Title: "What is it like to be a bat?" a pathway to the answer from the Integrated 1 Information Theory 2 3 By Naotsugu Tsuchiya1,2 4 5 1. School of Psychological Sciences, Monash University, Melbourne, Australia, 6 3168 7 2. Monash Institute of Cognitive and Clinical Neuroscience, Monash University, 8 Australia, 3168 9 10 May 10, 2016 11 12 Abstract: 1 What does it feel like to be a bat? Is conscious experience of echolocation closer to 2 that of vision or audition? Or, do bats process echolocation non-consciously, such 3 that they do not feel anything about echolocation? This famous question of bats' 4 experience, posed by a philosopher Thomas Nagel in 1974 (Nagel, 1974), clarifies 5 the difficult nature of the mind-body problem. Why a particular sense, such as vision, 6 has to feel like vision, but not like audition, is totally puzzling. This is especially so 7 given that any conscious experience is supported by neuronal activity. Activity of a 8 single neuron appears fairly uniform across modalities, and even similar to those for 9 non-conscious processing. Without any explanation on why a particular sense has 10 to feel the way it does, researchers cannot approach the question of the bats' 11 experience. Is there any theory that gives us a hope for such explanation? Currently, 12 probably none, except for one. Integrated Information Theory (IIT), proposed by 13 Tononi in 2004 (Tononi, 2004) has potential to offer a plausible explanation. IIT 14 essentially claims that any system that is composed of causally interacting 15 mechanisms can have conscious experience. And precisely how the system feels is 16 determined by the way the mechanisms influence each other in a holistic way. In 17 this article, I will give a brief explanation of the essence of IIT. Further, I will briefly 18 provide a potential scientific pathway to approach bats' conscious experience and 19 its philosophical implications. If IIT, or its improved or related versions, is validated 20 enough, the theory will gain credibility. When it matures enough, predictions from 21 the theory, including nature of bats' experience, will have to be accepted. I argue 22 that a seemingly impossible question about bats' consciousness will drive empirical 23 and theoretical consciousness research to make big breakthroughs, in a similar way 24 as an impossible question about the age of the universe has driven modern 25 cosmology. 26 27 1. Introduction 28 29 The title of Thomas Nagel's 1974 article "What is it like to be a bat?" articulates the 30 immense difficulty of the mind-body problem. Bats sense the outside world by what 31 is called "echolocation" (Jones, 2005). They produce a sound, receive its echo in 32 virtue of which they detect the presence of a prey at a certain distance and direction. 33 Despite extensive investigation into echolocation in terms of ecology and neural 34 mechanisms, we have no idea what it is like to be a bat. Do bats experience 35 echolocation as closer to their visual or auditory experience? Or do they not feel 36 anything, like our non-conscious processing? 37 38 We know that bat brains and our brains are composed of neurons. Each neuron 39 excites or inhibits other neurons that are connected via synapses. These 40 biophysical principles are conserved across biological species. We also know that 41 our conscious experience is generated by neurons in the brain, though we don't 42 know exactly how. If we knew the principles for how various conscious experiences 43 are generated in a human brain, it should be possible to understand1 what kinds of 44 experiences are generated in a bat brain. 45 46 Currently, most neuroscientists have no idea about what those principles could 47 possibly be. To come closer to such principles, we might need to know a lot more 48 about the brain. From microscopic to macroscopic levels, there are countless 49 questions, and neuroscientists worldwide are tackling them everyday. It might take 50 another 10, 20 or 100 years to come up principles for the mind-body problem. It is 51 also plausible, however, that it is not the lack of knowledge that keeps us from a 52 solution, but rather that there is a crucial idea that is missing: an idea that can 53 dissolve the mystery that stands in between consciousness and the brain. 54 1 I will clarify what I mean by "understand" here in later sections. 55 To address bat consciousness, what we need is a theory that can tell us "this is 56 what it's like to be a bat" if we understand all physical properties of the bat brain. 57 Specifically, the theory should consist of a set of laws, which jointly translate 58 information about the brain (connectivity and a pattern of neural activity) into a 59 subjective experience. The theory should be empirically testable and falsifiable in 60 some way. 61 62 Since it's impossible to become a bat, one may conclude any such theory is 63 untestable and unfalsifiable. Surely, we cannot directly test a theory on bat 64 consciousness, but neither can we directly test theories on how the universe started 65 or how the life has emerged and evolved. The theories of the latter kind, however, 66 are considered testable and falsifiable, because we have scientific constructs, such 67 as relativity and quantum mechanics, biochemistry and DNA, and indirect evidence 68 like astronomical observations and the fossil record, which give us answers to 69 questions that are not directly testable. The only difference is that these theories 70 have made many predictions in the past, and they have been supported by 71 accumulated evidence over time, to the extent that seemingly untestable 72 predictions are accepted. In this article, I propose to take a similar approach for 73 consciousness research; to empirically test a promising theory and to refine it to the 74 limit so that we can approach the seemingly untestable question of what it's like to 75 be a bat. I will focus on integrated information theory (IIT) (Tononi, 2004), which 76 makes many qualitative predictions that are empirically testable. 77 78 What should we expect from a theory of consciousness and the brain as a starting 79 point? Given that we can experience only our own consciousness, the theory has to 80 explain all enigmatic features of the relationship between our own consciousness 81 and the brain. To give examples of these mysteries: Why do I lose consciousness 82 when I sleep or go under general anesthesia? Why is the activity in some parts of 83 my brain (e.g. the cerebellum) seemingly irrelevant to what I am experiencing now? 84 Why are any two moments of visual experience much more similar to each other 85 than visual and auditory experiences? 86 87 Even if a theory provides answers to these problems, it is not enough. We should 88 also expect the theory to explain and predict the conscious experience of other 89 persons purely based on his/her neural connectivity and activity. To test the validity 90 of the theory's explanation and prediction, rare forms of conscious experience will 91 be most informative. For example, synesthetic experience (e.g., seeing color when 92 hearing sound (V. Ramachandran & Hubbard, 2001) and substituted sensory 93 experience (e.g., seeing through the auditory modality (Bach-y-Rita & S, 2003)) are 94 hard cases to imagine what it feels like. So far, none of these phenomena have 95 been theoretically explained based on connectivity and activation states of the 96 neural system. An ideal theory should be able to predict who experiences what 97 kinds of experience, just based on the brain data, without a need to ask their 98 experience. 99 100 Further, the theory should explain and make predictions about animal 101 consciousness, which is not directly verifiable by us. With certain animal species, 102 however, there are strong cases to believe in what trained animals reports about 103 their percept. For example, macaque monkeys can be trained to report on their own 104 percept while viewing ambiguous stimuli, such as binocular rivalry. Of course, we 105 cannot trust their reports as they are just by the fact that they can report percept in 106 such a situation. However, when these ambiguous trials can be interleaved with 107 unambiguous trials. In these unambiguous trials, stimulus characteristics are 108 carefully manipulated to reveal highly homologous behavioral performance to 109 humans'. Techniques such as this have strengthened the case to believe that 110 monkeys and humans have similar visual experience in various situations (Leopold, 111 Maier, & Logothetis, 2003; Wilke, Logothetis, & Leopold, 2006). Trained rats can 112 show the evidence of their ability for "metacognition". In a sensory discrimination 113 task, when they are given an option to "skip" a trial in addition to the two alternative 114 forced choices, rats do skip more trials when the stimulus is ambiguous and 115 decision is more difficult {Kepecs, 2008 #2307}. Based on fine details of neural 116 connectivity and activity patterns in these animals, the theory of consciousness 117 should predict conscious perception and metacognition in these animals, which fill 118 strongly validate the theory. 119 120 We would gain more confidence in accepting what a theory predicts about bat 121 consciousness if it can withstand the critical validations through human and animal 122 testing suggested above. If successful, there would be no real difference between 123 what we can accept about the predictions of the beginning of the universe, evolution 124 of life and the consciousness of a bat. 125 126 127 2. Puzzles of consciousness and the brain 128 129 In this section, I will consider several candidate theories that aim to explain what we 130 know about the relationship between consciousness and the brain. The more we 131 learn about the facts about neurons and brains, the more puzzled we become about 132 how brains generate consciousness. For example, people who know little about 133 brains may assume that we lose consciousness when we sleep because the brain 134 turns off like electrical equipment. However, according to various measures, brains 135 during deep sleep without dreams are far from inactive (Dang-Vu et al., 2008; 136 Schabus et al., 2007). Some brain-damaged patients who recovered from loss of 137 consciousness may show very low metabolic activity, while other patients who 138 remain unconscious can show high levels of metabolism. Thus, any theory that tries 139 to explain consciousness simply based on the degree of neural activity fails to 140 provide a reasonable explanation (Massimini & Tononi, 2015). 141 142 In the face of this puzzle, scientists have suggested that some form of "complexity" 143 is necessary and sufficient for consciousness. But our experience is generated only 144 by a part of the brain, and this fact is difficult to explain by complexity theories. 145 When critical parts of the cortex and thalamus (a connectivity hub beneath the 146 cortex) are impaired, we lose consciousness entirely (Bogen, 1995; Laureys, 2005). 147 On the other hand, restricted injury to specific parts of the cortex can lead to loss of 148 specific kinds of content, such as loss of some aspects of vision (V. S. 149 Ramachandran & Blakeslee, 1998). Further, loss of a cerebellum, which contains 150 four times more neurons than the cortico-thalamic system (Herculano-Houzel, 151 2012), hardly affects any aspects of consciousness (Lemon & Edgley, 2010; Yu, 152 Jiang, Sun, & Zhang, 2015). As long as the cortico-thalamic and the cerebellar 153 system cannot be distinguished in terms of "complexity", complexity explanations 154 are far from satisfactory. Further, complexity theories also fall short of explaining 155 contents of consciousness. Seeing, hearing, and touching are all supported by 156 neurons in the cerebral cortex and the thalamus. Just how vision could be 157 distinguished from audition in terms of complexity is very unclear: is vision more 158 complex than audition? Less? Is it a different kind of complexity? If so, what could it 159 be that makes both kinds of complexity different kinds of conscious? 160 161 As a neural correlate of consciousness, more specific forms of interactions between 162 neurons have also been proposed, such as synchronous activity of neurons (Engel 163 & Singer, 2001), global availability of information (Dehaene, 2014), and recurrent 164 feedback activation (Lamme, 2010). These processes are all suited to sustain 165 neural activity for a short-term and to facilitate communication across distant brain 166 areas. However, these can be observed during loss of consciousness as in 167 dreamless sleep or general anesthesia, and in the non-conscious cerebellum. More 168 critically, they have no specificity to explain the distinct phenomenology between 169 different senses. Why does vision feel like vision? Within vision, why does color feel 170 different from shape, despite both being generated in the visual cortex? Whatever is 171 critical for consciousness should be specific for the cortico-thalamic system during 172 the awake and the dreaming state, and should be differentiable in ways that allow 173 us to understand different modalities and their particular characteristics. What is this 174 critical factor? 175 176 All the neurons in the brain operate under the same principle; they are connected 177 with other neurons, receiving and sending electrical signals called spikes. The brain 178 regions that are responsible for visual discrimination of colors and auditory 179 discrimination of pitches both use the same spike mechanisms. Why do we 180 experience these differently, if they are supported by the same mechanisms? 181 Similarly puzzling is the fact that much of neural activity, even within the cerebral 182 cortex, does not correlate with any aspects of phenomenology (Koch, 2004). What 183 are the differences between neural activity resulting in consciousness and neural 184 activity resulting in unconsciousness? Without a theory that can account for all 185 these problems, we are very far from making reasonable predictions about what it is 186 like to be a bat. 187 188 189 3. Integrated information theory in a nutshell 190 191 One of the most promising approaches available at the moment is the Integrated 192 Information Theory (IIT), originally proposed by Tononi in 2004 (Tononi, 2004). IIT 193 indeed has claimed that it would address the problem of bat consciousness, if 194 sufficiently developed (e.g., p229 in (Tononi, 2008)). The original theory has 195 undergone several revisions over the years, especially in its mathematical 196 formulations (Balduzzi & Tononi, 2008, 2009; Oizumi, Albantakis, & Tononi, 2014; 197 Tononi, Boly, Massimini, & Koch, 2016), but the core ideas remain the same. 198 199 IIT gives adequate explanations for all the problems raised in the previous section. 200 In sum, IIT proposes that a system that is composed of multiple causal mechanisms 201 that influence each other will experience something. Contents of consciousness 202 (also known as qualia (Balduzzi & Tononi, 2009; Kanai & Tsuchiya, 2012)) are 203 determined by the way these mechanisms causally interact with one another. This 204 only gives an intuitive idea of IIT. To precisely understand the IIT, one needs to read 205 through math-heavy papers (Balduzzi & Tononi, 2008, 2009; Hoel, Albantakis, & 206 Tononi, 2013; Oizumi et al., 2014; Tononi et al., 2016). However, an intuitive 207 understanding of IIT is enough for my purpose here, which is to provide a pathway 208 to approaching the consciousness of bats. 209 210 IIT starts from seriously considering one's own phenomenology2 . The theory 211 identifies five fundamental properties of consciousness (Oizumi et al., 2014; Tononi, 212 2015): 1) existence: consciousness exists intrinsically and a conscious subject 213 cannot doubt one's ongoing experience; 2) composition: any experience is 214 composed of various modalities (e.g., vision, audition) and various aspects within 215 each modality (e.g., visual motion, faces and objects, colors within objects); 3) 216 information: one moment of consciousness is extremely "informative" and 217 differentiated to an experiencing subject, in a sense that one experience excludes 218 all other potential experiences that the subject could have had at that moment; 4) 219 integration: parts of a conscious experience are bound together and experienced as 220 a whole, that is, different aspects of an experience are not experienced separately 221 but always as integrated parts of one unified whole (e.g., one cannot separate 222 auditory experience and visual experience); and 5) exclusion: consciousness has a 223 2 Starting from phenomenology and proposing neural mechanisms is a highly distinguishing strategy of IIT. Most other approaches for consciousness, as reviewed in section 2, start from observing the neural activity in experimental situations, then try to think how such neural activity gives rise to consciousness. That pathway of explanation (neuron -> consciousness) may be very Hard (Chalmers, 1996), but possibly not the other way around as taken by IIT. definite spatiotemporal grain – it flows at a definite speed and has a definite scale – 224 and no other overlapping conscious experience exists at another scale or speed. 225 Any phenomenal distinction that does not meet the spatiotemporal grain (e.g., too 226 fast or too slow) is excluded and not experienced. 227 228 IIT attempts to discover the physical mechanisms that can support these 229 phenomenological properties. The exact forms of the postulated mathematical 230 expressions of these mechanisms have evolved across the versions of the theory 231 (Balduzzi & Tononi, 2008, 2009; Oizumi et al., 2014; Tononi, 2004). Common to all 232 is that they involve the critical notion of integrated information, usually denoted as "φ" 233 (phi). The more advanced and updated version has more sophisticated 234 mathematical formulations, but it comes at a cost for intuitive understanding and for 235 feasibility of validation through experiments. Below, I briefly explain the core 236 features of IIT using a framework based on the second generation of the IIT 237 (Balduzzi & Tononi, 2008; Oizumi, Amari, Yanagawa, Fujii, & Tsuchiya, 2016). The 238 second generation lacks some theoretically important aspects (e.g., distinction 239 between cause in the past and effects in the future) implemented in the third 240 generation, but it has several advantages. Most importantly for our purposes is that 241 it is easier to understand through simple numerical examples, such as the one 242 given below. Further, it is much more feasible to compute integrated information 243 patterns from empirical neuronal recordings (Haun et al., 2016). These properties 244 make the second generation of IIT perfectly suitable for the purpose of this paper. 245 246 [Figure 1 around here] 247 248 To explain how the concept of integrated information, φ, captures the fundamental 249 properties of consciousness (Oizumi et al., 2014; Tononi, 2004, 2008, 2012), let's 250 consider the simple example given in Figure 1. Figure 1a depicts all four possible 251 states (1-4) of a system, composed of two neurons. Each neuron is either "on" or 252 "off" at any time. Each neuron copies the state of the other with a time delay (τ). In 253 this situation (with the connectivity and the rule for each neuron's firing), if the 254 present state is "off-on" (Figure 1b, right), then the past state (Figure 1b, left) must 255 have been "on-off". 256 257 In an information theoretic jargon, the present state is said to remove uncertainty 258 about the past. If the present state is unknown, uncertainty about the past state is 259 maximal; the four states of this system (i.e., on-on, off-off, on-off, or off-on) are 260 equally likely. We can quantify the degree of uncertainty with a concept of entropy 261 (H). Entropy quantifies possible variability of the system (usually with the logarithm 262 with a base of 2 of the number of possible states of the system). Here, H=log2(the 263 number of possible states)=log2(4)=2. The remaining uncertainty after knowing the 264 present state is called conditional entropy (H*) and H*=log2(1)=0. 265 266 Now, the concept of "information" can be defined as reduction of uncertainty. The 267 more information you have, the less uncertain you are. Another mathematical 268 concept, called mutual information, I, is defined as H-H* to capture this idea formally. 269 In the above case, mutual information, I, between the present state and the past 270 state is I = H − H∗ = 2. 271 272 Integrated information (φ) is the difference between the information derived from the 273 whole system (I) compared with the sum of the information arising from its parts (I*): 274 φ = I − I∗. In the above example, if the system is cut into two parts (Figure 1c), 275 each part cannot specify its past state even if its present state is known, thus I*=0, 276 and φ=2. In other words, φ quantifies how much information is lost if the whole 277 system is cut into its constituent parts. 278 279 Importantly, φ can be exhaustively computed for any subset in the system. For a 280 system of three neurons, A, B, and C (Figure 1d), φ is defined for all subsets, 281 including AB, AC, BC as well as ABC. Once we exhaustively compute integrated 282 information for all subsets, there is a hierarchical pattern of integrated information. 283 Say, AB and ABC are high, BC is low, and AC is 0. This nested and compositional 284 structure of integrated information is postulated to correspond to compositionality of 285 experience. When we experience a face, it is composed of experience of parts, 286 such as eyes, nose and mouth. An experience of a face is also a subset of larger 287 experience of vision, composed of other objects and background. Visual experience 288 is also embedded in an experience composed of all sensory modalities. 289 290 [Figure 2 around here] 291 292 In addition to the basics described above, there are two concepts that are crucial to 293 understand how IIT treats non-conscious processing: the Minimum Information 294 Partition (MIP) and the exclusion principle. 295 296 The MIP can be considered as the most appropriate way to cut the system when 297 one tries to compute I* in the step depicted in Figure 1c. In Figure 2, we consider 2 298 pairs of 2 neurons, where there is an interaction within each pair, but not at all 299 between the pairs. If we compute φ of the entire system with a cut between the left 300 and the right pair, φ for the entire system is correctly identified as 0 (Figure 2a). 301 (This cut "minimizes" information between the cut parts, thus it is called the MIP.) 302 But if we cut it through the interacting pairs between the upper and the lower half 303 (Figure 2b), φ is overestimated as non-zero. 304 305 The exclusion principle relates how to find the most critical subset of the system. 306 According to the exclusion principle, which IIT postulated based on the exclusive 307 property of phenomenology, the subset that has the largest φ, which is called 308 "complex" in IIT, only matters for consciousness. In Figure 2c, we consider an 309 example of 3 strongly interacting neurons ABC and additional neuron D. In this case, 310 the maximal interactions can be identified within ABC. Any cut introduced to ABC 311 always reduces integrated information. Further, adding D to ABC will introduce a 312 very weak link to the system. In this case, the cut between ABC and D will make 313 φABCD to be nearly 0. Any neural interaction outside of the complex corresponds to 314 non-conscious processing. Here, IIT predicts that interaction between C and D is 315 not experienced by the complex, ABC. 316 317 Important for the discussion in this paper is IIT's explanation on how uniqueness of 318 each sensory modality arises. According to IIT, the uniqueness arises from the way 319 each mechanism in the complex causally interacts with others, constructing a 320 specific pattern of integrated information. For example, the "visualness" of visual 321 experience is determined not only by the way visual neurons interact with other 322 visual neurons, but it also depends on how the visual neurons interact with auditory 323 neurons and other neurons within the complex (Figure 2d). Likewise, within visual 324 quality, patterns of integrated information for color should have unique properties, 325 which distinguish them from patterns of integrated information for shape. 326 Relationships between these patterns define quality of color and shape. In other 327 words, the meaning of neural interactions, or quality of experience for which they 328 are responsible, can be determined only by the interactions with other neural 329 interactions in a holistic manner. 330 331 This intuitive summary of IIT will be our guide for the rest of the paper. According to 332 these principles, IIT explains the known neural basis of consciousness and makes 333 further predictions. The more variable interactions a system can have, the richer 334 conscious phenomenology it can entertain. Not all interactions matters, as any 335 interactions that are outside of the complex have no effects on the complex, leading 336 to non-conscious processing. It is the connectivity and the activation patterns that 337 eventually determine exactly what types of conscious experience a system has at 338 each moment. The theory, in principle, can get us closer to approaching bats' 339 experience. 340 341 342 4. A framework for empirical testing of IIT towards understanding bats' 343 consciousness 344 345 4.1 Computing integrated information patterns from neural activity 346 347 Applying these IIT concepts as they are to a real human brain, which is composed 348 of 1011 neurons and 1014 synaptic connections, is currently impossible for practical 349 purposes. Thus, we need some gross approximations for these concepts when we 350 empirically test explanations and predictions from IIT (Barrett & Seth, 2011; Chang 351 et al., 2012; Lee, Mashour, Kim, Noh, & Choi, 2009; Oizumi, Amari, et al., 2016; 352 Oizumi, Tsuchiya, & Amari, 2016; Tegmark, 2016). With approximations, our 353 research group has computed patterns of integrated information from real neural 354 activities recorded in awake human patients while they reported what they see in 355 each trial in several tasks (Haun et al., 2016) (Figure 3). The result is consistent with 356 an idea and prediction from IIT, which is that patterns of particular types of neural 357 interactions determine quality of a particular aspect of experience. While this 358 research program is still at an early stage, we can now compute patterns of 359 integrated information based on neural recordings and test if such patterns 360 correspond to what subjects experience. 361 362 [Figure 3 around here] 363 364 4.2 No-report paradigms to understand consciousness in non-speaking animals 365 366 Contents of consciousness at perceptual thresholds would require us to test if 367 patterns of integrated information correspond to perceptual reports in a trial-by-trial 368 manner (Haun et al., 2016). However, the act of perceptual reports may activate 369 various brain areas that are neither necessary nor sufficient for conscious 370 experience per se. Recently developed "no-report" paradigms remove strict 371 requirements of perceptual reports from subjects by manipulation of their conscious 372 experience through instructions/expectations or by reliable inference of conscious 373 contents through bodily signals, such as eye movements (Tsuchiya, Wilke, Frässle, 374 & Lamme, 2015). No-report paradigms have implied that certain parts of the brain 375 areas, such as the prefrontal areas, may not be related to consciousness, but more 376 to do with the act of the reports (Koch, Massimini, Boly, & Tononi, 2016). 377 378 No-report paradigms are especially powerful to infer the nature of experience in 379 animals, as they remove difficulties associated with training animals to reliably 380 reports their percepts. No-report paradigms for simple perceptual discriminations, 381 such as discriminations of visual and auditory stimuli would be feasible to develop 382 for humans and various animals, especially without any perceptual masking. Once 383 we establish no-report paradigms and record neuronal activities, we can then 384 compare the structure of conscious experience and the patterns of integrated 385 information across various sensory modalities and animal species, which brings us 386 closer to bat consciousness. A remaining difficulty is comparing structures of 387 consciousness and patterns of integrated information. Such comparisons can be 388 formally achieved by a mathematical formalism, called category theory. 389 390 391 4.3 Category theory to link consciousness and patterns of integrated information 392 across different modalities and animal species 393 394 Towards empirical studies of bat consciousness, we have to examine what types of 395 relationships exist among completely distinct domains. We need to compare visual 396 and auditory consciousness, consciousness for humans and bats, and crucially, the 397 domain of conscious experience and the domain of mathematics (integrated 398 information). Mathematical formalism, called category theory (Awodey, 2010; Mac 399 Lane, 1998) is a powerful tool to achieve such a goal (Tsuchiya, Taguchi, & Saigo, 400 2016). 401 402 Category theory can be thought of as a more flexible version of set theory. It can 403 precisely characterize relationships between two completely different domains of 404 knowledge to the extent that what types of mathematical conclusions can be 405 transferred from one domain to the other. Unlike set theory, category theory is 406 developed to characterize the nature of "relationships" between objects (Tsuchiya 407 et al., 2016). The category theory's focus on relationships rather than objects is very 408 well suited for its application to the problems of consciousness as well as IIT, as the 409 "relationships" are critical for both, as outlined above. 410 411 So far, category theory has been applied mainly in mathematics and physics. For 412 example, by establishing a certain similarity between geometry and algebra, a very 413 difficult theorem in geometry can be easily solved in algebra, which can be used as 414 a proof of the theorem in geometry. Also, quantum mechanics, logic, and 415 computation can be formally shown to be similar in some sense (Baez & Stay, 416 2009), which allows proofs in one of these domains to be directly applied to the 417 problems in the others. Importantly, category theory offers precise definitions about 418 "similarity" in different degrees (e.g., a very strong similarity of "isomorphism" is 419 weaker than "identity", (Tsuchiya et al., 2016)). Different levels of transfer of 420 knowledge between the categories can be achieved at different levels of similarity 421 between categories. 422 423 For our purpose, we need to formally compare across categories of consciousness, 424 which varies in modalities (e.g., vision, audition), animal species (e.g., humans, 425 bats), and categories of mathematical structures, such as integrated information 426 patterns. Being mathematical objects, integrated information patterns as a category 427 should be relatively easy to deal with in category theory. To characterize categories 428 of consciousness, some framework in mathematical psychophysics (Hoffman, 429 1966) combined with no-report paradigms in animals will be useful. 430 431 Once these domains are characterized as categories, we can investigate the nature 432 of the relationships among these categories (Figure 4). How are visual and auditory 433 consciousness different and similar to each other? Under no-report paradigms, is 434 visual experience in humans comparable with those in monkeys (Crick & Koch, 435 2003; Leopold et al., 2003), rats, and bats? What about auditory experience? We 436 can ask the corresponding questions with respect to patterns of integrated 437 information across animals and modalities. Of course, it is critical to ask if the 438 domain of consciousness and integrated information patterns correspond at each 439 level. If not, it implies the mathematical structures proposed by IIT are wrong, a 440 potentially powerful way to reject IIT in the currently proposed format. 441 442 Now, coming back to bats, what happens if we apply the same IIT analyses to bats' 443 brain? If patterns of integrated information from their echolocation area are more 444 similar to those generated in the visual than the auditory cortex, then, the theory 445 predicts that experience of echolocation should be similar to those of visual 446 experience. If they are closer to those originating from the auditory areas, quality of 447 echolocation is closer to sense of sound. If bats are not really experiencing anything 448 with echolocation, much like non-conscious processing, IIT would predict that 449 patterns from the echolocation area is very low in magnitude without much variety, 450 residing mainly outside of the complex. Perhaps, processing modules for 451 echolocation may be parallel and independent, like those of our non-conscious 452 cerebellar system. The outline above is a potential pathway to understand bat 453 consciousness. 454 455 456 5. Concluding remarks 457 458 Some explanations and predictions from IIT are still not yet developed and most of 459 them have not been directly empirically tested. Some of them are even untestable. 460 However, similar to the age of the universe or the evolutionary theory, the theory 461 can be grounded by available evidence and make progress. There are growing 462 interests in empirically testing the theory and the tools that enable testing are being 463 developed. Rejecting IIT as non-testable theory would be premature. 464 465 If IIT is validated, it will have significant philosophical implications. IIT is unlikely to 466 be easily categorized as one of the traditional options in philosophy, be it 467 physicalism, dualism, panpsychism or others. IIT starts from the phenomenology, 468 acknowledging that one cannot doubt one's own ongoing conscious experience. 469 But its essence is to try to find physical substrates of consciousness. Note that 470 essential relationships in IIT are those between consciousness and mathematical 471 structures derived from the physical substrates, not between consciousness and 472 matter as is usually debated in philosophy. This means that two distinct physical 473 substrates can generate identical consciousness. Also, IIT does not assume 474 everything is conscious (Tononi & Koch, 2015), which is a direct consequence of 475 the exclusion principle, which says that only the local maxima of integrated 476 information is relevant for consciousness. In other words, if a neuron (or a 477 fundamental particle, or whatever) participates in my current consciousness, it 478 cannot participate in any consciousness at smaller or bigger scales. This seems a 479 feature that is present in most versions of panpsychism (Skrbina, 2003)3. It would 480 be an interesting project in philosophy to clarify theoretical issues surrounding IIT 481 3 This exclusion principle solves the "combination problem" in panpsychism. and how it fits (or not) with the traditional classifications and options available in 482 philosophy of mind. 483 484 An approach outlined in this article is neuroscientific and empirical, allowing us to 485 attack the problem of bat consciousness. Especially with ever-advancing 486 techniques in identifying anatomical connections and recording from many neurons 487 simultaneously as well as in manipulating the connections and states of neurons, 488 this line of research will be highly possible and fruitful, especially when combined 489 with more sophisticated computational analyses (Tononi et al., 2016; Tsuchiya, 490 Haun, Cohen, & Oizumi, 2017). Starting with one's own phenomenology, the theory 491 tries to come up with a mathematical framework, which explains the quality of 492 consciousness based on neural connectivity and activity. The theory would start 493 explaining one's own phenomenology, but should be gradually extended and 494 confirmed to other humans who can report. Then, to animals who are trained to 495 report with careful manipulations (Kepecs, Uchida, Zariwala, & Mainen, 2008; Kiani 496 & Shadlen, 2009; Leopold et al., 2003), and through to humans in no-report 497 paradigms including people without report capability (e.g., babies, injured subjects) 498 (Tsuchiya et al., 2015). Across various modalities and animals, we need to verify if 499 the structure of consciousness corresponds to that of the proposed mathematical 500 structure, such as integrated information patterns. Category theory (Awodey, 2010; 501 Mac Lane, 1998) is a powerful mathematical tool to bridge these two distinct 502 domains of knowledge (Tsuchiya et al., 2016). If IIT makes a highly counter-intuitive 503 prediction, yet empirical tests confirm it, IIT will gain the credibility. As the credibility 504 of IIT gradually builds up, we can gradually increase our trust in the theory to infer 505 conscious experience in animals, eventually in bats. 506 507 Although it may be practically impossible to understand bats' phenomenology in 508 every detail, the research project I outlined above would be sufficient to give a 509 highly credible answer as to whether the bat's echolocation is closer to audition, 510 vision or non-conscious processing. Identifying the neuronal connectivity in bats' 511 brain and understanding their neural activation patterns, analysed according to the 512 IIT's principles will give a fairly educated and grounded answer, assuming that IIT is 513 correct. 514 515 Still, such an answer may be too far from the certainty that we would like to achieve 516 eventually. At the moment, however, the precision of any guess on quality of 517 consciousness in other species is very bad and it has no possibility of 518 generalization across species in a quantitative way. If IIT is validated enough to the 519 extent that we can believe, for example, bats' echolocation should feel like vision, 520 but not audition, that would be a tremendous breakthrough in consciousness 521 research! Consider the age of the universe. 1000 years ago, we had no idea about 522 the age of the universe. With current precision cosmology, however, the estimate of 523 13.7 billion years is believed to be with an estimated error of 1%. Actually, how 524 many of us know the age of grandmothers or friends with 1% error? 525 526 Predicting the sensory experience based on a mathematical framework, be it IIT or 527 anything else, might become possible and important soon in the future. Artificial 528 neural circuits for repairing damaged brain areas are being developed. If we can 529 attach/detach such circuits, we can test the prediction about how our sensory 530 experience changes as we attach and detach the device. As restoring sensory 531 deficits in patients due to disease or brain trauma is an important medical issue, 532 there will be needs and potentials for such technology. Eventually, we may be able 533 to develop an artificial "bat" circuit, which will allow us to directly experience "what it 534 is like to be bats"! 535 536 537 Acknowledgement 538 The author's research was supported by Australian Research Council Future 539 Fellowship (FT120100619) and Discovery Project (DP130100194). 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We can quantify entropy (H) as log2(the number of possible states) = 693 log2(4) = 2, in this case. b) If the present state (at time=t) of the two-neuron is 694 "on-off" (i.e., X(t)="state 3 in a"), then, its past state at t-τ had to be "off-on" (i.e., 695 X(t-tau)="state 2 in a"), because each neuron copies the state of the other. Thus, 696 knowing the present state removes all uncertainty about the past state. The 697 reduction of uncertainty is quantified as mutual information: I=H-H*=2-0=2. (H* 698 quantifies the reduced uncertainty; H*=log2(the number of possible states given the 699 present state)=log2(1)=0 in this case.) c) If each neuron is considered separately, 700 each cannot specify its own past state. A sum of the mutual information among 701 separated parts is I*. In this case, I*=0. How much information is lost when the 702 system is cut is integrated information: φ=I-I*=2-0=2. Note that all ingredients for 703 integrated information, from H, H*, I, I* and φ is a function of both the connectivity 704 and the state of the system. d) Integrated information, φ, can be considered for any 705 subset of the system. For example, φAB, φBC, φCA, and φABC, represent integrated 706 information between A and B, B and C, and C and A, as well as among A, B and C. 707 708 Figure 2. IIT in a nutshell (part 2). Non-conscious perception and uniqueness of 709 each sensory modality. a-c) Key concepts to understand how IIT treats 710 non-conscious processing are the Minimum Information Partition (MIP) and the 711 exclusion principle. a) Two independent systems, as the case of two sets of two 712 neurons depicted here, should have no integrated information because there is no 713 interaction between the two sets. Each subset is identical to the example in Figure 1, 714 with φ=2. When two non-interacting subsets are considered with the appropriate cut 715 (MIP), there is no loss of information across the cuts (I = I*, and φ=0). b) With an 716 inappropriate cut, there is loss of information (I* decreases), and integrated 717 information is overestimated (φ>0). Thus, it is critical to estimate the MIP accurately. 718 c) Another example of a 4-neuron system. If AB and AC are strongly interacting, 719 φABC will be above 0. If D just provides weak input to C, the MIP among ABCD is 720 identified as ABC vs. D, correctly identifying φABCD to be nearly 0. IIT claims that the 721 subset within a system that achieves the maximum φ only matters for conscious 722 experience of the system, and everything else is non-conscious (the exclusion 723 principle). The local maximum subset is called a "complex" in IIT. In this example, 724 the complex is ABC. A pattern of integrated information within ABC (i.e., φAB, φBC, 725 φCA, and φABC) determines quality of experience of ABC. Any integrated information 726 outside of the complex (e.g., φCD) corresponds to non-conscious processing. d) 727 Interactions among neurons in the complex determine the quality of experience in 728 each modality. Peculiar quality of experience in each modality (e.g., visualness) is 729 determined by patterns of integrated information within the neurons for that modality 730 as well as those across modalities in a holistic manner. In other words, vision 731 cannot feel like vision unless it is related with other senses. 732 733 Figure 3. An example of patterns of approximated integrated information, φ* (Oizumi, 734 Amari, et al., 2016), from the actual neural recordings (Haun et al., 2016). a) We 735 recorded intracranial neural activity in the fusiform gyrus, which is strongly 736 suspected to generate conscious percept of a face (Parvizi et al., 2012; Tong, 737 Nakayama, Vaughan, & Kanwisher, 1998). Recording was performed in awake 738 subjects under epilepsy monitoring. Subjects performed several tasks and saw 739 various stimuli under conscious and non-conscious conditions. 4 traces show the 740 evoked neural activity when the patient consciously saw a face from channel A, B, 741 C, and D. b) From the recordings, we computed necessary ingredient of integrated 742 information (φ*), which is entropy (H) and mutual information (I), for all subsets of A, 743 B, C and D, over time. Based on these, we computed φ* for each subset and each 744 time. c) A magnitude of φ* over 11 subsets is represented as a shape at 400 ms 745 after the stimulus onset. The height of each dot is the magnitude of φ* for each 746 subsystem. For details see (Haun et al., 2016). d) Based on patterns of integrated 747 information, we were able to infer what subjects consciously saw in each trial at a 748 high precision, when the electrodes were implanted in the object sensitive area. 749 Here, the dendrogram demonstrates that the pattern of integrated information was 750 closely related to the image that the subject saw on each trial. 751 752 Figure 4. Schematic of how we address the question of bat's consciousness, 753 combining IIT with category theory. Category theory allows us to compare 754 distinctive domains of knowledge, such as structures of conscious experience and 755 patterns of integrated information. If any change in experience changes integrated 756 information pattern and vice versa, a strong relationship of "isomorphism" can be 757 established between them, as IIT proposes (Oizumi et al., 2014). Other than 758 isomorphic relation, category theory offers varying degrees of "similarity" (Tsuchiya 759 et al., 2016). IIT needs to be validated to establish isomorphism between 760 consciousness and mathematical structure across various animals. When that is 761 achieved, similarity of integrated information patterns in bat's echolocation with 762 those for vision, audition or non-conscious processing will be decisive as to the 763 nature of bat consciousness. 764 Figure 1 287x110mm (96 x 96 DPI) Page 31 of 33 Philosophy Compass Philosophy Compass 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Figure 2 240x155mm (96 x 96 DPI) Page 32 of 33 Philosophy Compass Philosophy Compass 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Figure 3 342x200mm (96 x 96 DPI) Page 33 of 33 Philosophy Compass Philosophy Compass 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Figure 4. Schematic of how we address the question of bat's consciousness, combining IIT with category theory. Category theory allows us to compare distinctive domains of knowledge, such as structures of conscious experience and patterns of integrated information. If any change in experience changes integrated information pattern and vice versa, a strong relationship of "isomorphism" can be established between them, as IIT proposes (Oizumi et al., 2014). Other than isomorphic relation, category theory offers varying degrees of "similarity" (Tsuchiya et al., 2016). IIT needs to be validated to establish isomorphism between consciousness and mathematical structure across various animals. When that is achieved, similarity of integrated information patterns in bat's echolocation with those for vision, audition or non-conscious processing will be decisive as to the nature of bat's consciousness. 361x270mm (72 x 72 DPI) Page 34 of 33 Philosophy Compass Philosophy Compass 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59