Foundations in Psychology - Semester 1, Lecture 1

Study of the brain, human or animal behaviour & the relationship between the two   Application of principles of biology to the study of mental processes & behaviour   Goal - Understand the biological processes underlying psychological phenomena   Research - How psychological factors like cognition, mood & appraisal combine with biological events like stress physiology, changes in brain function & pharmacological effects to shape human experience   *See flow chart

WHAT IS PSYCHOBIOLOGY?

Study of mental processes   Key Features: Main approach to experimental psychology​​​​​​ - In cognitive psychology which investigates memory, language, perception & problem solving - Also used for other areas like social & developmental

WHAT IS COGNITIVE PSYCHOLOGY?

Emphasises active mental processes - The brain is seen as an information processor using the analogy of mind to computer - Mental processes are based on discrete modules   Uses experimental methods as well as computer modelling & neuropsychology

WHAT IS COGNITIVE PSYCHOLOGY?

Working Memory - Central executive: controls/directs attention - Visuo-spatial sketchpad, phonological loop & episodic buffer   Temporal Perception - Internal clock/pacemaker

Schemas - Mental representation of thought/behaviour organisation - Activated in relevant situations   *See flow charts

COGNITIVE PSYCH VS PSYCHOBIOLOGY?

Cognitive Psychology: How does the mind do it?   Psychobiology: How does the brain do it?   Both can use neurons to describe mind   Mental processes vs physical biology

EARLY BEGINNINGS

Early Philosophers:   Plato (387 BC) suggested the brain was the seat of all mental processes   Galen (AD 130-200) proposed a theory of brain function based on ventricles   Plotinus (AD 205-270) said that the soul is separate from the body (dualistic understanding of mind & body)

17TH-19TH CENTURY

Descartes (1596-1650) - Dualism (& the mind/body problem): interested in how does physical matter - Mechanistic view of the body (eg. reflexes) - Mind does not follow the laws of nature - Mind & body can influence each other - Pineal gland as the interface between body & soul

Debates during the 18th & early 19th centuries - Those who believed that brain function could be localised to particular brain regions - Those who believed the brain acted as a whole

DEBATE - EARLY-MID 19TH CENTURY

  Franz Joseph Gall (1757-1828) & Johann Spurzheim (1776-1832) developed phrenology (the idea that behaviours & characteristics could be deduced by the pattern & size of bumps on the skull ​​​​​​​ Marie-Jean-Pierre Flourens (1794-1867) believed that parts of the brain had separate functions but each of these areas functioned globally as a whole

DEBATE - LATE 1800-EARLY 1900

  Broca (1824-1888) & Wernicke (1848-1904) in the late 1800s provided strong data to support localisation of function, they identified specific areas of the brain central in the production & comprehension of speech   Golgi (1873) discovered silver staining to identify neuron structure

WILHELM WUNDT

Father of experimental psychology   Published 'Principles of Physiological Psychology' (1873)   Consciousness - analysis of the subjective experience of the mind ​​​​​​​ Opened first psychology laboratory in Leipzig, Germany (1879) ​​​​​​​ Scientific techniques to isolate subtle processes

EBBINGHAUS & MEMORY

Systematic & controlled study of memory in laboratory   Devised methods to measure memory & the speed with which forgetting occurred ​​​​​​​​​​​​​​​​​​​​​ Forgetting & learning curves

EARLY 20TH CENTURY ADVANCEMENTS

Growing evidence for localisation in the early 20th century, direct evidence from motor & sensory maps in the brain from early work of Wilder Penfield   Stimulating the temporal lobes could elicit meaningful integrated responses ​​​​​​​​​​​​​​ Physical basis of memory, an 'engram'

BIOLOGY OF MEMORY

Karl Lashley (1950) - Searched for the engram, the physical location of a memory - Trained rats to solve maze then cut out pieces of their cortex & re-tested their memory of the maze - Partial memory retained

WHAT ABOUT COGNITION?

Behaviourism dominant until the late 1950s, B. F. Skinner   Scientific approach ​​​​​​​ All our actions are the consequence of the response, reinforcement relationship ​​​​​​​​​​​​​​ Including 'Verbal Behaviour' (1957) ​​​​​​​ Chomsky's (1959) critique of 'Verbal Behaviour' is that it doesn't account for child sentence creation & an innate ability must exist

WHAT ABOUT COGNITION?

Bartlett (1886-1969), Piaget (1896-1980) & Lewin (1890-1947), early pioneers ​​​​​​​ WII shifted the need to look at cognitive skills ​​​​​​​ 1950s technological improvements gave a theoretical (information processing) as well as methodological boost ​​​​​​​ Studies software but not hardware of the brain

COGNITIVE NEUROSCIENCE

Psychobiology + Cognition = Cognitive Neuroscience   Study of the neurological basis of cognitive processing   Concerned with the scientific study of biological substrates underlying cognition with a specific focus on the neural substrates of mental processes   It addresses the questions of how psychological/cognitive functions are produced by neural circuits in the brain   Computational neuroscience - detailed simulation of neuronal mechanisms

WHY STUDY THEM?

  It's important to know how the brain works & how it affects our behaviours ​​​​​​​ Implications for many areas of psychology, abnormal psychology, developmental psychology, forensic & investigative psychology, HCI, etc.

METHODOLOGIES

EEG's (electroencephalogram's) & ERP's (event related potentials)   TMS (transcranial magnetic simulator) - a magnetic pulse applied to a brain region to 'switch it off' ​​​​​​​ CAT (computerised axial tomography) - series of multiple x-rays ​​​​​​​​​​​​​​ PET (positron emission tomography) - radioactive tracer introduced to body & traced ​​​​​​​ fMRI (functional magnetic resonance imaging) - measurement of blood flow through brain regions

COGNITIVE PSYCHOLOGY

Methods of Investigation Experimental methods - lab studies   Simulations ​​​​​​​ Case studies on acquired & developmental deficits

LANGUAGE & CONNECTIONIST MODELS

Overview The acquisition of the past-tense of verbs in English - over-regularisations & the U-shaped profile of learning   Computational model 1 - the symbolic model of the past tense learning ​​​​​​​ Brief introduction to connectionist or neural network models ​​​​​​​ Computational model 2 - a connectionist neural network model of the past tense learning  

PAST-TENSE IN ENGLISH

To form the past tense of regular verbs, -ed is added   The past tense of irregular verbs is not formed by adding -ed   When children add the -ed at the end of an irregular word, they are producing 'over-regularisations' ​​​​​​​​​​​​​​ Some authors claim that these over-regularisations provide evidence that children are learning rules

IRREGULAR VERBS - KUCZAJ (1977)

There are about 150-180 irregular verbs that can be classified into 5 different categories - Internal vowel change - Internal vowel change & addition of a final dental consonant - The final consonant is changed to a dental consonant - No change - Total change

U-SHAPED PROFILE OF LEARNING

At an early age, children produce correct forms of past tense for both regular & irregular verbs   When children are about 3/4 years old, they start producing over-regularisation errors of the type 'go-ed' & 'went-ed' ​​​​​​​ At a later age, children produce correct forms of past tense for both regular & irregular verbs ​​​​​​​ This is called the U-shaped profile of learning

COMPUTATIONAL MODEL 1

Computational Model 1 = The Symbolic Model of the Past Tense Learning   Symbolic Model: Dual-Route Model Two mechanisms - A memory storage device containing the past tense of irregular forms - A rule-based system that adds the '-ed' form to the stem of a regular verb to form the past tense   *See flow charts

SUMMARY OF THE SYMBOLIC MODEL

Two mechanism - A memory storage device containing the past tense of irregular forms - A rule-based system that adds the '-ed' form to the stem of a regular verb to form the past tense   Explanation of development​​​​​​

- At the beginning, all past tense verbs are stored in the memory storage device - The beginning of over-generalisation is explained by the interference of the two mechanisms - With time & practice, the two mechanisms discover the correct division of verbs into regulars & irregulars

THE SYMBOLIC MODEL & POSITIVES

Evidence Normally developing children make mistakes on irregular forms only Frequent irregular verbs are also less likely to be over-regularised Specific Language Impairment vs Williams Syndrome (Pinker, 1994) - Individuals with SLI make mistakes on the irregular verb by over-regularising them

- Individuals with Williams Syndrome have only a memory storage that stores both the regular & irregular verbs This dual architecture is innate It can account for the U-shaped profile of development Takes into account why some irregular verbs are more likely to be over-regularised

Limitations The model does not tell us how children learn the '-ed' rule Kuczaj (1977) indicated that irregular verbs could be classified into 5 different categories depending on the type of rules that lead to the formation of the irregular past tense

POSITIVES & LIMITATIONS

Cross-linguistic variations - In Arabic, the regular form of plural constitutes less than 20% of the plurals in the language - Norwegian has 2 ways of forming the past tense in regular verbs (add '-te'/'-ede'), plus 100 irregulars - Both mean it is very difficult for children

CONNECTIONIST MODELS

  Also called neural network models   Altman (1997) compares a neuron to the structure of a sunflower

NEURONS & THEIR ACTIVATION

Main Characteristics   Characteristic 1 Neurons send impulses to the other neurons to which they are connected The rate at which impulses are sent corresponds to the 'strength' of the signal Characteristic 2 The impulse from one neuron can either 

make it more likely that another neuron will become active & pass on the activation (excitatory) or less likely that another neuron will become active (inhibitory) Characteristic 3 The connections from one neuron to other neurons can change in response to the surrounding activity

COMMUNICATION BETWEEN NEURONS

Excitatory Postsynaptic Potential (EPSP) = may trigger a new action potential by depolarising the neuron   Inhibitory Postsynaptic Potential (IPSP) = may inhibit a new action potential by hyper-polarising the neuron ​​​​​​​ Summation (things added together) of the postsynaptic potentials determine whether a postsynaptic will fire

BASIC PRINCIPLES OF THIS MODEL

Connectionist/neural network models are computer models that try to simulate how the neurons in the brain communicate & learn These computer neurons are much simpler than real ones The computer allocates a number to a neuron to indicate how active each neuron is, this is similar to the activation of a real neuron

An algorithm calculates how active a neuron is, which will depend on how many neurons connected to it are activated & the strength of that activation The connection strength between two neurons is a number If the number is positive, then it is an excitatory connection, however  if the number is negative, then it is an inhibitory connection

MORE BASIC PRINCIPLES OF THIS MODEL

These models offer an alternative to the rule-based accounts of the inflectional system ​​​​​​​ The architecture of the connectionist models is not innate ​​​​​​​ Using the same principles as the Behaviourist perspective, connectionist models learn from the environment

COMPUTATIONAL MODEL 2

Connectionist/Neural Network Model of the Past Tense Learning   Plunkett & Marchman (1993-1996) This is a network that tries to explain the past learning & processing of the past tense of verbs in English Architecture of the 'feed-forward network' with 20 input units, 30 hidden units & 20 output units

PLUNKETT & MARCHMAN (1993-1996)

The aim of the network is to find the right set of connections between the neurons that will activate '-ed' when a regular verb is presented (excitatory) but will switch them off when an irregular verb is presented (inhibitory) ​​​​​​​ During training, it is likely that there would be temporary patterns of interference between the right connection for the regular verbs & the right ones for the irregular verbs, this leads to over-regularisation errors

PLUNKETT & MARCHMAN (1993-1996)

Learning Initially, the model learned the past tense of 10 regular & 10 irregular verb stems which reflects the current estimates of the balance between regular & irregular verbs in children's early vocabularies​​​​​​​ After that, the vocabulary was increased to mimic gradual uptake of verbs by children​​​​​​​ The network saw a total of 500 verbs, 90% of these being regular & verbs introduced early in training had a higher frequency​​​​​​​ The output pattern is compared to a teacher signal which specifies the correct past tense form of the current verb

PLUNKETT & MARCHMAN (1993-1996)

At the beginning, there are no errors The overall rate of over-regularisation errors is between 5% & 10% Over-regularisation recurs throughout the training period, they are not restricted to a particular stage of development

Over-regularisation of high frequent irregular verbs is very rare The phonological properties of some irregular verbs seem to block over-regularisation A very small number of irregularisation errors are observed

PLUNKETT & MARCHMAN (1993-1996)

Evidence The pattern of behaviour of the network is similar to the data found by Marcus et al (1992) when they studied the spontaneous speech of 83 English-speaking children The network demonstrates that we can simulate patterns of regular & irregular verbs without the need for an innate architecture

USES OF NEURAL NETWORK

Over-regularisation errors are not observed until the vocabulary size reaches around 120 verbs ​​​​​​​ While the network has been exposed to less than 50 verbs, it treats each novel verb stem unsystematically as the net still hasn't extracted a 'representation'   When the vocabulary expands from 50 to 120 verbs, the performance of the network improves by around 70%, there seems to be a critical period where the network moves from a 'rote representation' to a more systematic 'rule representation' ​​​​​​​ The number of regular verbs during training is crucial for the success of the network