Part II: Identity Thesis

Quantitative Predictions

Introduction
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Quantitative Predictions

The motif characterizations yield a direct empirical prediction: in controlled affect induction paradigms, affects should cluster by their defining dimensions:

  1. Joy conditions cluster in the (+Val,+reff,+Φ,−SM)(+\valence, +\effrank, +\intinfo, -\mathcal{SM}) region
  2. Suffering conditions cluster in the (−Val,+Φ,−reff)(-\valence, +\intinfo, -\effrank) region
  3. Fear and curiosity both show high CF\mathcal{CF} but separate on valence axis

If affects don't cluster by their predicted dimensions—or if other dimensions predict clustering better—the motif characterizations are wrong and require revision.