Graziano Lab

  Department of Psychology, Princeton University, Princeton, NJ 08544

Michael Graziano, Phone : (609) 258-7555, Fax : (609)258-1113, Email :


Consciousness Research

Past Research





Graziano's CV

Psych Home


at Princeton

Topics Studied in the Graziano Lab...

Over the past 25 years I contributed to three main topics in neuroscience. These contributions include studying how neurons in specific regions of the brain monitor the space near the body; discovering how the motor cortex may coordinate complex, ethologically relevant actions, functioning at a much more complex level than previously suspected; and proposing a novel theory of the brain basis of awareness.


1. The space near the body.

Through the 1990s I wrote a series of papers with Charles Gross on the coding of multisensory space near the body and how that representation changes flexibly as the body moves. This work rested on previous work by Rizzolatti and colleagues in the premotor cortex (1,2), by Hyvarinen and colleagues in the parietal cortex (3,4), and by Goldberg and colleagues in parietal area VIP (5).

We studied multisensory neurons in macaque monkeys in a set of anatomically connected brain areas including the putamen, the premotor cortex, parietal area 7b, and parietal area VIP (6-21). These neurons encoded the space immediately surrounding the body. Each multisensory neuron responded to a touch within a specific tactile receptive field on the body surface and also to visual stimuli near the tactile receptive field. The visual receptive field was therefore a region of space affixed to the relevant body part. Some neurons responded to sound sources near the tactile receptive field (16). Some neurons also responded mnemonically, becoming active when a part of the body moved in the dark and approached the remembered location of an object (13). The activity of these multisensory neurons therefore signaled the presence of an object near or touching a part of the body, regardless of whether the object was felt, seen, heard, or remembered.

In the premotor cortex, a high proportion of these multisensory neurons showed complex spatial properties (8,12,13,15). If the neuron had a tactile receptive field on the arm, it would respond to visual stimuli near that part of the arm, regardless of where the arm was placed in the animal’s visual field. If the tactile receptive field was on the face, the neuron would respond to visual stimuli near that part of the face regardless of the angle of the head or the direction of the animal’s gaze. These visual receptive fields behaved like inflated balloons glued to the skin. Their spatial properties were radically different from the standard retina-centered receptive fields found in other visual brain areas. We hypothesized that these “body-part-centered” receptive fields would be useful for guiding movement of the arm, head, and other body parts with respect to nearby objects. Body-part-centered spatial coding was common in the premotor cortex but not in the parietal cortex, suggesting some hierarchical processing.

While these multisensory neurons may contribute to many types of actions, they appear to emphasize actions that defend the body surface. In more recent experiments we found that electrical stimulation of cortical sites that contained these multisensory neurons almost always evoked a complex, coordinated movement that resembled a squinting, blocking, or defending action, protecting the part of the body where the tactile receptive field was located [21-24]. For example, a tactile receptive field on the arm was paired with a fast withdrawal of the arm, and a tactile receptive field on the cheek was paired with a fast turning away of the head, a squint, and a raising of the hand into a blocking position near the cheek. These stimulation-evoked defensive movements were stereotyped and reliable, repeatable over hundreds of trials, and could even be evoked in anesthetized animals. Chemical inhibition of these neurons produced a “nerves of steel” state in which natural defensive reactions to an air puff were reduced; chemical disinhibition produced a “super flincher” state in which any mild stimulus, such as an object gently moved toward the face, evoked an intense flinching reaction [23].

We suggested that these neurons help monitor the space near the body and coordinate actions with an emphasis on withdrawal and defensive actions. We further speculated (25-27) that this neuronal mechanism may form the basis of the protective personal space described in non-human animals by Hediger (28) and in humans by Hall (29).

The complete story of how we studied the space near the body, and the brain mechanisms that process it, is told in my book, The Spaces Between Us (Oxford University Press, 2018). This book is aimed at a general audience rather than a scholarly audience.


2. Action map in the motor cortex.

In the 2000s, my lab’s focus expanded from peripersonal space and defensive actions to the manner in which other complex, ethologically relevant actions may be organized in the cortical motor system.

Since the discovery of motor cortex more than 140 years ago by Fritsch and Hitzig (30), three prominent views of its function have been proposed. In one view, the motor cortex is a homunculus-like map of muscles, though the map may be partially overlapping and fractured in its somatotopy. In a second view, the motor cortex functions through a population of spatially tuned neurons. These neurons collectively pool or sum their outputs, thereby specifying an arm movement. Whether it is hand direction in particular that is specified, or some other parameter of movement such as speed or force, became controversial and was never fully resolved.

In the past decade we proposed a third view, the action map view of the motor cortex (22-26, 31-41). In the action map hypothesis, the motor cortex is organized around the common, useful behaviors performed by the animal. Different categories of action, such as hand-to-mouth actions, manipulation of objects in central space, reaching, or defensive actions, are relatively emphasized in different regions of cortex. In this view, to understand the highly non-uniform, complex organization of motor cortex, it is necessary to go beyond the musculature of the animal’s body and beyond a few movement parameters such as direction or speed. One must study the actual movement repertoire and how it is mapped onto the cortical surface.

We applied electrical microstimulation to the macaque motor cortex on a behaviorally relevant time scale. Most of our studies used electrical stimulation for half a second, approximately matching the duration of a monkey’s typical reach. While electrical stimulation on a behavioral time scale, even up to several minutes at a time, was common in the study of other brain systems, it was not used systematically in the motor cortex. Most previous mapping studies of the motor cortex used brief stimulation, for example 50 ms or less, and obtained brief muscle twitches arranged in a rough and partially overlapping map of muscles on the cortical surface. In our studies, the overlapping representations of muscles took on a new significance. The longer stimulation trains evoked complex movements that were coordinated among many muscles and joints.

For example, stimulation in one cortical region always caused the hand to close in a grip, the arm to bring the hand to the mouth, and the mouth to open (22,24). Stimulation in another region caused the grip to open, the palm to face away from the body, and the arm to extend, as if the monkey were reaching to grasp an object (22,24). Other regions evoked other complex movements. The main categories of the animal’s behavioral repertoire seemed to be mapped onto the cortical sheet, and the organization of the map was consistent from one monkey to the next.

We found convergent evidence to support the action map interpretation by comparing the electrically-evoked movements to the movement repertoire of monkeys in their home cages, in zoos, and in the wild (26,33,42); by comparing the effects of stimulation to the properties of single neurons during normal movement (21,36,38); by using chemical inhibition and disinhibition of cortical sites (23); and by using computational modeling (37,39,40). The computational models showed that when the complex movement repertoire of a monkey is arranged optimally on a flattened map, with similar movements represented near each other, the map closely resembles the actual arrangement of the motor cortex obtained in our experiments (37,39,40).

Other researchers have since found similar, complex actions and an ethological organization to motor cortical regions in monkeys, humans, prosimians, cats, rats, and mice (43-53). The evidence is therefore strong and increasing: the motor cortex is organized at least partly as an action map.

The homunculus — the textbook account of the motor cortex for the past century and a half — is not wrong, but is not complete and may not be the most fundamental principle of organization. Many of the complexities of the motor cortex, its partial maps of the body, its blurred borders, and its multiple areas with somewhat different mixtures of properties, may be a result of rendering a complex movement repertoire onto the cortical surface. Rather than contradicting more traditional views of motor cortex, the view of an action map may help explain why motor cortex is divided into heterogeneous fields and why its somatotopy exhibits so much overlap. A relatively simple underlying principle, a flattening or rendering of the movement repertoire onto the cortical surface, may be at work.


3. Brain basis of awareness

Most recently my lab has begun to study the brain basis of awareness. This work grew directly out of my work for many years on the body schema, the brain’s representation of the body. The theory of awareness that I proposed is different from many previous theories in several key respects. It is rational, experimentally testable, and falsifiable. It is not a theory of how the brain generates a non-physical and unmeasureable feeling. It is a theory of how, and for what adaptive advantage, brains arrive at that self-description. Brains arrive at many physically incoherent models – such as the brain’s model of white light as pure brightness with no color, a short-cut model that is not physically accurate. Why does the brain construct a self-descriptive model in which it has an awareness, or a non-physical subjective experience?

We proposed that awareness is the brain’s schematic and somewhat inaccurate model of attention (54-59). Control systems are greatly enhanced when they have access to a model of the thing they control, a now well-established principle of control engineering. Just as the brain constructs the body schema, an approximate internal model of the body used to help control the body, according to the ‘attention schema’ theory the brain constructs an approximate model of the process of attention and employs it in controlling attention. In this proposal, the brain not only pays attention to item X in the sense of enhancing that signal relative to other signals, but also constructs a rough model of that process, attributing to itself a state of being aware of X. That model can at least sometimes be cognitively accessed and reported, leading to the ubiquitous claim that we have an awareness of things in us and around us. This model of attention lacks any information about the neuronal, physical mechanisms of attention, and therefore when the brain accesses the model it concludes not only that it is aware of X, but also that its awareness is a non-physical essence. If this theory is correct, then awareness probably gradually co-evolved with attention, beginning in some form with the origin of sophisticated nervous systems half a billion years ago.

Although attention and awareness may seem similar, two aspects of them are consistent with the hypothesis that one is a schematic representation of the other. First, attention is a physical process of signal enhancement that occurs in the brain whereas awareness is in the form of knowledge that the brain can potentially report. Second, although the content of awareness and the content of attention overlap much of the time, the two can be dissociated. It is sometimes possible to attend to a stimulus without being aware of it (60-63). In that case, the brain’s reportable knowledge about what is currently “in mind” becomes dissociated from what it is actually attending to, suggesting that like all representations constructed by the brain, awareness is an imperfect model.

My book, Consciousness and the Social Brain (54), lays out the complete “attention schema” theory and its relation to the existing data and previous work. My lab has begun to publish a growing series of papers to test the theory experimentally and explore its implications (55-59). One of the more intriguing implications of the theory is that the construct of awareness might be used to model other people’s attentional state as well as one’s own. In that hypothesis, we attribute awareness to other people using the same mechanisms that we use to attribute it to ourselves. In an initial experiment along this line of research we found evidence that, at least in humans, there may be a fundamental connection between attributing awareness to oneself and the social attribution of awareness to others (59).

The attention schema theory has gained recognition since my book on the topic was published in 2013 (54). It is a promising scientific, rational explanation for how and why brains arrive at the remarkable conclusion that they have an inner subjective experience of things.


3. Other topics

My work has also touched on other topics including how humans can learn to navigate through a computer-generated environment that contains four spatial dimensions instead of the usual three (64); and how the visual system processes flow fields such as rotations, spirals, and expansions (65).



[1] Rizzolatti G, Scandolara C, Matelli M, Gentilucci M (1981). Afferent properties of periarcuate neurons in macaque monkeys. II. Visual responses. Beh Brain Res 2: 147–163.

[2] Fogassi L, Gallese V, di Pellegrino G, Fadiga L, Gentilucci M, Luppino M, Pedotti A Rizzolatti G (1992). Space coding by premotor cortex. Exp Brain Res 89: 686–690.

[3] Hyvarinen J (1981). Regional distribution of functions in parietal association are 7 of the monkey. Brain Res 206: 287–303.

[4] Hyvarinen J, Poranen A (1974). Function of the parietal associative area 7 as revealed from cellular discharges in alert monkeys. Brain 97: 673–692.

[5] Colby CL, Duhamel JR, Goldberg ME (1993). Ventral intraparietal area of the macaque: anatomic location and visual response properties. J Neurophysiol 69: 902-914.

[6] Graziano MSA, Gross CG (1993). A bimodal map of space: somatosensory receptive fields in the macaque putamen with corresponding visual receptive fields. Exp Brain Res 97: 96-109.

[7] Graziano MSA, Gross CG (1994). The representation of extrapersonal space: A possible role for bimodal, visual-tactile neurons, in The Cognitive Neurosciences, M.S. Gazzaniga, Ed. (MIT Press, Cambridge): pp. 1021 -1034.

[8] Graziano MSA, Yap GS, Gross CG (1994). Coding of visual space by pre-motor neurons. Science 266: 1054-1057.

[9] Graziano MSA, Gross CG (1994). Mapping space with neurons. Cur Dir Psychol Sci 3: 164-167.

[10] Gross CG, Graziano MSA (1995). Multiple representations of space in the brain. The Neuroscientist 1: 43-50.

[11] Graziano MSA, Gross CG (1996). Multiple pathways for processing visual space. In Attention and Performance XVI. Edited by T. Inui and J.L. McClelland. MIT Press, Cambridge MA, pp.181-207.

[12] Graziano MSA, Hu XT, Gross CG (1997). Visuo-spatial properties of ventral premotor cortex. J Neurophys, 77: 2268-2292.

[13] Graziano MSA, Hu XT, Gross CG (1997). Coding the locations of objects in the dark. Science 277: 239-241.

[14] Graziano MSA, Gross CG (1998). Spatial maps for the control of movement. Cur Opin Neurobiol 8: 195 -201.

[15] Graziano MSA, Gross CG (1998). Visual responses with and without fixation: Neurons in premotor cortex encode spatial locations independently of eye position. Exp Brain Res 118: 373-380.

[16] Graziano MSA, Reiss LAJ, Gross CG (1999). A neuronal representation of the location of nearby sounds. Nature 397: 428-430.

[17] Graziano MSA (1999). Where is my arm? The relative role of vision and proprioception in the neuronal representation of limb position. PNAS USA 96: 10418-10421.

[18] Graziano MSA, Gandhi S (2000). Location of the polysensory zone in the precentral gyrus of anesthetized monkeys. Exp Brain Res 135: 259-266.

[19] Graziano MSA, Cooke DF, Taylor CSR (2000). Coding the location of the arm by sight. Science 290: 1782-1786.

[20] Graziano MSA, Alisharan SA, Hu X, Gross CG (2002). The clothing effect: Tactile neurons in the precental gyrus do not respond to the touch of the familiar primate chair. PNAS USA 99: 11930-11933.

[21] Cooke DF, Graziano MSA (2004). Sensorimotor integration in the precentral gyrus: Polysensory neurons and defensive movements. J Neuroph 91: 1648-1660.

[22] Graziano MSA, Taylor CSR, Moore T (2002). Complex movements evoked by microstimulation of precentral cortex. Neuron 34: 841-851.

[23] Cooke DF, Graziano MSA (2004). Super-flinchers and nerves of steel: Defensive movements altered by chemical manipulation of a cortical motor area. Neuron 43: 585-593.

[24] Graziano MSA, Aflalo T, Cooke DF (2005). Arm movements evoked by electrical stimulation in the motor cortex of monkeys. J Neurophys 94: 4209-4223.

[25] Graziano MSA, Cooke DF (2006). Parieto-frontal interactions, personal space, and defensive behavior. Neuropsychologia 44: 845-859.

[26] Graziano MSA (2008). The Intelligent Movement Machine: An Ethological Perspective on the Primate Motor System. Oxford University Press, Oxford UK.

[27] Graziano MSA (2014, in press). A new view of the motor cortex and its relation to social behavior. In Shared Representations: Sensorimotor Foundations of Social Life. Eds. Obhi and Cross. Cambridge University Press.

[28] Hediger H (1955). Studies of the Psychology and Behavior of Captive Animals in Zoos and Circuses. New York, NY: Criterion Books.

[29] Hall ET (1966). The Hidden Dimension. Garden City, New York: Anchor Books.

[30] Fritsch G, Hitzig E (1870). Uber die elektrishe Erregbarkeit des Grosshirns. Arch. f. Anat., Physiol und wissenchaftl. Mediz., Leipzig, 300-332. (On the electrical excitability of the cerebrum.) Translated by von Bonin G. In: Some Papers On The Cerebral Cortex. 1960. Von Bonin G (Ed). Springfield IL: Charles Thomas Publisher, pp. 73-96.

[31] Graziano MSA, Taylor CSR, Moore T, Cooke DF (2002). The cortical control of movement revisited. Neuron 36: 349-362.

[32] Graziano MSA, Taylor CSR, and Moore T (2002). Probing cortical function with electrical stimulation. Nat Neurosci 5: 921.

[33] Graziano MSA, Cooke DF, Taylor CSR, Moore T (2004). Distribution of hand location in monkeys during spontaneous behavior. Exp Brain Res 155: 30-36.

[34] Graziano MSA, Patel KT, Taylor CSR (2004). Mapping from motor cortex to biceps and triceps altered by elbow angle. J Neurophys 92: 395-407.

[35] Graziano MSA (2006). The organization of behavioral repertoire in motor cortex. Ann Rev Neurosci 29: 105-134.

[36] Aflalo TN, Graziano MSA (2006). Partial tuning of motor cortex neurons to final posture in a free-moving paradigm. PNAS USA 103: 2909-2914.

[37] Aflalo TN, Graziano MSA (2006). Possible origins of the complex topographic organization of motor cortex: reduction of a multidimensional space onto a 2-dimensional array. J Neurosci 26: 6288-6297.

[38] Aflalo TN, Graziano MSA (2007). Relationship between unconstrained arm movement and single neuron firing in the macaque motor cortex. J Neurosci 27: 2760-2780.

[39] Graziano MSA, Aflalo TN (2007). Rethinking cortical organization: Moving away from discrete areas arranged in hierarchies. The Neuroscientist 13: 138-147.

[40] Graziano MSA, Aflalo TN (2007). Mapping behavioral repertoire onto the cortex. Neuron 56: 239-251.

[41] Meier JD, Aflalo TN, Kastner S, Graziano MSA (2008). Complex organization of human primary motor cortex: A high resolution fMRI study. J Neurophys 100: 1800-1812.

[42] Macfarlaine N, Graziano MSA (2009). Diversity of Grip in Macaca mulatta. Exp Brain Res 197: 255-268.

[43] Stepniewska I, Fang PC, and Kaas JH (2005). Microstimulation reveals specialized subregions for different complex movements in posterior parietal cortex of prosimian galagos. PNAS USA 102: 4878-4883.

[44] Stepniewska I, Fang PC, Kaas JH (2009). Organization of the posterior parietal cortex in galagos: I. Functional zones identified by microstimulation. J Comp Neurol 517: 765-782.

[45] Ethier C, Brizzi L, Darling WG, Capaday C (2006). Linear summation of cat motor cortex outputs. J Neurosci 26: 5574-5581.

[46] Overduin SA, d'Avella A, Carmena JM, Bizzi E (2012). Microstimulation activates a handful of muscle synergies. Neuron 76: 1071-1077.

[47] Van Acker GM 3rd, Amundsen SL, Messamore WG, Zhang HY, Luchies CW, Kovac A, Cheney PD (2013). Effective intracortical microstimulation parameters for evoking forelimb movements to stable spatial end-points from primary motor cortex. J Neurophysiol 110: 1180-1189.

[48] Caruana F, Jezzini A, Sbriscia-Fioretti B, Rizzolatti G, Gallese V (2011). Emotional and social behaviors elicited by electrical stimulation of the insula in the macaque monkey. Curr Biol 21: 195-199.

[49] Desmurget M, Song Z, Mottolese C, Sirigu A (2013). Re-establishing the merits of electrical brain stimulation. Trends Cogn Sci 17: 442-449.

[50] Haiss F, Schwarz C (2005). Spatial segregation of different modes of movement control in the whisker representation of rat primary motor cortex. J Neurosci 25: 1579-1587.

[51] Ramanathan D, Conner JM, Tuszynski MH (2006). A form of motor cortical plasticity that correlates with recovery of function after brain injury. PNAS USA 103: 11370-11375.

[52] Harrison TC, Ayling OG, Murphy TH (2012). Distinct cortical circuit mechanisms for complex forelimb movement and motor map topography. Neuron 74: 397-409.

[53] Bonazzi L, Viaro R, Lodi E, Canto R, Bonifazzi C, Franchi G (2013). Complex movement topography and extrinsic space representation in the rat forelimb motor cortex as defined by long-duration intracortical microstimulation. J Neurosci 33: 2097-2107.

[54] Graziano MSA (2013). Consciousness and the Social Brain. Oxford University Press, Oxford UK.

[55] Graziano MSA, Webb TW (2014). A mechanistic theory of consciousness. International Journal of Machine Consciousness, 2, DOI: 10.1142/S1793843014400174.

[56] Graziano MSA (2014). Speculations on the evolution of awareness. Journal of Cognitive Neuroscience, 26, 1300-1304.

[57] Graziano MSA, Kastner S (2011). Human consciousness and its relationship to social neuroscience: A novel hypothesis. Cog Neurosci 2: 98-113.

[58] Graziano MSA and Kastner S (2011). Awareness as a perceptual model of attention. Cog Neurosci 2: 125-133.

[59] Kelly YT, Webb TW, Meier JD, Arcaro MJ, Graziano MSA (2014). Attributing awareness to oneself and to others. Proceedings of the National Academy of Sciences 111: 5012-5017.

[60] Baars BJ (1997). Some essential differences between consciousness and attention, perception, and working memory. Conscious Cogn 6: 363-371.

[61] Kentridge RW, Heywood CA, Weiskrantz L (1999). Attention without awareness in blindsight. Proc Biol Sci 266: 1805-1811.

[62] Lamme VA (2003). Why visual attention and awareness are different. Trends Cogn Sci 7: 12-18.

[63] Koch C, Tsuchiya N (2007). Attention and consciousness: two distinct brain processes. Trends Cogn Sci 11: 16-22.

[64] Aflalo TN, Graziano MSA (2008). Four dimensional spatial reasoning in humans. J Exp Psychol: Hum Perc Perf 34: 1066-1077.

[65] Graziano MSA, Andersen RA, Snowden R (1994). Tuning of MST neurons to spiral stimuli. J Neuroscience 14: 54-67.