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Short article
The more intelligent people are, the more they use tools Plus nous sommes intelligents et plus nous utilisons des outils J. Navarro a,∗, F. Osiurak a,b a b
Laboratoire d’Étude des Mécanismes Cognitifs (EA 3082), Université Lyon 2, 69676 Bron, France Institut Universitaire de France, 75231 Paris, France
a r t i c l e
i n f o
Article history: Received 25 November 2014 Accepted 4 November 2015 Available online xxx Keywords: Tool use Intelligence Cognitive capacities Decision-making Automation
a b s t r a c t There are several apparent reasons for people to use tools, such as to save time and effort, or to earn money. In this report, a potentially deeper reason is pointed out: people’s intelligence. We show that intelligence level is linked to the propensity to use tools or more specifically an automatic tool. In our experiment, when confronted to choose between a manual or a tool assisted task completion, the most intelligent participants used the tool more often that other participants. This link was not found with other measures such as personality factors. This finding support the idea that human intelligence might be considered as an evolutional advantage that once helped our ancestors to survive. Nowadays, human intelligence is still favoring more effective tool use over manual performance. That would also explain the fact that humans conceive and use an exponential number of tools. © 2015 Société franc¸aise de psychologie. Published by Elsevier Masson SAS. All rights reserved.
r é s u m é Mots clés : Utilisation d’outil Intelligence
Un gain de temps, d’argent ou encore d’effort peut expliquer l’utilisation d’outils. Cet article présente une autre raison plus profonde : l’intelligence. L’étude rapportée ici indique que le niveau
∗ Corresponding author at: Laboratoire d’Étude des Mécanismes Cognitifs (EA 3082), Institut de Psychologie, 5, avenue PierreMendès-France, 69676 Bron cedex, France. E-mail addresses: [email protected] (J. Navarro), [email protected] (F. Osiurak). http://dx.doi.org/10.1016/j.psfr.2015.11.002 0033-2984/© 2015 Société franc¸aise de psychologie. Published by Elsevier Masson SAS. All rights reserved.
Please cite this article in press as: Navarro, J., & Osiurak, F. The more intelligent people are, the more they use tools. Psychol. fr. (2015), http://dx.doi.org/10.1016/j.psfr.2015.11.002
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Capacités cognitives Prise de décision Automatisation
d’intelligence est lié à la propension à utiliser un outil et plus spécifiquement un automate. Dans notre expérience, les participants devaient faire un choix entre la réalisation manuelle d’une tâche ou la délégation de cette tâche à un automate. Les participants les plus intelligents ont fait usage de l’automate plus souvent que le reste des participants. Ces résultats plaident en faveur de l’idée que l’intelligence chez les êtres humains serait à considérer comme un avantage évolutionnel qui aurait jadis aidé nos ancêtres à survire. De nos jours, l’intelligence favorise toujours l’utilisation d’outil lorsque cette utilisation se trouve être plus efficace que la réalisation manuelle d’une tâche. Nos résultats expliqueraient également pourquoi les êtres humains conc¸oivent et utilisent un nombre exponentiel d’outils. © 2015 Société franc¸aise de psychologie. Publié par Elsevier Masson SAS. Tous droits réservés.
1. Introduction It is always surprising to observe the variety of strategies and tools that different people may use to achieve a task, even if relatively simple. Inter-individual strategies are a well-known and studied phenomenon often referred as people activity in the psychology and human factors areas (Leplat, 1981, 1990). For decades many psychologists have intended to characterize, understand and measure these cognitive differences between people that result in different behavioral strategies. The present study aimed to explore why some people are more prone than others to use automatic tools based on cognitive differences. Humans are not unique in using tools. Rather, they are unique because they use frequently a great variety of tools (Osiurak, Jarry, & Le Gall, 2010; Osiurak, 2014). The use of tools has increased exponentially over the centuries (Isaac, 1976). Additionally, tools tend to be more and more complex and autonomous. Some tools can even replace human for several tasks, this tools category hereafter referred as “automatic tools” were investigated in the current study (Navarro, Mars, & Young, 2011). The human proneness to use tools might be explained by human brain capabilities that provided us an evolutional advantage over other species. Major cognitive differences between human and nonhuman primates were reported after a systematic comparison of major cognitive capacities related to tool use (Vaesen, 2012) and human intelligence has acquired unique characteristics (Matsuzawa, 2001). Compared to grand apes, humans’ benefits for instance from better hand-man coordination, a unique causal thought system and representation of functional knowledge, a remarkable inhibitory control and several sophisticated social learning strategies (Vaesen, 2012). Because of its specific cognitive capabilities, humans were able to conceptualize, design and use more and more complex tools (Gibson & Ingold, 1994; Gibson, 2012). Those tools may extend human intelligence contributing even more to the design and use of tools (Salomon, Perkins, & Globerson, 1991). Within this context, it is reasonable to hypothesize that the more intelligent people are the more they should use tools. Intelligence is defined here as the ability to understand, reason, and solve problems within an adaptive perspective (Piaget, 1970). Thus, the focus was set on the dimensions of intelligence directly related to tool use (i.e. perceptual organization and processing speed). The hypothesis that reasoning or intellectual skills support proneness to use tools has also been recently suggested by the 4 Constraints Theory of human tool use (4CT; Osiurak, 2014). According to 4CT, this proneness can be explained by specific cognitive skills allowing humans to assess the costs and benefits (e.g., in terms of effort of time) associated to different potential tool use or nontool use options. This approach diverges from the perspective that proneness to use tools is based on “personality factors” such as maximization tendency, boredom avoidance or locus of control (for a review see Osiurak, Wagner, Djerbi, & Navarro, 2013). For instance, decision of using a tool or not might be under the influence of the maximization tendency (Schwartz et al., 2002). The tendency to maximize (i.e. explore all available options to select the best) should result in a more frequent use
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of automatic tools as far as the automatic tool is the best option. The use of automatic tools might also be influenced by people need for busyness and boredom avoidance (Hsee, Yang, & Wang, 2010; Navarro & Osiurak, 2015). Proneness to boredom should prevent people to use automatic tools if no other occupation than waiting for the automatic task completion is offered. Finally the locus of control might also be linked to automatic tool use frequency (Levenson, 1974). It can be hypothesized that people relying on others and chance would favor automatic tool use more than internal people (i.e. who believe that their actions explain what happen to them). In sum, evidence seems to indicate that intellectual skills might support proneness to use tools. However, this hypothesis contrasts from the perspective that this proneness is more a matter of personality factors, such as tendency to maximize, boredom avoidance or locus of control. In order to assess the influence of (1) intelligence, (2) tendency to maximize, (3) proneness to boredom, and (4) locus of control, participants were asked to perform a task either manually or using a more effective automatic tool. 2. Method 2.1. Participants Fifty undergraduate students from the University of Lyon (22 years of mean age ± 2.3 years; 28 women) participated in this experiment. Participants provided informed consent following procedure approved by the local institutional review board. According to the Edinburgh test of handedness (Oldfield, 1971), 47 participants were right-handed (scores ranging from 41 to 100) and three were left-handed (scores ranging from −8 to −100). 2.2. Stimuli and procedure After completing the consent form, participants were asked to seat in front of an iMac equipped with a 19-in screen with the Superlab (Version 4.0, Cedrus Corp.) program on. A three key button-box was located in front of them. The three response keys (left, center and right) were located 10 cm the ones from the others. Participants were instructed that they would have to hammer virtually a given number of wooden signs appearing 3 by 3 on the monitor (left, center and right) by pressing the corresponding button of the button-box. Then, the experimenter explained that two possibilities were available to hammer the signs: the blue hand and the green hand. Participants were also said that they would face that choice several times. So as to make their choice between the blue and the green hand participants were given no information but to choose freely. To put some emphasis on that freedom, participants were told that they could choose only the blue hand or only the green hand or any combination of blue and green hand during the course of the experiment. After being presented the blue and green hand conditions, participants performed each condition once before starting the experiment. The first picture displayed on the computed screen was a “choice picture” where a green and a blue hand were presented. One of these two hands was located on the left side of the screen and the other on the right side of the screen. The location on the screen of the green and blue hands was counterbalanced across participants. Participants had to choose between the blue or the green hand by pressing the corresponding left or right button of the button-box. Whatever the color of the hand selected, three wooden signs then appeared on the monitor. These three signs always had to be hammered one by one from the left to the right. This sequence was played twelve times in a row until the next choice picture appeared on the monitor. The sequence between two choice pictures will be referred as a block. The experiment stopped when participants had completed 32 blocks. If participants selected the blue hand (no tool condition) they had to hammer all the signs of the block (36 signs) one by one by pressing the button located under the sign to hammer (left button for the left sign, center button for the center sign and right button for the right sign). Because a sign could only be hammered when the blue hand was on top of it, the time required to complete a trial was very
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consistent from a trial to the other. Based on previous studies a trial was expected and currently last about 27.2-s for a block (Navarro & Osiurak, 2015; Osiurak et al., 2013). If participants selected the green hand (automatic tool condition) they only had to wait for the hand to hammer all the signs of a block. As a result, the 36 signs of a block were hammered with no other participants’ action that the selection of the green hand on the picture choice. The completion time of a block in the automatic tool condition (15.2-s) was 44.1% faster than in the no tool condition. This automatic tool speed had been selected based on previous data showing an almost perfectly balanced repartition between no tool and automatic tool conditions selections at this particular automatic tool speed (Navarro & Osiurak, 2015). In other words, participants consider as equivalent a manual performance and an automatic tool completion of the task at that particular automatic tool speed. It was therefore the best option for a comparison. After that first experimental step, participants were confronted to the following cognitive assessments: • Matrix Reasoning, Digit Symbol Coding and Symbol Search sub-tests from the Wechsler Adult Intelligence Scale (WAIS IV) so as to assess their intelligence through perceptual organization and processing speed dimensions (Wechsler, 2008). The measures of the three sub-tests were normalized (z-scores) and averaged to collect a mean intelligence value per participant; • Schwartz’s Maximization Scale (Schwartz et al., 2002), translated and validated (Faure, Joulain, & Osiurak, 2015). A score of between 1 (optimizer) and 7 (maximizer) was collected for each participant; • a translated and validated version of the Boredom Proneness Scale (Gana & Akremi, 1998). A score of between 1 and 24 was collected for each participant. The higher the score the higher the proneness to boredom; • a translated and validated version of Levenson’s Locus of Control scale (Levenson, 1974; Rossier, Rigozzi, & Berthoud, 2002). A score of between 0 and 48 was collected for each of the three dimensions assessed: Internal, Powerful others and Chance measured. 2.3. Data analysis First, and in order to verify that the experimental task allowed an inter-individual variety in terms of condition selection, the automatic tool selection rate against the chance rate of 0.50 in a Wilcoxon signed-rank test was computed. In addition, binomial tests were conducted to determine whether each participant individually showed a significant preference for one of the conditions. Second, to examine the strength of the links between (1) intelligence, (2) tendency to maximize, (3) proneness to boredom, (4) locus of control, and frequency of automatic tool selections Pearson’s casewise correlations were carried on the percentage of automatic tool condition selections. Third, a stepwise regression was carried on to evaluate the proportion of variance explained by those different cognitive dimensions. 3. Results 3.1. Tool condition selection rate Wilcoxon signed-rank tests showed that if the selection rate was not significantly different from equal repartition (p < .010), a trend toward tool use was present. Participant-by-participant analyses indicated that 17 participants (34%) favored the no tool condition while 28 (56%) favored the tool condition. The last five participants (10%) selected the no tool condition as many times as the tool condition. Ten participants (20%) showed a significant preference for the no tool condition and 19 (38%) for the tool condition (Binomial test, p < .050). 3.2. Correlations The score of intelligence obtained was significantly correlated with the frequency of automatic tool use (r = .34; p < .016; r2 = .12; Fig. 1). The higher the intelligence score the more participants used the automatic tool.
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Percentage of automac tool selecons 25 50 75
5
100
Mean ntelligence score (-3/3)
1.65
0.825
0
-0.825
-1.65 Fig. 1. Percentage of automatic tool selection depending on the mean intelligence z score (−3/3) for the 50 participants. The black line represents the best-fit line.
None of the other dimensions assessed was significantly correlated with the percentage of automatic tool condition selection (tendency to maximize: r = .06, p = .671; proneness to boredom: r = −.01, p = .970; locus of control: internal r = .09, p = .517, powerful others: r = .07, p = .641 and chance: r = .19, p = .190). 3.3. Stepwise regression A stepwise regression indicated that only the intelligence score and the chance dimension of Levenson’s locus of control scale significantly contribute to explain the frequency of the tool condition selection [F (2, 47) = 3.91; p < .026; with 14% of variance explained]. The intelligence score explained 11% of the variance and the chance scale 3% more. 4. Discussion Experimental data demonstrated that the more intelligent people are the more they tend to use tools. This conclusion was obtained with a tool objectively more effective than a no tool completion of the task. The so-called automatic tool used for the experiment did not require any learning and completely replace people from the task. Therefore, intelligence is a key factor that triggers effective tool use more frequently. This result is in line with the definition of intelligence presented in introduction (Piaget, 1970). Because it is much faster, using the tool in the current experiment is objectively the best available option, as a consequence the most intelligent people might simply be more able to assess the situation precisely and therefore select more often the tool than less intelligent people. However, the experimental task is very simple and previous data indicates that time perceptions are not a good predictor of the tool condition selection (Navarro & Osiurak, 2015). Thus, this intuitive explanation cannot fully report the data collected. A better explanation is to consider human intelligence as an evolutional advantage that helped our ancestors to survive and is still favoring tool use over manual performance. In an evolutional perspective, we can hypothesize that our cognitive capacities are still growing from generation to generation and that we use those capabilities to conceive tools. That would explain the fact that humans conceive and use an exponential number of tools over centuries (Isaac, 1976). Our finding also fits with a cognitive archeology point of view that stresses the importance of cognitive capacities to handle natural and social environments as far as in prehistoric societies (Haidle, 2011;
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Peebles, 1993). The observed trend for intelligent people to use tools might be the heritage of this ancient evolutional advantage. This goes along with the description of human intelligence evolution made by Darwin (Darwin, 1874). In Darwin’s words, the more “sagacious” humans (i.e. the more intelligent as reported in the current study) might design and use the most efficient tools, thereby securing a reproductive advantage. In our experiment, participants were confronted to an unfamiliar task and automatic tool so as to insure that participants had no previous knowledge on the task and how to deal with it. Results should be confirmed in more everyday live tool use conditions. Adding a verbal and cultural intelligence assessment to the performance intelligence performed in the study may reinforce even more the importance of intelligence on tool use frequency (Gibson, 1991; Herrmann, Call, Hernández-Lloreda, Hare, & Tomasello, 2007). Moreover, only undergraduate students with relatively homogenous cognitive capabilities have been included in our participant sample. The strength of the relationship between intelligence and tool use frequency would probably increase with a more heterogeneous sample in terms of intelligence. Those additional intelligence assessments would probably increase the percentage of variance explained here. None of the other cognitive assessments (i.e. tendency to maximize, proneness to boredom and locus of control) that might have explained a more or less frequent tool use are correlated with the observed tool use frequency. Only the chance scale (one of the three scales of the locus of control) was found to explain 3% of the observed variance. Indicating that people attributing what happen in their life to chance are more prone to use tools than others. In agreement with our assumption that someone with an external locus of control is more disposed to use tools that replace him/her for a given task. All together data indicates a relative independence between individual characteristics in terms of tendency to maximize, proneness to boredom, and locus of control on the one hand and tool use frequency on the other hand. Supporting the idea of a more deeply routed intelligence based explanation. But those individual characteristics might also impact tool use under more naturalistic conditions. For instance, maximizers might need more confrontations to the task to determine the best option and people prone to boredom might have been kept busy thinking about the experiment objective. Our results offer new perspectives regarding tool use and might be valuable to take into account for tools development and future tools spreading strategies. If extended to all the tools we use in our everyday lives such as computer and mobile applications, those data are valuable for all of us.
Author contribution All authors contributed to the study concept, study design and data collection. J. Navarro performed the data analysis and interpretation under the supervision of F. Osiurak. J. Navarro drafted the manuscript, and F. Osiurak provided critical revisions. All authors approved the final version of the manuscript for submission.
Disclosure of interest The authors declare that they have no competing interest.
Acknowledgments We thank Gilles Avrillault and Antoine Danielou for their help with data collection and suggestions at different stages of the project. Funding: this work was performed within the framework of the LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the program “Investissements d’Avenir” (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). Ethical considerations: this research was carried out in accordance with the provisions of the World Medical Association Declaration of Helsinki.
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Short article
The more intelligent people are, the more they use tools Plus nous sommes intelligents et plus nous utilisons des outils J. Navarro a,∗, F. Osiurak a,b a b
Laboratoire d’Étude des Mécanismes Cognitifs (EA 3082), Université Lyon 2, 69676 Bron, France Institut Universitaire de France, 75231 Paris, France
a r t i c l e
i n f o
Article history: Received 25 November 2014 Accepted 4 November 2015 Available online xxx Keywords: Tool use Intelligence Cognitive capacities Decision-making Automation
a b s t r a c t There are several apparent reasons for people to use tools, such as to save time and effort, or to earn money. In this report, a potentially deeper reason is pointed out: people’s intelligence. We show that intelligence level is linked to the propensity to use tools or more specifically an automatic tool. In our experiment, when confronted to choose between a manual or a tool assisted task completion, the most intelligent participants used the tool more often that other participants. This link was not found with other measures such as personality factors. This finding support the idea that human intelligence might be considered as an evolutional advantage that once helped our ancestors to survive. Nowadays, human intelligence is still favoring more effective tool use over manual performance. That would also explain the fact that humans conceive and use an exponential number of tools. © 2015 Société franc¸aise de psychologie. Published by Elsevier Masson SAS. All rights reserved.
r é s u m é Mots clés : Utilisation d’outil Intelligence
Un gain de temps, d’argent ou encore d’effort peut expliquer l’utilisation d’outils. Cet article présente une autre raison plus profonde : l’intelligence. L’étude rapportée ici indique que le niveau
∗ Corresponding author at: Laboratoire d’Étude des Mécanismes Cognitifs (EA 3082), Institut de Psychologie, 5, avenue PierreMendès-France, 69676 Bron cedex, France. E-mail addresses: [email protected] (J. Navarro), [email protected] (F. Osiurak). http://dx.doi.org/10.1016/j.psfr.2015.11.002 0033-2984/© 2015 Société franc¸aise de psychologie. Published by Elsevier Masson SAS. All rights reserved.
Please cite this article in press as: Navarro, J., & Osiurak, F. The more intelligent people are, the more they use tools. Psychol. fr. (2015), http://dx.doi.org/10.1016/j.psfr.2015.11.002
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Capacités cognitives Prise de décision Automatisation
d’intelligence est lié à la propension à utiliser un outil et plus spécifiquement un automate. Dans notre expérience, les participants devaient faire un choix entre la réalisation manuelle d’une tâche ou la délégation de cette tâche à un automate. Les participants les plus intelligents ont fait usage de l’automate plus souvent que le reste des participants. Ces résultats plaident en faveur de l’idée que l’intelligence chez les êtres humains serait à considérer comme un avantage évolutionnel qui aurait jadis aidé nos ancêtres à survire. De nos jours, l’intelligence favorise toujours l’utilisation d’outil lorsque cette utilisation se trouve être plus efficace que la réalisation manuelle d’une tâche. Nos résultats expliqueraient également pourquoi les êtres humains conc¸oivent et utilisent un nombre exponentiel d’outils. © 2015 Société franc¸aise de psychologie. Publié par Elsevier Masson SAS. Tous droits réservés.
1. Introduction It is always surprising to observe the variety of strategies and tools that different people may use to achieve a task, even if relatively simple. Inter-individual strategies are a well-known and studied phenomenon often referred as people activity in the psychology and human factors areas (Leplat, 1981, 1990). For decades many psychologists have intended to characterize, understand and measure these cognitive differences between people that result in different behavioral strategies. The present study aimed to explore why some people are more prone than others to use automatic tools based on cognitive differences. Humans are not unique in using tools. Rather, they are unique because they use frequently a great variety of tools (Osiurak, Jarry, & Le Gall, 2010; Osiurak, 2014). The use of tools has increased exponentially over the centuries (Isaac, 1976). Additionally, tools tend to be more and more complex and autonomous. Some tools can even replace human for several tasks, this tools category hereafter referred as “automatic tools” were investigated in the current study (Navarro, Mars, & Young, 2011). The human proneness to use tools might be explained by human brain capabilities that provided us an evolutional advantage over other species. Major cognitive differences between human and nonhuman primates were reported after a systematic comparison of major cognitive capacities related to tool use (Vaesen, 2012) and human intelligence has acquired unique characteristics (Matsuzawa, 2001). Compared to grand apes, humans’ benefits for instance from better hand-man coordination, a unique causal thought system and representation of functional knowledge, a remarkable inhibitory control and several sophisticated social learning strategies (Vaesen, 2012). Because of its specific cognitive capabilities, humans were able to conceptualize, design and use more and more complex tools (Gibson & Ingold, 1994; Gibson, 2012). Those tools may extend human intelligence contributing even more to the design and use of tools (Salomon, Perkins, & Globerson, 1991). Within this context, it is reasonable to hypothesize that the more intelligent people are the more they should use tools. Intelligence is defined here as the ability to understand, reason, and solve problems within an adaptive perspective (Piaget, 1970). Thus, the focus was set on the dimensions of intelligence directly related to tool use (i.e. perceptual organization and processing speed). The hypothesis that reasoning or intellectual skills support proneness to use tools has also been recently suggested by the 4 Constraints Theory of human tool use (4CT; Osiurak, 2014). According to 4CT, this proneness can be explained by specific cognitive skills allowing humans to assess the costs and benefits (e.g., in terms of effort of time) associated to different potential tool use or nontool use options. This approach diverges from the perspective that proneness to use tools is based on “personality factors” such as maximization tendency, boredom avoidance or locus of control (for a review see Osiurak, Wagner, Djerbi, & Navarro, 2013). For instance, decision of using a tool or not might be under the influence of the maximization tendency (Schwartz et al., 2002). The tendency to maximize (i.e. explore all available options to select the best) should result in a more frequent use
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of automatic tools as far as the automatic tool is the best option. The use of automatic tools might also be influenced by people need for busyness and boredom avoidance (Hsee, Yang, & Wang, 2010; Navarro & Osiurak, 2015). Proneness to boredom should prevent people to use automatic tools if no other occupation than waiting for the automatic task completion is offered. Finally the locus of control might also be linked to automatic tool use frequency (Levenson, 1974). It can be hypothesized that people relying on others and chance would favor automatic tool use more than internal people (i.e. who believe that their actions explain what happen to them). In sum, evidence seems to indicate that intellectual skills might support proneness to use tools. However, this hypothesis contrasts from the perspective that this proneness is more a matter of personality factors, such as tendency to maximize, boredom avoidance or locus of control. In order to assess the influence of (1) intelligence, (2) tendency to maximize, (3) proneness to boredom, and (4) locus of control, participants were asked to perform a task either manually or using a more effective automatic tool. 2. Method 2.1. Participants Fifty undergraduate students from the University of Lyon (22 years of mean age ± 2.3 years; 28 women) participated in this experiment. Participants provided informed consent following procedure approved by the local institutional review board. According to the Edinburgh test of handedness (Oldfield, 1971), 47 participants were right-handed (scores ranging from 41 to 100) and three were left-handed (scores ranging from −8 to −100). 2.2. Stimuli and procedure After completing the consent form, participants were asked to seat in front of an iMac equipped with a 19-in screen with the Superlab (Version 4.0, Cedrus Corp.) program on. A three key button-box was located in front of them. The three response keys (left, center and right) were located 10 cm the ones from the others. Participants were instructed that they would have to hammer virtually a given number of wooden signs appearing 3 by 3 on the monitor (left, center and right) by pressing the corresponding button of the button-box. Then, the experimenter explained that two possibilities were available to hammer the signs: the blue hand and the green hand. Participants were also said that they would face that choice several times. So as to make their choice between the blue and the green hand participants were given no information but to choose freely. To put some emphasis on that freedom, participants were told that they could choose only the blue hand or only the green hand or any combination of blue and green hand during the course of the experiment. After being presented the blue and green hand conditions, participants performed each condition once before starting the experiment. The first picture displayed on the computed screen was a “choice picture” where a green and a blue hand were presented. One of these two hands was located on the left side of the screen and the other on the right side of the screen. The location on the screen of the green and blue hands was counterbalanced across participants. Participants had to choose between the blue or the green hand by pressing the corresponding left or right button of the button-box. Whatever the color of the hand selected, three wooden signs then appeared on the monitor. These three signs always had to be hammered one by one from the left to the right. This sequence was played twelve times in a row until the next choice picture appeared on the monitor. The sequence between two choice pictures will be referred as a block. The experiment stopped when participants had completed 32 blocks. If participants selected the blue hand (no tool condition) they had to hammer all the signs of the block (36 signs) one by one by pressing the button located under the sign to hammer (left button for the left sign, center button for the center sign and right button for the right sign). Because a sign could only be hammered when the blue hand was on top of it, the time required to complete a trial was very
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consistent from a trial to the other. Based on previous studies a trial was expected and currently last about 27.2-s for a block (Navarro & Osiurak, 2015; Osiurak et al., 2013). If participants selected the green hand (automatic tool condition) they only had to wait for the hand to hammer all the signs of a block. As a result, the 36 signs of a block were hammered with no other participants’ action that the selection of the green hand on the picture choice. The completion time of a block in the automatic tool condition (15.2-s) was 44.1% faster than in the no tool condition. This automatic tool speed had been selected based on previous data showing an almost perfectly balanced repartition between no tool and automatic tool conditions selections at this particular automatic tool speed (Navarro & Osiurak, 2015). In other words, participants consider as equivalent a manual performance and an automatic tool completion of the task at that particular automatic tool speed. It was therefore the best option for a comparison. After that first experimental step, participants were confronted to the following cognitive assessments: • Matrix Reasoning, Digit Symbol Coding and Symbol Search sub-tests from the Wechsler Adult Intelligence Scale (WAIS IV) so as to assess their intelligence through perceptual organization and processing speed dimensions (Wechsler, 2008). The measures of the three sub-tests were normalized (z-scores) and averaged to collect a mean intelligence value per participant; • Schwartz’s Maximization Scale (Schwartz et al., 2002), translated and validated (Faure, Joulain, & Osiurak, 2015). A score of between 1 (optimizer) and 7 (maximizer) was collected for each participant; • a translated and validated version of the Boredom Proneness Scale (Gana & Akremi, 1998). A score of between 1 and 24 was collected for each participant. The higher the score the higher the proneness to boredom; • a translated and validated version of Levenson’s Locus of Control scale (Levenson, 1974; Rossier, Rigozzi, & Berthoud, 2002). A score of between 0 and 48 was collected for each of the three dimensions assessed: Internal, Powerful others and Chance measured. 2.3. Data analysis First, and in order to verify that the experimental task allowed an inter-individual variety in terms of condition selection, the automatic tool selection rate against the chance rate of 0.50 in a Wilcoxon signed-rank test was computed. In addition, binomial tests were conducted to determine whether each participant individually showed a significant preference for one of the conditions. Second, to examine the strength of the links between (1) intelligence, (2) tendency to maximize, (3) proneness to boredom, (4) locus of control, and frequency of automatic tool selections Pearson’s casewise correlations were carried on the percentage of automatic tool condition selections. Third, a stepwise regression was carried on to evaluate the proportion of variance explained by those different cognitive dimensions. 3. Results 3.1. Tool condition selection rate Wilcoxon signed-rank tests showed that if the selection rate was not significantly different from equal repartition (p < .010), a trend toward tool use was present. Participant-by-participant analyses indicated that 17 participants (34%) favored the no tool condition while 28 (56%) favored the tool condition. The last five participants (10%) selected the no tool condition as many times as the tool condition. Ten participants (20%) showed a significant preference for the no tool condition and 19 (38%) for the tool condition (Binomial test, p < .050). 3.2. Correlations The score of intelligence obtained was significantly correlated with the frequency of automatic tool use (r = .34; p < .016; r2 = .12; Fig. 1). The higher the intelligence score the more participants used the automatic tool.
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Percentage of automac tool selecons 25 50 75
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-1.65 Fig. 1. Percentage of automatic tool selection depending on the mean intelligence z score (−3/3) for the 50 participants. The black line represents the best-fit line.
None of the other dimensions assessed was significantly correlated with the percentage of automatic tool condition selection (tendency to maximize: r = .06, p = .671; proneness to boredom: r = −.01, p = .970; locus of control: internal r = .09, p = .517, powerful others: r = .07, p = .641 and chance: r = .19, p = .190). 3.3. Stepwise regression A stepwise regression indicated that only the intelligence score and the chance dimension of Levenson’s locus of control scale significantly contribute to explain the frequency of the tool condition selection [F (2, 47) = 3.91; p < .026; with 14% of variance explained]. The intelligence score explained 11% of the variance and the chance scale 3% more. 4. Discussion Experimental data demonstrated that the more intelligent people are the more they tend to use tools. This conclusion was obtained with a tool objectively more effective than a no tool completion of the task. The so-called automatic tool used for the experiment did not require any learning and completely replace people from the task. Therefore, intelligence is a key factor that triggers effective tool use more frequently. This result is in line with the definition of intelligence presented in introduction (Piaget, 1970). Because it is much faster, using the tool in the current experiment is objectively the best available option, as a consequence the most intelligent people might simply be more able to assess the situation precisely and therefore select more often the tool than less intelligent people. However, the experimental task is very simple and previous data indicates that time perceptions are not a good predictor of the tool condition selection (Navarro & Osiurak, 2015). Thus, this intuitive explanation cannot fully report the data collected. A better explanation is to consider human intelligence as an evolutional advantage that helped our ancestors to survive and is still favoring tool use over manual performance. In an evolutional perspective, we can hypothesize that our cognitive capacities are still growing from generation to generation and that we use those capabilities to conceive tools. That would explain the fact that humans conceive and use an exponential number of tools over centuries (Isaac, 1976). Our finding also fits with a cognitive archeology point of view that stresses the importance of cognitive capacities to handle natural and social environments as far as in prehistoric societies (Haidle, 2011;
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Peebles, 1993). The observed trend for intelligent people to use tools might be the heritage of this ancient evolutional advantage. This goes along with the description of human intelligence evolution made by Darwin (Darwin, 1874). In Darwin’s words, the more “sagacious” humans (i.e. the more intelligent as reported in the current study) might design and use the most efficient tools, thereby securing a reproductive advantage. In our experiment, participants were confronted to an unfamiliar task and automatic tool so as to insure that participants had no previous knowledge on the task and how to deal with it. Results should be confirmed in more everyday live tool use conditions. Adding a verbal and cultural intelligence assessment to the performance intelligence performed in the study may reinforce even more the importance of intelligence on tool use frequency (Gibson, 1991; Herrmann, Call, Hernández-Lloreda, Hare, & Tomasello, 2007). Moreover, only undergraduate students with relatively homogenous cognitive capabilities have been included in our participant sample. The strength of the relationship between intelligence and tool use frequency would probably increase with a more heterogeneous sample in terms of intelligence. Those additional intelligence assessments would probably increase the percentage of variance explained here. None of the other cognitive assessments (i.e. tendency to maximize, proneness to boredom and locus of control) that might have explained a more or less frequent tool use are correlated with the observed tool use frequency. Only the chance scale (one of the three scales of the locus of control) was found to explain 3% of the observed variance. Indicating that people attributing what happen in their life to chance are more prone to use tools than others. In agreement with our assumption that someone with an external locus of control is more disposed to use tools that replace him/her for a given task. All together data indicates a relative independence between individual characteristics in terms of tendency to maximize, proneness to boredom, and locus of control on the one hand and tool use frequency on the other hand. Supporting the idea of a more deeply routed intelligence based explanation. But those individual characteristics might also impact tool use under more naturalistic conditions. For instance, maximizers might need more confrontations to the task to determine the best option and people prone to boredom might have been kept busy thinking about the experiment objective. Our results offer new perspectives regarding tool use and might be valuable to take into account for tools development and future tools spreading strategies. If extended to all the tools we use in our everyday lives such as computer and mobile applications, those data are valuable for all of us.
Author contribution All authors contributed to the study concept, study design and data collection. J. Navarro performed the data analysis and interpretation under the supervision of F. Osiurak. J. Navarro drafted the manuscript, and F. Osiurak provided critical revisions. All authors approved the final version of the manuscript for submission.
Disclosure of interest The authors declare that they have no competing interest.
Acknowledgments We thank Gilles Avrillault and Antoine Danielou for their help with data collection and suggestions at different stages of the project. Funding: this work was performed within the framework of the LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the program “Investissements d’Avenir” (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). Ethical considerations: this research was carried out in accordance with the provisions of the World Medical Association Declaration of Helsinki.
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