Results

To evaluate the proposed relationships between literary exposure, psychological traits, and social-cognitive outcomes, a series of statistical analyses were conducted. The analyses proceeded in three stages: (1) descriptive statistics and distributional checks were used to assess central tendencies, variability, and normality of continuous variables; (2) correlation analyses were conducted to examine the associations between key demographic, behavioural, and psychological variables; and (3) a series of hierarchical multiple regression models were conducted to assess the predictive value of literary exposure, anxiety, and existential concerns on empathy and mentalisation outcomes, including their interaction effects. These analyses are reported in the subsections that follow.

4.1. Descriptive Analyses

Descriptive statistics were computed for book count and age, as well as for reading behaviour variables that were originally ordinal but treated as approximately continuous for summarisation purposes. Table 2 presents the minimum, maximum, mean, standard deviation, skewness, and kurtosis values for each of the demographic and reading-related variables.

Variable	Min	Max	M	SD	Skewness	Kurtosis
Age	19	30	22.1	2.3	1.3	1.4
Books	0	68	13	10.1	1.9	5.7
Textbook	0	7	3	2.1	0.5	-0.7
Academic	0	7	2	1.7	1.2	1.2
Magazine	0	7	0	1.2	2.4	6.9
Newspaper	0	7	0	0.9	3.1	13.4
E-Mail	0	7	1	1.1	3.2	13.6
Internet	0	7	4	2.2	-0.3	-1.1

Table 2\ Descriptive statistics of age, book count, and reading habits

Note. Age is in years; Books refers to the total number of books read from the book list (out of 101). Reading time variables were assessed using 8 ordered categories (“0 hours” to “7 or more hours”); means and standard deviations reflect approximate midpoints for descriptive purposes only.

According to widely cited guidelines, univariate distributions can be regarded as acceptably normal when skewness is between ±2 and kurtosis between ±7 (West, Finch, & Curran, 1995); more stringent conventions treat values within ±1 for skewness and ±2 for kurtosis as strongly normal (George & Mallery, 2010).

Among the variables examined, textbook reading time and internet-text reading time approximated normality under both the regular and stricter criteria. Book count, age, and reading time for academic texts met the regular thresholds but not the stricter ones. In contrast, reading time for magazines, newspapers, and e-mails showed substantial right skew and high kurtosis, indicating heavy tails and a clustering of values near the lower end of the scale. The distributions of these variables can be better understood by consulting Figures 1, 2, and 3. Figure 1 illustrates the age distribution of the participants, while Figure 2 shows the distribution of the number of books read. Figure 3 presents the distributions of weekly reading time across various formats, including textbooks, academic papers, magazines, newspapers, e-mails, and internet content.

Figure 1
Distribution of the participants’ ages

Note. The overlaid curve is a kernel density estimate (KDE), providing a smoothed approximation of the age distribution.

*Figure 2* Distribution of the number of books read by the participants

Note. The overlaid curve is a kernel density estimate (KDE), providing a smoothed approximation of the book count distribution.

Figure 3
Distribution of participants’ reading habits across mediums

Although the age and book count variables met conventional thresholds for acceptable normality, they exceeded stricter criteria for strong normality. Log transformation was therefore applied to better satisfy the linearity assumption and reduce the influence of skew. An even more important rationale, however, was theoretical: changes at lower levels (e.g., reading 3 versus 6 books, or being 19 versus 23 years old) may carry greater psychological or developmental significance than equivalent increases at higher levels (e.g., 20 versus 23 books, or 24 versus 28 years old). The transformation thus also served to reflect the non-linear nature of these relationships in subsequent analyses.

Descriptive statistics were also computed for the total and subscale scores of the psychological instruments used in the study. These values are presented in Table 3.

Scale	Subscale	Component	Min	Max	M	SD	Skew.	Kurt.
STAI–S			20	77	41.8	12.4	0.36	-0.46
STAI–T			29	73	48.4	9.0	0.17	-0.38
ECQ			22	108	61.9	18.7	-0.02	-0.77
	General EA		13	64	37.7	11.9	-0.02	-0.65
	Death		5	25	14.5	5.3	0.16	-0.89
	Avoidance		4	20	9.7	3.8	0.43	-0.55
MentS			53	125	96.8	12.3	-0.39	0.04
	Self		8	40	27.2	6.7	-0.50	-0.31
	Other		19	45	36.8	4.9	-0.52	0.15
	Motivation		12	40	32.8	4.8	-0.84	1.21
QCAE			41	120	98.1	11.7	-0.75	1.39
	Cognitive		24	76	60.8	8.0	-0.56	0.85
		Perspective Taking	12	40	33.4	5.0	-0.68	0.34
		Online Simulation	9	36	27.4	4.4	-0.44	0.63
	Affective		17	47	37.3	5.4	-0.53	0.31
		Emotion Contagion	4	16	12.4	2.6	-0.56	0.10
		Proximal Responsivity	4	16	12.6	2.3	-0.64	0.47
		Peripheral Responsivity	5	16	12.2	2.2	-0.23	-0.54

Table 3\ Descriptive statistics of questionnaire scores

Note. Skew = Skewness, Kurt = Kurtosis.

All questionnaire scale and subscale scores comfortably met even the stricter normality criteria, with absolute skewness values not exceeding 0.84 and absolute kurtosis values remaining below 1.39. Combined with their strong internal consistency (as reported in the Methods section), the normal distributions of these scale scores suggest that the measures are both statistically reliable and appropriate for subsequent parametric analyses.

4.1. Correlation Analysis

Correlation analysis was conducted to examine the connection between the variables of the present study, the results of which revealed several statistically significant relationships. Refer to Table 4 for the correlation matrix. Since all scales used in the study had 13 subscales in total, as well as their overall score, which adds up to 16 variables, only their overall scores are presented in the table. The subscales’ and components’ correlation results with literary exposure can be seen in Table 5. All other significant findings not present in either table are discussed below.

Variable	1	2	3	4	5	6	7	8	9	10
1. Age	—
2. Books count	.08	—
3. Academic hours	-.08	.22**	—
4. Nonfiction hours	.12	.32**	.29**	—
5. Internet hours	.03	-.01	.13*	.01	—
6. STAI–S	-.08	.09	.03	.03	.03	—
7. STAI–T	-.15*	.02	.03	-.02	.02	.61**	—
8. ECQ	-.21**	.08	.10	.09	.04	.47**	.66**	—
9. MentS	.16*	.08	-.03	.03	-.03	-.17*	-.34**	-.18*	—
10. QCAE	-.04	.17*	-.01	.14*	.03	.12	.12	.25**	0.52**	—

Table 4\ Correlation matrix for key variables

Note. * p < .05; ** p < .001; Academic hours = average of textbook hours and academic hours; Nonfiction hours = average of Magazine and Newspaper hours; Internet hours = average of E-Mail and Internet hours.

Age was found to be significantly correlated with a number of psychological and behavioural variables. It was negatively correlated with trait anxiety (r = -.15, p < .05). It was also negatively correlated with overall existential concerns (r = -.21, p < .001), and each of its subscales; namely, general existential anxiety (r = -.22, p < .001), death anxiety (r = -.14, p < .05), and avoidance (r = -.17, p < .05). Additionally, age was positively correlated with overall mentalisation (r = .16, p < .05) and its self-related subscale (r = .18, p < .05). Age was not correlated with overall empathy and any of its subscales or components.

The number of books read by the participant was significantly correlated with time spent reading academic materials, such as textbooks and academic journal articles, (r = 0.22, p < .001) and nonfiction reading material (newspapers and magazines) (r = 0.32, p < .001), reflecting a broader pattern of reading engagement across formats.

Scale	Subscale	Component	r
ECQ			.08
	General EA		.12*
	Death		.00
	Avoidance		.03
MentS			.08
	Self		-.02
	Other		.10
	Motivation		.13*
QCAE			.17*
	Cognitive		.14*
		Perspective Taking	.09
		Online Simulation	.16*
	Affective		.16*
		Emotion Contagion	.07
		Proximal Responsivity	.17*
		Peripheral Responsivity	.14*

Table 5\ Correlation analysis for the questionnaires and literary exposure

Note. * p < .05.

Literary exposure was positively correlated with the ECQ’s general existential anxiety subscale (r = 0.12, p < .05) and MentS’ other-related mentalisation (r = 0.13, p < .05) and motivation to mentalise (r = .13, p < .05) subscales. As for empathy, book count was positively correlated with overall empathy (r = .17, p < .05), cognitive empathy (r = .14, p < .05), online simulation (r = .16, p < .05), affective empathy (r = .16, p < .05), proximal responsivity (r = .17, p < .05), and peripheral responsivity (r = .14, p < .05).

Overall mentalisation was associated with diminished state anxiety (r = -.17, p < .05), trait anxiety (r = -.34, p < .001), overall existential concerns (r = -.18, p < .05), general existential anxiety (r = -.17, p < .05), and avoidance (r = -.31, p < .001). On the other hand, overall mentalisation was linked to heightened overall empathy (r = .52, p < .001), cognitive empathy (r = .54, p < .001), perspective taking (r = .50, p < .001), online simulation (r = .41, p < .001), affective empathy (r = .32, p < .001), proximal responsivity (r = .38, p < .001), and peripheral responsivity (r = .29, p < .001), though not emotion contagion (r = .09, p ≥ .05). An important finding here is that self-related mentalisation and other-related mentalisation differed in their relationship to both state anxiety and trait anxiety. Self-related mentalisation was much more strongly negatively associated with both (r = -.36, p < .001 and r = -.58, p < .001, respectively), while other mentalisation showed either non-significant or weak results (r = -.02, p ≥ .05 and r = -.13, p < .05, respectively). The same pattern emerges in relation to existential concerns as well. Self-related mentalisation is negatively associated with the overall score as well as each of its subscales (-.55 ≥ rs ≥ -.35, ps < .001), whereas other-related mentalisation is not (-.07 ≤ rs ≤ 12, ps ≥ .05).

Overall empathy was significantly correlated with overall existential concerns (r = 0.25, p < .001) as well as its subscales general existential anxiety (r = .26, p < .001) and death anxiety (r = .24, p < .001), though not avoidance (r = .09, p ≥ .05). This reveals a connection between empathy and heightened sensitivity to existential themes, without an inclination to avoid such themes. While overall empathy and cognitive empathy were not associated with either state anxiety or trait anxiety, affective empathy was associated with both (r = .22, p < .001 and r = .27, p < .001, respectively).

To summarise and clarify these distinct patterns regarding mentalisation and empathy, Table 6 presents a side-by-side comparison of the correlations between each key construct and the primary distress-related variables. This layout highlights their divergent associations and functional profiles.

Table 6

Correlation analysis for key variables across MentS and QCAE subscales

	MentS		QCAE
Variable	Self	Other	Cognitive	Affective
STAI–S	-.36**	-.02	.03	.22**
STAI–T	-.58**	-.13*	-.01	.27**
ECQ – Overall	-.55**	.06	.16*	.31**
ECQ – General EA	-.54**	.07	.18*	.30**
ECQ – Death	-.35**	.12	.14*	.32**
ECQ – Avoidance	-.55**	-.07	.02	.16*
MentS – Overall	.73**	.76**	.54**	.32**
QCAE – Overall	.04	.64**	.92**	.81**

Note. * p < .05; ** p < .001

Table 6 displays a consistent pattern of associations: self-related mentalisation was negatively correlated with all anxiety and existential concern variables, while affective empathy was positively correlated with the same variables. In contrast, other-oriented mentalisation and cognitive empathy showed weak or non-significant correlations with distress-related outcomes. These results suggest that, within this sample, the self-related and affective dimensions of these constructs are more closely linked to reported anxiety and existential concern than their other-related or cognitive counterparts. Additionally, Table 6 highlights a clear distinction between self- and other-related Mentalisation in their associations with overall empathy. While other-related mentalisation was strongly correlated with overall empathy (r = .64, p < .001), self-related mentalisation showed no significant relationship (r = .04, p ≥ .05). This is also the case with empathy’s subscales and their components. This suggests that individual differences in empathy, as measured by the QCAE, are more closely aligned with other-related mentalising capacities than with self-related ones, supporting the theoretical linkage between the ability to reflect on others’ mental states and empathy.

Trait anxiety was positively correlated with state anxiety (r = .61, p < .001). Both showed significant positive correlation with existential concerns (STAI–S with ECQ – Overall, r = .47, p < .001; STAI–T with ECQ – Overall, r = .66, p < .001), and all of its subscales, supporting the conceptual overlap between trait anxiety and existential insecurity. Notably, trait anxiety showed consistently stronger correlations across all existential concern subdimensions, including general existential anxiety (r = .66, p < .001), death anxiety (r = .45, p < .001), and avoidance (r = 0.54, p < .001), compared to state anxiety (general existential anxiety: r = .48, p < .001; death anxiety: r = 0.32, p < .001; avoidance: r = .39, p < .001). These differences suggest that while both forms of anxiety are connected to existential concerns, trait anxiety, which reflects a stable predisposition to experience anxiety, may be more strongly linked to existential distress than situational anxiety, represented by State Anxiety.

4.2. Hierarchical Regression Analysis With Interaction Effects

Initially, two three-step hierarchical regression analyses were conducted with the target variables overall empathy (QCAE) and overall mentalisation (MentS). Afterwards, QCAE’s Cognitive Empathy and Affective Empathy subscales, and MentS’ Self-Related and Other-Related subscales were selected as target variables as well. In the first step, the demographic variables age, sex, and monthly spending were modelled as control variables. In the second step, the main predictors of book count, overall existential concerns score, state anxiety, and trait anxiety scores were added. In the last step, the interaction between book count and the other main predictors were added. Regarding the control variables, monthly spending and education level were recoded into broader categories to address low cell sizes. Collapsed groupings and their respective sample sizes are shown in Table 7 below.

Collapsed Group	Original Group(s)	n	%
Sex
Male		50	19.4
Monthly spending
0–15,000 TL	0–15,000 TL	102	39.5
15,001–30,000 TL	15,001–30,000 TL	88	34.1
30,001–45,000 TL	30,001–45,000 TL	35	13.6
Over 45,000 TL	All 4 groups over 45,000 TL	33	12.8
Highest education
Non-university	High school degree, Associate degree	181	70.2
University	Bachelor’s degree, Master’s degree, PhD	77	29.8

Table 7\ Collapsed groups for monthly spending and highest education

Note. Frequencies for the original categories are reported in Table 1 (see the Methods section)

4.2.1. Overall Empathy

Table 8 presents the results of the hierarchical regression analysis predicting overall empathy.

Table 8
Overall empathy hierarchical regression model

Predictor	B	SE	β	t	p
Step 1: Control Variables
Age	-6.60	8.54	-.06	-0.77	.440
Sex (ref = Male)
Female	9.43	1.76	.32	5.36	< .001
Highest Education (ref = Non-university)
University	-0.17	1.82	-.01	-0.09	.925
Monthly Spending (ref = 0–15,000 TL)
15,001–30,000 TL	1.74	1.63	.07	1.07	.287
30,001–45,000 TL	1.00	2.17	.03	0.46	.645
Above 45,000 TL	5.49	2.28	.16	2.41	.017
Step 2: Main Predictors
Books	2.37	0.96	.15	2.48	.014
ECQ	0.12	0.05	.19	2.39	.018
STAI–S	0.02	0.07	.02	0.33	.742
STAI–T	-0.08	0.11	-.06	-0.67	.504
Step 3: Interaction Terms
Books × ECQ	-0.05	0.07	-.06	-0.78	.435
Books × STAI–S	0.00	0.00	.01	0.10	.917
Books × STAI–T	0.00	0.01	.01	0.17	.866

Note. Step 1: F(6, 251) = 6.57, p = < .001, R² = .14.

Step 2: ΔR² = .05, F_change(4, 247) = 3.91, p_change = .004;

F(10, 247) = 5.69, p < .001, R² = .19.

Step 3: ΔR² = .00, F_change(3, 244) = 0.29, p_change = .831;

F(13, 244) = 4.41, p < .001, R² = .19.

Demographic variables explained 14% of the variance in empathy, R² = .14, F(6, 251) = 6.57, p < .001. Being female significantly predicted higher empathy scores (β = .32, p < .001), and participants with higher income (above 45,000 TL) showed significantly greater empathy (β = .16, p = .017); other covariates were non-significant (|β| ≤ .07, ps ≥ .287). Adding the main predictors accounted for an additional 5% of variance, ΔR² = .05, F_change(4, 247) = 3.91, p_change = .004; total R² = .19, F(10, 247) = 5.69, p < .001. Both book count (β = .15, p = .014) and existential concerns (β = .19, p = .018) were significant positive predictors of empathy, while trait and state anxiety were not (|β| ≤ .06, ps ≥ .504). Interaction terms did not contribute additional explanatory power, ΔR² = .00, F_change(3, 244) = 0.29, p_change = .831; final R² remained at .19, F(13, 244) = 4.41, p < .001. Overall, greater literary exposure and existential concerns were associated with higher empathy, independent of sex and income, while anxiety and interaction effects played no meaningful role.

4.2.2. Overall Mentalisation

Table 9 presents the results of the hierarchical regression analysis predicting overall mentalisation.

Table 9
Overall mentalisation hierarchical regression model

Predictor	B	SE	β	t	p
Step 1: Control Variables
Age	23.65	9.33	.19	2.53	.012
Sex (ref = Male)
Female	4.48	1.92	.14	2.33	.021
Highest Education (ref = Non-University)
University	-1.15	1.99	-.04	-0.58	.563
Monthly Spending (ref = 0–15,000 TL)
15,001–30,000 TL	-1.80	1.78	-.07	-1.01	.312
30,001–45,000 TL	-2.37	2.37	-.07	-1.00	.318
Above 45,000 TL	0.64	2.49	.02	0.26	.797
Step 2: Main Predictors
Books	1.01	1.01	.06	1.01	.315
ECQ	0.03	0.05	.05	0.57	.567
STAI–S	0.04	0.07	.04	0.48	.632
STAI–T	-0.54	0.12	-.40	-4.55	< .001
Step 3: Interaction Terms
Books × ECQ	-0.05	0.07	-.06	-0.75	.456
Books × STAI–S	-0.01	0.01	-.07	-0.91	.364
Books × STAI–T	-0.00	0.00	-.02	-0.22	.827

Note. Step 1: F(6, 251) = 2.54, p = = .021, R² = .06.

Step 2: ΔR² = .12, F_change(4, 247) = 9.04, p_change < .001;

F(10, 247) = 5.34, p < .001, R² = .18.

Step 3: ΔR² = .01, F_change(3, 244) = 0.64, p_change = .592;

F(13, 244) = 4.23, p < .001, R² = .18.

Demographic variables accounted for 6% of the variance in mentalisation, R² = .06, F(6, 251) = 2.54, p = .021. Age significantly predicted higher mentalisation (β = .19, p = .012), and female sex was associated with higher scores (β = .14, p = .021); education and income were not significant predictors (|β| ≤ .07, ps ≥ .312). Adding the main predictors increased explained variance by 12%, ΔR² = .12, F_change(4, 247) = 9.04, p_change < .001; total R² = .18, F(10, 247) = 5.34, p < .001. Trait anxiety was the only significant predictor in this block (β = –.40, p < .001); book count, existential concerns, and state anxiety were not significant (|β| ≤ .06, ps ≥ .315). Interaction terms did not contribute additional variance, ΔR² = .01, F_change(3, 244) = 0.64, p = .592; final R² remained .18, F(13, 244) = 4.23, p < .001. Overall, greater age, female sex, and lower trait anxiety predicted higher mentalisation, with no evidence that literary exposure or its interactions significantly contributed to the model.

4.2.3. Cognitive Empathy

Table 10 presents the results of the hierarchical regression analysis predicting cognitive empathy.

Table 10

Cognitive empathy hierarchical regression model

Predictor	B	SE	β	t	p
Step 1: Control Variables
Age	-5.89	5.99	-.07	-0.98	.326
Sex (ref = Male)
Female	4.21	1.23	.21	3.42	< .001
Highest Education (ref = Non-University)
University	0.21	1.28	.01	0.17	.868
Monthly Spending (ref = 0–15,000 TL)
15,001–30,000 TL	1.88	1.14	.11	1.64	.102
30,001–45,000 TL	0.28	1.52	.01	0.19	.853
Above 45,000 TL	3.60	1.60	.15	2.25	.025
Step 2: Main Predictors
Books	1.37	0.67	.12	2.04	.043
ECQ	0.09	0.04	.20	2.44	.015
STAI–S	0.01	0.05	.01	0.16	.875
STAI–T	-0.16	0.08	-.18	-2.05	.041
Step 3: Interaction Terms
Books × ECQ	-0.02	0.05	-.03	-0.40	.688
Books × STAI–S	0.00	0.00	.00	-0.06	.954
Books × STAI–T	0.00	0.00	-.03	-0.40	.690

Note. Step 1: F(6, 251) = 3.51, p = .002, R² = .08.

Step 2: ΔR² = .04, F_change(4, 247) = 3.05, p_change = .018;

F(10, 247) = 3.39, p < .001, R² = .12.

Step 3: ΔR² = .00, F_change(3, 244) = 0.14, p_change = .935;

F(13, 244) = 2.62, p = .002, R² = .12.

Demographic control variables accounted for 8% of the variance in cognitive empathy, R² = .08, F(6, 251) = 3.51, p = .002. Female sex predicted higher cognitive empathy (β = .21, p < .001), and higher monthly spending (above 45,000 TL) was also associated with higher scores (β = .15, p = .025); other covariates were non-significant (|β| ≤ .11, ps ≥ .102). The addition of main predictors explained an additional 4% of variance, ΔR² = .04, F_change(4, 247) = 3.05, p_change = .018; total R² = .12, F(10, 247) = 3.39, p < .001. Greater existential concerns (β = .20, p = .015), higher book count (β = .12, p = .043), and lower trait anxiety (β = –.18, p = .041) significantly predicted higher cognitive empathy, while state anxiety was not significant (β = .01, p = .875). Interaction terms did not improve model fit, ΔR² = .00, F_change(3, 244) = 0.14, p_change = .935; final R² = .12, F(13, 244) = 2.62, p = .002. No interaction term reached significance (|β| ≤ .03, ps ≥ .688). After accounting for demographic variables, cognitive empathy was significantly predicted by existential concerns, literary exposure, and trait anxiety, while interactions yielded no additional explanatory value.

4.2.4. Affective Empathy

Table 11 presents the results of the hierarchical regression analysis predicting affective empathy.

Table 11
Affective empathy hierarchical regression model

Predictor	B	SE	β	t	p
Step 1: Control Variables
Age	-0.71	3.87	-.01	-0.18	.854
Sex (ref = Male)
Female	5.21	0.80	.38	6.54	< .001
Highest Education (ref = Non-University)
University	-0.38	0.83	-.03	-0.46	.643
Monthly Spending (ref = 0–15,000 TL)
15,000–30,000 TL	-0.14	0.74	-.01	-0.19	.850
30,000–45,000 TL	0.72	0.99	.05	0.73	.466
Above 45,000 TL	1.89	1.03	.12	1.82	.069
Step 2: Main Predictors
Books	0.99	0.42	.13	2.34	.020
ECQ	0.03	0.02	.11	1.49	.137
STAI–S	0.02	0.03	.03	0.49	.624
STAI–T	0.09	0.05	0.15	1.76	.080
Step 3: Interaction Terms
Books × ECQ	-0.03	0.03	-.08	-1.12	.262
Books × STAI–S	0.00	0.00	.02	0.33	.744
Books × STAI–T	0.00	0.00	.07	1.02	.308

Note. Step 1: F(6, 251) = 8.59, p < .001, R² = .17.

Step 2: ΔR² = .08, F_change(4, 247) = 6.95, p_change < .001;

F(10, 247) = 8.42, p < .001, R² = .25.

Step 3: ΔR² = .01, F_change(3, 244) = 0.92, p_change = .430;

F(13, 244) = 6.69, p < .001, R² = .26.

Control variables explained 17% of the variance in affective empathy, R² = .17, F(6, 251) = 8.59, p < .001. Being female was a strong positive predictor (β = .38, p < .001), while age, education, and income brackets were non-significant (|β| ≤ .12, ps ≥ .069). Adding the main predictors increased explained variance by 8%, ΔR² = .08, F_change(4, 247) = 6.95, p_change < .001, yielding R² = .25, F(10, 247) = 8.42, p < .001. Literary exposure was a significant positive predictor (β = .13, p = .020), and trait anxiety showed a marginal positive association (β = .15, p = .080); existential concerns and state anxiety were non-significant (|β| ≤ .11, ps ≥ .137). Interaction terms added only 1% to the explained variance, ΔR² = .01, F_change(3, 244) = 0.92, p_change = .430, with no significant interactions (|β| ≤ .08, ps ≥ .262). Overall, affective empathy was higher in females and among those with greater literary exposure, and trait anxiety showed a trend-level positive association, though this did not reach significance.

4.2.5. Other-Related Mentalisation

Table 12 presents the results of the hierarchical regression analysis predicting other-oriented mentalisation.

Table 12
Other-related mentalisation hierarchical regression model

Predictor	B	SE	β	t	p
Step 1: Control Variables
Age	2.18	3.80	.04	0.57	.567
Sex (ref = Male)
Female	1.85	0.78	.15	2.36	.019
Highest Education (ref = Non-University)
University	0.57	0.81	.05	0.70	.486
Monthly Spending (ref = 0–15,000 TL)
15,001–30,000 TL	-0.51	0.73	-.05	-0.71	.480
30,001–45,000 TL	-0.54	0.97	-.04	-0.56	.579
Above 45,000 TL	0.34	1.02	.02	0.33	.739
Step 2: Main Predictors
Books	0.48	0.43	.07	1.12	.265
ECQ	0.06	0.02	.23	2.75	.006
STAI–S	0.02	0.03	.06	0.78	.434
STAI–T	-0.18	0.05	-.33	-3.61	< .001
Step 3: Interaction Terms
Books × ECQ	-0.04	0.03	-.10	-1.30	.196
Books × STAI–S	0.00	0.00	.00	0.04	.972
Books × STAI–T	0.00	0.00	-.05	-0.70	.482

Note. Step 1: F(6, 251) = 1.46, p = .191, R² = .03.

Step 2: ΔR² = .06, F_change(4, 247) = 4.12, p_change = .003;

F(10, 247) = 2.57, p = .006, R² = .09.

Step 3: ΔR² = .01, F_change(3, 244) = 0.94, p_change = .423;

F(13, 244) = 2.19, p = .001, R² = .10.

Demographic variables accounted for 3% of the variance in other-related mentalisation, R² = .03, F(6, 251) = 1.46, p = .191. None of the control variables reached significance (|β| ≤ .15, ps ≥ .019). Adding the main predictors increased explained variance by 6%, ΔR² = .06, F_change(4, 247) = 4.12, p_change = .003; total R² = .09, F(10, 247) = 2.57, p = .006. Higher existential concerns significantly predicted greater other-related mentalisation (β = .23, p = .006), while higher trait anxiety was associated with lower scores (β = –.33, p < .001); book count and state anxiety were non-significant (|β| ≤ .08, ps ≥ .153). Interaction terms added only 1% to the variance, ΔR² = .01, F_change(3, 244) = 0.94, p_change = .423; final R² = .10, F(13, 244) = 2.19, p = .001. No interaction reached significance (|β| ≤ .10, ps ≥ .196). Overall, greater existential concerns and lower trait anxiety were associated with stronger other-related mentalisation, while literary exposure and its interactions did not yield meaningful effects.

4.2.6. Self-Related Mentalisation

Table 13 presents the results of the hierarchical regression analysis predicting self-oriented mentalisation.

Table 13
Self-related mentalisation hierarchical regression model

Predictor	B	SE	β	t	p
Step 1: Control Variables
Age	15.14	5.17	.22	2.93	.004
Sex (ref = Male)
Female	-0.37	1.06	-.02	-0.34	.731
Highest Education (ref = Non-University)
University	-0.87	1.10	-0.06	-0.79	.430
Monthly Spending (ref = 0–15,000 TL)
15,000–30,000 TL	-0.97	0.99	-.07	-0.98	.329
30,000–45,000 TL	-0.27	1.31	-.01	-0.20	.838
Above 45,000 TL	-0.28	1.38	-.01	-0.21	.837
Step 2: Main Predictors
Books	-0.02	0.47	.00	-0.04	.964
ECQ	-0.11	0.02	-.30	-4.39	< .001
STAI–S	0.02	0.03	.03	0.47	.637
STAI–T	-0.30	0.06	-.40	-5.47	< .001
Step 3: Interaction Terms
Books × ECQ	0.03	0.03	.06	0.92	.358
Books × STAI–S	0.00	0.00	-.08	-1.26	.209
Books × STAI–T	0.00	0.00	-.06	-0.93	.352

Note. Step 1: F(6, 251) = 1.74, p = .113, R² = .04.

Step 2: ΔR² = .37, F_change(4, 247) = 38.04, p_change < .001;

F(10, 247) = 16.87, p < .001, R² = .41.

Step 3: ΔR² = .01, F_change(3, 244) = 0.89, p_change = .445;

F(13, 244) = 13.17, p < .001, R² = .41.

Demographic variables accounted for 4% of the variance in self-related mentalisation R² = .04, F(6, 251) = 1.74, p = .113. Age significantly predicted higher self-related mentalisation (β = .22, p = .004), whereas sex, education, and monthly spending were non-significant (|β| ≤ .07, ps ≥ .329). Adding the main predictors markedly increased explained variance, ΔR² = .37, F_change(4, 247) = 38.04, p_change < .001; total R² = .41, F(10, 247) = 16.87, p < .001. Greater existential concerns (β = –.30, p < .001) and higher trait anxiety (β = –.40, p < .001) predicted lower self-related mentalisation, whereas book count and state anxiety were non-significant (|β| ≤ .03, ps ≥ .637). Interaction terms did not improve the model, ΔR² = .01, F_change(3, 244) = 0.89, p_change = .445; final R² = .41, F(13, 244) = 13.17, p < .001. No interaction reached significance (|β| ≤ .08, ps ≥ .209). Overall, older participants reported higher self-related mentalisation, while existential concerns and trait anxiety were associated with lower scores; literary exposure, state anxiety, and their interactions did not yield reliable effects.