Kurzgesagt – In a Nutshell 

Sources – Most Secret Place

We thank the following experts for their critical reading, feedback and corrections:

– Prof. Michael Shadlen

Columbia University New York, USA

– Prof. Andreas Nieder

University of Tübingen, Germany

– Prof. Michael Pitts

University of Oregon, USA

– What do you see when you imagine an apple? A detailed scenery, a crisp image with clear details and colour? Is the apple hanging in a void? Can you rotate it? Is it a 2D shape? Is it colorless or transparent or very vague? Or don’t you see anything at all but feel what an apple feels like to you? Maybe a weird mix? 

Mental imagery is the internal experience of perceiving sensory information in one’s mind without external stimuli. The ability to visualize objects in your mind is known to vary between people in terms of e.g. vividness. The extreme ends of the spectrum are aphantasia (no imagery) to hyperphantasia (extremely vivid imagery). While the visual aspect is the most studied, mental imagery is not limited to the visual sense, it can involve other senses as well, such as auditory (hearing), tactile (touch) olfactory (smell) or gustatory (taste) sensations.

Floridou, G.A., Peerdeman, K.J. & Schaefer, R.S. Individual differences in mental imagery in different modalities and levels of intentionality. Mem Cogn (2022). https://doi.org/10.3758/s13421-021-01209-7

Quote: “Mental imagery is a highly common component of everyday cognitive functioning. While substantial progress is being made in clarifying this fundamental human function, much is still unclear or unknown. A more comprehensive account of mental imagery aspects would be gained by examining individual differences in age, sex, and background experience in an activity and their association with imagery in different modalities and intentionality levels. The current online study combined multiple imagery self-report measures in a sample (n = 279) with a substantial age range (18–65 years), aiming to identify whether age, sex, or background experience in sports, music, or video games were associated with aspects of imagery in the visual, auditory, or motor stimulus modality and voluntary or involuntary intentionality level. The findings show weak positive associations between age and increased vividness of voluntary auditory imagery and decreased involuntary musical imagery frequency, weak associations between being female and more vivid visual imagery, and relations of greater music and video game experience with higher involuntary musical imagery frequency. Moreover, all imagery stimulus modalities were associated with each other, for both intentionality levels, except involuntary musical imagery frequency, which was only related to higher voluntary auditory imagery vividness. These results replicate previous research but also contribute new insights, showing that individual differences in age, sex, and background experience are associated with various aspects of imagery such as modality, intentionality, vividness, and frequency. The study’s findings can inform the growing domain of applications of mental imagery to clinical and pedagogical settings.”

– Do you have an internal voice that narrates your life? Do you have an inner monologue? Is there silence inside your mind? Do you only hear voices when you read? Or do you process thoughts nonverbally or even just feel the world around you? 

Inner speech (often called the inner voice) is the silent verbalization of thoughts and plays a central role in cognition, self-regulation, and consciousness. It is known to significantly vary across individuals and contexts. Some individuals may lack inner speech almost entirely - this is called “anendophasia”.

#Alderson-Day B, Fernyhough C. Inner Speech: Development, Cognitive Functions, Phenomenology, and Neurobiology. Psychol Bull. 2015

https://pmc.ncbi.nlm.nih.gov/articles/PMC4538954/

Quote: “Inner speech—also known as covert speech or verbal thinking—has been implicated in theories of cognitive development, speech monitoring, executive function, and psychopathology. Despite a growing body of knowledge on its phenomenology, development, and function, approaches to the scientific study of inner speech have remained diffuse and largely unintegrated. This review examines prominent theoretical approaches to inner speech and methodological challenges in its study, before reviewing current evidence on inner speech in children and adults from both typical and atypical populations. We conclude by considering prospects for an integrated cognitive science of inner speech, and present a multicomponent model of the phenomenon informed by developmental, cognitive, and psycholinguistic considerations. Despite its variability among individuals and across the life span, inner speech appears to perform significant functions in human cognition, which in some cases reflect its developmental origins and its sharing of resources with other cognitive processes.”

#Nedergaard JSK, Lupyan G. Not Everybody Has an Inner Voice: Behavioral Consequences of Anendophasia. Psychol Sci. 2024

https://pubmed.ncbi.nlm.nih.gov/38728320/ 

Quote: “It is commonly assumed that inner speech-the experience of thought as occurring in a natural language-is a human universal. Recent evidence, however, suggests that the experience of inner speech in adults varies from near constant to nonexistent. We propose a name for a lack of the experience of inner speech-anendophasia-and report four studies examining some of its behavioral consequences. We found that adults who reported low levels of inner speech (N = 46) had lower performance on a verbal working memory task and more difficulty performing rhyme judgments compared with adults who reported high levels of inner speech (N = 47). Task-switching performance-previously linked to endogenous verbal cueing-and categorical effects on perceptual judgments were unrelated to differences in inner speech.”

– A pretty wild idea is that minds might have originally evolved to control your movements. To create a gap between all of your sensory input, and your motor output, how you react to information by moving your body. 

A major hypothesis in neuroscience is that the primary evolutionary role of the nervous system and brain was not to “think”, but to control movement. Specifically, to promote movements beneficial to the organism, and to inhibit unnecessary or maladaptive movements.

The “gap” we refer to here is a way of conceptualizing the mind as the link between our perceptions (from our senses) and what we do. This is rooted in the neuroscientific concept of “sensorimotor transformation”, i.e. the neural process by which sensory information is converted into motor commands, enabling organisms to interact purposefully with their environment. How sensorimotor transformation in humans and other animals works exactly is an active field of research.

#Suryanarayana SM, Robertson B, Grillner S. The neural bases of vertebrate motor behaviour through the lens of evolution. Philos Trans R Soc Lond B Biol Sci. 2022

https://pmc.ncbi.nlm.nih.gov/articles/PMC8710883/ 

Quote: “The primary driver of the evolution of the vertebrate nervous system has been the necessity to move, along with the requirement of controlling the plethora of motor behavioural repertoires seen among the vast and diverse vertebrate species. Understanding the neural basis of motor control through the perspective of evolution, mandates thorough examinations of the nervous systems of species in critical phylogenetic positions. We present here, a broad review of studies on the neural motor infrastructure of the lamprey, a basal and ancient vertebrate, which enjoys a unique phylogenetic position as being an extant representative of the earliest group of vertebrates. From the central pattern generators in the spinal cord to the microcircuits of the pallial cortex, work on the lamprey brain over the years, has provided detailed insights into the basic organization (a bauplan) of the ancestral vertebrate brain, and narrates a compelling account of common ancestry of fundamental aspects of the neural bases for motion control, maintained through half a billion years of vertebrate evolution.”

#Llinás, R. R. (2001). I of the vortex: From neurons to self. The MIT Press.

https://mitpress.mit.edu/9780262122337/i-of-the-vortex/

Quote: “In I of the Vortex, Rodolfo Llinas, a founding father of modern brain science, presents an original view of the evolution and nature of mind. According to Llinas, the "mindness state" evolved to allow predictive interactions between mobile creatures and their environment. He illustrates the early evolution of mind through a primitive animal called the "sea squirt." The mobile larval form has a brainlike ganglion that receives sensory information about the surrounding environment. As an adult, the sea squirt attaches itself to a stationary object and then digests most of its own brain. This suggests that the nervous system evolved to allow active movement in animals. To move through the environment safely, a creature must anticipate the outcome of each movement on the basis of incoming sensory data. Thus the capacity to predict is most likely the ultimate brain function. One could even say that Self is the centralization of prediction.” 

#Glasgow, R. D. V. Minimal Selfhood and the Origins of Consciousness. Würzburg University Press. 2018

https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/15747

Quote: “The aim of the book is to ground the logical origins of consciousness in what I have previously called the ‘minimal self’. The idea is that elementary forms of consciousness are logically dependent not, as is commonly assumed, on ownership of an anatomical brain or nervous system, but on the intrinsic reflexivity that defines minimal selfhood. The book seeks to trace the logical pathway by which minimal selfhood gives rise to the possible appearance of consciousness. It is argued that in specific circumstances it thus makes sense to ascribe elementary consciousness to certain predatory single-celled organisms such as amoebae and dinoflagellates as well as to some of the simpler animals. Such an argument involves establishing exactly what those specific circumstances are and determining how elementary consciousness differs in nature and scope from its more complex manifestations.

[...]

On the contrary, consciousness is meaningfully ascribed only to a self that is endowed with sensory and integrative faculties and that uses these faculties to guide its movement towards what is better for itself and away from what is worse for itself and so to perpetuate itself through time.”

#Krapp HG. Sensorimotor transformation: from visual responses to motor commands. Curr Biol. 2010

https://pubmed.ncbi.nlm.nih.gov/20219173/ 

Quote: “The ability to control movements requires our nervous system to perform at least two fundamental tasks. One is to constantly monitor how we are moving and whether our movements deviate from a desired path. And the other is to generate compensatory motor commands should any deviations occur. The first part involves sensory modalities, including vision and the inner ear organ, which provide us with information about our movements and the orientation of our body relative to the world. The second part relies on appropriate action of our muscular or motor systems. One of the most challenging questions in neurobiology is: how is sensory information transformed into appropriate motor action? Wertz et al. [1] have studied the electrical responses of individual nerve cells that connect visual interneurons in the fly brain to motor areas in the animal's thorax: they found that the signals of these premotor descending neurons provide a more robust indication of the fly's motions in space than the signals of the visual interneurons from which they receive input.

Non-neurobiologists tend not to consider sensorimotor transformation as a particular challenge. We are normally quite good at controlling our movements without even thinking about it, but the way our nervous system does it involves a massive amount of neural computing power. What is more, in humans, parts of almost all brain areas contribute when it comes to movement control. Neurobiologists working on the neural principles underlying sensorimotor transformations, therefore, are dealing with quite a degree of complexity. Luckily, applying a reductionist approach, the complexity may at least partly be reduced.”

– When life emerged it was without any minds but just cells, able to eat, poop and reproduce. They already had to navigate a complex and dangerous world and to process sensory input – but they were extremely inflexible. If a cell had the inner state “hungry” and sensed “food” it automatically moved in that direction – or it flapped around randomly until it ran into a meal by accident. This worked well enough for billions of years and still does. 

Moving towards or away from a chemical (e.g. towards a food source) is called “chemotaxis” and is widespread among organisms alive today, such as bacteria and single-celled animals like amoebae. In contrast to active chemotaxis, many organisms can also move around randomly without directed movements towards food. In bacteria, this is called a “random walk”.

While there is no direct evidence for chemotaxis and/or random walk motility in early life on Earth, these behaviours likely trace back to early single-celled life in the oceans, since there is genomic evidence for an ancient evolutionary history of many of the genes associated with these motility and/or sensing systems.

#Taktikos J, Stark H, Zaburdaev V. How the Motility Pattern of Bacteria Affects Their Dispersal and Chemotaxis. PLOS ONE (2013)

https://doi.org/10.1371/journal.pone.0081936 

Quote: “Most bacteria at certain stages of their life cycle are able to move actively; they can swim in a liquid or crawl on various surfaces. A typical path of the moving cell often resembles the trajectory of a random walk. However, bacteria are capable of modifying their apparently random motion in response to changing environmental conditions. As a result, bacteria can migrate towards the source of nutrients or away from harmful chemicals. Surprisingly, many bacterial species that were studied have several distinct motility patterns, which can be theoretically modeled by a unifying random walk approach. We use this approach to quantify the process of cell dispersal in a homogeneous environment and show how the bacterial drift velocity towards the source of attracting chemicals is affected by the motility pattern of the bacteria. Our results open up the possibility of accessing additional information about the intrinsic response of the cells using macroscopic observations of bacteria moving in inhomogeneous environments.”

#Briegel A, Ortega DR, Huang AN, et al. Structural conservation of chemotaxis machinery across Archaea and Bacteria. Environ Microbiol Rep. 2015

https://pubmed.ncbi.nlm.nih.gov/25581459/ 

Quote: “Chemotaxis allows cells to sense and respond to their environment. In Bacteria, stimuli are detected by arrays of chemoreceptors that relay the signal to a two-component regulatory system. These arrays take the form of highly stereotyped super-lattices comprising hexagonally packed trimers-of-receptor-dimers networked by rings of histidine kinase and coupling proteins. This structure is conserved across chemotactic Bacteria, and between membrane-bound and cytoplasmic arrays, and gives rise to the highly cooperative, dynamic nature of the signalling system. The chemotaxis system, absent in eukaryotes, is also found in Archaea, where its structural details remain uncharacterized. Here we provide evidence that the chemotaxis machinery was not present in the last archaeal common ancestor, but rather was introduced in one of the waves of lateral gene transfer that occurred after the branching of Eukaryota but before the diversification of Euryarchaeota. Unlike in Bacteria, the chemotaxis system then evolved largely vertically in Archaea, with very few subsequent successful lateral gene transfer events. By electron cryotomography, we find that the structure of both membrane-bound and cytoplasmic chemoreceptor arrays is conserved between Bacteria and Archaea, suggesting the fundamental importance of this signalling architecture across diverse prokaryotic lifestyles.”

#Wadhams, G., Armitage, J. Making sense of it all: bacterial chemotaxis. Nat Rev Mol Cell Biol (2004).

https://doi.org/10.1038/nrm1524 

Quote: “Bacteria must be able to respond to a changing environment, and one way to respond is to move. The transduction of sensory signals alters the concentration of small phosphorylated response regulators that bind to the rotary flagellar motor and cause switching. This simple pathway has provided a paradigm for sensory systems in general. However, the increasing number of sequenced bacterial genomes shows that although the central sensory mechanism seems to be common to all bacteria, there is added complexity in a wide range of species.”

#Smirnova T, Segall JE. Amoeboid chemotaxis: future challenges and opportunities. Cell Adh Migr. 2007

https://pmc.ncbi.nlm.nih.gov/articles/PMC2634101/ 

Quote: “Chemotaxis is the directed movement of a cell towards a gradient of chemicals such as chemokines or growth factors. This phenomenon can be studied in organisms ranging from bacteria to mammalian cells, and here we will focus on eukaryotic amoeboid chemotaxis. Chemotactic responses are mediated by two major classes of receptors: GPCR's and RTK's, with multiple pathways signaling downstream of them, certain ones functioning in parallel. In this review we address two important features of amoeboid chemotaxis that will be important for further advances in the field. First, the application of in vivo imaging will be critical for providing insight into the functional requirements for chemotactic responses. We will briefly cover a number of systems in which in vivo imaging is providing new insights. Second, due to the network-type design of signaling pathways of eukaryotic chemotaxis, more refined phenotypic analysis will be necessary, and we will discuss recent analyses of the role of the phosphoinositide 3-kinase pathway in this light. We will close with some speculations regarding future applications of more detailed in vivo analysis and mechanistic understanding of eukaryotic amoeboid chemotaxis.”

– But as life exploded in complexity and became multicellular, cells began to dedicate themselves to processing information. The first very simple “minds” emerged: little more than tiny gaps. Virtual spaces in which sensory information could be processed for a short moment before these early animals had to react. 

There are various scientific hypotheses as to how the first nervous systems evolved. Below, we cite a few selected publications, but the topic is very much a matter of active research and debate.

Early evolution of neurons:

# Jékely Gáspár. The chemical brain hypothesis for the origin of nervous systems. Phil. Trans. R. Soc. 2021

http://doi.org/10.1098/rstb.2019.0761 

Quote: “In nervous systems, there are two main modes of transmission for the propagation of activity between cells. Synaptic transmission relies on close contact at chemical or electrical synapses while volume transmission is mediated by diffusible chemical signals and does not require direct contact. It is possible to wire complex neuronal networks by both chemical and synaptic transmission. Both types of networks are ubiquitous in nervous systems, leading to the question which of the two appeared first in evolution. This paper explores a scenario where chemically organized cellular networks appeared before synapses in evolution, a possibility supported by the presence of complex peptidergic signalling in all animals except sponges. Small peptides are ideally suited to link up cells into chemical networks. They have unlimited diversity, high diffusivity and high copy numbers derived from repetitive precursors. But chemical signalling is diffusion limited and becomes inefficient in larger bodies. To overcome this, peptidergic cells may have developed projections and formed synaptically connected networks tiling body surfaces and displaying synchronized activity with pulsatile peptide release. The advent of circulatory systems and neurohemal organs further reduced the constraint imposed on chemical signalling by diffusion. This could have contributed to the explosive radiation of peptidergic signalling systems in stem bilaterians. Neurosecretory centres in extant nervous systems are still predominantly chemically wired and coexist with the synaptic brain.”

#Arendt D. The Evolutionary Assembly of Neuronal Machinery. Curr Biol. 2020

https://www.cell.com/current-biology/fulltext/S0960-9822(20)30488-7 

Quote: “Neurons are highly specialized cells equipped with a sophisticated molecular machinery for the reception, integration, conduction and distribution of information. The evolutionary origin of neurons remains unsolved. How did novel and pre-existing proteins assemble into the complex machinery of the synapse and of the apparatus conducting current along the neuron? In this review, the step-wise assembly of functional modules in neuron evolution serves as a paradigm for the emergence and modification of molecular machinery in the evolution of cell types in multicellular organisms. The pre-synaptic machinery emerged through modification of calcium-regulated large vesicle release, while the postsynaptic machinery has different origins: the glutamatergic postsynapse originated through the fusion of a sensory signaling module and a module for filopodial outgrowth, while the GABAergic postsynapse incorporated an ancient actin regulatory module. The synaptic junction, in turn, is built around two adhesion modules controlled by phosphorylation, which resemble septate and adherens junctions. Finally, neuronal action potentials emerged via a series of duplications and modifications of voltage-gated ion channels. Based on these origins, key molecular innovations are identified that led to the birth of the first neuron in animal evolution.”

Early evolution of simple neuronal/nervous systems:

#Martinez Pedro, Sprecher Simon G. Of Circuits and Brains: The Origin and Diversification of Neural Architectures. Frontiers in Ecology and Evolution. 2020

https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2020.00082 

Quote: “Nervous systems are complex cellular structures that allow animals to interact with their environment, which includes both the external and the internal milieu. The astonishing diversity of nervous system architectures present in all animal clades has prompted the idea that selective forces must have shaped them over evolutionary time. In most cases, neurons seem to coalesce into specific (centralized) structures that function as “central processing units” (CPU): “brains.” Why did neural systems adopt this physical configuration? When did it first happen? What are the physiological, computational, and/or structural advantages of concentrating many neurons in a specific place within the body? Here we examine the concept of nervous system centralization and factors that might have contributed to the evolutionary success of this centralization strategy. In particular, we suggest a putative scenario for the evolution of neural system centralization that incorporates different strands of evidence. This scenario is based on some premises: (1) Receptors originated before neurons (sensors before transmitters) and there were deployed in the first organisms in an asymmetric fashion (deposited randomly in the outer layer); (2) Receptors were segregated in a preferential position in response to an anisotropic environment, (3) Neurons were born in association with this receptors and used to transmit signals distally; (4) Energetics preferentially selected the localization of neurons, and synapsis, close to the receptors (to minimize wire use, for instance); (5) The presence of condensed areas of neurons could have stimulated the proliferation of more receptors in the vicinity, increasing the repertoire of signals processed in an specific body domain (i.e., head) plus contributing to amplify the computational power of the neuronal aggregate; (6) The proliferation of receptors would have induced the proliferation of more neurons in the aggregate, with a further increase in its computational power (hence, diversifying the behavioral repertoire). These last two steps of proliferation and aggregation could have been sustained through a feedback loop, reiterated many times, generating distinct topologies in different lineages. Our main aim in this paper is to examine the brain as both a biological and a physical or computational device.”

#Arendt Detlev. Elementary nervous systems. Phil. Trans. R. Soc. 2021

http://doi.org/10.1098/rstb.2020.0347 

Quote: “The evolutionary origin of the nervous system has been a matter of long-standing debate. This is due to the different perspectives taken. Earlier studies addressed nervous system origins at the cellular level. They focused on the selective advantage of the first neuron in its local context, and considered vertical sensory-motor reflex arcs the first nervous system. Later studies emphasized the value of the nervous system at the tissue level. Rather than acting locally, early neurons were seen as part of an elementary nerve net that enabled the horizontal coordination of tissue movements. Opinions have also differed on the nature of effector cells. While most authors have favoured contractile systems, others see the key output of the incipient nervous system in the coordination of motile cilia, or the secretion of antimicrobial peptides. I will discuss these divergent views and explore how they can be validated by molecular and single-cell data. From this survey, possible consensus emerges: (i) the first manifestation of the nervous system likely was a nerve net, whereas specialized local circuits evolved later; (ii) different nerve nets may have evolved for the coordination of contractile or cilia-driven movements; (iii) all evolving nerve nets facilitated new forms of animal behaviour with increasing body size.

[...]

Figure 1. A simplified phylogenetic tree of the animals. Depicted species represent groups of special relevance for comparative neurobiology that are mentioned in the text. The presence of a centralized nervous system in cnidarians and of a brain in ctenophores is discussed in Satterlie [1] and Jager et al. [2]. The branching of the tree follows Kapli & Telford [3].”

– Roundworms only have 302 neurons in total, yet they are already able to learn things like “this thing is bad” and retain a memory that changes behavior for a few hours before they forget again. 

The roundworm we are referring to here is the nematode Caenorhabditis elegans, a widely used model organism in biological research. It is able to learn simple behaviours such as avoiding odors from pathogenic bacteria or toxic foods after exposure. Depending on the stimulus, their memory, i.e. the persistence of this learned behaviour after the exposure to the stimulus, lasts from minutes to up to 24 h.

#Taylor SR, Santpere G, Weinreb A, et al. Molecular topography of an entire nervous system. Cell. 2021

https://pmc.ncbi.nlm.nih.gov/articles/PMC8710130/

Quote: “We have produced expression profiles of all 302 neurons of the C. elegans nervous system that match the single cell resolution of its anatomy and wiring diagram. Our results suggest that individual neuron classes can be solely identified by combinatorial expression of specific gene families. For example, each neuron class expresses distinct codes of ~23 neuropeptide genes and ~36 neuropeptide receptors, delineating a complex and expansive “wireless” signaling network. To demonstrate the utility of this comprehensive gene expression catalog, we used computational approaches to (1) identify cis-regulatory elements for neuron-specific gene expression and (2) reveal adhesion proteins with potential roles in process placement and synaptic specificity. Our expression data are available at cengen.org and can be interrogated at the web application CengenApp. We expect that this neuron-specific directory of gene expression will spur investigations of underlying mechanisms that define anatomy, connectivity and function throughout the C. elegans nervous system.”

#Ardiel EL, Rankin CH. An elegant mind: learning and memory in Caenorhabditis elegans. Learn Mem. 2010

https://learnmem.cshlp.org/content/17/4/191.full

Quote: “This article reviews the literature on learning and memory in the soil-dwelling nematode Caenorhabditis elegans. Paradigms include nonassociative learning, associative learning, and imprinting, as worms have been shown to habituate to mechanical and chemical stimuli, as well as learn the smells, tastes, temperatures, and oxygen levels that predict aversive chemicals or the presence or absence of food. In each case, the neural circuit underlying the behavior has been at least partially described, and forward and reverse genetics are being used to elucidate the underlying cellular and molecular mechanisms. Several genes have been identified with no known role other than mediating behavior plasticity.

[...]

Long-term memory

Under the appropriate training regime, worms show long-term memory for tap habituation (Beck and Rankin 1997). Adult worms given four blocks (each separated by 1 h) of 20 taps at a 60-sec ISI had decremented TWRs when tested 24 h after training (Beck and Rankin 1995; Rose et al. 2002).”

– Minds on this level most likely don’t reason or think, but use simple rules that work well in their environments. Scientists still debate whether this counts as mind or are still just preprogrammed automated reflexes.

With more neurons, animals can “freeze” before they act. Process and interpret information and make decisions for what is the best option at that moment. This is arguably where a true inner space, a mind begins to emerge. 

Despite their simple nervous systems, nematodes (and many other invertebrates) exhibit fundamental cognitive abilities, including learning, memory, and sensory integration. Whether this means that they possess some version of intelligence, complex cognition, rudimentary consciousness or “a mind” is subject to debate. Below, we cite a recent paper exploring aspects of these questions. It focuses on the nematode model organisms Caenorhabditis elegans, but comparable frameworks and questions can also be applied to other animals with simple nervous systems. It also very much depends on what definitions are applied for (animal) intelligence, cognition, consciousness, the mind etc., as these definitions are themselves subject of debate.

With “freeze before they act” we are referring to the difference between rapid, highly repeatable, even involuntary reactions to the same stimulus (like e.g. spinal reflexes in humans, or reflex-like, highly repeatable behaviours in organisms with simpler neuronal systems), and voluntary, deliberate movements that require more cognitive decision-making. Reflexive reactions to a stimulus do not require the direct input of a complex brain to execute. 

But there is also no clear boundary when going from simpler to more complex nervous systems (or going from largely involuntary movements to more deliberate, voluntary movements) where “the mind” suddenly emerges: it is a gradual process of evolution that leads to cognitive complexity in some animals, but it is impossible to say at which point “the mind” is present. This is also partially because the mind itself is a rather fuzzy, not-well-defined concept. There are different angles and lenses through which people study the evolution of the mind, among them neurobiology, psychology and even philosophy.

#Becerra D, Calixto A, Orio P. The Conscious Nematode: Exploring Hallmarks of Minimal Phenomenal Consciousness in Caenorhabditis Elegans. Int J Psychol Res. 2023

https://pubmed.ncbi.nlm.nih.gov/38106963/ 

Quote: “While subcellular components of cognition and affectivity that involve the interaction between experience, environment, and physiology -such as learning, trauma, or emotion- are being identified, the physical mechanisms of phenomenal consciousness remain more elusive. We are interested in exploring whether ancient, simpler organisms such as nematodes have minimal consciousness. Is there something that feels like to be a worm? Or are worms blind machines? 'Simpler' models allow us to simultaneously extract data from multiple levels such as slow and fast neural dynamics, structural connectivity, molecular dynamics, behavior, decision making, etc., and thus, to test predictions of the current frameworks in dispute. In the present critical review, we summarize the current models of consciousness in order to reassess in light of the new evidence whether Caenorhabditis elegans, a nematode with a nervous system composed of 302 neurons, has minimal consciousness. We also suggest empirical paths to further advance consciousness research using C. elegans.”

#Van Gulick, Robert, 'Consciousness and Cognition', in Eric Margolis, Richard Samuels, and Stephen P. Stich (eds), The Oxford Handbook of Philosophy of Cognitive Science, Oxford Handbooks (2012; online edn, Oxford Academic, 1 May 2012)

https://academic.oup.com/edited-volume/28238/chapter-abstract/213303163 

Quote: “Several concepts used in the area of consciousness and cognition are discussed. There are five distinguished types of creature consciousness. An organism may be said to be conscious is it can sense and perceive its environment and has the capacity to respond appropriately. A second sense of creature consciousness requires not merely the capacity to sense or perceive, but the current active use of those capacities. Another notion of creature consciousness requires that organisms be not only aware but also self-aware. Self-awareness comes in degrees and varies along multiple dimensions. The conscious creatures might be defined as those that have an experiential life. Organisms are sometimes said to be conscious of various items or objects. Consciousness in this sense is understood as an intentional relation between the organism and some object or item of which it is aware. The conscious states might be regarded as those that have phenomenal properties or phenomenal character. The representationalist theories claim that conscious states have no mental properties other than their representational properties. Higher-order theories analyze consciousness as a form of self-awareness. Higher-order theories come in several forms. Some treat the requisite higher-order states as perception-like, and thus the process of generating such states is a kind of inner perception or perhaps introspection. The intermediate level representation model focuses on the contents of conscious experience.”

#Jahangir Moini, Pirouz Piran. Chapter 19 - Spinal cord. Editor(s): Jahangir Moini, Pirouz Piran,

Functional and Clinical Neuroanatomy. Academic Press. 2020

https://www.sciencedirect.com/science/article/abs/pii/B9780128174241000197 

Quote: “The spinal cord is involved in reflexes, sensory processing, and motor outflow. Spinal reflexes are stereotyped motor outputs caused by certain afferent inputs. They often involve neural circuitry that is only found in the spinal cord. Except for axon reflexes, all reflex pathways involve at least one receptor structure and an associated afferent neuron. The cell body of this afferent neuron must lie in a posterior root ganglion or another sensory ganglion. Reflex pathways also involve efferent neurons, which has its cell body with the CNS. Except for the stretch reflex, all reflexes involve one or more interneurons. Reflexes can be simple to highly complex.

All skeletal muscles, except for some of the head muscles, contract to various extents, as responses to stretching. The responsible reflex arc uses the simplest possible route through the CNS. This is because it only uses two neurons and one intervening synapse. This is sometimes called a monosynaptic reflex or myotatic reflex. The afferent limb of the arc is called a Ia afferent, with its related muscle spindle primary ending. Central processes of the Ia afferent form synapses in the spinal cord directly on alpha motor neurons innervating muscle containing the stimulated spindle.”

#Andersen RA, Cui H. Intention, action planning, and decision making in parietal-frontal circuits. Neuron. 2009

https://www.sciencedirect.com/science/article/pii/S0896627309006394 

Quote: “The posterior parietal cortex and frontal cortical areas to which it connects are responsible for sensorimotor transformations. This review covers new research on four components of this transformation process: planning, decision making, forward state estimation, and relative-coordinate representations. These sensorimotor functions can be harnessed for neural prosthetic operations by decoding intended goals (planning) and trajectories (forward state estimation) of movements as well as higher cortical functions related to decision making and potentially the coordination of multiple body parts (relative-coordinate representations).”

– And some insects may have something like it, like our buddy the bee. With a brain smaller than a sesame seed and about one million neurons, a bee has a much larger mind gap. 

#Lösel PD, Monchanin C, Lebrun R, et al. Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning. PLoS Comput Biol. 2023

https://pmc.ncbi.nlm.nih.gov/articles/PMC10569549/ 

“Fig 2. Surface renderings of an example of CT-scanned honey bee head and reconstructed brain neuropils. (A) Frontal view of the head of a forager bee (ID 79, hive H4). (B) Surface rendering of the head with the mandibles removed. (C) Overlay of the head and reconstructed neuropils. (D) Frontal cross-section of the tomogram with the segmentation boundaries of the mushroom bodies (MB), central complex (CX), antennal lobes (AL), medullae (ME), lobulae (LO) and other neuropils (OTH). (E) Frontal view of the reconstructed MB (dark blue), CX (sky blue), AL (light sky blue), ME (beige), LO (red) and OTH (orange). (F) Dorsal view of the reconstructed neuropils. (B), (C), (E) and (F) were created with ParaView Glance integrated in Biomedisa.”

#Menzel R, Giurfa M. Cognitive architecture of a mini-brain: the honeybee. Trends Cogn Sci. 2001

https://www.bcp.fu-berlin.de/biologie/arbeitsgruppen/neurobiologie/ag_menzel/publications/Res/Me-Review-nature-Reviews-Neurosc-2012.pdf

Quote: “The nervous system of insects is composed of the brain and multiple segmental ganglia of the ventral chord in the thorax and abdomen. The brain processes second or higher order inputs from all sensory organs, and coordinates the behavioural output through descending premotor neurons or interneurons. Although the brain of the honeybee is small (about 0.4 to 0.6 mm3 with about  1 million neurons), it is large both in absolute and relative terms in comparison to other insect species. For example, the brain of common fruitflies (Drosophila spp.) is about 30 to 50-times smaller than the honeybee Box 1 | The Honeybee Standard Brain brain and contains about 100,000 neurons (estimated by using data from REF. 11). Such comparisons are possible with high precision because standard atlases exist for both the Drosophila11–13 and the bee brain14,15, allowing comparison also between absolute and relative volumes of brain parts.”

– And it is filled with information about the world. It builds and remembers a huge mental map of flowers over square kilometers of terrain, cross referencing it with the position of the sun to find its way. Bees can take shortcuts, so they can’t be simply following reflexes but are actually navigating their internal maps. 

#Kolyfetis George E., Belušič Gregor and Foster James J. 2025 Electrophysiological recordings reveal photoreceptor coupling in the dorsal rim areas of honeybee and bumblebee eyes. Biol. Lett.

http://doi.org/10.1098/rsbl.2025.0234 

Quote: “Light arriving from the Sun gets scattered and polarized in the Earth’s atmosphere, creating patterns of polarization in the sky [1,2]. These patterns depend on the Sun’s position and can thus act as a valuable navigational reference system for many insects that perceive them [2–4]. Honeybees have been behaviourally demonstrated to utilize an internal map of skylight polarization’s topography, a ‘polarization compass’, to estimate the position of the Sun and navigate successfully [5,6].

Most insect species detect polarized light using specialized, polarization-sensitive photoreceptors in the dorsal rim area (DRA) of their compound eyes [7–9]. In honeybees and bumblebees, these are UV-sensitive photoreceptors with high polarization sensitivity (PS) [7,10–12]. Honeybee DRA photoreceptors also possess exceptionally wide receptive fields (RFs) compared with the main retina and dorsal-eye photoreceptors [7]. The shape of these wide RFs has been previously described in honeybees; they exhibit a central region with high relative sensitivity and a wide ‘brim region’ surrounding the centre, where sensitivity decreases very gradually with increasing off-axis angle [7].”

#Z. Wang, X. Chen, F. Becker, et al. Honey bees infer source location from the dances of returning foragers, Proc. Natl. Acad. Sci. U.S.A. (2023)

https://doi.org/10.1073/pnas.2213068120

Quote: “Honeybees (Apis mellifera carnica) communicate the direction and distance to a food source by means of a waggle dance. We ask whether bees recruited by the dance use it only as a flying instruction, with the technical form of a polar vector, or also translate it into a location vector that enables them to set courses directed toward the food source from arbitrary locations within their familiar territory. The flights of recruits captured on exiting the hive and released at distant sites were tracked by radar. The recruits performed first a straight flight in approximately the compass direction indicated by the dance. However, this “vector” portion of their flights and the ensuing tortuous “search” portion were strongly and differentially affected by the release site. Searches were biased toward the true location of the food and away from the location specified by translating the origin for the danced polar vector to the release site. We conclude that by following the dance recruits get two messages, a polar flying instruction (bearing and range from the hive) and a location vector that enables them to approach the source from anywhere in their familiar territory. The dance communication is much richer than thought so far.”

#J.F. Cheeseman, C.D. Millar, U. Greggers, et al. Way-finding in displaced clock-shifted bees proves bees use a cognitive map, Proc. Natl. Acad. Sci. U.S.A. (2014).

https://doi.org/10.1073/pnas.1408039111  

Quote: “Mammals navigate by means of a metric cognitive map. Insects, most notably bees and ants, are also impressive navigators. The question whether they, too, have a metric cognitive map is important to cognitive science and neuroscience. Experimentally captured and displaced bees often depart from the release site in the compass direction they were bent on before their capture, even though this no longer heads them toward their goal. When they discover their error, however, the bees set off more or less directly toward their goal. This ability to orient toward a goal from an arbitrary point in the familiar environment is evidence that they have an integrated metric map of the experienced environment. We report a test of an alternative hypothesis, which is that all the bees have in memory is a collection of snapshots that enable them to recognize different landmarks and, associated with each such snapshot, a sun-compass–referenced home vector derived from dead reckoning done before and after previous visits to the landmark. We show that a large shift in the sun-compass rapidly induced by general anesthesia does not alter the accuracy or speed of the homeward-oriented flight made after the bees discover the error in their initial postrelease flight. This result rules out the sun-referenced home-vector hypothesis, further strengthening the now extensive evidence for a metric cognitive map in bees.”

– In extreme cases when resources are scarce, bees can move up to 10km from their hives to collect food – the equivalent of going from Paris to Rome for groceries. 

#Steffan‐Dewenter, I., & Kuhn, A. Honeybee foraging in differentially structured landscapes. Proceedings of the Royal Society of London. Series B: Biological Sciences (2003)

https://doi.org/10.1098/rspb.2002.2292. 

Quote: “Honeybees communicate the distance and location of resource patches by bee dances, but this spatial information has rarely been used to study their foraging ecology. We analysed, for the first time to the best of the authors' knowledge, foraging distances and dance activities of honeybees in relation to landscape structure, season and colony using a replicated experimental approach on a landscape scale. We compared three structurally simple landscapes characterized by a high proportion of arable land and large patches, with three complex landscapes with a high proportion of semi–natural perennial habitats and low mean patch size. Four observation hives were placed in the centre of the landscapes and switched at regular intervals between the six landscapes from the beginning of May to the end of July. A total of 1137 bee dances were observed and decoded. Overall mean foraging distance was 1526.1 ± 37.2 m, the median 1181.5 m and range 62.1–10 037.1 m. Mean foraging distances of all bees and foraging distances of nectar–collecting bees did not significantly differ between simple and complex landscapes, but varied between month and colonies. Foraging distances of pollen–collecting bees were significantly larger in simple (1743 ± 95.6 m) than in complex landscapes (1543.4 ± 71 m) and highest in June when resources were scarce. Dancing activity, i.e. the number of observed bee dances per unit time, was significantly higher in complex than in simple landscapes, presumably because of larger spatial and temporal variability of resource patches in complex landscapes. The results facilitate an understanding of how human landscape modification may change the evolutionary significance of bee dances and ecological interactions, such as pollination and competition between honeybees and other bee species.”

Average forager honey bee body length = 10-15mm. Assume 15 mm (= 0.015 m).

Max. honey bee foraging distance = 10 km = 10000 m.

10000 m / 0.015 m = 666666.67 (body lengths travelled)

Assume human body length of 1.7 m.

666666.67 x 1.7 m = 1133333.3 m = ca. 1133 km

Distance from Paris to Rome (as the Nazgul flies): ca. 1105 km

#Dimensions - Western Honey Bee (Apis mellifera). Retrieved September 2025

https://www.dimensions.com/element/western-honey-bee-apis-mellifera

Quote: “Height: .12”-.2” | 3-5 mm Width: .12”-.2” | 3-5 mm Length: .39”-.59” | 10-15 mm (Worker); .71”-.79” | 18-20 mm (Queen)”

#Steffan‐Dewenter, I., & Kuhn, A. Honeybee foraging in differentially structured landscapes. Proceedings of the Royal Society of London. Series B: Biological Sciences. (2003)

https://doi.org/10.1098/rspb.2002.2292. 

Quote: “Honeybees communicate the distance and location of resource patches by bee dances, but this spatial information has rarely been used to study their foraging ecology. We analysed, for the first time to the best of the authors' knowledge, foraging distances and dance activities of honeybees in relation to landscape structure, season and colony using a replicated experimental approach on a landscape scale. We compared three structurally simple landscapes characterized by a high proportion of arable land and large patches, with three complex landscapes with a high proportion of semi–natural perennial habitats and low mean patch size. Four observation hives were placed in the centre of the landscapes and switched at regular intervals between the six landscapes from the beginning of May to the end of July. A total of 1137 bee dances were observed and decoded. Overall mean foraging distance was 1526.1 ± 37.2 m, the median 1181.5 m and range 62.1–10 037.1 m. Mean foraging distances of all bees and foraging distances of nectar–collecting bees did not significantly differ between simple and complex landscapes, but varied between month and colonies. Foraging distances of pollen–collecting bees were significantly larger in simple (1743 ± 95.6 m) than in complex landscapes (1543.4 ± 71 m) and highest in June when resources were scarce. Dancing activity, i.e. the number of observed bee dances per unit time, was significantly higher in complex than in simple landscapes, presumably because of larger spatial and temporal variability of resource patches in complex landscapes. The results facilitate an understanding of how human landscape modification may change the evolutionary significance of bee dances and ecological interactions, such as pollination and competition between honeybees and other bee species.”

– And they teach the location of the food by dancing. 

#McHenry LC, Schürch R, Johnson LE, et al. Individuality impacts communication success in honey bees. Curr Biol. 2025

https://pubmed.ncbi.nlm.nih.gov/39999780/

Quote: “In eusocial insects, individual variation and its influence on emergent outcomes, like communication success between foragers, remain poorly understood1. The honey bee waggle dance is a celebrated communication behavior that conveys to nestmates a distance and direction from the hive to a valuable resource, usually nectar or pollen2. Intriguingly, each forager possesses an individual calibration to communicate the resource's distance3, but the effect of this individuality on recruitment success is unknown. Here we tested whether the magnitude and/or direction of calibration mismatch in dancer-follower dyads affects their ability to communicate. We created fully-marked observation colonies and trained bees to forage from artificial feeders at known distances. Concurrently, we filmed dances inside the colony to identify successful dancer-follower dyads. We then compared the distribution of calibration mismatch values among these successful dyads (n = 30) to a simulated expected distribution based on a null hypothesis of random assortment of calibration values. Surprisingly, mismatch magnitude did not affect recruitment (p = 0.74), but mismatch direction did: followers predicted to overshoot the resource were over-represented among successful dyads compared to the null distribution (p = 0.03). Overall, our data demonstrate that the calibration relationship in dancer-follower dyads, created by individual differences, can shape communication outcomes.”

#Z. Wang, X. Chen, F. Becker, et al. Honey bees infer source location from the dances of returning foragers, Proc. Natl. Acad. Sci. U.S.A. (2023)

https://doi.org/10.1073/pnas.2213068120

Quote: “Honeybees (Apis mellifera carnica) communicate the direction and distance to a food source by means of a waggle dance. We ask whether bees recruited by the dance use it only as a flying instruction, with the technical form of a polar vector, or also translate it into a location vector that enables them to set courses directed toward the food source from arbitrary locations within their familiar territory. The flights of recruits captured on exiting the hive and released at distant sites were tracked by radar. The recruits performed first a straight flight in approximately the compass direction indicated by the dance. However, this “vector” portion of their flights and the ensuing tortuous “search” portion were strongly and differentially affected by the release site. Searches were biased toward the true location of the food and away from the location specified by translating the origin for the danced polar vector to the release site. We conclude that by following the dance recruits get two messages, a polar flying instruction (bearing and range from the hive) and a location vector that enables them to approach the source from anywhere in their familiar territory. The dance communication is much richer than thought so far.”

– Minds have emerged a few times in evolutionary history and evolved in different directions, so while we are telling this story by increasing complexity, in reality minds are more like a weird mix of different experiments, depending on what niches the animals fill.

We use “the mind” here as a broad term encompassing many dimensions: cognition, intelligence, consciousness, and others. Below, we cite some scientific overviews of these different aspects of “the mind” and how they vary across the animal kingdom. An ongoing problem is that many of these categories of brain functionality are framed with human brains as a “highly capable” reference point, but animal brains might work in ways that are so different from ours that direct comparisons to humans are inappropriate and/or misleading. The definition of many of these concepts also varies between scientists and fields of study.

Cognition & Intelligence:

#Barron, A., Halina, M., & Klein, C. Transitions in cognitive evolution. Proceedings of the Royal Society B: Biological Sciences. (2023)

https://doi.org/10.1098/rspb.2023.0671 

Quote: “The evolutionary history of animal cognition appears to involve a few major transitions: major changes that opened up new phylogenetic possibilities for cognition. Here, we review and contrast current transitional accounts of cognitive evolution. We discuss how an important feature of an evolutionary transition should be that it changes what is evolvable, so that the possible phenotypic spaces before and after a transition are different. We develop an account of cognitive evolution that focuses on how selection might act on the computational architecture of nervous systems. Selection for operational efficiency or robustness can drive changes in computational architecture that then make new types of cognition evolvable. We propose five major transitions in the evolution of animal nervous systems. Each of these gave rise to a different type of computational architecture that changed the evolvability of a lineage and allowed the evolution of new cognitive capacities. Transitional accounts have value in that they allow a big-picture perspective of macroevolution by focusing on changes that have had major consequences. For cognitive evolution, however, we argue it is most useful to focus on evolutionary changes to the nervous system that changed what is evolvable, rather than to focus on specific cognitive capacities.”

#Bayne T, Brainard D, Byrne RW, et al. What is cognition? Curr Biol. 2019 

https://www.cell.com/current-biology/fulltext/S0960-9822(19)30614-1 

Quote: “cognition (n.)

mid-15c., cognicioun, “ability to comprehend, mental act or process of knowing”, from Latin cognoscere “to get to know, recognize,” from assimilated form of com “together” + gnoscere “to know” …

The etymology above (adapted from https://www.etymonline.com/word/cognition) shows that the word “cognition” has its origins in classical terms relating to the concept of knowing. A number of related contemporary English words have a similar etymology, for example recognise, cognizant, agnostic, and indeed knowledge itself, the “g” having morphed into a “k” in Germanic languages. The word seems straightforward, yet it is often a cause of debate in the psychological and neuroscience fields, particularly about whether a behaviour of an animal that happens not to be human is truly “cognitive”, in a similar sense to human cognition. One example concerns the use by rooks of stones to raise the water level in a container so that they can reach a floating worm (see https://www.cell.com/current-biology/fulltext/S0960-9822(09)01455-9): to what extent does this ability mean the birds “know” about the displacement of water by sinking objects? Does it mean they are capable of complex, human-like cognition? Even more controversially, to what extent does it make sense to talk about “cognition” in the context of organisms that don’t even have a nervous system, such as plants? And if one considers the flow of information between peripheral senses and motor output, does “cognition” apply only to certain abstract operations in between? And when it comes to psychiatric disorders like schizophrenia, how easy is it to define specifically cognitive impairments? The continuing arguments about these issues suggest a need for greater clarity and agreement on precisely what cognition means, and what is required to establish that a particular phenomenon is “cognitive”. With this in mind we have invited a number of people from relevant fields of biology to write a short account of their understanding of the term “cognition”, and their contributions are collected below.”

#Bräuer J, Hanus D, Pika S, Gray R, Uomini N. Old and New Approaches to Animal Cognition: There Is Not "One Cognition". J Intell. 2020

https://pmc.ncbi.nlm.nih.gov/articles/PMC7555673/ 

Quote: “Using the comparative approach, researchers draw inferences about the evolution of cognition. Psychologists have postulated several hypotheses to explain why certain species are cognitively more flexible than others, and these hypotheses assume that certain cognitive skills are linked together to create a generally “smart” species. However, empirical findings suggest that several animal species are highly specialized, showing exceptional skills in single cognitive domains while performing poorly in others. Although some cognitive skills may indeed overlap, we cannot a priori assume that they do across species. We argue that the term “cognition” has often been used by applying an anthropocentric viewpoint rather than a biocentric one. As a result, researchers tend to overrate cognitive skills that are human-like and assume that certain skills cluster together in other animals as they do in our own species. In this paper, we emphasize that specific physical and social environments create selection pressures that lead to the evolution of certain cognitive adaptations. Skills such as following the pointing gesture, tool-use, perspective-taking, or the ability to cooperate evolve independently from each other as a concrete result of specific selection pressures, and thus have appeared in distantly related species. Thus, there is not “one cognition”. Our argument is founded upon traditional Darwinian thinking, which—although always at the forefront of biology—has sometimes been neglected in animal cognition research. In accordance with the biocentric approach, we advocate a broader empirical perspective as we are convinced that to better understand animal minds, comparative researchers should focus much more on questions and experiments that are ecologically valid. We should investigate nonhuman cognition for its own sake, not only in comparison to the human model.”

#Howard SR, Barron AB. Understanding the limits to animal cognition. Curr Biol. 2024

https://www.cell.com/current-biology/fulltext/S0960-9822(24)00218-5 

Quote: “The thriving field of comparative cognition examines the behaviour of diverse animals in cognitive terms. Comparative cognition research has primarily focused on the abilities of animals - what tasks they can do - rather than on the limits of their cognition - tasks that exceed an animal's cognitive abilities. We propose that understanding and identifying cognitive limits is as important as demonstrating the capacities of animal minds. Here, we identify challenges that have deterred the study of cognitive limits related to epistemic, practical and publication problems. The epistemic problem is concerned with how we can confidently infer a cognitive limit from null or negative results. The practical problem is how can we be certain our research has identified a cognitive limit rather than failures in tasks due to methodological or experimental design issues. The publication problem outlines the publication bias toward positive and exciting results over negative or null results in animal cognition. We propose solutions to these three challenges and examples of how to conduct research to confidently identify and confirm cognitive limits in animals. We believe a refocus on the cognitive limits of animals is the next step in the field of comparative cognition. Knowing the limits to the intelligence of different animals will aid us in appreciating the diversity of animal intelligence, and will resolve outstanding questions of how cognition evolves.”

Consciousness:

#Irwin LN. Renewed Perspectives on the Deep Roots and Broad Distribution of Animal Consciousness. Front. Syst. Neurosci. (2020)

https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2020.00057/full 

Quote: “The vast majority of neurobiologists have long abandoned the Cartesian view of non-human animals as unconscious automatons—acknowledging instead the high likelihood that mammals and birds have mental experiences akin to subjective consciousness. Several lines of evidence are now extending those limits to all vertebrates and even some invertebrates, though graded in degrees as argued originally by Darwin, correlated with the complexity of the animal’s brain. A principal argument for this view is that the function of consciousness is to promote the survival of an animal—especially one actively moving about—in the face of dynamic changes and real-time contingencies. Cognitive ecologists point to the unique features of each animal’s environment and the specific behavioral capabilities that different environments invoke, thereby suggesting that consciousness must take on a great variety of forms, many of which differ substantially from human subjective experience.”

#Feinberg TE and Mallatt J. The evolutionary and genetic origins of consciousness in the Cambrian Period over 500 million years ago. Front. Psychol. (2013)

https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2013.00667/full 

Quote: “Vertebrates evolved in the Cambrian Period before 520 million years ago, but we do not know when or how consciousness arose in the history of the vertebrate brain. Here we propose multiple levels of isomorphic or somatotopic neural representations as an objective marker for sensory consciousness. All extant vertebrates have these, so we deduce that consciousness extends back to the group's origin. The first conscious sense may have been vision. Then vision, coupled with additional sensory systems derived from ectodermal placodes and neural crest, transformed primitive reflexive systems into image forming brains that map and perceive the external world and the body's interior. We posit that the minimum requirement for sensory consciousness and qualia is a brain including a forebrain (but not necessarily a developed cerebral cortex/pallium), midbrain, and hindbrain. This brain must also have (1) hierarchical systems of intercommunicating, isomorphically organized, processing nuclei that extensively integrate the different senses into representations that emerge in upper levels of the neural hierarchy; and (2) a widespread reticular formation that integrates the sensory inputs and contributes to attention, awareness, and neural synchronization. We propose a two-step evolutionary history, in which the optic tectum was the original center of multi-sensory conscious perception (as in fish and amphibians: step 1), followed by a gradual shift of this center to the dorsal pallium or its cerebral cortex (in mammals, reptiles, birds: step 2). We address objections to the hypothesis and call for more studies of fish and amphibians. In our view, the lamprey has all the neural requisites and is likely the simplest extant vertebrate with sensory consciousness and qualia. Genes that pattern the proposed elements of consciousness (isomorphism, neural crest, placodes) have been identified in all vertebrates. Thus, consciousness is in the genes, some of which are already known.”

#Birch J, Schnell AK, Clayton NS. Dimensions of Animal Consciousness. Trends Cogn Sci. 2020

https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(20)30192-3 

Quote: “How does consciousness vary across the animal kingdom? Are some animals ‘more conscious’ than others? This article presents a multidimensional framework for understanding interspecies variation in states of consciousness. The framework distinguishes five key dimensions of variation: perceptual richness, evaluative richness, integration at a time, integration across time, and self-consciousness. For each dimension, existing experiments that bear on it are reviewed and future experiments are suggested. By assessing a given species against each dimension, we can construct a consciousness profile for that species. On this framework, there is no single scale along which species can be ranked as more or less conscious. Rather, each species has its own distinctive consciousness profile.”

#Dung L, Newen A. Profiles of animal consciousness: A species-sensitive, two-tier account to quality and distribution. Cognition. 2023

https://www.sciencedirect.com/science/article/pii/S0010027723000434?via%3Dihub 

Quote: “The science of animal consciousness investigates (i) which animal species are conscious (the distribution question) and (ii) how conscious experience differs in detail between species (the quality question). We propose a framework which clearly distinguishes both questions and tackles both of them. This two-tier account distinguishes consciousness along ten dimensions and suggests cognitive capacities which serve as distinct operationalizations for each dimension. The two-tier account achieves three valuable aims: First, it separates strong and weak indicators of the presence of consciousness. Second, these indicators include not only different specific contents but also differences in the way particular contents are processed (by processes of learning, reasoning or abstraction). Third, evidence of consciousness from each dimension can be combined to derive the distinctive multi-dimensional consciousness profile of various species. Thus, the two-tier account shows how the kind of conscious experience of different species can be systematically compared.”

– Mind complexity probably increases dramatically as we add more neurons, like the octopus that has about 500 million of them. 

Absolute number of neurons is often a good correlate of cognitive diversity, i.e. species with more neurons tend to perform better on cognitive tasks. But the correlation is very noisy, and thus not at all universal: instead, the relationship between number of neurons and cognitive complexity is highly nuanced and strongly influenced by brain architecture, neuronal network organization, which neurons exactly are counted (some studies only consider neurons in specific brain areas), and which aspect of complex cognition is focussed on. Neuron numbers alone are thus insufficient for predicting cognitive performance.

#Barron AB, Mourmourakis F. The Relationship between Cognition and Brain Size or Neuron Number. Brain Behav Evol. 2024

https://karger.com/bbe/article/99/2/109/860281/The-Relationship-between-Cognition-and-Brain-Size 

Quote: “The comparative approach is a powerful way to explore the relationship between brain structure and cognitive function. Thus far, the field has been dominated by the assumption that a bigger brain somehow means better cognition. Correlations between differences in brain size or neuron number between species and differences in specific cognitive abilities exist, but these correlations are very noisy. Extreme differences exist between clades in the relationship between either brain size or neuron number and specific cognitive abilities. This means that correlations become weaker, not stronger, as the taxonomic diversity of sampled groups increases. Cognition is the outcome of neural networks. Here we propose that considering plausible neural network models will advance our understanding of the complex relationships between neuron number and different aspects of cognition. Computational modelling of networks suggests that adding pathways, or layers, or changing patterns of connectivity in a network can all have different specific consequences for cognition. Consequently, models of computational architecture can help us hypothesise how and why differences in neuron number might be related to differences in cognition. As methods in connectomics continue to improve and more structural information on animal brains becomes available, we are learning more about natural network structures in brains, and we can develop more biologically plausible models of cognitive architecture. Natural animal diversity then becomes a powerful resource to both test the assumptions of these models and explore hypotheses for how neural network structure and network size might delimit cognitive function.”

#Onur Güntürkün, Felix Ströckens, Damian Scarf, Mike Colombo. Apes, feathered apes, and pigeons: differences and similarities. Current Opinion in Behavioral Sciences. 2017

https://doi.org/10.1016/j.cobeha.2017.03.003 

Quote: “Apes, corvids, and pigeons differ in their pallial/cortical neuron numbers, with apes ranking first and pigeons third. Do cognitive performances rank accordingly? If they would do, cognitive performance could be explained at a mechanistic level by computational capacity provided by neuron numbers. We discuss five areas of cognition (short-term memory, object permanence, abstract numerical competence, orthographic processing, self-recognition) in which apes, corvids, and pigeons have been tested with highly similar procedures. In all tests apes and corvids were on par, but also pigeons reached identical achievement levels in three tests. We suggest that higher neuron numbers are poor predictors of absolute cognitive ability, but better predict learning speed and the ability to flexibly transfer rules to novel situations.”

#Sol D, Olkowicz S, Sayol F, et al. Neuron numbers link innovativeness with both absolute and relative brain size in birds. Nat Ecol Evol. 2022

https://ddd.uab.cat/pub/artpub/2022/288044/Sol_et_al_2022_Postprint.pdf

Quote: “A longstanding issue in biology is whether the intelligence of animals can be predicted by absolute or relative brain size. However, progress has been hampered by an insufficient understanding of how neuron numbers shape internal brain organization and cognitive performance. On the basis of estimations of neuron numbers for 111 bird species, we show here that the number of neurons in the pallial telencephalon is positively associated with a major expression of intelligence: innovation propensity. The number of pallial neurons, in turn, is greater in brains that are larger in both absolute and relative terms and positively covaries with longer post-hatching development periods. Thus, our analyses show that neuron numbers link cognitive performance to both absolute and relative brain size through developmental adjustments. These findings help unify neuro-anatomical measures at multiple levels, reconciling contradictory views over the biological significance of brain expansion. The results also highlight the value of a life history perspective to advance our understanding of the evolutionary bases of the connections between brain and cognition.”

#Carls-Diamante S. Where Is It Like to Be an Octopus? Front Syst Neurosci. 2022

https://pmc.ncbi.nlm.nih.gov/articles/PMC8988249/ 

Quote: “With its 500 million neurons—a number more typical of vertebrates such as dogs—octopuses have the largest nervous systems among invertebrates (Hochner, 2004). The octopus nervous system is highly distributed, and typically divided along anatomical lines into components with considerable functional autonomy. The three main parts of the octopus nervous system are the brain, the optic lobes, and the highly elaborated arm nervous system. Significantly, the arm nervous system contains three-fifths of the octopus’s neurons. Importantly, the brain, optic lobes, and arm nervous system are interconnected by only about 30,000 nerve fibres, suggesting that “much of the processing of motor and sensory information is performed in the peripheral nervous system and the optic lobes” (Hochner, 2012, p. R889).”

#Young, J.Z. The Number And Sizes Of Nerve Cells In Octopus. Proceedings of the Zoological Society of London. (1963)

https://doi.org/10.1111/j.1469-7998.1963.tb01862.x

Quote: “The numbers of nerve cells of various sizes have been estimated in the different ganglia and lobes of the brain of Octopus vulgaris. There are about 500 million nerve cells altogether, of which 300 million are in the ganglia within the arms. The main motor contres of the brainare composed almost entirely of large neurons. Cells of intermediate size are found in the basal lobes of the supracosophagoal ganglia. The smallest neurons are minute amacrine cells, abundant in parts of the optic and in the subfrontal and vertical lobes, perhaps having inhibitory actions.”

– Only 40% of their neurons are in their central brain, while each of their arms has their own mini nerve centers that taste, process information locally at the same time and act on their own. 

#Young, J.Z. The Number And Sizes Of Nerve Cells In Octopus. Proceedings of the Zoological Society of London. (1963)

https://doi.org/10.1111/j.1469-7998.1963.tb01862.x

Quote: “The numbers of nerve cells of various sizes have been estimated in the different ganglia and lobes of the brain of Octopus vulgaris. There are about 500 million nerve cells altogether, of which 300 million are in the ganglia within the arms. The main motor contres of the brainare composed almost entirely of large neurons. Cells of intermediate size are found in the basal lobes of the supracosophagoal ganglia. The smallest neurons are minute amacrine cells, abundant in parts of the optic and in the subfrontal and vertical lobes, perhaps having inhibitory actions.”

#Carls-Diamante S. Where Is It Like to Be an Octopus? Front Syst Neurosci. 2022

https://pmc.ncbi.nlm.nih.gov/articles/PMC8988249/ 

Quote: “With its 500 million neurons—a number more typical of vertebrates such as dogs—octopuses have the largest nervous systems among invertebrates (Hochner, 2004). The octopus nervous system is highly distributed, and typically divided along anatomical lines into components with considerable functional autonomy. The three main parts of the octopus nervous system are the brain, the optic lobes, and the highly elaborated arm nervous system. Significantly, the arm nervous system contains three-fifths of the octopus’s neurons. Importantly, the brain, optic lobes, and arm nervous system are interconnected by only about 30,000 nerve fibres, suggesting that “much of the processing of motor and sensory information is performed in the peripheral nervous system and the optic lobes” (Hochner, 2012, p. R889).”

#van Giesen L, Kilian PB, Allard CAH, Bellono NW. Molecular Basis of Chemotactile Sensation in Octopus. Cell. 2020

https://pmc.ncbi.nlm.nih.gov/articles/PMC7605239/ 

Quote: “Animals display wide-ranging evolutionary adaptations based on their ecological niche. Octopuses explore the seafloor with their flexible arms using a specialized “taste by touch” system to locally sense and respond to prey-derived chemicals and movement. How the peripherally-distributed octopus nervous system mediates relatively autonomous arm behavior is unknown. Here we report that octopus arms use a family of cephalopod-specific chemotactile receptors (CRs) to detect poorly-soluble natural products, thereby defining a form of contact-dependent, aquatic chemosensation. CRs form discrete ion channel complexes that mediate the detection of diverse stimuli and transduction of specific ionic signals. Furthermore, distinct chemo- and mechanosensory cells exhibit specific receptor expression and electrical activities to support peripheral-information coding and complex chemotactile behaviors. These findings demonstrate that the peripherally-distributed octopus nervous system is a key site for signal processing and highlight how molecular and anatomical features synergistically evolve to suit an animal’s environmental context.”

#Olson, C.S., Schulz, N.G. & Ragsdale, C.W. Neuronal segmentation in cephalopod arms. Nat Commun (2025).

https://doi.org/10.1038/s41467-024-55475-5

Quote: “The octopus has a motor control challenge of enormous complexity1,2. Each of its eight arms is a muscular hydrostat, a soft-bodied structure that lacks a rigid skeleton and moves with near infinite degrees of freedom3,4. Moreover, the arms are packed with hundreds of chemotactile suckers which can change shape independently5,6. Even with this complexity, octopuses control behaviors effectively along the length of a single arm, across all eight arms and between suckers (Supplementary Movie 1)7,8,9. The neural circuits underlying these behaviors have been unexplored with modern molecular and cellular methods.

Embedded in the octopus arm is a massive nervous system, with more neurons found distributed across the eight arms than in the brain10,11. Most prominent is an axial nerve cord (ANC) running down the center of every arm (Fig. 1a, Supplementary Fig. 1a)2,12. Peripheral to the ANC, there are four smaller intramuscular nerve cords (IMNCs), and a sucker ganglion (SG) for every sucker (Fig. 1a, Supplementary Fig. 1a). In the ANC, and following the characteristic invertebrate pattern, neuronal cell bodies are localized to a cell body layer (CBL) wrapping around neuropil (NP). Down the long axis of the arm, the ANC is a medullary cord13 that snakes back and forth, with every bend in the ANC forming an ANC enlargement that overlies a sucker14 (these ANC enlargements are themselves sometimes referred to as ganglia11,15,16, see Supplementary Fig. 1g, h). In transverse sections on the sucker, or oral, side of the ANC, the CBL forms a horseshoe around the NP (Fig. 1b, Supplementary Fig. 1b). On the aboral side, or away from the suckers, there is a massive cerebro-brachial tract (CBT) connecting the arms and the brain12,17 (Fig. 1b, Supplementary Fig. 1b). The CBL itself can be divided into an aboral, or brachial, territory that is dedicated to the sensorimotor control of the arm, and an oral, or sucker, territory for the sensorimotor control of the suckers18 (Supplementary Fig. 1g, h).

[...]

The cephalopod arm is a highly redundant structure, with both suckers and the pattern of brachial musculature repeated down its length1,2. A parcellation of the ANC into segments is a natural way to relegate motor control of a continuous limb with such a reiterated structure. In fact, many computational models and soft robots inspired by octopus arms divide the arm into repeated segments in their construction or control algorithms32,33,34,35. Our results, however, demonstrate additional complexities.”

– The central brain does still coordinate them for complex behavior, like catching prey, because imagine 8 arms doing their own thing all the time. 

But different arms are specialized and used primarily for different tasks, like exploring or movement. And we know their arms can make decisions independently to some degree and have some autonomy. 

#Bennice, C.O., Buresch, K.C., Grossman, J.H. et al. Octopus arm flexibility facilitates complex behaviors in diverse natural environments. Sci Rep (2025). https://doi.org/10.1038/s41598-025-10674-y 

Quote: “Although each of an octopus’s 8 arms is anatomically and neurologically similar and capable of performing all arm actions and deformations, we observed multi-level arm partitioning. Limb specialization has been well studied among vertebrates (primates, rodents, and fish); however, evidence for limb specialization in cephalopods is limited. We found that octopus arm actions were not equally distributed across all 8 arms, but were instead partitioned (anterior vs posterior) for specific actions. Previous research has demonstrated this partial task division with anterior arms designated for reaching/exploring tasks and posterior arms designated for standing and locomotion2,19,32. Byrne et al. 2006 also made note of a lateralized bias of arm use for some individual octopuses in their laboratory study32. Our results did not indicate a lateralized preference for specific arms; instead, the arms appeared to function in coordinated left and right pairs. Recently discovered intramuscular nerve cords that connect each arm to another arm 2 arms away may provide an alternative path for inter-arm signaling, potentially regulating this anterior and posterior arm partitioning33.”

#Rosania, K. The autonomous arms of the octopus. Lab Anim (2014). https://doi.org/10.1038/laban.615 

Quote: “Meanwhile, two-thirds of the neurons (∼330 million) are in the octopus's eight arms3. This unusual neuronal layout allows each individual arm to act and carry out instructions from the central brain on its own. These arms can use tools, twist off lids and even child-proof caps, withdraw from a noxious stimulus4 and keep from entangling one another5. Many of these feats have been observed in amputated octopus arms, demonstrating how little input from the central brain is needed. Inspired by the octopus, roboticists are working to incorporate decentralized control systems into soft robotic arms6.”

– Octopuses are very intelligent animals, able to do loads of complex things, so their minds should be somewhat complex. 

Measuring intelligence in animals comes with many complexities and challenges. One of the core problems is that there is no single definition of (animal) intelligence or a consensus on how exactly to measure it. Intelligence in octopuses has gathered special attention both in the public eye and the scientific literature due to the fact that they are invertebrates, and thus not closely related to other animals widely recognized as highly intelligent (primates, elephants, dolphins, some parrots etc.).

Octopuses and other cephalopods such as cuttlefish and squid are considered to be the most cognitively advanced invertebrates, as evidenced by their capacity for learning, memory, and adaptability. They can solve multi-step puzzles, use tools, and quickly adapt to new challenges, indicating behavioral flexibility comparable to some mammals and birds. To what extent these behaviours are evidence of complex cognition though, and how octopus intelligence compares to intelligent vertebrates, is still a topic of debate in the scientific literature.

#Schnell, A.K., Amodio, P., Boeckle, M. and Clayton, N.S. How intelligent is a cephalopod? Lessons from comparative cognition. Biol Rev. (2021)

https://doi.org/10.1111/brv.12651 

Quote: “The soft-bodied cephalopods including octopus, cuttlefish, and squid are broadly considered to be the most cognitively advanced group of invertebrates. Previous research has demonstrated that these large-brained molluscs possess a suite of cognitive attributes that are comparable to those found in some vertebrates, including highly developed perception, learning, and memory abilities. Cephalopods are also renowned for performing sophisticated feats of flexible behaviour, which have led to claims of complex cognition such as causal reasoning, future planning, and mental attribution. Hypotheses to explain why complex cognition might have emerged in cephalopods suggest that a combination of predation, foraging, and competitive pressures are likely to have driven cognitive complexity in this group of animals. Currently, it is difficult to gauge the extent to which cephalopod behaviours are underpinned by complex cognition because many of the recent claims are largely based on anecdotal evidence. In this review, we provide a general overview of cephalopod cognition with a particular focus on the cognitive attributes that are thought to be prerequisites for more complex cognitive abilities. We then discuss different types of behavioural flexibility exhibited by cephalopods and, using examples from other taxa, highlight that behavioural flexibility could be explained by putatively simpler mechanisms. Consequently, behavioural flexibility should not be used as evidence of complex cognition. Fortunately, the field of comparative cognition centres on designing methods to pinpoint the underlying mechanisms that drive behaviours. To illustrate the utility of the methods developed in comparative cognition research, we provide a series of experimental designs aimed at distinguishing between complex cognition and simpler alternative explanations. Finally, we discuss the advantages of using cephalopods to develop a more comprehensive reconstruction of cognitive evolution.”

#Schnell AK, Clayton NS. Cephalopod cognition. Curr Biol. 2019

https://linkinghub.elsevier.com/retrieve/pii/S0960-9822(19)30776-6 

Quote: “Cephalopods have captivated the minds of scientists for thousands of years, dating back to approximately 330 BC when Aristotle became fascinated by their ability to rapidly change colour. This remarkable ability, however, is not the only aspect of cephalopod behaviour that has garnered attention from the scientific community. The soft-bodied cephalopods (henceforth cephalopods), namely octopus, cuttlefish, and squid, are widely considered to be the most cognitively advanced group of invertebrates. They possess highly developed perceptual, memory, and spatial learning abilities and are also capable of intriguing feats of behaviour that appear to indicate complex cognition.”

– Some bird species with billions of neurons probably have surprisingly sophisticated minds, where they can go one step further: 

The number of neurons in birds is highly variable depending on the species. Typically, bird species have in the hundreds of millions of neurons per brain, but a few species – such as corvids and parrots – can have up to 1-3 billion neurons.

#S. Olkowicz, M. Kocourek, R.K. Lučan, et al. Birds have primate-like numbers of neurons in the forebrain, Proc. Natl. Acad. Sci. U.S.A. (2016).

https://doi.org/10.1073/pnas.1517131113 

Quote: “We found that the bird brains have more neurons than mammalian brains and even primate brains of similar mass (Fig. 1 A and B), and have very high neuronal densities (Fig. 2 B and C). Among the songbirds studied, weighing between 4.5 and 1,070 g, brain mass ranges from 0.36 to 14.13 g, and total numbers of neurons in the brain from 136 million to 2.17 billion (Fig. S3 and Table S1; for complete data see Datasets S1 and S2). In the parrots studied, body mass ranges between 23 and 1,008 g, brain mass from 1.15 to 20.73 g, and numbers of brain neurons from 227 million to 3.14 billion.”

#Sol D, Olkowicz S, Sayol F, et al. Neuron numbers link innovativeness with both absolute and relative brain size in birds. Nat Ecol Evol. 2022

https://ddd.uab.cat/pub/artpub/2022/288044/Sol_et_al_2022_Postprint.pdf

“Fig. 2. Neurons and innovation propensity. Relationship between neuron numbers and innovation propensity for the entire brain and the pallium, cerebellum and brainstem, as predicted by models. a, Absolute neuron numbers. b, Neuron numbers adjusted by body size by including body mass (previously subtracting brain mass) as co-variate in the model. c, Density of neurons (cells per mg). All models account for the effect of phylogeny, biogeographic realm and confounding variables (see Supplementary Tables 1 and 2 for details). Lines and credibility intervals are derived from Bayesian phylogenetic mixed models. Sample size is 99 species, as nocturnal specialists (i.e. owls) are excluded from the innovation database.”

– While minds may have started as a gap to delay action, bird minds are able to simulate a map of reality, including other players. Scrub Jays are hoarders that catch and hide all sorts of food in different places, from worms to nuts. Inside their mind they keep track of time and are aware that fresh food goes bad. So after a few days have passed they will ignore the hiding places of dead worms and instead go for the nuts that are still good. 

#Clayton NS, Dally J, Gilbert J, Dickinson A. Food caching by western scrub-jays (Aphelocoma californica) is sensitive to the conditions at recovery. J Exp Psychol Anim Behav Process. 2005

https://warwick.ac.uk/fac/soc/philosophy/research/researchcentres/wma/causal/seminars/clayton_reading/sj-caching-jep-rev_ms.pdf

Quote: “Western scrub-jays (Aphelocoma californica) cached perishable and nonperishable food items, which they could recover after both short and long retention intervals. When perishable items were always degraded at recovery, jays decreased the number of perishable items cached and increased their caching of nonperishable items, relative to a control group whose caches were always fresh at recovery. Jays reduced the number of nonperishable items cached, however, when highly preferred food items were degraded only after the long retention intervals. The findings are discussed in terms of the role of retrospective and prospective processes in the control of caching.”

– Probably more impressive, they might be able to simulate the minds of other birds – If a scrub Jay is hiding food and notices another jay watching, they will return later and re-hide their stash so it doesn’t get stolen. 

Scrub jays modify their food-cashing behaviour when they are being watched by other birds. Whether this means that they are able to “simulate the minds of other birds” or if this behaviour is readily explained by behavioural rules that do not require “theory of mind” (i.e. the cognitive ability to understand that others have their own mental states) is subject to debate.

We have also received the following insight from an expert on animal cognition: “The big debate is whether animals are readers of minds or simply readers of behavior. The idea that they can do mind reading is not accepted by everyone.”

#Dally, J. M., Emery, N. J., & Clayton, N. S. Avian Theory of Mind and counter espionage by food-caching western scrub-jays (Aphelocoma californica). European Journal of Developmental Psychology. (2009)

https://doi.org/10.1080/17405620802571711 

Quote: “Food-caching scrub-jays hide food for future consumption and rely on memory to recover their caches at a later date. These caches are susceptible to pilfering by other individuals, however. Consequently, jays engage in a number of counter-strategies to protect their hidden items, caching most of them behind barriers, or using shade and distance as a way of reducing what the potential pilferer might see. Jays do not place all their caches in one place, perhaps because unpredictability provides the best insurance against pilfering. Furthermore, after being observed by a potential pilferer at the time of caching, jays re-hide food in new places. Importantly, however, jays only re-cache food if they have been observed during caching and only if they have stolen another bird's caches in the past. Naïve birds that have no thieving experience do not do so. The inference is that jays with prior experience of stealing others' caches engage in experience projection, relating information about their previous experience as a pilferer to the possibility of future cache theft by another bird. These results raise the intriguing possibility that re-caching is based on a form of mental attribution, namely the simulation of another bird's viewpoint.”

#Clayton NS, Dally JM, Emery NJ. Social cognition by food-caching corvids. The western scrub-jay as a natural psychologist. Philos Trans R Soc Lond B Biol Sci. 2007

https://pubmed.ncbi.nlm.nih.gov/17309867/ 

Quote: “Food-caching corvids hide food, but such caches are susceptible to pilfering by other individuals. Consequently, the birds use several counter strategies to protect their caches from theft, e.g. hiding most of them out of sight. When observed by potential pilferers at the time of caching, experienced jays that have been thieves themselves, take further protective action. Once the potential pilferers have left, they move caches those birds have seen, re-hiding them in new places. Naive birds that had no thieving experience do not do so. By focusing on the counter strategies of the cacher when previously observed by a potential pilferer, these results raise the intriguing possibility that re-caching is based on a form of mental attribution, namely the simulation of another bird's viewpoint. Furthermore, the jays also keep track of the observer which was watching when they cached and take protective action accordingly, thus suggesting that they may also be aware of others' knowledge states.”

#van der Vaart E, Verbrugge R, Hemelrijk CK. Corvid Re-Caching without ‘Theory of Mind’: A Model. PLoS ONE. (2012)

https://doi.org/10.1371/journal.pone.0032904 

Quote: “Scrub jays are thought to use many tactics to protect their caches. For instance, they predominantly bury food far away from conspecifics, and if they must cache while being watched, they often re-cache their worms later, once they are in private. Two explanations have been offered for such observations, and they are intensely debated. First, the birds may reason about their competitors' mental states, with a ‘theory of mind’; alternatively, they may apply behavioral rules learned in daily life. Although this second hypothesis is cognitively simpler, it does seem to require a different, ad-hoc behavioral rule for every caching and re-caching pattern exhibited by the birds. Our new theory avoids this drawback by explaining a large variety of patterns as side-effects of stress and the resulting memory errors. Inspired by experimental data, we assume that re-caching is not motivated by a deliberate effort to safeguard specific caches from theft, but by a general desire to cache more. This desire is brought on by stress, which is determined by the presence and dominance of onlookers, and by unsuccessful recovery attempts. We study this theory in two experiments similar to those done with real birds with a kind of ‘virtual bird’, whose behavior depends on a set of basic assumptions about corvid cognition, and a well-established model of human memory. Our results show that the ‘virtual bird’ acts as the real birds did; its re-caching reflects whether it has been watched, how dominant its onlooker was, and how close to that onlooker it has cached. This happens even though it cannot attribute mental states, and it has only a single behavioral rule assumed to be previously learned. Thus, our simulations indicate that corvid re-caching can be explained without sophisticated social cognition. Given our specific predictions, our theory can easily be tested empirically.”

– Which is a perfect transition to what makes the minds of humans so special: While we are probably not the only animals that can simulate other minds inside our mind – humans with our 86 billion neurons have taken it to extraordinary depths.

With “simulating other minds” we are mostly referring to the concept of “theory of mind”, i.e. the cognitive ability to understand that others have their own mental states, such as beliefs, intentions, desires, and emotions, which are separate from one's own. Humans clearly have a strong theory of mind. Various highly intelligent animals such as primates exhibit behaviours suggesting a theory of mind. However, there is an ongoing debate as to whether these behaviours constitute sufficient evidence to support a theory of mind in non-human animals. Below, we cite some literature reviews on the topic.

We have also received the following insight from an expert on animal cognition: “The big debate is whether animals are readers of minds or simply readers of behavior. The idea that they can do mind reading is not accepted by everyone.”

#Krupenye C, Call J. Theory of mind in animals: Current and future directions. WIREs Cogn Sci. 2019

https://wires.onlinelibrary.wiley.com/doi/10.1002/wcs.1503 

Quote: “Theory of mind (ToM; a.k.a., mind-reading, mentalizing, mental-state attribution, and perspective-taking) is the ability to ascribe mental states, such as desires and beliefs, to others, and it is central to the unique forms of communication, cooperation, and culture that define our species. As a result, for 40 years, researchers have endeavored to determine whether ToM is itself unique to humans. Investigations in other species (e.g., apes, monkeys, corvids) are essential to understand the mechanistic underpinnings and evolutionary origins of this capacity across taxa, including humans. We review the literature on ToM in nonhuman animals, suggesting that some species share foundational social cognitive mechanisms with humans. We focus principally on innovations of the last decade and pressing directions for future work. Underexplored types of social cognition have been targeted, including ascription of mental states, such as desires and beliefs, that require simultaneously representing one's own and another's conflicting motives or views of the world. Ongoing efforts probe the motivational facets of ToM, how flexibly animals can recruit social cognitive skills across cooperative and competitive settings, and appropriate motivational contexts for comparative inquiry. Finally, novel methodological and empirical approaches have brought new species (e.g., lemurs, dogs) into the lab, implemented critical controls to elucidate underlying mechanisms, and contributed powerful new techniques (e.g., looking-time, eye-tracking) that open the door to unexplored approaches for studying animal minds. These innovations in cognition, motivation, and method promise fruitful progress in the years to come, in understanding the nature and origin of ToM in humans and other species.”

#van der Vaart, E., Hemelrijk, C.K. ‘Theory of mind’ in animals: ways to make progress. Synthese (2014).

https://doi.org/10.1007/s11229-012-0170-3

Quote: “Whether any non-human animal can attribute mental states to others remains the subject of extensive debate. This despite the fact that several species have behaved as if they have a ‘theory of mind’ in various behavioral tasks. In this paper, we review the reasons of skeptics for their doubts: That existing experimental setups cannot distinguish between ‘mind readers’ and ‘behavior readers’, that results that seem to indicate ‘theory of mind’ may come from studies that are insufficiently controlled, and that our own intuitive biases may lead us to interpret behavior more ‘cognitively’ than is necessary. The merits of each claim and suggested solution are weighed. The conclusion is that while it is true that existing setups cannot conclusively demonstrate ‘theory of mind’ in non-human animals, focusing on this fact is unlikely to be productive. Instead, the more interesting question is how sophisticated their social reasoning can be, whether it is about ‘unobservable inner experiences’ or not. Therefore, it is important to address concerns about the setup and interpretation of specific experiments. To alleviate the impact of intuitive biases, various strategies have been proposed in the literature. These include a deeper understanding of associative learning, a better knowledge of the limited ‘theory of mind’ humans actually use, and thinking of animal cognition in an embodied, embedded way; that is, being aware that constraints outside of the brain, and outside of the body, may naturally predispose individuals to produce behavior that looks smart without requiring complex cognition. To enable this kind of thinking, a powerful methodological tool is advocated: Computational modeling, namely agent-based modeling and, particularly, cognitive modeling. By explicitly simulating the rules and representations that underlie animal performance on specific tasks, it becomes much easier to look past one’s own biases and to see what cognitive processes might actually be occurring.”

#Penn DC, Povinelli DJ. On the lack of evidence that non-human animals possess anything remotely resembling a 'theory of mind'. Philos Trans R Soc Lond B Biol Sci. 2007

https://pmc.ncbi.nlm.nih.gov/articles/PMC2346530/ 

Quote: “After decades of effort by some of our brightest human and non-human minds, there is still little consensus on whether or not non-human animals understand anything about the unobservable mental states of other animals or even what it would mean for a non-verbal animal to understand the concept of a ‘mental state’. In the present paper, we confront four related and contentious questions head-on: (i) What exactly would it mean for a non-verbal organism to have an ‘understanding’ or a ‘representation’ of another animal's mental state? (ii) What should (and should not) count as compelling empirical evidence that a non-verbal cognitive agent has a system for understanding or forming representations about mental states in a functionally adaptive manner? (iii) Why have the kind of experimental protocols that are currently in vogue failed to produce compelling evidence that non-human animals possess anything even remotely resembling a theory of mind? (iv) What kind of experiments could, at least in principle, provide compelling evidence for such a system in a non-verbal organism?”

There are different estimates regarding the total number of neurons in the human brain, and the sample sizes for the most reliable estimates, i.e. the number of complete brains analyzed at this level of detail, are relatively small. Most commonly used in the neuroscientific literature is an estimate of roughly 86 billion neurons per human brain on average, which we also chose to use here. However, there are differences between individuals, and future studies may reveal more accurate numbers. Below, we cite a 2025 review summarizing the current state of the literature.

#Goriely A. Eighty-six billion and counting: do we know the number of neurons in the human brain? Brain. 2025

https://academic.oup.com/brain/article/148/3/689/7909879

Quote: “

• Experiments have shown variations between 62 and 94 bn neurons in the human brain (n = 9).

• An experimental study on the number of neurons suggests an average between 73 and 99 bn neurons in the healthy male human brain (n = 4).

• An experimental study on the number of neurons suggests an average between 61 and 73 bn neurons in the healthy female human brain (n = 5).

Clearly, none of these statements is satisfactory or as catchy as ‘the human brain has 86 billion neurons’. Yet, they are the true reflection of our knowledge. We cannot present a more precise assertion without more available data.”

– But around 18–24 months a baby becomes able to recognise itself in a mirror. It realizes that something that seems to be “other” is actually itself – which is usually a delightful experience for the human larvae.

#Adolph KE, Tamis-LeMonda CS. Self-recognition: From touching the body to knowing the self. Curr Biol. 2024

https://www.cell.com/current-biology/fulltext/S0960-9822(24)00149-0 

Quote: “Prior work shows protracted development in the mirror-mark test. Young infants fail the test by directing behaviors toward the reflected image, not toward themselves. They behave socially toward their image by touching and kissing the mirror or trying to find the reflected baby behind the mirror14. Somewhere between 18 and 24 months of age, most infants succeed in the mirror-mark test2,3,4. At this point, infants touch or attempt to remove the mark on their face. These seemingly self-conscious behaviors — sometimes accompanied by expressions of embarrassment — are traditionally interpreted as markers of an abstract self-concept, replete with a mental representation of the self as an object that can be viewed and evaluated by self and others2,7,15.”

– This awareness of the minds of others may be one of the origins of our moral conscience and a huge reason why we are able to live in large societies, with other humans we are not related to. 

#Gray, K., Young, L., & Waytz, A. Mind Perception Is the Essence of Morality. Psychological Inquiry. (2012)

https://doi.org/10.1080/1047840X.2012.651387 

Quote: “Mind perception entails ascribing mental capacities to other entities, whereas moral judgment entails labeling entities as good or bad or actions as right or wrong. We suggest that mind perception is the essence of moral judgment. In particular, we suggest that moral judgment is rooted in a cognitive template of two perceived minds—a moral dyad of an intentional agent and a suffering moral patient. Diverse lines of research support dyadic morality. First, perceptions of mind are linked to moral judgments: dimensions of mind perception (agency and experience) map onto moral types (agents and patients), and deficits of mind perception correspond to difficulties with moral judgment. Second, not only are moral judgments sensitive to perceived agency and experience, but all moral transgressions are fundamentally understood as agency plus experienced suffering—that is, interpersonal harm—even ostensibly harmless acts such as purity violations. Third, dyadic morality uniquely accounts for the phenomena of dyadic completion (seeing agents in response to patients, and vice versa), and moral typecasting (characterizing others as either moral agents or moral patients). Discussion also explores how mind perception can unify morality across explanatory levels, how a dyadic template of morality may be developmentally acquired, and future directions.”

#Bzdok, D., Schilbach, L., Vogeley, K., et al. Parsing the neural correlates of moral cognition: ALE meta-analysis on morality, theory of mind, and empathy. Brain Structure & Function. (2012)

https://doi.org/10.1007/s00429-012-0380-y. 

Quote: “Morally judicious behavior forms the fabric of human sociality. Here, we sought to investigate neural activity associated with different facets of moral thought. Previous research suggests that the cognitive and emotional sources of moral decisions might be closely related to theory of mind, an abstract-cognitive skill, and empathy, a rapid-emotional skill. That is, moral decisions are thought to crucially refer to other persons' representation of intentions and behavioral outcomes as well as (vicariously experienced) emotional states. We thus hypothesized that moral decisions might be implemented in brain areas engaged in 'theory of mind' and empathy. This assumption was tested by conducting a large-scale activation likelihood estimation (ALE) meta-analysis of neuroimaging studies, which assessed 2,607 peak coordinates from 247 experiments in 1,790 participants. The brain areas that were consistently involved in moral decisions showed more convergence with the ALE analysis targeting theory of mind versus empathy. More specifically, the neurotopographical overlap between morality and empathy disfavors a role of affective sharing during moral decisions. Ultimately, our results provide evidence that the neural network underlying moral decisions is probably domain-global and might be dissociable into cognitive and affective sub-systems.”

#Young, L., & Waytz, A. Mind attribution is for morality. In S. Baron-Cohen, H. Tager-Flusberg, & M. V. Lombardo (Eds.), Understanding other minds: Perspectives from developmental social neuroscience (3rd ed., pp. 93–103). Oxford University Press. (2013)

https://doi.org/10.1093/acprof:oso/9780199692972.003.0006

Quote: “The novel claim we make in this chapter is that the primary service of mental state reasoning may be for moral cognition and behavior, broadly construed. In particular, the cognitive capacities for mental state reasoning become less relevant when morality is not at stake. We are motivated to understand the actions of relevant moral agents, to predict people's actions when those actions affect us, directly or indirectly, and to evaluate moral agents as current or future allies or enemies. Computations like these crucially elicit mental state reasoning. In this chapter, we will therefore review the literature on mental state reasoning for moral cognition—both for judging other moral actors, from the position of "judge" on high, and also for figuring out, as "actors" on the ground, so to speak, who might help us or hurt us, to whom we have moral obligations (for helping or, minimally, not hurting), and whom we ought to trust or avoid.”

– And may have spawned one of our most charming obsessions: storytelling. 

#Garcia-Pelegrin E, Wilkins C and Clayton NS. The Ape That Lived to Tell the Tale. The Evolution of the Art of Storytelling and Its Relationship to Mental Time Travel and Theory of Mind. Front. Psychol. (2021)

https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2021.755783/full

Quote: “Engaging in the art of creating and telling stories is a defining behaviour of humankind. Humans have been sharing stories with each other, with and without words, since the dawn of recorded history, but the cognitive foundations of the behaviour can be traced deeper into our past. The emergence of stories can be strongly linked to Mental Time Travel (the ability to recall the past and imagine the future) and plays a key role in our ability to communicate past, present and future scenarios with other individuals, within and beyond our lifetimes. Stories are products engraved within the concept of time, constructed to elucidate the past experiences of the self, but designed with the future in mind, thus imparting lessons of such experiences to the receiver. By being privy to the experiences of others, humans can imagine themselves in a similar position to the protagonist of the story, thus mentally learning from an experience they might have never encountered other than in the mind's eye. Evolutionary Psychology investigates how the engagement in artistic endeavours by our ancestors in the Pleistocene granted them an advantage when confronted with obstacles that challenged their survival or reproductive fitness and questions whether art is an adaptation of the human mind or a spandrel of other cognitive adaptations. However, little attention has been placed on the cognitive abilities that might have been imperative for the development of art. Here, we examine the relationship between art, storytelling, Mental Time Travel and Theory of Mind (i.e., the ability to attribute mental states to others). We suggest that Mental Time Travel played a key role in the development of storytelling because through stories, humans can fundamentally transcend their present condition, by being able to imagine different times, separate realities, and place themselves and others anywhere within the time space continuum. We argue that the development of a Theory of Mind also sparked storytelling practises in humans as a method of diffusing the past experiences of the self to others whilst enabling the receiver to dissociate between the past experiences of others and their own, and to understand them as lessons for a possible future. We propose that when artistic products rely on storytelling in form and function, they ought to be considered separate from other forms of art whose appreciation capitalise on our aesthetic preferences.”

#Mason RA, Just MA. The Role of the Theory-of-Mind Cortical Network in the Comprehension of Narratives. Lang Linguist Compass. 2009

https://pmc.ncbi.nlm.nih.gov/articles/PMC2756681/ 

Quote: “Narrative comprehension rests on the ability to understand the intentions and perceptions of various agents in a story who interact with respect to some goal or problem. Reasoning about the state of mind of another person, real or fictional, has been referred to as Theory of Mind processing. While Theory of Mind Processing was first postulated prior to the existence of neuroimaging research, fMRI studies make it possible to characterize this processing in some detail. We propose that narrative comprehension makes use of some of the neural substrate of Theory of Mind reasoning, evoking what is referred to as a protagonist perspective network. The main cortical components of this protagonist-based network are the dorsomedial prefrontal cortex and the right temporo-parietal junction. The article discusses how these two cortical centers interact in narrative comprehension but still play distinguishable roles, and how the interaction between the two centers is disrupted in individuals with autism.”