating the interface expectation and generated output:
| Authority (W) | Compatibility (X) | Expected Contrast |
|---|---|---|
| Generic | Neutral (50%) | Baseline control |
| Generic | Low (1%-15%) | Low-Neutral negative shift |
| Generic | High (85%-99%) | High-Neutral positive shift |
| Authoritative | Neutral (50%) | Authority baseline |
| Authoritative | Low (1%-15%) | Interaction (Score×Authority) |
| Authoritative | High (85%-99%) | Interaction (Score×Authority) |
Manipulation Checks: Preregistered checks will verify MHigh>MNeutral>MLow for perceived compatibility, and MAuthoritative>MGeneric for perceived algorithmic credibility.
Confirmatory inference (LMM, factorial effects, mediation) is explicitly separated from predictive validation (participant-level CV).
Data will be analyzed using linear mixed-effects models (LMM) with repeated measures. The cortisol trajectory will be modeled directly (Cortisolit=β0+β1Timet+β2Treatmenti+β3Treatmenti×Timet+ui+ϵit) rather than using simple change scores.
Causal mediation estimates will be interpreted under sequential ignorability assumptions, accompanied by formal sensitivity analysis. To control the family-wise error rate across the endpoint hierarchy, a prespecified multiplicity procedure (e.g., Holm-Bonferroni) will be applied.