توجه متمرکز بر خود خصلتی، دشواری وظیفه و تلاش مربوط به واکنش پذیری قلبی عروقی

Abstract Using motivational intensity theory as a framework, the present experiment examined how individual differences in self-focused attention interact with task difficulty to predict effort, assessed via cardiovascular reactivity. Participants (n = 50) worked on a cognitive task fixed at an easy, medium, or hard level of difficulty, and individual differences in private self-consciousness and self-reflection were measured. Regression models indicated that trait self-focus interacted with task difficulty to predict cardiovascular reactivity, particularly systolic blood pressure (SBP) reactivity. Participants low and high in trait self-focus showed similar SBP reactivity in the easy and medium conditions, but they diverged in the hard condition: High trait focus was associated with higher SBP reactivity, indicating greater effort, whereas low trait self-focus was associated with low SBP reactivity, indicating disengagement. The findings thus support the motivational intensity approach to effort and its interpretation of self-focus's role in effort mobilization.

Introduction Self-focused attention is a major construct in the psychology of self-regulation and motivation (Carver, 2003). A large literature shows that directing attention to the self causes people to compare themselves to relevant standards. When people feel able to meet a standard, high self-focus increases their motivation to do so, as seen in a variety of affective, cognitive, and behavioral outcomes (for reviews, see Carver and Scheier, 1998 and Duval and Silvia, 2001). In the present research, we build upon recent applications of motivational intensity theory (Brehm et al., 1983 and Wright, 1996) to self-focused attention. This theory makes new predictions about how self-focus affects the intensity of effort, measured with physiological outcomes. In particular, we examine how individual differences in trait self-focused attention and task difficulty jointly determine effort-related cardiovascular reactivity in an active coping situation.

. Results 5.1. Data reduction and preliminary analyses We averaged the four baseline assessments to form baseline scores for SBP (α = .94), DBP (α = .93), and HR (α = .97). Table 1 displays the baseline values. One-way ANOVAs with S-N-K follow-up tests found no significant between-group differences in SBP, DBP, or HR for the baseline scores. The five task assessments were averaged to form overall task scores for SBP (α = .97), DBP (α = .92), and HR (α = .97). Baseline-to-task change was then measured by computing difference (delta) scores. To test for possible carry-over or initial-value effects, we tested whether the baseline scores correlated significantly with the difference scores. We found significant correlations for SBP (r = −.34, p = .016) and DBP (r = −.39, p = .005) but not for HR (r = −.10, p = .48), so the analyses of SBP and DBP were conducted using baseline-adjusted scores ( Llabre et al., 1991). Gender wasn't analyzed because too few men participated, but gender was distributed evenly between the conditions (n = 3 men in each condition). Table 1. Cardiovascular baseline values. Easy Medium Hard SBP M 112.58 107.75 106.41 SE 3.99 1.97 2.08 DBP M 66.69 63.43 65.03 SE 2.24 1.09 1.73 HR M 80.88 81.19 79.48 SE 3.35 1.74 2.61 Note. SBP = systolic blood pressure; DBP = diastolic blood pressure; HR = heart rate. SBP and DBP are in mmHg; HR is in beats per minute. Cell ns are n = 16 in the Easy condition, n = 18 in the Medium condition, and n = 16 in the Hard condition. Table options The two measures of trait self-focus—the revised private self-consciousness scale (α = .64) and the self-reflection scale (α = .88)—were highly correlated (r = .65, p < .001), as expected. Each scale was thus standardized, and a trait self-focus composite was created by averaging the standardized scores. 5.2. Cardiovascular reactivity We expected that trait self-focus, a continuous variable, would interact with task difficulty—as task difficulty increased, people low in trait self-focus would disengage more quickly. To test this, we estimated a regression model for SBP, DBP, and HR. The regression equation included the following effects: (1) two orthogonal contrast terms for task difficulty—a linear contrast (easy = − 1, medium = 0, hard = 1) and a quadratic contrast (easy = 1, medium = − 2, hard = 1)—that represent its main effects; (2) a main effect of trait self-focus, which was centered at zero; (3) the two interactions (trait self-focus by the linear contrast, and trait self-focus by the quadratic contrast); and (4) an intercept. 5.2.1. SBP reactivity For SBP, the regression analysis found a marginal effect of the linear contrast (b = 1.81, SE = 1.10, p = .107) and a significant effect of the quadratic contrast (b = − 1.56, SE = .61, p = .014). Table 2 shows the descriptive statistics. There was no main effect of trait self-focus (b = 1.01, SE = 1.06, p = .345), but trait self-focus significantly interacted with both the linear contrast (b = 2.84, SE = 1.38, p = .045) and the quadratic contrast (b = 1.72, SE = .70, p = .018). Adding the interaction effects increased R2 by 13.2%, a significant change, F(2, 44) = 4.02, p = .025, for a total model R2 of 27.9%. Table 2. Cardiovascular reactivity and task difficulty. Easy Medium Hard SBP M − 3.16 3.14 −.37 SE 1.34 1.67 1.69 DBP M − 1.94 .81 1.04 SE .90 1.07 1.22 HR M − 2.30 1.17 −.07 SE 1.37 1.18 1.16 Note. SBP = systolic blood pressure; DBP = diastolic blood pressure; HR = heart rate. SBP and DBP are in mmHg; HR is in beats per minute. Values for SBP and DBP are baseline-corrected. All means and standard errors are descriptive statistics, not predicted values from the regression models. Cell ns are n = 16 in the Easy condition, n = 18 in the Medium condition, and n = 16 in the Hard condition. Table options Fig. 1 displays the predicted values based on the regression equation. Trait self-focus was estimated at values of −1.5 (low) and 1.5 (high). As predicted, trait self-focus and task difficulty jointly influenced SBP reactivity. SBP increased as task difficulty increased, but when task difficulty was high, SBP reactivity declined when trait self-focus was low but increased when trait self-focus was high. This pattern conceptually replicates the experiments that manipulated self-focus (Gendolla et al., 2008 and Silvia et al., 2010). Another way to represent the interaction is to consider the within-condition correlations between trait self-focus and SBP reactivity. Trait self-focus was unrelated to SBP reactivity in the easy condition (r = −.022, p = .935), non-significantly negatively related in the medium condition (r = −.322, p = .192), but strongly and positively related in the hard condition (r = .560, p = .024). Predicted values for trait self-focus, task difficulty, and SBP reactivity. Fig. 1. Predicted values for trait self-focus, task difficulty, and SBP reactivity. Figure options 5.2.2. DBP reactivity For DBP, the linear contrast was significant (b = 1.67, SE = .74, p = .029) but the quadratic contrast was not (b = −.45, SE = .41, p = .277): DBP increased as task difficulty increased. Table 2 displays the descriptive statistics. There was no main effect of trait self-focus (b = −.08, SE = .71, p = .909) and no interaction between trait self-focus and the linear contrast (b = .89, SE = .92, p = .335). Trait self-focus did, however, significantly interact with the quadratic contrast (b = 1.36, SE = .47, p = .006). Adding the interaction effects increased R2 by 14.5%, a significant change, F(2, 44) = 4.21, p = .021, for a total model R2 of 24.5%. As with SBP, we estimated the interaction pattern for DBP using the regression equation; Fig. 2 displays the predicted values for low (− 1.5) and high (1.5) levels of trait self-focus. Like SBP, DBP reactivity overall increased as task difficulty increased, but it diverged when difficulty was high: Reactivity increased for people high in trait self-focus but declined for people low in trait self-focus. Predicted values for trait self-focus, task difficulty, and DBP reactivity. Fig. 2. Predicted values for trait self-focus, task difficulty, and DBP reactivity. Figure options 5.2.3. HR reactivity For HR, neither the linear contrast (b = 1.31, SE = .91, p = .159) nor the quadratic contrast (b = −.76, SE = .51, p = .140) was significant; Table 2 displays the descriptive statistics. Trait self-focus had no main effect (b = .765, SE = .88, p = .388) and no interactions with the linear contrast (b = .78, SE = 1.14, p = .500) or quadratic contrast (b = .78, SE = .58, p = .187). Total model R2 was 12.3%. 5.3. Task performance and subjective measures How did trait self-focus and task difficulty affect task performance? Response time (RT) was quantified as the average RT for correct trials; errors were quantified as the proportion of incorrect trials (rather than the raw score, given the different number of trials in each condition). Table 3 displays the effects across levels of task difficulty. Not surprisingly, there were large effects of difficulty on both RT and errors. For RT, a regression model found only a significant linear effect of task difficulty (b = − 125.02, SE = 14.48, p < .001, β = −.797). For errors, there was a significant linear effect (b = .086, SE = .015, p < .001, β = .644) and quadratic effect (b = .018, SE = .008, p = .030, β = .242) of task difficulty; no other effects were significant. As Table 2 shows, response times became faster and the percentage of errors increased as the task became harder. To expand upon this analysis, we explored within-condition correlations between trait self-focus, RT, and errors. Trait self-focus did not significantly predict RT (correlations ranged from r = .00 to r = −.24) or errors (correlations ranged from r = −.06 to r = .26) in any of the conditions. Table 3. Task difficulty effects on response time, errors, and subjective reports. Easy Medium Hard Response time 768 (24) 632 (19) 516 (13) Proportion errors .039 (.013) .071 (.014) .22 (.029) Rated task difficulty 2.31 (.29) 2.47 (.32) 3.87 (.42) Rated performance expectancies 5.79 (.28) 5.64 (.26) 3.96 (.33) Rated importance 5.38 (.36) 5.59 (.36) 5.07 (.36) Rated challenge 2.69 (.35) 3.29 (.42) 4.80 (.37) Rated threat 1.50 (.26) 2.00 (.33) 2.60 (.51) Note. Standard errors are in parentheses. Response times are rounded to the nearest millisecond. All means and standard errors are descriptive statistics, not predicted values from the regression models. Due to missing questionnaire responses, cell ns for the rated variables are n = 16 in the Easy condition, n = 17 in the Medium condition, and n = 15 in the Hard condition. Table options Finally, we conducted regression analyses of the subjective measures; Table 3 displays the descriptive statistics. For perceptions of the task's difficulty, only the expected linear effect of task difficulty appeared (b = .748, SE = .262, p = .007); for performance expectancies, regression models found a linear (b = −.933, SE = .217, p < .001, β = −.538) and a quadratic (b = −.248, SE = .120, p = .044, β = −.258) effect of task difficulty. Not surprisingly, people found the task harder and had less optimistic performance expectancies as task difficulty increased. For self-reported challenge and threat, only a linear effect of task difficulty appeared (challenge: b = 1.08, SE = .286, p < .001, β = .501; threat: b = .572, SE = .283, p = .050, β = .301): People rated the task as more challenging and as more threatening as task difficulty increased. No significant main effects or interactions were found for self-reported importance.