In order to decide whether nonhuman animals are conscious, it is helpful to begin with an empirically motivated account of the precise conditions under which consciousness arises in human beings. Reliance on behavior alone can lead to shaky inferences. Many animals behave very differently from humans, and behaviors associated with consciousness can be carried out without a central nervous system; for example, an excised octopus leg will avoid noxious stimuli (Alupay et al., 2014), and scratching behavior in sea turtles can be controlled by the spine (Stein, et al. 1995). In humans, much of what can be done consciously can also be done unconsciously (e.g., Prinz, 2017). Confidence about other animals will increase if we can identify mechanisms that match the correlates of consciousness in us. Here I will briefly summarize an account of those mechanisms, defended at great length elsewhere, and I will suggest that a surprising number of taxa may satisfy its fairly demanding conditions.
The account of consciousness that I favor is called the AIR theory, which stands for Attended Intermediate-Level Representations (Prinz, 2012). Drawing on a large body of empirical evidence, the theory makes two main claims. First, consciousness arises only at an intermediate-level of abstraction in hierarchical sensory systems. The senses move from very local feature detectors, corresponding to cellular transduction mechanisms, on up to categorical representations that are invariant across a wide range of stimulus conditions; in human beings, all consciousness seems to arise between these extremes. Second, consciousness seems to arise when and only when sensory states are modulated by attention. There have been some empirical challenges to this later claim, but I think they are largely terminological. “Attention” can refer to several different processes. In the AIR theory, it refers to just one: a change in processing that allows information in sensory systems to activate working memory. In a slogan: consciousness is the categorical basis of availability to working memory. The AIR theory also advances a hypothesis about this categorical basis, stated in neurocomputational terms: cellular activity in intermediate-level sensory systems becomes available to working memory when and only when assemblies of sensory neurons fire in phase-synched oscillations in the gamma frequency (about 25–100 Hz).
Each component of the AIR theory has been defended on its own by others. There are those who locate consciousness in the intermediate level (Jackendoff, 1987), those who identify consciousness with attention (Mack and Rock, 1998), those who relate consciousness to working-memory access (Baars, 2002), and those who suggest that gamma oscillations are the neural correlates of consciousness (Singer and Gray, 1995; Crick and Koch, 1990; Engel et al., 1999; Gaillard et al., 2009). The AIR theory integrates all of these proposals. It is therefore a synthesis of the theories that enjoy most empirical support. If it can be shown that a nonhuman animal satisfies all of these conditions, that would be powerful evidence for the attribution of consciousness, since such a creature would satisfy a number of different theories. Of course, there might be creatures that satisfy some of these conditions and not others. Here, the AIR theory would deliver a negative verdict. There could also be creatures that have mechanisms that are similar, but not identical to those specified in the AIR theory; for example, different oscillation frequencies underlying working-memory access. Such cases would be very difficult to settle empirically, unless future work can establish such variation in human correlates of consciousness. One might worry, then, that the AIR theory is too stringent or too difficult to apply to creatures very different from us. It turns out, however, that a surprising number of taxa satisfy the conditions of the AIR theory. Therefore, even if we make the conservative assumption that consciousness requires neurofunctional correlates like those found in us, a strong case can be made for the conclusion that consciousness is widespread in nature.
With the AIR theory of consciousness in hand, we can look at evidence for consciousness in nonhuman animals. In this brief review, I will try to indicate that indicators of consciousness have been measured in a wide range of taxa. I will not attempt to cover the biological work exhaustively, and I will rely on some folk categories (such as fish) that include much more diversity than represented here. Still, a modest sample will reveal that evidence for consciousness is impressive. Though much empirical work remains to be done, the available evidence suggests that consciousness may be more widespread than many people would be antecedently disposed to believe. I will begin with some brief remarks on the intermediate-level hypothesis, and then I will turn in greater detail to attention and working memory.
I am not aware of any research that attempts to locate consciousness at the intermediate level of sensory processing in nonhuman animals. That said, human visual neuroscience often uses animal models (cats were used in Hubel and Wiesel’s discovery of edge detectors, and macaques have been sacrificed in countless studies of visual processing, including those that led to Weiskrantz’s discovery of blindsight). There are many homologies between mammalian brains. All mammals have a laminar neocortex and hierarchically organized sensory areas. Mammals also face the same challenge as humans when it comes to perception: converting local cells at the sensory periphery into representations of complex properties and objects. This gives some reason for thinking that mammals, at least, have brain organization capable of supporting intermediate-level sensory representations. There is even evidence in monkeys that intermediate-level vision is the first and primary locus of attention modulation (Mehta et al., 2000). This does not prove that the intermediate level is the locus of consciousness in mammals, but it is certainly suggestive that there is a sensory hierarchy that interacts with attention in ways that parallel observations in humans.
What if we move beyond mammals? A surprising range of taxa show evidence for hierarchically organized senses. This is true of cephalopods, birds, and insects. Feinberg and Mallat (2016: 176: Table 9.2) provide what may be the most complete review of this issue in the literature. They argue that multi-level hierarchies can be found in the sensory systems of most multi-cellular taxa. Exceptions include gastropods, such as snails, as well as flatworms, roundworms, and other simple protozomes. Feiberg and Mallat suggest that their hierarchies may be too simple to support consciousness (p. 187).
The picture that emerges from this work is consistent with the conjecture that animals with greater sensory complexity than snails might perceive the world consciously. In what follows, I will restrict myself to these more complex creatures and I will assume they meet the basic condition of organization within their sensory system. This assumption must be treated as both speculative and tentative. For while sensory hierarchies abound, they differ in a variety of ways. Whether these differences make a difference is an open empirical question.
To illustrate, consider some of the variety in mammals (Northcutt and Kaas, 1995). Despite widespread homology, there are also interesting differences. For example, one of the layers in squirrel visual pathways has three sublayers, and these seem to have evolved independently from sibling species. Similarly, cats and monkeys both have layers in their lateral geniculate nuclei (a hub for sensory processing), but these appear to have evolved independently. There are variations in complexity as well. Rodents have five to eight visual areas, whereas we have dozens.
There are even differences among primates. Primates have many subregions in their visual pathways, but they differ in number across species (20–40), and not all are homologous. Functional differences in visual systems of humans and monkeys have been observed using neuroimaging (Orban et al., 2004), and there are also structural differences distinguishing humans and great apes (Preuss, 2007). We simply don’t know whether these differences make a difference with respect to consciousness.
Let’s turn now from the intermediate level to attention and working memory. Here, evidence is clearer. Beginning with mammals, there is overwhelming behavioral evidence for both attention and working memory, and there is also some evidence implicating related brain systems.
Mammalian attention shows a broadly consistent functional profile. When an object of interest is presented to a mammal, it will process it in ways that indicate enhanced encoding. In monkeys, attention is known to increase the sensitivity of intermediate-level visual neurons and to reshape receptive field sizes. Monkey attention also has a similar time course as measured by “inhibition of return,” which is interpreted as the rate of endogenous attentional resetting in visual search tasks (Torbaghan et al., 2012). Inhibition of return has not been observed in rats (Wagner et al., 2014), but rats’ parietal cortex is known to regulate attention, much as it does in humans (Bucci, 2012). Parietal cortex also gates activity to frontal working-memory structures in monkeys and rats. Such findings indicate similar circuits of attention across mammals.
Mammals also show similar capacities for working memory. All mammals can retain information for brief intervals, after a stimulus has been removed. This is frequently measured by delayed match-to-sample tasks. After a stimulus is removed, an animal must select it from options. The delay period between cue and test provides a measure of working-memory duration. Lind et al. (2015) report similar durations for chimpanzees, monkeys, dolphins, rats, and other mammals. Primates do not stand out here from other mammals (p. 55). In direct comparisons between monkeys and humans, our species shows stronger working-memory performance, as we mature, but monkey performance rivals human four-year-olds (Chelonis et al., 2014).
Anatomically, working memory is associated with prefrontal cortex in both rats (Kesner et al., 1996) and monkeys (Goldman-Rakic, 1990). Homologies are close enough that animal research on working memory is often conducted to draw conclusions about humans. That said, there are some differences in prefrontal areas that should be taken into account. Croxson et al. (2005) report a number of differences in the connectional anatomy of human and macaque prefrontal cortex, though they note similarities in connections to parietal areas that have been implicated in working memory. Pruess (1995) reports that rats lack an anatomical homologue of dorsolateral prefrontal cortex, which is a locus of visual working memory in humans, but others have shown that rats have frontal brain areas that function in analogous ways (Birrell and Brown, 2000; Dalley et al., 2004).
In addition, there is evidence that gamma-band oscillations contribute to attention in mammalian species. This is well demonstrated using cellular recordings in monkeys (Fries et al., 2001). Gamma has also been associated with visual stimulus presentation in mice (Nase et al., 2003).
Despite some cross-species differences mentioned here, the overall picture suggests that the psychological and neural correlates of human consciousness can be found in other mammals. If the AIR theory is right, I think we can be relatively confident about attribution in these cases.
Let’s move now to animals whose brains are quite different from our own. I will begin with birds. Mammalian brains have many parts with no homologues in the brains of birds, but there are functional similarities that have emerged through convergent evolution. Therefore, they are a good test case for the applicability of AIR to creatures that differ from us anatomically and evolutionarily.
Bird working memory has been studied using some of the same tasks that are used in mammals, including delayed matching to samples. This task has been tested in a number of species, including various jays, crows, chickadees, chickens, pigeons, and juncos. Performance measures favorably to mammals, with similar psychometric profiles (Lind et al., 2015). There is also evidence for a similar anatomic implementation. Milmine et al. (2008) trained pigeons on a selective forgetting version of the delayed match-to-sample task. To succeed, the birds needed to retain a memory of a previous cue during a delay period. In this interval, the researchers measured activity in the nidopallium caudolaterale, a structure that has been called the avian prefrontal cortex, because it is a highly integrative area implicated in executive functions. They found evidence for temporary memory encoding, akin to what has been observed in mammals. Similar results are reported by Rose and Colombo (2005). They find that delay-period activity in the nidopallium caudolaterale is present on experimental trials when pigeons must hold information in working memory, and absent on trials where retention is not called for.
Attention has been studied in birds in a variety of different ways. The most impressive results adapt a paradigm that is frequently used in human and monkey research. Sridharan et al. (2014a) presented chickens with a spatial cue followed by a target. Target detection improved with cueing, the effect of distractors reduced, response times shortened, and confidence increased (as measured by choice behavior). Birds do not have a parietal cortex for controlling attention, but they do have a homologue of the mammalian superior colliculus, which is a subcortical structure that can control attention. This structure is called the optic tectum. Research in pigeons suggests that it controls attention (Marín et al., 2005). Strikingly, it appears to do so by modulating gamma activity (Neuenschwander et al., 1996; Sridharan et al., 2014b).
Turning to (nonavian) reptiles and amphibians, evidence is both harder to come by and harder to interpret. Most standard tests for working memory and attention have not been applied to frogs, lizards, snakes, toads, or turtles, much less crocodiles. There is, however, some research that lends itself to speculative inference. It should be noted at the outset that although the categories of reptiles and amphibians are philogenetically close, there may be significant psychological differences between them. Cabanac et al. (2009) argue that reptiles show a greater capacity for play and reward learning, and they take this to suggest that they are more likely than amphibians to be conscious. I share this assessment, for slightly different reasons, but I also think there are some grounds for doubting consciousness in both classes of animals.
Evidence for attention in reptiles and amphibians is inconclusive. There are various findings that indicate selective orienting responses. For example, Schwartz and Gerhardt (1989) presented green tree frogs with aggression calls set against a background of noise. They find that the frogs were able to detect the calls and orient towards them. They compare this behavior to the cocktail party effect, where we hear our own names against the clatter of a crowded room. Strictly speaking, however, they show only that frogs orient towards a meaningful stimulus, not that they attend. In primates, orienting and attending are correlated by dissociable responses. Orienting is a matter of changing bodily position to improve stimulus processing, whereas attention is a change in processing itself. For example, when we shift gaze direction, that is an orienting response, but we can look one way while attending the other way. Orienting may predate attending phylogenetically, so results like this do not decisively establish attention in frogs. Similarly, there are studies that use increased tongue flicking and postural changes in lizards as an indicator of attention (e.g., Greenberg, 2002), but these behaviors constitute orienting, rather than attending, since they are ways to more effectively sample the environment, rather than changes in how a sample, once perceived, gets processed. More direct measures of attention would be helpful.
Herpetological research on working memory also tends to be somewhat indirect. For example, Wilkinson et al. (2007) investigate tortoise performance in radial mazes, and mention that success depends on something akin to working memory, since information about successful routes must be retained. But success could also reflect another kind of short-term (or even long-term) memory. Amphibians are less successful than reptiles in radial mazes, which may indicate a lack of working memory, but more research is needed to confirm this conclusion (Bilbo et al., 2000).
There is evidence that reptiles’ and amphibians’ brains make use of synchronized oscillations in perception. Prechtl (1994) reports neural synchrony in the turtle visual system, averaging around 20 Hz, and Hall and Delaney (2001) report synchrony between 7 and 12 Hz in the frog olfactory bulb. Notice that these oscillations are appreciably slower than gamma. In fact, gamma reduces during visual perception in turtles. This indicates that the temporal neurodynamics of perception differs from what has been found in mammals and birds.
It is difficult to deliver a decisive verdict about reptiles and amphibians, given findings such as these. It may be that their information-processing systems are similar to ours in certain respects, but also different in ways that reduce confidence when attributing consciousness. Without a fixed metric for similarity, and with a need for more research, a decisive conclusion would be premature, but I am tentatively inclined to say that we lack solid grounds for believing that reptiles and amphibians have the psychological and neural processes required for consciousness.
Let’s move on to fish. It must first be stressed that this folk category is highly heterogeneous, so generalization may be impossible (Allen, 2013). With that caveat, I think we can say that there is intriguing but inconsistent and incomplete evidence regarding consciousness in various species.
There is some indirect evidence for attention in fish. For example, Piffer et al. (2012) show that guppies can differentiate small quantities. This indicates attention, they argue, since some models of such numerical behavior in mammals make reference to attention; the limits in competence with exact numerosities is said to correspond to the capacity of multi-object attention (Hyde and Wood, 2011). In another study, Jun et al. (2016) argue for attention in electric fish. They investigate active sensing and novelty responses in a dark environment, and conclude that the fish are showing signs of selection and intensive processing, which are signature features of attention. On its own, active sensing behavior might just indicate orienting, but the presence of novelty response and evidence of learning indicates that something more may be going on here. Perhaps the fish are attending and information is being retained in working memory to guide ongoing behavior.
There have been various efforts to investigate working memory in fish more directly. In one unsuccessful effort, Newport et al. (2014) administered a delayed match-to-sample task to archerfish. This species could not learn the task. Other species may work differently. For example, Guttridge and Brown (2014) show that Port Jackson sharks are susceptible to trace conditioning. In this paradigm, there is a 10-second delay between the unconditioned and conditioned stimuli, indicating brief retention in memory.
In addition, there is intriguing evidence for neural synchrony. For example, Friedrich et al. (2004) recorded synchronous gamma-band oscillations in zebrafish during an odor discrimination task. In another study, Ramcharitar et al. (2006) investigated the “jamming avoiding response” (JAR) in electric fish. JAR is used to block out electric signals from conspecifics that might prevent a fish from obtaining accurate sensory information. As the authors point out, JAR is, in that sense, like a filter for attention. They find that JAR is associated with gamma-band synchronization.
The fish findings reviewed here are intended to illustrate lines of research that indicate that some kinds of fish may have the kinds of psychological and neurophysiological resources posited by the AIR theory of consciousness. The evidence is far from conclusive, but sufficient, I hope, to suggest that we cannot rule out consciousness in fish.
Sticking with sea creatures, I want to turn to cephalopods – the class that includes squids, cuttlefish, and octopuses. Some of the animals, especially octopuses, are known for their impressive cognitive abilities, such as strategic hunting, but what shall we say about their consciousness?
Evidence that bears on the AIR theory is hard to come by. There is a lot of research on orienting responses in cephalopods, but little that tries to differentiate orienting and attending. For example, when cuttlefish see a prey animal, they change gaze direction toward it, erect their first pair of harms, and alter their body pattern. Hanlon and Messenger (1996: 51) call this attention, but it is an orienting task. There are not, to my knowledge, studies that use methods that are common in mammal attention research, such as tests of discrimination accuracy in cued locations (these tests are most convincing when they control for orienting, which may be difficult with cephalopods). More suggestive are studies indicating selection processes. For example, Alves et al. (2007) trained cuttlefish to navigate using different spatial cues. They found that the animals could flexibly adapt to new cues and choose between cues when more than one was present, using cue salience and other factors. This may be explained by selective attention, but it could also be achieved by learned associations between cues and motor planning centers.
This brings us to the issue of working memory. To my knowledge, cephalopods have not been shown to succeed at standard tests such as delayed match-to-sample or trace conditioning. They do show the ability to learn in mazes, but the learning lasts for days, suggesting that they may not require working memory for this. Admittedly, maze performance requires a brief store of where the animal has been. Mather (1991) argues that this requires working memory, but this can be achieved using a specialized spatial memory that operates independently of working memory, as with the hippocampal place system in rodents. Graindorge et al. (2006) has obtained evidence based on lesion studies that the vertical lobe in the cephalopod brain works similarly to the hippocampus.
There is other evidence for brief memory in cephalopods. For example, Dickel et al. (1998) measure cuttlefish capacity to remember that prey are inedible when trapped in a glass tube. This is true just for the duration of the task, so it does not require long-term memory. The effect does last many minutes after training, however, so it is not necessarily evidence for working memory.
Mather (2008) argues that octopuses have working memory. She points out that the animals do no react immediately to prey, but rather engage in strategic planning once prey are identified. They also show flexibility, learning multiple responses and cues and choosing between them. This suggests that sensory information is passed on to brief storage for executive processing. On the whole, I find such evidence convincing, but more direct tests would be welcome.
What about gamma in cephalopods? Here, evidence is scant, but there are studies indicating that synchronized neural activity is used by the octopus nervous system and that such synchrony is sometimes in the gamma range (Bullock and Budelmann, 1991). There is no evidence as yet that gamma synchrony is linked to attention, but it does seem to reflect transient states in these animals, rather than standing waveforms, which indicates that it could serve such a function.
Putting this altogether, I think we can conclude that cephalopods may indeed have the necessary substrates for consciousness, though further investigation is needed. The mere possibility is intriguing, given how different they are from mammals. As with birds, cephalopods could establish that the mechanisms of consciousness can emerge through convergent evolution.
Let’s turn, finally, to insects. Here, again, there is much variety, but also impressive similarities across taxa, and impressive signs of conservation, linking insects to each other, and to many more remotely related species, spanning the range from tardigrades to humans.
In recent years, there has been a flurry of impressive work on insect attention (see de Bivort and van Swinderen, 2016, for a review). Much of it takes advantage of an apparatus in which a flying insect can be tethered in a harness and surrounded by a controlled environment that contains visual information. Sareen et al. (2011) used such a setting to show that flies respond to cues. After cuing in one of two locations, two stumuli were concurrently presented. Torque responses indicated preferential processing of the cued location. Measurements from the insect nervous system indicate that such attention effects do not operate at the earliest stages of visual processing, but on secondary stages (Seelig and Jayaraman, 2015). This might indicate an analogue of the intermediate level.
There is also research indicating that insects have working memory. Pahl et al. (2013) report numerical cognition in various insects, which may suggest both attention and brief storage. In a more direct investigation of working memory, Giurfa et al. (2001) show that honeybees perform well in a delayed match-to-sample task. Interestingly, attention and working memory may be genetically linked in insects (van Swinderen, 2007), suggesting that, as with mammals, these processes are closely related.
There is even evidence that these processes involve gamma oscillations. Van Swinderen and Greenspan (2003) found that salience modulates 20–30 Hz brain activity in fruit fly brains, which pushes into gamma. Gamma frequencies have also been implicated in locust brains during sensory discrimination (Stopfer, et al. 1997).
Strikingly, then, insects do seem to have the basic ingredients of consciousness as postulated by the AIR theory.
In searching for consciousness in other creatures, we cannot rely on the presence of mere behavior, such as pain avoidance. We should look instead for the psychological and neural mechanisms that underlie consciousness in us. This may seem chauvinistic, but, given the enormous behavioral complexity that we ourselves can exhibit without consciousness, coarse behavioral measures run the risk of being too permissive. To find a more principled test, I introduced the AIR theory, which integrates some of the leading empirically based approaches to consciousness. According to that theory, consciousness requires hierarchical sensory processing, attention, and working memory, along with high-frequency, phase-locked neural oscillations. There is very strong evidence for these components in mammals, but also in a range of creatures whose brains are very different from our own. The case is especially strong in avian species, but also surprisingly strong in insects. Gastropods probably lack consciousness, given the simplicity of their nervous systems, but cephalopods are good candidates. The case for fish is less clear at this point, and the evidence for consciousness in reptiles and, especially, amphibians, is compatatively low.
Many questions remain. How much similarity to us is required? For example, must neural oscillations fall in a specific range, or just any range adequate for flow to working memory? Here, I have treated working memory as a system for ephemeral retention of perceptual information, but working memory can also encompass processes that allow for executive control. It may turn out that consciousness involves sophisticated forms of working memory. This would add further constraints to our search. The proposals advanced here must therefore be regarded as provisional. Still, they give us an empirical basis for attribution of consciousness that can be applied on the basis of extant data, and the conclusions reached, however tentative, provided principled answers rather than mere speculation based on gross behavior and phylogenetic similarity.
I am grateful for helpful feedback from Kristin Andrews, Jake Beck, and Peter Godfrey-Smith.
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