The Interactive Role of Emotion and Expected Efficacy and Reward in Improving Cognitive Control in Patients with Depression

doi:10.21203/rs.3.rs-5299101/v1

Background: Difficulties in cognitive control over negative emotional stimuli are a key depression characteristic. The Expected Value of Control (EVC) provides a framework for understanding how cognitive control is allocated, focusing on the motivational factors of efficacy and reward. Efficacy is the likelihood that an effort will result in a specific result, while reward is the value assigned to that outcome. However, the impact of emotion on the estimation of EVC has not been explored. We investigated the interplay between emotion and motivation (EVC) in depression.

Methods: We utilized a within-between-subject design. The subjects were healthy controls (n=31) and those with depression (n=36), who underwent a clinical diagnostic interview, completed the General Health Questionnaire-12, the Beck Depression Inventory-II, and participated in an incentivized Emotional Stroop Paradigm where participants received cues indicating different levels of efficacy (low vs. high) and reward (low vs. high) prior to the targeted stimuli.

Results: Significant interactions were detected between a) group × emotional valence × efficacy and b) group × reward regarding accuracy rates on the Emotional Stroop Task. Follow-up analyses revealed that during high-efficacy trials, the Control group demonstrated significantly greater accuracy than the Depressed group for both positive and neutral stimuli. In low-efficacy trials, the Controls were also significantly more accurate than the Depressed group when responding to negative stimuli. Additionally, the Depressed group performed significantly worse compared to the Controls on high-reward trials, no significant difference was detected between the two groups on low-reward trials.

Conclusion: The emotional valence of stimuli can influence the assessment of reward efficacy, and individuals with depression struggle to focus on reward cues. Further research is necessary to incorporate emotion into the EVC framework.

Clinical trial number: not applicable.

Depression

Executive Function

Expected Value of Control

Cognitive control

Emotion

Motivation

Reward

Efficacy

Cognitive control is essential for motivated, goal-oriented behavior, which encompasses many processes enabling flexible adjustments in behavior and cognition to align with present goals (Botvinick & Cohen, 2014). Key aspects of cognitive control are set shifting, updating working memory, and inhibition (Miyake & Friedman, 2012). All three of these components are compromised in individuals with depression (Joormann & Tanovic, 2015). Such impairments are viewed as a diminished ability to exert cognitive control (Grahek et al., 2019). People with depression often find it difficult to let go of negative information and have trouble controlling irrelevant thoughts (inhibition), making it difficult for them to switch their focus from one task to another to achieve an objective causing them difficulties in regulating emotions and adapting to changing environments (Gotlib & Joormann, 2010). Given that central features of depression are consistently associated with deficits in cognitive control, it is an important area for understanding and addressing depression (Grahek et al., 2019). However, our current comprehension of cognitive control in depression is limited by two main factors: a) an emphasis on descriptive models rather than exploring the underlying mechanisms, and b) a failure to integrate emotional, cognitive, and motivational impairments into a unified theoretical framework. These limitations hinder our understanding regarding why and how cognitive control is affected during depression (Grahek et al., 2018; Grahek et al., 2019).

Researchers need to investigate the mechanisms underlying cognitive control impairments in depression. The EVC theory offers a normative mechanistic explanation regarding how motivation affects the cognitive control application (Shenhav et al., 2013). The EVC for a special control signal in a certain condition is determined by the probability of an outcome resulting from that control signal (efficacy), the outcome value, and the costs involved in exerting control (Shenhav et al., 2013; Shenhav et al., 2021). Expanding on EVC theory, Grahek et al. introduced an approach combining learning, motivation, and cognitive control impairments to deepen our mechanistic awareness of cognitive control impairments in depression. Grahek et al. contend that behavior based on a goal is influenced by three key factors: outcome value (the anticipated reinforcement—either punishment or reward —linked to achieving an outcome), outcome controllability (the evaluation of a person’s ability to affect outcomes in their environment), and effort costs (the amount of effort needed to attain an objective, which comes with a cost) (Grahek et al., 2019). All of these motivational components are according to previous learning experiences (Daw & O’Doherty, 2014) and are impaired in depressed individuals (Grahek et al., 2019; Barch et al., 2015).

Within this approach, cognitive control deficits in depression are related to disrupted reward processing and shifts in how controllable individuals perceive their environment (Grahek et al., 2019). This approach suggests that changes in motivational components diminish the EVC, resulting in a reduced allocation of cognitive control. Thus, the cognitive control deficits seen in depression because of lowered expectations regarding the exercising control value, rather than a diminished capacity to exert that control(Grahek et al., 2019). The behavioral prediction of this model has been confirmed in healthy, community samples (Frömer et al., 2021) and individuals with depression (Grahek et al., 2019; Toobaei et al., 2023).

Cognitive control in relation to negative emotional content has been studied. These investigations have revealed specific difficulties in not focusing on negative material, removing negative information from working memory, and inhibiting negative stimuli (Gotlib & Joormann, 2010; Joormann & Vanderlind, 2014; Grahek et al., 2019; Koster et al., 2011). Furthermore, some researchers have proposed that individuals with depression develop biases in their cognitive control over emotional data, while not exhibiting widespread deficits in non-emotional cognitive control tasks, such as non-emotional Stroop tasks (Quigley et al., 2020). Within this framework, some studies indicated that the amount of reward can enhance cognitive control over emotional material in healthy individuals (Padmala et al., 2019; Padmala et al., 2017), but to date, this has not been investigated among individuals with clinical depression. Additionally, the role of efficacy in implicit emotional processing has not yet been investigated in depression and healthy controls. Therefore, although the effect of negative emotional material on cognitive control has been well studied, the effect of negative materials in cognitive control tasks such as Stroop Tasks on reward processing has not yet been investigated. Examining the influence of negative materials on reward processing (for example, examining the effect of negative material on efficacy estimates) can further examine the proposed framework (Grahek et al., 2019).

Current Study

We incorporated emotion into the mechanistic framework of cognitive control impairments as outlined by the EVC model within a clinically depressed population. This integration is crucial because, although the effect of motivation on elucidating the mechanisms behind cognitive control impairments in depression has been examined, the impact of emotion on reward processing and, subsequently, on cognitive control performance has not yet been investigated. Moreover, this aim will be investigated among Iranian individuals with clinical depression. A recent systematic review found the prevalence of depression in Iran was moderate, growing, and concerning (Tahan et al., 2020). The authors highlighted the urgent need for research to inform effective measures and treatment targets to address depression among Iranian patients (Tahan et al., 2020). Moreover, while research has confirmed cognitive control deficits in Iranian depression patients ( Bodaghi et al., 2016; Akbari et al., 2015), very little research has examined the mechanisms accounting for this deficit, such as exploring the interactive role of emotion and motivation in the cognitive control impairments. Therefore, it is not yet clear how negative emotional material affects reward processing and, consequently, cognitive control performance; factors which could all inform treatment targets.

Design and Participants

We utilized a within-between-subject design. Ethical approval was received from the Ethics Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1400.815) and subjects signed written informed consent. Prior to data gathering, a power analysis was performed by G*Power software, suggesting a sample size of N=40 to achieve 95% test power to achieve an effect size of d=0.30 at an α error of 0.05 for mixed ANOVA. Seventy-seven subjects were included; however, 10 were excluded as they did not meet the inclusion criteria, and finally, 67 participants were regarded. The study included two groups: the Depressed Group (n=36) and the Control Group (n=31) .

The Depressed Group included those with Major Depressive Disorder (MDP) from psychological and psychiatric clinics in Shiraz, Iran. They met the diagnostic criteria for a current MDE within the last two weeks (following the DSM-5 criteria, assessed using the SCID-5-RV) and scored > 14 on the BDI-II (Beck et al., 1966). They aged between 18 and 50, had no color blindness (Ishihara, 1918), had at least a primary school education to ensure reading and comprehension of the instructions, and were not using antidepressants. The exclusion criteria encompassed having schizophrenia and/or other psychotic diseases, substance abuse, obsessive-compulsive disorder, bipolar disorder, and/or neurological conditions, like epilepsy brain injuries, and tumors. The depressed group had 36 people(age M=32.64 years, SD=6.07; 2 men; 34 women).

The Controls were 31 individuals and included the general public selected via social media platforms. All participants in this group had no DSM-5 disorder considering their answers on the SCID-5-RV. They had no psychiatric disorder or had not visited a psychiatric facility in the last six months (determined through a clinical interview and the GHQ-12) and were not using any drug to treat a psychiatric condition (mean age = 30.68 years, SD = 6.847; 9 men and 22 women) .

Measures

Structured Clinical Interview For DSM-V -Research Version (SCID-5-RV)

The DSM-5 diagnoses were assessed by this scale (First et al., 2015). It demonstrates strong psychometric features, such as in Iranians (Mohammadkhani et al., 2020). According to Mohammadkhani et al. (2020), the sensitivity index for MDP is 0.68, and the specificity is 0.75. In this study, a clinical psychologist administered the full SCID-5-RV to diagnose MDP and to ensure that the healthy control participants had no psychiatric disorders.

Beck Depression Inventory-II (BDI-II)

This scale with 21 items assessed the severity and existence of depressive symptoms in the past two weeks (Beck et al., 1996). The items are graded between 0 and 3 (total score: 0 - 63); scores above 14 indicate at least mild depression (Beck et al., 1996). The Persian BDI-II has established validity and reliability (Ghasemzadeh et al., 2005). In our research, Cronbach’s alpha was found to be 0.95.

General Health Questionnaire-12 (GHQ-12)

This scale with 12 items evaluated the general health status of subjects in the last four weeks. The items are graded between 0 and 3 (total score: 0 - 36), and higher scores indicate poorer health status (Goldberg, 1988). Yaghubi et al. reported that the GHQ-12 has strong psychometric properties in Iran. Here, Cronbach’s alpha was 0.93.

Cognitive Control Task

The Emotional Word Stroop Task (Williams et al., 1996) measured cognitive control exertion over emotional words (negative, positive, and neutral). Initially, a total of 120 words were selected for the stimuli pool and these words were piloted using 10 individuals with depression, who were asked to rank the words based on the valence (0 = completely unpleasant to 10 = completely pleasant). Finally, 22 negative words, 22 positive words, and 22 neutral words were selected for the Emotional Word Stroop task (see Appendix 1). Words were matched in length. Overall, each category of words was repeated three times across the main task. Participants were instructed to focus on the words’ ink color while disregarding the meaning of the emotionally charged words. We employed a previous paradigm for reward cues (Frömer et al., 2021) following ECV Theory (Shenhav et al., 2013). The subjects received rewards according to the reward cues presented, which included the reward amount (high and low = 150,000 and 20,000 Rials, respectively) and the efficacy level (high and low: rewards were completely based on subjects’ function and were not contingent on the subjects’ function but were randomly assigned, respectively) for each trial. The trials (Fig 1.) began with a fixation display lasting 1500 milliseconds, and then a reward cue was presented for another 1500 milliseconds. Next, the considered stimulus was shown for 1000 milliseconds. When the subject responded, a blank screen appeared for 800 milliseconds, and then feedback was displayed for 750 milliseconds, indicating the reward they would receive in the next trial. The interval between the two attempts was set at 800 milliseconds. To enhance the task’s difficulty, the response threshold for every trial was 750 milliseconds, although reaction times (RTs) were noted for up to 1000 milliseconds when the target stimulus appeared. The stimulus was provided at a screen center (12 inches) on a Microsoft Surface Pro 3 tablet, which was positioned 60 cm far from the participant.

Subjects started by completing 3 practice blocks. In the initial block, which included 16 trials, we displayed a square stimulus in yellow (255, 237, 0), blue (0, 5, 255), red (255, 0, 0), and green (0, 128, 0). Subjects familiarized themselves with the key-color mapping by putting pressure on the related keys on the keyboard (the F, D, K, and J keys were respectively assigned to green, blue, red, and yellow). The second practice block consisted of 20 trials, where subjects associated cues with different levels of efficacy and reward. Ultimately, subjects finished the third practice block, which comprised 32 trials and closely mirrored the actual task. Instructions for the incentive blocks are provided below:

In the next block, you again need to press the key associated with the color of the text on the screen. From now on, you will have the opportunity to get an additional bonus based on how you perform the task. You will be told on each trial how performance could affect your bonus. Before each word appears, you will see an image that tells you two things: (1) the amount of reward you could earn; and (2) whether or not your performance will determine if you get that reward. When you see one of the two images above, you can get a low (20000 Rials) or high reward (150000 rials) if you respond quickly and accurately. In other words, the blue hand shows that your reward is directly related to the speed and accuracy of your response. The gray bag represents a low reward and the pink bag represents a high reward. The two images above ALSO indicate that you can get a low or high reward, BUT the gray hands indicate that your reward will have NOTHING to do with how quickly or accurately you perform. Instead, these rewards will be determined randomly. As long as you provide some response on that trial, you have some possibility of getting a low (20000 Rials) or high (150000 Rials) reward. Although these rewards will be random, you will be just as likely to get a reward on these trials as the trials with the blue hands. (Frömer et al., 2021).

After each block of practice, the following statement was presented to ensure whether the practice was suitable for every subject: “Was the practice sufficient in this part? If this practice is enough, press the Y key. If you need more practice, press the N key”. Following completing the practice stage, Subjects were guided to perform the main task: “The main task is similar to the last practice, but it will not be practice. Every trial can affect your ultimate reward. At the end of the session, 10 trials will be chosen randomly and the total amount of money you earned across those 10 trials will be paid to you” (Frömer et al., 2021). The main task had a 198-trial block, which was counterbalanced between subjects. The JAVA language developed the experiment.

Procedure

The study took place in a single 90-minute session. Initially, subjects underwent a clinical interview using the SCID-5-RV, followed by the Ishihara task (Lezak et al., 2012) to evaluate color blindness. Those who met the eligibility criteria were allocated to either the healthy control group or the depressed group. Subjects filled out some assessments, such as the BDI-II, a demographic questionnaire, and the GHQ-12. Afterward, they engaged in the cognitive control approach. They then were provided with the rewards they earned during the task.

Statistical Analysis

Data analyses were performed by SPSS 27. The analyses assessed the average RT for correct responses (Accurate RTs) and the accuracy rate, calculated as the ratio of correct responses to total responses. A mixed repeated measures analysis of variance (ANOVA) with a 2 (Group: Depressed vs. Control) × 3 (Valence: positive, negative, and neutral) × 2 (Efficacy: low vs. high) × 2 (Reward: low vs. high) design examined the impact of expected efficacy and reward on cognitive control allocation. In this analysis, Group served as a between-subjects factor, while Valence, Efficacy, and Reward were treated as within-subjects factors. The dependent variables included average accurate RTs and the accuracy rate. Univariate outliers were identified by determining mean RTs that exceeded 3 standard deviations, leading to the exclusion of six participants (the RT data showed normal distribution when these outliers were removed) from the ANOVA, which used average RTs as the dependent variable. This left 27 subjects in the Depressed Group and 29 in the Controls for these assessments. Additionally, subjects (N = 22) who acted poorly (i.e., achieving < 60% accuracy on high efficacy trials) were excluded from the ANOVA that used accuracy rate as the dependent variable, resulting in 24 subjects in the Depressed Group and 21 in the Controls for these assessments. Effect sizes were reported as partial eta (medium: ηp2 = .06; small: ηp2 = .01; and large: ηp2 = .14) and Cohen’s d (medium: d = .09; small: d = .01; and large: d = .25) (Cohen, 1988).

Subjects’ Characteristics

Clinical and Demographic data can be found in Table 1. The two groups showed no significant differences regarding education, age, marital status, or occupation. However, there was a significant difference in gender between the groups. To account for this difference, we included gender as a covariate in the subsequent analyses, which revealed a similar trend of results. The Depression Group scored markedly higher on the GHQ-12 and BDI-II compared to the Controls.

Cognitive Control

Mean RT

The mixed repeated measures ANOVA regarding RT revealed non-significant interaction effects related to the group. The main effect of the Group was also not significant, F(1,54) = .01, p = .91, ηp2 < .001. Also, the main effects of valence, F (2,108) = 0.48, p= .95, η_p²=. 001, reward, F (1,54) = .01, p= .93, η_p²< .001, and efficacy, F (1,54) =.76, p= .38, η_p²= .01, were all not significant.

Accuracy Rate

The mixed repeated measures ANOVA regarding accuracy rate indicated that the main effect of the Group was non-significant, F(1,42) = 2.12, p = .15, ηp2 = .05. The main effects of valence, F(2,84)= .60, p= .54, η_p²= .01, reward, F(1,42)= 1.14, p= .29, η_p²= .03, and efficacy, F(1,42)= 2.08, p= .15, η_p²= .05, were also non-significant. Significant interaction effects of group× valence× efficacy, F(2,84)= 4.01, p= .03, η_p²= .08, and group × reward, F(1,42)= 11.86, p< .001, η_p²= .22 were found. Other interactions were non-significant.

Follow-up analyses of the valence × efficacy × group interaction revealed that (Table 2) the Control Group was significantly more accurate than the Depressed Group for positive and neutral words in high-efficacy trials. Additionally, for negative valenced words in low efficacy trials, the Control Group also demonstrated significantly greater accuracy than the Depressed Group (see Figure 2). Within the Depressed Group, participants showed significantly higher accuracy for neutral words in the low efficacy status (M = 91.8%, SD = 6.13) compared to the high efficacy status (M = 80.35%, SD = 7.27), t(23) = 2.44, p = .02, d = .50. No other follow-up interactions were found to be significant (Tables 3).

Follow-up analyses of the group × reward interaction indicated that (Figure 3) the Depressed group (M = 89.97%, SD = 6.56) and the Control group (M = 91.69%, SD = 5.57) had no significant difference in the low reward condition, t(43) = .94, p = .35, d = .28. However, in the high reward condition, the Depressed Group (M = 88.59%, SD = 5.61) was significantly less accurate than the Controls (M = 93.58%, SD = 4.44), t(43) = 3.26, p < .01, d = .97. The Controls performed significantly better (more accurately) in the high reward trials (M = 93.58%, SD = 4.44) in comparison to the low reward trials (M = 91.69%, SD = 5.57), t(20) = 2.14, p < .05, d = .46. Conversely, participants in the Depressed Group showed significantly better performance in low reward trials (M = 89.97%, SD = 6.56) than in high reward trials (M = 88.59%, SD = 5.61), t(23) = 2.50, p = .02, d = .51.

This study explored how emotion and motivational factors (reward and efficacy) interact as a potential mechanism for cognitive control deficit against emotionally valenced stimuli in individuals with depression. Our findings showed that, while no significant differences were detected in RT between the Control and Depressed Groups, notable differences were observed in response accuracy. We found that the interaction between group, efficacy and emotional valence was significant. Specifically, the Depressed Group had significantly less accurate performance than the Control Group against positive and neutral words, but not negative words, on high efficacy trials, with large effect sizes observed. The Depressed Group did have significantly less accurate performance than the Control Group in response to negative words on low efficacy trials, with a large effect size detected. It seems, therefore, that the Depressed group, when compared to the Control Group, still displayed cognitive control deficits for positive and neutral stimuli despite increasing efficacy. However, for negative stimuli the Depressed Group, while having poorer accuracy performance in the low efficacy condition, when efficacy was high the two groups did not differ significantly. Thus, it seems that the emotional valence of stimuli can affect the evaluation of reward efficacy.

The study also found that there were no significant differences in accuracy in low-reward trials between the Depressed Group and Controls. However, in high-reward trials, the Control Group performed significantly better than the Depressed group, with a large effect size found. Within-subject comparisons showed that while Controls performed significantly better in the high reward compared to low reward trials, the Depressed Group had significantly poorer performance in high-reward trials than low reward trials. In other words, increasing the amount of reward in the Control Group led to improved performance, but in the Depressed Group, increasing the reward resulted in a decrease in performance. A possible explanation for these findings is that people with depression have difficulty paying attention to rewarding cues. Anderson et al. (2014) showed that reward sensitivities were lower in people with depression than in healthy controls. Auerbach et al. (2022) also showed that the volume and activity of the accumbens nucleus, the brain area responsible for reward, was reduced in people with depression. Therefore, those with depression may be less sensitive to reward. These findings align with the EVC model (Grahek, 2019) as they show that the EVC in those with depression has decreased when compared to healthy controls. Therefore, individuals with depression may not want to exert cognitive control, which leads to difficulties in emotion regulation and cognitive biases (LeMoult & Gotlib, 2019). Another possible explanation is that due to the long duration of the experimental session, fatigue affected the performance of participants, especially those with depression. This may have resulted in participants paying attention only to completing the task. Since those with depression often experience fatigue earlier (Costa et al., 2023), it seems that fatigue may account for some of the findings in this study. Additionally, such results could be influenced by existing medical conditions and older age. Therefore, future research should compare various age groups and diseases to investigate the impacts of these factors.

Our research has several important implications. It is better to incorporate the role of emotion alongside motivational components into the EVC theory, which would enhance our understanding of the mechanisms involved in cognitive control allocation in depression. Also, employing emotion-focused techniques might enhance reward processing in individuals with depression. Consequently, it may be essential to incorporate emotional and motivational components into therapeutic approaches, particularly within cognitive control training paradigms, to improve EVC leading to enhancing cognitive control and alleviating depression symptoms. Considering that cognitive control deficits are transdiagnostic and that emerging classification methods for mental health disorders—like the Research Domain Criteria (RDoC) from the National Institute of Mental Health (NIMH; Insel et al., 2010)—emphasize transdiagnostic frameworks across different disorders, the results of this study could help investigate the connection between the positive valence system (reward processing) and cognitive systems in a broader context.

Our research faced many limitations. No electrophysiological and neuroimaging tools (such as Q-EEG EEG, and fMRI) to explore the underlying neural processes can be a drawback. Future research is encouraged to combine neurological and behavioral assessments to examine the influence of emotion and motivation on neurological and behavioral reactions in depression. No use of eye-tracking methods was another limitation. Incorporating eye tracking can help determine whether participants were focusing on the reward cues. Future studies should utilize eye-tracking methods. We focused on the EVC theory solely within the clinical population of depression; since cognitive control deficits are present in various psychiatric diseases, it would be beneficial to explore this model in other clinical conditions. Lastly, the study had a disproportionate representation of women, which may affect the generalizability of the findings.

The Depressed Group had significantly less accurate performance than the Control Group against positive and neutral words on high-efficacy trials and negative words on low-efficacy trials. The study also found that in high-reward trials the Control Group performed significantly better than the Depressed group on accuracy but not for low-reward trials. Additionally, increasing the amount of reward in the Control Group led to improved performance, but in the Depressed Group, increasing the reward resulted in a decrease in performance.

In summary, our findings support certain elements of EVC theory. Specifically, in line with the suggestion by Grahek et al. (2019), EVC is diminished in individuals with depression. Nevertheless, additional studies are necessary to investigate the role of emotion in assessing EVC and to determine the relevance of EVC in the context of depression.

Ethics approval and consent to participate:

Ethical approval was received from the Ethics Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1400.815) in accordance to the ethical principles and the national norm and standards for conducting Medical Research in Iran. participants signed written informed consent.

Consent for publication: not applicable.

Availability of data and materials:

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Funding

This study was supported by the Cognitive Sciences and Technologies Council (11354).

Competing interest

The authors declare that they have no competing interest

Acknowledgment: not applicable

Authors’ contributions:

Mostafa Toobaei: Conceptualization, Methodology, Investigation, Writing, Review & Editing, Formal Analysis, Writing of the Original Draft. Mohammadreza Taghavi: Writing, Review & Editing, Supervision Laura Jobson: Writing, Review & Editing, Supervision. All authors have approved the final manuscript.

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Table 1 Participant’s characteristics

Variable	Depressed subjects (N=36)	Control subjects (N=31)	Statistical Parameter	p
Age, Mean (SD)	32.64 (6.07)	30.68 (6.84)	t = 1.24	.21
Sex, No. (%) Male female	2 34	9 22	χ ² = 6.69	.01^*
Education, No. (%) Diploma Bachelor Master Ph.D.	2 24 8 2	0 19 9 3	χ ² = 2.48	.47
Marriage status, No. (%) Single Married Divorced	22 12 2	16 14 1	χ ² = 1.06	.58
Occupation, No. (%) Employed Unemployed	24 12	24 7	χ ² = 0.948	.33
GHQ-12, Mean (SD)	23.19 (6.64)	10.13 (4.52)	t = 9.50	<.001^*
BDI-II, Mean (SD)	29.44 (11.22)	5.39 (7.17)	t = 10.59	<.001^*

Note. BDI-II = Beck’s depression inventory-version 2 and GHQ-12 = general health questionnaire-12 items.

Table 2 Follow up analysis of interaction effects of valence× efficacy× group

Variable		Group	Mean	SD	df	t (p- value)	Cohen’s d
Positive words	Low Efficacy	Controls	92.36	6.01	43	1.13 (.26)	0.33
		Depressed	90.25	6.41
	High Efficacy	Controls	93.48	6.97	43	2.90 (<.001)	0.86
		Depressed	88.05	6.97
Negative words	Low Efficacy	Controls	92.78	7.31	43	2.16 (.03)	0.64
		Depressed	87.78	8.09
	High Efficacy	Controls	91.21	6.33	43	0.79 (.42)	0.23
		Depressed	89.47	8.04
Neutral words	Low Efficacy	Controls	93.5	5.39	43	0.98 (.33)	0.29
		Depressed	91.8	6.13
	High Efficacy	Controls	92.48	5.39	43	2.13 (.03)	0.63
		Depressed	88.35	7.27

Table 3 Follow up analysis of interaction effects of group× valence× efficacy

Group	Variables			Mean	SD	df	t (p- value)	Cohen’s d
Depressed Group		Positive words	Low Efficacy	90.25	6.41	23	1.87 (0.074)	0.38
			High Efficacy	88.05	6.97
		Negative words	Low Efficacy	87.78	8.09	23	1.52 (0.141)	0.31
			High Efficacy	89.47	8.04
		Neutral words	Low Efficacy	91.8	6.13	23	2.44 (0.02)^**	0.50
			High Efficacy	80.35	7.27
Controls		Positive words	Low Efficacy	92.36	6.01	20	0.94 (0.35)	0.20
			High Efficacy	93.48	5.31
		Negative words	Low Efficacy	92.78	7.31	20	1.05 (0.30)	0.23
			High Efficacy	91.21	6.33
		Neutral words	Low Efficacy	93.50	5.39	20	0.64 (0.52)	0.14
			High Efficacy	92.48	5.39

No competing interests reported.

Appendix1.docx

The Interactive Role of Emotion and Expected Efficacy and Reward in Improving Cognitive Control in Patients with Depression

Status:

Version 1

Abstract

Figures

Introduction

Current Study

Materials and Methods

Design and Participants

Measures

Cognitive Control Task

Statistical Analysis

Results

Subjects’ Characteristics

Cognitive Control

Mean RT

Accuracy Rate

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1