Srry I should have mentioned in the original post that ANT_WIN_VS_NOWIN and ANT_LOSS_VS_NOLOSS correspond to Win ($1 and $5) vs No Win ($0) and Loss ($1 and $5) vs No Loss ($0), respectively. Each magnitude separated is not listed here. I'd have to include those to the script. In fact, I tried that using the following script, and did not get the same output. Thoughts on how to fix this? Thanks.

# run afni_proc.py to create a single subject processing script
afni_proc.py -subj_id $subj \
-script proc.$subj -scr_overwrite \
-blocks despike tshift align tlrc volreg blur mask scale regress \
-copy_anat $anat_dir/anatSS.${subj}.nii \
-anat_has_skull no \
-tcat_remove_first_trs 0 \
-tshift_opts_ts -tpattern seqplus \
-dsets \
$epi_dir/${subj}.MID.epi+orig. \
-volreg_align_to MIN_OUTLIER \
-tlrc_base $top_dir/nlWarp/MNI152_2009_template_SSW.nii.gz \
-volreg_align_e2a \
-volreg_tlrc_warp \
-tlrc_NL_warp \
-tlrc_NL_warped_dsets \
$anat_dir/anatQQ.${subj}.nii \
$anat_dir/anatQQ.${subj}.aff12.1D \
$anat_dir/anatQQ.${subj}_WARP.nii \
-blur_size 5.0 \
-mask_epi_anat yes \
-regress_stim_times \
$stim_dir/loss_0_ant.txt \
$stim_dir/loss_1_ant.txt \
$stim_dir/loss_5_ant.txt \
$stim_dir/loss_ant.txt \
$stim_dir/win_0_ant.txt \
$stim_dir/win_1_ant.txt \
$stim_dir/win_5_ant.txt \
$stim_dir/win_ant.txt \
$stim_dir/fix.txt \
$stim_dir/win_fb_hit.txt \
$stim_dir/win_fb_nhit.txt \
$stim_dir/loss_fb_hit.txt \
$stim_dir/loss_fb_nhit.txt \
-regress_stim_labels \
L0ANT \
L1ANT \
L5ANT \
LANT \
W0ANT \
W1ANT \
W5ANT \
WANT \
FIX \
WFB_HIT \
WFB_NHIT \
LFB_HIT \
LFB_NHIT \
-regress_basis_multi \
'GAM' \
'GAM' \
'GAM' \
'GAM' \
'GAM' \
'GAM' \
'GAM' \
'GAM' \
'GAM' \
'GAM' \
'GAM' \
'GAM' \
'GAM' \
-regress_stim_types \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
-regress_censor_motion 0.3 \
-regress_apply_mot_types demean deriv \
-regress_motion_per_run \
-regress_opts_3dD \
-num_glt 10 \
-gltsym 'SYM: 1.0*WANT -1.0*W0ANT' -glt_label 1 ANT_WIN_VS_NOWIN \
-gltsym 'SYM: 1.0*LANT -1.0*L0ANT' -glt_label 2 ANT_LOSS_VS_NOLOSS\
-gltsym 'SYM: 1.0*WANT -1.0*LANT' -glt_label 3 ANT_WIN_VS_LOSS \
-gltsym 'SYM: 0.5*WANT 0.5*LANT -0.5*W0ANT -0.5*L0ANT' -glt_label 4 ANT_W15L15_VS_W0L0 \
-gltsym 'SYM: WFB_HIT -WFB_NHIT' -glt_label 5 WINHIT_VS_MISS \
-gltsym 'SYM: LFB_HIT -LFB_NHIT' -glt_label 6 LOSSHIT_VS_MISS \
-gltsym 'SYM: 1.0*W1ANT -1.0*W0ANT' -glt_label 7 ANT_WIN1_VS_NOWIN \
-gltsym 'SYM: 1.0*W5ANT -1.0*W0ANT' -glt_label 8 ANT_WIN5_VS_NOWIN \
-gltsym 'SYM: 1.0*L1ANT -1.0*L0ANT' -glt_label 9 ANT_LOSS1_VS_NOLOSS \
-gltsym 'SYM: 1.0*L5ANT -1.0*L0ANT' -glt_label 10 ANT_LOSS5_VS_NOLOSS \
-regress_anaticor_fast \
-regress_reml_exec \
-regress_make_ideal_sum sum_ideal.1D \
-regress_est_blur_epits \
-regress_est_blur_errts \
-regress_run_clustsim no