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Amidst many focuses in the area of research related to factors influencing cognitive ability as well as cognition in general, constructs measuring aspects of cognitive motivation or cognitive investment have in recent years increasingly provoked interest and lead to diverse findings in specifically psychological research and beyond. Probably the construct most focused on is Need for Cognition (NFC) which has originally been conceptualized by Cacioppo and Petty (1982) as well as following researchers (Cacioppo et al., 1996) and characterized as an individual’s stable tendency to actively search for and engage in effortful intellectual challenges and endeavours as well as enjoy such an activity. Individuals scoring high on NFC have been found to be highly intrinsically motivated and naturally tending to seek, acquire and evaluate information to make sense of their surroundings (Cacioppo et al., 1996). Given the characterisation of the construct of NFC as well as early findings by Cacioppo et al. (1996), it might be presumed that the principal characteristic of individuals high on NFC to actively search for and enjoy challenging intellectual endeavours is linked to a particular pattern of general cognitive dispositions and processes. Das et al. (2003) for instance found NFC to be moderately to highly and significantly positively associated with information seeking behaviour on the internet, the latter being referred to by the authors as a more complex source of information compared to others. Mokhtari et al. also (2013) reported a high association between NFC and a self-constructed questionnaire to measure information seeking behaviour (Mokhtari et al., 2013). Verplanken et al. (1992) also found individuals scoring high on NFC to show a greater desire for and a more resilient search of information in a task concerning the evaluation of presented brands. Also, the cognitive effort put into this evaluation was positively associated with NFC. Curşeu (2011) on the other hand found students high on NFC to have a greater tendency to seek information from other students in their study group. Furthermore, Cacioppo et al. (1996) suggests that individuals lower on NFC invest less cognitive effort when processing information and also rely more on mental short-cuts (e.g., the belief in experts) to make sense out of information presented to them. Results by Levin et al. (2000) support this conception, as differences between high and low scoring individuals on NFC in the amount of acquired knowledge were found primarily when it was demanded to make a choice about which few options to include into further consideration. This might be explained by an increased demand for thorough evaluation when consequences of a choice are weightier (Levin et al., 2000). Furthermore, the way in which a problem is framed seems to have a significant effect on the individuals’ choice when scoring low on NFC but not for those with high scores on the construct (Smith & Levin, 1996). Regarding motivational factors, See et al. (2009) found individuals with high levels on NFC to be more motivated to process the information contained in a message described as complex in opposition to simple, with individuals scoring low showing the reversed pattern. Also, persons scoring high on NFC indeed applied their cognitive resources only when the message was labelled as complex (See et al., 2009). Also, Cazan and Indreica (2014) found NFC to be moderately up to highly and positively associated to learning-strategies like “deep processing” as well as “self-regulation strategies”, suggesting a deeper engagement with information as well the utilization of metacognitive skills to attain a specific goal (Cazan & Indreica, 2014). Various studies have investigated the relationship between NFC and measures of crystallized as well as fluid intelligence. Stuart-Hamilton and McDonald (2001) operationalized crystallized intelligence as the score in the Mill Hill Vocabulary Test (Raven, Court, & Raven, 1994, as cited in Stuart-Hamilton & McDonald, 2001) and reported only small correlations between the measures in a sample of older adults. In Tidwell et al. (2000), participants’ knowledge was operationalized as the score in a multiple-choice test and verbal ability measured with the Shipley-Hartford scale (Shipley, 1940, as cited in Tidwell et al., 2000). NFC was found to be lightly associated with knowledge and moderately with verbal ability, the former correlation being small but significant even with verbal ability partialed out. Woo et al. (2007) reported a significant moderate correlation between NFC and knowledge in various areas, which was however operationalized as the participants estimation of their own self-rated knowledge, while controlling for self-enhancement. Concerning fluid intelligence, Stuart-Hamilton and McDonald (2001) found a moderate to high positive and significant association between NFC and scores in Raven’s matrices. The latter measure an individual’s ability to detect a pattern in a series of figures and complete it by choosing a figure from the possible options. Furnham and Thorne (2013) examined the relationship of NFC to the Wonderlic Personnel Test (Wonderlic, 1992, as cited in Furnham and Thorne, 2013) as an indicator of intellectual ability. NFC was moderately up to highly correlated to the test, yet as this measure is regarded as measuring general intelligence rather than either fluid or crystallized intelligence separately (Bell et al., 2002), their results are difficult to interpret in this regard. Fleischhauer et al. (2010) used the I-S-T 2000 R, an intelligence test that allows e.g., for measuring verbal intelligence, figural and spacial intelligence, arithmetical intelligence, working memory, deductive reasoning as well as fluid and crystallized intelligence (Amthauer et al., 2001). They reported NFC to be moderately positive and significantly correlated to measures of fluid intelligence with crystallized intelligence being however only lightly positively and not significantly associated with the construct. Additionally, individuals scoring high on NFC did in fact not respond correctly to more tasks than individuals low on NFC but answered more correctly on those that they did approach. This supports the conclusion of a particular style of answering and suggests primarily a greater investment of cognitive resources and inclination to solve a given puzzle. Hill et al. (2013) used the Wechsler Adult Intelligence Scale-III (Wechsler, 1997, as cited in Hill et al., 2013) as well as Raven’s matrices and found NFC and fluid intelligence to be moderately and significantly positively associated. Other than in Fleischhauer et al. (2010) however, also three categories measuring crystallized intelligence were significantly associated with NFC to a moderate degree. One should further suspect individuals with higher scores on NFC to be e.g., more apt to being effectively involved in a prolonged and demanding cognitive effort due to their tendency to engage in and enjoy complex puzzles and other cognitive challenges. This conception of NFC suggests that these individuals could display greater capacities in several executive functions such as working memory and inhibitory control (Miyake et al., 2000): A prolonged engagement in intellectually challenging situations will for example potentially demand from individuals to constantly take up, retain and update information necessary for the tasks ahead as well as show a certain resilience to distracting influences. The former might consequently be represented in an enhanced working memory capacity and the latter in a heightened inhibitory control – the ability to deliberately inhibit dominant and automatic responses when it is required (Miyake et al., 2000; Diamond, 2013). In a preliminary literature screening, comparatively few studies have been found that – directly or indirectly – link NFC to executive functions. However, a cognitive function that has occasionally been conceptualized as an overlying construct which increases due to enhanced executive functions is “self-control” (Katzir et al., 2010). It describes the capacity to choose in a given situation long-term beneficial global goals or values over short-term situational benefits (Fujita, 2011) and has been found to be significantly and positively correlated to NFC (Bertrams & Dickhäuser, 2009; Grass et al., 2019). Also, self-control seems to mediate the relationship between NFC and the school grades attained by participants (Bertrams & Dickhäuser, 2009). While no studies have been found, that directly addressed the relationship to NFC, the Stroop task is an often-used measure to assess inhibitory control as an individual’s capacity to suppress a dominant response to the word information (e.g., West & Alain, 2000; Bratzke et al., 2012). Though labelled with “self-control”, findings by Bertrams and Dickhäuser (2012) who examined the relationship between NFC and participants’ performance in the Stroop task might therefore be also regarded as also directly concerning inhibitory control. These authors reported a positive small to moderate association but no correlation to the reaction times in interference trials when administering a Stroop task without a previous depleting task (Bertrams & Dickhäuser, 2012). However, in a recent attempt to clarify the relationship between NFC and central executive functions, Gärtner et al. (2021) also examined inhibitory control next to other functions such as shifting and updating. Applying a Bayesian Correlation Analysis to the data and providing sufficient power to detect the suspected effects, their findings do not allow the conclusion that NFC is related to any of these functions. Furthermore, only a few studies have focused on the relationship between working memory and NFC. With NFC being generally characterized as an individual’s intrinsically motivated tendency to engage in effortful cognitive endeavours and enjoy this activity, it would have to be suspected that such activities required enhanced mnestic abilities, yet e.g., results by Fleischhauer et al. (2010) suggest no noticeable relationship to NFC. Similar results have been reported by Hill et al. (2016), who also did not find significant associations of NFC to e.g., a task measuring listening span as well as a N-back task. However, Hill et al. (2016) further analysed the same data used by Hill et al. (2013) and examined the possible influence of working memory as a moderator on the relationship between NFC and intelligence measures. Their results suggest that working memory does indeed serve as a moderator on the relationship insofar as an average degree of working memory capacity is required for the positive relationship between NFC and intelligence to be observed (Hill et al., 2013). The goal of the present review and meta-analysis is to provide an answer to the questions concerning the relationship between Need for Cognition (NFC) and fluid and crystallized intelligence as well as executive functions. Though the association between these variables has been investigated over the last decades, the results must partially be regarded as very dispersed over multiple fields of psychological research as well as provided using sometimes different kind of measures. To allow for a clearer and more comprehensive picture, the present study will therefore aim at reviewing the available literature up to the present date as well as provide a metanalytic synthetization of study results. In order to examine this relationship, the study aims at answering the following questions by estimating the mean effect size (calculated as Pearson’s r) across primary studies. The specific kinds of measurement of the cognitive variable mentioned in research question 3 refers to potential differences in the way in which a cognitive variable could have been measured over studies. Hill et al. (2016) for instance used several measures as indicators of working memory capacity. As the theoretical background does not clearly indicate a relationship between NFC and executive functions, no specific hypotheses can be derived and formulated. However, the definition of NFC as well as the respective functions does conceptually suggest potential associations to some degree, which is why the association of NFC with these cognitive functions will be examined next to intelligence. Yet with no explicit expectations in this regard, research question 2 can consequently be considered as exploratory. Research question 3 is equally of exploratory kind because the set of moderating variables that have been specified here cannot be regarded as being exhaustive and will over the course of the literature search potentially be extended. The research questions are the following: 1. What are the relations between NFC and measures of fluid and crystallized intelligence? 2. What are the relations between NFC and executive functions such as self-control, inhibitory control, shifting, updating, attention-related variables and working memory? 3. Do variables like age, sex, risk of bias and the specific kind of measurement of the cognitive variable in the primary studies moderate the relationship between NFC and the respective cognitive variables? |
