Week 9 – The Reproduction of Technology

This early draft was authored by Dietrich Stout.

What is Technology?

 Neuroscientific and psychological research commonly treats tool-use as an obvious behavioral category that does not require explicit delineation (e.g. Gönül, Takmaz, Hohenberger, & Corballis, 2018; Heald, Ingram, Flanagan, & Wolpert, 2018; Orban & Caruana, 2014). This is a misapprehension, as illustrated by extensive (and inconclusive) ethological attempts to define tool-use for comparative purposes (Crain, Giray, & Abramson, 2013; Fragaszy & Mangalam, 2018). For example, Osiurak and colleagues (Osiurak & Heinke, 2018; Osiurak & Reynaud, 2019; Reynaud, Lesourd, Navarro, & Osiurak, 2016) have argued that the prevailing cognitive science conception of tool-use as object manipulation is too narrow because it excludes “tools” such as machines, computers, containers, and structures and fails to emphasize a uniquely human capacity for “technical reasoning.” Osiurak et al. propose neologisms like intoolligence (Osiurak & Heinke, 2018) to describe this broader sphere of investigation but the existing term technology might prove more apt if its meaning can be suitably constrained.

Technology has been defined as everything from a physical assemblage of hardware to a set of rules or methods for problem solving (Dusek, 2006).  Language and number systems have been described as “cognitive technologies” (e.g. Frank, Everett, Fedorenko, & Gibson, 2008), B.F. Skinner (Skinner, 2002) envisioned a “technology of behavior,” and Michel Foucault (Foucault, 1988) enumerated technologies of production, sign systems, social control, and “the self.” Potential meanings of technology thus range from the smartphone in your pocket to essentially all of human culture and cognition. It may be helpful to ground this concept with comparative and evolutionary perspectives on technology as a distinctly human domain of activity that has helped to shape the modern human brain and mind. This “technological niche” perspective converges usefully with the “technological systems” approach developed in the social sciences, which identifies a technology as an integrated system of hardware, people, skills, knowledge, social relations, and institutions applied to practical tasks (Dusek, 2006; Hughes, 1987). The technological niche perspective enhances this definition by providing an evolutionarily motived interpretation of “practical.” 

The Human Technological Niche

As Darwin (Darwin, 1871: 136) noted, humans are a highly successful species. Even without agriculture, it has been estimated that Homo sapiens would have attained a global population of more than 70 million and a total biomass greater than any other large vertebrate (Hill, Barton, & Hurtado, 2009). This paradoxical demographic potential in a large-brained primate with notoriously costly young (Miller, Churchill, & Nunn, 2019) is enabled by a human strategy of alloparenting (Burkart, van Schaik, & Griesser, 2017) or biocultural reproduction (Bogin, Bragg, & Kuzawa, 2014), in which individuals other than the parents donate resources (e.g., time, effort, food) to help support offspring. For alloparents to have resources available for contribution, at least some individuals must reliably produce a surplus beyond what they require for survival. Embodied capital theory (Kaplan, Gurven, Winking, Hooper, & Stieglitz, 2010) thus proposes that humans have evolved a tightly integrated strategy in which a focus on high-value, difficult-to-acquire food resources provides the surplus nutrition needed to fund growth, survival, and reproduction, and is in turn enabled by the increased longevity and brain size that allow teaching, learning, and the cultural evolution of increasingly effective skills, knowledge, and equipment. This human-constructed niche (Laland et al., 2015) is thereby populated by increasingly complex technological systems focused on evolutionarily practical tasks of material production.

Such an evolutionary framing of technology specifies a behavioral domain of socially reproduced and elaborated activities typically involving the manipulation and modification of objects to enact changes in the physical environment (Stout, 2013). This extends well beyond conventional conceptions of tool use to include much longer causal chains (e.g. use of a tool to construct a mechanism to harvest a resource to make a product, and so on) (Stout, 2013), involving the coordinated activity of many individuals (Powers, van Schaik, & Lehmann, 2016), and the use of objects and materials in a wide range of roles other than as hand-held instruments (Osiurak & Heinke, 2018). It also encompasses processes of social reproduction that enable the accumulation of technological complexity (Stout & Hecht, 2017). At the same time, a focus on material production restricts the concept of technology from spreading to encompass all of human culture.

Technology is not, however, limited to activities that directly increase net energy capture or survival rates (cf. Isler & Van Schaik, 2014; White, 1943) since the nature of production and the currency of returns to individuals can vary so widely across economic and cultural systems. A more robust distinction can be made between materially instrumental tasks primarily intended to achieve physical changes in the world and communicative tasks that seek to alter the thoughts, behaviors, and/or experiences of the self or others (cf. Legare & Nielsen, 2015). Human culture clearly encompasses both, but technology as defined here includes only the former. This distinction is important for cognitive science because instrumental and communicative goals present very different functional demands and design constraints and will thus tend to implicate different cognitive processes (Finkel, Hogrefe, Frey, Goldenberg, & Randerath, 2018; Tylén, Philipsen, Roepstorff, & Fusaroli, 2016), social learning mechanisms (Legare & Nielsen, 2015), and cultural evolutionary dynamics (Derex & Mesoudi, 2020; Rogers & Ehrlich, 2008). By more rigorously defining the object of study, the technological niche concept provides the unifying framework for a cognitive science of human technology.

Cognitive Foundations of Technology

Attempts to specify a critical “essence” of technology have invoked everything from skilled prehension (Buxbaum, 2017; Fragaszy & Mangalam, 2018; Heald et al., 2018; Yildirim, Wu, Kanwisher, & Tenenbaum, 2019), to causal reasoning (Osiurak & Reynaud, 2019; Wolpert, 2003), mental time travel (Suddendorf, Bulley, & Miloyan, 2018), imitation (Derex, Bonnefon, Boyd, & Mesoudi, 2019; Legare & Nielsen, 2015), and mentalizing (Tomasello, Kruger, & Ratner, 1993). Rather than prioritizing one of these, a technological niche perspective recognizes all as relevant and provides a framework for considering their interaction. For example, the remarkable complexity and efficacy of human technology is often attributed to a process of incremental improvement over time referred to as cumulative cultural evolution (CCE) (Mesoudi & Thornton, 2018). CCE is widely held to rely upon “high fidelity” social reproduction mechanisms requiring mentalizing and/or imitation in order to enable the lossless accumulation of innovations over time (Dean, Kendal, Schapiro, Thierry, & Laland, 2012; Derex et al., 2019; Tomasello et al., 1993). In the case of technology, however, capacities for causal and analogical reasoning (Gentner & Hoyos, 2017; Osiurak & Reynaud, 2019), cognitive control (Gönül et al., 2018; McDougle, Ivry, & Taylor, 2016; Stout, Hecht, Khreisheh, Bradley, & Chaminade, 2015), memory (Gruber & Ranganath, 2019), and perceptual-motor control (Sánchez et al., 2017) will often be implicated in the behavioral exploration necessary for the generation and identification of beneficial innovations (Legare & Nielsen, 2015; Miu, Gulley, Laland, & Rendell, 2020). Many technologies will also require substantial learning as a foundation for such exploration and discernment, so that processes of knowledge reproduction (Gentner & Hoyos, 2017; Pan et al., 2020), skill acquisition (Gowlland, 2019), and innovation (Legare & Nielsen, 2015) are thoroughly intertwined (Osiurak & Reynaud, 2019; Stout & Hecht, 2017). Finally, the same capacities for intersubjectivity (Tomasello et al., 1993) and interactive alignment (Pagnotta, Laland, & Coco, 2020; Pan et al., 2020) that support the social reproduction of technology also underpin the cooperation and coordination (Hill et al., 2009; Powers et al., 2016) that have allowed the complexity of human technology to so far exceed that of individual animal tool-use.

Technological Reproduction

Although social learning is commonly treated as the “transmission” or “copying” of information (e.g. Boyd, Richerson, & Henrich, 2011), technological learning is a protracted, collaborative process (Gobet, 2015; Gowlland, 2019; Pargeter, Khreisheh, & Stout, 2019; Suddendorf, Brinums, & Imuta, 2016) better described as the reproduction of skill. This reflects demands for the precise control of physical contingencies in pursuit of complex technological goals, and has important implications for understanding the processes of high-fidelity imitation and innovation that have been termed the twin “engines” of cumulative cultural evolution (CCE) (Legare & Nielsen, 2015). In particular, the complexity of real-world technological reproduction problematizes dichotomies of social vs. asocial learning (Heyes, 2018), product vs. process copying (Tennie, Call, & Tomasello, 2009), and “blind” vs. guided innovation (Mesoudi, in press) that have been prevalent in experimental and modeling approaches to CCE. This is exemplified in technological apprenticeship (Gowlland, 2019; Sterelny, 2012) which alternates social learning with individual practice (Stout, 2013) in an iterative process of increasing refinement that Whiten (Whiten, 2015) refers to as a “helical curriculum.” Social information ranging from exemplar artifacts, tools, and observable behavior available in a constructed “learning niche” (Flynn, Laland, Kendal, & Kendal, 2013; Stout & Hecht, 2017) to intentional demonstration, explicit instruction, and affective feedback from teachers (Kline, 2015) guides learners to re-create increasingly sophisticated skills though deliberate practice over extended periods, with each round of individual practice allowing deeper appreciation of the available social information (Stout, 2013; Stout & Hecht, 2017; Whiten, 2015).

The end result is technological expertise that combines refined internal models for efficient action perception, control, and prediction (McNamee & Wolpert, 2019; Sokolov, Miall, & Ivry, 2017) with flexible task-related knowledge structures assembled in hierarchical systems of increasing depth and complexity (Gobet, 2015; Stout, 2013). The absolute and relative importance of these different elements are expected to vary across technologies ranging from stone tool making [Box 3] to engineering (Purzer, Moore, & Dringenberg, 2018) or computer programming (Blackwell, Petre, & Church, 2019), and will help determine the relevance and efficacy of strategies such as trial-and-error experimentation (Truskanov & Prat, 2018), end-product emulation (Reindl, Apperly, Beck, & Tennie, 2017), body movement mimicry (Heyes, 2018; Tennie et al., 2009), intention sharing (Tomasello et al., 1993), and various forms of social scaffolding and teaching (Kline, 2015). For example, demanding perceptual-motor skills require deliberate practice over extended periods, often in the absence of apparent progress or immediate rewards (Gray & Lindstedt, 2017; Pargeter et al., 2019). For learners, this requires the prospective ability to imagine payoffs of investment in one’s future self (Suddendorf et al., 2016; Suddendorf et al., 2018). For teachers, it places a premium on mentalizing and metacognitive strategies for motivation and encouragement, including intervention to help learners manage (e.g. “you’re getting impatient, take a break”) or reappraise (e.g. “think of it as an opportunity”) their own affective and mental states (Buhle et al., 2013). Rather than one key reproductive mechanism enabling the buildup of cumulative technology (cf. Osiurak & Reynaud, 2019; Tennie et al., 2009), we should expect context-dependent diversity (Caldwell, 2020).

Similarly, a focus on technological reproduction helps to resolve the false dichotomy between directed exploration and blind variation and selection as drivers of technological innovation (Derex et al., 2019; Mesoudi, in press; Osiurak & Reynaud, 2019) by drawing attention to the requirements for individual skill that underpin both. Thus, even the strongest examples of blind evolution (e.g. the gradual optimization of 10^th – 18^th century violin sound holes absent any understanding of acoustic engineering principles (Nia et al., 2015)) require reproductive accuracy and discernment of desired outcomes by expert craftspeople with detailed understanding of their tools, materials, and products. Acquired skills and knowledge are expected to constrain and guide variation (e.g. Derex et al., 2019) by biasing the likelihood of copying and adoption (Boyd et al., 2011; Henrich, 2016; Kendal et al., 2018), the direction and probability of copy errors (Truskanov & Prat, 2018) and the generation of innovations both through intentional trail-and-error and by insight (Legare & Nielsen, 2015). Furthermore, cultural norms and practices for skill reproduction can influence both rates of random variation and tendencies toward intentional innovation (Lew-Levy, Milks, Lavi, Pope, & Friesem, 2020). Much as in biological evolution (Laland et al., 2015), a technological niche perspective does not always allow a clean analytical separation between proximate cognitive and behavioral mechanisms and ultimate population-level processes of change.

To be added: Paleolithic stone-tool making as a case study.

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