Attentional shift (or shift of attention) occurs when directing attention to a point to increase the efficiency of processing that point and includes inhibition to decrease attentional resources to unwanted or irrelevant inputs. Shifting of attention is needed to allocate attentional resources to more efficiently process information from a stimulus. Research has shown that when an object or area is attended, processing operates more efficiently. Task switching costs occur when performance on a task suffers due to the increased effort added in shifting attention. There are competing theories that attempt to explain why and how attention is shifted as well as how attention is moved through space.
Here, we update our 1990 Annual Review of Neuroscience article, “The Attention System of the Human Brain.” The framework presented in the original article has helped to integrate behavioral, systems, cellular, and molecular approaches to common problems in attention research. Our framework has been both elaborated and expanded in subsequent years. Research on orienting and executive functions has supported the addition of new networks of brain regions. Developmental studies have shown important changes in control systems between infancy and childhood. In some cases, evidence has supported the role of specific genetic variations, often in conjunction with experience, that account for some of the individual differences in the efficiency of attentional networks. The findings have led to increased understanding of aspects of pathology and to some new interventions.
Published on May 16, 2013
The possibility that our personal memory can play strange tricks on us has been the focus of Giuliana’s research for many years. Her work, based at the University of Hull, has also examined the cognitive and behavioural consequences of suggestion. Giuliana is a recognised memory expert and has recently been part of Channel 4’s documentary The Boy Who Can’t Forget where she examined Aurelien, a boy who claims he can remember every day of his life. This condition is considered impossible by current models of memory.
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Imagine the brain could reboot, updating its damaged cells with new, improved units. That may sound like science fiction — but it’s a potential reality scientists are investigating right now. Ralitsa Petrova details the science behind neurogenesis and explains how we might harness it to reverse diseases like Alzheimer’s and Parkinson’s.
Dunbar’s number is a suggested cognitive limit to the number of people with whom one can maintain stable social relationships. These are relationships in which an individualknows who each person is and how each person relates to every other person. This number was first proposed in the 1990s by British anthropologist Robin Dunbar, who found a correlation between primate brain size and average social group size. By using the average human brain size and extrapolating from the results of primates, he proposed that humans can only comfortably maintain 150 stable relationships. Proponents assert that numbers larger than this generally require more restrictive rules, laws, and enforced norms to maintain a stable, cohesive group. It has been proposed to lie between 100 and 250, with a commonly used value of 150. Dunbar’s number states the number of people one knows and keeps social contact with, and it does not include the number of people known personally with a ceased social relationship, nor people just generally known with a lack of persistent social relationship, a number which might be much higher and likely depends on long-term memory size.
Dunbar theorized that “this limit is a direct function of relative neocortex size, and that this in turn limits group size … the limit imposed by neocortical processing capacity is simply on the number of individuals with whom a stable inter-personal relationship can be maintained.” On the periphery, the number also includes past colleagues, such as high schoolfriends, with whom a person would want to reacquaint himself if they met again.
Dunbar has argued that 150 would be the mean group size only for communities with a very high incentive to remain together. For a group of this size to remain cohesive, Dunbar speculated that as much as 42% of the group’s time would have to be devoted to social grooming. Correspondingly, only groups under intense survival pressure.
Dunbar, in Grooming, Gossip, and the Evolution of Language, proposes furthermore that language may have arisen as a “cheap” means of social grooming, allowing early humans to maintain social cohesion efficiently. Without language, Dunbar speculates, humans would have to expend nearly half their time on social grooming, which would have made productive, cooperative effort nearly impossible. Language may have allowed societies to remain cohesive, while reducing the need for physical and social intimacy.
Dunbar’s number has since become of interest in anthropology, evolutionary psychology, statistics, and business management. For example, developers of social software are interested in it, as they need to know the size of social networks their software needs to take into account; and in the modern military, operational psychologists seek such data to support or refute policies related to maintaining or improving unit cohesion and morale. A recent study has suggested that Dunbar’s number is applicable to online social networks and communication networks (mobile phone).
Philip Lieberman argues that since band societies of approximately 30-50 people are bounded by nutritional limitations to what group sizes can be fed without at least rudimentary agriculture, big human brains consuming more nutrients than ape brains, group sizes of approximately 150 cannot have been selected for in paleolithic humans.Brains much smaller than human or even mammalian brains are also known to be able to support social relationships, including social insects with hierachies where each individual knows its place (such as the paper wasp with its societies of approximately 80 individuals ) and computer-simulated virtual autonomous agents with simple reaction programming emulating what is referred to in primatology as “ape politics”.
The ability to communicate through spoken language may be the trait that best sets humans apart from other animals. Last year researchers identified the first gene implicated in the ability to speak. This week, a team shows that the human version of this gene appears to date back no more than 200,000 years–about the time that anatomically modern humans emerged. The authors argue that their findings are consistent with previous speculations that the worldwide expansion of modern humans was driven by the emergence of full-blown language abilities.
The researchers who identified the gene, called FOXP2, showed that FOXP2 mutations cause a wide range of speech and language disabilities (ScienceNOW, 3 October 2002). In collaboration with part of this team, geneticist Svante Pääbo’s group at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, set about tracing the gene’s evolutionary history.
As a uniquely human trait, language has long baffled evolutionary biologists. Not until FOXP2was linked to a genetic disorder that caused problems in forming words could they even begin to study language’s roots in our genes. Soon after that discovery, a team at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, discovered that just two bases, the letters that make up DNA, distinguished the human and chimp versions ofFOXP2. To try to determine how those changes influenced the gene’s function, that group put the human version of the gene in mice. In 2009, they observed that these “humanized” mice produced more frequent and complex alarm calls, suggesting the human mutations may have been involved in the evolution of more complex speech.
When humanized mice and wild mice were put in mazes that engaged both types of learning,the humanized mice mastered the route to the reward faster than their wild counterparts, report Schreiweis, Graybiel, and their colleagues
The results suggest the human version of the FOXP2 gene may enable a quick switch to repetitive learning—an ability that could have helped infants 200,000 years ago better communicate with their parents. Better communication might have increased their odds of survival and enabled the new version of FOXP2 to spread throughout the entire human population, suggests Björn Brembs, a neurobiologist at the University of Regensburg in Germany, who was not involved with the work.
“The findings fit well with what we already knew about FOXP2 but, importantly, bridge the gap between behavioral, genetic, and evolutionary knowledge,” says Dianne Newbury, a geneticist at the Wellcome Trust Centre for Human Genetics in Oxford, U.K., who was not involved with the new research. “They help us to understand how the FOXP2 gene might have been important in the evolution of the human brain and direct us towards neural mechanisms that play a role in speech and language acquisition.”
Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field’s heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the “new AI” — focused on using statistical learning techniques to better mine and predict data — is unlikely to yield general principles about the nature of intelligent beings or about cognition.
Published on Oct 6, 2012
Steven Pinker – Psychologist, Cognitive Scientist, and Linguist at Harvard University
How did humans acquire language? In this lecture, best-selling author Steven Pinker introduces you to linguistics, the evolution of spoken language, and the debate over the existence of an innate universal grammar. He also explores why language is such a fundamental part of social relationships, human biology, and human evolution. Finally, Pinker touches on the wide variety of applications for linguistics, from improving how we teach reading and writing to how we interpret law, politics, and literature.
The Floating University
Hebbian theory is a theory in neuroscience which proposes an explanation for the adaptation of neurons in the brain during the learning process. It describes a basic mechanism for synaptic plasticity, where an increase in synaptic efficacy arises from the presynaptic cell’s repeated and persistent stimulation of the postsynaptic cell. Introduced by Donald Hebb in his 1949 book The Organization of Behavior, the theory is also called Hebb’s rule, Hebb’s postulate, and cell assembly theory. Hebb states it as follows:
“Let us assume that the persistence or repetition of a reverberatory activity (or “trace”) tends to induce lasting cellular changes that add to its stability.… When anaxon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.”
The theory is often summarized as “Cells that fire together, wire together”. However, this summary should not be taken literally. Hebb emphasized that cell A needs to ‘take part in firing’ cell B, and such causality can only occur if cell A fires just before, not at the same time as, cell B. This important aspect of causation in Hebb’s work foreshadowed what we now know about spike-timing-dependent plasticity, which requires temporal precedence. The theory attempts to explain associative or Hebbian learning, in which simultaneous activation of cells leads to pronounced increases in synaptic strength between those cells, and provides a biological basis for errorless learning methods for education and memory rehabilitation.
Hebbian theory concerns how neurons might connect themselves to become engrams. Hebb’s theories on the form and function of cell assemblies can be understood from the following:
- “The general idea is an old one, that any two cells or systems of cells that are repeatedly active at the same time will tend to become ‘associated’, so that activity in one facilitates activity in the other.” (Hebb 1949, p. 70)
- “When one cell repeatedly assists in firing another, the axon of the first cell develops synaptic knobs (or enlarges them if they already exist) in contact with the soma of the second cell.” (Hebb 1949, p. 63)
Gordon Allport posits additional ideas regarding cell assembly theory and its role in forming engrams, along the lines of the concept of auto-association, described as follows:
- “If the inputs to a system cause the same pattern of activity to occur repeatedly, the set of active elements constituting that pattern will become increasingly strongly interassociated. That is, each element will tend to turn on every other element and (with negative weights) to turn off the elements that do not form part of the pattern. To put it another way, the pattern as a whole will become ‘auto-associated’. We may call a learned (auto-associated) pattern an engram.” (Allport 1985, p. 44)
Hebbian theory has been the primary basis for the conventional view that when analyzed from a holistic level, engrams are neuronal nets or neural networks.
Experiments on Hebbian synapse modification mechanisms at the central nervous system synapses of vertebrates are much more difficult to control than are experiments with the relatively simple peripheral nervous system synapses studied in marine invertebrates. Much of the work on long-lasting synaptic changes between vertebrate neurons (such aslong-term potentiation) involves the use of non-physiological experimental stimulation of brain cells. However, some of the physiologically relevant synapse modification mechanisms that have been studied in vertebrate brains do seem to be examples of Hebbian processes. One such study reviews results from experiments that indicate that long-lasting changes in synaptic strengths can be induced by physiologically relevant synaptic activity working through both Hebbian and non-Hebbian mechanisms.
From the point of view of artificial neurons and artificial neural networks, Hebb’s principle can be described as a method of determining how to alter the weights between model neurons. The weight between two neurons increases if the two neurons activate simultaneously—and reduces if they activate separately. Nodes that tend to be either both positive or both negative at the same time have strong positive weights, while those that tend to be opposite have strong negative weights.
The following is a formulaic description of Hebbian learning: (note that many other descriptions are possible)
where is the weight of the connection from neuron to neuron and the input for neuron . Note that this is pattern learning (weights updated after every training example). In a Hopfield network, connections are set to zero if (no reflexive connections allowed). With binary neurons (activations either 0 or 1), connections would be set to 1 if the connected neurons have the same activation for a pattern.
Another formulaic description is:
where is the weight of the connection from neuron to neuron , is the number of training patterns, and the th input for neuron . This is learning by epoch (weights updated after all the training examples are presented). Again, in a Hopfield network, connections are set to zero if (no reflexive connections).
A variation of Hebbian learning that takes into account phenomena such as blocking and many other neural learning phenomena is the mathematical model of Harry Klopf. Klopf’s model reproduces a great many biological phenomena, and is also simple to implement.
Generalization and stability
Hebb’s Rule is often generalized as
or the change in the th synaptic weight is equal to a learning rate times the th input times the postsynaptic response . Often cited is the case of a linear neuron,
and the previous section’s simplification takes both the learning rate and the input weights to be 1. This version of the rule is clearly unstable, as in any network with a dominant signal the synaptic weights will increase or decrease exponentially. However, it can be shown that for any neuron model, Hebb’s rule is unstable. Therefore, network models of neurons usually employ other learning theories such as BCM theory, Oja’s rule, or the Generalized Hebbian Algorithm.
Despite the common use of Hebbian models for LTP, there exists several exceptions to Hebb’s principles and examples that demonstrate some aspects of the theory are oversimplified. One of the most well-documented of these exceptions pertains to how synaptic modification may not simply occur only between activated neurons A and B, but to neighboring neurons as well. This is due to how Hebbian modification depends on retrograde signaling in order to modify the presynaptic neuron. The compound most commonly identified as fulfilling this retrograde transmitter role is nitric oxide, which, due to its high solubility and diffusibility, often exerts effects on nearby neurons. This type of diffuse synaptic modification, known as volume learning, counters, or at least supplements, the traditional Hebbian model.
Hebbian learning account of mirror neurons
Hebbian learning and what we know about spike timing dependent plasticity has also been used in an influential theory of how mirror neurons emerge. Mirror neurons are neurons in that fire both when an individual performs an action and when the individual sees or hears  another perform a similar action. The discovery of these neurons has been very influential in explaining how individuals make sense of the actions of others, by showing that when we perceive the actions of others, we activate the motor programs we would use to perform similar actions. The activation of these motor programs then adds information to the perception and help predict what the person will do next based on the perceiver’s own motor program. A challenge has been to explain how individuals come to have neurons that respond both while performing an action and while hearing or seeing another perform similar actions. Christian Keysers and David Perrett suggested that while an individual performs a particular action, the individual will see, hear and feel himself perform the action. These re-afferent sensory signals will trigger activity in neurons responding to the sight, sound and feel of the action. Because the activity of these sensory neurons will consistently overlap in time with those of the motor neurons that caused the action, Hebbian learning would predict that the synapses connecting neurons responding to the sight, sound and feel of an action and those of the neurons triggering the action should be potentiated. The same is true while people look at themselves in the mirror, hear themselves babble or are imitated by others. After repeated experience of this re-afference, the synapses connecting the sensory and motor representations of an action would be so strong, that the motor neurons would start firing to the sound or the vision of the action, and a mirror neuron would have been created. Evidence for that perspective comes from many experiments that show that motor programs can be triggered by novel auditory or visual stimuli after repeated pairing of the stimulus with the execution of the motor program (see  for a review of the evidence). For instance, people that have never played the piano do not activate brain regions involved in playing the piano when listening to piano music. Five hours of piano lesson, in which the participant is exposed to the sound of the piano each time he presses a key, suffices to later trigger activity in motor regions of the brain upon listening to piano music. Consistent with the fact that spike timing dependent plasticity occurs only if the presynaptic neuron’s firing predicts the post-synaptic neuron’s firing, the link between sensory stimuli and motor programs also only seem to be potentiated if the stimulus is contingent on the motor program.