Table 1 Summary of past MVPA studies of BOLD data.

From: Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience

Study

N

# Emot. categories

# Stimuli per category (unique)

Induction method

Relevant preprocessing

Feature selection

Classification algorithm

Kassam et al.24

10

9

2

Participant-generated scenario immersion

No spatial smoothing

Voxels with the most stable activation profile

Gaussian Naive Bayes

Z-scoring

Anatomical normalization

Kragel and LaBar25

32

7

4

Movies

Music

No spatial smoothing

All grey matter voxels

Partial least squares discriminant analysis

Mean centering

Anatomical normalization

Saarimäki et al.29

48

5 (study 1)

6 (study 2)

10 (study 1)

6 (study 2)

Participant-generated scenario immersion

Movies

No spatial smoothing

Voxels most sensitive to manipulation using ANOVA

Linear neural network with no hidden layers

Z-scoring

Anatomical normalization

Saarimäki et al.30

25

15

4

Narrative-guided scenario immersion

No spatial smoothing

Voxels most sensitive to manipulation using ANOVA

Linear neural network with no hidden layers

Z-scoring

Anatomical normalization

Wager et al.32

2159

5

Variable

Variable

Meta-analysis (peak based)

Whole brain

Bayesian spatial point process model

Binarized data

Anatomical normalization

  1. These studies all claim to identify unique patterns of brain activity for specific emotion categories, yet these patterns are inconsistent across studies. Other existing MVPA studies of affect (e.g.,38), and conceptual knowledge (e.g.,39) are less relevant and so are not listed here.
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