Age-Related Increases in Verbal Knowledge Are Not Associated With Word Finding Problems in the Cam-CAN Cohort: What You Know Won’t Hurt You

Abstract

Objective: We tested the claim that age-related increases in knowledge interfere with word retrieval, leading to word finding failures. We did this by relating a measure of crystallized intelligence to tip-of-the-tongue (TOT) states and picture naming accuracy.Method: Participants were from a large (N = 708), cross-sectional (aged 18–88 years), population-based sample from the Cambridge Centre for Ageing and Neuroscience cohort (Cam-CAN; ). They completed (a) the Spot-the-Word Test (STW), a measure of crystallized intelligence in which participants circled the real word in word/nonword pairs, (b) a TOT-inducing task, and (c) a picture naming task.Results: Age and STW independently predicted TOTs, with higher TOTs for older adults and for participants with lower STW scores. Tests of a moderator model examining interactions between STW and age indicated that STW was a significant negative predictor of TOTs in younger adults, but with increasing age, the effect size gradually approached zero. Results using picture naming accuracy replicated these findings.Discussion: These results do not support the hypothesis that lifelong knowledge acquisition leads to interference that causes an age-related increase in TOTs. Instead, crystallized intelligence supports successful word retrieval, although this relationship weakens across adulthood.

Resting-state and vocabulary tasks distinctively inform on age-related differences in the functional brain connectome

“…The ability to understand the meaning of written or oral words (e.g., semantic judgment, word association) is typically spared until 65 years of age (Salthouse, 2014) and beyond (Fabiani, 2012;Schaie, 2005;Schaie & Willis, 2010;Verhaegen & Poncelet, 2013;Verhaeghen, 2003). Tasks that tap into lexical production (e.g., picture naming, fluency, word definition) bring more inconsistencies, according to the chosen method (see for example Shafto, 2016;Goulet 1994;Feyereisen 1994). Performance is often similar to that of younger adults, and sometimes better (Salthouse, 2014), especially when accuracy measures, rather than response times, are used (Vereaghen, 2013) in a constrained (Schmitter-Edgecombe, 2000) and untimed task (Wieranga, 2008).…”

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

“…By contrast, greater difficulties in semantic access were only observed in the MCI-worsened relative to the SCC-stable and the MCI-stable groups. Group differences were maintained despite the statistical control for age and level of vocabulary, suggesting no longitudinal influence of the vocabulary on ToTs as indicated in previous cross-sectional studies with cognitively unimpaired old adults (Facal et al, 2012;Salthouse and Mendell, 2013;Shafto et al, 2017). Thus, phonological access seems to be an early lexical marker of post-semantic impairment in the cognitive continuum from SCC to MCI (Juncos-Rabadán et al, 2011.…”

“…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).…”

Detecting the Information of Functional Connectivity Networks in Normal Aging Using Deep Learning From a Big Data Perspective

“…), which included 220 young adults whose ages ranged from 18 to 45 and 192 elderly adults whose ages ranged from 66 to 88. The binary young/old classes were based on the published paper of Cam-CAN open dataset (Shafto et al, 2017). In each group, the number of participants was approximately equal when divided into 10 year-wide bins.…”

“…The limitations of current work are following: first, the aim of this work was to detect the information between health young group and old group (aging), and the current age division was based on the published paper of Cam-CAN open dataset (Shafto et al, 2017). However, a finer bin-based age classification may be investigated in the future effort.…”