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Posts Tagged ‘psychology’

Psychologists have argued that components of emotional functioning are a small part of cognition as a whole. I however, believe that emotion plays a more fundamental role. Through enactive theory (Varela et al 1991), we can explore and flesh out much more comprehensive accounts of consciousness. This can be done through integrating phenomenological research methods and third person sciences.

Research in emotion can play a particularly vital role in this. Research is currently being carried out (Colombetti 2008; Hutto 2010 and others) which is particularly interesting. My own research centres on exploring phenomenological accounts of emotional experience and linking it with the physiological elements associated with the experience. This may lead to a more comprehensive understanding of cognitive functioning and the role that emotion plays. Similar to neurophenomenological understanding, this allows a mixed method approach to cognitive behaviour.

Our desires, motivations and actions all require understanding. Without an emotional or affective dimension of this understanding, we would be acting in and experiencing the world in a purely passive way. However (and this is where the enactive literature steps in) we actively engage with the environment in every possible way. We can even liken our dreams or fantasies down to the experience we have (or do not have) with the world, as we are an active agent in it. In enactive terms, we en-act our Umwelt (or life world) and bring about our own understanding of the world.

Enactive theorists use the example of a bacterium striving toward an area of high glucose concentration to survive. Instead of cognition being a computational interaction, it is much more of a dynamic and fluid system. To use another enactive reference, cognition is much more like a handshake between environment and living organism that an organism psychologically and physiologically interacting with the environment in a passive and reflexive way. An important point to mention is that I would hardly attribute emotional understanding or higher cognitive functioning to basic organisms like bacterium.

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Connectionism was explicitly put forward as an alternative to the classical computer based paradigm of the cognitive science approach. Many philosophers see connectionism as a basis for denying structured symbols. Empirical evidence from neuroscience shows that no symbol, proposition, sentence, or algorithm can be found in the brain. There must be an alternative, more basic, mechanism for the representation and processing of knowledge. The goal of the connectionist approach is to construct an abstract model of the neural processes taking place in the brain.

Connectionist models seem particularly well matched to what we know about neurology. Neural networks are also particularly well adapted for problems that require the resolution of many conflicting constraints in parallel. There is ample evidence from research in artificial intelligence that cognitive tasks such as object recognition, planning, and even coordinated motion present problems of this kind.

The main components of connectionism:

Knowledge is distributed: One of the central claims associated with the parallel distributed processing approach is that knowledge is coded in a distributed fashion. Localist representations within this perspective are widely rejected. Bowers (2002) notes that connectionist networks can learn localist representations and many connectionist models depend on localist coding for their functioning. He argues that there are fundamental challenges that have not been addressed by connectionist theories that are readily accommodated within localist approaches. In word and non-word naming tasks, it has been found that distributed representations make it difficult for participants to name monosyllabic items and deal with more complex language phenomena. Neural networks have great difficulty in representing information that specifies who is doing what with whom and with what (eg John hit Paul with the hammer). In contrast, models that learn localist representations support many of the core language functions that connectionist models fail to account for. It is concluded that the common refection of localist coding schemes and complete reliance on distributed representations within many connectionism theories may be premature, and more research needs to be conducted in order to understand it fully.

Knowledge is stored by content: The connectionist processing and learning paradigm has many implications. Due to its associative processing mechanism, it has a content‐addressable memory. Connectionism suggests that this is due to the fact that the incoming pattern of activation that occurs when thinking of something, has matching parts to a previous pattern and this is sufficient to reactivate other parts of the pattern. It is hard to achieve in classical architectures, where items are typically accessed on the basis of knowing where they were stored.

Norman (1981) “information is not stored anywhere in particular. Rather, it is stored everywhere”. An immediate consequence of connectionism is that memories are deeply sensitive to context.

Graceful degradation is another feature that is typical of natural and artificial nervous systems; if small parts of the network are damaged, this has only small effects on its overall performance. Learning is based on a process of self‐organization on a pre-linguistic level.

Information is processed in parallel:  Neural networks often have many hundreds of thousands of small units, each processing different information. Connectionism implies that information is not serially processed, but many computations are performed simultaneously in parallel. Townsend (2004) argues that it is extremely difficult to entirely separate reasonable serial and parallel models on the basis of typical data. The study found strong evidence for pure serial and pure parallel processing, with differences occurring across individuals and inter-stimulus conditions.

Inactive knowledge is nowhere: Knowledge is represented by a pattern of activation in connectionism. When that pattern is not active, the information is not represented in the system. Activation flows directly from inputs to hidden units and then on to the output units. More realistic models of the brain would include many layers of hidden units, and recurrent connections that send signals back from higher to lower levels. Such recurrence is necessary in order to explain such cognitive features as short term memory. Connectionists tend to avoid recurrent connections because little is understood about the general problem of training recurrent nets. However Elman (1991) and others have made some progress with simple recurrent nets, where the recurrence is tightly constrained (Garson, 2007).

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Why ABA?

Interventions developed by the discipline of ABA have allowed individuals even with the most severe behavioural problems to make progress. ABA interventions for problem behaviours focus on establishing and reinforcing new skills, provide access to preferred activities and items, provide choice-making opportunities, increase appropriate communication, making complex situations more predictable and reducing maladaptive behaviours. Effective ABA techniques range from focused interventions for increasing specific functional skills and/or reducing specific problem behaviours to comprehensive programming.

 The goal of ABA is to determine the function that problem behaviour serves for an individual in a specific situation so that more socially appropriate replacement behaviours serving the same function can be facilitated. Most school-based behavioural interventions for problem behaviours are based on ABA techniques (Smith, 2001). The literature examining the effectiveness of ABA techniques with individuals with autism is substantial, and has contributed significantly to the development of a range of educational techniques.

Components of effective ABA in educational settings

In educational settings, a number of important components have been recognised toward making consistent progress through the ABA technique. These include:

  • Early intervention
  •  Parent involvement
  •  Mainstreaming children with typically developing children
  •  Intensive one-to-one teaching – Research has shown that 30-40 hours per week of one-to-one intervention for at least 2 years may be required to produce maximum effect
  •  Comprehensiveness of program
  •  Individualised programming

 

The Autism Society of America (1998) claim that properly designed and implemented ABA programs contain most if not all of the components of treatment approached found to be most successful in supporting individuals with autism. These include individualised instruction, structured learning experiences, low student-teacher ratio, early intervention and family involvement.

The beginning of ABA in educational settings

Lovaas and his colleagues have published several reviews of the UCLA early intervention program in educational settings for children with autism. Follow up data from a treatment group of 19 showed that 47% achieved normal intellectual and educational functioning with normal-range IQ scores and successful performance in public schools. Another 40% displayed mild intellectual disability and were assigned to special classes and 10% showed profound intellectual disability and were assigned to classes for children with severe disabilities. In contrast, only 2% of the control group children achieved normal intellectual functioning; 45% showed mild intellectual disability and were placed in appropriate classes and 53% displayed severe intellectual disability and were placed in other special classes (Lovaas, 1987).

Replication studies and ABA techniques

Green (1996) concluded that early intervention based on ABA can produce large, comprehensive, lasting and meaningful improvements in many important domains for a large portion of children with autism. She found early intervention to be more effective and the best results from children who began the program at 2 or 3 years of age in educational settings.

Weiss (1999) supports Lovaas’ claim of using early, intensive, behavioural intervention for children with autism. She found that children whose learning rates are slower may be helped by earlier alterations in approached and strategies to education. Such changes or additions to educational intervention may augment the degree to which these children benefit from treatment.

 Matson et al (1996) concluded that ABA has made significant contributions in demonstrating practical techniques to address the problems associated with autism. Eikseth et al (2002) provide evidence that some 4 – 7 year old children with autism may make substantial gains from ABA techniques and interventions in educational settings.

 Dillenburger et al (2004) show convincing evidence that ABA techniques offer a highly effective form of intervention for children with autistic spectrum disorder (ASD). Harris and Delmolino (2002) suggest that early intensive treatment using methods of ABA enables a significant number of children to enter the educational mainstream and achieve normal intellectual functioning. The study utilised school based models and Discrete Trial Training (DTT). Smith (2001) explored DTT as an important component of ABA treatment but concluded that it should not be the only component. This suggests that for ABA to be more effective, a multitude of techniques must be utilised.

 PECS (Picture Exchange Communication System) might have helped children to develop functional receptive language and meaningful communication skills (Bondy, 1988). The benefits of this ABA technique used in an educational setting might also have been helpful to provide children with more functional programming, focusing on the development of daily living skills and self-help skills. An earlier shift in the focus of instruction might be more beneficial.

 Pivotal Response Training (PRT) has been effectively used for peer interaction in school settings with children with autism (Pierce and Schreibman, 1995). Children with autism were seen to maintain prolonged interactions with peers, initiated play and conversations, and increased engagement in language and joint attention behaviours. In addition, teachers reported positive changes in social behaviour, with the largest increases in peer-preferred social behaviour. Further, these effects showed generality and maintenance.

 Smith, Groen and Wynn (2000) examined effects of ABA treatment for children with autism. Children received a mean of 24.5 h per week over 2 years of one-to-one ABA treatment. ABA groups in educational settings scored significantly greater than parent training groups. 27% of ABA group succeeded in regular educational classrooms.

 Eikeseth et al (2002) found ABA treatment to be produce greater outcomes that the eclectic approach. ABA treatment group scored significantly higher as compared to the eclectic treatment group on intelligence, language, adaptive functioning, and maladaptive functioning and on two of the subscales on the socio-emotional assessment (social and aggression).

 Cohen et al (2006) Compared effects of ABA treatment with special education provided at local public schools for children with autism. Six of the 21 ABA treated children were fully included into regular education without assistance, and 11 others were included with support; in contrast, only 1 comparison child was placed primarily in regular education.

 Birnbrauer and Leach (1993) showed significant improvement in autistic children using 1:1 ABA tutoring in educational settings using a pretest-posttest design. It was pertinent that parents developed ABA skills for improvement of child’s behaviour to cross over into all aspects of child’s life. 

Significant results show that ABA techniques improve child’s IQ, developmental language functioning, and developmental adaptive functioning and social skills.

Drawbacks

There are no adequate explanations why some children show dramatic improvements while others do not. Consequently, generalisations from the current research should be made with caution in many cases (Green, 1996). Also, comparisons with other competing treatments need further investigation.

 Dempsey and Foreman (2001) importantly note that while ABA has been found to be effective for some children, it is unlikely that it is effective for all children. In the future we would hope that diagnosis of autism will be sufficiently sophisticated to include specific recommendations for treatment. Some children may be prescribed sensory integration, while others are diagnosed as needing auditory integration. This can be overcome with individualised programming and low student-teacher ratios: Teachers are aware of the child’s abilities and individual program can be developed to actualise the child’s capabilities.

Some of the older procedures used by ABA tutors had let to ethical issues: e.g. hand slapping. This has been overcome through the development of ethics and would hardly be seen as an effective procedure to use as punishment.

Conclusion

It is important that we recognise that the research in ABA has not concluded that it is the cure for ASD’s. We can however see that it is a useful technique to approaching the maladaptive behaviours associated with the disorders.

From the evidence, there is a lack of research in comparison studies for ABA and other techniques. However ABA shows promise. Intervention practices have developed over time through the adherence to ABA principles and techniques continue to flourish. Effective communication instruction has drawn heavily from ABA (Ogeltree and Oren, 2001). The evidence suggests that for greatest efficacy, ABA should be implemented in all aspects of the child’s life. Evidence of ABA implementation in schools settings supports this.

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Eye-tracking equipment has been used for a multitude of studies including Internet Consumer gazing behaviours, eye-movements during word-reasoning tasks and a study I conducted for my undergrad psychology degree– gazing behaviour of social and unsocial individuals.  My hypothesis was that highly social individuals would make increased eye-contact with the images of faces. Further hypothesised was that highly social individuals would show higher rates of gazing behaviour for images previously rated as social (in a pretest – it was a rather lengthy process to perfect the experiment!).

Some shameless self promotion, the paper I have written will be published in the coming months, I thought I would write a piece on what eye-tracking equipment can and cannot do.

The following image shows a scan path that the equipment recorded for my experiment. Larger circles show increased time (ms) on that area and you will notice that circles are numbered 1, 2, 3 etc… This shows the gazing path, 1 being the first area that the equipment recorded the eye hitting. 2, 3 etc are the where the eye had moved.

The following image shows the attention map for the gazing behaviour for another participant. The attention map shows the areas of the image that the eyes of the participant looked at. While the participant perceived the whole image for sure, the areas shown display the parts of the image that the participant had increased gaze.

The eye tracking equipment was a desk mounted monitor and the images shown were displayed in a slideshow format – each image was shown for 7 seconds (7000 milliseconds) and there were 10 images.

Some issues I had with the eye tracking equipment:

  • It froze a number of times. I was working off of a safe computer with no internet connection that didn’t allow USB sticks (in case of infection – the equipment is over 10000 euro). In spite of this it was glitch and I had to disregard 2 participants’ data as it didn’t record efficiently. I think that if the computer was a mac, things may have been a little easier, but maybe not.
  • The amount of data that is recorded is staggering. It took me 3 months of sifting through and adding up data, a lot of which I didn’t use. In the end I did some of the totals by hand out of convenience. I wouldn’t advise it on anybody unless they have more than 6 months to do the study.

Hopefully useful advice:

  1. Filter out what things you DON’T want the equipment to record BEFORE you start collecting the data. It will save so much time
  2. Keep an eye on the control monitor that the participants heads are in the right position
  3. Leave yourself plenty of time to go through the data, I found it easier to do by hand. If i were to do another study with a lot of data on excel files, I would create intermittent excel files and do the calculations on that

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