{
  "description": "",
  "input": {
    "task": "rhymejudgment"
  },
  "name": "ds003_model001",
  "steps": [
    {
      "contrasts": [
        {
          "condition_list": [
            "trial_type.word",
            "trial_type.pseudoword"
          ],
          "name": "word_gt_pseudo",
          "type": "t",
          "weights": [
            1,
            -1
          ]
        },
        {
          "condition_list": [
            "trial_type.word",
            "trial_type.pseudoword"
          ],
          "name": "task_vs_baseline",
          "type": "t",
          "weights": [
            0.5,
            0.5
          ]
        }
      ],
      "level": "run",
      "model": {
        "x": [
          "trial_type.word",
          "trial_type.pseudoword",
          "framewise_displacement",
          "trans_x",
          "trans_y",
          "trans_z",
          "rot_x",
          "rot_y",
          "rot_z",
          "a_comp_cor_00",
          "a_comp_cor_01",
          "a_comp_cor_02",
          "a_comp_cor_03",
          "a_comp_cor_04",
          "a_comp_cor_05"
        ]
      },
      "transformations": [
        {
          "input": [
            "trial_type"
          ],
          "name": "Factor"
        },
        {
          "input": [
            "trial_type.word",
            "trial_type.pseudoword"
          ],
          "model": "spm",
          "name": "Convolve"
        }
      ]
    },
    {
      "auto_contrasts": [
        "word_gt_pseudo",
        "task_vs_baseline"
      ],
      "level": "dataset"
    }
  ]
}

Correlation matrix

The correlation matrix of a design matrix shows the correlation between each pair of regressors. Very high or low correlations among variables of interest (top left) or between variables of interest and nuisance regressors (top right) can indicate deficiency in the design. High correlations among nuisance regressors will generally have little effect on the model.