Running head: A MODULE IS A MODULE
Published as:
Grossi, G. (2014). A Module is a module is a module: Evolution of modularity in
Evolutionary Psychology. Dialectical Anthropology, DOI: 10.1007/s10624-014-9355-0.
A Module is a Module is a Module:
Evolution of Modularity in Evolutionary Psychology
Giordana Grossi
State University of New York at New Paltz
Address and correspondence
Giordana Grossi
Department of Psychology, SUNY New Paltz
600 Hawk Drive, New Paltz, NY 12561 ph: (845) 257-2674 fax: (845) 257-3474 email: [email protected]
Acknowledgments: Many thanks to Alison Nash, Suzanne Kelly, Gowri Parameswaran, and two anonymous reviewers for their thoughtful comments and suggestions on an earlier version of this manuscript.
1
Abstract
The concept of modularity has been central in behavioral and neural sciences since the publication of Fodor’s The Modularity of Mind (1983). Fodor strived to explain the functional architecture of the mind based on the distinction between modular and central systems. Modular systems were deemed to have certain architectural features, such as automaticity, encapsulation, and domain-specificity. Evolutionary psychologists have adopted the concept to characterize purportedly evolved human adaptations. In an influential paper, Barrett and Kurzban (2006) proposed a definition of modules purely in terms of functional specialization. It is here argued that such strategy marks a shift in
Evolutionary Psychology’s theoretical emphasis, as it trivializes the investigation of proximate causes in evolutionary theorizing; furthermore, it leaves the door open to too much flexibility on what counts as evidence for purportedly evolved modules.
2
History and definition of modularity
Modularity is a central concept in the cognitive and neural sciences. Generally speaking, modularity is the idea that the mind and the brain are organized in systems that are specialized to process specific types of information and that are functionally independent
(Coltheart, 1999). The concept became predominant in psychology after the publication of Fodor’s The Modularity of Mind (1983). Fodor’s (1983) hypothesis about the functional architecture of the mind was proposed to explain the types of psychological mechanisms underlying the different facts of mental life (e.g., sensation, perception, volition, and learning). In his account, the architecture of the mind is divided into modular and central systems. Modular systems include perceptual (or input) and linguistic mechanisms, whereas central systems pertain to decision-making systems, knowledge, and beliefs. The latter systems were deemed not to be modular and cut across a variety of cognitive domains.
Without clearly defining the term “module,” Fodor associated modular systems with specific characteristics, such as automaticity, informational encapsulation, and domain-specificity:
- Automaticity: A system was conceptualized as automatic if its mechanisms are to be
“obligatorily applied” (p. 53), such as those involved in spoken word recognition in
native speakers of a language: even when participants are asked to focus on low-level
(acoustic-phonetic) characteristics of speech, it is impossible to turn off word
recognition mechanisms.
3 - Informational encapsulation: A system was deemed to be encapsulated if it is less
permeable to background information, contextual factors, or beliefs than other
systems. Fodor illustrated this property by discussing the mechanisms responsible for
visual illusions. In the Müller-Lyer illusion, the line flanked by two arrow tails looks
longer than the line flanked by two arrowheads. This perception persists even after
the viewer is informed that the two lines are identical in length.
- Domain-specificity: Modules are specialized, in the sense that only a restricted class
of stimuli has access to them. For example, the input systems involved in recognizing
speech are different from the auditory systems involved in the recognition of non-
speech sounds (Fodor, 1983).
Modular systems tend to have other related characteristics, such as being hardwired (i.e., “associated with specific, localized, and elaborately structured neural systems,” p. 37) and innate (i.e., they “develop according to specific, endogenously determined patterns under the impact of environmental releasers”, p. 100). According to
Fodor, modules needed not share all these characteristics; furthermore, modularity is a matter of degree.
Fodor’s speculative view of the mind, born within philosophy, prompted a considerable amount of research and discussion among cognitive psychologists and neuroscientists. Much of the empirical research focused on determining how perceptual and linguistic systems function and whether they exhibit characteristics such as encapsulation, automaticity, and domain-specificity. Empirical findings revealed a complex picture in which not even perceptual systems turned out to exhibit some of the
4 defining characteristics of modularity. For example, some charged that input systems do not exhibit strict encapsulation because their functioning is modulated by attention (e.g.,
Wojciulik, Kanwisher, & Driver, 1998) or higher-level cognitive processes (Farah, 1994).
Furthermore, behavioral studies had already shown that perceptual mechanisms are modulated by contextual factors and information from other sensory modalities. For example, in the McGurk effect (McGurk & MacDonald, 1976), the auditory perception of a syllable (e.g., /ba/) changes when the syllable is visually presented with the face of a person pronouncing another sound (e.g., /ga/): in this case, participants hear /da/. These and other empirical results led to a more refined understanding of how the brain is organized and functions, as I will discuss later.
Evolutionary Psychology1 and modularity
In the late 1980s, the concept of modularity was borrowed by a group of evolutionary psychologists (e.g., John Tooby, Leda Cosmides, Steven Pinker, David
Buss) and reframed in terms of evolved adaptations. In their view, the human mind is comprised of numerous specialized modules (or “mini-computers”) that have evolved under selective pressure to solve recurrent problems in humans’ ancestral past (Cosmides
& Tooby, 1997, p. 11). Therefore, the Evolutionary Psychology (EP henceforth) version of modules rests on the assumptions that they are supported by dedicated neural circuits, shaped by natural selection, and genetically specified (Pinker, 1997, p. 21). Examples of
1 The term “Evolutionary Psychology” will be used in this paper to denote the thought of an influential group of researchers including, among others, Leda Cosmides, John Tooby, David Buss, and Steven Pinker. It is not to be confused with “evolutionary psychology” as the general field of inquiry. Please see the Introduction to this issue for more information.
5 modules include face recognition, tool-use, fear, social-exchange, kin-oriented motivation, child-care, social-inference, sexual-attraction, friendship, grammar acquisition, and theory of mind (Tooby & Cosmides, 1992, p. 113).
A number of assumptions are made regarding these modules. For example, they are domain-specific, that is, “relatively well-engineered for solving ancestral adaptive problems” (Cosmides & Tooby, 1994, p. 88). This assumption relies on the claim that such problems cannot be solved by domain-general mechanisms. Evolutionary psychologists argue that different problems require sensitivity to different types of information and different solutions. For example, selecting food and selecting a mate typically lead to different solutions; therefore, they rely on different, and specialized, mechanisms (Confer et al., 2010).
Another assumption is that the number of specialized modules has increased in humans, compared to other primates and mammals, and this increase ensures humans’ flexible and complex behavior. In this context, modules are “pre-specified” structures, as they contain information about the world:
“The more a system initially "knows" about the world and its persistent
characteristics, and the more evolutionarily proven "skills" it starts out with, the
more it can learn, the more problems it can solve, the more it can accomplish.”
(Tooby & Cosmides, 1992, p. 113)
Evolutionary psychologists’ focus is on a computational, or functional, analysis of behavior, based on Marr’s distinction between levels of description (Cosmides & Tooby,
2003; Tooby & Cosmides, 1994). In his influential book Vision (1982/2010), Marr
6 posited that informational systems can be analyzed at three different levels of description: computational, or functional (what a system does and why; the problem that the system solves); algorithmic/representational (how a system does what it does, what representations and processes it uses), and implementational (how a system is neurally/physically implemented). Marr considered the three levels of description “rather loosely related” (2010, p. 25) and constrained by different issues (independence among levels)2. For this reason, for some phenomena, there might be no unitary theory connecting all three levels.
By privileging a computational analysis of behavior, evolutionary psychologists adopt reverse engineering to identify traits that have been advantageous (in terms of reproductive fitness) during human evolution in “ancestral times”. These “design features” are identified based on hypotheses concerning the problems faced by our ancestors and speculations about the ancestral environments in which they lived. Design features, which characterize evolved adaptations, can be recognized by three characteristics: they are specialized to solve an adaptive problem; their phenotypic characteristics are unlikely to have arisen by chance; and they are not explained as the byproduct of other mechanisms to solve other ancestral problems (Cosmides & Tooby,
2003).
It is important to note that many of the EP assumptions and claims (e.g., domain- specificity or modularity of a given system) are rarely accompanied by supporting empirical data (a point already observed by others; e.g., Richardson, 2007). For example,
2 Marr’s distinction among levels of description and the importance he gave to questions of function were not informed by evolutionary theory but by work on brain-lesioned patients in human neuropsychology. For a discussion, see Shapiro and Epstein (1998).
7 in Tooby and Cosmides’ writings, the existence of specific modules is typically assumed, not demonstrated. The existence of a behavior that is hypothesized to be evolutionary relevant is simply explained by the existence of a dedicated module: humans’ use of tools is explained by the existence of a module specific for using tools; humans’ use of language characterized by grammar is explained by the existence of a grammar acquisition module. However, there is no convincing evidence for the existence, for example, of a grammar acquisition module. Children exhibit a predisposition to learn language, but this does not imply that they have a mechanism or a circuit specialized to learn grammar per se. Indeed, researchers have shown that, in children, grammatical complexity is tied to vocabulary size, indicating that the acquisition of grammar relies on other aspects of linguistic communication (Bates & Goodman, 1997). In addition, children whose language reveals grammatical difficulties also exhibit a range of other linguistic and/or nonlinguistic problems (e.g., Karmiloff-Smith, 2006; Vargha-Khadem,
Watkins, Alcock, Fletcher, & Passingham, 1995). Therefore, the claim that humans have a grammar acquisition module remains a presupposition.
Furthermore, the characteristics of such hypothesized modules remain vague and underspecified. For example, speaking of depth perception, Tooby and Cosmides (1992) remarked that, “Our depth perception mechanism has this property, for example: It works well because it combines the output of many small modules, each sensitive to a different cue correlated with depth.” (p. 104). These descriptions, typically shared without pertinent details from the empirical literature, inevitably raise issues of circularity, as what needs to be demonstrated is actually included in the premises (depth perception works well because the system picks up cues of depth).
8 Numerous scholars have raised issues regarding the concept of modularity adopted by evolutionary psychologists. For example, focusing on cognitive architecture,
Shapiro and Epstein (1998) have remarked that whether or not a system is domain- specific or domain-general remains a matter of empirical evidence, not theoretical assumptions. Based on empirical data, DeSteno, Bartlett, Braveman, and Salovey (2002) have questioned the evolutionary meaning of the sex differences in jealousy reported by
Buss, Larsen, Westen, and Semmelroth (1992) and the existence of the “jealousy module.” Other researchers have rejected mass modularity (the assumption that the mind is mostly comprised by domain-specific modules) on neuroscientific grounds (Bolhuis,
Brown, Richardson, & Laland, 2011; Buller, 2005). Such critiques have raised counter- arguments from some evolutionary psychologists (e.g., Barrett, Frederick, Haselton, &
Kurzban, 2006). In an influential paper, Barrett and Kurzban (2006; B&K henceforth) claimed that these critiques often misrepresent the concept of modularity proposed by evolutionary psychologists. The authors emphasize that that EP version of modularity centers on the notion of “functional specialization”:
“Here we argue and provide evidence for the view that constructive progress has
been undermined by the fact that opponents of modern views of modularity have
critiqued modern positions as though the original (Fodorian) conception of
modularity were intended. We also assert, as have other evolutionary
psychologists (Barrett, 2005; Cosmides & Tooby, 1994; Pinker, 1997; Sperber,
1994; Tooby & Cosmides, 1992; Tooby, Cosmides, & Barrett, 2005), that a
broader notion of modularity than the one Fodor advanced is possible: in
particular, a modularity concept based on the notion of functional specialization,
9 rather than Fodorian criteria such as automaticity and encapsulation.” (p. 628-629,
italics in the original)
This position has been echoed by others, such as Confer et al. (2010), who claimed that “psychological adaptations are not separate “modules” in the Fodorian
(Fodor, 1983) sense of informational encapsulation; rather, they often share components and interact with each other to produce adaptive behavior ...”3 (p. 111; see also Cosmides
& Tooby, 2003, p. 63, for a similar position).
After showing that, despite B&K’s claim, evolutionary psychologists have described modules or specific information processing systems in terms of Fodorian characteristics4, I will argue that the strategy of defining modules only in terms of functional specialization is problematic. Indeed, by stripping them of defining architectural features or treating them loosely and vaguely, such strategy reduces constraints on hypothesis testing and causes too much flexibility when researchers test hypotheses regarding the modularity of specific systems. Here I will discuss these issues by focusing on two modular features: domain-specificity and neural specificity.
Fodorian modularity in Evolutionary Psychology
Contrary to B&K’s claims, work in EP has in fact focused on Fodorian characteristics of modularity. In most cases, the reference to Fodor’s features is not explicitly
3 It is important to note that such interactions are also assumed in EP: when and how they take place is typically not a matter of empirical exploration but post-hoc speculation (see Lickliter & Honeycutt, 2003, for a similar point). 4 For a similar argument, see Chiappe and Gardner (2012).
10 acknowledged, but the research strategy adopted by the authors clearly indicates a reliance on them. For example, the concept of automaticity surfaces in Buss and colleagues’ study on sex differences concerning jealousy, as they measured changes in autonomic nervous system responses triggered by infidelity scenarios (Buss et al., 1992).
Jealousy is “triggered” in a variety of papers (Buss, 1998; Buss & Haselton, 2005; Buss,
Larsen, & Westen, 1996; Buss et al., 1999); furthermore, male jealousy is considered to be “obligate” in long-term relationships (Buss, 1998, p. 26). The “fear module” hypothesized by Mineka & Öhman (2002) is “selective, automatic, and encapsulated”
(that is, “preferentially activated by stimuli related to survival threats in evolutionary history…. automatically activated by fear-relevant stimuli… relatively impenetrable to conscious cognitive control…”, p. 927). Similarly, New et al. (2007) have hypothesized the existence of a category-specific attention mechanism aimed at automatically checking the status of people and animals in environment. They used a well-known paradigm
(change blindness) to test whether changes concerning humans or animals in a scene are found more easily than changes concerning non-animate objects.
The evidence of specialized neural mechanisms has also been taken as a marker of modularity by evolutionary psychologists. Duchaine, Cosmides, and Tooby (2001) provided a review of “some of the recent evidence for functionally specialized problem- solving machinery in the brain” (p. 225). Tooby and Cosmides (2005) remark that specialized mechanisms rely on “neurocognitive specializations” that are “functionally and neurally distinct from more general abilities to process information or behave intelligently” (p. 587), and have attempted to find evidence of a cognitive mechanism specialized to identify cheaters in social exchanges (cheater-detection module) at the
11 neural level (Stone et al., 2002). Jung et al. (2012) claim that perception of facial attractiveness is a “premier” example of psychological adaptation and cite, among other forms of evidence, the fact that it is associated with the activation of specialized brain areas. Confer et al. (2010) mention the “sex-differentiated patterns of neural activation”
(p. 114) associated with jealousy (found by Takahashi et al., 2006) as a support for the tenet that sex differences in jealousy have evolved.
These are just some examples that demonstrate that B&K’s claim that evolutionary psychologists have not relied on Fodorian definitions of modularity is incorrect. Therefore, it is not clear how “… constructive progress has been undermined by the fact that opponents of modern views of modularity have critiqued modern positions as though the original (Fodorian) conception of modularity were intended” (p.
628). If anything, constructive progress has actually been made thanks to studies that have tested the modularity of purported evolved modules and their development. These studies have complicated the picture provided by evolutionary psychologists. For example, according to Confer et al. (2010), the existence of “evolved fear adaptations”, such as fear of snakes, are relatively uncontroversial, as shown by evidence in terms of automaticity, neural specificity, and difficulty of extinction. Also, fear of snakes exists in both humans and other primates, whereas humans do not exhibit fear of objects that are historically more recent, even if more dangerous than snakes (e.g., guns, electric outlets):
“A programmatic series of studies has shown that an intense fear of snakes exists
in humans and other primates; snakes and spiders embedded in complex visual
arrays automatically capture attention far more than do harmless objects—they
“pop out” of the visual array; humans rapidly condition to fear snakes more than
12 most other stimuli; and the snake fear adaptation is selectively and automatically
activated, it is difficult to extinguish, and it can be traced to specialized neural
circuitry ...” (p. 111).
However, research has shown that objects like guns capture as much attention as snakes in spatial attention experiments (Carlson, Fee, & Reinke, 2009) and visual search tasks in which participants are asked to find a target object in an array of distractors (e.g.,
Blanchette, 2006; Brosch & Sharma, 2005; Fox, Griggs, & Mouchlianitis, 2007). Tipples et al. (2002) also showed that non-threatening animals such as kittens and bunnies are processed similarly to snakes in visual search experiments. Therefore, conclusions from studies in which snakes were presented among non-animal pictures were drawn incorrectly, as a threat manipulation was confounded with animateness.
Furthermore, Hugdahl & Johnsen (1989) showed that people can be rapidly conditioned to guns pointed at them and this conditioned response is as difficult to extinguish as the response to snakes. As for the existence of “specialized neural circuits,”
Mineka and Öhman (2002) (cited by Confer et al., 2010), discuss the role of the amygdala in fear conditioning, not specific neural circuits for fear of snakes (which have never been identified, to my knowledge). Finally, fear of snakes is not present in all humans and is acquired in monkeys based on specific and non-obvious experiences, as developmental studies have shown. Mineka, Davidson, Cook, and Keir (1984) had already noted that laboratory-reared monkeys do not exhibit fear of snakes, but will exhibit it after observing their wild-rear parents’ reactions to snakes. In a remarkable and rarely cited study, Masataka (1993) showed that fear of snakes was present in laboratory- reared monkeys fed with fruit and live insects but absent in laboratory-reared monkeys
13 fed on fruit and monkey chow. It appears that fear of snakes is acquired in monkeys based on a variety of experiences, for example observing their parents or encountering and feeding on live insects (part of the normal monkey’s diet in the wild). Monkeys do not develop a fear of snakes without these experiences. Similarly, Berger et al. (2001) have shown that moose’s fear of wolves and bears (moose’s “natural predators”, based on their evolutionary history) disappeared in animals where these predators were decimated within a few generations (1959-1992). When predators were reintroduced, fear was relearned in one generation through specific experiences, such as the loss of offspring, and then transmitted culturally to the next generation. In summary, this body of research sheds light on the actual experiences and mechanisms that, within each generation, foster the development of fear of snakes or predators. Explaining such behavior simply in terms of an evolved adaptation or module does not capture its complexity.
Confer et al. (2010) use the term “evolved adaptions” to refer to fear of snakes and other evolutionary relevant faculties (e.g., jealousy). They maintain that such a concept does not coincide with Fodor’s concept of modularity (p. 111). However, the characteristics that they list to support the existence of a snake fear adaptation mostly overlap with the characteristics proposed by Fodor (1983). Therefore, such features are used as evidence for modularity in some cases and dismissed in others, in particular when experiments do not provide support for them (e.g., Barrett et al., 2006). In their paper,
B&K propose to remove these features from the definition of modularity or to use them flexibly, as discussed next.
14 From Fodorian modularity to untethered functionalism
Domain-specificity
B&K’s view of modularity stems from Pinker (1997), “who argued that modules should be defined by the specific operations they perform on the information they receive, rather than by a list of necessary and sufficient features …" (p. 629). Modules are "… functionally specialized mechanisms with formally definable informational inputs" (p.
630). They have design features that evolved by natural selection "acting on the developmental systems that build modules during development” (p. 630). Through natural selection, they become domain-specific, as they process specific information in a specific way (they have “specific input criteria,” p. 630). For example, "systems specialized for assessing the numerosity of objects accept only representations previously parsed into distinct objects; systems specialized for speech perception process only transduced representations of sound waves; and systems specialized for making good food choices process only representations relevant to the nutritional value of different potential food items.” (p. 630).
B&K's concept of domain specificity seems not to differ substantially from
Fodor's. However, domain is here conceptualized in terms of "formal properties of representations":
“…, we wish to stress that we intend the broadest construal of the term domain to
include, in principle, any possible means of individuating inputs. We do not
intend a reading of domain as content domain, in the folk sense of domains
individuated by the meaning of their constituents. Rather, we define domains as
15 individuated by the formal properties of representations because, we believe, this
is the only possible means by which brain systems could select inputs. As a
corollary, by virtue of the fact that formal properties determine which inputs are
processed, a mechanism specialized for processing information of a particular sort
can, as a by-product, come to process information for which it was not originally
designed, ...” (p. 630)
These claims raise some issues. First of all, how are formal and non-formal properties defined, empirically identified, and distinguished from each other? These questions assume relevance when we try to identify the systems specialized for a given function. For example, what are the systems specialized for speech perception, and what are their formal properties? Importantly, how do they differ from the formal properties of non-speech perceptual auditory systems? To state, as B&K do, that “systems specialized for speech perception process only transduced representations of sound waves” (p. 630) is to state the obvious, as speech is a signal based on sound waves, and transduction is the translation of a form of energy (mechanical, in auditory perception) into neural signals.
The key word in their claim is, perhaps, “only”, but without clarifying what the specific systems are and how they work, the claim of input specificity remains vague and unsubstantiated5.
A second, and related, issue concerns the input criteria. Systems can accept only
5 As previously discussed, speech perception is indeed a complicated affair. It is affected by the presentation of both visual (McGurk &Donald, 1976) and tactile (Gick & Derrick, 2009) stimuli. Such influence is highly specific and was hypothesized to occur based on an in-depth knowledge of how speech perception systems work. For example, inaudible air puffs applied on participants’ right hand or neck while they listened to syllables caused participants to mishear ‘b’ (non-aspirated) as ‘p’ (aspirated; Gick & Derrick, 2009). These results demonstrate that perceivers integrate event-relevant tactile information in auditory perception and provide evidence for an extensive degree of integration across sensory modalities.
16 specific types of input, but they can process information for which they were not originally designed. According to B&K, the fact that a system is activated by other types of input (that is, lacks domain-specificity) does not undermine its modularity. Specificity of input is then substituted with “vocabulary on inputs”:
“…, if every cognitive mechanism has specifiable information that it accepts as
inputs, even if some systems accept information in multiple formats (or can be
controlled or influenced horizontally or top-down by other systems), then the
crucial issue is the vocabulary of inputs a given mechanism accepts. No
mechanism is either encapsulated or unencapsulated in an absolute sense.
Cognitive mechanisms can be referred to as encapsulated with respect to certain
information types but not others. What is important is to specify how information
is accessed and how it is processed, including the input criteria that must be met
for processing to occur.” (p. 631, italic added)
This move is seen as necessary:
“The focus on input conditions and function clarifies what is meant by “domains,”
because a theory of function will constrain hypothesized formal input conditions
for information-processing devices. Sperber (1994) referred to the “proper”
domain of a module as the class of inputs the module was designed by natural
selection to process. For example, the proper domain of a face recognition system
would be, putatively, faces of conspecifics (Kanwisher, 2000). The “actual
domain” might be, and indeed in many cases must be, a broader class of tokens
17 than the type for which certain modular systems evolved: for example, perhaps
not only faces but the wider set of stimuli that have formal properties that cause
them to be processed by the face recognition system. In addition, a view of
evolved function informs hypotheses about inputs, including the contextual
mediation of processing, as information about context can itself be an input to
modular systems … .” (p. 631)
B&K maintain that specialization is identified by the presence of specific input criteria, which are identified by an analysis of function. It is the input that indicates what types of information is processed by a system. However, this input might include other aspects of stimuli that share “formal properties” with the specialized input. The authors do not address the conditions under which one stimulus or a broader class of tokens characterizes the “actual domain” of a module. Why “must” we have, in some cases, a variety of inputs and in others a specific type of input? This lack of specificity opens the door to too much flexibility on what counts as evidence for purportedly evolved modules.
With respect to faces, B&K do not discuss what the formal properties shared by other visual objects might be. Evolutionary psychologists’ hypotheses are typically driven by hypothesized evolutionary scenarios; as a consequence, they do not focus on how the information is actually accessed or processed. Specific hypotheses are rarely made. For example, it is difficult to imagine how a functional analysis of face perception or the hypothesis that the face recognition system has evolved to allow humans to recognize their conspecifics might explain and predict some recent empirical findings.
Specifically, in car experts, face processing is disrupted by the visual presentation of cars
18 (Gauthier, Curran, Curby, & Collins, 2003; see also McKeeff, McGugin, Tong, &
Gauthier, 2010). Furthermore, the neural region of the temporo-occipital cortex associated with face processing (face fusiform area, Kanwisher & Yovel, 2006) is significantly activated by objects of expertise: the presentation of cars and birds activates this area in car and bird experts, respectively (Gauthier, Skudlarski, Gore, & Anderson,
2000); furthermore, this area is activated in individuals who became expert in recognizing exemplars of a new made-up category (i.e., Greebles; Gauthier et al., 1999).
The question of what purported formal properties are shared by faces and cars in car experts, or by faces and birds in bird experts, must be addressed to explain these findings.
In cognitive neuroscience, these data have been used to challenge the domain- specificity hypothesis proposed by Kanwisher, according to whom face perception is carried out by neural systems specialized for processing faces (e.g., Kanwisher & Yovel,
2006). Gauthier and colleagues proposed that faces might preferentially activate specific brain regions not because they are specialized for faces per se, but because they involve a level of visual representation that is not typical of other object categories (i.e., processing information beyond single parts, or holistic processing, and individuation of exemplars, not just types; Gauthier & Tarr, 2002). Therefore, functional specialization is driven by expertise, not faces per se. B&K might propose that both theories can be incorporated within an evolutionary framework where objects of expertise share some formal properties with faces (for an attempt to integrate them, see Barrett, 2012). However, without a definition of “formal properties” (based on physical characteristics? type of processing?) and an in-depth knowledge of how a given system works, this a posteriori integration would remain problematic. The underspecification of a system’s
19 characteristics (here in terms of input and processes) makes the theory amenable to subsequent adjustments by following a postdiction logic. In order for theories to be useful, they should explicitly identify what type of empirical findings would falsify them.
If any data can be adjusted to fit a theory, the theory becomes too powerful and ultimately superfluous.
Therefore, the concern is that the reformulation of the concept of modularity in terms of functional specialization leaves too much leeway on the type of evidence needed to support the hypothesis that a given system is modular. It does not matter what features a system has; as long as it has design characteristics that one might define as functional, based on hypothesized selective environmental pressures, the system is considered a mechanism evolved through natural selection. The theory itself then becomes sufficient reason (or evidence) to establish whether or not a mechanism is considered to be specialized and adaptive. This is a critical issue as studies devised to test EP theories typically do not test the hypothesis that a trait was adaptive in ancestral times, but establish the presence of some sort of effect, ability, or group difference that is consistent with the hypothesis (e.g., Bolhuis et al., 2011; Richardson, 2007). The effect is in turn taken as evidence for the hypothesis that an adaptation has been found (see also Lloyd,
1999, and Nash, this issue). However, an effect or a group difference says nothing about their origins.
Neural specificity
B&K apply the same flexible logic to another important criterion for modularity, that is, specificity of neural circuits (Fodor’s ‘hardwiring’). Specificity of input entails
20 specificity at the neural level. If it is true that a module needs not to be localized in a single and small area of the brain (B&K, p. 641), its neural implementation should be clearly defined in terms of brain regions involved, input projections, output, etc. While some EPs have researched the neurophysiological underpinning of purported modules
(e.g., Stone et al., 2002), examination of brain mechanisms to substantiate hypothesis regarding evolved modules has been rare in EP (see Bolhuis et al., 2011, for a similar point). Specialization of circuits is typically assumed. On the one hand, B&K concede that isomorphism between functional and structural architecture exists; on the other hand,
“at a larger, macroscopic level, there is no reason to assume that there must be spatial units or chunks of brain tissue that neatly correspond to information-processing units.” (p.
641). As is typical in EP papers, instead of providing scientific evidence for such claim, the authors present an analogy and discuss how, in an electronic device,
“Individual wires have specific functions, but at the level of the entire machine,
wires with different functions might cross and overlap. For this reason, removing,
say, a three-inch square chunk from the machine would not necessarily remove
just one of the machine’s functions and leave the rest intact. In brain terms, it
could be, and probably is, that macroscopic regions of brain tissue include
neurons from multiple information-processing systems with multiple functions ...”
(p. 641)
Based on such analogy, B&K are ready to dismiss evidence from neuroimaging and brain-lesion studies:
“Here, we argue that modularity in the sense of functionally specialized
21 information processing can exist even in the absence of evidence of spatial
localization from, for example, fMRI or lesion studies.”6 (p. 641)
While it is true that large lesions typically compromise a variety of functions, that modules need not be localized in one single region, and that neuroimaging techniques have limitations in terms of spatial resolution (besides, they provide an indirect measure of brain activity, as they measure changes in blood flow metabolism; Friston, 1997),
B&K fail to provide alternative methods to investigate how modular systems are neurally implemented. Their strategy consists in dismissing the usefulness of two major methods used by cognitive neuroscientists to test models of neural architecture and implicitly suggesting that such an enterprise might be worthless when claims of modularity are tested within an EP framework. Therefore, we are asked to evaluate hypotheses about evolved modular systems, characterized by specific neurally circuits, without a scientific investigation of their neurophysiological underpinnings or correlates.
Similarly, activation of the same neural system by two different sets of stimuli is rejected by B&K as evidence for shared mechanisms, based on the limitation of neuroimaging techniques in terms of spatial resolution: the two stimuli might activate different populations of neurons even in a very circumscribed region7 (p. 641). However, there are other techniques and research paradigms that can be used to overcome the
6 Contrast this position with that of Price and Friston (2005), who call for a rigorous investigation before claiming that a brain region exhibits functionally specialization: “To infer functional specificity requires a demonstration that an area is activated only by tasks that engage its function and no others” (p. 265). 7 B&K consider informative the activation of different brain areas by different types of stimuli to establish that the two stimulus sets engage different systems, and I agree. However, even in this case, careful investigation is necessary to establish the meaning of differential brain activation, as it might reflect a variety of factors (e.g., differences in non-critical physical characteristics, levels of practice, levels of attention, etc.). In order to identify what brain regions are necessary for a given function, functional specialization at the neural level needs to be investigated.
22 localization issues mentioned by B&K. For example, Gauthier et al. (2003) employed a different methodology to investigate whether neural regions for faces and objects of expertise are functionally independent. They adopted an interference paradigm where participants were asked to process two stimuli at the same time. If processing two types of stimuli relies on shared cognitive and neural resources, performance is negatively impacted (for example in terms of response time) compared to a condition in which the two types of stimuli draw from different resources. Gauthier and colleagues (2003) reasoned that face processing would be disrupted by the presentation of cars in car experts if faces and cars were processed by the same brain circuits. This is exactly what the authors found. Importantly, the interference effect arose during the early stages of visual processing. The authors recorded Event-Related Potentials, or ERPs, while participants performed the experiment. ERPs are voltage fluctuations in the electroencephalogram elicited by the presentation of a controlled stimulus. The latencies of different positive and negative components in an ERP reveal the time course of activation (in the order of milliseconds) of the neuronal populations that are recruited during the processing of that stimulus (Picton et al., 2000). Asking car experts to simultaneously process faces and cars influenced the amplitude of a component associated with the early stages of face perception, known as the N170, elicited over posterior regions between 140 and 190 ms. Therefore, in car experts, processing of cars and faces relies on shared neural circuits or, in the authors’ words, “are not functionally independent.” (p. 431).
23 These results cast doubts on the hypothesis that these brain regions are functionally specialized for faces per se. However, B&K still maintain that such systems are functionally specialized for faces:
“Unlike intuitive ideas about domain specificity, functional specificity affords a
means of individuating functional domains using evolutionary functionalist
principles to do so (in this case, for example, the importance of faces as a domain
is suggested by the fitness benefits of being able to recognize individual
conspecifics; Cosmides & Tooby, 1994; Tooby & Cosmides, 1992).” (p. 634)
However, the cited works do not provide independent evidence of the “fitness benefits” related to recognizing conspecifics. These benefits are only assumed to have existed during early human evolution, raising once again issues of circularity (see Nash, this issue).
Modularity, thirty years later
Fodor (1983) proposed the modular framework as a working hypothesis to understand the functional organization of the mind. Such organization relied on the distinction between modular and non-modular systems. Modular systems were hypothesized to have specific characteristics and architectural constraints. This hypothesis prompted a considerable amount of empirical research aimed at testing the modularity of a variety of cognitive systems. These studies have refined our understanding of how the brain is organized and functions, as empirical findings revealed a complex picture in which not even perceptual
24 and linguistic systems turned out to exhibit the defining characteristics of modularity
(e.g., Farah, 1994; McGurk & MacDonald, 1976; Price, Thierry, & Griffith, 2005;
Wojciulik et al., 1998). The concept of informational encapsulation further appeared to break down at the neural level, as revealed by studies on multisensory integration during early perceptual processing (for a review, see Ghazanfar & Schroeder, 2006).
Furthermore, functional neuroimaging data revealed no reliable one-to-one mapping between cognitive functions and specific neural regions (Price & Friston, 2005). This empirical work led to a redefinition of the architecture of the mind and the brain, mostly in terms of interconnectivity, where specialization of a region is not an intrinsic property but depends on connections with other regions (e.g., Friston & Price, 2001; McIntosh,
2000).
At the same time, work in developmental cognitive neuroscience has shown that the brain organization found in adults emerges during development (e.g., Cohen Kadosh
& Johnson, 2007; Neville & Mills, 1997). This development is context-depended and shaped by a variety of factors at different levels of organization: from the activity within and between single neurons, to the activity of different brain regions, to the behavior of the organism itself, in turn influenced by its environment (Westermann et al., 2007).
Changes at one level can dramatically alter brain organization, as shown by the reorganization of the cortex documented in individuals with congenital blindness (e.g.,
Amedi, Merabet, Bermpohl, & Pascual-Leone, 2005), whose visual areas are recruited to support linguistic functions during speech production. Therefore, an approach where levels of descriptions are seen as independent (Marr, 1982/2000) is untenable as brain organization and cognitive development emerge through local and interactive processes.
25 In this context, cognitive and brain systems that are specialized in adults develop in a highly interconnected brain where regions co-develop with other brain regions, not in isolation. What a brain region or neuron does, in terms of function, depends on its interaction with other regions and neurons (e.g., Friston & Price, 2001; Price & Friston,
2005); it even depends on the state of distributed neural networks (e.g., Fontanini & Katz,
2008). Within this framework, the specialization of neural systems (modularity) assumes a different meaning, one that is anchored into the physical system of a developing organism (Karmiloff-Smith, 2013). Development is an interactive phenomenon, or, in
Balaban’s (2006) words, “a conversation, not a blueprint” (p. 310), where the characteristics of an organism, including systems considered modular, are constructed here and now. Current contextual influences are not factored into EP, except in terms of conjectures about recurrent selective pressures from the ancestral past.
Therefore, the empirical research conducted after the publication of The
Modularity of Mind has put the concept of modularity in perspective, contextualized it, and redefined it (in terms of partial and emergent specialization). At the same time, it has given it a more defined place, one that is rooted in the actual mechanisms that characterize neural and cognitive development. Such research focused on testing, not assuming, modularity. In contrast, the modules proposed by evolutionary psychologists have remained elusive, disembodied, unanchored to a rigorous analysis of proximate causes, including their neural underpinnings. B&K’s reframing of the concept in terms of functional specialization exacerbates this elusiveness. B&K do not consider it necessary to provide evidence of structural modularity to show that a system is an evolved module: the adoption of functional specialization as the defining feature of modularity
26 accommodates a flexible form of domain-specificity, which can be used as needed.
Flexibility is further adopted in establishing evidence of neural specificity for evolved adaptations. Using Karmiloff-Smith’s (2006) words, they want to have their modular cake and eat it too (p. 565). Indeed, in a later paper, Barrett and Kurzban (2012) adopt a definition of modularity that strips purportedly evolved modules of any architectural constraints: “if X is a mechanism, and if it has a design (i.e., has been shaped by the process of natural selection acting over evolutionary time), then it is what we are calling a
“module”” (pp. 684-685, italics in the original). This definition includes systems that have traditionally been considered non-modular according to Fodorian characteristics.
It is perhaps ironic that a concept so central to the EP approach has become so ineffable. Humans supposedly have many instincts, many more than other animals
(Cosmides & Tooby, 1997), and specific neurocognitive mechanisms to support them.
Yet, evidence of modularity for such mechanisms is based less on reliable evidence of architectural features and more on functional guesses based on hypothesized evolutionary scenarios. When issues of modularity are raised (e.g., DeSteno et al., 2002), key constraints are not considered critical (e.g., Barrett et al., 2006) or removed from the definition of modularity (Barrett & Kurzban, 2006, 2012). The adoption of words like machine and replicators (Tooby & Cosmides, 2005), along with the recurrent (and ill- conceived) brain-as-computer metaphor, reveal a progressive estrangement from the study of biological organisms as real and embodied, and stands in stark contrast with the call for an integrated study of behavior by computational neuroscientists. Indeed, in his
Afterword to the 2010 edition of Marr’s Vision, Poggio remarked how the connections between levels must be reemphasized:
27 “ (1) insights gained on higher levels help us ask the right questions and do the
right experiments at the lower levels, and (2) it is necessary to study nervous
systems at all levels simultaneously. From this perspective, the importance of
coupling experimental and theoretical work in the neurosciences follows directly:
without close interaction with experiments, theory is very likely to be sterile” (p.
364).
The trend to isolate functional analyses from the investigation of proximate causes of behavior (such as its physical implementation and development)8 is evident in other recent writings. For example, in a paper addressing the relationship between exposure to disease threats and preference for physically attractive leaders, White,
Kenrick, and Neuberg (2013) write that such relationship was predicted based on an evolutionary perspective and that, “ … predictions couched in terms of more proximal mechanisms do not necessarily constitute “alternatives.” Instead, it may be through these mechanisms that more distal processes have their effects.” (p. 2435; see Barrett, 2012, p.
10739, for a similar point). In this view, the study of the neural underpinnings of behavior has the purpose of simply revealing how distal functions are implemented; it does not inform the scientist, and it provides information that can only validate, not challenge, a functional analysis. Thus, it becomes irrelevant. As B&K have stated: “We remain agnostic with regard to the way that functional specificity is implemented in the brain and look forward to the accumulating evidence from developmental neuroscience to inform
8 I am not endorsing a rigid distinction between levels of explanation, as the utility of such distinction has been questioned in the light of new empirical findings (see Laland, Sterelny, Odling-Smee, Hoppitt, & Uller, 2011, and Bateson & Laland, 2013, for recent discussions). What I am arguing is that the study of proximal causes (development and neurobiological processes) cannot be neglected in evolutionary sciences, as such processes have been shown to shape evolutionary pathways (Lickliter & Honeycutt, 2003).
28 the details of these processes.” (p. 642). It is difficult to reconcile such a position with the claim that evolutionary psychologists identify “the proximate causal mechanisms of human behavior in the brain” (Barrett, 2008, p. 173).
B&K’s words seem to suggest a theoretical shift in EP, one that is associated with the abandonment of rigorous analyses at the implementational level and the retreat into a functionalist framework. This shift reveals a fundamental contradiction within EP. Some evolutionary psychologists have adopted concepts developed in other theoretical approaches (e.g., probabilistic epigenesis, Gottlieb, 1998; progressive modularization,
Karmiloff-Smith, 2006; developmental system, Oyama, 2000) and incorporated recent empirical findings from cognitive, developmental, and brain sciences in their theoretical contributions (e.g., Barrett, 2012; Ellis & Bjorklund, 2005). This incorporation demonstrates how important the analysis of proximate causes is for evolutionary theorizing. However, evolutionary psychologists do not implement such an integrative approach in their empirical investigations and rarely contribute to our understanding of evolutionary-relevant behaviors in terms of developmental and neural processes (see also
Nash, this issue, and Bolhuis et al., 2011). B&K’s proposal exacerbates this trend.
Until such integration is reached, rather than reframing modularity in terms of functional specialization, evolutionary psychologists should refrain from using the term when they refer to purportedly evolved adaptations. Barrett and Kurzban (2012) stated that they “would be happy to discard the term in favor of some mutually agreed upon term: “psychological specialization”, “mental adaptation”, “evolved cognitive
29 mechanism” 9, or any equivalent that designates an aspect of mental structure that was shaped by natural selection” (p. 684). Following their suggestion, they should adopt a terminology that clearly acknowledges the focus on the functional (computational) level and removes any reference to architectural constraints and neural processes, as these are not investigated systematically. The vagueness and disconnect of EP theories will then emerge, along with the contradictions intrinsic to an approach that claims to treat psychology as a branch of biology “… that studies (1) brains, (2) how brains process information, and (3) how the brain's information-processing programs generate behavior”
(Cosmides & Tooby, 1997, p. 5).
9 See Gantt, Melling, & Reber (2012) for a critique of the use of the term “mechanism” in EP.
30
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