Same or Different? Clarifying the Relationship of Need for Cognition to Personality and Intelligence

Abstract

Need for cognition (NFC) refers to an individual’s tendency to engage in and enjoy effortful cognitive processing. So far, little attention has been paid to a systematic evaluation of the distinctiveness of NFC from traits with similar conceptualization and from intelligence. The present research contributes to filling this gap by examining the relation of NFC to well-established personality concepts (Study 1) and to a comprehensive measure of intelligence in a sample with broad educational backgrounds (Study 2). We observed NFC to be positively correlated with openness, emotional stability, and traits indicating goal orientation. Using confirmatory factor analysis and event-related potentials, incremental validity of NFC and openness to ideas was demonstrated, showing that NFC is more predictive of drive-related and goal-oriented behavior and attentional resource allocation. Regarding intelligence, NFC was more associated with fluid than with crystallized aspects of intelligence. Altogether, the results provide strong support for the conceptual autonomy of NFC.

Assessing the Willingness to Elaborate among Young Students: Psychometric Evaluation of a Swedish Need for Cognition Scale

“…Based on these studies we expected high temporal stability (i.e., test-retest reliability) of the METQ. In contrast to temporal stability, the internal stability of NFC has been frequently examined, with most studies showing high internal stability that is above 0.80 [see, e.g., Venkatraman and Price (1990), Cacioppo et al (1996), Fleischhauer et al (2010), and Preckel (2014)]. A similar result is expected in the present study.…”

“…The significance of NFC as a moderator of information processing was also shown in a study of information search and interactive web pages (Sicilia et al, 2005). Cacioppo and Petty (1982) developed a scale to measure NFC, which has been explored in relation to a variety of individual differences, such as intelligence, personality, and academic achievement [see, e.g., Cacioppo et al (1996), Fleischhauer et al (2010), and Preckel (2014)]. The original and short version of the NFC-scale is, according to Cacioppo et al (1996), reasonable, reliable, and valid.…”

Do Individual Differences Predict Change in Cognitive Training Performance? A Latent Growth Curve Modeling Approach

“…Even on the meta-analytic level, evidence is mixed regarding the effectiveness of cognitive training in both younger and older adults (e.g., Au et al 2015;Dougherty et al 2016;Karbach and Verhaeghen 2014;Kelly et al 2014;Lampit et al 2014;Melby-Lervåg and Hulme 2013;Melby-Lervåg et al 2016;Schwaighofer et al 2015;Soveri et al 2017). Aside from design and methodological choices potentially explaining the diverging findings (e.g., Noack et al 2009;Shipstead et al 2012), many authors increasingly articulated the potentially important influence of individual differences on cognitive training trajectories and outcomes (e.g., Buitenweg et al 2012;Guye et al 2016;Könen and Karbach 2015;Shah et al 2012;von Bastian and Oberauer 2014 Individual differences in cognitive functioning (e.g., Ackerman and Lohman 2006) and learning potential (e.g., Stern 2017) accentuate with increasing age (e.g., Rabbitt et al 2004) and have been shown to be related to personality (e.g., Graham and Lachman 2012), cognition-related beliefs such as need for cognition (NFC; e.g., Fleischhauer et al 2010;Hill et al 2013), and everyday life activities (e.g., Jopp and Hertzog 2007). Investigating which of these individual differences potentially predict cognitive training outcomes may not only explain inconsistencies concerning the effectiveness of cognitive training, but also identify possible subgroups of individuals that are more or less responsive to cognitive training, thereby constituting the conceptual groundwork for developing individually tailored interventions to boost training effectiveness.…”

“…Individual differences in cognitive functioning (e.g., Ackerman and Lohman 2006) and learning potential (e.g., Stern 2017) accentuate with increasing age (e.g., Rabbitt et al 2004) and have been shown to be related to personality (e.g., Graham and Lachman 2012), cognition-related beliefs such as need for cognition (NFC; e.g., Fleischhauer et al 2010;Hill et al 2013), and everyday life activities (e.g., Jopp and Hertzog 2007). Investigating which of these individual differences potentially predict cognitive training outcomes may not only explain inconsistencies concerning the effectiveness of cognitive training, but also identify possible subgroups of individuals that are more or less responsive to cognitive training, thereby constituting the conceptual groundwork for developing individually tailored interventions to boost training effectiveness.…”

Does Brain Sweat Pay Off?: The Association between the Need for Cognition and Cognitive Function among the American Elderly

“…Our primary outcome measures involved a series of cognitive abilities tests measuring constructs that have been previously demonstrated to be involved with activities related to the Need for Cognition [22][23][24]: (1) Similarities -participants describe how similar two words or concepts are, measuring verbal concept formation and reasoning; (2) Number series -respondent looks at a number series with a missing number, determining the pattern and identifies the missing number to complete a numerical sequence. It measures quantitative reasoning; (3) Picture vocabulary -respondents identify familiar and unfamiliar pictured objects, measuring aspects of lexical knowledge; (4) Auditory working memory -respondent listens to mixed series of words and digits while attempting to reorder them by words and numbers in order.…”