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Reduced Autobiographical Memory Specificity as a Mediating Factor between General Anxiety Symptoms and Performance on Problem-Solving Tasks

Hallford, D. J. 1, 2, Noory, N. 1, Mellor, D. 1

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School of Psychology, Deakin University, 221 Burwood Hwy, Burwood, Victoria 3125, Melbourne, Australia 2 Corresponding Author, School of Psychology, Deakin University, 221 Burwood Hwy, Burwood, Victoria 3125, Melbourne, Australia, Phone: + 61 3 9251 7777, Email: [email protected]

Please cite as: Hallford, D. J., Noory, N., & Mellor, D. (2018). Reduced Autobiographical Memory Specificity as a Mediating Factor between General Anxiety Symptoms and Performance on Problem-Solving Tasks. Applied Cognitive Psychology. Inpress.

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Abstract This study sought to further explain the association between general anxiety symptoms and impaired problem-solving by testing whether this occurs, in part, through a reduced ability to retrieve event-level, specific autobiographical memory (AM). Participants (N = 301; M age=28.2 SD=7.7, 55.8% female) completed assessments of the retrieval of specific AM, anxiety symptoms, depressive symptoms, and rumination. They then completed the Means-End Problem Solving Task, which assessed their ability to produce relevant problem-solving steps. Participants who were higher in anxiety reported a lesser number of relevant problem-solving steps and this association was, in part, related to anxiety being associated with reduced AM specificity (after controlling for depressive symptoms). Rumination did not mediate anxiety and problem-solving, nor anxiety and AM specificity. These findings provide further evidence that elevated anxiety is associated with reduced ability to retrieve specific AM, and a specific cognitive pathway through which anxiety may affect problem-solving performance.

Keywords: Anxiety, Autobiographical Memory Specificity, Problem Solving, Rumination,

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Specific autobiographical memory (AM) refers to information about personally-experienced events that occurred within a 24-hour period of one's life (Williams et al., 2007). The ability to recall specific AM is important in adaptive functioning, and is implicated in a wide range of psychological processes such as planning, emotional regulation, and self-identity (Bluck, Alea,

Habermas, & Rubin, 2005). The association between psychopathology and the retrieval of specific AM has been widely researched, and a range of psychological disorders have been found to be associated with a reduced ability to recall specific AM, including major depression, posttraumatic stress disorder (PTSD: Moradi et al., 2014; Williams et al., 2007), anorexia nervosa (Bomba et al., 2014), and schizophrenia-spectrum disorders (Berna et al., 2015). Although there is an increasingly developed body of research highlighting how various psychological states are associated with impairments in AM retrieval, there has been limited research relating to anxiety. Given that a significant proportion of the general population experience problematic levels of anxiety over their lifetime (Bandelow & Michaelis, 2015), identification of further cognitive markers may lead to potential points of intervention. Anxiety is an affective state defined by threat perception and physiological arousal that occurs as a part of normal psychological functioning as well as in varying degrees among psychological disorders. To date, studies have shown mixed evidence for reduced AM specificity in anxiety disorders, although this may be dependent on the type of disorder. For example, it is not found in social anxiety disorder (Wenzel, Werner, Cochran, & Holt, 2004) or specific phobias (Wenzel, Jackson, Brendle, & Pinna, 2003). Evidence suggests that general anxiety symptoms are related to difficulties recalling specific AM (Hallford et al., 2018), although some data from community samples have shown that this association may persist after controlling for depressive symptoms (Hallford & Mellor, 2017) or may not (Boelen et al., 2014).This disparity may be due to the fact that generalized anxiety symptoms represent a more pervasive and chronic state of elevated anxiety, and may be more likely to lead to pervasive disruptions in cognitive processing, relative

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to anxiety triggered by context-specific stimuli. One theoretical framework that may help explain this disruption is Attentional Control Theory. This theory proposes that anxiety focuses our attention on stimuli that are perceived threats and reduces the flexibility of our attention, diminishes our ability to disinhibit and filter out irrelevant stimuli, and reduces the capacity of working memory (Eysenck, 2014; Eysenck, Derakshan, Santos, & Calvo, 2007). The retrieval of specific AM is thought to involve a generative search of self-related memory that is stored in a hierarchical structure, progressing from more abstracted information about the self to information about specific events (Conway & Pleydell- Pearce, 2000). Elevated levels of anxiety may disrupt this generative search due to impairments in attentional control, and truncate memory retrieval at less specific levels of information, for example memories of longer periods in one’s life such as a schooling year or a vacation, or semantic information about the self (e.g., I have always had patience). Indeed, impaired executive functioning is one factor shown to be related to specific AM (Dalgleish et al., 2007; Williams et al., 2007), whereby difficulties in inhibiting distracting information, or planning and sequencing of information in working memory, may impact the generative retrieval of specific AM. In addition to its effect on AM, anxiety can also interfere with the generation of solutions to real or anticipated problems that may arise (Eysenck & Derakshan, 2011; Ladouceur, Blais, Freeston, & Dugas, 1998; Sutherland & Bryant, 2008). Social problem-solving, as defined by D’Zurilla and Nezu (1982) to encompass interpersonal and non-interpersonal problems, refers to the self-directed cognitive-behavioural process through which individuals identify adaptive ways of coping with problematic situations in the real world. It is posited then, that this type of problem-solving is conscious, rationale, and and purposeful (D’Zurilla & Chang, 1995). This relationship between anxiety and problem-solving can also be understood through Attentional Control Theory (Bardeen et al., 2014; Eysenck & Derakshan, 2011). For example, switching focus to stimuli viewed as threatening might demean cognitive resources otherwise dedicated to

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the problem-solving process (Eysenck & Derakshan, 2011; Johnson, 2009), as might the impairments in disengaging from such stimuli that are found in anxious individuals (Cisler & Koster, 2010). Further, even where the accuracy of performance on tasks is maintained, anxious individuals show an inefficiency in their processing (Derakshan & Koster., 2010). Where there are impairments in attentional control due to anxiety, this facilitates less effective problemsolving. One pathway through which anxiety may indirectly affect problem-solving is through the failure to retrieve detailed specific memories that might be used to generate solutions (Eysenck & Derakshan, 2011; Williams et al., 2006). In effect, how well an individual is able to recombine details from past events has a direct impact on how well they are able to simulate and resolve anticipated problems (Schacter, Addis & Buckner, 2008). In support of this proposition, research has indicated that when AM retrieval is more specific, problem-solving improves (Beaman, Pushkar, Etezadi, Bye, & Conway, 2007; Williams et al., 2006) and when negative affective states such as dysphoria reduce AM specificity, problem-solving ability decreases (Goddard et al., 1996). Rumination may also play a role in the relationship between anxiety and problemsolving, as well as anxiety and AM specificity. Rumination is defined by repetitive thinking about negative events and causes of distress (Nolen-Hoeksema, 1991). Rumination is typically elevated in the context of anxiety (Olatunji, Cisler, & Tolin, 2007), and is also related to poorer problem-solving (Lyubomirsky & Nolen-Hoeksema, 1995). Further to this, recent findings have indicated that rumination partially mediates the association between anxiety and reduced AM specificity (Hallford & Mellor, 2017). rumination, increased in the context of anxiety, is thought reinforce and increase the saliency of intermediate and schematic levels of information about the self, capturing attention before specific event-level knowledge can be retrieved (Williams et al., 2007). Therefore, anxiety may directly disrupt the retrieval of specific AM through deficits in

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attentional control, and indirectly disrupt it through increased rumination and the process of attention capture during generative retrieval. In the present study we aimed to extend on existing research by empirically testing whether heightened levels of anxiety might impact adversely on the outcomes of a problemsolving tasks, and whether this impact might occur, in part, through a reduced retrieval of specific AM. We also sought to examine the role of rumination as a mediating variable, both between anxiety and problem-solving and between anxiety and AM specificity. We predicted that higher general symptoms of anxiety would be directly related to the ability to generate relevant problem-solving steps, and also indirectly related through increased rumination, and reduced AM specificity.. We also included depressive symptoms in the model to control for the overlapping variance with anxiety symptoms (Clark & Watson, 1991). Methods Participants The sample consisted of 301 participants (M age=28.2 SD=7.7, 55.8% female). The participants all resided in the United States. With respect to highest educational achievement, 12.6% of the sample had postgraduate qualifications, 41.5% had bachelor degrees, 21.9% had diplomas or certificates, 22.9% had completed high school, and 1% had finished elementary school as their highest attainment. The majority of the participants were employed, either fulltime (58.8%), part-time (15.9 %), or casually (8.6 %), and 36.2% reported currently studying. The participants were recruited from Amazon’s Mechanical Turk (MTurk; https://www. mturk.com/mturk/welcome), a crowd-sourcing website facilitating online recruitment of participants to complete tasks for which they are remunerated. Research indicates that data from MTurk is comparable to that obtained using other recruitment methods (Paolacci & Chandler, 2014), and is considered to have utility in clinically-relevant research on par with other non-

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probability sampling methods such as student samples of convenience (Chandler & Shapiro, 2016). Materials Autobiographical memory specificity. The Sentence Completion for Events from the Past Test (SCEPT; Raes, Hermans, Williams, & Eelen, 2007) was used to assess the ability to retrieve specific AM. The SCEPT consists of 11 sentence stems that probe for past experiences (e.g., “When I think back to…”, and “Last year I…”). Participants are advised they can finish the sentences any way they like, but each sentence must relate to a different topic. The SCEPT is an implicit measure of AM specificity, as participants are not instructed in terms of how specific or detailed the memory should be. The SCEPT has been shown to be a valid measure of AM specificity, and more sensitive to individual differences in non-clinical populations relative to explicit methods (Raes et al., 2007). The responses to the SCEPT were coded as specific (i.e., an event that occurred within the space of one day) or non-specific. Two coders, the first author and another researcher not otherwise related to the study, rated 10% of the responses. An intraclass correlation coefficient was calculated which showed acceptable inter-rater reliability across the items (ICC = .81, p < .001). The unaffiliated researcher coded the remaining responses. The number of specific AM responses were summed and reported as a total. Rumination. Rumination was assessed using the Rumination on Sadness Scale (RSS; Conway et al., 2000). The RSS uses 13 self-report items to which respondents rate the strength with which they identify with statements about repetitively thinking about distress and its cause on a scale from 0 (not at all) to 10 (very much). The RSS is a psychometrically robust measure of rumination, with divergent validity in the context of depressive symptoms (Conway et al., 2000). In the current study, the internal reliability of the RSS items was excellent (Cronbach’s alpha = .96).

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Anxiety symptoms. The short-form version of the Depression, Anxiety, and Stress Scale (DASS; Lovibond & Lovibond, 1995) was used to assess general anxiety symptoms. Participants responded to seven self-report items by indicating the presence of general anxiety symptoms over the last week using a four-point scale ranging from 0 (did not apply to me at all) to 3 (applied to me very much, or most of the time). This short-form subscale has good psychometric properties in the measure of clinical anxiety symptoms (Antony et al., 1998), and had good internal reliability in the current study (Cronbach’s alpha = .87). Depressive symptoms. We used the Patient Heath Questionnaire (PHQ; Kroenke, Spitzer, Williams, & Löwe, 2010) to assess depressive symptoms. The PHQ consists of nine items that correspond to the Diagnostic and Statistical Manual for Mental Disorders 4th edition text revision criteria for a Major Depressive Episode (American Psychiatric Association, 2000). Each item is rated in terms of the self-reported frequency of experiencing the respective symptom over the last two weeks using a 0 (not at all) to 3 (nearly every day) scale. The PHQ is a valid and reliable measure of symptoms of clinical depression (Kroenke et al., 2010). In the current study, internal reliability was excellent (Cronbach’s alpha = .96) Problem-Solving. To assess the ability to generate relevant problem-solving steps to a defined problem, we used the Means-End Problem Solving Task (MEPS; Platt & Spivack, 1975). The MEPS procedure required participants to view three different problems with an ending solution (a dirty living space that ends up clean, an avoidant friend with whom the relationship is improved, and a poor diet that becomes healthier). Given that some of the original items from the MEPS were not relevant for this cohort (e.g., killing an SS soldier), we used items from a recent study using MEPS methodology that used both interpersonal and non-interpersonal problems (Madore & Schacter, 2014). We chose these items because of their general relevance for individuals, and therefore they would be able to generate solution steps to solve. We limited it to three items to reduce participant burden and risk of dropout, especially given the measure was

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completed remotely. Participants were given three minutes to write out the steps that they would take to reach the final solution to each problem in as much detail as possible. Instructions were provided prior to the first problem being presented, and when the three minutes were up the online survey was automated to take them to the next problem. Responses for the MEPS were scored in terms of how many relevant steps were generated to solve the problem, where relevant was considered a step or event that logically led towards the stated solution. Again, the first author and a researcher unaffiliated with the study coded 10% of the participants’ responses to the three items for the number of relevant steps. The inter-rater reliability for coding of the number of relevant steps over the three MEPS items was excellent (mean ICC = .92). The unaffiliated author then coded the remaining responses. The internal reliability of these three items was acceptable (Cronbach’s alpha = .81), and they were averaged to produce a mean score for each participant of relevant problem-solving steps. To assess the factor structure of these items an exploratory factor analysis using maximum-likelihood extraction was conducted. The results showed the extraction of one underlying factor (eigenvalue = 2.2), which accounted for 72.8% of the variance in the responses, with item factor loadings of 0.69, 0.82, and 0.80. Procedure Ethics approval was obtained from the Deakin University human research ethics committee prior to commencement of the study. Participants self-selected to take part in the study advertised on MTurk, and they were then taken to an external website to complete the questionnaires and tasks. They were presented with the plain language statement, and then asked to provide demographic information. The measures were completed as part of a larger survey, and in this order: the SCEPT, the anxiety and depression measures, the RSS, and then the MEPS. Informed consent was implied by completion and submission of the questionnaire. Participants were compensated for their time with a nominal sum of US$1. Data Analytic Strategy

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SPSS 24.0 was used to generate descriptive statistics and bivariate correlations. Path analysis was used to test the study hypotheses. This allowed for simultaneous tests of the direct and indirect associations between the variables. The path model was estimated using a singlestep model with maximum likelihood estimation through AMOS 24.0 software. Bootstrapping with 10,000 bootstrap samples and a bias-corrected and accelerated 95% confidence interval (CI) were used to test for indirect effects. The following fit indices were used to assess the statistical fit of the model to the data: the chi-square value (CMIN) and p-value, the relative chi-square statistic (CMIN/df), the root mean square error of approximation (RMSEA), the standardized root mean square residual (SRMR), and the comparative fit index (CFI). We drew on commonly used guidelines provided by Hu and Bentler (1999) to help assess the degree to which the model fit the data: RMSEA ≤ .06, SRMR ≤ .09, and CFI ≥ .95. We aimed to recruit at least 300 participants, as this sample size is considered adequate to accurately estimate parameters in a path analysis model (Kline, 2015). Using G*Power 3.1, it was estimated that our sample size would allow for detection of small effects between the study variables (r ≥ .16) using a two-tailed significance test with an alpha level of .05 and power level of .80. The hypothesized model is shown in Figure 1. As indicated, we sought to test a multiple mediation model, whereby anxiety was predicted to be directly associated with a lower number of relevant problem-solving steps, and indirectly related through increased rumination and decreased AM specificity. We included depressive symptoms in the model to control for its known association with AM specificity (Liu, Li, Xiao, Yang, & Jiang, 2013), rumination (NolenHoeksema, 2000) and social problem-solving (Marx, Williams, & Claridge, 1992). We also correlated depressive symptoms and anxiety symptoms. Lastly, we included age in the model to control for known associations with anxiety (Scott et al., 2008), AM specificity (Ros, Latorre, Serrano, & Ricarte, 2017), and problem-solving (Thornton & Dumke, 2005).

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Results Descriptive statistics and zero-order correlations are shown in Table 1. As anticipated, anxiety was found to be related to lower AM specificity, higher rumination, and less relevant problem-solving steps, and depressive symptoms showed correlations consistent in direction and magnitude. Anxiety and depressive symptoms were strongly correlated. Rumination and AM specificity also had significant bivariate correlations with problem-solving steps, thereby satisfying the conditions necessary for assessment of indirect effects of anxiety on problemsolving through these variables. Age was not found to be related to any of the study variables. The results from the initial model test showed it was a good fit to the data, CMIN = 2.3 (df = 2, p = .314), CMIN/df = 1.1, RMSEA = .02, SRMR = .024, CFI = .99. Anxiety was a significant, direct predictor of relevant problem-solving steps, and AM specificity and rumination. AM specificity was found to be related to problem-solving steps. Rumination was not related to problem-solving directly, nor was it related to AM specificity. Given this, rumination was removed from the model, and analysis was re-run. The resulting model was again a good fit, CMIN = 1.4 (df = 1, p = .228), CMIN/df = 1.4, RMSEA = .03, SRMR = .022, CFI = .99. Consistent with the zero-order correlations, age was not associated with anxiety, AM specificity, or problem-solving (all p values ≥ .170), and nor were depressive symptoms related to AM specificity (p = .482) or problem-solving (p = .237). Therefore, these pathways were trimmed and the model was re-run, again with a good fit, CMIN = 2.2 (df = 2, p = .336), CMIN/ df = 1.1, RMSEA = .017, SRMR = .017, CFI = .99. Figure 2 shows the standardized regression coefficients, covariance, and squared multiple correlations of this model. Tests of indirect effects indicated that anxiety was indirectly associated with problem-solving steps through decreased AM specificity (standardized indirect effect = -.03, CI95% L-.07 U-.01, p = .002).

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Discussion This study examined the mediating role of the retrieval of specific AM and rumination in the relationship between general anxiety symptoms and problem-solving. As hypothesized, higher general symptoms of anxiety were found to be directly related to relevant problem-solving steps, and also indirectly through reduced AM specificity. Our results indicate that participants who reported experiencing stronger symptoms of general anxiety were also less specific in their retrieval of autobiographical memory, and less adept at formulating relevant steps to solve the problems that were presented. These findings support previous research linking anxiety symptoms with problem-solving (Sutherland & Bryant., 2008), and reduced AM specificity with problem-solving (Beaman et al., 2007). This study provides the first evidence that as levels of general anxiety symptoms increase, there is a reduced likelihood of retrieving of specific, event-level memories, and that this less specific retrieval is associated with reduced ability in problem-solving tasks. This is consistent with Attentional Control Theory (Eysenck & Derakshan, 2011), whereby increases in anxiety may impair our ability to engage in the retrieval of information and our capacity to shift focus from perceived sources of threat, and filter out irrelevant information (Bardeen et al., 2013). Indeed, the retrieval of specific autobiographical memories does requires the ability to inhibit irrelevant information through executive control (Dalgleish et al., 2007). In our initial path analysis model rumination was not observed to be related to AM specificity or problem-solving. This might be explained in several ways. Firstly, rumination has typically been found to correlate only weakly with AM specificity in previous studies of nonclinical samples (Raes et al., 2007). The variance that rumination would typically account for in AM specificity may have been accounted for by anxiety, with both representing factors that may disrupt attentional control in retrieval of specific autobiographical memories. Anxiety may account independently for this variance given that it is understood to be a more proximal factor

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in disrupting executive functioning relative to rumination which requires some level of sustained attention. This may also apply to the lack of association between rumination and problemsolving. Depressive symptoms correlated with AM specificity and problem-solving, however, they were not a significant predictor in the full model, with only anxiety symptoms predicting unique variance. This was not a hypothesis of the study, and it is unclear why this was the case. Given that the effects were generally small in the model, and that anxiety and depression shared large amounts of variance, this finding may have have due to random variations in this sample. The results, which were modest with respect to the magnitude of effects, support current theoretical frameworks through which we understand the functional impact of anxiety on problem-solving. As anxiety facilitates a reduction in cognitive resources, the ability to draw on past experiences and generate useful solutions to problems deteriorates (Dugas et al, 1998; Johnson, 2009; Sutherland & Bryant, 1998). The relationship between anxiety and problemsolving may be, in part, attributable to the meditating influence of reduced AM specificity. In this respect, AM training interventions that can improve retrieval of specific AM, such as Memory Specificity Training (MeST; Raes, Williams, Hermans, 2009) may compensate somewhat for the effects of anxiety. Some evidence indicates that improvements in AM specificity are associated with reduced attention to distracting information (Takano, Moriya, & Raes, 2017), potentially attenuating the influence of anxiety on attentional control. The current study also extends on previous work by showing that associations between anxiety and AM specificity can be detected using an implicit measure of AM specificity, in addition to explicit measures (Hallford & Mellor, 2017). Several limitations of the current study should be noted. One significant point is that attentional control itself was not directly measured in our study, but suggested as a process to explain how anxiety might affect AM specificity (Dalgleish et al., 2007), and problem-solving (Eysenck & Derakshan, 2011). Therefore, subsequent tests of this model might include

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attentional control. We used a self-selected community sample, and it is unknown whether these results would generalize to samples with diagnosed clinical anxiety. These findings must be interpreted in the context of anxiety within a normal population, rather than one with particularly heightened anxiety. Further, we did not collect information on history of a diagnosis of mental illness, which might affect findings given that even remitted disorders, such as depression, may chronically modify access to specific AM (Nandrino, Pezard, Posté, Réveillère, & Beaune, 2002). Although the tasks and instructions were standardized across the sample, exerting experimental control was difficult given the study was conducted online. The cross-sectional design also prevents strong causal inferences from being made. Tests of indirect effects between variables using cross-sectional designs have obvious limitations regarding directionality of causation, and it can only be concluded that variables are related with each other in some form. Although we specified a particular model in this study, as noted above, further tests are needed to establish the casual relationships, in particular the anxiety and AM specificity association. Future studies may use longitudinal or experimental designs to test this causal pathway. In conclusion, these findings provide further evidence of how elevated anxiety is associated with retrieval of specific AM, and are illustrative of a specific cognitive pathway through which anxiety may affect problem-solving performance

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References Antony, M. M., Bieling, P. J., Cox, B. J., Enns, M. W., & Swinson, R. P. (1998). Psychometric properties of the 42-item and 21-item versions of the Depression Anxiety Stress Scales in clinical groups and a community sample. Psychological Assessment, 10(2), 176. doi: 10.1037/1040-3590.10.2.176 Bandelow, B., & Michaelis, S. (2015). Epidemiology of anxiety disorders in the 21st century. Dialogues in Clinical Neuroscience, 17(3), 327-335. Retrieved from https://www.ncbi.nlm.nih.gov /pmc/articles /PMC4610617/ Bardeen, J. R., Tull, M. T., Stevens, E. N., & Gratz, K. L. (2014). Exploring the relationship between positive and negative emotional avoidance and anxiety symptom severity: The moderating role of attentional control. Journal of Behavior therapy and Experimental Psychiatry, 45(3), 415-420. doi: 10.1016/j.jbtep.2014.04.006 Beaman, A., Pushkar, D., Etezadi, S., Bye, D., & Conway, M. (2007). Autobiographical memory specificity predicts social problem-solving ability in old and young adults. Quarterly Journal of Experimental Psychology, 60, 1275-1288. doi:10.1080/17470210600943450 Berna, F., Potheegadoo, J., Aouadi, I., Ricarte, J. J., Allé, M. C., Coutelle, R., ... & Danion, J. M. (2015). A meta-analysis of autobiographical memory studies in schizophrenia spectrum disorder. Schizophrenia Bulletin, 42(1), 56-66. doi: 10.1093/schbul/sbv099 Bluck, S., Alea, N., Habermas, T., & Rubin, D. C. (2005). A tale of three functions: The self– reported uses of autobiographical memory. Social Cognition, 23(1), 91-117. doi: 10.1521/soco.23.1.91.59198 Boelen, P. A., Huntjens, R. J., & van den Hout, M. A. (2014). Concurrent and prospective associations of habitual overgeneral memory and prospection with symptoms of depression, general anxiety, obsessive compulsiveness, and post-traumatic stress. Memory, 22(7), 747-758. doi: 10.1080/09658211.2013.824985

ANXIETY, MEMORY AND PROBLEM-SOLVING

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Bomba, M., Marfone, M., Brivio, E., Oggiano, S., Broggi, F., Neri, F., & Nacinovich, R. (2014). Autobiographical memory in adolescent girls with anorexia nervosa. European Eating Disorders Review, 22(6), 479-486. doi: 10.1002/erv.2321 Chandler, J., & Shapiro, D. (2016). Conducting clinical research using crowd sourced convenience samples. Annual Review of Clinical Psychology, 53-81. doi: https://doi.org/10.1146/ annurev-clinpsy-021815-093623 Cisler, J. M., & Koster, E. H. (2010). Mechanisms of attentional biases towards threat in anxiety disorders: An integrative review. Clinical Psychology Review, 30(2), 203-216. doi: 10.1016/j.cpr.2009.11.003 Clark, L. A., & Watson, D. (1991). Tripartite model of anxiety and depression: psychometric evidence and taxonomic implications. Journal of Abnormal Psychology, 100(3), 316-336. doi:10.1037//0021-843x.100.3.316 Conway, M., Csank, P. A., Holm, S. L., & Blake, C. K. (2000). On assessing individual differences in rumination on sadness. Journal of Personality Assessment, 75(3), 404-425. doi: 10.1207/S15327752JPA7503_04 Conway, M. A., & Pleydell-Pearce, C. W. (2000). The construction of autobiographical memories in the self-memory system. Psychological Review, 107(2), 261-288. doi:10.1037//0033-295X .107.2.261 Dalgleish, T., Williams, J. M. G., Golden, A. M. J., Perkins, N., Barrett, L. F., Barnard, P. J., ... & Watkins, E. (2007). Reduced specificity of autobiographical memory and depression: the role of executive control. Journal of Experimental Psychology: General, 136(1), 2342. doi:10.1037/0096-3445.136.1.23 Derakshan, N., & Koster, E. H. (2010). Processing efficiency in anxiety: Evidence from eyemovements during visual search. Behaviour Research and Therapy, 48(12), 1180-1185. Doi: 10.1016/j.brat.2010.08.009

Running head: ANXIETY, MEMORY AND PROBLEM-SOLVING

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D'Zurilla, T. J., & Chang, E. C. (1995). The relations between social problem solving and coping. Cognitive Therapy and Research, 19, 547-562. doi:10.1007/BF02230513 D'Zurilla, T. J., & Nezu, A. M. (1982). Social problem-solving in adults. In P. C. Kendall (Ed.), Advances in cognitive-behavioral research and therapy (Vol. 1, pp. 201-274). New York: Academic Press. Ladouceur, R., Blais, F., & Freeston, M. H. Dugas, M.J. (1998). Problem solving and problem orientation in generalized anxiety disorder. Journal of Anxiety Disorders, 12(2), 139-152. doi:10.1016/S0887-6185(98)00002-4 Eysenck, M. (2014). Anxiety and cognition: A unified theory. Psychology Press. Eysenck, M. W., & Derakshan, N. (2011). New perspectives in attentional control theory. Personality and Individual Differences, 50(7), 955-960. doi: 10.1016/j.paid.2010.08.019 Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7(2), 336-353. doi:10.1037/15283542.7.2.336 Ehlers, A. & Kleim, B., (2008). Reduced autobiographical memory specificity predicts depression and posttraumatic stress disorder after recent trauma. Journal of Consulting and Clinical Psychology, 76(2), 231. doi: 10.1037/0022-006X.76.2.231 Goddard, L., Dritschel, B., & Burton, A. (1996). Role of autobiographical memory in social problem solving and depression. Journal of Abnormal Psychology, 105, 609-616. doi: 10.1037/0021-843X.105.4.609 Hallford, D. J., & Mellor, D. (2017). Autobiographical memory specificity and general symptoms of anxiety: Indirect associations through rumination. International Journal of Mental Health, 46(2), 74-88. doi: 10.1080/00207411.2017.1294968

ANXIETY, MEMORY AND PROBLEM-SOLVING

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Hallford, D. J., Mellor, D., Bafit, L., Devenish, B., Bogeski, T., Austin, D. W., & Kaplan, R. (2018). The Effect of Increased State Anxiety on Autobiographical Memory Specificity and Future Thinking. Under Review. Heeren, A., Van Broeck, N., & Philippot, P. (2009). The effects of mindfulness on executive processes and autobiographical memory specificity. Behaviour Research and Rherapy, 47(5), 403-409. doi:10.1016/j.brat.2009.01.017 Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. doi:10.1080/10705519909540118 Johnson, D. R. (2009). Goal-directed attentional deployment to emotional faces and individual differences in emotional regulation. Journal of Research in Personality, 43(1), 8-13. doi: 10.1016/j.jrp.2008.09.006 Kline, R. B. (2015). Principles and practice of structural equation modeling. New York, US: Guilford Publications. Kofman, O., Meiran, N., Greenberg, E., Balas, M., & Cohen, H. (2006). Enhanced performance on executive functions associated with examination stress: Evidence from task-switching and Stroop paradigms. Cognition & Emotion, 20(5), 577-595. doi: 10.1080/02699930500270913 Kroenke, K., Spitzer, R. L., Williams, J. B., & Löwe, B. (2010). The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. General Hospital Psychiatry, 32(4), 345-359. doi: 10.1016/j.genhosppsych.2010.03.006 Liu, X., Li, L., Xiao, J., Yang, J., & Jiang, X. (2013). Abnormalities of autobiographical memory of patients with depressive disorders: A meta-analysis. Psychology and Psychotherapy: Theory, Research and Practice, 86(4), 353-373. doi: 10.1111/j.2044-8341.2012.02077.x

Running head: ANXIETY, MEMORY AND PROBLEM-SOLVING

19

Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 33(3), 335-343. doi:10.1016/0005-7967(94)00075-U Lyubomirsky, S., & Nolen-Hoeksema, S. (1995). Effects of self-focused rumination on negative thinking and interpersonal problem solving. Journal of Personality and Social Psychology, 69(1), 176-190: doi:10.1037/0022-3514.69.1.176 Madore, K. P., & Schacter, D. L. (2014). An episodic specificity induction enhances means-end problem solving in young and older adults. Psychology and Aging, 29(4), 913-924. doi:10.1037/a0038209 Marx, E. M., Williams, J. M., & Claridge, G. C. (1992). Depression and social problem solving. Journal of Abnormal Psychology, 101(1), 78-86. doi:10.1037/0021-843X.101.1.78 Moradi, A. R., Moshirpanahi, S., Parhon, H., Mirzaei, J., Dalgleish, T., & Jobson, L. (2014). A pilot randomized controlled trial investigating the efficacy of MEmory Specificity Training in improving symptoms of posttraumatic stress disorder. Behaviour Research and Therapy, 56, 68-74. doi: 10.1016/j.brat.2014.03.002 Nandrino, J. L., Pezard, L., Posté, A., Réveillère, C., & Beaune, D. (2002). Autobiographical memory in major depression: A comparison between first-episode and recurrent patients. Psychopathology, 35(6), 335-340. doi:10.1159/000068591 Nolen-Hoeksema, S. (2000). The role of rumination in depressive disorders and mixed anxiety/depressive symptoms. Journal of abnormal psychology, 109(3), 504. Olatunji, B. O., Cisler, J. M., & Tolin, D. F. (2007). Quality of life in the anxiety disorders: a meta-analytic review. Clinical Psychology Review, 27(5), 572-581. doi: 10.1016/j.cpr.2007.01.015

ANXIETY, MEMORY AND PROBLEM-SOLVING

20

Platt, J. J., & Spivack, G. (1975). Manual for the Mean-end Problem-solving Procedure (MEPS): A Measure of Interpersonal Cognitive Problem-solving Skill. Hahnemann Community Mental Health/Mental Retardation Center, Hahnemann Medical College and Hospital. Paolacci, G., & Chandler, J. (2014). Inside the Turk understanding mechanical Turk as a participant pool. Current Directions in Psychological Science, 23(3), 184-188. doi:10.1177/0963721414531598 Raes, F., Hermans, D., Williams, J. M. G., & Eelen, P. (2007). A sentence completion procedure as an alternative to the Autobiographical Memory Test for assessing overgeneral memory in non-clinical populations. Memory, 15(5), 495-507. doi:10.1080/09658210701390982 Raes, F., Hermans, D., Williams, J. M. G., Demyttenaere, K., Sabbe, B., Pieters, G., & Eelen, P. (2005). Reduced specificity of autobiographical memory: A mediator between rumination and ineffective social problem-solving in major depression?. Journal of Affective Disorders, 87(2), 331-335. Doi: 10.1016/j.jad.2005.05.004 Raes, F., Williams, J. M. G., & Hermans, D. (2009). Reducing cognitive vulnerability to depression: A preliminary investigation of MEmory Specificity Training (MEST) in inpatients with depressive symptomatology. Journal of Behavior Therapy and Experimental Psychiatry, 40(1), 24-38. doi:10.1016/j.jbtep.2008.03.001 Ros, L., Latorre, J. M., Serrano, J. P., & Ricarte, J. J. (2017). Overgeneral autobiographical memory in healthy young and older adults: Differential age effects on components of the capture and rumination, functional avoidance, and impaired executive control (CaRFAX) model. Psychology and Aging, 32(5), 447-459. doi:10.1037/pag0000175 Schacter, D. L., Addis, D. R., & Buckner, R. L. (2008). Episodic simulation of future events. Annals of the New York Academy of Sciences, 1124(1), 39-60. Doi: 10.1196/annals.1440.001

Running head: ANXIETY, MEMORY AND PROBLEM-SOLVING

21

Scott, K. M., Von Korff, M., Alonso, J., Angermeyer, M., Bromet, E. J., Bruffaerts, R., ... & He, Y. (2008). Age patterns in the prevalence of DSM-IV depressive/anxiety disorders with and without physical co-morbidity. Psychological Medicine, 38(11), 1659-1669. doi:10.1017/S0033291708003413 Sutherland, K., & Bryant, R. A. (2008). Social problem solving and autobiographical memory in posttraumatic stress disorder. Behaviour Research and Therapy, 46(1), 154-161. doi: 10.1016/j.brat.2007.10.005 Takano, K., Ueno, M., Moriya, J., Mori, M., Nishiguchi, Y., & Raes, F. (2017). Unraveling the linguistic nature of specific autobiographical memories using a computerized classification algorithm. Behavior Research Methods, 49(3), 835-852. doi: 10.3758/s13428-016-0753-x Teasdale, J. D. & Watkins. E. (2001). Rumination and overgeneral memory in depression: effects of self-focus and analytic thinking. Journal of Abnormal Psychology, 110(2), 353. doi: 10.1037/0021-843X.110.2.333 Thornton, W. J., & Dumke, H. A. (2005). Age differences in everyday problem-solving and decision-making effectiveness: A meta-analytic review. Psychology and Aging, 20(1), 85-99. doi:10.1037/0882-7974.20.1.85 Van Vreeswijk, M. F., & De Wilde, E. J. (2004). Autobiographical memory specificity, psychopathology, depressed mood and the use of the Autobiographical Memory Test: A meta-analysis. Behaviour Research and Therapy, 42(6), 731-743. Wenzel, A., Jackson, L. C., Brendle, J. R., & Pinna, K. (2003). Autobiographical memories associated with feared stimuli in fearful and nonfearful individuals. Anxiety, Stress & Coping, 16(1), 1-15. doi: 10.1080/1061580021000009656

ANXIETY, MEMORY AND PROBLEM-SOLVING

22

Wenzel, A., Werner, M. M., Cochran, C. K., & Holt, C. S. (2004). A differential pattern of autobiographical memory retrieval in social phobic and nonanxious individuals. Behavioural and Cognitive Psychotherapy, 32(1), 1-13. doi:10.1017/s1352465804001006 Williams, J. M. G., Barnhofer, T., Crane, C., Herman, D., Raes, F., Watkins, E., & Dalgleish, T. (2007). Autobiographical memory specificity and emotional disorder. Psychological Bulletin, 133(1), 122-148. doi: 10.1037/0033-2909.133.1.122

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Table 1. Descriptive Statistics and Zero-Order Correlations between the Study Variables Age

Age

Specific

DASS

AM

Anxiety

Rumination

Problem-

PHQ

Mean (SD)

solving

Possible Range

-

Specific AM

.03

-

DASS Anxiety

-.02

-.23***

-

Rumination

-.07

-.13*

.54***

-

Problem-

-.07

.24***

-.32***

-.16*

-

solving PHQ

-.07

-.22***

.68***

.58***

-.17**

-

1.9 (1.5)

0 – 11

4.5 (4.7)

0 – 21

54.5 (31.7)

0 – 120

11.3 (6.3)

/

7.0 (6.4)

0 - 27

* p < .05, **p < .01, ***p < .001. Note: DASS = Depression, Anxiety, and Stress Scale, PHQ = Depressive symptoms as rated by the Patient Health Questionnaire, Specific AM reported as number of specific responses on the Sentence Completion for Events from the Past Test.

Running head: ANXIETY, MEMORY AND PROBLEM-SOLVING

Figure 1. Hypothesized Path Model.

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Running head: ANXIETY, MEMORY AND PROBLEM-SOLVING

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Figure 2. Final path model showing standardized regression coefficients, covariance between depression and anxiety symptoms, and squared multiple correlations indicating explained variance in AM specificity and problem-solving Steps. Non-significant pathways shown here, but not included in the model, *p < .05, ***p < .001, n.s. = not statistically significant.