{"version":"2.0.0","groups":[{"id":"animals","name":"Animals and other organisms","line":"From single cells with no neurons to the human brain. Neuron counts are measured where they exist.","subs":[{"id":"none","name":"No neurons"},{"id":"nets","name":"Nerve nets, no brain"},{"id":"inverts","name":"Invertebrates with brains"},{"id":"fish","name":"Fish and amphibians"},{"id":"birds","name":"Birds"},{"id":"mammals","name":"Mammals"}]},{"id":"collective","name":"Collective systems","line":"Groups that make decisions no single member makes alone. They're studied as collective intelligence, not as minds."},{"id":"artificial","name":"Artificial systems","line":"Kinds of machine intelligence, sorted by how they work. No current system is considered a strong candidate for consciousness."},{"id":"hypothetical","name":"Hypothetical minds","line":"Minds proposed in the scientific and philosophical literature. Nobody has observed one."}],"entries":[{"id":"ecoli","name":"Escherichia coli","sci":"Escherichia coli","group":"animals","sub":"none","status":"observed","summary":"A single cell with no neurons. It steers toward food and has a chemical memory that lasts a few seconds.","neurons":{"v":0,"text":"0","scope":"no neurons (single-celled bacterium)","tag":"measured","src":""},"cortical":null,"evidence":[{"text":"Chemotaxis uses a short-term molecular memory. The cell compares current attractant concentration with the level a few seconds earlier, through methylation of its receptors. That's a temporal comparison, not a nervous system.","src":"Segall JE, Block SM, Berg HC (1986) Temporal comparisons in bacterial chemotaxis. Proc Natl Acad Sci USA 83(23):8987-8991. doi:10.1073/pnas.83.23.8987."}],"declaration":null,"contested":false},{"id":"physarum","name":"Slime mould","sci":"Physarum polycephalum","group":"animals","sub":"none","status":"observed","summary":"A single giant cell with no neurons. It solves mazes and grows efficient transport networks.","neurons":{"v":0,"text":"0","scope":"no neurons (single giant multinucleate cell)","tag":"measured","src":""},"cortical":null,"evidence":[{"text":"An amoeboid plasmodium found the shortest path through a maze.","src":"Nakagaki T, Yamada H, Toth A (2000) Maze-solving by an amoeboid organism. Nature 407(6803):470. doi:10.1038/35035159."},{"text":"Its network of tubes organises itself. Its cost, efficiency and fault tolerance are comparable to the Tokyo rail network.","src":"Tero A, Takagi S, Saigusa T, Ito K, Bebber DP, Fricker MD, Yumiki K, Kobayashi R, Nakagaki T (2010) Rules for biologically inspired adaptive network design. Science 327(5964):439-442. doi:10.1126/science.1177894."}],"declaration":null,"contested":false},{"id":"mimosa","name":"Sensitive plant","sci":"Mimosa pudica","group":"animals","sub":"none","status":"observed","summary":"A plant whose leaves fold when touched. One study reported that it learns to ignore harmless touches. A later re-analysis disputed that.","neurons":{"v":0,"text":"0","scope":"no neurons (plant)","tag":"measured","src":""},"cortical":null,"evidence":[{"text":"Reported habituation: the leaflets stopped folding after repeated harmless drops, and the response lasted for weeks.","src":"Gagliano M, Renton M, Depczynski M, Mancuso S (2014) Experience teaches plants to learn faster and forget slower in environments where it matters. Oecologia 175(1):63-72. doi:10.1007/s00442-013-2873-7."},{"text":"A re-analysis found the evidence wasn't enough to show habituation.","src":"Biegler R (2017). Insufficient evidence for habituation in Mimosa pudica. Oecologia 186:33-35. doi:10.1007/s00442-017-4012-3."}],"declaration":null,"contested":true},{"id":"sponge","name":"Sponge","sci":"Porifera (e.g. Amphimedon queenslandica)","group":"animals","sub":"none","status":"observed","summary":"An animal with no neurons or synapses that still contracts its whole body in a coordinated way.","neurons":{"v":0,"text":"0","scope":"no neurons or synapses","tag":"measured","src":"Leys SP (2015) Elements of a 'nervous system' in sponges. J Exp Biol 218(4):581-591. doi:10.1242/jeb.110817."},"cortical":null,"evidence":[{"text":"Sponges have no neurons or synapses, but they coordinate whole-body contractions (for example, 'sneezing') with non-neuronal signalling.","src":"Leys SP (2015) Elements of a 'nervous system' in sponges. J Exp Biol 218(4):581-591. doi:10.1242/jeb.110817."}],"declaration":null,"contested":false},{"id":"hydra","name":"Hydra","sci":"Hydra vulgaris","group":"animals","sub":"nets","status":"observed","summary":"A small freshwater cnidarian. It's controlled by a diffuse nerve net and has no brain.","neurons":{"v":null,"text":"about 3,000 to 5,000","scope":"whole nerve net; no exact count exists","tag":"estimate","src":"Bode H, Berking S, David CN, Gierer A, Schaller H, Trenkner E (1973) Quantitative analysis of cell types during growth and morphogenesis in Hydra. Wilhelm Roux Arch Entwickl Mech Org 171(4):269-285. doi:10.1007/BF00577725."},"cortical":null,"evidence":[{"text":"Its behaviour (contracting, feeding, somersaulting) is driven by a nerve net with no brain. Calcium imaging of the whole animal has mapped its groups of neurons.","src":"Dupre C, Yuste R (2017) Non-overlapping neural networks in Hydra vulgaris. Curr Biol 27(8):1085-1097. doi:10.1016/j.cub.2017.02.049."}],"declaration":null,"contested":false},{"id":"jellyfish","name":"Box jellyfish","sci":"Tripedalia cystophora","group":"animals","sub":"nets","status":"observed","summary":"A box jellyfish has no central brain, yet it showed associative learning.","neurons":{"v":null,"text":null,"scope":"","tag":"unknown","src":""},"cortical":null,"evidence":[{"text":"It showed associative learning (a visual cue paired with a mechanosensory cue). The learning happens in the rhopalial nervous system, even though the animal has no centralised brain.","src":"Bielecki J, Dam Nielsen SK, Nachman G, Garm A (2023) Associative learning in the box jellyfish Tripedalia cystophora. Curr Biol 33(19):4150-4159.e5. doi:10.1016/j.cub.2023.08.056."}],"declaration":null,"contested":false},{"id":"celegans","name":"Roundworm","sci":"Caenorhabditis elegans","group":"animals","sub":"inverts","status":"observed","summary":"A worm about a millimetre long with exactly 302 neurons. It was the first animal to have its complete wiring diagram mapped.","neurons":{"v":302,"text":"302","scope":"whole nervous system (hermaphrodite)","tag":"measured","src":"White JG, Southgate E, Thomson JN, Brenner S (1986) The structure of the nervous system of the nematode Caenorhabditis elegans. Phil Trans R Soc Lond B 314(1165):1-340. doi:10.1098/rstb.1986.0056."},"cortical":null,"evidence":[{"text":"It shows habituation, associative learning and memory. Its full connectome is known (now updated for both sexes).","src":"Cook SJ, Jarrell TA, Brittin CA, Wang Y, et al. (2019) Whole-animal connectomes of both Caenorhabditis elegans sexes. Nature 571(7763):63-71. doi:10.1038/s41586-019-1352-7."},{"text":"A review of learning and memory in the worm.","src":"Ardiel EL, Rankin CH (2010). An elegant mind: learning and memory in C. elegans. Learn Mem 17:191-201. doi:10.1101/lm.960510."}],"declaration":null,"contested":false},{"id":"drosophila","name":"Fruit fly","sci":"Drosophila melanogaster","group":"animals","sub":"inverts","status":"observed","summary":"The largest complete brain wiring diagram mapped so far, covering about 139,000 neurons.","neurons":{"v":139255,"text":"139,255","scope":"whole adult brain (proofread neurons, one female); excludes ventral nerve cord","tag":"measured","src":"Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, et al.; FlyWire Consortium (2024) Neuronal wiring diagram of an adult brain. Nature 634(8032):124-138. doi:10.1038/s41586-024-07558-y."},"cortical":null,"evidence":[{"text":"Flies learn visual place memory in a virtual arena.","src":"Ofstad TA, Zuker CS, Reiser MB (2011) Visual place learning in Drosophila melanogaster. Nature 474(7350):204-207. doi:10.1038/nature10131."}],"declaration":"realistic","contested":false},{"id":"honeybee","name":"Honeybee","sci":"Apis mellifera","group":"animals","sub":"inverts","status":"observed","summary":"About a million neurons, and bees can learn abstract concepts such as same and different.","neurons":{"v":950000,"text":"about 1 million","scope":"whole brain","tag":"estimate","src":"Menzel R (2001). Searching for the memory trace in a mini-brain, the honeybee. Learn Mem 8:53-62, citing Witthöft (1967)."},"cortical":null,"evidence":[{"text":"Bees learn the concepts of 'sameness' and 'difference'.","src":"Giurfa M, Zhang S, Jenett A, Menzel R, Srinivasan MV (2001) The concepts of 'sameness' and 'difference' in an insect. Nature 410(6831):930-933. doi:10.1038/35073582."},{"text":"Agitated (shaken) bees show a pessimistic-like cognitive bias, an affect-like state.","src":"Bateson M, Desire S, Gartside SE, Wright GA (2011) Agitated honeybees exhibit pessimistic cognitive biases. Curr Biol 21(12):1070-1073. doi:10.1016/j.cub.2011.05.017."}],"declaration":"realistic","contested":false},{"id":"ant","name":"Ant (Camponotus / Formica)","sci":"Camponotus spp.","group":"animals","sub":"inverts","status":"observed","summary":"A small brain that handles long-distance navigation by counting steps (path integration) and remembering landmarks.","neurons":{"v":null,"text":null,"scope":"","tag":"unknown","src":""},"cortical":null,"evidence":[{"text":"Desert ants navigate long distances using path integration and visual landmark memory, with a tiny brain.","src":"Wehner R (2003) Desert ant navigation: how miniature brains solve complex tasks. J Comp Physiol A 189(8):579-588. doi:10.1007/s00359-003-0431-1."}],"declaration":"realistic","contested":false},{"id":"portia","name":"Portia jumping spider","sci":"Portia fimbriata / labiata","group":"animals","sub":"inverts","status":"observed","summary":"A jumping spider that plans detours. It takes routes that lead away from its prey and out of sight.","neurons":{"v":null,"text":null,"scope":"","tag":"unknown","src":""},"cortical":null,"evidence":[{"text":"It makes detours to reach prey, planning routes that first lead away from the prey and out of sight.","src":"Tarsitano MS, Jackson RR (1997) Araneophagic jumping spiders discriminate between detour routes that do and do not lead to prey. Anim Behav 53(2):257-266. doi:10.1006/anbe.1996.0372."}],"declaration":null,"contested":false},{"id":"decapod","name":"Shore crab / crayfish","sci":"Carcinus maenas; Procambarus / Astacus spp.","group":"animals","sub":"inverts","status":"observed","summary":"Crabs and crayfish. A 2021 government review found strong evidence that true crabs are sentient.","neurons":{"v":null,"text":null,"scope":"","tag":"unknown","src":""},"cortical":null,"evidence":[{"text":"Shore crabs learn to avoid a shelter that gives shocks. They also trade off avoiding shocks against how good the shelter is. That fits the pain criteria.","src":"Magee B, Elwood RW (2013) Shock avoidance by discrimination learning in the shore crab (Carcinus maenas) is consistent with a key criterion for pain. J Exp Biol 216(3):353-358. doi:10.1242/jeb.072041."}],"declaration":"realistic","contested":false},{"id":"octopus","name":"Common octopus","sci":"Octopus vulgaris","group":"animals","sub":"inverts","status":"observed","summary":"Most of its neurons are in its arms. A 2021 review found very strong evidence that it's sentient.","neurons":{"v":500000000,"text":"500 million","scope":"whole nervous system: about 300 million in the arms, 200 million in the central brain","tag":"estimate","src":"Young JZ (1963) The number and sizes of nerve cells in Octopus. Proc Zool Soc Lond 140(2):229-254. doi:10.1111/j.1469-7998.1963.tb01862.x; Young JZ (1971) The Anatomy of the Nervous System of Octopus vulgaris. Oxford: Clarendon Press."},"cortical":null,"evidence":[{"text":"A review of the behavioural evidence for consciousness in cephalopods.","src":"Mather JA (2008) Cephalopod consciousness: Behavioural evidence. Conscious Cogn 17(1):37-48. doi:10.1016/j.concog.2006.11.006."}],"declaration":"realistic","contested":false},{"id":"zebrafish","name":"Zebrafish","sci":"Danio rerio","group":"animals","sub":"fish","status":"observed","summary":"A small fish widely used in neuroscience. Its neurons haven't been fully counted. The adult brain has about 10 million cells.","neurons":{"v":null,"text":null,"scope":"","tag":"unknown","src":""},"cortical":null,"evidence":[{"text":"Fish have nociceptors and change their behaviour in ways linked to pain. This was shown in trout, and it's used here as the reference for fish, not as a result for zebrafish.","src":"Sneddon LU, Braithwaite VA, Gentle MJ (2003) Do fishes have nociceptors? Evidence for the evolution of a vertebrate sensory system. Proc R Soc Lond B 270(1520):1115-1121. doi:10.1098/rspb.2003.2349."}],"declaration":"realistic","contested":false},{"id":"wrasse","name":"Cleaner wrasse","sci":"Labroides dimidiatus","group":"animals","sub":"fish","status":"observed","summary":"A reef fish that passed a version of the mirror mark test. People disagree about what that shows.","neurons":{"v":null,"text":null,"scope":"","tag":"unknown","src":""},"cortical":null,"evidence":[{"text":"It passed the mirror mark test: it reacted to a coloured mark visible only in its reflection. How to interpret that is debated.","src":"Kohda M, Hotta T, Takeyama T, Awata S, et al. (2019) If a fish can pass the mark test, what are the implications for consciousness and self-awareness testing in animals? PLoS Biol 17(2):e3000021. doi:10.1371/journal.pbio.3000021."}],"declaration":"realistic","contested":false},{"id":"frog","name":"Frog","sci":"Anura (e.g. Xenopus / Rana)","group":"animals","sub":"fish","status":"observed","summary":"The 2024 New York Declaration says there's a realistic possibility of conscious experience in amphibians.","neurons":{"v":null,"text":null,"scope":"","tag":"unknown","src":""},"cortical":null,"evidence":[{"text":"Amphibians are covered by the New York Declaration's 'realistic possibility' of conscious experience in all vertebrates.","src":"New York Declaration on Animal Consciousness (2024). https://sites.google.com/nyu.edu/nydeclaration/declaration."}],"declaration":"realistic","contested":false},{"id":"pigeon","name":"Rock pigeon","sci":"Columba livia","group":"animals","sub":"birds","status":"observed","summary":"Hundreds of millions of neurons in a small brain. Pigeons can sort complex images into categories.","neurons":{"v":310000000,"text":"310 million","scope":"whole brain (isotropic fractionator)","tag":"measured","src":"Olkowicz S et al. (2016). Birds have primate-like numbers of neurons in the forebrain. PNAS 113:7255-7260. doi:10.1073/pnas.1517131113."},"cortical":{"v":72000000.0,"text":"about 72 million (pallium, derived from telencephalon counts)","src":"Olkowicz S et al. (2016). Birds have primate-like numbers of neurons in the forebrain. PNAS 113:7255-7260. doi:10.1073/pnas.1517131113."},"evidence":[],"declaration":"strong","contested":false},{"id":"raven","name":"Common raven","sci":"Corvus corax","group":"animals","sub":"birds","status":"observed","summary":"Over two billion neurons, densely packed. Ravens remember individual competitors.","neurons":{"v":2171000000,"text":"2.17 billion","scope":"whole brain (isotropic fractionator)","tag":"measured","src":"Olkowicz S et al. (2016). Birds have primate-like numbers of neurons in the forebrain. PNAS 113:7255-7260. doi:10.1073/pnas.1517131113."},"cortical":{"v":1204000000.0,"text":"1.2 billion (pallium)","src":"Olkowicz S et al. (2016). Birds have primate-like numbers of neurons in the forebrain. PNAS 113:7255-7260. doi:10.1073/pnas.1517131113."},"evidence":[{"text":"Ravens judge competitors based on what happened in earlier play-caching.","src":"Bugnyar T, Schwab C, Schloegl C, Kotrschal K, Heinrich B (2007) Ravens judge competitors through experience with play caching. Curr Biol 17(20):1804-1808. doi:10.1016/j.cub.2007.09.048."}],"declaration":"strong","contested":false},{"id":"nccrow","name":"New Caledonian crow","sci":"Corvus moneduloides","group":"animals","sub":"birds","status":"observed","summary":"It makes hooked tools from twigs and wire. Its neurons haven't been counted.","neurons":{"v":null,"text":null,"scope":"","tag":"unknown","src":""},"cortical":null,"evidence":[{"text":"It makes hooked tools.","src":"Weir AAS, Chappell J, Kacelnik A (2002) Shaping of hooks in New Caledonian crows. Science 297(5583):981. doi:10.1126/science.1073433."}],"declaration":"strong","contested":false},{"id":"greyparrot","name":"African grey parrot","sci":"Psittacus erithacus","group":"animals","sub":"birds","status":"observed","summary":"About 1.6 billion neurons. Decades of laboratory study of numerosity and categorisation.","neurons":{"v":1566000000,"text":"1.57 billion","scope":"whole brain (isotropic fractionator)","tag":"measured","src":"Olkowicz S et al. (2016). Birds have primate-like numbers of neurons in the forebrain. PNAS 113:7255-7260. doi:10.1073/pnas.1517131113."},"cortical":{"v":850000000.0,"text":"about 850 million (pallium, derived from telencephalon counts)","src":"Olkowicz S et al. (2016). Birds have primate-like numbers of neurons in the forebrain. PNAS 113:7255-7260. doi:10.1073/pnas.1517131113."},"evidence":[{"text":"Long-term lab studies of numerical and categorical concepts in a grey parrot.","src":"Pepperberg IM (1999) The Alex Studies: Cognitive and Communicative Abilities of Grey Parrots. Cambridge, MA: Harvard University Press. doi:10.4159/9780674041998."}],"declaration":"strong","contested":false},{"id":"mouse","name":"Mouse","sci":"Mus musculus","group":"animals","sub":"mammals","status":"observed","summary":"About 71 million neurons. Mice show facial expressions of pain that can be measured.","neurons":{"v":70900000,"text":"70.9 million","scope":"whole brain (isotropic fractionator)","tag":"measured","src":"Herculano-Houzel S, Mota B, Lent R (2006). Cellular scaling rules for rodent brains. PNAS 103:12138-12143. doi:10.1073/pnas.0604911103."},"cortical":{"v":13700000.0,"text":"about 13.7 million","src":"Herculano-Houzel S, Mota B, Lent R (2006). Cellular scaling rules for rodent brains. PNAS 103:12138-12143. doi:10.1073/pnas.0604911103."},"evidence":[{"text":"Mice show a facial expression that codes for pain.","src":"Langford DJ, Bailey AL, Chanda ML, Clarke SE, et al. (2010) Coding of facial expressions of pain in the laboratory mouse. Nat Methods 7(6):447-449. doi:10.1038/nmeth.1455."}],"declaration":"strong","contested":false},{"id":"rat","name":"Rat","sci":"Rattus norvegicus","group":"animals","sub":"mammals","status":"observed","summary":"About 200 million neurons. Rats release trapped cagemates.","neurons":{"v":200000000,"text":"200 million","scope":"whole brain (isotropic fractionator)","tag":"measured","src":"Herculano-Houzel S, Mota B, Lent R (2006). Cellular scaling rules for rodent brains. PNAS 103:12138-12143. doi:10.1073/pnas.0604911103."},"cortical":{"v":31000000.0,"text":"31 million","src":"Herculano-Houzel S, Lent R (2005). Isotropic fractionator. J Neurosci 25:2518-2521. doi:10.1523/JNEUROSCI.4526-04.2005."},"evidence":[{"text":"Rats free trapped cagemates. This has been interpreted as empathy-driven helping.","src":"Ben-Ami Bartal I, Decety J, Mason P (2011). Empathy and pro-social behavior in rats. Science 334:1427-1430. doi:10.1126/science.1210789."}],"declaration":"strong","contested":false},{"id":"cat","name":"Domestic cat","sci":"Felis catus","group":"animals","sub":"mammals","status":"observed","summary":"About 250 million neurons in the cerebral cortex.","neurons":{"v":1215000000,"text":"1.22 billion","scope":"whole brain","tag":"measured","src":"Jardim-Messeder D et al. (2017). Dogs have the most neurons, though not the largest brain: trade-off between body mass and number of neurons in the cerebral cortex of large carnivoran species. Front Neuroanat 11:118. doi:10.3389/fnana.2017.00118."},"cortical":{"v":249830000,"text":"250 million","src":"Jardim-Messeder D et al. (2017). Dogs have the most neurons, though not the largest brain: trade-off between body mass and number of neurons in the cerebral cortex of large carnivoran species. Front Neuroanat 11:118. doi:10.3389/fnana.2017.00118."},"evidence":[],"declaration":"strong","contested":false},{"id":"dog","name":"Domestic dog","sci":"Canis familiaris","group":"animals","sub":"mammals","status":"observed","summary":"Up to about 600 million cortical neurons, depending on the dog. That's more than cats.","neurons":{"v":null,"text":"1.8 to 2.6 billion, depending on the dog","scope":"whole brain","tag":"measured","src":"Jardim-Messeder D et al. (2017). Dogs have the most neurons, though not the largest brain: trade-off between body mass and number of neurons in the cerebral cortex of large carnivoran species. Front Neuroanat 11:118. doi:10.3389/fnana.2017.00118."},"cortical":{"v":null,"text":"429 to 623 million, depending on the dog","src":"Jardim-Messeder D et al. (2017). Dogs have the most neurons, though not the largest brain: trade-off between body mass and number of neurons in the cerebral cortex of large carnivoran species. Front Neuroanat 11:118. doi:10.3389/fnana.2017.00118."},"evidence":[{"text":"fMRI scans of awake dogs show reward-related responses in the caudate to trained cues.","src":"Berns GS, Brooks AM, Spivak M (2012) Functional MRI in awake unrestrained dogs. PLoS ONE 7(5):e38027. doi:10.1371/journal.pone.0038027."}],"declaration":"strong","contested":false},{"id":"pig","name":"Domestic pig","sci":"Sus scrofa domesticus","group":"animals","sub":"mammals","status":"observed","summary":"Pigs learn what a mirror shows and use it to find food.","neurons":{"v":2220000000.0,"text":"2.22 billion","scope":"whole brain, one domestic pig","tag":"measured","src":"Jelsing J, Nielsen R, Olsen AK, Grand N, Hemmingsen R, Pakkenberg B (2006) The postnatal development of neocortical neurons and glial cells in the Gottingen minipig and the domestic pig brain. J Exp Biol 209(8):1454-1462. doi:10.1242/jeb.02141. Whole brain: Kazu RS, Maldonado J, Mota B, Manger PR, Herculano-Houzel S (2014) Cellular scaling rules for the brain of Artiodactyla include a highly folded cortex with few neurons. Front Neuroanat 8:128. doi:10.3389/fnana.2014.00128."},"cortical":{"v":432000000.0,"text":"432 million in the neocortex (stereology); 293 million by fractionator","src":"Jelsing J, Nielsen R, Olsen AK, Grand N, Hemmingsen R, Pakkenberg B (2006) The postnatal development of neocortical neurons and glial cells in the Gottingen minipig and the domestic pig brain. J Exp Biol 209(8):1454-1462. doi:10.1242/jeb.02141. Whole brain: Kazu RS, Maldonado J, Mota B, Manger PR, Herculano-Houzel S (2014) Cellular scaling rules for the brain of Artiodactyla include a highly folded cortex with few neurons. Front Neuroanat 8:128. doi:10.3389/fnana.2014.00128."},"evidence":[{"text":"Pigs learn what a mirror image stands for, and use it to find food.","src":"Broom DM, Sena H, Moynihan KL (2009) Pigs learn what a mirror image represents and use it to obtain information. Anim Behav 78(5):1037-1041. doi:10.1016/j.anbehav.2009.07.027."}],"declaration":"strong","contested":false},{"id":"dolphin","name":"Bottlenose dolphin","sci":"Tursiops truncatus","group":"animals","sub":"mammals","status":"observed","summary":"Passes mirror self-recognition tests. There's no measured neuron count for the bottlenose dolphin.","neurons":{"v":null,"text":null,"scope":"","tag":"unknown","src":""},"cortical":null,"evidence":[{"text":"Dolphins pass mirror self-recognition tests.","src":"Reiss D, Marino L (2001) Mirror self-recognition in the bottlenose dolphin: A case of cognitive convergence. Proc Natl Acad Sci USA 98(10):5937-5942. doi:10.1073/pnas.101086398."}],"declaration":"strong","contested":false},{"id":"elephant","name":"African elephant","sci":"Loxodonta africana","group":"animals","sub":"mammals","status":"observed","summary":"257 billion neurons, three times as many as a human, almost all in the cerebellum.","neurons":{"v":257000000000,"text":"257 billion","scope":"whole brain (~98% in cerebellum)","tag":"measured","src":"Herculano-Houzel S et al. (2014). The elephant brain in numbers. Front Neuroanat 8:46. doi:10.3389/fnana.2014.00046."},"cortical":{"v":5600000000,"text":"5.6 billion","src":"Herculano-Houzel et al. 2014."},"evidence":[{"text":"Asian elephants pass the mirror mark test.","src":"Plotnik JM, de Waal FBM, Reiss D (2006) Self-recognition in an Asian elephant. Proc Natl Acad Sci USA 103(45):17053-17057. doi:10.1073/pnas.0608062103."}],"declaration":"strong","contested":false},{"id":"chimp","name":"Chimpanzee","sci":"Pan troglodytes","group":"animals","sub":"mammals","status":"observed","summary":"One of our two closest living relatives (along with the bonobo). It passes the mirror test.","neurons":{"v":null,"text":"about 28 billion (predicted from scaling rules, not counted)","scope":"whole brain","tag":"estimate","src":"Herculano-Houzel S, Kaas JH (2011), prediction from primate scaling rules."},"cortical":{"v":null,"text":"3.7 billion in one hemisphere (measured)","src":"Collins CE et al. (2016). Cortical cell and neuron density estimates in one chimpanzee hemisphere. PNAS 113:740-745. doi:10.1073/pnas.1524208113."},"evidence":[{"text":"Chimpanzees pass the mirror mark test.","src":"Gallup GG Jr (1970) Chimpanzees: self-recognition. Science 167(3914):86-87. doi:10.1126/science.167.3914.86."}],"declaration":"strong","contested":false},{"id":"human","name":"Human","sci":"Homo sapiens","group":"animals","sub":"mammals","status":"observed","summary":"86 billion neurons, 16 billion of them in the cerebral cortex. We're the only species whose members can give verbal reports of their experience.","neurons":{"v":86100000000,"text":"86.1 billion","scope":"whole brain (isotropic fractionator, 4 male brains)","tag":"measured","src":"Azevedo FAC et al. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J Comp Neurol 513:532-541. doi:10.1002/cne.21974."},"cortical":{"v":16340000000,"text":"16.3 billion","src":"Azevedo et al. 2009 (16.34 B cortex; value confirmed in Olkowicz 2016 supplement)."},"evidence":[{"text":"Verbal report is the reference standard for consciousness. The New York Declaration (2024) restates the case for animals.","src":""}],"declaration":"strong","contested":false},{"id":"c-ants","name":"Ant colonies","group":"collective","status":"observed","summary":"Colonies pick the better nest because individual ants follow a quorum threshold. No ant compares the sites overall.","measured":"Recruitment rates, quorum thresholds and the choices colonies make between nest sites of different quality.","evidence":[{"text":"Colonies pick the better nest because individual ants follow a quorum threshold. No ant compares the sites overall.","src":"Pratt SC, Mallon EB, Sumpter DJT, Franks NR (2002) Quorum sensing, recruitment, and collective decision-making during colony emigration by the ant Leptothorax albipennis. Behav Ecol Sociobiol 52(2):117-127. doi:10.1007/s00265-002-0487-x."}],"note":"Research on collective intelligence studies how groups decide. It doesn't claim that the group itself has experiences."},{"id":"c-bees","name":"Honeybee swarms","group":"collective","status":"observed","summary":"Swarms pick the best cavity through competition between scouts and a quorum. Stop signals provide cross-inhibition, which breaks deadlocks.","measured":"Counts of scout dances, support for each nest site, the quorum at the chosen site, and stop signals.","evidence":[{"text":"Swarms pick the best cavity through competition between scouts and a quorum. Stop signals provide cross-inhibition, which breaks deadlocks.","src":"Seeley TD (2010). Honeybee Democracy. Princeton UP; Seeley TD et al. (2012). Stop signals provide cross inhibition in collective decision-making by honeybee swarms. Science 335:108-111. doi:10.1126/science.1210361."}],"note":"Research on collective intelligence studies how groups decide. It doesn't claim that the group itself has experiences."},{"id":"c-starlings","name":"Starling flocks","group":"collective","status":"observed","summary":"Each bird tracks a fixed number of neighbours (about 6-7), not every bird within a fixed distance. This is called topological, not metric, interaction.","measured":"3D positions of individual birds in flocks of up to about 2,600, reconstructed by stereo photography.","evidence":[{"text":"Each bird tracks a fixed number of neighbours (about 6-7), not every bird within a fixed distance. This is called topological, not metric, interaction.","src":"Ballerini M, Cabibbo N, Candelier R, Cavagna A, et al. (2008) Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proc Natl Acad Sci USA 105(4):1232-1237. doi:10.1073/pnas.0711437105."}],"note":"Research on collective intelligence studies how groups decide. It doesn't claim that the group itself has experiences."},{"id":"c-fish","name":"Fish shoals","group":"collective","status":"observed","summary":"Shoals use quorum rules to decide. Large groups can sense environmental gradients that no single fish can.","measured":"The rules individual fish follow when they interact, and the quorum-based decisions shoals make.","evidence":[{"text":"Shoals use quorum rules to decide. Large groups can sense environmental gradients that no single fish can.","src":"Ward AJW, Sumpter DJT, Couzin ID, Hart PJB, Krause J (2008) Quorum decision-making facilitates information transfer in fish shoals. Proc Natl Acad Sci USA 105(19):6948-6953. doi:10.1073/pnas.0710344105."}],"note":"Research on collective intelligence studies how groups decide. It doesn't claim that the group itself has experiences."},{"id":"ai-expert","name":"Rule-based expert systems","group":"artificial","status":"observed","summary":"Programs that encode a domain expert's knowledge as explicit if-then rules. An inference engine applies the rules in sequence to reach conclusions, usually with an explanation of its reasoning.","description":"Programs that encode a domain expert's knowledge as explicit if-then rules. An inference engine applies the rules in sequence to reach conclusions, usually with an explanation of its reasoning. People wrote down everything the system knows. It learns nothing from data. These programs were prominent in the 1970s-80s.","evidence":[],"sources":["Buchanan BG, Shortliffe EH (1984). Rule-Based Expert Systems: The MYCIN Experiments. Addison-Wesley."]},{"id":"ai-chess","name":"Game-tree search engines (chess)","group":"artificial","status":"observed","summary":"Programs that enumerate possible move sequences to a fixed depth and score the resulting positions with a hand-built evaluation function. They prune branches that can't change the choice (minimax with alpha-beta pruning).","description":"Programs that enumerate possible move sequences to a fixed depth and score the resulting positions with a hand-built evaluation function. They prune branches that can't change the choice (minimax with alpha-beta pruning). Their strength comes from search speed and a well-tuned evaluation, not from general understanding.","evidence":[],"sources":["Shannon CE (1950). Programming a computer for playing chess. Philosophical Magazine 41:256-275.","Campbell M, Hoane AJ, Hsu F-h (2002). Deep Blue. Artificial Intelligence 134:57-83."]},{"id":"ai-drl","name":"Deep reinforcement learning agents","group":"artificial","status":"observed","summary":"Neural networks trained by trial and error to maximise a reward signal. They learn a policy or value function by interacting with an environment, often a simulated one such as a game.","description":"Neural networks trained by trial and error to maximise a reward signal. They learn a policy or value function by interacting with an environment, often a simulated one such as a game. They can reach superhuman skill at narrow tasks, but they're optimised for a single reward goal.","evidence":[],"sources":["Mnih V, Kavukcuoglu K, Silver D, Rusu AA, et al. (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529-533. doi:10.1038/nature14236.","Silver D et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature 529:484-489. doi:10.1038/nature16961."]},{"id":"ai-llm","name":"Large language models","group":"artificial","status":"observed","summary":"Transformer neural networks trained to predict the next token in very large text corpora, then often tuned on human feedback.","description":"Transformer neural networks trained to predict the next token in very large text corpora, then often tuned on human feedback. They use self-attention over the whole context window. One 2020 paper reported a scale of 175 billion parameters. They produce fluent language, so human-sounding output is a poor test of inner experience.","evidence":[],"sources":["Vaswani A et al. (2017). Attention is all you need. NeurIPS. arXiv:1706.03762.","Brown TB et al. (2020). Language models are few-shot learners. NeurIPS 33. arXiv:2005.14165 (175B parameters)."]},{"id":"ai-robots","name":"Embodied robots","group":"artificial","status":"observed","summary":"Machines with sensors and actuators that act in the physical world. They often use layered reactive control instead of a central world model, so behaviour emerges from the loop between body and environment.","description":"Machines with sensors and actuators that act in the physical world. They often use layered reactive control instead of a central world model, so behaviour emerges from the loop between body and environment.","evidence":[],"sources":["Brooks RA (1991) Intelligence without representation. Artif Intell 47(1-3):139-159. doi:10.1016/0004-3702(91)90053-M."]},{"id":"ai-neuromorphic","name":"Neuromorphic hardware","group":"artificial","status":"observed","summary":"Chips whose circuits imitate spiking neurons and synapses. They are event-driven and use little power, unlike a conventional processor-and-memory design.","description":"Chips whose circuits imitate spiking neurons and synapses. They are event-driven and use little power, unlike a conventional processor-and-memory design. One published chip has one million digital neurons and 256 million synapses.","evidence":[],"sources":["Mead C (1990). Neuromorphic electronic systems. Proc IEEE 78:1629-1636.","Merolla PA, Arthur JV, Alvarez-Icaza R, Cassidy AS, et al. (2014) A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197):668-673. doi:10.1126/science.1254642."]},{"id":"h-wbe","name":"Whole-brain emulation","group":"hypothetical","status":"hypothetical","summary":"Scanning a brain in fine detail and running a software model of it.","description":"Scanning a brain in fine detail and running a software model of it. The roadmap lists eleven emulation levels, with increasing biological detail, from high-level computational modules up to molecular and quantum scale. Its estimates of the processing a human brain needs are about 10^18 FLOPS for a spiking-network model, 10^22 for electrophysiology and 10^25 for the metabolome. Unobserved: nobody has built a human-scale emulation.","evidence":[],"sources":["Sandberg A, Bostrom N (2008) Whole Brain Emulation: A Roadmap. Technical Report #2008-3, Future of Humanity Institute, Oxford University."]},{"id":"h-ancestor","name":"Simulated minds in ancestor simulations","group":"hypothetical","status":"hypothetical","summary":"Bostrom's trilemma: either almost no civilisations reach a posthuman stage, or almost none run ancestor simulations, or we almost certainly live in one.","description":"Bostrom's trilemma: either almost no civilisations reach a posthuman stage, or almost none run ancestor simulations, or we almost certainly live in one. It assumes the mind doesn't depend on what it runs on (substrate independence). It's a philosophical argument, not an observation.","evidence":[],"sources":["Bostrom N (2003). Are we living in a computer simulation? Philosophical Quarterly 53(211):243-255. doi:10.1111/1467-9213.00309."]},{"id":"h-boltzmann","name":"Boltzmann brains","group":"hypothetical","status":"hypothetical","summary":"Brain-like observers that appear out of rare thermal or quantum fluctuations.","description":"Brain-like observers that appear out of rare thermal or quantum fluctuations. In some cosmological models they'd outnumber ordinary observers, and that's treated as a problem for the theory. Purely theoretical.","evidence":[],"sources":["Albrecht A, Sorbo L (2004) Can the universe afford inflation? Phys Rev D 70(6):063528. doi:10.1103/PhysRevD.70.063528.","Carroll SM (2017). Why Boltzmann brains are bad. arXiv:1702.00850."]},{"id":"h-uploading","name":"Mind uploading via gradual replacement","group":"hypothetical","status":"hypothetical","summary":"Replacing neurons one by one with functionally identical artificial parts.","description":"Replacing neurons one by one with functionally identical artificial parts. Chalmers discusses this in his analysis of the singularity. He uses a fading-qualia argument to say that functional organisation suffices for consciousness. That claim is contested. Unobserved.","evidence":[],"sources":["Chalmers DJ (2010) The Singularity: a philosophical analysis. J Conscious Stud 17(9-10):7-65."]},{"id":"h-superintelligence","name":"Superintelligence","group":"hypothetical","status":"hypothetical","summary":"A hypothetical intellect greatly exceeding human performance in virtually all domains. Bostrom's book analyses the routes to it (whole-brain emulation, biological cognition, AI), its forms and the control problems.","description":"A hypothetical intellect greatly exceeding human performance in virtually all domains. Bostrom's book analyses the routes to it (whole-brain emulation, biological cognition, AI), its forms and the control problems. The definition doesn't say whether it would be conscious. Unobserved.","evidence":[],"sources":["Bostrom N (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press."]}],"butlin":{"conclusion":"Their assessment: “no current system appears to be a strong candidate for consciousness.” They add that, if computational functionalism is true, conscious AI systems “could realistically be built in the near term.”","source":"Butlin P, Long R, Elmoznino E, Bengio Y, Birch J, et al. (2023). Consciousness in Artificial Intelligence: Insights from the Science of Consciousness. arXiv:2308.08708. https://arxiv.org/abs/2308.08708 (Table 1 text read directly from the PDF)","indicators":[{"code":"RPT-1","theory":"Recurrent processing theory","text":"Input modules using algorithmic recurrence"},{"code":"RPT-2","theory":"Recurrent processing theory","text":"Input modules generating organised, integrated perceptual representations"},{"code":"GWT-1","theory":"Global workspace theory","text":"Multiple specialised systems capable of operating in parallel (modules)"},{"code":"GWT-2","theory":"Global workspace theory","text":"Limited capacity workspace, entailing a bottleneck in information flow and a selective attention mechanism"},{"code":"GWT-3","theory":"Global workspace theory","text":"Global broadcast: availability of information in the workspace to all modules"},{"code":"GWT-4","theory":"Global workspace theory","text":"State-dependent attention, giving rise to the capacity to use the workspace to query modules in succession to perform complex tasks"},{"code":"HOT-1","theory":"Computational higher-order theories","text":"Generative, top-down or noisy perception modules"},{"code":"HOT-2","theory":"Computational higher-order theories","text":"Metacognitive monitoring distinguishing reliable perceptual representations from noise"},{"code":"HOT-3","theory":"Computational higher-order theories","text":"Agency guided by a general belief-formation and action selection system, and a strong disposition to update beliefs in accordance with the outputs of metacognitive monitoring"},{"code":"HOT-4","theory":"Computational higher-order theories","text":"Sparse and smooth coding generating a \"quality space\""},{"code":"AST-1","theory":"Attention schema theory","text":"A predictive model representing and enabling control over the current state of attention"},{"code":"PP-1","theory":"Predictive processing","text":"Input modules using predictive coding"},{"code":"AE-1","theory":"Agency and embodiment","text":"Agency: Learning from feedback and selecting outputs so as to pursue goals, especially where this involves flexible responsiveness to competing goals"},{"code":"AE-2","theory":"Agency and embodiment","text":"Embodiment: Modeling output-input contingencies, including some systematic effects, and using this model in perception or control"}]},"birch":{"source":"Birch J, Burn C, Schnell A, Browning H, Crump A (2021). Review of the Evidence of Sentience in Cephalopod Molluscs and Decapod Crustaceans. LSE Consulting, 19 Nov 2021. https://www.lse.ac.uk/business/consulting/assets/documents/Sentience-in-Cephalopod-Molluscs-and-Decapod-Crustaceans-Final-Report-November-2021.pdf (read directly)","levels":["very high confidence","high confidence","medium confidence","low confidence","very low confidence","no confidence"],"gradingScheme":{"7-8 criteria at high/very high":"very strong evidence","5-6":"strong evidence","3-4":"substantial evidence","2":"some evidence","0-1":"sentience unknown or unlikely"},"criteria":[{"n":1,"text":"The animal possesses receptors sensitive to noxious stimuli (nociceptors)"},{"n":2,"text":"The animal possesses integrative brain regions capable of integrating information from different sensory sources"},{"n":3,"text":"The animal possesses neural pathways connecting the nociceptors to the integrative brain regions"},{"n":4,"text":"The animal's behavioural response to a noxious stimulus is modulated by chemical compounds affecting the nervous system (e.g. local anaesthetics, analgesics, opioid-type systems)"},{"n":5,"text":"The animal shows motivational trade-offs, in which the disvalue of a noxious or threatening stimulus is weighed against the value of an opportunity for reward, leading to flexible decision-making"},{"n":6,"text":"The animal shows flexible self-protective behaviour (e.g. wound-tending, guarding, grooming, rubbing) of a type likely to involve representing the bodily location of an injury or noxious stimulus"},{"n":7,"text":"The animal shows associative learning in which noxious stimuli become associated with neutral stimuli, and/or novel ways of avoiding noxious stimuli are learned through reinforcement (habituation and sensitisation do not count)"},{"n":8,"text":"The animal shows that it values a putative analgesic or anaesthetic when injured (e.g. self-administers it, or learns to prefer a location where it is available)"}],"findings":{"summary":"Very strong evidence of sentience in octopods. Substantial evidence in other coleoids (high confidence for criteria 1, 2, 3 and 7). Strong evidence in true crabs (high or very high for 1, 2, 4, 6 and 7). Substantial evidence in anomuran crabs (high for 1, 2 and 6, medium for 5) and in astacid lobsters and crayfish (high or very high for 1, 2 and 4). Low confidence means the evidence is thin, not that the trait is absent. No group had high confidence of failing a criterion.","table1_confidence_by_criterion_1_to_8":{"Octopods":"VH VH H H M VH VH H","Cuttlefish":"H VH H L M M VH L","Other coleoids (squid)":"H VH H L M L H L","Nautiloids":"H L L L L L M VL","True crabs (Brachyura)":"H VH L VH L VH H VL","Anomuran crabs":"H VH L L M H L VL","Astacid lobsters/crayfish":"H VH L VH L L M VL","Spiny lobsters":"H VH L L L L M VL","Caridean shrimps":"H VH L M L M L VL","Penaeid shrimps":"H L L M L L L VL"},"key":"VH very high, H high, M medium, L low, VL very low"},"law":"The UK Animal Welfare (Sentience) Act 2022 (Royal Assent 28 April 2022) recognises animals as sentient and sets up an Animal Sentience Committee. The committee reports on whether government policy pays due regard to animal welfare. After the Birch report, the Lords amended the Bill so that 'animal' includes decapod crustaceans and cephalopod molluscs as well as vertebrates. That covers all cephalopods and decapods, not just octopods and true crabs. https://www.legislation.gov.uk/ukpga/2022/22"},"declaration":{"date":"2024-04-19","text":"\"First, there is strong scientific support for attributions of conscious experience to other mammals and to birds. Second, the empirical evidence indicates at least a realistic possibility of conscious experience in all vertebrates (including reptiles, amphibians, and fishes) and many invertebrates (including, at minimum, cephalopod mollusks, decapod crustaceans, and insects). Third, when there is a realistic possibility of conscious experience in an animal, it is irresponsible to ignore that possibility in decisions affecting that animal. We should consider welfare risks and use the evidence to inform our responses to these risks.\" Announced at NYU; initiated by Jeff Sebo, Kristin Andrews, Jonathan Birch; 40 initial signatories, 611 on the page when fetched.","url":"https://sites.google.com/nyu.edu/nydeclaration/declaration"}}