Currently, no one theory has been agreed upon to explain the synaptic, neuronal or systemic basis of learning. Prominent since 1973 however, is the idea that long-term potentiation (more commonly known as LTP) of population of synapses induces learning, through both pre- and postsynaptic mechanisms (Bliss & Lømo, 1973; Bliss & Gardner-Medwin, 1973). LTP is a form of Hebbian learning, proposing that high frequency, tonic activation of a circuit of neurones will increase the efficacy with which they are activated and will also give a larger response to a smaller stimulus than the standard neurone (Hebb, 1949). These mechanisms are the principles behind Hebb’s famously simple explanation: “Those that fire together, wire together.” - Donald Hebb, 1949
LTP has received much support since it was first observed by Terje Lømo in 1966 and is still the subject of many modern studies and clinical research. However, there are possible alternative mechanisms underlying LTP, as presented by Enoki, Hu, Hamilton and Fine in 2009, published in the highly esteemed Neuron. They concede that LTP is the basis of learning but firstly propose that LTP occurs in individual synapses, and this plasticity is graded (as opposed to in a binary mode) and bidirectional (Enoki et al., 2009). Secondly, the group suggest that the synaptic changes are expressed solely presynaptically, via changes in the probability of transmitter release (Enoki et al., 2009). Finally, the team predict that the occurrence of LTP could be age-dependent, as the plasticity of a neonatal brain would be higher than that of a mature one. Therefore the theories differ as one proposes an on/off occurrence of LTP by pre and postsynaptic mechanisms and the second proposes only presynaptic changes, graded ability and age-dependence. Now these theories differ largely but do agree on one element of LTP: that it must occur through physical changes to the synaptic membrane/s, i.e. synaptic plasticity. A psychological theory, for which support has been growing exponentially since the 1960s and 70s, encompasses both of these views and is known as Perceptual Control Theory, or PCT. The founder of PCT, William T. Powers, proposes the mechanism of ‘reorganisation’ as the basis of learning. Powers (1973) suggests that an organism’s behaviour is the control of its perceptions and that these perceptions are being continuously adjusted to decrease the discrepancy between the organism’s output and its internally-devised reference values. This is done by maintaining internal variables (which Powers names ‘ intrinsic variables’) at level which will optimise the organism’s ability to survive (Powers, 1973). This is in line with Darwin’s theory of Natural Selection, as if either perceptions or reference values disadvantageous to survival ability are held, the organism would die and not pass on these genetic attributes (Darwin, 1859). Reorganisation occurs within the inherent control system of a human or animal by restructuring the inter- and intraconnections of its hierarchical organisation, akin to the neuroscientific phenomenon of neural plasticity. This reorganisation initially allows the trial-and-error form of learning, which is seen in babies, and then progresses to more structured learning through association, apparent in infants, and finally to systematic learning, covering the adult ability to learn from both internally and externally- generated stimuli and events. Therefore PCT is a valid model for learning as it combines the biological mechanisms of LTP and also explains the progression and change of mechanisms which are associated with developmental ability.
Powers (2008) produced a simulation of arm co-ordination. He suggested that in order to move your arm, fourteen control systems that control fourteen joint angles are involved, and they reorganise simultaneously and independently. It was found that for optimum performance, the output functions must be organised in a way so as each control system's output only affects the one environmental variable it is percieving. In this simulation, the reorganising process is working how it should do, and how Powers suggests it works in humans, and reducing outputs that cause error and increasing those that reduce error. Initially, the disturbances have large effects on the angles of the joints, however over time, the joint angles match the reference signals more closely, this is due to the system being reorganised. Powers (2008) suggests that in order to achieve coordination of joint angles to produce desired movements, the brain must calculate how multiple joint angles must change to produce this movement. Coordination also involves the use of negative feedback systems to generate the joint angles that are required. A single reference signal that is varied in a higher-order system can generate a movement that required several joint angles to change at the same time.


Botvinick (2008) identified that the recognition of hierarchical structure in human behaviour was one of the founding insights of the cognitive revolution. Despite decades of research, however, the computational mechanisms underlying hierarchically organized behaviour are still not fully understood. It has been identified that the functional organization of the frontal lobe remains unknown thus,Bedre, Hoffman, Cooney & D’Esposito (2009) identified that fundamental goal in cognitive neuroscience is to characterize the functional organization of frontal cortex that supports the control of action.
Recent neuroimaging data has supported the hypothesis that the frontal lobes are organized hierarchically, such that control is supported in progressively caudal regions as decisions are made at more concrete levels of action. Bedre, Hoffman, Cooney & D’Esposito (2009) identified that it hasn’t been addressed whether lower-order control processors are differentially affected by impairments in higher-order control when between-level interactions are required to complete a task, or whether there are feedback influences of lower-level on higher-level control.
Botvinik (2008) Identified that all existing models of hierarchically structured behaviour share at least one general assumption – that the hierarchical, part–whole organization of human action is mirrored in the internal or neural representations underlying it. Specifically, the assumption is that there exist representations not only of low-level motor behaviours, but also separable representations of higher-level behavioural units. The latest crop of models provides new insights, but also poses new or refined questions for empirical research, including how abstract action representations emerge through learning, how they interact with different modes of action control and how they sort out within the PFC.
Perceptual Control theory (PCT) can provide an explanation of neural organisation that deals with the current issues. PCT describes the hierarchical control of behaviour as being determined by control of perception rather than behaviour, and identifies that the brain is a means toward transferring perceptual signals derived from the external environment into the internal environment of billions of interconnected neurons. Control systems within the brain and body are responsible for keeping perceptual signals within survivable limits, regardless of the nature of the environment that they are derived from. PCT does not propose that there is an internal model that the brain simulates behaviour within, but rather that one of the characteristic features of cerebral organisation of behaviour is the principle lack of cerebral organisation, and that it sets reference values based on various external and internal inputs and attempts to reduce the discrepancy between the reference value and perception (Cools, 1985). Because PCT argues that there is no internal representation of behaviour, as behaviour needs to constantly adapt and change for an organism to maintain its perceptual goals, it can provide an explanation of how abstract action representations emerge through learning through spontaneous reorganisation of the hierarchy. Mansell (2011) identified that PCT proposes that conflict occurs between reference values for perception rather than between different responses, and that learning is implemented as trial-and-error changes of the properties of control systems (Marken & Powers, 1989), rather than any specific response being ‘‘reinforced.’’ Arguably, this allows behaviour to remain adaptive to the environment as it unfolds, rather than relying on learned action patterns that may not fit. Hierarchies of perceptual control have been simulated in computer models and have been shown to provide a close match to behavioural data. Marken (1986) conducted an experiment comparing the behaviour of a perceptual control hierarchy computer model with that of 6 healthy volunteers in 3 experiments. The participants were required to keep the distance between a left line and a centre line equal to that of the centre line and a right line. They were also instructed to keep both distances equal to 2cm. They had 2 paddles in their hands, one controlling the left line and one controlling the middle line. They had to react to random disturbances applied to the positions of the line. The results showed that the participants managed to nullify the expected effect of the disturbances by moving their paddles, suggesting that control was reached. These results also showed that the correlation between the behaviour of subjects and the model in all the experiments approached .99. The preceding explanation of PCT principles provides justification of how the PCT theory can provide a valid explanation of neural organisation and how it can explain some of the Current issues of conceptual models.

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