Thinking styles in implicit and explicit learning

Thinking styles in implicit and explicit learning

Learning and Individual Differences 23 (2013) 267–271 Contents lists available at SciVerse ScienceDirect Learning and Individual Differences journal...

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Learning and Individual Differences 23 (2013) 267–271

Contents lists available at SciVerse ScienceDirect

Learning and Individual Differences journal homepage: www.elsevier.com/locate/lindif

Thinking styles in implicit and explicit learning Qiuzhi Xie a, b,⁎, 1, Xiangping Gao a, Ronnel B. King b, c a b c

Department of Psychology, Shanghai Normal University, No. 100 Guilin Road, Shanghai, PR China Faculty of Education, The University of Hong Kong, Pokfulam Road, Hong Kong Department of Special Education and Counselling, The Hong Kong Institute of Education, Tai Po, Hong Kong

a r t i c l e

i n f o

Article history: Received 18 March 2012 Received in revised form 23 September 2012 Accepted 18 October 2012 Keywords: Implicit learning Explicit learning Thinking styles

a b s t r a c t This study investigated whether individual differences in thinking styles influence explicit and implicit learning. Eighty-seven university students in China participated in this study. Results indicated that performance in the explicit learning condition was positively associated with Type I thinking styles (i.e. legislative and liberal styles) and the internal style and negatively associated with a Type II thinking style (i.e. conservative style) and the external style. There was no significant relationship between thinking styles and performance in the implicit learning condition. Taken together, these findings suggest that implicit and explicit learning are distinct, each influenced by different individual difference variables. It also provides support to the value-laden nature of styles, giving further evidence to the adaptiveness of Type I over Type II styles. © 2012 Elsevier Inc. All rights reserved.

1. Introduction

& Cleeremans, 2001; Gebauer & Mackintosh, 2007; Mathews et al., 1989).

1.1. Implicit learning Reber (1967) categorized learning mechanisms into explicit and implicit learning. Explicit learning refers to learning that involves consciousness and effort. Implicit learning, on the other hand, is largely independent of conscious awareness of either the learning process or the learning products. Experimental tasks have been designed to study implicit learning, the three most popular ones being artificial grammar learning (Reber, 1976), sequence learning (Lewicki, Czyzewska, & Hoffman, 1987), and process control (Berry & Broadbent, 1988). With regard to research on implicit learning, it is important to note that implicit learning is distinct from explicit learning. The dissociation between explicit and implicit learning has been supported by differences in verbal reports: participants can report explicitly acquired knowledge, but fail to report implicitly acquired knowledge (e.g. Berry & Broadbent, 1984; Cleeremans & McClelland, 1991). Several scholars have criticized such evidence as weak, arguing that verbal reports may not be sensitive enough to detect implicit learning (Dulany, Carlson, & Dewey, 1985; Shanks & St. John, 1994). The dissociation between these two learning modes, however, was also supported by studies that documented differences in behavioral outcomes (e.g. Destrebecqz

⁎ Corresponding author at: Faculty of Education, the University of Hong Kong, Hong Kong. Tel.: +852 66876774; fax: +852 25471924. E-mail address: [email protected] (Q. Xie). 1 Qiuzhi Xie conducted this study at Department of Psychology, Shanghai Normal University. 1041-6080/$ – see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.lindif.2012.10.014

1.2. Individual differences in implicit learning and explicit learning Reber and Allen (2000) reviewed a large body of work on implicit learning and concluded that individual differences in implicit learning do exist. They also raised the question as to whether inter-individual variation in implicit learning is distinct from that found in explicit learning. From an evolutionary perspective, Reber and Allen (2000) hypothesized that unlike explicit learning, implicit learning is independent of psychometric intelligence. Several studies have examined whether intelligence is related to implicit and explicit learning. In general, it was found that intelligence was positively related to performance on explicit learning tasks (e.g. Gebauer & Mackintosh, 2007; Kaufman et al., 2010). However, the findings regarding the association between intelligence and performance on implicit learning tasks were rather inconsistent. Some studies reported a non-significant correlation between the two variables (e.g. Gebauer & Mackintosh, 2007; Reber, Walkenfeld, & Hernstadt, 1991), while other studies reported a significant relationship (e.g. Danner, Hagemann, Schankin, Hager, & Funke, 2011). Kaufman et al. (2010) found that performance on the implicit learning task was associated with some intelligence dimensions (i.e. verbal reasoning), while being independent of some other dimensions (i.e. perceptual reasoning and mental rotation ability). Nevertheless, Kaufman et al. (2010) concluded that the relationship between intelligence and performance was stronger for explicit learning tasks and weaker for implicit learning tasks. The relationship between intelligence and implicit learning is still equivocal; however, based on previous research outlined above, it is possible that even if

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there is a relationship between the two variables, the relationship would be rather weak. Several studies also examined the relationship between performance on implicit learning tasks and basic cognitive functions. For instance, it was found that implicit learning was related to processing speed, but was independent of working memory capacity (e.g. Kaufman et al., 2010). Rathus, Reber, Manza, and Kushner (1994) investigated the relationship of affective factors to both implicit and explicit cognitive processes and found that: (1) anxiety interfered with explicit memory but not implicitly acquired knowledge and (2) depressive symptoms were related to neither explicit memory nor implicit learning. Several studies also investigated the impact of personality on implicit learning. It has been argued that implicit learning and intuition are closely associated (e.g. Lieberman, 2000). Woolhouse and Bayne's (2000) study showed that when people were unaware of the underlying rules, those who preferred intuition (seeking possibility and initiation) outperformed those who preferred sensing (seeking reality and convention) in an implicit learning task. Kaufman et al. (2010) reported the associations of two lower facets of Openness to Experience – Intellect and Openness – with performance on an implicit learning task. Intellect pertains to quickness, ingenuity, and ideas, whereas Openness pertains to aesthetics, imagination, and fantasy. It was found that participants' performance on the implicit learning task was related to Openness, but not to Intellect. Kaufman et al. (2010) also claimed that implicit learning and impulsivity share a common characteristic, in the sense that both entail automatic processes. They found that the lack of premeditation (one of the dimensions of impulsivity) was positively related to performance on the implicit learning task. Pretz, Totz, and Kaufman (2010) found that the rational cognitive style, rather than the experiential one, predicted better performance on implicit learning tasks. In addition, Kassin and Reber's (1979) study showed that performance in the implicit learning condition was positively related to locus of control: those having an internal locus of control outperformed those with an external locus of control on an implicit artificial grammar learning task. To date, there is still a dearth of research about individual differences in implicit learning. In addition, extant studies are plagued by a methodological problem. Most studies, except for two (Gebauer & Mackintosh, 2007; Maybery, Taylor, & O'Brien-Malone, 1995), used different tasks to measure performance under the implicit learning condition and the explicit learning condition. Therefore, it is possible that their different relationships to individual differences factors might have been due to task differences (Gebauer & Mackintosh, 2007; Reber & Allen, 2000). This study addresses this weakness by using the same task for both learning conditions. 1.3. Thinking styles Sternberg's (1997) theory of thinking styles, also known as the theory of mental self-government, is one of the most recent and influential theories on styles (Zhang & Sternberg, 2005). According to Sternberg (1997), thinking styles refer to mental tendencies to approach tasks in a certain manner. Sternberg (1997) used societal organization as a metaphor for understanding thinking styles. He proposed five dimensions of thinking styles: function (legislative, executive, and judicial styles), form (monarchic, hierarchic, oligarchic, and anarchic styles), level (global and local styles), scope (internal and external styles), and leaning (liberal and conservative styles). Descriptions for each of these styles can be found in Appendix A. Insomuch as the 13 thinking styles are subsumed into five dimensions, the theory can furnish a general profile of styles, instead of just relying on one or two style categories to describe an individual (Sternberg, 1997). Zhang and Sternberg (2005) categorized the 13 thinking styles into three types. Thinking styles (i.e. legislative, judicial, hierarchical, global, and liberal styles) pertinent to creativity and cognitive complexity are classified as Type I. Thinking styles (i.e. executive,

monarchic, local, and conservative styles) which are related to a preference for norms and cognitive simplicity are categorized as Type II. Type I thinking styles are regarded as more adaptive and are related to some positive attributes such as higher cognitive-developmental level and the use of a deep learning approach. Type II styles are considered to be less adaptive and are related to lower cognitivedevelopmental level and the use of a surface learning approach (e.g. Zhang & Sternberg, 2005). The other four styles (i.e. oligarchic, anarchic, internal, and external styles) are largely reckoned as valuedifferentiated and, thus far, no clear relationship has been found between them and important educational outcomes. These styles are labeled as Type III styles. They may express the characteristics of either Type I or II styles, depending on situational contingencies. The relationship between thinking styles and academic achievement has been widely investigated in Western and Eastern countries (e.g. Cano-Garcia & Hughes, 2000; Zhang, 2004). It was consistently found that thinking styles contribute to learning achievement beyond intelligence. Furthermore, Zhang (2004, 2007) found that the contribution of thinking styles to academic achievement varied as a function of subject matter. Although the predictive power of thinking styles for learning performance has been widely investigated, in all these studies, learning performance was tested through students' examination scores in schools rather than through learning tasks under rigorous experimental controls. Therefore, the contribution of thinking styles to the specific underlying learning processes has not yet been investigated. 1.4. Research purpose and hypotheses The current study aims to investigate whether differences in thinking styles influence implicit and explicit learning. First, it was hypothesized that Type I styles and the internal (Type III) style would be positively related to performance on the explicit learning task, whereas Type II styles and the external style would be negatively related to it. This prediction was based on the cognitive-complexity/simplicity characteristic of Type I and II styles. In addition, those preferring the internal style tend to be more introverted, whereas those preferring the external style tend to be more extraverted (Sternberg, 1997). The introverts usually have a higher resting level of arousal (Eysenck & Eysenck, 1985) and thus may outperform the extraverts in tasks requiring reflection (Chamorro-Premuzic & Furnham, 2005; Matthews, 1992). The explicit learning task, therefore, would facilitate those preferring the internal style to the external style. Moreover, we did not expect any relationship between thinking styles and performance on the implicit learning task. Because thinking styles arise from both cognition and personality (Sternberg, 1997), we reckoned that styles on the dimensions of function, level, and leaning should be pertinent to the subscale of Intellect under the broader trait of Openness to Experience. Additionally, thinking styles on the dimension of form should be relevant to the subscale of Orderliness under the broader trait of Conscientiousness (DeYoung, Quilty, & Peterson, 2007). However, Intellect and Orderliness were found to be unrelated to implicit learning (Kaufman et al., 2010; Norman, Price, & Duff, 2006). Thus, we did not posit any relationship between thinking styles and performance on the implicit learning task. 2. Method 2.1. Participants and research design Eighty-seven students (6 males and 81 females) between ages 20 years and 24 years (M = 21.3 years, SD = 1.03) in a university in Shanghai, China participated in this research. Thirty-four students were sophomores, 22 were juniors, and 31 were seniors. These students majored in Humanities, Science, and Management.

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We adopted a between-subject design, because the same task was employed for both implicit and explicit learning tasks. Forty-six (Mage = 20.8 years, SD = 1.04) students were randomly assigned the implicit learning task and forty-one (Mage = 21.9 years, SD = .65) students were assigned the explicit learning task. 2.2. Materials and procedure 2.2.1. Thinking Styles Inventory-Revised II (TSI-R2, Sternberg, Wagner, & Zhang, 2007) The TSI-R2 contains 65 items with 13 scales corresponding to the 13 thinking styles. Each scale has 5 items. The questionnaire was scored on a 7 point Likert scale with higher scores indicating a greater endorsement of the item (1 = does not fit you at all; 7 = fits you extremely well). The present study employed the Chinese version of this inventory that was developed through translation and back translation. The Chinese version has been used in a number of studies and has been found to have good psychometric properties (e.g. Zhang, 2002, 2004, 2007). The Cronbach alpha coefficient for each scale typically ranged from .60 to .80 in previous research, indicating acceptable internal consistency reliability of this inventory. 2.2.2. Artificial Grammar Learning (AGL, Gebauer & Mackintosh, 2007) The material and testing procedure of AGL was adopted from Gebauer and Mackintosh's (2007) study. This computer-based task consisted of two phases: the learning phase and the testing phase. In the learning phase, each of the 20 letter strings following artificial grammar (see Fig. 1) was presented for 4 s on a computer screen one by one. Participants were required to type each item (letter string) right after its presentation was finished. If typed incorrectly, the previous item would appear again until it could be correctly typed. After the presentation of every 10 items, all the previous 10 items were presented in order altogether for 65 s for the participants to review. All the participants were informed of such procedures before the task began. In the following testing phase, 32 new items were presented one after another. Half of these items followed the artificial grammar in the learning phase and the other half violated the grammar. Participants were required to judge whether each of the new items followed or violated the grammar. Each right answer deserved one point and a total score of right answers was calculated. Different instructional sets were used to dissociate implicit learning from explicit learning. Participants in the implicit learning condition were informed at the beginning that it was a memory task and that they were required to memorize these letter strings, which were described as nonsense and randomized. In the testing phase, they were informed that half of the items had appeared in the learning phase and the other half

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were new. They were asked to judge whether each item was old or new. Participants in the explicit learning condition were informed at the beginning that the letter strings were generated from a rule system determining which letter could follow another and which letters could appear at the beginning or the end of a letter string. Participants were encouraged to discover as many rules as possible. In the following testing phase, they were asked to judge whether the new letter strings followed the previous grammatical rules. 2.2.3. Task order All the participants commenced with the AGL task, followed by the paper-and-pencil test measuring thinking styles. Participants given the implicit instruction were tested prior to those given the explicit instruction. 3. Results 3.1. Preliminary investigation The mean score of percentage correct (Pc) of performance under the implicit instruction was .57 (95% CI = .54 to .59) and that of performance under the explicit instruction was .61 (95% CI = .58 to .64). 2 One sample t-tests showed that the proportions of correct answers in both groups were significantly higher than what would have been obtained by chance alone, thus suggesting that the performances were above chance (timplicit = 5.30, p b .001; texplicit = 7.76, p b .001). The standard deviation for learning under the implicit instruction (2.76) was comparable to that for learning under the explicit instruction (2.90). Such result did not support Reber and Allen's (2000) argument that the inter-individual variation of implicit learning should be smaller than that of explicit learning. Moreover, in this study, performance under the explicit instruction and implicit instruction was not correlated with age or year of study (see Table 1). 3.2. The relationships of thinking styles to performance under implicit and explicit instructions Bivariate correlations were used to examine the relationships of thinking styles to performance on the learning task (see Table 1). As expected, performance under the explicit instruction was correlated positively with Type I styles (i.e. the legislative and the liberal styles) and the internal style, whereas it correlated negatively with a Type II style (i.e. the conservative style) and the external style. No significant correlation was found between performance under the implicit instruction and thinking styles. We also conducted stepwise regression to examine how individual differences in thinking styles predict performance. Performance under the explicit instruction was designated as the dependent variable and thinking styles were designated as the independent variable (see Table 2). The external style and the conservative style contributed negatively to and accounted for approximately 33% of the variance in performance under the explicit instruction. 4. Discussion The results of this study supported the hypothesis concerning the relationships between thinking styles and performance upon the explicit learning task. The internal style and Type I styles – the ones concerning the preferences for creating rules (i.e. the legislative style) and going beyond existent rules (i.e. the liberal style) – were positively related to performance under the explicit instruction. The external style and the Type II conservative style, which pertain to

Fig. 1. Artificial Grammar from “Psychometric Intelligence Dissociates Implicit and Explicit Learning”, by G. F. Gebauer and N. J. Mackintosh, 2007, Journal of Experimental Psychology: Learning Memory, and Cognition, 33(1), p. 38. APA as publisher.

2 The mean scores of Pc in this study were not substantially higher than .60, meaning that learning rates observed in this study were lower than typical.

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Table 1 Pearson correlations of learning performances with age, year of study, and thinking styles.

Age Year of study legislative Executive Judicial Global Local Liberal conservative hierarchic monarchic oligarchic Anarchic Internal External

Implicit

Explicit

.07 .13 .10 .01 .04 −.01 −.03 −.12 −.15 .03 −.06 −.05 −.04 .24 −.08

−.73 .25 .46⁎⁎ −.07 −.01 .01 −.24 .31⁎ −.43⁎⁎ .13 −.06 −.30 −.09 .37⁎ −.52⁎⁎⁎

Note. Implicit = performance under implicit instruction and Explicit = performance under explicit instruction. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.

2010). This study showed that people's thinking styles are associated with explicit learning but not with implicit learning. Further, the findings have significant implications to the theory of thinking styles, proposed to explain individual differences in learning performance. This is the first study that investigates the contributions of thinking styles to performance on an experimental learning task and, thus, the findings can suggest the contributions of thinking styles to specific learning processes. This study implies that thinking styles are more strongly associated with the conscious learning process (in explicit learning condition) rather than the unconscious learning process (in implicit learning condition). Also, the results imply that those having Type I thinking styles seem to be at a considerable advantage in the acquisition of knowledge entailing reasoning and reflection. The results support Zhang and Sternberg's (2005) argument that Type 1 styles are more adaptive compared to Type II styles. To the best of our knowledge, this is the first study that investigated whether thinking styles are related to implicit and explicit learning. Future studies that use other types of tasks to assess implicit and explicit learning and those involving larger sample sizes are needed to replicate our findings. Appendix A. Descriptions of thinking styles

the preference for adhering to existing rules, were negatively associated with performance under the explicit instruction. Results of the regression equation indicated that the external thinking style was the strongest predictor of performance on the explicit learning task. The negative contributions of the external and the conservative thinking styles exceeded the positive contributions of the internal and Type I thinking styles. Also as expected, thinking styles were not found to be related to performance in the implicit learning condition. Because the same task was employed for both implicit and explicit learning conditions, the findings rule out the possibility that the different relationships of thinking styles to performance under the two learning modes were due to task differences. It presents a considerable improvement over previous research which has failed to disentangle effects due to task differences from that due to different learning processes. This study has implications for both the literature on implicit and explicit learning and the theory of thinking styles. First, this study supported the dissociation between explicit and implicit learning. In line with extant research, this study showed that there was a pertinent consequential behavioral difference between explicit and implicit learning: the performance under explicit instruction was related to thinking styles, whereas the performance under implicit instruction was not. This finding is also congruent with Evans and Frankish's (2009) dual-process theories about two distinguishable processing systems. Second, this study showed that individual differences variables that influence implicit learning are distinguishable from those that impact explicit learning. Previous studies have indicated that implicit learning and explicit learning are associated with different individual differences factors (e.g. Gebauer & Mackintosh, 2007; Kaufman et al.,

Table 2 Stepwise regression for predicting explicit learning task score from thinking styles.

1 2

External External Conservative

Note. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.

Adj. R2

F

Beta

t

.255 .328

92.082⁎⁎⁎ 6.805⁎⁎⁎

−1.124 −.949 −.992

−3.835⁎⁎⁎ −3.286⁎⁎ −2.286⁎

Dimension

Thinking style

Description

Function

Legislative Executive Judicial Monarchic Hierarchic Oligarchic

Preference for creating rules and autonomy Preference for following rules Preference for evaluating rules Tendency to be single-minded and driven Tendency to set hierarchy for goals and tasks Tendency to be motivated by several things with equally perceived importance Tendency to be driven by an assortment of needs and goals with disorder Preference for something abstract and large Preference for something concrete and with details Inclination to work independently Inclination to work with others Liking for going beyond rules and maximizing changes Liking for adhering to existing rules and minimizing changes

Form

Anarchic Level Scope Leaning

Global Local Internal External Liberal Conservative

Adapted from "Thinking styles" by Sternberg, 1997.

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