Mercier and Sperber's (M&S's) theory of the adaptive value of argumentative reasoning is intriguing from a computational perspective, since the search for arguments that support a given conclusion is computationally more difficult (viewed as a reasoning problem) than logical reasoning. The first logical solvers were developed in the 1950s (Newell & Simon Reference Newell and Simon1956). Argumentative computers are yet to be developed.

Argumentative reasoning, defined broadly as the discovery of statements to support a given conclusion can be cast as a form of adbuctive reasoning, or inferring a precondition from a consequent (following Peirce Reference Peirce1931–35). Such reasoning is logically fallacious, but as M&S's target article details, it is typical of human behaviour to select arguments and conclusions that together are mutually reinforcing.

We accept M&S's arguments for the adaptive value of argumentative reasoning as a communicative skill. However, just as questions have been raised in other fields about the evolution of the sophisticated communicative abilities of humans, we can also ask how an argumentative ability could have evolved. Many evolutionary adaptations are thought to be exaptations; that is, new uses for existing structures. Verbal argumentative reasoning obviously draws on linguistic ability, but it need not postdate it. We consider the possibility that cognitive abilities underlying argumentative reasoning may predate the evolution of language by exapting abductive abilities from other domains.

Reasoning is not the only domain where adaptive behaviour may utilise abductive reasoning. A much more ancient evolutionary ability, which humans share with other mammals, birds, reptiles, and even insects, is the ability to navigate. Much is known about the navigational systems of mammals, including the neural representations of places (O'Keefe & Dostrovsky Reference O'Keefe and Dostrovsky1971) linked into cognitive maps (O'Keefe & Nadel Reference O'Keefe and Nadel1978; Tolman Reference Tolman1948), grid cells (Moser et al. Reference Moser, Kropff and Moser2008), and head-direction cells (Taube et al. Reference Taube, Muller and Ranck1990). Complementing neural studies are computational models and embodied robots, and it is the fully functional robotic systems (Arleo & Gerstner Reference Arleo and Gerstner2000; Kuipers Reference Kuipers2000; Milford & Wyeth Reference Milford and Wyeth2003; Thrun Reference Thrun, Lakemeyer and Nebel2003) that provide insight for this commentary.

Two approaches can be contrasted for robotic navigational systems: a logically correct approach based on Bayesian reasoning (analogous to deductive reasoning), and one based on a bio-inspired approach that exploits a form of abductive reasoning to constructive a cognitive map. In mobile robots, a key problem is to maintain an estimate of one's current location while exploring and mapping a new environment (called simultaneous localisation and mapping [SLAM]).Given information about localisation (such as a Global Positioning System [GPS]), mapping is a relatively straightforward deductive reasoning problem, and conversely, given a map, localisation is straightforward. However, when both tasks must be solved simultaneously (in the absence of GPS), the errors in each compound. Many locations do not have unique landmarks; apparently unique features of one environment may turn out to be present only transiently or to be shared by other locations. Even recognising a previously visited location at a later time can be challenging. In vision-only SLAM, one of the best-performing systems is the RatSLAM system (Milford Reference Milford2008), inspired by the hippocampal mapping system of the rodent. Initially developed using place cells and head-direction cells, it was discovered early on that the robots also needed something akin to grid cells (although when the model was first developed in 2003, grid cells themselves were yet to be discovered). RatSLAM learns the paths that a robot traverses through its environment and links them into maps. It uses a unique optimisation system that maintains information that is locally consistent, while also estimating a global map.

If a location is considered a “conclusion” in a mapping task, and features of the environment are considered “arguments to support that conclusion,” then systems that are effective at navigation are of necessity abductive reasoners. Maps are constructed by using locations for which there is evidence, and evidence is retained when it is useful for localisation. Maps and their evidence need to be mutually reinforcing to be useful. The hippocampus has been linked to many aspects of cognition as well as spatial memory. Argumentative reasoning may well be the latest of its exapted abilities.