# Commentary on François Osiurak and Emanuelle Reynaud's "The Elephant in the Room: What Matters Cognitively in Cumulative Technological Culture" (2019)

Alessandro Guida

► To cite this version:

Alessandro Guida. Commentary on François Osiurak and Emanuelle Reynaud's "The Elephant in the Room: What Matters Cognitively in Cumulative Technological Culture" (2019). Journal of expertise, 2020. hal-05503947

HAL Id: hal-05503947

https://hal.science/hal-05503947v1

Submitted on 10 Feb 2026

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.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

Journal of Expertise
2020. Vol. 3(3)
©2020. The author
licenses this article
under the terms of the
Creative Commons
Attribution 3.0 License.
ISSN 2573-2773

# Commentary on François Osiurak and Emanuelle Reynaud's "The Elephant in the Room: What Matters Cognitively in Cumulative Technological Culture" (2019)

Alessandro Guida

Department of Psychology, Université Rennes, France

Correspondence: Alessandro Guida. alessandro.guida@univ-rennes2.fr; alessandro.guida.psychology@gmail.com

In an article published in *Behavioral and Brain Sciences*, François Osiurak and Emanuelle Reynaud (2019) have proposed a new framework to understand cumulative technological culture (CTC). They define CTC as an increase in the efficiency and complexity of tools and techniques in human populations over generations (e.g., Boyd et al., 2011; Richerson & Boyd, 2008), which distinguishes humans from other species¹. Two concepts seem to be central to explain CTC (Legare & Nielsen, 2015): imitation and innovation. According to Osiurak and Reynaud, previous researchers (e.g., Dean et al., 2012; Lewis & Laland, 2012; Tennie et al., 2009; Tomasello et al., 2005) have mainly focused their attention on the former to explain CTC. Although the authors do not deny the importance of imitation, which is necessary to pass on the *content* of technical information, they champion the idea that CTC originates in non-social cognitive skills instead of in social cognitive skills. They argue that CTC emerges uniquely because a non-social *cognitive structure* (i.e., technical reasoning²) enables humans to acquire and develop the *content*.

In this article I will not argue against this hypothesis; Instead I will focus on two points that draw on memory expertise. The first one concerns the distinction between *cognitive structure* and *content* on which the authors base their argumentation. The second point is related to humans' unique capacity to imitate a combination of interdependent mechanical

actions³, which is explained by Wynn and Coolidge (2007) through an enhancement of working memory (WM).

My first point is related to the knowledge principle (Guida & Campitelli, 2019), which states that individuals always use knowledge to process and remember information; in fact we cannot *not* use knowledge. We constantly use knowledge structures to interact with the world. In the domain of expertise, knowledge structures have been highly theorized in terms of chunks (e.g., Chase & Simon, 1973; De Groot, 1946/1978), retrieval structure (Chase & Ericsson, 1982; Ericsson & Kintsch, 1995) or templates (Gobet & Simon, 1996) for example. Applied to Osiurak and Reynaud's framework, it means that there is a recursion between *content* and *cognitive structure*, once a new content is learned, it will change the structure, which will help in the acquisition of new content, and so on. This highly interactive process between *content* and *cognitive structure* is so extreme that our (mental) *content* becomes our *cognitive structure*. Knowledge structures such as chunks, retrieval structures, or templates are first *contents* that we store.

Eventually, however, they become *cognitive structures*, which enable us to interact with the world and efficiently store new *content* that becomes *cognitive structure*. The fact that these two entities are difficult to disentangle is not new (e.g., De Groot, 1946/1978), and computational models such as EPAM-IV
(Richman et al., 1995) and CHREST (e.g., Gobet & Simon, 2000) are based on a discrimination network where information from a new *content* can be incorporated within the discrimination network expanding it via the creation of new nodes (*cognitive structure*)⁴. The development of knowledge structures has profound changes in terms of brain structural plasticity (e.g., Maguire et al., 2000; for a review see Zatorre et al., 2012) and functional plasticity (e.g., Bilalić et al., 2016; for a review see Buschkuehl et al., 2012) especially when it comes to templates or retrieval structures (Guida et al., 2012). Concerning the latter, studies on the Spatial Positional Association Response Codes effect (Guida et al., 2018; van Dijck & Fias, 2011) have shown that once individuals know how to read/write—through an increase of their knowledge of written language (*content*)— they will be able to use their knowledge as a retrieval structure (*cognitive structure*) to store information, organizing it according to their reading/writing system direction.

Going back to Osiurak and Reynaud's argumentation, it means that although technical reasoning is the necessary *cognitive structure* to acquire and develop new *content*, the elements that compose technical reasoning were certainly acquired as *content* before being used as a *cognitive structure*. I think that the authors' distinction is too clear-cut, as *cognitive structure* and *content* are related in a recursive fashion. There is a specific point in the manuscript where this is more patent, which brings me to the second point.

Osiurak and Reynaud explain that imitation in nonhumans generally concerns one mechanical action or a sequence of independent mechanical actions but not a combination of interdependent mechanical actions (“combined mechanical actions” from here). The latter would be crucial to account for humans’ superiority in terms of CTC. But this explanation begs the question, where does the ability of humans to acquire combined mechanical actions come from? One explanation analyzed by the authors is based on Wynn and Coolidge’s (2007) proposal: Our ability would come from an enhancement in

WM. However, Osiurak and Reynaud rebut this explanation because WM only stores information and cannot generate content⁵.

I do not agree with their view that considers WM just as a storage facility, which is reminiscent of the von Neumann architecture (von Neumann, 1945) applied to the mind. I believe that three points are crucial. First, WM enable us not only to temporarily store information but also to manipulate it (Baddeley, 2003; Baddeley & Hitch, 1974). Second, recent models (Barrouillet et al., 2004; Cowan, 2012; Oberauer, 2002) envisage WM as the activated portion of long-term memory (although this could be considered controversial⁶), which has, for consequence, that we cannot think about something we do not know, we can only think with our knowledge (see above the knowledge principle). Third, our mental capacity to store information and manipulate it, as measured by a WM span, is highly dependent on knowledge structures such as chunks⁷. The authors are aware of the chunking theory (Chase & Simon, 1973) or the long-term working memory theory (Ericsson & Kintsch, 1995) but seem to disconnect them from the concept of WM. Modern views of WM see it as a combination of processes (Cowan, 2012; Postle, 2006), which should encompass chunking or even retrieval structures⁸. Based on these elements, I believe that the authors should not depict WM just as an empty *cognitive structure*.

In conclusion, while I welcome this new framework of François Osiurak and Emanuelle Reynaud, I hope they can enrich it by relating it to a more interactive view of the concepts of “content” and “cognitive structure.” After all, sometimes the elephant is in the details.

## Endnotes

1. Human cultural transmission is described by a “ratchet effect” (Tomasello, 1999): Once an improvement is made, backward slippage is rare.

2. It is noteworthy that Osiurak and Reynaud propose that technical reasoning is also crucial for imitation as it allows to extract relevant technical information from a social demonstration.
3. For example, consider the making of a simple spear: A flake tool is produced in order to cut down a tree and carve from it a spear (i.e., tool-object interaction).

4. Gobet et al. (2018) write: “EPAM [and CHREST] stores information using a discrimination network...Learning consists of creating new nodes (discrimination) and elaborating existing nodes (familiarization).

5. Osiurak and Reynaud write (2019, p.23): “Working memory is not a cognitive mechanism that is used to generate content, but instead temporarily stores content that is processed by other cognitive mechanisms.”

6. Baddeley's model (Baddeley, 2003; Baddeley & Hitch, 1974) does not consider WM as the active part of long-term memory and is the most widespread WM model.

7. When considering chunks in laypersons, WM span can pass from 3 to 7 as modelized by Mathy and Feldman (2012).

8. The theory of long-term working memory is, after all, a WM theory.

## Author's Declaration

The author declares that there are no personal or financial conflicts of interest regarding the research in this article.

## References

Baddeley, A. (2003). Working memory: Looking back and looking forward. *Nature Reviews Neuroscience*, *4*(10), 829-839.

Baddeley, A. D., & Hitch, G. (1974). Working memory. *Psychology of Learning and Motivation*, *8*, 47-89.

Barrouillet, P., Bernardin, S., & Camos, V. (2004). Time constraints and resource sharing in adults' working memory spans. *Journal of Experimental Psychology: General*, *133*(1), 83.

Bilalić, M., Grottenthaler, T., Nägele, T., & Lindig, T. (2016). The faces in radiological images: Fusiform face area supports radiological expertise. *Cerebral Cortex*, *26*(3), 1004-1014.

Boyd, R., Richerson, P. J., & Henrich, J. (2011). The cultural niche: Why social learning is

essential for human adaptation. *Proceedings of the National Academy of Sciences*, *108*(Supplement 2), 10918-10925.

Buschkuehl, M., Jaeggi, S. M., & Jonides, J. (2012). Neuronal effects following working memory training. *Developmental Cognitive Neuroscience*, *2*, S167-S179.

Chase, W. G., & Ericsson, K. A. (1982). Skill and working memory. *Psychology of Learning and Motivation*, *16*, 1-58.

Chase, W. G., & Simon, H. A. (1973). Perception in chess. *Cognitive Psychology*, *4*(1), 55-81.

Cowan, N. (2012). *Working memory capacity*. Psychology press.

De Groot, A. D. (1978). *Thought and choice in chess* (Vol. 4). Walter de Gruyter GmbH & Co KG.

Dean, L. G., Kendal, R. L., Schapiro, S. J., Thierry, B., & Laland, K. N. (2012). Identification of the social and cognitive processes underlying human cumulative culture. *Science*, *335*(6072), 1114-1118.

Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. *Psychological Review*, *102*(2), 211.

Gobet, F., & Simon, H. A. (1996). Templates in chess memory: A mechanism for recalling several boards. *Cognitive Psychology*, *31*(1), 1-40.

Gobet, F., & Simon, H. A. (2000). Five seconds or sixty? Presentation time in expert memory. *Cognitive Science*, *24*(4), 651-682.

Gobet, F., Lloyd-Kelly, M., & Lane, P. C. (2018). Computational models of expertise. *The Science of Expertise*, 347-364.

Guida, A., & Campitelli, G. (2019). Explaining the SPoARC and SNARC effects with knowledge structures: An expertise account. *Psychonomic Bulletin & Review*, *26*(2), 434-451.

Guida, A., Gobet, F., Tardieu, H., & Nicolas, S. (2012). How chunks, long-term working memory and templates offer a cognitive explanation for neuroimaging data on expertise acquisition: A two-stage framework. *Brain and Cognition*, *79*(3), 221-244.
Guida, A., Megreya, A. M., Lavielle-Guida, M., Noël, Y., Mathy, F., van Dijck, J.-P., & Abrahamse, E. (2018). Spatialization in working memory is related to literacy and reading direction: Culture “literarily” directs our thoughts. *Cognition*, 175, 96-100.

Henrich, J., & Gil-White, F. J. (2001). The evolution of prestige: Freely conferred deference as a mechanism for enhancing the benefits of cultural transmission. *Evolution and Human Behavior*, 22(3), 165-196.

Legare, C. H., & Nielsen, M. (2015). Imitation and innovation: The dual engines of cultural learning. *Trends in Cognitive Sciences*, 19(11), 688-699.

Lewis, H. M., & Laland, K. N. (2012). Transmission fidelity is the key to the buildup of cumulative culture. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 367(1599), 2171-2180.

Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S., & Frith, C. D. (2000). Navigation-related structural change in the hippocampi of taxi drivers. *Proceedings of the National Academy of Sciences*, 97(8), 4398-4403.

Mathy, F., & Feldman, J. (2012). What's magic about magic numbers? Chunking and data compression in short-term memory. *Cognition*, 122(3), 346-362.

Oberauer, K. (2002). Access to information in working memory: Exploring the focus of attention. *Journal of Experimental Psychology: Learning, Memory, and Cognition*, 28(3), 411.

Osiurak, F., & Reynaud, E. (2019). The Elephant in the Room: What Matters Cognitively in Cumulative Technological Culture. *Behavioral and Brain Sciences*, 1-57.

Postle, B. R. (2006). Working memory as an emergent property of the mind and brain. *Neuroscience*, 139(1), 23-38.

Richerson, P. J., & Boyd, R. (2008). *Not by genes alone: How culture transformed human evolution*. University of Chicago press.

Richman, H. B., Staszewski, J. J., & Simon, H. A. (1995). Simulation of expert memory

using EPAM IV. *Psychological Review*, 102(2), 305-30.

Tennie, C., Call, J., & Tomasello, M. (2009). Ratcheting up the ratchet: On the evolution of cumulative culture. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 364(1528), 2405-2415.

Tomasello, M. (1999). The human adaptation for culture. *Annual Review of Anthropology*, 28(1), 509-529.

Tomasello, M., Carpenter, M., Call, J., Behne, T., & Moll, H. (2005). Understanding and sharing intentions: The origins of cultural cognition. *Behavioral and Brain Sciences*, 28(5), 675-691.

van Dijck, J.-P., & Fias, W. (2011). A working memory account for spatial-numerical associations. *Cognition*, 119(1), 114-119.
https://doi.org/10.1016/j.cognition.2010.12.013

Wynn, T., & Coolidge, F. L. (2007). Did a small but significant enhancement in working memory capacity power the evolution of modern thinking. *Rethinking the Human Revolution*, 79-90.

Zatorre, R. J., Fields, R. D., & Johansen-Berg, H. (2012). Plasticity in gray and white: Neuroimaging changes in brain structure during learning. *Nature Neuroscience*, 15(4), 528-536.

Submitted: 28 May 2020

Revision submitted: 17 July 2020

Accepted: 22 July 2020

<figure><img src="image_3.png" /></figure>