# An Evolutionary Approach to Motivation and Learning: Differentiating Biologically Primary and Secondary Knowledge

Kate Xu, Sarah Coertjens, Florence Lespiau, Kim Ouwehand, Hanke Korpershoek, Fred Paas, David Geary

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## ► To cite this version:

Kate Xu, Sarah Coertjens, Florence Lespiau, Kim Ouwehand, Hanke Korpershoek, et al.. An Evolutionary Approach to Motivation and Learning: Differentiating Biologically Primary and Secondary Knowledge. Educational Psychology Review, 2024, 36 (2), pp.45. 10.1007/s10648-024-09880-3. hal-04565594

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# An Evolutionary Approach to Motivation and Learning: Differentiating Biologically Primary and Secondary Knowledge

Kate Xu, Sarah Coertjens, Florence Lespiau, Kim Ouwehand, Hanke Korpershoek, Fred Paas, David Geary

► To cite this version:

Kate Xu, Sarah Coertjens, Florence Lespiau, Kim Ouwehand, Hanke Korpershoek, et al.. An Evolutionary Approach to Motivation and Learning: Differentiating Biologically Primary and Secondary Knowledge. Educational Psychology Review, 2024, 36 (2), pp.45. 10.1007/s10648-024-09880-3. hal-04565594

HAL Id: hal-04565594

https://hal.science/hal-04565594v1

Submitted on 21 May 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

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Distributed under a Creative Commons Attribution 4.0 International License
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# An Evolutionary Approach to Motivation and Learning:
## Differentiating Biologically Primary and Secondary
## Knowledge

Kate M. Xu¹ Ⓓ · Sarah Coertjens¹ · Florence Lespiau² · Kim Ouwehand³ · Hanke Korpershoek⁴ · Fred Paas³,⁵ · David C. Geary⁶

Accepted: 19 March 2024
© The Author(s) 2024

## Abstract

The ubiquity of formal education in modern nations is often accompanied by an assumption that students' motivation for learning is innate and self-sustaining. The latter is true for most children in domains (e.g., language) that are universal and have a deep evolutionary history, but this does not extend to learning in evolutionarily novel domains (e.g., mathematics). Learning in evolutionarily novel domains requires more cognitive effort and thus is less motivating. The current study tested the associated hypothesis that learning will feel easier and more motivating for evolutionarily relevant (e.g., "mother," "food") than evolutionarily novel (e.g., "computer," "gravity") word pairs and that a growth mindset emphasizing the importance of effort in learning might moderate this effect. Specifically, 144 adults were presented with 32 word pairs (half evolutionarily relevant and half evolutionarily novel) and were randomly assigned to a growth mindset or a control condition. Evolutionarily relevant words were better remembered than evolutionarily novel words ($d=0.65$), and the learning was reported as more enjoyable ($d=0.49$), more interesting ($d=0.38$), as well as less difficult ($d=-0.96$) and effortful ($d=-0.78$). Although the growth mindset intervention fostered a mindset belief, compared to the control condition, it did not lead to improved recall performance or changes in motivational beliefs. These results are consistent with the prediction of higher motivation and better learning of evolutionarily relevant words and concepts than for evolutionarily novel words and concepts. Implications for future research and educational practice are discussed.

**Keywords** Motivation · Evolution · Learning · Biologically primary knowledge · Biologically secondary knowledge

Motivation contributes to children's learning in school, and thus, it is not surprising that motivation theories are at the heart of modern theories of educational

<footnote>Extended author information available on the last page of the article</footnote>
<figure><img src="image_2.png" /></figure>

Check for
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# An Evolutionary Approach to Motivation and Learning:
## Differentiating Biologically Primary and Secondary
## Knowledge

Kate M. Xu¹ Ⓓ · Sarah Coertjens¹ · Florence Lespiau² · Kim Ouwehand³ · Hanke Korpershoek⁴ · Fred Paas³,⁵ · David C. Geary⁶

Accepted: 19 March 2024
© The Author(s) 2024

## Abstract

The ubiquity of formal education in modern nations is often accompanied by an assumption that students' motivation for learning is innate and self-sustaining. The latter is true for most children in domains (e.g., language) that are universal and have a deep evolutionary history, but this does not extend to learning in evolutionarily novel domains (e.g., mathematics). Learning in evolutionarily novel domains requires more cognitive effort and thus is less motivating. The current study tested the associated hypothesis that learning will feel easier and more motivating for evolutionarily relevant (e.g., "mother," "food") than evolutionarily novel (e.g., "computer," "gravity") word pairs and that a growth mindset emphasizing the importance of effort in learning might moderate this effect. Specifically, 144 adults were presented with 32 word pairs (half evolutionarily relevant and half evolutionarily novel) and were randomly assigned to a growth mindset or a control condition. Evolutionarily relevant words were better remembered than evolutionarily novel words ($d=0.65$), and the learning was reported as more enjoyable ($d=0.49$), more interesting ($d=0.38$), as well as less difficult ($d=-0.96$) and effortful ($d=-0.78$). Although the growth mindset intervention fostered a mindset belief, compared to the control condition, it did not lead to improved recall performance or changes in motivational beliefs. These results are consistent with the prediction of higher motivation and better learning of evolutionarily relevant words and concepts than for evolutionarily novel words and concepts. Implications for future research and educational practice are discussed.

**Keywords** Motivation · Evolution · Learning · Biologically primary knowledge · Biologically secondary knowledge

Motivation contributes to children's learning in school, and thus, it is not surprising that motivation theories are at the heart of modern theories of educational

<footnote>Extended author information available on the last page of the article</footnote>
psychology (Deci et al., 1991; Wigfield & Eccles, 2000). Contemporary theories of motivation portray it as “the energy [students] bring to [school related] tasks, the beliefs, values, and goals that determine which tasks they pursue and their persistence in achieving them, and the standards they set to determine when a task has been accomplished” (Wentzel & Wigfield, 2007, p. 1). The development of motivation for learning emerges during the preschool years (see also Geary & Xu, 2022a; Wigfield et al., 2015; Wigfield et al., 2015) and often in the context of social play (Gray, 2011; Moll & Tomasello, 2007). Although it may seem logical that motivation to learn should continue into formal education settings, this is not typically the case. Children’s motivation for school learning declines as the knowledge taught in school becomes more complex (Wigfield et al., 2006). In other words, there is not a domain-general motivational bias to learn, but the domains in which children’s learning motivation is stronger or weaker are not fully understood.

An evolutionary perspective has been proposed to explain this variation in learning motivation (Geary, 1995; Geary & Xu, 2022a; Marsh et al., 2021). It has been suggested that children are motivated to engage in activities, such as social play or exploration of the environment, that support the development to universal forms of knowledge, such as language and the location of important things (e.g., water source) in the ecology. The motivation to engage in these types of activities does not, in theory, extend to the activities that promote academic learning and are often in conflict with them (Geary, 2024). The present study assessed the hypothesis that learning potentially evolutionary-relevant (e.g., survival-related) will be easier (i.e., greater accuracy), more motivating, and less effortful than learning evolutionarily novel information. A related aim was to determine if framing the learning in a growth mindset emphasizing on effort investment differentially influenced learning and motivation across these evolutionarily relevant and evolutionarily novel conditions.

## An Evolutionary Distinction of Knowledge and Motivation

Geary (2008; see also Geary & Xu, 2022b for broader literature) proposed an evolutionary framework that distinguishes biologically secondary knowledge and skills learned in schools from those acquired nearly automatically during development (e.g., language, face recognition). The biologically primary knowledge associated with the latter emerges through a combination of built-in scaffolds to ensure its learning and a motivation to engage the species-typical (e.g., social play) activities that enhance these abilities and adapt them to local conditions (e.g., the local language). These built-in cognitive and motivational biases do not extend to most academic domains, such as physics and mathematics, and thus, these require more explicit, organized instruction (Paas & Sweller, 2012; Sweller et al., 2019). The evolutionary novelty of these domains also explains why the motivation to learn them declines during schooling (Gnambs & Hanfstingl, 2016; Gottfried et al., 2001; Spinath & Spinath, 2005).

The basic idea is that the combination of built-in cognitive and motivational biases results in the universal emergence of biologically primary cognitive domains organized as folk psychology, folk biology, and folk physics (Atran, 1998; Geary, 2005; Gelman, 2003; Leslie et al., 2004; Wellman, 2017; Wellman & Gelman,

<figure><img src="image_4.png" /></figure>
psychology (Deci et al., 1991; Wigfield & Eccles, 2000). Contemporary theories of motivation portray it as “the energy [students] bring to [school related] tasks, the beliefs, values, and goals that determine which tasks they pursue and their persistence in achieving them, and the standards they set to determine when a task has been accomplished” (Wentzel & Wigfield, 2007, p. 1). The development of motivation for learning emerges during the preschool years (see also Geary & Xu, 2022a; Wigfield et al., 2015; Wigfield et al., 2015) and often in the context of social play (Gray, 2011; Moll & Tomasello, 2007). Although it may seem logical that motivation to learn should continue into formal education settings, this is not typically the case. Children’s motivation for school learning declines as the knowledge taught in school becomes more complex (Wigfield et al., 2006). In other words, there is not a domain-general motivational bias to learn, but the domains in which children’s learning motivation is stronger or weaker are not fully understood.

An evolutionary perspective has been proposed to explain this variation in learning motivation (Geary, 1995; Geary & Xu, 2022a; Marsh et al., 2021). It has been suggested that children are motivated to engage in activities, such as social play or exploration of the environment, that support the development to universal forms of knowledge, such as language and the location of important things (e.g., water source) in the ecology. The motivation to engage in these types of activities does not, in theory, extend to the activities that promote academic learning and are often in conflict with them (Geary, 2024). The present study assessed the hypothesis that learning potentially evolutionary-relevant (e.g., survival-related) will be easier (i.e., greater accuracy), more motivating, and less effortful than learning evolutionarily novel information. A related aim was to determine if framing the learning in a growth mindset emphasizing on effort investment differentially influenced learning and motivation across these evolutionarily relevant and evolutionarily novel conditions.

## An Evolutionary Distinction of Knowledge and Motivation

Geary (2008; see also Geary & Xu, 2022b for broader literature) proposed an evolutionary framework that distinguishes biologically secondary knowledge and skills learned in schools from those acquired nearly automatically during development (e.g., language, face recognition). The biologically primary knowledge associated with the latter emerges through a combination of built-in scaffolds to ensure its learning and a motivation to engage the species-typical (e.g., social play) activities that enhance these abilities and adapt them to local conditions (e.g., the local language). These built-in cognitive and motivational biases do not extend to most academic domains, such as physics and mathematics, and thus, these require more explicit, organized instruction (Paas & Sweller, 2012; Sweller et al., 2019). The evolutionary novelty of these domains also explains why the motivation to learn them declines during schooling (Gnambs & Hanfstingl, 2016; Gottfried et al., 2001; Spinath & Spinath, 2005).

The basic idea is that the combination of built-in cognitive and motivational biases results in the universal emergence of biologically primary cognitive domains organized as folk psychology, folk biology, and folk physics (Atran, 1998; Geary, 2005; Gelman, 2003; Leslie et al., 2004; Wellman, 2017; Wellman & Gelman,
1992). These abilities support the development and maintenance of social relation-
ships (folk psychology), knowledge about other species in the ecology (e.g., edible
plants, folk biology), and the ability to navigate and use objects as tools (folk phys-
ics). These abilities have a deep evolutionary history, in contrast with the more recent
emergence of formal education about 5000 years ago (Eskelson, 2020). Indeed, uni-
versal education slowly emerged across many parts of Europe and globally over the
past 400 years (Goldin, 1999) (Ramirez & Boli, 1987), and universal education in
some Western contexts was not achieved until well into the nineteenth century and
sometimes the early twentieth century and remains to be achieved in some parts
of the world today (Goldin, 1999). In the context of schooling, children eventually
learn biologically secondary knowledge, such as reading and mathematics.

The demands of modern schooling and modern economies result in an evolu-
tionary mismatch between primary knowledge and motivational systems and the
secondary knowledge needed to be successful in the modern world (Bjorklund,
2022). Still, aspects of primary knowledge and motivation can at times be used to
facilitate engagement in secondary learning (Alipour et al., 2023; Paas & Sweller,
2012). Embedded in logical problem-solving training context, Lespiau and Tricot
(2018, 2019) conducted a series of experiments in university and secondary school
students. These studies consistently showed that participants scored higher for logi-
cal problems infused with primary knowledge content in comparison to problems
infused with secondary knowledge content. Furthermore, they also reported higher
motivation and less perceived cognitive load when solving problems with primary
content. They largely replicated the same pattern of findings in statistical problems
(e.g., Lespiau & Tricot, 2022b).

Except for one small study on mathematics learning (Alipour et al., 2023), there
are (to our knowledge) no studies that contrast learning in the context of primary and
secondary content other than logic and statistics, for example language learning. Spo-
ken language is a key part of folk psychology and essential to developing survival-
related social networks in traditional contexts. Reading skills are secondary knowl-
edge and are built onto the language system but are not needed in traditional contexts
and have only recently become universally important in modern contexts (Geary,
2002). Learning and remembering written vocabulary words are directly influenced
by the evolutionary salience of the meaning of these words. Nairne (2022; 2013)
showed that adults' memory for nouns representing animate, living things (e.g., pred-
ator) is better remembered than those representing inanimate, non-living things (e.g.,
book). The results support the hypothesis that the human memory system is biased
towards fitness-based events and things. Although Nairne and colleagues' studies
were based on a vocabulary-related task, the experiment was not based on an authen-
tic learning task specifically. In other studies, the animacy principle was subject to
word learning tasks including foreign words—Swahili words paired with English
translations (VanArsdall et al., 2015) or nonwords (Laurino & Kaczer, 2019). In both
studies, participants (undergraduate students) could produce the Swahili or nonword
translation better for animate as compared to inanimate words.

While the studies on animacy are indeed relevant to the primary versus secondary
knowledge distinction, the focus there is more specifically on remembering nouns
representing things that are living (e.g., animals) or non-living (e.g., furniture,
1992). These abilities support the development and maintenance of social relation-
ships (folk psychology), knowledge about other species in the ecology (e.g., edible
plants, folk biology), and the ability to navigate and use objects as tools (folk phys-
ics). These abilities have a deep evolutionary history, in contrast with the more recent
emergence of formal education about 5000 years ago (Eskelson, 2020). Indeed, uni-
versal education slowly emerged across many parts of Europe and globally over the
past 400 years (Goldin, 1999) (Ramirez & Boli, 1987), and universal education in
some Western contexts was not achieved until well into the nineteenth century and
sometimes the early twentieth century and remains to be achieved in some parts
of the world today (Goldin, 1999). In the context of schooling, children eventually
learn biologically secondary knowledge, such as reading and mathematics.

The demands of modern schooling and modern economies result in an evolu-
tionary mismatch between primary knowledge and motivational systems and the
secondary knowledge needed to be successful in the modern world (Bjorklund,
2022). Still, aspects of primary knowledge and motivation can at times be used to
facilitate engagement in secondary learning (Alipour et al., 2023; Paas & Sweller,
2012). Embedded in logical problem-solving training context, Lespiau and Tricot
(2018, 2019) conducted a series of experiments in university and secondary school
students. These studies consistently showed that participants scored higher for logi-
cal problems infused with primary knowledge content in comparison to problems
infused with secondary knowledge content. Furthermore, they also reported higher
motivation and less perceived cognitive load when solving problems with primary
content. They largely replicated the same pattern of findings in statistical problems
(e.g., Lespiau & Tricot, 2022b).

Except for one small study on mathematics learning (Alipour et al., 2023), there
are (to our knowledge) no studies that contrast learning in the context of primary and
secondary content other than logic and statistics, for example language learning. Spo-
ken language is a key part of folk psychology and essential to developing survival-
related social networks in traditional contexts. Reading skills are secondary knowl-
edge and are built onto the language system but are not needed in traditional contexts
and have only recently become universally important in modern contexts (Geary,
2002). Learning and remembering written vocabulary words are directly influenced
by the evolutionary salience of the meaning of these words. Nairne (2022; 2013)
showed that adults' memory for nouns representing animate, living things (e.g., pred-
ator) is better remembered than those representing inanimate, non-living things (e.g.,
book). The results support the hypothesis that the human memory system is biased
towards fitness-based events and things. Although Nairne and colleagues' studies
were based on a vocabulary-related task, the experiment was not based on an authen-
tic learning task specifically. In other studies, the animacy principle was subject to
word learning tasks including foreign words—Swahili words paired with English
translations (VanArsdall et al., 2015) or nonwords (Laurino & Kaczer, 2019). In both
studies, participants (undergraduate students) could produce the Swahili or nonword
translation better for animate as compared to inanimate words.

While the studies on animacy are indeed relevant to the primary versus secondary
knowledge distinction, the focus there is more specifically on remembering nouns
representing things that are living (e.g., animals) or non-living (e.g., furniture,
or tools) as examples of evolutionarily relevant and novel information. A more
complete and direct assessment of the evolutionary distinction should be based
on a vocabulary task with words more widely reflecting both the biologically
primary and biologically secondary knowledge types (Geary & Bjorklund, 2000).
It is worth noting that such a vocabulary learning task is based on paired words
consisting of a native word matched with a nonword (De Groot & Keijzer, 2000),
which also resembles aspects of paired-associate learning tasks (Karantzoulis et al.,
2011), presented in the form of word pairs (Rohwer & Litrownik, 1983), or visual
imagery pairs (Pressley & Levin, 1977). The word association paradigm invokes a
“stimulus–response” type of mental process, thus enables examining learning of the
memorization (Rohwer & Litrownik, 1983), including the extent to which nonwords
representing different knowledge types can be recalled. Such tasks allow the learning
process (e.g., the cognitive load, the motivational aspects) to be directly contrasted
between different knowledge content by using words directly presenting those
concepts. Furthermore, this task affords control over possible confounds of task
variations compared to some other tasks we considered. For example, it is possible
to balance task elements based on word features such as letter lengths, syllables,
and word abstract/concreteness (De Groot & Keijzer, 2000). With such a study, it is
then possible to assess the motivation associated with learning words from different
knowledge categories. It is expected that compared to words presenting biologically
secondary knowledge, words representing biologically primary knowledge would be
better recalled and learned with greater enjoyment and possibly with less perceived
cognitive load (e.g., less effort and less difficulty).

The effortful learning follows from the lack of built-in cognitive scaffolds and
motivational biases to facilitate secondary knowledge learning; in the context of
animacy research, one scaffold is the bias to attend to living things that in turn facilitates
learning about them. The learning of secondary knowledge requires more effort, which
in addition to the novel content contributes to reduced motivation (Bjorklund, 2022;
Geary & Bjorklund, 2000). Thus, from an evolutionary perspective, explicitly or
implicitly promoting motivation and effortful engagement in learning might be more
important for secondary than primary learning. This lies at the heart of growth mindset
(Burnette et al., 2022; Yeager et al., 2019), a motivational belief that emphasizes the
importance of effort investment in achieving academic proficiency. While some students
believe their abilities can be improved with perseverance and practice, others believe
their abilities are fixed and further effort will not make a difference. These beliefs are
respectively called “growth” or “fixed” mindset. Learners with a growth mindset are
more likely to adopt more positive attitudes towards learning, whereas learners with
a fixed mindset develop maladaptive motivations and behaviors including lower effort
during learning. The concept of mindset and its roles in motivation and learning have
been extensively studied in the last few decades (Canning & Limeri, 2023; Dweck &
Leggett, 1988). It was shown that fostering a growth mindset can have a positive effect
on motivation and reduce learners’ perceived effort and difficulty for physics learning,
a biologically secondary knowledge domain (Xu et al., 2021). It is expected that a
growth mindset intervention will be more effective for effortful learning and thus more
important for learning words representing biologically secondary knowledge compared
to words representing biologically primary knowledge.
or tools) as examples of evolutionarily relevant and novel information. A more
complete and direct assessment of the evolutionary distinction should be based
on a vocabulary task with words more widely reflecting both the biologically
primary and biologically secondary knowledge types (Geary & Bjorklund, 2000).
It is worth noting that such a vocabulary learning task is based on paired words
consisting of a native word matched with a nonword (De Groot & Keijzer, 2000),
which also resembles aspects of paired-associate learning tasks (Karantzoulis et al.,
2011), presented in the form of word pairs (Rohwer & Litrownik, 1983), or visual
imagery pairs (Pressley & Levin, 1977). The word association paradigm invokes a
“stimulus-response” type of mental process, thus enables examining learning of the
memorization (Rohwer & Litrownik, 1983), including the extent to which nonwords
representing different knowledge types can be recalled. Such tasks allow the learning
process (e.g., the cognitive load, the motivational aspects) to be directly contrasted
between different knowledge content by using words directly presenting those
concepts. Furthermore, this task affords control over possible confounds of task
variations compared to some other tasks we considered. For example, it is possible
to balance task elements based on word features such as letter lengths, syllables,
and word abstract/concreteness (De Groot & Keijzer, 2000). With such a study, it is
then possible to assess the motivation associated with learning words from different
knowledge categories. It is expected that compared to words presenting biologically
secondary knowledge, words representing biologically primary knowledge would be
better recalled and learned with greater enjoyment and possibly with less perceived
cognitive load (e.g., less effort and less difficulty).

The effortful learning follows from the lack of built-in cognitive scaffolds and
motivational biases to facilitate secondary knowledge learning; in the context of
animacy research, one scaffold is the bias to attend to living things that in turn facilitates
learning about them. The learning of secondary knowledge requires more effort, which
in addition to the novel content contributes to reduced motivation (Bjorklund, 2022;
Geary & Bjorklund, 2000). Thus, from an evolutionary perspective, explicitly or
implicitly promoting motivation and effortful engagement in learning might be more
important for secondary than primary learning. This lies at the heart of growth mindset
(Burnette et al., 2022; Yeager et al., 2019), a motivational belief that emphasizes the
importance of effort investment in achieving academic proficiency. While some students
believe their abilities can be improved with perseverance and practice, others believe
their abilities are fixed and further effort will not make a difference. These beliefs are
respectively called “growth” or “fixed” mindset. Learners with a growth mindset are
more likely to adopt more positive attitudes towards learning, whereas learners with
a fixed mindset develop maladaptive motivations and behaviors including lower effort
during learning. The concept of mindset and its roles in motivation and learning have
been extensively studied in the last few decades (Canning & Limeri, 2023; Dweck &
Leggett, 1988). It was shown that fostering a growth mindset can have a positive effect
on motivation and reduce learners’ perceived effort and difficulty for physics learning,
a biologically secondary knowledge domain (Xu et al., 2021). It is expected that a
growth mindset intervention will be more effective for effortful learning and thus more
important for learning words representing biologically secondary knowledge compared
to words representing biologically primary knowledge.

<figure><img src="image_3.png" /></figure>
# Present Study

The present study thus aims to empirically investigate the effect of knowledge types on motivation and learning performance based on a novel vocabulary learning task, and the effect of an effort-inducing motivation intervention (growth mindset) on the motivation and learning outcomes based on different knowledge types. It is predicted that participants will perform better, experience more motivation, and less cognitive demands when learning words with a biologically primary content as compared to words with a biologically secondary content. It is also predicted that the growth mindset intervention will be more effective for biologically secondary word learning than biologically primary word learning.

# Method

## Participants and Design

The study applied a randomized controlled trial design and was based on data collected from 144 first year psychology bachelor students attending a Dutch university. The mean age was 21.08 (SD = 3.1) years (18 men, 123 women, and three choosing not to identify their gender). The experiment was based on a two-way within and between-factor mixed design. The within factor was evolutionary context embedded in a learning task, distinguishing biologically primary knowledge and biologically secondary knowledge. The between factor distinguished a growth mindset intervention (n = 72) or a control condition (n = 72), randomized across participants.

The current sample size exceeded the 48 participants that were minimally required, based on an a priori power calculation for a mixed design ANOVA analysis, with a medium effect size of Cohen's d = 0.5 (or *f* = 0.25), for power = 80%, and type I error rate = 5%.

## Learning Task

The learning task contained 32 pairs of Dutch-fictional words, presented to learners sequentially in eight lists of four word pairs (Table 1). The goal of the task was to learn the fictional ('foreign') words. The Dutch (native) words that appeared in the learning task were selected in terms of the number of syllables, abstract (e.g., sad) or concrete (e.g., fruit) word (Brysbaert et al., 2014), number of letters contained in the word (De Groot & Keijzer, 2000), and frequency (Baayen et al., 1996). The full details of Dutch word construction are presented in Table 1. The fictional words were constructed with random letters with comparable lengths (six to seven letters) with two syllables.

The learning task consisted of a total of eight study lists. In each list, there were four Dutch-fictional word pairs. Each word pair was automatically shown on the screen for 8 s. The word pairs within each list were repeated four times in
# Present Study

The present study thus aims to empirically investigate the effect of knowledge types on motivation and learning performance based on a novel vocabulary learning task, and the effect of an effort-inducing motivation intervention (growth mindset) on the motivation and learning outcomes based on different knowledge types. It is predicted that participants will perform better, experience more motivation, and less cognitive demands when learning words with a biologically primary content as compared to words with a biologically secondary content. It is also predicted that the growth mindset intervention will be more effective for biologically secondary word learning than biologically primary word learning.

# Method

## Participants and Design

The study applied a randomized controlled trial design and was based on data collected from 144 first year psychology bachelor students attending a Dutch university. The mean age was 21.08 (SD = 3.1) years (18 men, 123 women, and three choosing not to identify their gender). The experiment was based on a two-way within and between-factor mixed design. The within factor was evolutionary context embedded in a learning task, distinguishing biologically primary knowledge and biologically secondary knowledge. The between factor distinguished a growth mindset intervention (n = 72) or a control condition (n = 72), randomized across participants.

The current sample size exceeded the 48 participants that were minimally required, based on an a priori power calculation for a mixed design ANOVA analysis, with a medium effect size of Cohen's d = 0.5 (or *f* = 0.25), for power = 80%, and type I error rate = 5%.

## Learning Task

The learning task contained 32 pairs of Dutch-fictional words, presented to learners sequentially in eight lists of four word pairs (Table 1). The goal of the task was to learn the fictional ('foreign') words. The Dutch (native) words that appeared in the learning task were selected in terms of the number of syllables, abstract (e.g., sad) or concrete (e.g., fruit) word (Brysbaert et al., 2014), number of letters contained in the word (De Groot & Keijzer, 2000), and frequency (Baayen et al., 1996). The full details of Dutch word construction are presented in Table 1. The fictional words were constructed with random letters with comparable lengths (six to seven letters) with two syllables.

The learning task consisted of a total of eight study lists. In each list, there were four Dutch-fictional word pairs. Each word pair was automatically shown on the screen for 8 s. The word pairs within each list were repeated four times in
<table><caption>**Table 1** Word lists and knowledge type presented during learning task</caption><thead><tr><th colspan="3">Study list 1 (primary concepts)</th><th colspan="3">Study list 2 (secondary concept)</th></tr></thead><tbody><tr><td colspan="3">Dutch-fictional</td><td colspan="3">Dutch-fictional</td></tr><tr><td>1</td><td>English</td><td>Butterfly</td><td>5</td><td>English</td><td>Telephone</td></tr><tr><td>2</td><td>Liefde-breefje</td><td>Love</td><td>10</td><td>5</td><td>4.87</td></tr><tr><td>3</td><td>Peer-miftee</td><td>Pear</td><td>170</td><td>6</td><td>1.87</td></tr><tr><td>4</td><td>Wraak-ellaan</td><td>Revenge</td><td>10</td><td>7</td><td>4.80</td></tr><tr><td colspan="3">Study list 3 (primary concepts)</td><td>8</td><td>Umbrella</td><td>9</td></tr><tr><td colspan="3">Dutch-fictional</td><td>8</td><td>Multiplication</td><td>1</td></tr><tr><td>9</td><td>English</td><td>Hair</td><td>18</td><td>Frequency (per million)</td><td>Frequency (per million)</td></tr><tr><td>10</td><td>Haar-morees</td><td>Enemy</td><td>97</td><td>Frequency (per million)</td><td>Frequency (per million)</td></tr><tr><td>11</td><td>Vijand-plarker</td><td>Mother</td><td>60</td><td>13</td><td>4.93</td></tr><tr><td>12</td><td>Moeder-zappel</td><td>Mother</td><td>596</td><td>14</td><td>3.07</td></tr><tr><td>12</td><td>Gevaar-bekaar</td><td>Danger</td><td>116</td><td>15</td><td>5.00</td></tr><tr><td colspan="3">Study list 5 (primary concepts)</td><td>16</td><td>Auto-soeluup</td><td>137</td></tr><tr><td colspan="3">Dutch-fictional</td><td>16</td><td>History</td><td>1.86</td></tr><tr><td>17</td><td>English</td><td>Frog</td><td>9</td><td>Study list 6 (secondary concepts)</td><td>Frequency (per million)</td></tr><tr><td>18</td><td>Kikker-schomik</td><td>Favor</td><td>22</td><td>21</td><td>4.67</td></tr><tr><td>19</td><td>Gunst-aaluuk</td><td>Lemon</td><td>11</td><td>22</td><td>1.73</td></tr><tr><td>20</td><td>Citroen-geschak</td><td>Hate</td><td>37</td><td>23</td><td>4.87</td></tr><tr><td colspan="3">Study list 7 (primary concepts)</td><td>24</td><td>Crane</td><td>14</td></tr><tr><td colspan="3">Dutch-fictional</td><td>24</td><td>Mathematics</td><td>11</td></tr><tr><td>25</td><td>Lichaam-ipseel</td><td>Body</td><td>292</td><td>Study list 8 (secondary concepts)</td><td>Frequency (per million)</td></tr><tr><td>26</td><td>Vriend-rufoen</td><td>Friend</td><td>284</td><td>29</td><td>387</td></tr><tr><td>27</td><td>Vader-boddelt</td><td>Father</td><td>576</td><td>30</td><td>2.60</td></tr><tr><td>28</td><td>Dreiging-spodent</td><td>Threat</td><td>16</td><td>31</td><td>4.67</td></tr><tr><td colspan="3">Dutch-fictional</td><td>32</td><td>Century</td><td>20</td></tr><tr><td colspan="3">English</td><td>4.93</td><td>Frequency (per million)</td><td>Frequency (per million)</td></tr><tr><td colspan="3">Abstract/concrete</td><td>4.47</td><td>Abstract/concrete</td><td>387</td></tr><tr><td colspan="3">Frequency (per million)</td><td>2.00</td><td>Book</td><td>7</td></tr><tr><td colspan="3">Frequency (per million)</td><td>4.87</td><td>Atom</td><td>20</td></tr><tr><td colspan="3">Frequency (per million)</td><td>1.67</td><td>Tram</td><td>229</td></tr><tr><td colspan="3">Frequency (per million)</td><td>2.47</td><td>Eeuw-fileek</td><td>229</td></tr></tbody></table>

Note. Word ratings representing the degree of abstractness/concreteness were extracted from Brysbaert et al. (2014), with 1 representing an abstract word and 5 representing a concrete word. Word frequency was extracted from the Dutch vocabulary Corpus CELEX (Baayen et al., 1996). There was no statistically significant difference between words representing knowledge types in terms of abstractness/concreteness ($t = -0.68, p = 0.50, d = -0.24; M = 3.30, SD = 1.28 for primary knowledge words; M = 3.61, SD = 1.32 for secondary knowledge words), nor word frequency ($t = 1.15, p = 0.26, d = 0.41; M = 145.54, SD = 195.54 for primary knowledge words; M = 80.56, SD = 110.90 for secondary knowledge words)

<figure><img src="image_5.png" /></figure>
a Latin-square fashion (see Fig. 1 for illustration of word pairs presented in one study cycle for list 1). In the first, second, and fourth rounds, the word pairs were presented; while in the third round, only the Dutch word was presented, with a prompt for the participant to type in the corresponding fictional word. The learning phases concluded after all eight study lists with a total of 32 word pairs were shown.

## Experimental Conditions

Two experimental conditions were implemented: the evolutionary knowledge type distinctions (within factors) and growth mindset intervention (between factors).

### Evolutionary Knowledge Type Distinction

The evolutionary contexts were embedded in the learning task by way of counterbalancing equal numbers of word pairs across each context. There were four lists of 16 words representing primary objects (e.g., mother) and concepts (e.g., love), and another four lists representing 16 secondary objects (e.g., computer) or concepts (e.g., gravity; see Table 1). In total, there were eight themes for primary knowledge and eight themes for secondary knowledge (see Appendix Table S1). They were broadly comparable to those used in previous research investigating biological knowledge types (e.g., Lespiau & Tricot, 2018; Lespiau & Tricot, 2022a).

<figure><img src="image_6.png" /></figure>

Note. This figure demonstrates the presentation sequence of the word pairs in Study List 1 (see Table 1 for all study lists). Each word pair was presented for 8 seconds each, thus in total 32 seconds in each round.

<caption>**Fig. 1** An illustration of the presentation of study list 1 during the learning phase</caption>
a Latin-square fashion (see Fig. 1 for illustration of word pairs presented in one study cycle for list 1). In the first, second, and fourth rounds, the word pairs were presented; while in the third round, only the Dutch word was presented, with a prompt for the participant to type in the corresponding fictional word. The learning phases concluded after all eight study lists with a total of 32 word pairs were shown.

## Experimental Conditions

Two experimental conditions were implemented: the evolutionary knowledge type distinctions (within factors) and growth mindset intervention (between factors).

### Evolutionary Knowledge Type Distinction

The evolutionary contexts were embedded in the learning task by way of counterbalancing equal numbers of word pairs across each context. There were four lists of 16 words representing primary objects (e.g., mother) and concepts (e.g., love), and another four lists representing 16 secondary objects (e.g., computer) or concepts (e.g., gravity; see Table 1). In total, there were eight themes for primary knowledge and eight themes for secondary knowledge (see Appendix Table S1). They were broadly comparable to those used in previous research investigating biological knowledge types (e.g., Lespiau & Tricot, 2018; Lespiau & Tricot, 2022a).

<figure><img src="image_4.png" /></figure>

Note. This figure demonstrates the presentation sequence of the word pairs in Study List 1 (see Table 1 for all study lists). Each word pair was presented for 8 seconds each, thus in total 32 seconds in each round.

<caption>**Fig. 1** An illustration of the presentation of study list 1 during the learning phase</caption>
## Growth Mindset Intervention

In both the growth mindset and its control conditions, the participants performed reading and writing tasks (Appendix) adapted from previous research (Xu et al., 2021). The tasks were designed to be similar across intervention and control conditions. In particular, the reading tasks contained comparable lengths of texts and numbers of illustrative images.

**Growth Mindset Condition Tasks** A growth mindset intervention was presented to the learners in the form of a text to read and a writing task. The reading text concerned scientific explanations on brain functions and the importance of effort and persistence when learning new knowledge. The writing task instructed the participants to write a short motivating letter to a student who is struggling to learn to reinforce the growth mindset text.

**Control Condition Tasks** The control group completed comparable reading and writing tasks. The reading task provided a scientific explanation of cognitive functions but not specific to learning situations, nor concepts related to growth mindset (e.g., effortful practice). After reading the text, participants were asked to produce a short-written summary of it.

## Measurements

During and after the learning task, participants reported measures regarding motivational beliefs, cognitive load perceptions, and learning performance.

## Growth Mindset Belief

Growth mindset belief (Dweck, 2000) was measured on a six-point Likert scale ("No matter who you are, you can significantly change your intelligence level," ranging from "completely disagree" to "completely agree"). Participants reported growth mindset belief twice, once before the growth mindset intervention ($α=0.85$) and afterwards ($α=0.91$).

## Enjoyment and Interest

Single-item ratings of enjoyment and interest (Lespiau & Tricot, 2018) were assessed during the vocabulary task on nine-point Likert response scales ("not at all" to "very much"). After each vocabulary list, the participants rated the extent to which they enjoyed and were interested in the words they just studied. In total, there were eight items rated for enjoyment (test-retest reliability ICC=0.96) and eight items rated for interest (ICC=0.97).
## Growth Mindset Intervention

In both the growth mindset and its control conditions, the participants performed reading and writing tasks (Appendix) adapted from previous research (Xu et al., 2021). The tasks were designed to be similar across intervention and control conditions. In particular, the reading tasks contained comparable lengths of texts and numbers of illustrative images.

**Growth Mindset Condition Tasks** A growth mindset intervention was presented to the learners in the form of a text to read and a writing task. The reading text concerned scientific explanations on brain functions and the importance of effort and persistence when learning new knowledge. The writing task instructed the participants to write a short motivating letter to a student who is struggling to learn to reinforce the growth mindset text.

**Control Condition Tasks** The control group completed comparable reading and writing tasks. The reading task provided a scientific explanation of cognitive functions but not specific to learning situations, nor concepts related to growth mindset (e.g., effortful practice). After reading the text, participants were asked to produce a short-written summary of it.

## Measurements

During and after the learning task, participants reported measures regarding motivational beliefs, cognitive load perceptions, and learning performance.

## Growth Mindset Belief

Growth mindset belief (Dweck, 2000) was measured on a six-point Likert scale ("No matter who you are, you can significantly change your intelligence level," ranging from "completely disagree" to "completely agree"). Participants reported growth mindset belief twice, once before the growth mindset intervention ($α=0.85$) and afterwards ($α=0.91$).

## Enjoyment and Interest

Single-item ratings of enjoyment and interest (Lespiau & Tricot, 2018) were assessed during the vocabulary task on nine-point Likert response scales ("not at all" to "very much"). After each vocabulary list, the participants rated the extent to which they enjoyed and were interested in the words they just studied. In total, there were eight items rated for enjoyment (test-retest reliability ICC=0.96) and eight items rated for interest (ICC=0.97).

<figure><img src="image_5.png" /></figure>
## Cognitive Load Perceptions

Cognitive load perceptions (Paas, 1992) were assessed with single-item ratings of perceived mental effort and difficulty during the vocabulary task using nine-point Likert response scales (from “not at all” to “very much”). After each vocabulary list, the participants rated the extent to which they perceived their mental effort and found it difficult for learning the words they were just presented with. In total, there were eight items rated for mental effort (ICC=0.94) and eight items rated for difficulty (ICC=0.91).

## Learning Performance

Learning performance was measured via recall of the words from the learning task. After the learning task was completed, the native (Dutch) version of the word was given as a prompt for the participants to fill in the fictional version of the word. The order was randomized between primary and secondary knowledge words. Each correctly spelled word was awarded one point with a maximum possible score of 32 for the performance measure (ICC=0.91).

## Procedure

The main phases of the experiment are illustrated in Fig. 2. The experiment was implemented online through Limesurvey (Limesurvey GmbH n.d.). Participants signed up for the study through the university experiment recruitment portal. To increase participation, an online lottery was offered in the form of a gift voucher worth 50 euro. The participants and experimenter joined the experimental session via MS Teams. A link to the study was shared by the experimenter with the participants. The participants worked independently on their own computers. The assignment of the experiment's link was randomized in terms of growth mindset intervention or control conditions. At the start of the experiment, the participants were first presented with an example of the word learning task. Then, they filled out questionnaire items on growth mindset both before and after they received growth mindset intervention or control task (see “Growth Mindset Intervention” for details). Afterwards, participants completed the learning task with embedded biological primary and secondary knowledge contexts (see “Learning Task” and “Evolutionary Knowledge Type Distinction” under the “Experimental Conditions” section for details). They also completed motivation and cognitive load questionnaire items for each of the eight-word learning lists throughout the learning phase. In the last step, they completed a recall test on the words learned in the learning task (see “Learning Performance” for details).

Ethical guidelines were followed, and approval was obtained from the institutional ethical committee review board.
## Cognitive Load Perceptions

Cognitive load perceptions (Paas, 1992) were assessed with single-item ratings of perceived mental effort and difficulty during the vocabulary task using nine-point Likert response scales (from “not at all” to “very much”). After each vocabulary list, the participants rated the extent to which they perceived their mental effort and found it difficult for learning the words they were just presented with. In total, there were eight items rated for mental effort (ICC=0.94) and eight items rated for difficulty (ICC=0.91).

## Learning Performance

Learning performance was measured via recall of the words from the learning task. After the learning task was completed, the native (Dutch) version of the word was given as a prompt for the participants to fill in the fictional version of the word. The order was randomized between primary and secondary knowledge words. Each correctly spelled word was awarded one point with a maximum possible score of 32 for the performance measure (ICC=0.91).

## Procedure

The main phases of the experiment are illustrated in Fig. 2. The experiment was implemented online through Limesurvey (Limesurvey GmbH n.d.). Participants signed up for the study through the university experiment recruitment portal. To increase participation, an online lottery was offered in the form of a gift voucher worth 50 euro. The participants and experimenter joined the experimental session via MS Teams. A link to the study was shared by the experimenter with the participants. The participants worked independently on their own computers. The assignment of the experiment's link was randomized in terms of growth mindset intervention or control conditions. At the start of the experiment, the participants were first presented with an example of the word learning task. Then, they filled out questionnaire items on growth mindset both before and after they received growth mindset intervention or control task (see “Growth Mindset Intervention” for details). Afterwards, participants completed the learning task with embedded biological primary and secondary knowledge contexts (see “Learning Task” and “Evolutionary Knowledge Type Distinction” under the “Experimental Conditions” section for details). They also completed motivation and cognitive load questionnaire items for each of the eight-word learning lists throughout the learning phase. In the last step, they completed a recall test on the words learned in the learning task (see “Learning Performance” for details).

Ethical guidelines were followed, and approval was obtained from the institutional ethical committee review board.
<figure><img src="image_7.png" /><figcaption>**Fig. 2** Experimental phases</figcaption></figure>

# Results

## The Interaction Between Knowledge Type and Growth Mindset

Firstly, an independent sample t-test was used to check whether participants were randomized across conditions. Results showed that in comparison to the control condition, participants in the growth mindset intervention condition had higher baseline growth mindset beliefs ($t(142) = -2.90, p = 0.004, d = 0.48$; $M = 3.4, SD = 0.7$ for control condition; $M = 3.76, SD = 0.78$ for growth mindset condition). Thus, baseline growth mindset was included as a covariate in subsequent analyses involving growth mindset intervention factor. Furthermore, a potential interaction was checked for between baseline growth mindset and intervention. Controlling for baseline growth mindset, the interaction term in an ANCOVA showed that the effect of the growth mindset was not affected by the baseline growth mindset ($F(1, 140) = 0.62, p = 0.434$); thus, this interaction was not included in subsequent analyses.

Then, to assess the interaction effect between the two main experimental factors, a mixed ANCOVA was used to test whether the effect of growth mindset

<figure><img src="image_8.png" /></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 2 Experimental phases</figcaption></figure>

# Results

## The Interaction Between Knowledge Type and Growth Mindset

Firstly, an independent sample t-test was used to check whether participants were randomized across conditions. Results showed that in comparison to the control condition, participants in the growth mindset intervention condition had higher baseline growth mindset beliefs ($t(142) = -2.90, p = 0.004, d = 0.48$; $M = 3.4, SD = 0.7$ for control condition; $M = 3.76, SD = 0.78$ for growth mindset condition). Thus, baseline growth mindset was included as a covariate in subsequent analyses involving growth mindset intervention factor. Furthermore, a potential interaction was checked for between baseline growth mindset and intervention. Controlling for baseline growth mindset, the interaction term in an ANCOVA showed that the effect of the growth mindset was not affected by the baseline growth mindset ($F(1, 140) = 0.62, p = 0.434$); thus, this interaction was not included in subsequent analyses.

Then, to assess the interaction effect between the two main experimental factors, a mixed ANCOVA was used to test whether the effect of growth mindset

<figure><img src="image_7.png" /></figure>
intervention is dependent on knowledge types. Results indicated there were no significant interaction effects (also see means, marginal means in Table 2), including for interest ($F(1, 141)=2.52, p=0.115$), enjoyment ($F(1, 141)=2.39, p=0.124$), perceived difficulty ($F(1, 141)=2.37, p=0.126$), perceived effort ($F(1, 141)=0.09, p=0.769$), or recall performance ($F(1, 140)=1.38, p=0.243$).

Since there was no interaction effect between growth mindset intervention and knowledge type in any of the dependent variables, the following result sections proceeded to present the analysis of the effects of knowledge type and growth mindset intervention separately, without the interaction term. The analysis for growth mindset intervention was based on one-way ANCOVA. The analysis for knowledge type was based on the paired sample *t*-test (equivalent to one-way ANOVA when no covariate is included).

## The Effect of Knowledge Type

Paired sample *t*-tests showed that in comparison to secondary knowledge words (Table 3), participants rated learning primary knowledge words as more interesting ($t=4.53, p<0.001, d=0.38$; $M=4.56, SD=1.79$ for primary knowledge words; $M=4.32, SD=1.80$ for secondary knowledge words), more enjoyable ($t=5.83, p<0.001, d=0.49$; $M=4.51, SD=1.72$ for primary knowledge words; $M=4.13, SD=1.72$ for secondary knowledge words), and experienced less difficulty ($t=-11.48, p<0.001, d=-0.96$; $M=4.89, SD=1.41$ for primary knowledge words; $M=5.65, SD=1.49$ for secondary knowledge words) and less effort ($t=-9.35, p<0.001, d=-0.78$; $M=5.38, SD=1.49$ for primary knowledge words; $M=5.89, SD=1.45$ for secondary knowledge words). They also recalled more primary knowledge words than secondary knowledge words ($t=5.85, p<0.001, d=0.49$; $M=7.29, SD=3.99$ for primary knowledge words; $M=6.08, SD=4.17$ for secondary knowledge words).

## The Effect of Fostering Growth Mindset

Further ANCOVA analyses revealed no differences across the growth mindset and control groups for the outcomes (Table 4), including interest ($F(1, 141)=1.02, p=0.315, d=0.27, M=4.20, SD=1.63$ for control condition; $M=4.68, SD=1.87$ for growth mindset condition), enjoyment ($F(1, 141)=1.58, p=0.211, d=0.29, M=4.08, SD=1.52$ for control condition; $M=4.56, SD=1.79$ for growth mindset condition), difficulty ($F(1, 141)=0.97, p=0.325, d=-0.07, M=5.32, SD=1.26$ for control condition; $M=5.22, SD=1.52$ for growth mindset condition), mental effort ($F(1, 141)=0.47, p=0.496, d=0.02, M=5.62, SD=1.39$ for control condition; $M=5.65, SD=1.49$ for growth mindset condition), or word recall performance ($F(1, 140)=0.24, p=0.623, d=0.05, M=13.15, SD=7.82$ for control condition; $M=13.57, SD=7.77$ for growth mindset condition).
intervention is dependent on knowledge types. Results indicated there were no significant interaction effects (also see means, marginal means in Table 2), including for interest ($F(1, 141)=2.52, p=0.115$), enjoyment ($F(1, 141)=2.39, p=0.124$), perceived difficulty ($F(1, 141)=2.37, p=0.126$), perceived effort ($F(1, 141)=0.09, p=0.769$), or recall performance ($F(1, 140)=1.38, p=0.243$).

Since there was no interaction effect between growth mindset intervention and knowledge type in any of the dependent variables, the following result sections proceeded to present the analysis of the effects of knowledge type and growth mindset intervention separately, without the interaction term. The analysis for growth mindset intervention was based on one-way ANCOVA. The analysis for knowledge type was based on the paired sample t-test (equivalent to one-way ANOVA when no covariate is included).

## The Effect of Knowledge Type

Paired sample t-tests showed that in comparison to secondary knowledge words (Table 3), participants rated learning primary knowledge words as more interesting ($t=4.53, p<0.001, d=0.38$; $M=4.56, SD=1.79$ for primary knowledge words; $M=4.32, SD=1.80$ for secondary knowledge words), more enjoyable ($t=5.83, p<0.001, d=0.49$; $M=4.51, SD=1.72$ for primary knowledge words; $M=4.13, SD=1.72$ for secondary knowledge words), and experienced less difficulty ($t=-11.48, p<0.001, d=-0.96$; $M=4.89, SD=1.41$ for primary knowledge words; $M=5.65, SD=1.49$ for secondary knowledge words) and less effort ($t=-9.35, p<0.001, d=-0.78$; $M=5.38, SD=1.49$ for primary knowledge words; $M=5.89, SD=1.45$ for secondary knowledge words). They also recalled more primary knowledge words than secondary knowledge words ($t=5.85, p<0.001, d=0.49$; $M=7.29, SD=3.99$ for primary knowledge words; $M=6.08, SD=4.17$ for secondary knowledge words).

## The Effect of Fostering Growth Mindset

Further ANCOVA analyses revealed no differences across the growth mindset and control groups for the outcomes (Table 4), including interest ($F(1, 141)=1.02, p=0.315, d=0.27, M=4.20, SD=1.63$ for control condition; $M=4.68, SD=1.87$ for growth mindset condition), enjoyment ($F(1, 141)=1.58, p=0.211, d=0.29, M=4.08, SD=1.52$ for control condition; $M=4.56, SD=1.79$ for growth mindset condition), difficulty ($F(1, 141)=0.97, p=0.325, d=-0.07, M=5.32, SD=1.26$ for control condition; $M=5.22, SD=1.52$ for growth mindset condition), mental effort ($F(1, 141)=0.47, p=0.496, d=0.02, M=5.62, SD=1.39$ for control condition; $M=5.65, SD=1.49$ for growth mindset condition), or word recall performance ($F(1, 140)=0.24, p=0.623, d=0.05, M=13.15, SD=7.82$ for control condition; $M=13.57, SD=7.77$ for growth mindset condition).
<table><caption>**Table 2** Means, marginal means, and interaction effect between growth mindset condition and knowledge type condition</caption><thead><tr><th rowspan="2">Outcome variables</th><th rowspan="2">Evolution distinction</th><th colspan="2">Marginal means</th><th colspan="2">Control condition</th><th colspan="2">Mindset condition</th><th colspan="4">Mixed ANCOVA interaction effect (knowledge type × mindset intervention)</th></tr><tr><th>N</th><th>Mean</th><th>SD</th><th>N</th><th>Mean</th><th>SD</th><th>N</th><th>Mean</th><th>SD</th><th>dF1</th><th>dF2</th><th>F</th><th>p</th></tr></thead><tbody><tr><td rowspan="3">Interest</td><td>Marginal means</td><td>144</td><td></td><td>72</td><td>4.20</td><td>1.63</td><td>72</td><td>4.68</td><td>1.87</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>144</td><td>4.56</td><td>1.79</td><td>72</td><td>4.36</td><td>1.69</td><td>72</td><td>4.76</td><td>1.87</td><td>1</td><td>141</td><td>2.515</td><td>0.115</td></tr><tr><td>Secondary</td><td>144</td><td>4.32</td><td>1.8</td><td>72</td><td>4.04</td><td>1.64</td><td>72</td><td>4.60</td><td>1.91</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Enjoyment</td><td>Marginal means</td><td>144</td><td></td><td>72</td><td>4.08</td><td>1.52</td><td>72</td><td>4.56</td><td>1.79</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>144</td><td>4.51</td><td>1.72</td><td>72</td><td>4.32</td><td>1.64</td><td>72</td><td>4.70</td><td>1.78</td><td>1</td><td>141</td><td>2.389</td><td>0.124</td></tr><tr><td>Secondary</td><td>144</td><td>4.13</td><td>1.72</td><td>72</td><td>3.85</td><td>1.53</td><td>72</td><td>4.42</td><td>1.85</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Perceived difficulty</td><td>Marginal means</td><td>144</td><td></td><td>72</td><td>5.32</td><td>1.26</td><td>72</td><td>5.22</td><td>1.52</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>144</td><td>4.89</td><td>1.41</td><td>72</td><td>4.90</td><td>1.27</td><td>72</td><td>4.88</td><td>1.54</td><td>1</td><td>141</td><td>2.368</td><td>0.126</td></tr><tr><td>Secondary</td><td>144</td><td>5.65</td><td>1.49</td><td>72</td><td>5.75</td><td>1.37</td><td>72</td><td>5.56</td><td>1.59</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Perceived effort</td><td>Marginal means</td><td>144</td><td></td><td>72</td><td>5.62</td><td>1.39</td><td>72</td><td>5.65</td><td>1.49</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>144</td><td>5.38</td><td>1.49</td><td>72</td><td>5.32</td><td>1.41</td><td>72</td><td>5.44</td><td>1.57</td><td>1</td><td>141</td><td>0.087</td><td>0.769</td></tr><tr><td>Secondary</td><td>144</td><td>5.89</td><td>1.45</td><td>72</td><td>5.92</td><td>1.44</td><td>72</td><td>5.85</td><td>1.48</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Recall performance</td><td>Marginal means</td><td>143</td><td></td><td>71</td><td>13.15</td><td>7.82</td><td>72</td><td>13.57</td><td>7.77</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>143</td><td>7.29</td><td>3.99</td><td>71</td><td>7.27</td><td>4.01</td><td>72</td><td>7.30</td><td>3.99</td><td>1</td><td>140</td><td>1.377</td><td>0.243</td></tr><tr><td>Secondary</td><td>143</td><td>6.08</td><td>4.17</td><td>71</td><td>5.88</td><td>4.27</td><td>72</td><td>6.26</td><td>4.08</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note. Baseline growth mindset belief was included as a covariate

<figure><img src="image_9.png" /></figure>
<table><caption>Table 2 Means, marginal means, and interaction effect between growth mindset condition and knowledge type condition</caption><thead><tr><th rowspan="2">Outcome variables</th><th rowspan="2">Evolution distinction</th><th colspan="2">Marginal means</th><th colspan="2">Control condition</th><th colspan="2">Mindset condition</th><th colspan="4">Mixed ANCOVA interaction effect (knowledge type × mindset intervention)</th></tr><tr><th>N</th><th>Mean</th><th>SD</th><th>N</th><th>Mean</th><th>SD</th><th>N</th><th>Mean</th><th>SD</th><th>dF1</th><th>dF2</th><th>F</th><th>p</th></tr></thead><tbody><tr><td rowspan="3">Interest</td><td>Marginal means</td><td>144</td><td></td><td>72</td><td>4.20</td><td>1.63</td><td>72</td><td>4.68</td><td>1.87</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>144</td><td>4.56</td><td>1.79</td><td>72</td><td>4.36</td><td>1.69</td><td>72</td><td>4.76</td><td>1.87</td><td>1</td><td>141</td><td>2.515</td><td>0.115</td></tr><tr><td>Secondary</td><td>144</td><td>4.32</td><td>1.8</td><td>72</td><td>4.04</td><td>1.64</td><td>72</td><td>4.60</td><td>1.91</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Enjoyment</td><td>Marginal means</td><td>144</td><td></td><td>72</td><td>4.08</td><td>1.52</td><td>72</td><td>4.56</td><td>1.79</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>144</td><td>4.51</td><td>1.72</td><td>72</td><td>4.32</td><td>1.64</td><td>72</td><td>4.70</td><td>1.78</td><td>1</td><td>141</td><td>2.389</td><td>0.124</td></tr><tr><td>Secondary</td><td>144</td><td>4.13</td><td>1.72</td><td>72</td><td>3.85</td><td>1.53</td><td>72</td><td>4.42</td><td>1.85</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Perceived difficulty</td><td>Marginal means</td><td>144</td><td></td><td>72</td><td>5.32</td><td>1.26</td><td>72</td><td>5.22</td><td>1.52</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>144</td><td>4.89</td><td>1.41</td><td>72</td><td>4.90</td><td>1.27</td><td>72</td><td>4.88</td><td>1.54</td><td>1</td><td>141</td><td>2.368</td><td>0.126</td></tr><tr><td>Secondary</td><td>144</td><td>5.65</td><td>1.49</td><td>72</td><td>5.75</td><td>1.37</td><td>72</td><td>5.56</td><td>1.59</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Perceived effort</td><td>Marginal means</td><td>144</td><td></td><td>72</td><td>5.62</td><td>1.39</td><td>72</td><td>5.65</td><td>1.49</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>144</td><td>5.38</td><td>1.49</td><td>72</td><td>5.32</td><td>1.41</td><td>72</td><td>5.44</td><td>1.57</td><td>1</td><td>141</td><td>0.087</td><td>0.769</td></tr><tr><td>Secondary</td><td>144</td><td>5.89</td><td>1.45</td><td>72</td><td>5.92</td><td>1.44</td><td>72</td><td>5.85</td><td>1.48</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Recall performance</td><td>Marginal means</td><td>143</td><td></td><td>71</td><td>13.15</td><td>7.82</td><td>72</td><td>13.57</td><td>7.77</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>143</td><td>7.29</td><td>3.99</td><td>71</td><td>7.27</td><td>4.01</td><td>72</td><td>7.30</td><td>3.99</td><td>1</td><td>140</td><td>1.377</td><td>0.243</td></tr><tr><td>Secondary</td><td>143</td><td>6.08</td><td>4.17</td><td>71</td><td>5.88</td><td>4.27</td><td>72</td><td>6.26</td><td>4.08</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note. Baseline growth mindset belief was included as a covariate

<figure><img src="image_8.png" /></figure>
<table><caption>**Table 3** Outcomes by knowledge types</caption><thead><tr><th rowspan="2">Outcome variables</th><th rowspan="2"></th><th rowspan="2">Evolutionary<br>distinction</th><th rowspan="2">N</th><th rowspan="2">Mean</th><th rowspan="2">SD</th><th rowspan="2">Cohen's d</th><th colspan="3">Paired sample t-test</th></tr><tr><th>t</th><th>df</th><th>p</th></tr></thead><tbody><tr><td rowspan="2">Interest</td><td>Primary</td><td>144</td><td>4.56</td><td>1.79</td><td rowspan="2">0.38</td><td rowspan="2">4.53</td><td rowspan="2">143</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Secondary</td><td>144</td><td>4.32</td><td>1.80</td></tr><tr><td rowspan="2">Enjoyment</td><td>Primary</td><td>144</td><td>4.51</td><td>1.72</td><td rowspan="2">0.49</td><td rowspan="2">5.83</td><td rowspan="2">143</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Secondary</td><td>144</td><td>4.13</td><td>1.72</td></tr><tr><td rowspan="2">Perceived difficulty</td><td>Primary</td><td>144</td><td>4.89</td><td>1.41</td><td rowspan="2">-0.96</td><td rowspan="2">-11.48</td><td rowspan="2">143</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Secondary</td><td>144</td><td>5.65</td><td>1.49</td></tr><tr><td rowspan="2">Perceived effort</td><td>Primary</td><td>144</td><td>5.38</td><td>1.49</td><td rowspan="2">-0.78</td><td rowspan="2">-9.35</td><td rowspan="2">143</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Secondary</td><td>144</td><td>5.89</td><td>1.45</td></tr><tr><td rowspan="2">Recall performance</td><td>Primary</td><td>143</td><td>7.29</td><td>3.99</td><td rowspan="2">0.49</td><td rowspan="2">5.86</td><td rowspan="2">142</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Secondary</td><td>143</td><td>6.08</td><td>4.17</td></tr></tbody></table>

Discussion

An evolutionary approach to learning predicts differences in the ease of and motivation to learn evolutionarily relevant or biologically primary knowledge and evolutionarily novel or biologically secondary knowledge (Geary, 2008). The latter largely refers to knowledge and skills needed to be successful in developed economies and largely taught through organized schooling. Results from the current experimental study confirmed these predictions for adults' learning of vocabulary with evolutionarily relevant versus evolutionarily novel content. We further hypothesized that a motivation intervention focused on increasing growth mindset and effort investment would benefit secondary knowledge vocabulary learning more than primary vocabulary learning, but this was not supported by the findings of this study.

Participants found vocabularies representing primary knowledge categories to be more interesting, more enjoyable to learn, and less difficult and effortful compared to learning vocabularies representing secondary knowledge. Moreover, primary knowledge words also were recalled better than secondary knowledge words. These results are consistent with previous research based on logic learning (Lespiau & Tricot, 2019) and also to some extent for statistics learning (Lespiau & Tricot, 2022b). Since the biologically primary knowledge concerns fitness and survival-related activities, the current findings are also in agreement with the literature in survival processing where words associated with living things are often better recalled than words associated with non-living things (Nairne, 2022; VanArsdall et al., 2015). The present study broadens the scope of living/non-living concepts to include more broadly defined primary and secondary knowledge types and further generalizes the knowledge distinction from logic/statistics to vocabulary learning. The present study suggests a promising approach to utilize the motivational and cognitive advantages of primary knowledge to design language learning instruction for adults. For example, it may be more motivating to learn vocabularies when the curricula first present primary knowledge content (Lespiau & Tricot, 2022a) and perhaps also set contexts
<table><caption>**Table 3** Outcomes by knowledge types</caption><thead><tr><th rowspan="2">Outcome variables</th><th rowspan="2"></th><th rowspan="2">Evolutionary<br>distinction</th><th rowspan="2">N</th><th rowspan="2">Mean</th><th rowspan="2">SD</th><th rowspan="2">Cohen's d</th><th colspan="3">Paired sample t-test</th></tr><tr><th>t</th><th>df</th><th>p</th></tr></thead><tbody><tr><td rowspan="2">Interest</td><td>Primary</td><td>144</td><td>4.56</td><td>1.79</td><td rowspan="2">0.38</td><td rowspan="2">4.53</td><td rowspan="2">143</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Secondary</td><td>144</td><td>4.32</td><td>1.80</td></tr><tr><td rowspan="2">Enjoyment</td><td>Primary</td><td>144</td><td>4.51</td><td>1.72</td><td rowspan="2">0.49</td><td rowspan="2">5.83</td><td rowspan="2">143</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Secondary</td><td>144</td><td>4.13</td><td>1.72</td></tr><tr><td rowspan="2">Perceived difficulty</td><td>Primary</td><td>144</td><td>4.89</td><td>1.41</td><td rowspan="2">-0.96</td><td rowspan="2">-11.48</td><td rowspan="2">143</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Secondary</td><td>144</td><td>5.65</td><td>1.49</td></tr><tr><td rowspan="2">Perceived effort</td><td>Primary</td><td>144</td><td>5.38</td><td>1.49</td><td rowspan="2">-0.78</td><td rowspan="2">-9.35</td><td rowspan="2">143</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Secondary</td><td>144</td><td>5.89</td><td>1.45</td></tr><tr><td rowspan="2">Recall performance</td><td>Primary</td><td>143</td><td>7.29</td><td>3.99</td><td rowspan="2">0.49</td><td rowspan="2">5.86</td><td rowspan="2">142</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Secondary</td><td>143</td><td>6.08</td><td>4.17</td></tr></tbody></table>

Discussion

An evolutionary approach to learning predicts differences in the ease of and motivation to learn evolutionarily relevant or biologically primary knowledge and evolutionarily novel or biologically secondary knowledge (Geary, 2008). The latter largely refers to knowledge and skills needed to be successful in developed economies and largely taught through organized schooling. Results from the current experimental study confirmed these predictions for adults' learning of vocabulary with evolutionarily relevant versus evolutionarily novel content. We further hypothesized that a motivation intervention focused on increasing growth mindset and effort investment would benefit secondary knowledge vocabulary learning more than primary vocabulary learning, but this was not supported by the findings of this study.

Participants found vocabularies representing primary knowledge categories to be more interesting, more enjoyable to learn, and less difficult and effortful compared to learning vocabularies representing secondary knowledge. Moreover, primary knowledge words also were recalled better than secondary knowledge words. These results are consistent with previous research based on logic learning (Lespiau & Tricot, 2019) and also to some extent for statistics learning (Lespiau & Tricot, 2022b). Since the biologically primary knowledge concerns fitness and survival-related activities, the current findings are also in agreement with the literature in survival processing where words associated with living things are often better recalled than words associated with non-living things (Nairne, 2022; VanArsdall et al., 2015). The present study broadens the scope of living/non-living concepts to include more broadly defined primary and secondary knowledge types and further generalizes the knowledge distinction from logic/statistics to vocabulary learning. The present study suggests a promising approach to utilize the motivational and cognitive advantages of primary knowledge to design language learning instruction for adults. For example, it may be more motivating to learn vocabularies when the curricula first present primary knowledge content (Lespiau & Tricot, 2022a) and perhaps also set contexts
<table><caption>**Table 4** Outcomes by a growth mindset intervention</caption><thead><tr><th rowspan="2"></th><th colspan="2">Control</th><th colspan="2">Mindset</th><th colspan="2">Cohen's d</th><th colspan="2">ANCOVA*</th><th rowspan="2">p</th></tr><tr><th>N</th><th>Mean</th><th>SD</th><th>N</th><th>Mean</th><th>SD</th><th>d|1</th><th>d|2</th><th>F</th></tr></thead><tbody><tr><td>Growth mindset post-intervention</td><td>72</td><td>3.46</td><td>0.72</td><td>72</td><td>4.22</td><td>0.94</td><td>0.91</td><td>1</td><td>141</td><td>22.87</td><td>&lt; 0.001</td></tr><tr><td>Interest</td><td>72</td><td>4.20</td><td>1.63</td><td>72</td><td>4.68</td><td>1.87</td><td>0.27</td><td>1</td><td>141</td><td>1.02</td><td>0.315</td></tr><tr><td>Enjoyment</td><td>72</td><td>4.08</td><td>1.52</td><td>72</td><td>4.56</td><td>1.79</td><td>0.29</td><td>1</td><td>141</td><td>1.58</td><td>0.211</td></tr><tr><td>Perceived difficulty</td><td>72</td><td>5.32</td><td>1.26</td><td>72</td><td>5.22</td><td>1.52</td><td>-0.07</td><td>1</td><td>141</td><td>0.97</td><td>0.325</td></tr><tr><td>Perceived effort</td><td>72</td><td>5.62</td><td>1.39</td><td>72</td><td>5.65</td><td>1.49</td><td>0.02</td><td>1</td><td>141</td><td>0.47</td><td>0.496</td></tr><tr><td>Recall performance*</td><td>71</td><td>13.15</td><td>7.82</td><td>72</td><td>13.57</td><td>7.77</td><td>0.05</td><td>1</td><td>140</td><td>0.24</td><td>0.623</td></tr></tbody></table>

Note: For ANCOVA analysis, baseline growth mindset belief was included as a covariate

*The recall performance was the sum score from the primary knowledge content and secondary knowledge content

<figure><img src="image_10.png" /></figure>
<table><caption>**Table 4** Outcomes by a growth mindset intervention</caption><thead><tr><th rowspan="2"></th><th colspan="2">Control</th><th colspan="2">Mindset</th><th colspan="2">Cohen's d</th><th colspan="2">ANCOVA*</th><th rowspan="2">p</th></tr><tr><th>N</th><th>Mean</th><th>SD</th><th>N</th><th>Mean</th><th>SD</th><th>d|1</th><th>d|2</th><th>F</th></tr></thead><tbody><tr><td>Growth mindset post-intervention</td><td>72</td><td>3.46</td><td>0.72</td><td>72</td><td>4.22</td><td>0.94</td><td>0.91</td><td>1</td><td>141</td><td>22.87</td><td>&lt;0.001</td></tr><tr><td>Interest</td><td>72</td><td>4.20</td><td>1.63</td><td>72</td><td>4.68</td><td>1.87</td><td>0.27</td><td>1</td><td>141</td><td>1.02</td><td>0.315</td></tr><tr><td>Enjoyment</td><td>72</td><td>4.08</td><td>1.52</td><td>72</td><td>4.56</td><td>1.79</td><td>0.29</td><td>1</td><td>141</td><td>1.58</td><td>0.211</td></tr><tr><td>Perceived difficulty</td><td>72</td><td>5.32</td><td>1.26</td><td>72</td><td>5.22</td><td>1.52</td><td>-0.07</td><td>1</td><td>141</td><td>0.97</td><td>0.325</td></tr><tr><td>Perceived effort</td><td>72</td><td>5.62</td><td>1.39</td><td>72</td><td>5.65</td><td>1.49</td><td>0.02</td><td>1</td><td>141</td><td>0.47</td><td>0.496</td></tr><tr><td>Recall performance*</td><td>71</td><td>13.15</td><td>7.82</td><td>72</td><td>13.57</td><td>7.77</td><td>0.05</td><td>1</td><td>140</td><td>0.24</td><td>0.623</td></tr></tbody></table>

Note: For ANCOVA analysis, baseline growth mindset belief was included as a covariate

*The recall performance was the sum score from the primary knowledge content and secondary knowledge content

<figure><img src="image_9.png" /></figure>
related to these themes (Nairne, 2022; see also studies using survival versus nonsurvival contexts Nairne et al., 2019; Nairne & Pandeirada, 2010; Raymaekers et al., 2014).

Despite of the hypothesis that a growth mindset intervention focusing on effort investment may be especially beneficial for learning vocabularies representing biologically secondary knowledge, results showed no difference in motivation and performance between the intervention and control conditions for learning associated with either knowledge types. It was also predicted that an intervention focusing on the importance of effortful practice would reduce the perceived cognitive load of secondary knowledge learning. This hypothesis was not supported in the current study. The finding is inconsistent with a previous experimental study where a growth mindset intervention increased motivation and performance and lowered perceived cognitive load for a learning task based on a physics topic (Xu et al., 2021). We refer to this study specifically, because most growth mindset research has been based on semester-long interventions (Burnette et al., 2022; Macnamara & Burgoyne, 2022) rather than short-term experiments.

Although both the previous study (Xu et al., 2021) and the current one were based on Dutch samples, there are important differences. The earlier study was based on a sample of vocational secondary education students and a physics topic learning task (i.e., about the Doppler effect) whereas the current study was based on a sample of academic university participants and a vocabulary learning task. In the Netherlands, vocational education is less selective academically than university education, like the institution where participants in the current study were enrolled. Thus, it is possible that the growth mindset intervention was less effective for the more academically advanced participants in the current study. University students presumably are well aware of the importance of effort and persistence which has enabled them to attend a prestigious university. In fact, this is also suggested by recent growth mindset literature that such an intervention is more effective for students who come from less advantaged background and who are performing less well (Burnette et al., 2022). Thus, this may explain, from a short intervention, why in the current study sample, the effect of the growth mindset intervention did not differentiate between learning vocabularies across biologically primary and secondary knowledge categories.

In terms of broad educational implications, an interesting point is whether the motivating nature of biologically primary knowledge can be used to formulate educational approaches in ways to make secondary knowledge learning intrinsically motivating (Bjorklund, 2022), for example, incorporating “productive failure” as a method of learning that guides children to learn by self-exploration (Kapur, 2016; Niu et al., 2021). The notion of productive failure is in line with the features of primary knowledge according to the evolutionary theoretical framework. This idea has sparked interesting debates as to whether this is a beneficial practice (Kirschner et al., 2006; Sweller, 2022). In his review, Bjorklund examined relevant literature and specifically suggested “guided play” as more beneficial than both direct instruction and play without any guidance (Bjorklund, 2022), at least for young children. However, “play” and “exploration” are not the only approaches that have an evolutionary basis. In evolutionary context, it has also been shown that learning can be
related to these themes (Nairne, 2022; see also studies using survival versus nonsurvival contexts Nairne et al., 2019; Nairne & Pandeirada, 2010; Raymaekers et al., 2014).

Despite of the hypothesis that a growth mindset intervention focusing on effort investment may be especially beneficial for learning vocabularies representing biologically secondary knowledge, results showed no difference in motivation and performance between the intervention and control conditions for learning associated with either knowledge types. It was also predicted that an intervention focusing on the importance of effortful practice would reduce the perceived cognitive load of secondary knowledge learning. This hypothesis was not supported in the current study. The finding is inconsistent with a previous experimental study where a growth mindset intervention increased motivation and performance and lowered perceived cognitive load for a learning task based on a physics topic (Xu et al., 2021). We refer to this study specifically, because most growth mindset research has been based on semester-long interventions (Burnette et al., 2022; Macnamara & Burgoyne, 2022) rather than short-term experiments.

Although both the previous study (Xu et al., 2021) and the current one were based on Dutch samples, there are important differences. The earlier study was based on a sample of vocational secondary education students and a physics topic learning task (i.e., about the Doppler effect) whereas the current study was based on a sample of academic university participants and a vocabulary learning task. In the Netherlands, vocational education is less selective academically than university education, like the institution where participants in the current study were enrolled. Thus, it is possible that the growth mindset intervention was less effective for the more academically advanced participants in the current study. University students presumably are well aware of the importance of effort and persistence which has enabled them to attend a prestigious university. In fact, this is also suggested by recent growth mindset literature that such an intervention is more effective for students who come from less advantaged background and who are performing less well (Burnette et al., 2022). Thus, this may explain, from a short intervention, why in the current study sample, the effect of the growth mindset intervention did not differentiate between learning vocabularies across biologically primary and secondary knowledge categories.

In terms of broad educational implications, an interesting point is whether the motivating nature of biologically primary knowledge can be used to formulate educational approaches in ways to make secondary knowledge learning intrinsically motivating (Bjorklund, 2022), for example, incorporating “productive failure” as a method of learning that guides children to learn by self-exploration (Kapur, 2016; Niu et al., 2021). The notion of productive failure is in line with the features of primary knowledge according to the evolutionary theoretical framework. This idea has sparked interesting debates as to whether this is a beneficial practice (Kirschner et al., 2006; Sweller, 2022). In his review, Bjorklund examined relevant literature and specifically suggested “guided play” as more beneficial than both direct instruction and play without any guidance (Bjorklund, 2022), at least for young children. However, “play” and “exploration” are not the only approaches that have an evolutionary basis. In evolutionary context, it has also been shown that learning can be
improved from incorporating primary knowledge skills such as human motor systems involving body movement and hand gestures (Choi et al., 2014; Ginns & King, 2021).

In sum, the present study contributes to the developing field of evolutionary educational psychology, which is already showing promise to better understand and improve academic learning and instruction. We also note that many studies, if not all, are lab-based, short-term experimental research. As such short-term experiments may fundamentally differ from regular curricula learning, the field can benefit from future more classroom-based, intervention research (see Alipour et al., 2023).

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s10648-024-09880-3.

## Declarations

**Conflict of Interest** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

Alipour, M., Aminifar, E., Geary, D. C., & Ebrahimpour, R. (2023). Framing mathematical content in evolutionarily salient contexts improves students' learning motivation. *Learning and Motivation, 82*, 101894. https://doi.org/10.1016/j.lmot.2023.101894

Atran, S. (1998). Folk biology and the anthropology of science: Cognitive universals and cultural particulars. *Behavioral and Brain Sciences, 21*(4), 547–569. https://doi.org/10.1017/S0140525X98001277

Baayen, R. H., Piepenbrock, R., & Gulikers, L. (Eds.). (1996). *The CELEX lexical database* (cd-rom). University of Pennsylvania.

Bjorklund, D. F. (2022). Children's evolved learning abilities and their implications for education. *Educational Psychology Review, 34*(4), 2243–2273. https://doi.org/10.1007/s10648-022-09688-z

Brysbaert, M., Stevens, M., De Deyne, S., Voorspoels, W., & Storms, G. (2014). Norms of age of acquisition and concreteness for 30,000 Dutch words. *Acta Psychologica, 150*, 80–84.

Burnette, J. L., Billingsley, J., Banks, G. C., Knouse, L. E., Hoyt, C. L., Pollack, J. M., & Simon, S. (2022). A systematic review and meta-analysis of growth mindset interventions: For whom, how, and why might such interventions work?. *Psychological Bulletin*.

Canning, E. A., & Limeri, L. B. (2023). Theoretical and methodological directions in mindset intervention research. *Social and Personality Psychology Compass, e12758*.

Choi, H.-H., Van Merriënboer, J. J., & Paas, F. (2014). Effects of the physical environment on cognitive load and learning: towards a new model of cognitive load. *Educational Psychology Review, 26*(2), 225–244.

De Groot, A. M., & Keijzer, R. (2000). What is hard to learn is easy to forget: The roles of word concreteness, cognate status, and word frequency in foreign-language vocabulary learning and forgetting. *Language Learning, 50*(1), 1–56.

Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. *Educational Psychologist, 26*(3–4), 325–346.
improved from incorporating primary knowledge skills such as human motor systems involving body movement and hand gestures (Choi et al., 2014; Ginns & King, 2021).

In sum, the present study contributes to the developing field of evolutionary educational psychology, which is already showing promise to better understand and improve academic learning and instruction. We also note that many studies, if not all, are lab-based, short-term experimental research. As such short-term experiments may fundamentally differ from regular curricula learning, the field can benefit from future more classroom-based, intervention research (see Alipour et al., 2023).

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s10648-024-09880-3.

## Declarations

**Conflict of Interest** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

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Baayen, R. H., Piepenbrock, R., & Gulikers, L. (Eds.). (1996). *The CELEX lexical database* (cd-rom). University of Pennsylvania.

Bjorklund, D. F. (2022). Children's evolved learning abilities and their implications for education. *Educational Psychology Review, 34*(4), 2243–2273. https://doi.org/10.1007/s10648-022-09688-z

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Burnette, J. L., Billingsley, J., Banks, G. C., Knouse, L. E., Hoyt, C. L., Pollack, J. M., & Simon, S. (2022). A systematic review and meta-analysis of growth mindset interventions: For whom, how, and why might such interventions work?. *Psychological Bulletin*.

Canning, E. A., & Limeri, L. B. (2023). Theoretical and methodological directions in mindset intervention research. *Social and Personality Psychology Compass, e12758*.

Choi, H.-H., Van Merriënboer, J. J., & Paas, F. (2014). Effects of the physical environment on cognitive load and learning: towards a new model of cognitive load. *Educational Psychology Review, 26*(2), 225–244.

De Groot, A. M., & Keijzer, R. (2000). What is hard to learn is easy to forget: The roles of word concreteness, cognate status, and word frequency in foreign-language vocabulary learning and forgetting. *Language Learning, 50*(1), 1–56.

Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. *Educational Psychologist, 26*(3–4), 325–346.
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Gray, P. (2011). The decline of play and the rise of psychopathology in children and adolescents. *American Journal of Play, 3*(4), 443–463.

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Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. *Psychological Review, 95*(2), 256–273. https://doi.org/10.5539/jel.v9n2p29

Eskelson, T. C. (2020). How and why formal education originated in the emergence of civilization. *Journal of Education and Learning, 9*(2), 29–47.

Geary, D. C. (1995). Reflections of evolution and culture in children's cognition: Implications for mathematical development and instruction. *American Psychologist, 50*(1), 24.

Geary, D. C. (2002). Principles of evolutionary educational psychology. *Learning and Individual Differences, 12*(4), 317–345.

Geary, D. C. (2005). *The origin of mind: Evolution of brain, cognition, and general intelligence*. American Psychological Association.

Geary, D. C. (2008). An evolutionarily informed education science. *Educational Psychologist, 43*(4), 179–195.

Geary, D. C. (2024). *The evolved mind and modern education: Status of evolutionary educational psychology*. Cambridge University Press.

Geary, D. C., & Bjorklund, D. F. (2000). Evolutionary developmental psychology. *Child Development, 71*(1), 57–65.

Geary, D. C., & Xu, K. M. (2022a). Evolution of Self-Awareness and the Cultural Emergence of Academic and Non-academic Self-Concepts. *Educational Psychology Review, 34*, 2323–2349. https://doi.org/10.1007/s10648-022-09669-2

Geary, D. C., & Xu, K. M. (2022b). Evolutionary Perspectives on Educational Psychology: Motivation, Instructional Design, and Child Development. *Educational Psychology Review, 34*(4), 2221–2227. https://doi.org/10.1007/s10648-022-09710-4

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Ginns, P., & King, V. (2021). Pointing and tracing enhance computer-based learning. *Educational Technology Research and Development, 1*–17.

Gnambs, T., & Hanfstingl, B. (2016). The decline of academic motivation during adolescence: An accelerated longitudinal cohort analysis on the effect of psychological need satisfaction. *Educational Psychology, 36*(9), 1691–1705.

Goldin, C. (1999). A brief history of education in the United States (0898–2937). In.

Gottfried, A. E., Fleming, J. S., & Gottfried, A. W. (2001). Continuity of academic intrinsic motivation from childhood through late adolescence: A longitudinal study. *Journal of Educational Psychology, 93*(1), 3.

Gray, P. (2011). The decline of play and the rise of psychopathology in children and adolescents. *American Journal of Play, 3*(4), 443–463.

Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. *Educational Psychologist, 51*(2), 289–299.

Karantzoulis, S., Scorpio, K., Borod, J. C., & Bender, H. A. (2011). Paired-associate learning. In J. S. Kreutzer, J. DeLuca, & B. Caplan (Eds.), *Encyclopedia of Clinical Neuropsychology*. Cham: Springer (Vol. 10, pp. 1949–1850). Springer. https://doi.org/10.1007/978-0-387-79948-3_1137

Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. *Educational Psychologist, 41*(2), 75–86.

Laurino, J., & Kaczer, L. (2019). Animacy as a memory enhancer during novel word learning: Evidence from orthographic and semantic memory tasks. *Memory, 27*(6), 820–828.

Leslie, A. M., Friedman, O., & German, T. P. (2004). Core mechanisms in ‘theory of mind.’ *Trends in Cognitive Sciences, 8*(12), 528–533. https://doi.org/10.1016/j.tics.2004.10.001

Lespiau, F., & Tricot, A. (2018). Primary knowledge enhances performance and motivation in reasoning. *Learning and Instruction, 56*(1), 10–19.

Lespiau, F., & Tricot, A. (2019). Using Primary Knowledge: an Efficient Way To Motivate Students and Promote the Learning of Formal Reasoning. *Educational Psychology Review, 1*–24.

Lespiau, F., & Tricot, A. (2022a). Primary vs. secondary knowledge contents in reasoning: Motivated and efficient vs. overburdened. *Acta Psychologica, 227*, 1036–1040.

Lespiau, F., & Tricot, A. (2022b). Using primary knowledge in unpopular statistics exercises. *Educational Psychology Review, 34*(4), 2297–2322.
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Macnamara, B. N., & Burgoyne, A. P. (2022). Do growth mindset interventions impact students' academic achievement? A systematic review and meta-analysis with recommendations for best practices. *Psychological Bulletin*.

Marsh, H. W., Xu, K. M., Parker, P. D., Hau, K.-T., Pekrun, R., Elliot, A., ... Basarkod, G. (2021). Moderation of the Big-Fish-Little-Pond Effect: Juxtaposition of Evolutionary (Darwinian-Economic) and Achievement Motivation Theory Predictions Based on a Delphi Approach. *Educational Psychology Review, 33*(4), 1353-1378. https://doi.org/10.1007/s10648-020-09583-5

Moll, H., & Tomasello, M. (2007). Cooperation and human cognition: The Vygotskian intelligence hypothesis. *Philosophical Transactions of the Royal Society B: Biological Sciences, 362*(1480), 639-648.

Nairne, J. S. (2022). Adaptive education: Learning and remembering with a stone-age brain. *Educational Psychology Review, 34*(4), 2275-2296. https://doi.org/10.1007/s10648-022-09696-z

Nairne, J. S., Coverdale, M. E., & Pandeirada, J. N. (2019). Adaptive memory: The mnemonic power of survival-based generation. *Journal of Experimental Psychology: Learning, Memory, and Cognition, 45*(11), 1970.

Nairne, J. S., & Pandeirada, J. N. (2010). Adaptive memory: Ancestral priorities and the mnemonic value of survival processing. *Cognitive Psychology, 61*(1), 1-22.

Nairne, J. S., VanArsdall, J. E., Pandeirada, J. N., Cogdill, M., & LeBreton, J. M. (2013). Adaptive memory: The mnemonic value of animacy. *Psychological Science, 24*(10), 2099-2105.

Niu, X., Zhang, J., Xu, K. M., & Wang, X. (2021, 12-15 July 2021). The Impact of Productive Failure on Learning Performance and Cognitive Load: Using Hypervideo to Facilitate Online Interactions. *IEEE 2021 International Conference on Advanced Learning Technologies (ICALT)*.

Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. *Educational Psychology Review, 24*(1), 27-45.

Paas, F. G. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. *Journal of Educational Psychology, 84*(4), 429-434.

Pressley, M., & Levin, J. R. (1977). Task parameters affecting the efficacy of a visual imagery learning strategy in younger and older children. *Journal of Experimental Child Psychology, 24*(1), 53-59.

Ramirez, F. O., & Boli, J. (1987). The political construction of mass schooling: European origins and worldwide institutionalization. *Sociology of Education, 2*-17. https://doi.org/10.2307/2112615

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**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Authors and Affiliations

Kate M. Xu¹☐ · Sarah Coertjens¹ · Florence Lespiau² · Kim Ouwehand³ · Hanke Korpershoek⁴ · Fred Paas³,⁵ · David C. Geary⁶

<figure><img src="image_11.png" /></figure>

Kate M. Xu
kate.xu@ou.nl

¹ Faculty of Educational Sciences, Open University of the Netherlands, Heerlen, the Netherlands

² APSY-V, UNIV. NIMES, Nîmes, France

³ Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands

⁴ GION Education/Research, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, the Netherlands

⁵ School of Education/Early Start, University of Wollongong, Wollongong, Australia

⁶ Department of Psychological Sciences, University of Missouri, Columbia, USA
Wigfield, A., Eccles, J. S., Fredricks, J. A., Simpkins, S., Roeser, R. W., & Schiefele, U. (2015). Development of achievement motivation and engagement. *Handbook of child psychology and developmental science*.

Wigfield, A., Eccles, J. S., Schiefele, U., Roeser, R. W., & Davis-Kean, P. (2006). *Development of achievement motivation*. John Wiley & Sons Inc.

Xu, K. M., Koorn, P., De Koning, B., Skuballa, I. T., Lin, L., Henderikx, M., ... Paas, F. (2021). A growth mindset lowers perceived cognitive load and improves learning: Integrating motivation to cognitive load. *Journal of Educational Psychology, 113*(6), 1171–1191.

Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., ... Hinojosa, C. P. (2019). A national experiment reveals where a growth mindset improves achievement. *Nature, 573*(7774), 364–369.

**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Authors and Affiliations

Kate M. Xu¹☐ · Sarah Coertjens¹ · Florence Lespiau² · Kim Ouwehand³ · Hanke Korpershoek⁴ · Fred Paas³,⁵ · David C. Geary⁶

<figure><img src="image_10.png" /></figure>

Kate M. Xu
kate.xu@ou.nl

¹ Faculty of Educational Sciences, Open University of the Netherlands, Heerlen, the Netherlands

² APSY-V, UNIV. NIMES, Nîmes, France

³ Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands

⁴ GION Education/Research, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, the Netherlands

⁵ School of Education/Early Start, University of Wollongong, Wollongong, Australia

⁶ Department of Psychological Sciences, University of Missouri, Columbia, USA