Longitudinal Patterns of the Tip-of-the-Tongue Phenomenon in People With Subjective Cognitive Complaints and Mild Cognitive Impairment

fpsyg-11-00425 March 13, 2020 Time: 17:38 # 1 ORIGINAL RESEARCH published: 13 March 2020 doi: 10.3389/fpsyg.2020.00425 Edited by: Montserrat Comesaña, University of Minho, Portugal Reviewed by: Gloria Gagliardi, University of Naples “L’Orientale”, Italy Ruth Elaine Mark, Tilburg University, Netherlands *Correspondence: David Facal [email protected] Specialty section: This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology Received: 25 October 2019 Accepted: 24 February 2020 Published: 13 March 2020 Citation: Campos-Magdaleno M, Leiva D, Pereiro AX, Lojo-Seoane C, Mallo SC, Nieto-Vieites A, Juncos-Rabadán O and Facal D (2020) Longitudinal Patterns of the Tip-of-the-Tongue Phenomenon in People With Subjective Cognitive Complaints and Mild Cognitive Impairment. Front. Psychol. 11:425. doi: 10.3389/fpsyg.2020.00425 Longitudinal Patterns of the Tip-of-the-Tongue Phenomenon in People With Subjective Cognitive Complaints and Mild Cognitive Impairment María Campos-Magdaleno1, David Leiva2, Arturo X. Pereiro1, Cristina Lojo-Seoane1, Sabela C. Mallo1, Ana Nieto-Vieites1, Onésimo Juncos-Rabadán1and David Facal1* 1Department of Developmental Psychology, University of Santiago de Compostela, Galicia, Spain, 2Department of Social Psychology and Quantitative Psychology, Institute of Neurosciences, University of Barcelona, Barcelona, Spain Background: The Tip-of-the-Tongue (ToTs) state is considered a universal phenomenon and is a frequent cognitive complaint in old age. Previous cross-sectional studies have found that ToT measures successfully discriminate between cognitively unimpaired adults and adults with Mild Cognitive Impairment (MCI). The aim of this study was to identify longitudinal patterns of ToTs in individuals with subjective complaints and with MCI regarding progress of their cognitive status. Method: The study included 193 participants with subjective cognitive complaints (SCC) and 56 participants with MCI who completed a baseline and two followup assessments, with an interval of about 18 months between each assessment. Participants were classified into three groups by considering cognitive stability or deterioration from the baseline diagnosis: SCC-stable, MCI-stable and MCI-worsened. Participants performed a ToT task involving recognition and naming of famous people depicted in 50 photographs. Generalized Linear Mixed Models (GLMM) were used to model longitudinal changes in familiarity, feeling of knowing, semantic access, phonological access and verbal fluency. Results: Phonological access differentiated MCI patients, stable and worsened, from adults with SCCs at all evaluation times. Phonological access declined over time in the three groups, without significant interactions between groups and time. Discussion: This study provides the first longitudinal evidence of differences in ToT measures for adults with MCI. The findings indicate that phonological access measures successfully differentiated between the diagnostic groups. However, slopes remain irrespective of the diagnostic group and progression toward more advance stages of cognitive impairment. Keywords: tip-of-the-tongue, lexical access, mild cognitive impairment, linear mixed models, longitudinal study, Compostela Aging Study Frontiers in Psychology | www.frontiersin.org 1March 2020 | Volume 11 | Article 425 fpsyg-11-00425 March 13, 2020 Time: 17:38 # 2 Campos-Magdaleno et al. Longitudinal Pattern of ToTs INTRODUCTION Cognitive impairment in old adults has been considered a continuum including different stages (Jack et al., 2018): a cognitively unimpaired phase (CU), with performance within the expected range for age and education; presence of subjective cognitive complaints (SCC), without objective cognitive impairment (Jessen et al., 2014;Molinuevo et al., 2017); Mild Cognitive Impairment (MCI), characterized by the presence of cognitive complaints, objective mild cognitive deterioration and relative preservation of instrumental activities of daily living (Petersen, 2004;Petersen et al., 2018); and dementia or major neurocognitive disorder, characterized by cognitive affectation and psychological symptoms that cause dependency (American Psychiatric Association, 2013). In MCI, single and multiple domain subtypes (deterioration in only one or in more cognitive domains) have been used to describe different degrees of severity, with the multiple domain subtype being the most serious condition (Brambati et al., 2009;Han et al., 2012;Campos- Magdaleno et al., 2016). Progression into this continuum is a complex process characterized by cognitive changes, transitions and diagnostic instability, with an increased risk of conversion to dementia but also the possibility of regression to CU (Facal et al., 2015;Petersen et al., 2018). MCI entity is heterogeneous, and different subtypes according evolutionary trajectories and severity need to be addressed (Díaz-Mardomingo et al., 2017). Language measures such as verbal fluency, naming and word learning have been successfully used as predictors of MCI and its progression to dementia (Murphy et al., 2006;Clague et al., 2011;Campos-Magdaleno et al., 2017). Tip-of-the-Tongue (ToT) constitutes one of the most frequent age-related language complaints and is characterized as a strong feeling of knowing in parallel with an inability to recall a lexical item which is known and that might eventually be recalled if enough attention and encoding feedback is provided (Brown, 2012;Bloom et al., 2018). Age-related increases in ToT experiences (hereafter ToTs) are not related to increased vocabulary knowledge throughout adulthood (Facal et al., 2012;Salthouse and Mendell, 2013; Shafto et al., 2017). Consistent evidence supports the hypothesis that the higher frequency of ToTs in older adults is caused by a decline in transmission of the activation from semantic to phonological representations (Burke et al., 1991;James and Burke, 2000;Shafto et al., 2007;Juncos-Rabadán et al., 2010;White et al., 2013). According to this hypothesis, ToTs occur when the activated semantic representation of a word fails to spread the necessary activation to its corresponding phonological representation, making lexical access impossible. The increase in the frequency of ToTs in older adults is consistent with an age-related decline in activation transmission, and proper names seem to be more vulnerable to this decline than common nouns, as proper names are represented by the individual characteristics of a person rather than by more general information connected to multiple semantic nodes (Burke et al., 1991). Other relevant hypothesis on cognitive aging, such as the inhibition deficit, explained ToT as a deficient inhibition of different phonological representations (competitors) that arise when semantic representation of the target word is successfully activated (Woodworth, 1938). However, few experimental studies (Jones and Langford, 1987;Jones, 1989) have supported that hypothesis, and other studies have not been able to replicate them (Meyer and Bock, 1992;Perfect and Hanley, 1992). According to the cognitive continuum between unimpaired cognition and dementia, MCI represents an intermediate stage in the ability to retrieve proper names and is characterized by greater difficulty in phonological access, relative to cognitively unimpaired old adults, and only mild difficulties in semantic access more commonly associated with the onset of Alzheimer’s disease (Juncos-Rabadán et al., 2014). Several ToT measures, including semantic access (calculated as a proportional measure that represents successful access in the total number of target names) and phonological access (calculated as the proportion of successful semantic retrievals in which success in phonological access is also achieved) (Gollan and Brown, 2006;Juncos-Rabadán et al., 2010), have been successfully used as language predictors of MCI (Juncos-Rabadán et al., 2013). A multivariate logistic regression model including feeling of knowing, semantic knowledge, semantic access and phonological access was used to assess the predictive value of ToT measures for discriminating between normal controls and MCI patients within the Compostela Aging Study (CompAS). In a cross-sectional study, Juncos-Rabadán et al. (2013) found that a model including these four ToT measures together correctly classified 70% of controls (specificity) and 71.6 of MCI patients (sensitivity), with an Area Under Curve Roc (AUC) value of 0.74, and accounted for 23.5% of the variance. Although the model comprised all ToT variables, only the phonological access measure remained significantly associated with amnestic MCI. The authors also found that specificity, sensitivity and AUC values were higher than those obtained using semantic fluency as a language measure to discriminate MCI (total classification value, AUC = 0.66 and accounted variance = 15.4%). We have to mention two studies (Poppe et al., 2006;Oh and Ha, 2015) that did not find differences between normal oldest people and MCI patients, but they used the percent or the total number of produced ToTs that have been criticized as not appropriate measures because they do not explain the semantic and phonological representation and processes involved in ToT (Gollan and Brown, 2006). Longitudinal studies on ToTs in MCI are very scarce. As far as we know, apart of the aforementioned by Poppe et al. (2006) that used the total number of reported ToTs, only one follow-up study of changes in ToT in MCI has been carried out to date (Facal et al., 2016a). In the aforementioned study, proportional measures of change between baseline and one follow-up assessment (around 18 months) were calculated for familiarity, semantic access, phonological access and semantic fluency in a sample of 15 individuals with multiple domain amnestic MCI, 41 individuals with single domain amnestic MCI and 41 cognitively unimpaired controls. Comparisons revealed significant differences between baseline and follow-up only in semantic and phonological access, with improvements in semantic access in the control group and decline in phonological access in the two groups with amnestic MCI. Nevertheless, full longitudinal models have been used to study change in semantic and phonological access and their potential role in explaining diagnostic change in MCI. Frontiers in Psychology | www.frontiersin.org 2March 2020 | Volume 11 | Article 425 fpsyg-11-00425 March 13, 2020 Time: 17:38 # 3 Campos-Magdaleno et al. Longitudinal Pattern of ToTs FIGURE 1 | Representation of the tasks included in the ToT procedure. The photograph shown in the example is of Rafael Nadal, a famous Spanish tennis player. Photography by Valentina Alemanno (CC), modified according to figure size requirements from https://www.flickr.com/photos/thevhale/14309864633. Facal et al. (2016a) also considers “familiarity” as a ToT measure of meta-cognitive processes involved in ToTs that indicate that the name knowledge is present (Schwartz and Metcalfe, 2011). Although some cross-sectional studies suggest that familiarity-based memory measures may be sensitive markers of preclinical and prodromal Alzheimer’s Disease (AD, Wolk et al., 2013;Pitarque et al., 2016), the longitudinal approach did not show any evidence of their predictive value (Facal et al., 2016a). The aim of the present study was to determine longitudinal patterns of several ToT measures (mainly semantic and phonological access) by using linear mixed models and data from longitudinally assessed individuals with SCC and MCI classified on the basis of diagnostic stability or deterioration. With this objective we expected to obtain new evidence regarding the usefulness of these measures as linguistic markers to characterize the cognitive profile of adults with MCI. MATERIALS AND METHODS Participants Two hundred forty-nine adults in the range of 50–87 years old already participating in the Compostela Aging Study (CompAS) and who completed 3 extensive clinical and neuropsychological assessments (Baseline, Time 1, and Time 2) were included in this study. At baseline there were 407 participants who performed the ToTs tasks, but only 249 completed the 3 assessments, being the total rate of attrition around 38% (158 participants) due to motivation, mobility or morbidity. CompAS is an ongoing longitudinal project (Juncos-Rabadán et al., 2012) in which participants are recruited after referral by general practitioners from primary care centers in Galicia (an autonomous region in north-western Spain) subjective cognitive complaints. A study on the attrition in the general CompAS project and their raisons may be see in Facal et al. (2016b). Exclusion criteria included previous diagnosis of any neurological or psychiatric disease, dementia, MCI, clinical stroke, motor-sensory defects, alcohol or drug abuse/dependency and traumatic brain injury at baseline. All participants underwent the same extensive assessment, and were classified into SCC or MCI groups at a special meeting of the research team. MCI subjects were classified into four subtypes following standard criteria (Petersen, 2004; Dubois et al., 2007;Albert et al., 2011): single-domain amnestic MCI (sda-MCI); multiple-domain amnestic MCI (mda-MCI); single-domain non-amnestic MCI (sdna-MCI); and multipledomain non-amnestic MCI (mdna-MCI). All MCI participants fulfilled the general criteria outlined by the National Institute Frontiers in Psychology | www.frontiersin.org 3March 2020 | Volume 11 | Article 425 fpsyg-11-00425 March 13, 2020 Time: 17:38 # 4 Campos-Magdaleno et al. Longitudinal Pattern of ToTs TABLE 1 | Mean values and standard deviations (in parentheses) of the demographic, cognitive and TOT measures by the three cognitively normal groups: subjective cognitive complaints (SCC) that remained Stable (SCC Stable), Mild Cognitive Impairment that remained Stable (MCI Stable), and MCI that worsened (MCI Worsened). SCC Stable Group 1 N= 193 MCI Stable Group 2N= 33 MCI Worsened Group 3 N= 23 Group differences -Kruskal–Wallis χ2(gl) Groups comparison – Mann–Whitney tests Groups differences- ANOVAs F(2,146) Groups comparison Bonferroni tests Age 64.94 (8.84) Range: 50–87 69.27 (7.35) Range: 54–82 73.91 (7.15) Range: 61–87 24.15 (2)** Group3 >Group2 >Group1 41.66** Group3 >Group2 >Group1 Years Education 10.18 (4.66) Range: 2–22 9.06 (4.58) Range: 3–21 8.35 (3.70) Range: 2–18 4.51 (2) 6.64*Group3 <Group1 CCI 0.87 (0.89) Range: 0–3 0.75 (−93) Range: 0-4 1.04 (0.97) Range: 0–3 1.56 (2) 0.68 Lawton 7.60 (0.88) Range: 4–8 6.84 (1.40) Range: 3–8 6.55 (1.93) Range: 3–8 16.33 (2)** Group3, Group2 <Group1 11.80** Group3 <Group2, Group1 SCC- Patient 18.66 (4.34) Range: 7–32 20.22 (4.21) Range: 10–29 19.47 (4.45) Range: 13–33 7.09 (2) *Group2 >Group1 2.02 SCC-Informant 15.59 (4.49) Range: 2–29 16.53 (3.72) Range: 9–22 18.25 (4.31) Range: 12–26 6.83 (2)*Group3 >Group1 3.78*Group3 >Group1 MMSE 28.16 (1.64) Range: 21–30 25.75 (2.63) Range: 21–30 25.17 (2.53) Range: 19–29 50.82 (2)** Group3, Group2 <Group1 42.62** Group3, Group2 <Group1 CVLT-SDFR 10.64 (2.71) Range: 3–16 4.96 (3.15) Range: 0–11 4.43 (3.08) Range: 0–11 87.23 (2)** Group3, Group2 <Group1 96.77** Group3, Group2 <Group1 CVLT-LDFR 11.44 (2.91) Range: 3–16 6.27 (3.91) Range: 0–14 4.82 (2.99) Range: 0–11 79.95 (2)** Group3, Group2 <Group1 78.25** Group3, Group2 <Group1 WAIS vocabulary subscale 51.18 (13.07) Range: 19–75 41.27 (13.00) Range: 20–71 41.48 (14.47) Range: 15–64 20.89 (2)** Group3, Group2 <Group1 36.43** Group3, Group2 <Group1 Semantic access proportion 0.92 (0.09) Range:0.40–1 0.87 (0.12) Range:0.40–1 0.74 (0.20) Range: 0.22–1 15.97 (2)** Group3, Group2 <Group1 70.54** Group3 <Group2 <Group1 Phonological access proportion 0.82 (0.13) Range:0.09 – 1 0.66 (0.20) Range:0.08–1 0.62 (0.61) Range:0.18–0.98 13.95 (2)** Group3, Group2 <Group1 70.37** Group3, Group2 <Group1 Familiarity 222.87 (28–28) Range: 89–250 223.44 (27.96) Range: 127–250 204.14 (36.42) Range: 116–250 16.98 (2)** Group3 <Group2, Group1 10.78** Group3 <Group2, Group1 Feeling of Knowing 48.05 (3.57) Range: 23–50 46.75 (4.64) Range: 24–50 41.40 (9.53) Range: 17–50 45.29 (2)** Group3 <Group2 <Group1 51.79** Group3 <Group2, Group1 Semantic fluency 17.75 (5.79) Range: 6–35 13.66 (4.11) Range: 7–25 11.17 (4.13) Range: 5–20 26.32 (2)** Group3, Group2 <Group1 54.67** Group3 <Group2 <Group1 CCI, Charlson Comorbidity Index. SCC, Subjective Cognitive Complaints. MMSE, MiniMental State Examination. CVLT-SDFR, California Verbal Learning Test, Short Delay Free Recall. CVLTLDFR, California Verbal Learning Test, Long Delay Free Recall. *p<0.05, **p<0.01. Frontiers in Psychology | www.frontiersin.org 4March 2020 | Volume 11 | Article 425 fpsyg-11-00425 March 13, 2020 Time: 17:38 # 5 Campos-Magdaleno et al. Longitudinal Pattern of ToTs TABLE 2 | Summary of models compared for Familiarity. Dependent variable: Familiarity Model 1 Model 2 Model 3 Age at baseline 1.283 (1.529) 2.558 (1.556) 2.552 (1.555) Vocabulary- WAIS 2.453 (1.527) 1.953 (1.549) 1.938 (1.549) Evaluation Time 4.764*** (1.196) 5.050*** (1.332) MCI-Stable 1.103 (4.529) 6.311 (8.679) MCI-Worsened −18.798*** (5.526) −19.531 (0.134) Evaluation Time ×MCI- Stable −2.596 (3.685) Evaluation Time ×MCI- Worsened 0.429 (4.815) Intercept 221.769*** (1.468) 213.637*** (2.904) 213.064*** (3.134) Observations 676 676 676 Log Likelihood −3,186.721 −3,166.436 −3.161.473.160 Akaike Inf. Crit. 6,391.442 6,356.871 6,350.947 Bayesian Inf. Crit. 6,432.048 6,410.959 6,414.007 Bayes Factor – 37966.384 0.2178 All models include random effects for intercepts and slopes, heteroskedasticity due to the group, and Age and Vocabulary at baseline as covariates. Model 1 is the null mixed model (i.e., intercepts and covariates only); Model 2 is the mixed model with main effects; and Model 3 is the mixed model with main effects and interactions. Coefficients and standard errors (in parentheses). ***p<0.01. on Aging-Alzheimer’s Association (Albert et al., 2011): (a) informant-corroborated memory complaints, assessed by a short version of the Subjective Memory Complaints Questionnaire (SMCQ; Benedet and Seisdedos, 1996); (b) performance of 1.5 standard deviations below age and education norms in at least one cognitive domain, assessed by the subscales of the Spanishadapted version of the Cambridge Cognitive Examination (CAMCOG-R, Huppert et al., 1996; Spanish version: López- Pousa, 2003;Pereiro et al., 2015), apart from the memory domain, which was assessed by the Short and Long Delay Free Recall from the Spanish-adapted version of the California Verbal Learning Test (CVLT, Delis et al., 1987; Spanish version: Benedet and Alejandre, 1998); (c) no significant impact on activities of daily living, assessed by the Lawton and Brody Index (Lawton and Brody, 1969); and (d) no dementia, according the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA), and the Diagnostic and Statistical Manual of Mental Disorders- Fourth Edition (DSM-IV) criteria. Participants were classified as SCC when, presenting subjective cognitive complains to their general practitioners confirmed by their own responses and that from their relatives to the SMCQ, they performed as cognitively unimpaired adults according to norms for age and years of education in general functioning and specific domain tests assessed with CAMCOG-R and the CVLT. All participants and their proxies were informed of the longitudinal nature of the project and were contacted twice regarding participation in two successive follow-up assessments with an interval of 18.67 ±2.73 months between each assessment. This time interval maximizes participation and motivation, while and reduces attrition due morbidity, mobility and mortality (Facal et al., 2016b). After the second follow-up assessment, participants were classified into three groups by considering FIGURE 2 | Estimated marginal means and errors bars from Model 2 for Familiarity in the three groups across the three evaluation times. SE, Standard Error; BL, Baseline assessment; T1, Time 1 assessment; T2, Time 2 assessment. Frontiers in Psychology | www.frontiersin.org 5March 2020 | Volume 11 | Article 425 fpsyg-11-00425 March 13, 2020 Time: 17:38 # 6 Campos-Magdaleno et al. Longitudinal Pattern of ToTs stability or progression from the diagnostic established at baseline: SCC participants at Baseline assessment who remained stable at Time 2 follow-up (SCC-Stable group, n= 193, 77.52%, 136 women/56 men); MCI participants at Baseline assessment who remained stable at Time 2 follow-up (MCI-Stable group, n= 33, 13.24%, 20 women/13 men); and sda-MCI or sdna- MCI participants at Baseline assessment who had progressed to mda-MCI, mdna-MCI or dementia either at Time 1 or Time 2 follow-up evaluations (MCI-Worsened group, n= 23, 9.24%, 16 women/7 men). Differences in the groups size reflect the incidence of MCI in people with subjective cognitive complains who attend primary care centers and the different rates of stability or progression/worsening (Facal et al., 2019). Assessment of participants who progressed to probable AD or dementia was conducted according to the DMS-IV and NINCDSADRDA criteria, by checking the medical history and recording the date of neurological diagnosis. All participants gave their written informed consent prior to participation in the study. The research project was approved by the Galician Clinical Research Ethics Committee (Xunta de Galicia, Spain), and the study was performed in accordance with the ethical standards established in the Declaration of Helsinki, updated in Seoul in 2008. Materials and Procedure The target items were 50 color photographs of famous people of the last 50 years (actors, singers, politicians, sportsmen, arts personalities, etc. from Spain and other countries, see TABLE 3 | Summary of models compared for Feeling of Knowing. Dependent variable: Feeling of Knowing Model 1 Model 2 Model 3 Age at baseline −0.017*** (0.006) −0.008 (0.006) −0.008 (0.006) Vocabulary- WAIS 0.015** (0.006) 0.010 (0.006) 0.010 (0.006) Evaluation Time 0.013 (0.007) 0.011 (0.008) MCI-Stable −0.017 (0.018) −0.056 (0.046) MCI-Worsened −0.132*** (0.024) −0.118** (0.060) Evaluation Time ×MCI- Stable 0.020 (0.022) Evaluation Time ×MCI- Worsened −0.008 (0.031) Intercept 3.857***(0.006) 3.845***(0.015) 3.849*** (0.017) Observations 751 751 751 Log Likelihood −1,999.809 −1,981.637 −1,981.159 Akaike Inf. Crit. 4,007.618 3,977.273 3,980.318 Bayesian Inf. Crit. 4,025.470 4,025.514 4,020.485 Bayes Factor – 4806.453 0.0025 All models included random effects for intercepts and Age and Vocabulary at baseline as covariates. Model 1 is the null mixed model (i.e., intercepts and covariates only); Model 2 is the mixed model with main effects; and Model 3 is the mixed model with main effects and interactions. Coefficients and standard errors (in parentheses). **p<0.05, ***p<0.01. Supplementary Material 1) selected from a set of 70. They were previously presented to a small control group of cognitively unimpaired users (20 persons) of a life-long learning association from Santiago de Compostela (ATEGAL) aged between 55 and 80 years. Final 50 photographs correspond with those that obtained the highest punctuation in familiarity and semantic information (age, residence, marital status. . . of the celebrity), in order to maximize the probability of ToT states. The ToT procedure included in the CompAS has been described in detail in a previous study (Juncos-Rabadán et al., 2011). In brief, the ToT procedure consisted of three tasks: (i) a naming task; (ii) a task to determine whether the ToTs were positive (when the name on the ToT was indeed the correct name) or negative (when the name on the ToT was not the target name); and (iii) a familiarity task, to assess the subjective degree of knowledge that each participant declared having about each celebrity depicted in the photographs (see Figure 1). In the naming task, 50 photographs of celebrities were presented separately on a screen (with E-Prime for Windows). Participants were asked to press the green key on a response box if they knew the name and the red key if they did not know the name. They were also asked to say the name out loud or to say either “I don’t know the name” or “I can’t recall the name at the moment” at the same time as pressing the response key. The names and responses were registered as follows: (a) correct (CORs) or incorrect, according to the accuracy of the name; (b) “Don’t know,” when the participant did not know the name; and (c) ToT state, when the participant said that they knew the name but could not recall it at the moment. In the second phase, the photographs that produced ToT responses were presented in a second task, in which participants were again asked for the celebrity’s name. If the participant correctly produced the name during the task, the response was classified as a resolved ToT. When the ToT was maintained or an incorrect name was produced, participants were encouraged to answer several questions that appeared on the screen in order to test their knowledge about the person and their name: ‘What is the person’s profession?’, ’What is the first letter or syllable of the name?’, ‘Does any name come to your mind?’. After these questions were scored, the target name was presented with two non-target names. For each such triad, participants were asked to state which of the names presented separately on the screen was the correct name of the person in the previously presented photograph and if it was the name that they had been trying to remember when they said “I know the name but I can’t recall it.” The ToT was then classified as a positive ToT (pToT) when participants correctly recognized the target name and said that it was the name that they had been trying to remember, and negative ToT when they recognized it but said that it was not name on their mind. In the third phase, the 50 target pictures were presented to each participant to determine how familiar the famous people were. Responses were scored on a scale of 1–5 (where 5 represents maximum familiarity and 1, unfamiliarity). The following measures were considered for the purposes of this study: (A) Familiarity, which represents the subjective knowledge that participants had about the people represented in the target pictures. This was calculated by summing the familiarity responses for all 50 photographs. (B) Feeling of Frontiers in Psychology | www.frontiersin.org 6March 2020 | Volume 11 | Article 425 fpsyg-11-00425 March 13, 2020 Time: 17:38 # 7 Campos-Magdaleno et al. Longitudinal Pattern of ToTs FIGURE 3 | Estimated marginal means and errors bars from Model 2 for Feeling of Knowing in the three groups across the three evaluation times. SE, Standard Error; BL, Baseline assessment; T1, Time 1 assessment; T2, Time 2 assessment. Knowing, which represents the security that participants have about the knowing the name, independently of whether the name was recalled or not (Schwartz, 2002). This was measured as the number of times that participants pressed the green key in the naming task. (C) Semantic access was calculated by the equation [(CORs +pToTs)/N] and represents successful access to the semantic representations of the names. (D) Phonological access was calculated by the equation [CORs/(CORs +pToTs)] and represents the proportion of both successful semantic and phonological retrievals (Juncos-Rabadán et al., 2010). In addition to these ToT measures, two lexical measures were considered: (A) Verbal fluency-animals (Semantic fluency), defined as the ability to produce words within a fixed time interval (Lezak et al., 2004) and considered suitable for detecting MCI (Taler and Phillips, 2009), was used as a general measure of lexical access; and (B) Total scoring in the vocabulary test of the Wechsler Adult Intelligence Scale (WAIS; Wechsler, 1988), used to measure the general verbal knowledge of the participant. Statistical Analysis Considering the heterogeneity in the sample size of the groups, non-parametric tests (e.g., Kruskal–Wallis and Mann–Whitney tests) were used to analyze between-group differences in sociodemographic and ToT measures at baseline. Complementarily, parametric tests were included also analyzing between-group differences. We initially selected Generalized Linear Mixed Models (GLMM) for modeling longitudinal changes in the language, including random intercepts and random slopes. Thus, patterns of performance in lexical access can be represented by different slopes and longitudinal trajectories can be defined by the intercepts. However, and due to convergence problems, random slopes were excluded from the analyses. We created the statistical models including Evaluation Time (Baseline, Time 1, and Time 2), Group (SCC-Stable, MCI- Stable and MCI-Worsened), and the interactions (Evaluation Time ×Group) as independent variables or predictors as fixed effects. Pairwise comparisons of the estimated marginal means for the dependent variables Evaluation Time and Group was carried out after they were specified as factors. We included heteroskedasticity due to group, random effects for intercepts, and the covariates age at baseline and previously standardized vocabulary score in all models (see Supplementary Material 2). Separate models were obtained for each dependent variable, with SCC-Stable as the reference group and Baseline assessment as the reference evaluation time. LMMs assuming a Gaussian response were used for modeling changes in proportional measures. GLMMs assuming Poissonian response were selected for counting measures. When statistical assumptions (e.g., overdispersion of data) were not met in the GLMMs, a negative binomial distribution was used for modeling count data. Log Likelihood, Akaike and Bayesian Information Criteria indices of goodness of fit were used to select the best models for each response. Thus, in order to select the best model for predicting the intercepts and slopes in each group by the ToT measures, we first compared all possible models including fixed effects (i.e., Evaluation time, Group and their interaction) Frontiers in Psychology | www.frontiersin.org 7March 2020 | Volume 11 | Article 425 fpsyg-11-00425 March 13, 2020 Time: 17:38 # 8 Campos-Magdaleno et al. Longitudinal Pattern of ToTs TABLE 4 | Summary of models compared for Semantic Access. Dependent variable: Semantic Access percentage Model 1 Model 2 Model 3 Age at baseline −1.798*** (0.609) −1.357** (0.629) −1.335** (0.629) Vocabulary-WAIS 2.418*** (0.611) 1.943*** (0.632) 1.961*** (0.632) Evaluation Time 2.234*** (0.265) 2.162*** (0.280) MCI-Stable −3.594 (1.835) −5.578**(2.504) MCI-Worsened −15.690*** (2.623) −11.971** (4.855) Evaluation Time ×MCI-Stable 1.065 (0.913) Evaluation Time ×MCI- Worsened −2.199 (2.403) Intercept 91.063***(0.584) 87.996***(0.826) 88.129*** (0.843) Observations 641 641 641 Log Likelihood −2,273.151 −2,221.715 −2,217.955 Akaike Inf. Crit. 4,560.302 4,463.430 4,459.910 Bayesian Inf. Crit. 4,591.511 4,507.967 4,513.315 Bayes Factor – 1.38479 ·1018 0.069 All models included random effects for intercepts and Age and Vocabulary at baseline as covariates. Model 1 is the null mixed model (i.e. intercepts and covariates only); Model 2 is the mixed model with main effects; and Model 3 is the mixed model with main effects and interactions. Coefficients and standard errors (in parentheses). **p<0.05, ***p<0.01. and including random effects or not. After optimizing the structure for the fixed and random effects, we then added heteroskedasticity (between-group variability) to the model and chose the best-fit model. Finally, we included standardized covariates in the model to allow intercept interpretation (see Supplementary Material 2 to reproduce the steps to get these intermediate models as well as the final regression models hereby detailed). Cross-sectional statistical analysis was performed with SPSS for Windows, version 21.0 (SPSS, Chicago, IL, United States); (G)LMMs were estimated in R environment (version 3.5.3; R Core Team, 2019) with the nlme (version 3.1-1137; Pinheiro et al., 2018) and lme4 packages (version 1.1-21; Bates et al., 2015). RESULTS Socio-demographic, neuropsychological and ToT measures of the groups at baseline are summarized in Table 1. The MCI- Worsened group was the oldest, followed by MCI-Stable. The SCC-Stable group obtained higher scores than the two MCI groups at baseline for the cognitive measures, MiniMental State Examination (MMSE; Folstein et al., 1975; Spanish version: Lobo et al., 1999) and CLVT Short (CVLT-SDFR) and Long Delay Free Recall (CVLT-LDFR), as well as for the TOT measures, semantic access and phonological access and semantic fluency. Familiarity was higher in the SCC-Stable and MCI- Stable groups than in the MCI-Worsened group. Vocabulary level was highest for the SCC-Stable than the other two groups. The lowest feeling of knowing was obtained in the MCI- Worsened group. No differences were found at baseline in FIGURE 4 | Estimated marginal means and errors bars from Model 2 for Semantic Access in the three groups across the three evaluation times. SE, Standard Error; BL, Baseline assessment; T1, Time 1 assessment; T2, Time 2 assessment. Frontiers in Psychology | www.frontiersin.org 8March 2020 | Volume 11 | Article 425 fpsyg-11-00425 March 13, 2020 Time: 17:38 # 9 Campos-Magdaleno et al. Longitudinal Pattern of ToTs TABLE 5 | Summary of models compared for Phonological Access. Dependent variable: Phonological Access percentage Model 1 Model 2 Model 3 Age at baseline −4.946*** (0.854) −3.769*** (0.835) −3.742***(0.835) Vocabulary-WAIS 5.350*** (0.854) 4.301*** (0.831) 4.332*** (0.830) Evaluation Time −0.958** (0.449) −1.059** (0.485) MCI-Stable −11.044 ***(2.573) −15.663***(4.171) MCI-Worsened −12.160*** (2.898) −10.437** (4.215) Evaluation Time ×MCI-Stable 2.461 (01.749) Evaluation Time ×MCI- Worsened −0.987 (1.728) Intercept 78.651***(0.830) 82.874***(1.219) 83.061*** (1.265) Observations 637 637 637 Log Likelihood −2,402.963 −2,382.531 −2,378.374 Akaike Inf. Crit. 4,823.925 4,789.062 4,784.747 Bayesian Inf. Crit. 4,863.994 4,842.429 4,846.965 Bayes Factor – 4.65930445815727e +47 0 All models included random effects for intercepts and slopes, heteroskedasticity due to the group, and Age and Vocabulary at baseline as covariates. Model 1 is the null mixed model (i.e., intercepts and covariates only); Model 2 is the mixed model with main effects; and Model 3 is the mixed model with main effects and interactions. Coefficients and standard errors (in parentheses). **p<0.05, ***p<0.01. comorbidity, and differences in years of education were only obtained between the SCC Stable and MCI Worsened group in the parametric comparisons. Familiarity GLMMs considering normal response (Gaussian) showed that the best fit model for Familiarity score (see Table 2) was model 2, which includes random effects for the intercepts and slopes, and fixed effects for Evaluation Time [χ2(1) = 15.87; p<0.001] and Group [χ2(2) = 12.33; p<0.001] but not the effect of the Group ×Evaluation time interaction. Neither of the covariates (Age and WAIS-vocabulary score at baseline) or the Evaluation Time ×Group interaction were significant. According to this model, the estimated means showed a significant increase in familiarity across the evaluation times in all groups (p<0.001). Familiarity was significantly lower for the MCI-Worsened group than for the SCC-Stable or the MCI-Stable groups at any Evaluation time (p<0.001). The Group ×Evaluation time interaction did not reach significance, showing that between-group differences in familiarity were maintained over time (Figure 2). Feeling of Knowing We used GLMMs selecting a Poisson model because of the presence of equi-dispersion. Model 2 provided the best fit (Table 3) and included random effects for the intercepts and fixed effects only for Evaluation time and Group. Model 2 showed a significant effect only for Group [χ2(2) = 31.27; p<0.001], but not for Evaluation time or for the Group ×Evaluation time interaction. The covariates Age and WAIS-vocabulary score at baseline did not reach significance. The estimated means model indicated that Feeling of Knowing scoring was significantly lower for the FIGURE 5 | Estimated marginal means and errors bars from Model 2 for Phonological Access in the three groups across the three evaluation times. SE, Standard Error; BL, Baseline assessment; T1, Time 1 assessment; T2, Time 2 assessment. Frontiers in Psychology | www.frontiersin.org 9March 2020 | Volume 11 | Article 425