Expanding the Interpretive Potential of Eye Tracking for the Rorschach: Replication and Extension of the Findings of Ales et al. (2020)

Figures & data

Table 1. Descriptive statistics of engagement and cognitive processing domain variables.

Raw Scores					Standard Scores
	M	SD	Skew	Kurtosis	M	SD	Skew	Kurtosis	ICCs
Page 1
Complexity	78.9	21.1	.33	−.51	103.2	13.4	−.13	−.49	.95
R	23.7	3.1	.81	.47	100.3	9.1	.25	.14	1.0
F%	36.0	16.6	.48	.28	94.8	15.0	−.06	.43	.92
Blend	5.7	3.2	.83	1.5	107.6	13.5	−.17	.66	.82
Sy	7.4	4.0	.37	−.62	102.8	14.7	.06	−.83	.90
MC	7.1	3.0	.55	.32	99.8	12.4	−.12	−.13	.93
MC - PPD	−3.8	4.3	−.76	1.0	95.2	13.1	−.28	−.11	.85
M	3.9	2.4	.68	.07	101.1	13.2	.03	−.26	.95
M - WSumC	.77	2.7	.27	−.15	2.32	16.36	.06	−.04	.88
(CF + C) – FC	1.7	1.8	.51	.18	112.87	17.15	.31	−.19	.87
Page 2
W%	37.77	15.78	.47	−.08	98.43	11.25	.245	−.33	.96
Dd%	15.60	9.10	.63	.49	100.92	12.27	−.082	−.21	.67
SI	2.15	1.81	1.29	2.49	94.98	14.40	.338	−.32	.87
IntCont	1.75	1.63	1.31	2.76	96.33	11.62	.228	−.38	.75
Vg%	1.09	2.16	1.69	1.36	89.37	6.61	1.578	.77	.92
V	1.00	1.38	.506	−.571	104.85	13.65	.608	−.56	.50
FD	3.88	1.86	.14	−.49	126.85	13.18	−.737	.81	.50
R8910	7.45	1.42	.66	−.63	98.55	9.92	.521	−.82	1.0
WSumC	3.15	1.70	.58	.67	98.77	11.43	−.104	.23	.83
C	.10	.30	2.74	5.67	96.90	5.75	2.74	5.67	*
Mp - Ma	−.50	2.31	.40	.78	.67	18.6	−.37	−.10	.72

Note: Values in bold refer to variables after square root transformation. Intraclass correlation coefficients (ICCs) were calculated based on raw scores. Variables that are underlined are proportion variables computed as subtraction. *All protocols for ICC contained C = 0.

Table 2. Descriptive statistics and correlation matrix for eye-tracking variables.

	M	SD	Skew	Kurtosis	1	2	3	4	5	6	7	8	9
1. NF	1300.79	610.21	.93	.33	1.00
2. FD (msec)	334.55	54.01	−.19	.44	−.04	1.00
3. URV	367.90	101.17	.92	1.64	.79**	−.15	1.00
4. SA (o visual angle)	2.58	.53	.03	−.80	−.10	−.03	.05	1.00
5. ISA (o visual angle)	1.91	.64	.49	−.43	−.13	−.02	−.06	.50**	1.00
6. ISL (msec)	279.29	73.94	2.04	7.78	.16	.00	.08	−.25	−.30*	1.00
7. URV/NF	.31	.08	.28	−.14	−.82**	.02	−.44**	.15	.11	−.20	1.00
8. NF/sec	2.54	.41	.25	.79	−.05	−.82**	−.13	−.11	−.05	−.06	−.07	1.00
9. URV/sec	.79	.24	.45	−.66	−.73**	−.47**	−.44**	.07	.06	−.22	.81**	.52**	1.00

Note: NF (Number of Fixations); FD (Fixation Duration); URV (Unique Regions Visited); SA (Saccade Amplitude); ISA (Initial Saccade Amplitude); URRV/NF (Unique Regions Visited per number of fixations); NF/sec (Number of fixations per second); URV/sec (Unique Regions Visited per second).

*p<.05; **p<.01.

Table 3. Correlations of Rorschach performance assessment system engagement and cognitive processing variables, symbol search, and eye tracking variables.

	NF	FD	URV	SA	ISA	ISL	URV/NF	NF/sec	URV/sec
Page 1
Complexity	.475**	−.060	.435**	.013	.132	−.156	−.463**	.084	−.368**
R	.211	−.269*	.350**	−.053	−.072	−.180	−.090	.190	.029
F%	−.207	.027	−.129	−.102	−.117	.112	.290*	−.085	.208
Blend	.355**	−.003	.291*	.096	.138	−.154	−.327*	−.020	−.300*
Sy	.405**	.156	.329*	−.019	.195	−.106	−.444**	−.036	−.423**
MC	.305*	−.092	.231	.010	.127	−.132	−.373**	.105	−.284*
MC – PPD	−.244	.079	−.337**	−.159	−.127	.143	.031	−.022	−.008
M	.340**	−.010	.219	−.105	.068	−.071	−.384**	.116	−.287*
M – WsumC	.243	.087	.119	−.193	−.027	.026	−.242	.081	−.175
(CF + C) – FC	.026	−.217	.142	.227	.073	−.085	.061	.002	.044
Page 2
W%	−.020	.071	−.089	−.093	.014	−.001	−.147	−.091	−.200
Dd%	.238	.209	.171	.164	.025	−.194	−.190	−.102	−.241
SI	.094	.187	.006	−.142	−.110	−.162	−.230	−.020	−.209
IntCont	.260*	−.105	.174	−.084	−.012	.242	−.299*	.160	−.174
Vg%	−.151	−.259*	−.052	.110	.143	−.136	.219	.171	.299*
V	−.021	−.146	−.065	.047	.285*	−.107	−.037	.145	.064
FD	.059	−.020	−.014	.017	−.033	.043	−.160	−.029	−.167
R8910%	−.176	.112	−.142	.171	−.119	−.085	.109	−.043	.096
WsumC	.082	−.153	.114	.164	.137	−.140	−.144	.031	−.117
C	−.109	−.112	−.066	−.001	.093	−.184	.123	−.111	.017
Mp – Ma	−.056	−.018	−.141	.022	−.233	.230	.010	.088	.075
Symbol Search from WAIS-IV
SS	−.069	−.165	−.036	.028	.055	−.129	.034	.072	.096

*p < .05, **p < .01.

Table 4. Hierarchical multiple regressions predicting eye-tracking variables.

NF
Model		β		Adj R^2
Step1	Step 2	M1	M2^a	M1	M2	ΔR^2
Complexity	Blend	.475**	.544**/−.085	.212	.201	.003
Complexity	Sy	.475**	.525**/−.057	.212	.199	.001
Complexity	MC	.475**	.680**/−.251	.212	.220	.021
Complexity	M	.475**	.473**/.003	.212	.199	.000
Complexity	InCont	.475**	.440**/.093	.212	.206	.007
Blend	Complexity	.355**	.085/.544**	.111	.201	.102**
Sy	Complexity	.405**	−.057/.525**	.150	.199	.062*
MC	Complexity	.305*	−.251/680**	.078	.220	.153**
M	Complexity	.340**	.003/.473**	.100	.199	.110**
InCont	Complexity	.260*	.093/.440**	.052	.206	.165**
URV
Complexity	R	.435**	.350**/−.202	.175	.195	.033
Complexity	Blend	.435**	.578**/−.177	.175	.172	.011
Complexity	Sy	.435**	.641*/−.235	.175	.174	.012
Complexity	MC−PPD	.435**	359**/−203	.175	.197	.035
R	Complexity	.350**	.202/.350**	.107	.195	.100**
Blend	Complexity	.291*	−.177/.578**	.069	.172	.115**
Sy	Complexity	.329*	−.235/.641*	.093	.174	.093*
MC−PPD	Complexity	−.337**	−.203/.359**	.098	.197	.111**
URV/NF
Complexity	Blend	−.463**	−.576**/.139	.201	.194	.007
Complexity	F%	−.463**	−.549**/−.116	.201	.193	.006
Complexity	Sy	−.463**	−.320/−.163	.201	.193	.006
Complexity	MC	−.463**	−.478*/.018	.201	.187	.000
Complexity	M	−.463**	−.385*/−.109	.201	.193	.006
Complexity	InCont	−.463**	−.408**/−.144	.201	.205	.018
Blend	Complexity	−.327*	.139/−.576**	.092	.194	.114**
F%	Complexity	.250*	−.116/−.549	.069	.193	.136**
Sy	Complexity	−.444**	−.163/−.320	.183	.193	.023
MC	Complexity	−.373**	.018/−.478**	.124	.215	.076*
M	Complexity	−.384**	−.109/−.385*	.132	.193	.073*
InCont	Complexity	−.299*	−.144/−.408**	.074	.205	.143**
URV/sec
Complexity	Blend	−.368**	−.363/−.007	.121	.105	.000
Complexity	Sy	−.368**	.019/-.440	.121	.151	.044
Complexity	MC	−.368**	−410/.051	.121	.106	.001
Complexity	M	−.368**	−.332/−.050	.121	.106	.001
Complexity	Vg%	−.368**	−358**/.286*	.121	.190	.082*
Blend	Complexity	−.300*	−.007/−.363	.074	.105	.045
Sy	Complexity	−.423**	−.440/.019	.165	.151	.000
MC	Complexity	−.284*	.051/−.410	.065	.106	.056
M	Complexity	−.287*	−.050/−.332	.066	.106	.055
Vg%	Complexity	.299*	.286*/−.358**	.074	.190	.128**

Note: M 1= Model 1; M2 = Model 2.

^aValues to the left of the slash refer to variables entered at Step 1; values on the right of the slash refer to the variable entered at Step 2.

*p<.05; **p<.01.

Data availability statement

Data from the study will be made available upon reasonable request.