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Abstract

Diverse studies of human foraging have revealed behavioral strategies that may have evolved as adaptations for foraging. Here, we used an outdoor experimental search task to explore the effect of three sources of information on participants’ performance: (i) information obtained directly from performing a search, (ii) information obtained prior to testing in the form of a distilled snippet of knowledge intended to experimentally simulate information acquired culturally about the environment, and (iii) information obtained from experience of foraging for natural resources for economic gain. We found that (i) immediate searching experience improved performance from the beginning to the end of the short, 2-min task, (ii) information priming improved performance notably from the very beginning of the task, and (iii) natural resource foraging experience improved performance to a lesser extent. Our results highlight the role of culturally transmitted information as well as the presence of mechanisms to rapidly integrate and implement new information into searching choices, which ultimately influence performance in a foraging task.

Keywords

cultural transmission information acquisition large-scale tests foraging experience searching behavior

Human foraging has been studied from a wide range of perspectives, from detailed descriptions of hunter-gatherer populations (Berbesque, Wood, Crittenden, Mabulla, & Marlowe, 2016; de Boer, Blijdenstein, & Longamane, 2002; Hill, Kaplan, Hawkes, & Hurtado, 1987; Pacheco-Cobos, Rosetti, Cuatianquiz, & Hudson, 2010; Sosis, 2002; Thomas, 2007) to computer-based tests in highly controlled situations that provide insight into searchers’ decisions (De Lillo, Kirby, & James, 2014; Ehinger & Wolfe, 2016; Hutchinson, Wilke, & Todd, 2008; Krasnow et al., 2011; Neave, Hamilton, Hutton, Tildesley, & Pickering, 2005; Wilke et al., 2015; Wilke, Hutchinson, Todd, & Czienskowski, 2009; Wolfe, 2013; Zhang, Gong, Fougnie, & Wolfe, 2015). While such approaches have been of importance in helping elucidate cognitive processes relevant to foraging, there is a limit in the extent to which these findings can be extrapolated to realistic situations. Thus, a third approach consisting in simulating natural environments aims to mimic the spatial characteristics of the environment in which foraging takes place (e.g., large open areas, caloric expense) while being able to manipulate variables experimentally (New, Krasnow, Truxaw, & Gaulin, 2007; Rosetti, Rodríguez, Pacheco-Cobos, & Hudson, 2015; Smith, Hood, & Gilchrist, 2010). These studies can be far less demanding to conduct than those in the field, while adding richer behavioral descriptions than those offered by virtual setups.

For humans, as for other animals, foraging success is strongly tied to choices and context. Foraging bouts involve a range of decisions: when to start foraging, what to forage for, where, how, for how long, and so on (Bell, 1991). In addition, searches are often made under constraints of time and limited information and thus require ecologically efficient mechanisms specifically adapted to exploit information in the environment that may result in an adaptive advantage (Hutchinson & Gigerenzer, 2005). Following this approach, recent studies of human foraging suggest that some of our cognitive processes may have evolved as adaptations to a foraging lifestyle such as the expectation of resources aggregated in time and space (Scheibehenne, Wilke, & Todd, 2011; Wilke & Barrett, 2009).

Adapted, ecologically rational behavioral responses can also arise indirectly through individual and social learning, as well as from the integration of simpler existing heuristic mechanisms into new, more complex ones (Todd & Gigerenzer, 2000). Regarding human foraging behavior, many authors have noted the importance of acquiring environmental information (i.e., learning) to achieve a successful outcome (Dall, Giraldeau, Olsson, McNamara, & Stephens, 2005; Kolling & Akam, 2017; McNamara & Houston, 1985). This is of particular relevance in humans because social learning is considered an important form of human behavioral adaptation (Boyd, Richerson, & Henrich, 2011). For instance, Koster and Venegas (2012) showed that knowledge of prey choice among the Arang Dak of lowland Nicaragua results mostly from social learning, as less experienced hunters have the same degree of information regarding prey choice as those with more experience. Thus, human foraging behavior seems to be the result of the convergence of the operation of endogenous, evolved cognitive processes, the acquisition of environmental information through learning, and culturally transmitted knowledge. Dissecting out the influence of each of these could help us better understand how each shapes foraging performance.

In this study, we aimed to investigate how searching success can be influenced by different means of acquiring environmental information. For this, we designed a large-scale search task that takes place in an outdoor arena and incorporates several of the physical constraints of searching in a patchy environment, while allowing us to control for the kind of environmental information available to the searcher. In three experiments, we evaluated the effect of three forms of information acquisition on searching performance. We present and discuss the results in three sections: the effect on performance of (i) information directly acquired while performing the search task, (ii) information priming by the experimenter that was intended to simulate socially transmitted knowledge of the environment, and (iii) information obtained from experience of foraging for natural resources.

General Methods

Participants

These were all young adult university students of both sexes in approximately equal numbers. Further details are given for each experiment below.

Task Setup

The task, modified from Rosetti, Rodríguez, Pacheco-Cobos, and Hudson (2015), was designed to test the behavioral outcome of searching in a patchy environment over an outdoor area larger than usually available in laboratory settings. We arranged a series of small, opaque cones (8 cm high, 7.5 cm base diameter) over a flat concrete area (basketball court, 15 m × 28 m), so as to form five differently colored patches of 30 cones and 1.2 m in diameter. Within each patch, six golf balls were placed beneath the cones in a nearest neighbor fashion. The position of the cluster of balls in each patch was randomly determined and the same arrangement was used for all tests (Figure 1).

Figure 1.

Search arena. Arena dimensions and the spatial arrangement of each group of cones (patch) and hidden balls (black cones) are shown.

Search Task

Participants were instructed to collect into a cloth shoulder bag as many balls as they could within 2 min, thereby forcing their decision to stay on or to leave a patch. The combination of a short searching time and an equal chance in each patch to encounter targets led us to use a constant number of cones and balls to reduce variability in the searching experience among participants.

Participants were tested individually. General instructions included not to lift simultaneously more than one cone and to leave the cones upright. Additional instructions for each condition are described below. Each participant was fitted with a bike helmet with an attached GoPro wide-angle camera (Hero 4, Go Pro Inc., San Mateo, CA) to record the searching process from a first-person perspective with high resolution (see sample video clip in Supplementary Material). Trials were stopped strictly after 2 min and included counts of cones lifted and balls collected up to that point.

After the task, participants were briefly interviewed regarding their foraging experience. Informed consent was obtained from participants before any evaluation was performed.

Behavioral Analysis

Using event logging software (BORIS; Friard & Gamba, 2016), videos were coded as a sequence noting the time that each cone was lifted and that a ball was collected. From this, we calculated for each participant the number of patches visited, the number of cones lifted, and the number of balls collected.

Statistical Analysis

We assessed performance in two ways: (i) we calculated the collection rate per patch, defined as the number of balls collected per cones lifted in a patch, and (ii) we used successes and failures (full and empty cones lifted, respectively) as a binomial variable. The latter was used because in this way we could consider the differential effort that may be concealed by considering the collection rate alone; for example, lifting 30 cones and finding six balls has the same collection rate as lifting 5 cones and finding one ball (see participants’ variation in execution in Section 2 of Supplementary Material).

To evaluate changes in the collection rate, first, we compared the value obtained in each patch against the depletion rate (collecting all balls by lifting all cones, 0.2) using a one-sample t test. Afterward, we tested the linear relationship between collection rate and the patch sequence, that is, the advance of the subjects across the different patches, and used one-sample t tests to compare the regression slopes against zero. In order to test performance changes between experimental conditions, we performed generalized linear mixed models (GLMMs) on (i) the collection rate (continuous response variable) and (ii) successes and failures (binomial response variable) to test how performance was influenced by the experimental condition and the patch sequence (fixed variables), while including the participants’ ID as a random factor. In all analyses, we used the data for the first five patches to avoid including the potentially reduced collection rate of a second visit, which, however, rarely occurred, and to account for the possible bias of unfinished patches, we removed patches where the search was cut short by time.

All analyses were performed using R (R Core Team, 2018) and significance was set at p < .05 but adjusted for multiple comparisons using the Benjamini–Hochberg procedure (Benjamini & Hochberg, 1995). For GLMMs, significance was calculated using the lmerTest package (Kuznetsova, Brockhoff, & Christensen, 2017), and multiple comparisons were calculated using multcomp (Hothorn et al., 2016) and lsmeans (Lenth, 2017) packages.

Experiment 1: Effect of Immediate Experience on Search Performance

Studies of searching behavior have shown that humans can rapidly obtain and implement novel environmental information regarding their performance (e.g., target location; Kerster, Rhodes, & Kello, 2016; Smith et al., 2010). Thus, in a first baseline (BL) condition, we aimed to identify the effect of immediate experience on searching by investigating whether participants’ performance improved as they moved through successive patches.

Method

Participants (n = 71, 49% men, mean age = 20.99, SD = 2.19) were urbanites who reported little or no experience of searching for natural resources. They were recruited on a university campus and tested in the setup described in the General Methods section.

Results and Discussion

Despite collection rate never significantly surpassing depletion rate (Table 1), improvement was evident from the significant linear relationship between collection rate and patch sequence. Also, we found a significant linear relationship between collection rate and patch sequence (estimate = 0.014; SE = 0.005; df = 302.5; t = 2.71; p = .006).

Table 1.

Collection Rate for Each Sequential Patch Compared With a Chance Depletion Rate of 0.2 Using a One-Sample t Test.

Condition	Patch	Mean Collection Rate	t Value	95% CI	df	p	p Adjusted
BL	1	0.179	−1.355	[0.149, 0.21]	70	.18	.397
	2	0.202	0.202	[0.18, 0.224]	70	.841	.841
	3	0.229	1.921	[0.199, 0.26]	62	.059	.297
	4	0.229	1.2	[0.18, 0.277]	34	.238	.397
	5	0.257	0.982	[0.134, 0.379]	15	.342	.427
INFO	1	0.283	5.356	[0.252, 0.314]	66	<.001	< .001
	2	0.288	5.607	[0.256, 0.319]	66	<.001	<.001
	3	0.308	7.357	[0.278, 0.337]	60	<.001	<.001
	4	0.309	5.592	[0.269, 0.348]	41	<.001	<.001
	5	0.330	3.067	[0.23, 0.431]	7	.018	.019
EXP	1	0.202	0.128	[0.166, 0.238]	52	.899	.899
	2	0.247	2.67	[0.217, 0.282]	50	.01	.034
	3	0.25	2.563	[0.211, 0.289]	48	.014	.034
	4	0.236	1.364	[0.182, 0.29]	31	.182	.304
	5	0.234	0.939	[0.155, 0.313]	13	.365	.456

Note. BL = base line; INFO = information condition; EXP = experience.

Based on these results, we suggest that participants’ behavior reflected a capacity to rapidly detect distribution patterns within patches. Participants’ performance significantly improved toward the end of the task, after they had passed through several patches, reflecting their ability to acquire relevant information as they advanced. Their improvement presumably came from learning probabilistic properties of the distribution of the balls, as has been observed in other studies of target location (Geng & Behrmann, 2002; Smith et al., 2010). The decision rule could have been from the observation that balls were clustered, from the idea that patches contained a fixed number of balls, or a combination of both. Behaviorally, this was difficult to distinguish since participants moved through a patch lifting cones mostly in a nearest-neighbor fashion (see Section 1 of Supplementary Material, for an analysis of the pattern of cone-lifting by participants while searching). An alternative explanation for the current results is that the improvement observed on Patch 5 reflects mainly a subset of participants reaching the fifth patch (23%) who started off with a preconceived notion matching how the balls were distributed. However, comparing the slope of the relationship between performance and patch sequence, we found no effect of the interaction term (number of patches visited and patch sequence, χ² = 1.11, df = 2, p = .57) nor a difference between participants who reached Patch 5 and those who did not (5 versus 3: estimate = 0.22; SE = 0.21, z = 1.04, p = .55; 5 versus 4: estimate = 0.001; SE = 0.07; z = 0.02; p = .99.). Presumably, a longer test might show that participants reach a performance plateau by applying a counting rule in the present case (Wilke et al., 2015).

Experiment 2: Effect of Information Priming on Performance

An important form of information acquisition that impacts foraging has to do with culture (Horner, Whiten, Flynn, & de Waal, 2006). For instance, Ladio and Lozada (2003) described how Mapuche groups of Patagonia still make use of culturally appreciated forest resources despite the high costs of long travel from the steppe, their actual place of residence. Similarly, Sosis (2002) describes how Ifaluk fishermen of Micronesia keep visiting faraway patches with suboptimal returns for reasons that could be considered to be culturally transmitted. In order to test the influence of the transmission of information on searching performance, we implemented a condition [information condition (INFO)] in which participants were primed with a minimal snippet of information prior to the test (a clue as to the spatial distribution rather than the number of balls in the patch; see below). This priming was intended to simulate distilled and factual knowledge of the foraging environment, with the aim of evaluating to what extent such information would modify performance of the task.

Method

Participants (n = 67, 46% men, mean age = 21.46, SD = 2.31 years), who reported to be urbanites with no foraging experience, were recruited on the same university campus as for Experiment 1. In addition to general instructions on the task, participants received the following snippet of information: “If you find a ball, it is likely there are other balls nearby.” In Spanish, “Si encuentras una pelota, es probable que haya otras pelotas cerca.”

Results and Discussion

Compared to BL, performance in INFO was significantly better from the very first patch, exceeding the chance-level depletion rate on every patch (Figure 2; Table 1). However, we found no relationship between collection rate and patch sequence (estimate = 0.011; SE = 0.005; df = 268.5; t = 1.91; p = .056).

Figure 2.

Performance in the search task for each experimental condition: (a) the collection rate (the number of balls collected per cones lifted in a patch) and (b) the probability of success assessed using a binomial model (see text for description). The dotted horizontal line of intercept 0.20 represents the richness of each patch consisting in six balls, each under one of 30 cones = 0.2. BL = baseline condition; INFO = information condition; EXP = experienced foragers. Means ± SE are shown.

In INFO, we could observe the influence that even a minimal transmission of knowledge about the distribution of resources had on the foraging performance of participants who had little to no experience of foraging for natural resources. We observed a clear improvement in performance from the very beginning of the search task (Table 1), as well as a significant improvement in the ratio of successes to failures when compared to BL (Table 2, Model 2), which may not be surprising considering the importance of socially transmitted information in foraging (Koster, Bruno, & Burns, 2016; Koster & Venegas, 2012; Scalise Sugiyama, 2001). The magnitude of this improvement was evident as even the value of the first patch was almost twice as large as that of the first patch in BL. This suggests information had a strong effect, resulting in participants rapidly incorporating the information into their search strategy, which resulted in a larger collection rate from the start (presumably by changing the pattern in which cones were lifted; see Figure 2S in Section 1 of Supplementary Material).

Table 2.

Summary of the Linear Mixed Models Describing the Relationship Between Performance, Condition, and Patch Sequence.

Model 1: Collection rate by condition per patch sequence, considering participant’s ID as a random factor
Variance (SD) of random effect = 0.003 (± 0.05)
Contribution of condition, χ² = 40.62, df = 2, p < .001
Post hoc comparison	Estimate	SE	df	t value	p
BL vs. INFO	−0.087	0.014	185.03	−6.139	<.001
BL vs. EXP	−0.022	0.015	181.34	−1.482	.14
INFO vs. EXP	0.064	0.015	180.55	4.252	<.001
Contribution of patch sequence, χ² = 11.99, df = 2, p <.001.
Model 2: Successes and failures by condition per patch sequence, considering participant’s ID as a random effect
Variance (SD) of random effect = 0.053 (± 0.23)
Contribution of condition, χ² = 63.73, df = 2, p < .001.
Post hoc comparison	Estimate	SE	z value		p
BL vs. INFO	−0.499	0.064	−7.798		<.001
BL vs. EXP	−0.151	0.069	−2.203		.028
INFO vs. EXP	0.348	0.069	5.078		<.001
Contribution of patch sequence, χ² = 7.73, df = 1, p = .005.

Note. No significant effects for the interaction of condition and patch sequence were found. See text for more details. BL = base line; INFO = information condition; EXP = experience; SE = standard error; df = degrees of freedom.

Experiment 3: Effect of Real-Life Foraging Experience on Performance

Lastly, information can also be obtained empirically, that is, acquired by repeated foraging in natural environments. For instance, Chipeniuk (1995) has shown that foraging experience during childhood is a good predictor of the capacity to assess biodiversity, while Bock (2005) has reported how skill learning and strength are both relevant to children in order for them to become proficient foragers at each developmental stage. We therefore conducted a third experiment (EXP) in which the task was as in BL but where participants had considerable real-life experience in searching for natural resources, hypothesizing that this might give them an advantage when tested in the searching task. Experienced foragers may start out with the preconceived notion that items have an aggregated distribution and thus show a better performance. Nevertheless, despite the urbanites reporting not to have foraging experience, we cannot discard their extensive experience with situations that could be considered as foraging such as shopping for groceries or looking for parking spaces.

Method

Participants (n = 53, 47% men, mean age = 20.85, SD = 1.89 years) were university students from different localities within Sierra de Zongolica, a mountainous rural region in the state of Veracruz, Mexico, who reported undertaking weekly foraging trips in search of natural resources of economic relevance such as firewood, coffee, fungi, or edible and medicinal plants (Escamilla-Prado et al., 2012; Weimann & Heinrich, 1998). The experimental procedure was the same as for Experiment 1 (BL).

Results and Discussion

In this task, participants’ collection rate for Patches 2 and 3 was significantly greater than the chance depletion rate (Figure 2; Table 1). However, we found no relationship between collection rate and patch sequence (estimate = 0.004; SE = 0.006; df = 185.2; t = 0.76; p = .44). Overall performance was below that of participants in INFO, while performance between the participants in EXP and BL differed significantly, only when successes and failures were considered (Table 2). We attribute this difference to the fact that participants in EXP had more successes than BL in the earlier patches (estimate = −0.16, SE = 0.66, z = −2.47, p = .01). No difference was found for successive patches (estimate = −0.1, SE = 0.15, z = −0.7, p = .48). This suggests a steady performance by EXP participants where they started off with an above chance performance that did not improve as they visited more patches and were overtaken in the later patches by the positive slope of the collection rate of BL participants (Table 1).

In our study, participants’ previous experience may explain the somewhat better performance of EXP in earlier patches when compared to BL (Figure 2; Table 2). However, the lack of a significant relationship between collection rate and patch sequence may be attributed to a possible expectation of a distribution pattern that allowed EXP participants to perform better but that hindered their ability to adapt their decisions to the new environment in such a short time, similar to the concept of decision inertia, described in the study of Alós-Ferrer, Hügelschäfer, and Li (2016). Previous experience in foraging has been associated with a stronger expectation of clumped resources (Wilke, Scheibehenne, Gaissmaier, McCanney, & Barrett, 2014), as Wilke and Barrett (2009) found when testing the expectation of aggregation in sequences of events (i.e., positive recency) among Shuar hunter-horticulturalists of Amazonian Ecuador and undergraduate students. Our results suggest that the expectation of clumped resources associated with previous experience with foraging for natural resources may hinder performance when this expectation interferes with the acquisition of novel information.

The unexpectedly large disparity in performance, however, between the EXP and INFO conditions (Figure 2) may reflect the heterogeneous foraging experience reported by the EXP participants in this study, ranging from forest environments to crop fields. For example, firewood (often collected from fallen trees) is usually rather conspicuous, and once a patch is located, intensive searching may not be required until it is depleted, whereas looking for harvestable vegetables in crop fields must be performed by systematically walking along the furrows in order to avoid damaging unharvested plants.

General Discussion

From the experimental conditions implemented in this study, we aimed to evaluate the influence of three forms of experience on searching performance: (i) In BL, our aim was to evaluate the searching behavior of urbanites, who had little to no experience of foraging for natural resources and were faced with a novel searching environment where they could only acquire information during a brief search, (ii) In INFO, we intended to simulate the effect of culturally transmitted knowledge by supplying a brief snippet of information about resource distribution (in this case, patchiness) on the searching behavior of urbanites, and (iii) in EXP, our aim was to evaluate the possible role of experience in foraging for natural resources on the searching behavior of regular foragers when faced with a novel searching environment with a resource distribution intended to simulate that found in nature for some resources (Taylor, 1961), and where they had the possibility to translate their experience to the new experimental environment. For each of these aims, we found empirical support for the influence on searching performance of all three proposed methods of acquiring information.

Regarding the experimental search paradigm, we consider that its current configuration, which involved a large open area and greater energetic cost to participants than is usual in most laboratory settings (cf. Rosetti, Valdez, & Hudson, 2017), shows potential for further studies of human foraging behavior and decision-making. It involves a low cost, easily implemented setup, which can be mounted in almost any location, as we were able to do in two distant urban and rural regions. It can also be readily modified to experimentally explore a variety of questions relating to human foraging; for example, providing monetary rewards to explore the influence of motivation, modifying time limits to explore the effect of time constraints, using different forms of information priming, and varying the degree to which the balls are clustered under the cones. Longer and more complex contexts can be used to evaluate predictions of optimal foraging theory related to decision-making; for example, using heterogeneous patches with different numbers of cones but same depletion rates to evaluate patch-leaving decision rules. Searching tasks have been used to explore decision-making involving potential cognitive dysfunction associated with psychopathology (Pellicano et al., 2011; Rosetti et al., 2016; Wilke et al., 2014). This opens a range of possibilities to explore human foraging behavior hypotheses, from evolutionary, ecological, cognitive, and even psychopathological perspectives.

In conclusion, we implemented a novel searching task that allowed us to explore the role of different forms of experience on searching performance and decision-making in a large-scale, open arena where we simulated a foraging situation. Immediate experience during the search was quickly incorporated into the searching behavior of participants with little to no experience of foraging for natural resources, resulting in a rapid improvement in performance. Minimal information priming about the pattern of resource distribution effectively modified the behavior of participants, tuning their searching strategy to the expectation of clumped resources, thereby almost immediately improving their performance. The speed and efficiency with which participants incorporated the information priming into their decisions suggests an evolutionary adaptation, not only in terms of the implementation of information gained within a foraging context but also in the transmission of information, which has the potential to change behavior within a much shorter time frame. Foraging experience for diverse natural resources, we suggest, can result in considerable decision inertia that can help foragers specialize their searching strategies to match the particular distribution of resources in their environment. Additionally, this searching task proved useful by providing a more realistic, yet experimental, method with which to approach the study of the effect of several variables on human searching behavior, thereby hopefully contributing to an increased understanding of the cognitive and evolutionary processes underpinning such a central aspect of human behavior.

Supplemental Material

SupplementaryMaterial_EP - Human Foragers: Searchers by Nature and Experience

SupplementaryMaterial_EP for Human Foragers: Searchers by Nature and Experience by César Maya, Marcos F. Rosetti, Luis Pacheco-Cobos and Robyn Hudson in Evolutionary Psychology

Footnotes

Acknowledgments

We thank the staff and students of the Universidad Nacional Autónoma de México and the Universidad Veracruzana Intercultural Región Grandes Montañas.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This work was supported by the CONACYT scholarship 672442/594738 and DGAPA PAPIIT research grant no. IA202617.

ORCID iD

Marcos F. Rosetti

Supplemental Material

Supplemental material for this article is available online.

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