At this point, it is important to address the fact that many do not think that statements like “the brain is a computer,” “the mind is a computer,” “cognition is computation”21, and others mentioned above, are metaphorical, instead, like Pylyshyn, they think that

And Tim van Gelder further attests that,

However, while it is correct to say that a description of cognitive activity as “rule-governed manipulation of internal symbolic representations” is a literal description, this does not exhaust the content of statements like “cognition is computation” (or “the brain is a computer”). In other words, statements like these typically do not function merely as shorthand for the former kinds of descriptions. Such computational statements are indeed often apt and explanatorily powerful; however, this does not negate the fact that the vast majority are metaphors. Roughly, this means that they function as ‘automatic, ubiquitous conceptual devices’ that enable us to understand the brain, mind, and cognition in terms of computers and computational processes—our understanding of the former has been “mapped onto” or “scaffolded upon” the latter. One thing complicating the use of these metaphors and their recognition as such is the fact that the concept of computer is rarely addressed or unpacked when invoking these metaphors. As Piccinini explains, “philosophers interested in computationalism have devoted most of their attention to explaining mental phenomena, leaving computation per se largely unanalyzed” (2010: 282). There is thus a fair bit of unacknowledged confusion between, and conflation of, distinct notions of computer in much of the relevant literature.

Any relevant notion of computer must be one focused on function rather than physical properties. In other words, whether the brain (or mind) is literally a computer is a matter of how it functions, not what it is “made of” (or grounded in), so to speak. The weakest version of Computationalism understands a computer as a physical apparatus that can “in theory compute any computable function”—where a “computable function” is one for which an algorithm (a finite, specific set of instructions) could specify the calculations or operations used to generate an output from the corresponding input (Richards & Lillicrap 2022). Relatedly, computation is information processing involving the manipulation of information-bearing structures or representations to produce outputs from inputs (Piccinini 2009: 516–517).22 This account of computers emerges from, and is most prevalent in, the computer science literature (Richards & Lillicrap 2022; Ralston et al. 2003).

When statements like “the mind is a computer” invoke this sense of ‘computer’ they are, strictly speaking, committing a category error in that the mind cannot be a physical apparatus, and thus saying something false. However, when statements like “the brain is a computer” invoke this sense of ‘computer,’ they are literally true—a brain is a physical apparatus that can in theory (bracketing concerns of time, energy, and resources) compute any computable function. Unfortunately, they are also explanatorily weak and fail to promote further insight into the kinds of questions about cognition at issue in philosophy, psychology, and neuroscience. This is because, depending on how broadly ‘information,’ ‘algorithm,’ ‘input,’ and ‘output’ are defined, the resulting notion of computation may be

This very general notion of computer does not enable us to account for mental states and processes as I have delimited them above—namely, as higher-level neural states and processes involved in things like abstraction, reasoning, etc. computation, on this account, is fairly ubiquitous and might be said to occur in a thermometer representing the ambient temperature—thus it cannot be used to capture or explain much of anything meaningful about cognition, including those topics addressed by Classical Computationalist and Embodied theories of cognition.23 Rather, these notions only typically serve as starting points for the concepts of computer and computation—and related specifications of relevant ‘inputs,’ ‘outputs,’ ‘algorithms,’ and ‘information processing’—most often used by philosophers and cognitive scientists.

Thus, a stronger version of Computationalism is typically relied upon in these disciplines according to which the mind is ‘the software’ of the brain—in other words, a program or set of programs contained within and executed by the brain, as well as the states and processes resulting from their execution. In this view, the relevant notion of computer at work in the claim that “the brain is a computer” is a physical apparatus that not only can “in theory compute any computable function,” but is also capable of functioning as hardware for the execution of such programs. Piccinini illustrates the centrality of these concepts in his articulations of the key claims of computationalism, functionalism, and computational functionalism. He explains that, according to computational functionalism,

This version of Computationalism derives much of its philosophical benefit and explanatory power from this conceptual framework—ostensibly enabling it to “demystify” the mind’s supervenience on the brain and the operation of particular cognitive processes (Piccinini 2010; Simon 1996; Fodor 2000).24 However, it is important to note that such statements are clearly metaphorical—which is not to say that they are vague, imprecise, or somehow “mysterious,” but instead to highlight that they enable and promote scaffolding of our understanding of the mind, brain, and cognition upon our understanding of computers (software, hardware, and computation).25

This issue is further compounded in stronger versions of Computationalism which engage in more detailed scaffolding of brain and cognition on computer and computation—informed by features of the kinds of computers that we are most familiar with—digital computers, especially those that we use regularly (e.g. laptops, phones, etc.). As Piccinini explains, “the most relevant and explanatory notion of computation is that associated with digital computers…[which is]…based on the electronic devices we use on a regular basis and how they operate” (Piccinini 2009: 223). Here, the relevant notions of computer and computation are digital computers and computation, respectively, and often (explicitly or implicitly) draw upon the following features.

First, the architecture of digital computers, which includes a central processing unit (CPU)—a computer chip responsible for coordinating and controlling the functions of a computer: it receives input (e.g. from a keyboard), processes the input (carries out arithmetic and logical operations based on step-by-step instructions (algorithms) provided by its control unit), and then sends the output to other devices (e.g. a display or printer). Additionally, digital computers include a random-access memory (RAM) module—a small circuit board containing a set of memory chips—which stores information the computer is currently using or processing (such as the aforementioned algorithms, information needed to run a web browser) and an external memory or hard drive which is used for long-term storage of things like documents and software programs. The CPU, control unit, RAM, and external memory all work together to allow a computer to perform tasks and store information.

Second, information processing in digital computers, which involves constant communication between the CPU, control unit, RAM, and often the hard drive and occurs through the transformation or manipulation of strings of binary digits (‘bits’ which can be a 1 or 0) which represent input from various sources including text, images, numbers, and audio which have been transduced into this form (e.g. the number ‘5’ is represented as ‘101’ and the letter ‘A’ as ‘01000001’). The output of this processing can then be transduced back into text, images, numbers, audio, etc. (e.g. ‘101’ becomes ‘5’). This information processing is largely sequential, discrete, and passive. It processes information sequentially in that it executes instructions and performs computations in the specific order determined by the instructions in the program being executed—as opposed to parallel processing. It processes information in a discrete manner in that it operates on distinct pieces of data, one at a time, that have been encoded using a set of discrete states (represented as ‘1’ and ‘0’)—rather than data in the form of a continuous range of values, as is done in analog computing.26 And its processing is passive in that the computer does not actively generate or modify the information or data being processed—rather it simply executes the instructions to perform the computations—in contrast to active systems.

These are just some of, although perhaps the most relevant, aspects of digital computers informing stronger versions of Computationalism, and their central concepts of computer and computation, that are ubiquitous in philosophy and the cognitive sciences. These aspects have clearly contributed to explanatorily powerful conceptual tools and frameworks for understanding the mind, brain, and cognition. For example, aspects of the aforementioned architecture and information processing of digital computers are often used to represent how different parts of the mind work together and engage in conceptual processing.27 Processes involved in executive functioning (such as decision-making and problem-solving) are represented as operating like a CPU, working or short-term memory like RAM, and long-term memory like a hard drive or external memory. Sensorimotor input is thought to be transduced into symbolic, mental representations in order to be processed, and once processed, transduced into outputs (e.g. actions)—on the model of information processing in digital computers. Relatedly, the relationship between cognition and the external world is understood using the model of on-line and off-line computer processing, and as mentioned above, the relationship between the mind and brain is modeled on that between software and hardware.

Again, these stronger versions of Computationalism clearly derive much of their philosophical benefit and explanatory power from these conceptual frameworks; and again, these frameworks and associated claims must be understood as metaphors. If brains were literally computers, on this notion of computer, they would clearly need to share their essential features, some of which I have outlined above.28 However, while they share some of them, they do not share others. To take just a few examples, they are similar to computers (architecturally) in that they both contain short-term and long-term memory (in computers, RAM and a hard drive, respectively), as well as a ‘control unit’ of sorts which acts as the central processing and ‘decision-making’ center, involved in controlling and regulating functions (i.e. the regions involved in executive functioning like the prefrontal cortex—in computers, the CPU).29 However, they differ (with regards to information processing) in that, while computers process information passively by simply executing a set of instructions that dictate how to process input, one might think that brains additionally often engage in active processing—they manipulate information, generate new connections, solve problems, and make decisions.

To take a more detailed example, take memory—the processes of memory acquisition or learning, as well as storage and retrieval, are often “mapped onto” the processes of “encoding” and “indexing” in computers, respectively. Roughly, in computers, input data are encoded into bits (1s and 0s) which are then stored in the computer’s memory and indexed through the use of locations called ‘addresses’—and strings of bits can then be later retrieved from storage by their address. Thus, these data are encoded (stored such that it can be accessed and used) and indexed (organized in a way that makes it easy to retrieve). These concepts have clearly provided metaphorical scaffolding for understanding human memory and its operations in work on cognition—and terms like ‘encoding’ and ‘indexing’ are often used in work in cognitive science on memory.

A clear example comes in references to ‘neural encoding’ which is the process by which sensory information is converted into a neural signal that can be processed and interpreted by the brain and is thought to play a key role in memory acquisition. Specific neurons are activated in response to sensory stimuli, which then transmit this information through the ‘complex circuitry’ of neural pathways for further processing—the specific patterns of neural activity that result from this process form the ‘neural code,’ from which the mind can then extract relevant information and generate a response to the stimuli (Dayan & Abbott: 2005).

There are some similarities between these two processes in brains and computers that make the latter, in some senses, an apt metaphor for the former—in both instances, patterns of activity are used to store information, enabling later retrieval. There are some similarities between these two processes in brains and computers that make the latter, in some senses, an apt metaphor for the former—in both instances, patterns of activity are used to store information, enabling later retrieval. However, there are of course also dissimilarities between the relevant types of ‘codes’ used—computer codes are formed by the presence or absence of electrical signals in digital circuits and logic gates to encode data in binary, enabling computer coding to be exact; whereas ‘neural codes’ are formed by the activation (or deactivation) of specific networks of interconnected neurons to encode information into patterns of activation, resulting in approximation rather than exactitude. And whereas neural encoding relates environmental stimuli to neural responses and is approximate, and only approximately reversible, (digital) computational coding relates classes of digital strings, is exact, and (typically) exactly reversible (Piccinini & Bahar 2013). Thus, due to key dissimilarities between the latter and the former, brains are best understood as metaphorical computers and cognitive processes, like those involved in memory, as metaphorically computational. Again, this is not to say that they are (necessarily) inaccurate, misleading, or less rigorous, but rather, to note that these metaphors provide key “conceptual scaffolding” through which we understand brains and cognitive processes in some sense in terms of or through our understanding of (in this case, digital) computers and computation—highlighting points of similarity and deemphasizing points of dissimilarity.

Thus, to summarize—first, there is confusion or at best inconsistency among authors about whether the mind or the brain is thought to be a (literal) computer. Second, in regards to the latter, weaker or more general notions of computer and computation result in “ubiquitous computation”—in which every physical system is a computer—and are thus apt to be explanatorily useless in the cognitive sciences. Further specifying this notion by fleshing out the mind-brain relationship in terms of the software-hardware relationship results in explanatory power largely rooted in this metaphorical scaffolding. And even stronger and more specific notions of computer are almost exclusively modeled on digital computers, resulting in additional metaphors providing further metaphorical scaffolding for our understanding of the brain and cognition. Thus, it is difficult to maintain a reading of the “brain is a computer” that is both explanatorily powerful and thoroughly literal.

One might protest that when they say “the brain is a computer” they have in mind a concept of computer which only includes the points of similarity, not dissimilarity between brains and computers—and that thus understood, this and related computational statements are both explanatorily powerful and literal. For example, one might claim that all they mean when they describe the brain as a computer (or invoke other computational metaphors) is that the brain literally has an architecture containing a central processing or control unit, short-term, and long-term memory; or that the brain literally transforms input into output by carrying out “rule-governed transformations on intentionally interpreted symbolic expressions.” Bracketing the fact that the second claim about information processing is precisely what is at issue between Embodied Cognition and Computationalists and thus by no means an uncontroversial claim about the mind—this is an attempt to sketch a notion of computer that cuts between weaker and stronger versions addressed above. However, this objector still must make the case that this account is both explanatorily helpful and does not end up collapsing into and thus facing the issues of either the weaker or stronger notions.

Perhaps one might think that this account does not collapse into the first account because it is merely further specifying the architecture and kind of information processing involved. However, in doing so, it appears to be merely cherry-picking features of digital computers that happen to fit with our current understanding of the brain, in an ad hoc manner. In so doing, it is constructing an understanding of ‘computer’ on the model of our brain, resulting in ‘computer’ functioning as a kind of shorthand for these cherry-picked traits. Statements using this notion of ‘computer’ then arise from utilizing an object (i.e. a computer) initially designed to emulate certain aspects of human cognition as the basis of a metaphorical framework for characterizing the mind and brain—thus attempting to ‘read back’ characteristics of the modeled object into the complex entity and processes on which it was modeled (e.g. the human brain and cognition, respectively). Thus, it is unclear how this notion of ‘computer’ might play a non-circular explanatory or informative role—helping to structure, inform, and deepen our understanding of cognition; furthermore, this notion does not capture much of the use of computational metaphors in the relevant literature.

Much of the resistance to acknowledging that statements like ‘the brain is a computer” are metaphorical can be explained by appealing to the following factors. First, some mistakenly assume that if a statement is metaphorical it is necessarily inaccurate, imprecise, or misleading. However, as addressed above, this is not the case—metaphors are oftentimes complex, precise, and apt conceptual frameworks that structure our understanding of abstract domains and concepts. As Rapaport explains “the brain doesn’t have to be a computer in order for its behavior to be describable computationally” (Rapaport 2018: 415)—in other words, acknowledging ‘the brain is a computer” as a metaphor does not amount to a threat to Computationalism. Second, the ubiquitous, entrenched, and foundational nature of many of these metaphors, may lead to psychological difficulties recognizing or acknowledging them as metaphors.30 For example, some studies have demonstrated that subjects who are impacted by metaphors are often unaware of this impact and even the presence of metaphors having the impact (Thibodeau & Boroditsky 2011; Amin et al. 2018).31 These effects are further compounded by the fact that many philosophers, psychologists, and neuroscientists have limited knowledge of the underlying concepts and principles of computation (Richards & Lillicrap 2022). This can result in a lack of awareness and/or acknowledgement of when a researcher is drawing on one as opposed to another of these notions, especially in work that is not focused on addressing these notions.

In summary, understanding the statement “the brain is a computer” literally restricts one to making only very basic claims about the brain and its capacities. Thus, it is reasonable to assume that often when this statement (or many of the related statements in the same metaphor family) is made in academic literature—specifically in philosophy, psychology, or neuroscience—it is being used to make more substantive claims about the brain and/or cognition. However, grounding these more substantive claims relies on invoking more explanatorily powerful notions of computer—that are grounded in specific traits of program-based and/or digital computers—to metaphorically scaffold such claims. Now that I have made the case that many statements invoking a Computationalist framework are metaphorical rather than literal—I will turn to argue that they are specifically conceptual metaphors (as opposed to merely linguistic metaphors), and embodied metaphors (rather than disembodied ones). Their status as conceptual, embodied (and conventional) metaphors strongly supports claims that Computational metaphors powerfully shape cognition.

Recall that conceptual metaphors are “automatic, ubiquitous conceptual device[s]” that structure and inform our thinking. Specifically, they enable us to think about and understand less familiar (and in some sense, more abstract) things in terms of more familiar (and in some sense, more concrete) things (Finsen et al. 2019). While a merely linguistic metaphor only enables us to communicate about more abstract concepts in terms of more familiar and concrete concepts; conceptual metaphors also enable us to think about more abstract concepts in terms of more familiar and concrete concepts. Thus, much of what I addressed in the section above in support of the claim that statements like “the brain is a computer” invoke a metaphor, also can provide support for the claim that such statements invoke a conceptual metaphor, specifically.

Clearly, such metaphors are integral to how we speak about cognition and ubiquitous in academic work (as well as in casual conversation) addressing the brain and cognition, even in work that does not explicitly invoke or engage with Computationalism. As Richard Boyd attests, these computational metaphors have played a central role in work on cognition. He explains that exploring

The centrality and foundational role of computational metaphors then give us reason to believe that these metaphors reflect an underlying conceptual framework in which our understanding of the brain and cognition is shaped by and associated with computers—specifically our understanding of and experiences with them. Much of current mainstream work in cognitive science is explicit about the central role of such conceptual frameworks—for example, Gauthier et al. (2019: 1) proclaim that “representation and computation are the best tools we have for explaining intelligent behavior.”32

According to some, computational metaphors are so central to Computationalist accounts of cognition that they are what is known as “theory-constitutive metaphors.” For example, Finsen et al., echoing Boyd, claim that,

In brief, statements about and descriptions of cognition often invoke computational metaphors and because these metaphors are so foundational and ubiquitous, it stands to reason that they are conceptual metaphors—meaning that they speak to the ways in which our understanding of the brain and cognition has been shaped by Computationalist conceptual frameworks. Naturally, one would seek empirical support for such claims, demonstrating the conceptual effects of such metaphors; however, there has thus far been a dearth of such work—either investigating the influence of computational metaphors or more generally metaphors for the brain.33 However, we can draw on more established empirical research investigating the influence of conceptual metaphors which are somewhat similar to computational metaphors (in that they deal with similarly abstract and complex target concepts) to draw further support for claims that computational metaphors are similarly conceptual. One such study comes from Amin et al. who explain that,

These metaphors are integral to communication on these topics and many studies indicate that they are also integral to understanding and thinking about them—in other words, they are conceptual metaphors. For example, Brookes & Etkina have conducted multiple such studies (2007; 2009; 2015). In their most recent, they address the role of metaphors for heat in students’ reasoning about heat in thermodynamic processes. On the basis of their own and other studies, they conclude that undergraduate physics students’ reasoning tendencies in solving problems about thermodynamic processes reflect the metaphorical ways of understanding heat encoded in the language that they use. Specifically, they found correlations between the use of substance-based metaphors for heat (heat is a “substance that moves from one location to another”, thermodynamic systems are envisioned as “containers” of heat, and temperature is seen as measuring the amount of heat “in” an object) and a tendency to reason about heat “as a state function” even in problem-solving contexts in which it is inappropriate. Jeppsson et al. (2013) reported similar findings from their studies conducted with physics Ph.D. students on metaphors for heat and entropy.

Notably, in a later study—also on the use of conceptual metaphors in the reasoning of physics Ph.D. students—Jeppsson et al. (2013) conclude that through metaphors, “concrete construals of abstract concepts such as entropy, energy, and heat in terms of possessions, movement of possession and containment in the context of advanced scientific problem-solving can be more prevalent at higher levels of expertise, and not necessarily a sign of naïve reasoning” (799). They go on to suggest that familiarity with and deep integration of conceptual metaphors may play a key role in the development of scientific expertise. In a similar vein, Alibali and Nathan (2012), Goldin-Meadow et al. (2009), Lakoff & Nunez (2000), Nemirovsky et al. (2012), and many others have investigated the impact of metaphors in mathematics; and others like Bleakley (2017), Nie et al. (2016), and Tate (2020) in medicine; Luhrmann (2011) in anthropology; and Niebert & Gropengiesser (2015) in psychology.34

The metaphors in the studies above are clearly conceptual metaphors in that their use or activation shapes reasoning about the target domain to be more in line with the similarities between the source and target concepts highlighted by the metaphors. Furthermore, this research demonstrates that such metaphors can impact the thinking of lay people and students, as well as experts and practitioners—thus indicating that at least some conceptual metaphors truly function as conceptual metaphors for the latter group, who know the most about the target domain.35 Because conceptual metaphors help us think about and understand more abstract and less familiar concepts in terms of more concrete and familiar concepts, it makes sense that they are integral to how we understand many scientific concepts (e.g. heat, energy), mathematical concepts (e.g. addition), and I argue, philosophical concepts like brain and cognition.

Thus far I have presented reasons to think that computational metaphors are specifically conceptual metaphors for cognition and now I turn to address why we ought to think that these metaphors are also embodied.36 Recall that conceptual metaphors result from cross-domain mappings which enable us to understand less familiar, more abstract concepts through association with more familiar, more concrete concepts. Often these more concrete concepts are closely tied to perceptual domains and thus likely grounded in one’s sensorimotor experiences—because of this, some claim that most if not all conceptual metaphors are embodied. I think there are additional reasons specific to computational metaphors to believe that these metaphors are embodied. For most people (specifically those without an advanced understanding of computers) many of their relevant concepts in the source domain (e.g. computer, computational, encoding) are in large part constituted by their (embodied) experiences of computers (e.g., typing on a keyboard, moving a mouse, clicking buttons on a screen, viewing representations of computers and computation in popular media, etc.)—and perhaps even constituted by more concrete concepts grounded in these experiences. Recall that when different computer concepts are properly disambiguated, it becomes clear that the computer concepts (and related computational concepts) used in relevant literature are grounded in our everyday, embodied experiences of digital computers. And when one tries to understand computers or specific computational functions by abstracting or generalizing away from the instantiation of such functions in literal computers, these descriptions lose much of their explanatory power (Boyd 1993; Finsen et al. 2019).

Interestingly, among embodied metaphors, computational metaphors may even be especially influential on our thinking because the sensorimotor experiences of computational devices that they rely on are ubiquitous, everyday experiences (Adbo & Taber 2009; Niebert et al. 2012).37 Furthermore, these sensorimotor interactions with computers often occur while engaging in higher-level cognitive processing (e.g., typing out a paper, recording notes on course material, researching, etc.)—which may thus further strengthen the association between concepts in the source and target domains. Again, naturally one would hope to find empirical support for such claims—demonstrating the role of embodied experiences with computers in mediating the cognitive effects of these computational metaphors—but again, there is a dearth of such work.

However, we can again draw on empirical research investigating the influence of embodied, conceptual metaphors in the sciences and mathematics to further support the plausibility of these claims. In one study, Robert Goldstone et al. argue for the occurrence of a kind of neural reuse in those engaging in mathematical reasoning. Their participants made use of gestures and embodied metaphors which were clearly grounded in sensorimotor activities when reasoning through the problems they were given. Summarizing their results, they explain that,

As they note, while mathematical reasoning is often thought of as a paradigm instance of abstract thinking that wouldn’t rely on sensorimotor states and processes, it appears that it does. Furthermore, they note that this reliance on sensorimotor strategies simply changes rather than dissipates in ‘sophisticated’ or expert reasoners. Similarly, Lakoff and Núñez address the embodiment of mathematical concepts extensively in their book Where Mathematics Comes From: How the Embodied Mind brings Mathematics into Being (2000). One example they address at length is the embodiment of the metaphor in which the target concept of equation is mapped onto the source concept of balance—which again is grounded in past physical experiences of balance. In a similar vein, in the sciences, Scherr et al. (2013) and Close and Scherr (2018), have conducted multiple studies surrounding their embodied learning activities which they call ‘Energy Theater’ that reveal compelling connections between student gestures and movements around the room in different configurations and their learning and reasoning about energy through metaphorical frameworks. Based on students’ articulation of their reasoning about energy after such activities, the researchers hypothesize that this reasoning was impacted by the relevant metaphor (energy is a substance) which was developed through their sensorimotor activities.38

Additionally, while there are many studies addressing the role of conceptual metaphors in the reasoning of experts, as opposed to learners—there are few that address the explicitly embodied elements of it. However, there’s no reason to suspect that the embodied element of conceptual metaphors would play less of a role in impacting the reasoning of experts, and recall that some hold that all conceptual metaphors are necessarily embodied. Furthermore, it is important to keep in mind that those with expertise in any subject were at one point merely learners and thus the metaphors they used to learn about the relevant subject are essentially still “baked into” their now expert understanding—as is evidenced by the continued appearance of these metaphors in academic writing on these topics. For example, researchers have found persistent misconceptions about how heat and energy operate that can be traced back to the influence of these metaphors even among experts researching these topics (Brookes & Etkina 2015; Chi et al. 1994). And again, there are some empirical findings that suggest that, at least in some disciplines, they may play even more of a role in the reasoning of experts than in that of non-experts. For example, in Jeppsson et al.’s (2013) study cited above, they note that when comparing the role of conceptual metaphors in the reasoning of physics undergraduate and Ph.D. students (at the end of graduate school) the use of conceptual metaphors by the latter to a much greater extent than the former “transformed what might have been expected to be highly formal reasoning to a process of reasoning that contained many elaborate concrete, imagistic scenarios …” (798). Roughly, the reasoning articulated by the Ph.D. students relied even more on embodied, conceptual metaphors than that of the undergraduate students.

These are just a few of the many examples that, when taken together, make a compelling case for the role of embodiment in such metaphors. Crucially, in many of these studies, researchers demonstrated not only that sensorimotor processes played a role in participants’ learning and understanding of the relevant concept(s) but that they played a direct role that was distinct from the other kinds of instruction they received. For example, Goldin-Meadow et al. (2009) demonstrated that gestures taught to some participants contributed to their learning of addition by providing information distinct from the verbal instruction they received. These studies demonstrate the role of sensorimotor activities in mediating the effects of metaphors on cognition, specifically on learning—thus making a compelling case that these metaphors are embodied. Furthermore, it is notable that these studies report a measurable impact in real-time, short-term embodied activities—repeated embodied experience may have even stronger effects. In summary, this research on embodied, conceptual metaphors in mathematics and the sciences gives us reason to think that computational metaphors may be similarly embodied, conceptual, and have measurable effects on cognition. Now I will turn to detailing the potential effects and implications of these effects on our understanding of and debates about theories of cognition.

First, metaphors affect the content of their target concepts because of associations drawn between the target and source concepts. For example, take Thibodeau and Boroditsky’s study (2011; 2013) in which they gave two groups of subjects information about crime in a fictional town: both groups were given the same crime statistics, accompanied by one of two vignettes that employed either the metaphor “crime is a beast” (e.g. metaphorical statements about needing to “hunt down” and “capture” criminals) or the metaphor “crime is a virus” (e.g. metaphorical statements about needing to “diagnose” and “treat” the crime that had “infected” the city). When presented with a list of potential approaches to addressing the city’s crime, subjects were more likely to support enforcement-focused approaches (such as catching and jailing criminals and enacting harsher enforcement laws) when they were given the “crime is a beast” vignette and were more likely to support reform-focused approaches (such as eradicating poverty and improving education) when given the “crime is a virus” vignette. Researchers hypothesize that this occurred because reading the metaphors affected participants’ understanding of crime to be more like a virus or to be more like a beast: the former a view in which the systematic nature of crime was emphasized and the latter one in which the importance of individual aggressors was emphasized.

These effects are stronger the more conventional (familiar and widespread) a metaphor is. According to the Career of Metaphor theory when someone is exposed to a novel or unfamiliar metaphor, it is processed as a comparison, in which pre-existing similarities between the target and source concepts are highlighted. However, when presented with a conventional metaphor, it is processed as a categorization, in which the target concept is in some way thought of as belonging to the category defined by the source concept, thus “inheriting” some properties of the source concept (Bowdle & Gentner 2005; Gentner & Bowdle 2001; Glucksberg et al. 1997). Sometimes, this “inheritance” can occur to such an extent that the conventional metaphor is often thought to be literally true. Ervas et al. (2018), after analyzing theirs and others’ research on the topic, claim that in the studies they examined, in virtue of the metaphors with them, “the majority of sentences with conventional metaphors [were] perceived as [literally] true, even though they [were] literally false.”

Research on the target concepts of heat and energy, addressed above, provides additional evidence of these kinds of effects. Conventional metaphors, like those mentioned above, “map” heat and energy onto material substances. As a result, in those who have learned these concepts through such metaphors, heat and energy are metaphorically categorized as kinds of material substances, thus “inheriting” some properties of material substances, and in some cases being misunderstood to be literally material substances. These effects manifest in how these concepts are spoken about (e.g. as mentioned in the quote above, using phrases like: “the molecule has kinetic energy…[and] heat was lost to the surroundings” (Amin et al., 2018, emphases mine), and have implications for how they are thought about. (As addressed above, these metaphors seemingly contributed to students assuming this material understanding of heat and/or energy when attempting to solve problems involving these concepts.) Thus, the target concepts involved in these metaphors (heat and energy) seem clearly affected by their metaphor associations with material source concepts, and these effects are perhaps even stronger because they are conventional metaphors.

Now, turning to (conventional) computational metaphors, the target concepts would presumably “inherit” some properties of their corresponding source concepts: brain inheriting some properties of computer, our concept of memory inheriting some properties of RAM or external hard drives, and the process of information storage within them. Thus, the brain, cognition, and cognitive activities would unsurprisingly be seen as more computational. It is important to highlight that because computational metaphors are conventional it is not the case that they merely make salient pre-existing similarities between brains and computers, but rather that the brain is metaphorically categorized as a kind of computer. This effect is likely especially pronounced in the case of computational metaphors because not only are they conventional metaphors for the brain, they are by far the most common—in contrast to many of the concepts in the sciences addressed above, like energy, for which there are multiple common metaphors. This means that concepts like brain and memory are most often “inheriting” computational properties and not properties of other, potentially more embodied, metaphors for the brain. One upshot of this is then that the dominance of computational metaphors for the brain may lead the central concepts themselves (e.g. brain, memory) to be seemingly more aligned with computational as opposed to embodied theories of cognition. Circling back to §2.1, the phenomenon addressed in this section perhaps helps make further sense of the temptation to maintain that statements like “the brain is a computer” are literally true. While there may be other reasons behind attempting to maintain the literal status of such statements, some of which I addressed above, it is notable that many who employ these computational metaphors do take them to be literally true, and that this is a well-known effect of conventional metaphors.

In addition to affecting the content of their target concepts, metaphors also affect the cognitive availability of other metaphors—as Thibodeau and Durgin note, metaphors “encourage the speaker to use, and prepare the listener to understand, other metaphors that rely on the same mapping” (2008: 532). Roughly, when a certain metaphor is “activated” this affects the “cognitive availability” of other metaphors—in other words how easily these metaphors can be called to mind and thus how intuitive and automatic it is to use them. When a metaphor is activated, it increases the cognitive availability of other, related metaphors (those that rely on mappings between the same domains) and correspondingly, decreases the relative cognitive availability of metaphors that rely on different mappings. The existence of metaphor families—sets of metaphors in which concepts from the same target domain are mapped onto other concepts from the same source domain—provides compelling evidence of this phenomenon (Lakoff & Johnson 2008; Thibodeau & Durgin 2008). Metaphor families form because conventional metaphors (those that are familiar) enable us to more easily understand and use novel (unfamiliar) metaphors that rely on similar mappings.

To see some of the effects of this, let’s return to Thibodeau and Boroditsky’s study (2011; 2013). After being presented with the “crime is a virus/beast” vignettes, subjects were asked open-ended questions including “how they would recommend solving [the fictional city] Addison’s crime problem.” (Thibodeau & Boroditsky 2011; 2013). Subjects’ responses were more likely to contain metaphors that belonged to the same metaphor family as the metaphor they were given in the vignette: those given the ‘crime is a beast’ vignette were more likely to mention things like ‘hunting down’ and ‘capturing’ in their responses, while those given the ‘crime is a virus’ vignette were more likely to mention things like ‘diagnosing’, ‘treating’, and ‘inoculating’ in their responses. Because of the metaphors that participants were initially given, they found it more natural to use metaphors that, while not explicitly mentioned in the vignette, were in the same metaphor family as those originally given to them (e.g. thinking of crime as a virus made more cognitively available by metaphors like “the city is a body” which thus prompted body-related metaphors to be preferred in some of the recommended solutions like “inoculating the city to further crime through education”).

As I’ve addressed above, the family of computational metaphors is ubiquitous in how we think, write, and talk about cognition. According to the aforementioned effects of metaphors, when metaphors in this family are activated, related metaphors become relatively more cognitively available while unrelated metaphors become relatively less cognitively available. This then means that using computational metaphors to address cognition may make other computational metaphors more readily spring to mind, seem more intuitive, apt, and thus more likely to be used. By the same token, it may make other metaphors for cognition, say those in-line with an embodied approach (e.g. “the brain/mind is a muscle” or “the brain/mind is a garden”) less cognitively available, less intuitive, seemingly more ill-fitting or less apt, and thus less likely to be used. This effect is especially notable in the case of computational metaphors because they are the dominant metaphors for cognition. This means that the domination of computational metaphors may lead to an inherent friendliness towards further metaphors that fit with a computational framework and inherent resistance towards those that do not.

Metaphors also tend to increase the cognitive accessibility and plausibility of non-metaphorical claims that fit with the metaphor and decrease that of those that don’t. For example, take Niebert and Gropengiesser’s (2015) study in which they looked at subjects who learned about myelin sheaths (layers formed around nerve cells that enable electrical impulses to travel along them) using the metaphor of a container (i.e., “myelin is a container for the nerve cells”). They found that it was easier for these subjects to make sense of (non-metaphorical) claims about functions that fit with the metaphor (e.g., “myelin sheaths protect the nerve cells”) and more difficult for them to make sense of claims about functions that did not (e.g., “myelin sheaths affect the signal speed of electrical impulses traveling along nerve cells”). As one student explained, “I cannot imagine how myelin affects the traveling time of signals, it prevents ions from leaving the neuron. The distance a signal has to travel in the neuron is the same with or without myelin” (2015: 12, emphasis mine).

The authors propose that because subjects learned about myelin through the “myelin is a container” metaphor, this made it easier for them to understand and find plausible functions of myelin that made sense as being carried out by a container than it was for them to understand those that did not. For some, like the subject quoted above, this occurred to the extent that the functions that did not fit with the container metaphor were unimaginable. And these metaphor effects seem to be particularly strong when the metaphor in question plays a central role in one’s learning about the concept. According to some, this also has implications for argument evaluation.39

As discussed above, computational metaphors are ubiquitous and central to how we learn about and understand concepts like the brain and cognition. This may in turn contribute to people finding claims and theories about cognition that fit with computational metaphors—most obviously, Computational theories of cognition—more understandable and perhaps more plausible than those that do not, e.g. Embodied Cognition theories. In some cases, this may even contribute to difficulty in fully conceptualizing these claims or theories that do not fit. We may see some of this “imaginative resistance” towards non-computational theories often reflected in statements about Computationalism. For example, Van Gelder (1995: 346) remarks that “…one of the most influential arguments in favor of the computational view is the claim that there is simply no alternative. This is sometimes known as the ‘what else could it be?’ argument.” Here we see Van Gelder noting that a primary motivation for many in adopting a Computationalist theory of cognition is that this theory is, in some sense, the only imaginable theory of cognition. Of course, these effects of metaphor are not the only potential cause of such imaginative resistance; however, as I will address below, it is notable that in debates about theories of cognition, there are often expressions of strong imaginative resistance against embodied theories of cognition, and that this is a well-known effect of metaphors.

Bringing together the effects addressed above yields the following picture of computational metaphors and their purported impact on our understanding of cognition. Computational metaphors constitute a family of conventional, conceptual metaphors in which the brain and cognitive processes are mapped onto, and thus closely associated with and understood in terms of, computers and computational processes. These metaphors are ubiquitous and when activated may result in the following. Within the relevant target concepts (e.g., brain, memory, etc.), pre-existing attributes shared with computers and computational processing may be more cognitively salient, and additional computational attributes may also be “inherited”—resulting in the target concepts being understood as more computational. Additionally, other metaphors (e.g. memories are encoded) and non-metaphorical statements (e.g. perceptions are transduced into amodal, symbolic representations) about cognition that align with these concepts may become more cognitively available and thus be seen as more plausible and intuitive—and those that do not align may become less so.

A final notable finding about the influence of computational metaphors is that people who experience these effects are often unaware of them as well as the fact that metaphors caused them and thus tend to misattribute these effects (Correia 2011; Ervas et al. 2018; Robins & Mayer 2000; Thibodeau & Boroditsky 2013).40 Relatedly, Glucksburg (2003) argues that metaphor comprehension is both automatic and unavoidable, in that people cannot ignore metaphors even when a literal interpretation makes sense in context—in such situations, they still experience measurable effects of metaphor. For example, in Thibodeau and Boroditsky’s study (2011; 2013) above, they found that although the majority of participants seemed to be impacted by the “crime as a virus/beast” vignettes they were presented with, most subjects were unaware of this impact. Many were even unaware of the mere presence of the metaphor in their vignettes—and some were even unable to recall which metaphor they were given when asked. Because of this, when subjects in the study above were asked what informed their decision to favor the approaches that they did, the majority of subjects did not cite the metaphors or vignettes and instead cited things like the crime statistics (which all participants in both metaphor groups were given). In other words, a participant in the “virus” group might say that they selected reform-focused approaches to address the crime because they thought it justified by the crime statistics and other details presented to them—and a participant in the “beast” group would say the same thing, except maintaining that the same statistics and details instead justified an enforcement-focused approach.

Even more striking, metaphors that are very conventional—in other words, those that recur frequently in language, imagery, etc.—appear to become activated and influence judgments after only a single mention of the metaphor (Thibodeau & Boroditsky 2011), or sometimes in the absence of any mention at all. For example, in the absence of activation of these metaphors, words related to goodness are more quickly recognized when presented higher in vertical space (as opposed to lower), consistent with the metaphor “good is up” that we invoke when saying someone is “moving up in the world” or “rising to the top” (Meier & Robinson 2004). And objects are perceived to be more important when they are physically heavier, consistent with the metaphor heavy is important we refer to when we speak of things “carrying weight” or “weighing us down” (Chandler et al. 2012). All of this highlights the ways in which metaphors can serve as “lenses” through which incoming information is filtered. The issue is not that we need or often use these “lenses” (clearly metaphors play an important conceptual role), that they impact how we “see things” (metaphors help scaffold understanding), nor that they are of a particular “color” (inevitably, metaphors will have particular content)—instead the issue is that we (often) don’t even “see” that the “lenses” are there, instead attributing the effects of metaphor to the information we are taking in.

Turning back to computational metaphors—the upshot of this is that, if computational metaphors lead to effects like those sketched above, then those who experience these effects will likely be unaware of them and thus likely to misattribute them—or fail to attribute them altogether.41 And again, this effect is also likely compounded by the fact that, as addressed above, computational metaphors provide much of the foundational theoretical vocabulary for discussing and thinking about the brain and cognition, thus it is even more difficult to recognize them as metaphors. However, if the effects of metaphors are “mandatory and automatic” then being unaware of the presence of computational metaphors or their status as metaphors does not exempt one from their cognitive effects.

Bringing this together with the research above means that even though someone may think that: 1) computational metaphors are literal statements about cognition; 2) computational metaphors just “fit” better with our understanding of the brain or are much more intuitive, plausible, or apt; 3) certain statements or arguments about cognitive capacities that “fit” with a computational framework are more plausible than those that don’t—the research presented above gives us strong reason to suspect that (1)-(3) may all in some measure be caused by computational metaphors, which at the very least ought to give them reason for increased skepticism towards these beliefs. Furthermore, they may also think 4) that they are unaffected by computational metaphors—and again, the current research on this topic should give them reason to hesitate. Roughly, computational metaphors—along multiple dimensions—likely bias us towards a more Computationalist understanding of cognition and are also unlikely to register as the causes of this bias, especially, and ironically, on a Computationalist understanding of cognition.