COGNITIVE PSYCHOLOGY REPORT

QUESTION

General Descriptives

Poor motor skill

Good motor skill

Gender breakdown per group

Reaction Time Data

Movement Time Data

Movement Time Data (tests of simple main effects)

Comparing groups just on non-jump trials

Errors

SOLUTION

The term predictive modeling comprises of two words, prediction and modeling. Thus predictive modeling is a process in which a statistical model is created to forecast the future behavior of an individual. The models are prepared on the basis of experimentation carried out in a psychology laboratory. By far experimentation is considered the most accurate, reliable, valid and objective method of studying behavior (Walia, J., 1999). Collins and Drever have beautifully remarked, “Psychology without an experimental part is an anachronism.” In predictive modeling, probabilities and trends are forecasted. A number of variable factors called predictors are made which may influence future behavior. All the data relevant to predictors is collected to articulate a statistical model. Predictions are made and model is confirmed with addition of some more available data.
The purposeful and goal directed movements made by an individual is because of the motor development. Not all the movements made by a person are just reflexes, but they are planned to achieve some goal. There are delays inherent in sensorimotor system and therefore for skilled movement to happen, the forthcoming state of motor system becomes indispensable to forecast. The predictive models also known as forward models are supposed to be neural systems that are impersonator of bodily movement.The current motion of body and environment can be accurately estimated based on motor commands.
Desired behavior and sensory feedback provide information that helps to guesshow the movement will happen (Karniel, 2002). Thus predictive models use motor commands to generate estimates of outcome. Both the predictive control and sensory feedback mechanism is required for any movement to happen. Predictive control contains information based on previous experience and sensory feedback guides the movement if in case actual sensory feedback is different from expected. For example, if a person holds an empty jug, then it is because of predictive control. If we fill that jug with water, then the sensory feedback from the brain will help the person to continue holding the jug full of water avoiding it to fall from hand. Predictive modeling helps to identify the motor abilities of a person. The ability of a person to do the assigned tasks in a model, correctly and efficiently may be because of his good motor abilities.
Problem
Predictive modeling plays an important role in determining the motor abilities of a person. There is some percentage of school going children having some kind of developmental coordination disorder. Due to this disorder children have poor coordination and clumsiness. They face difficulty in motor coordination as compared to other children of same age group. They get trouble in handling objects;problem arises in both gross and fine coordination of motor abilities. Such children find it difficult to jump, write, and stand on one foot. They cannot properly use a scissor or tie their shoelaces.
This type of distinction based on DCD may or may not be present in early adulthood also. The purpose of the study is to demonstrate the role of predictive modeling in determining motor abilities in young adults. Researchers like Hyde & Wilson, 2011 has shown that DCD may be the result of an impaired ability to use predictive modeling mechanism. Such an insufficiency may be a reason that persons with DCD get difficulty in correcting their movements. There are differences in the movement capability of individuals which can be explained by their differences in the ability to employ predictive modeling mechanisms to correct movements.
Method
Developmental coordination disorder is present in children but the present study aims to find whether similar dissimilarity is present in early childhood also. An online control that required predictive modeling mechanism was employed. Through this online control, adults with good motor skills were ought to be differentiated from those having poor motor skills. Adults who were found to have a reduced capacity to use predictive model, may have poor coordination as compared to those adults having a better capacity to use predictive model.
Subject
The Adult subjects needed for the study were undergraduate psychology students from Victoria University, Melbourne, Australia.
Apparatus
An on-line control involving a double step reaching task was used as an apparatus. The online control was dependent on predictive control mechanism. Three targets were made on a touch screen and the subjects need to reach and press one of the three targets. The experiment was conducted using the ‘jump’ and ‘non-jump’ trials. In the ‘non-jump trials’ the target remained same while in the ‘jump’ trials, target used to change unexpectedly. The subjects had to choose the correct target accordingly. By the use of predictive modeling strategies, subjects needed to correct their movements online in the jump trails in order to complete the task accurately and efficiently.
Design
Subjects were divided into two groups, Group A and Group B using a median split method. One of the group enclosed participants with good movement skills and other group contained participants showing comparative movement difficulties. Subjects were not knowledgeable to which group they belong either before or all through the experimentation.
Independent and dependent variables were defined. In the words of James N. Shafer, the word ‘variable’ itself refers to any event or process that may assume different values. Independent variable or stimulus variable is one which is systematically and independently varied or manipulated by the experimenter (Walia, J. 1999). For example, while studying the effect of noise on mental activity, noise is an independent variable from which we predict changes in mental activity. In the present study, independent variables are the subjects which are grouped according to the characteristics of the individuals like those participants having good motor skills and those with motor difficulties were assigned different groups. Second independent variable is the trial condition involving ‘jump’ and ‘non-jump’ trials. The target used to remain the same in ‘non-jump’ trial while it used to change in the ‘jump’ trial.
The dependent variables also known as response variables is that variable which we predict will change with changes in independent variable. The effect is being studied on dependent variable (Walia, J. 1999). For example, while studying effect of punishment on learning, punishment is the stimulus (independent) variable and slow or quick speed of learning is response (dependent) variable. In the present study, dependent variables to be studied areTime of Reaction, Time of movement and the errors made in performing movements. With the change in Independent variable which is condition of trial, the dependent variable will also change. Therefore the effect is to be studied on each participant’s movement time taken, reaction time and errors made while pressing the correct target.
Procedure
Undergraduate psychology students, who were participating as subjects for the present study, were called in psychology laboratory to carry out the experimentation. Instructions were specified to the subjects and a statement was formulated concerning the administration of independent variables and recording of dependent variables. The experimenter had carefully planned how to treat the subjects, how the stimuli (independent variable) will be administered, how the response (dependent variable) will be administered and how the response will be observed and recorded. An outline of each point to be covered in the actual data collection phase was prepared. A planned procedure also helps a person to check and verify the results by repeating the experiment in the same manner.
After the division of groups, participants were instructed to complete the motor ability test. Each participant had to perform the task individually. The double-step reaching task was communicated to them. There were three targets on a touch screen and the subjects had to reach and touch one of the three targets. In the non-jump trial, target remained the same throughout movement but in the jump trial, there was an unexpected change in the movement of target. The whole process involved participants to correct their movement accordingly to reach online. The subjects needed to use predictive modeling strategies to correct their movements and complete the jump trials accurately and efficiently. Two blocks of 40 trials were administered to complete the double-step reaching task. For each block, movement time, reaction time and movement errors made were recorded for each participant. The motor abilities of the subjects were determined on the basis of their performance in the online predictive modeling task.
Results and Discussion
The entire data collected and the quantitative results obtained are given in the form of table.
Reaction Time Data
Multivariate Tests (c)
Effect    Value    F    Hypothesis df    Error df    Sig.    Partial Eta Squared    Noncent. Parameter    Observed Power(b)
ConditionWilks’ Lambda    .924    2.466a    1.000    30.000    .127    .076    2.466    .330
Condition * GroupWilks’ Lambda    .903    3.225a    1.000    30.000    .083    .097    3.225    .412
a.    Exact statistic
b.    Computed using alpha = .05
c.    Design: Intercept + Group
Within Subjects Design: Condition
Tests of Between-Subjects Effects
Measure: RT
Transformed Variable: Average
Source    Type III Sum of Squares    df    Mean Square    F    Sig.    Partial Eta Squared    Noncent. Parameter    Observed Power(a)
Group    15656.266    1    15656.266    1.276    .268    .041    1.276    .194
a. Computed using alpha = .05
Movement Time data
Multivariate Tests (c)
Effect    Value    F    Hypothesis df    Error df    Sig.    Partial Eta Squared    Noncent. Parameter    Observed Power(b)
ConditionWilks’ Lambda    .028    1038.759a    1.000    30.000    .000    .972    1038.759    1.000
Condition * GroupWilks’ Lambda    .703    12.650a    1.000    30.000    .001    .297    12.650    .931
a.    Exact statistic
b.    Computed using alpha = .05
c.    Design: Intercept + Group
Within Subjects Design: Condition
Tests of Between-Subjects Effects
Measure: MT
Transformed Variable: Average
Source    Type III Sum of Squares    df    Mean Square    F    Sig.    Partial Eta Squared    Noncent. Parameter    Observed Power(a)
Group    23677.516    1    23677.516    2.928    .097    .089    2.928    .381
a.    Computed using alpha = .05

Movement Time Data (tests of simple main effects)
Comparing groups just on non-jump trials
Independent Samples Test
t-test for Equality of Means
t    df    Sig.(2- tailed)    Mean Difference    Std. Error Difference
Movement              Equal variances
Time                       assumed
Non-jump trials    .452    30    .655    8.12500    17.99198

Independent Samples Test
t-test for Equality of Means
t    df    Sig. (2- tailed)    Mean Difference    Std. Error Difference    95% Confidence Interval of the Difference
Lower    Upper
Movement    Equal
Time             variances
Jump trials    assumed    2.385    30    .024    68.81250    28.85785    9.87690    127.74810

Movement Errors
Independent Samples Test

Levene’s Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)    Mean Difference    Std. Error Difference    95% Confidence Interval of the Difference
Lower    Upper
Errors (Equal variances assumed)    1.074    .308    .528    30    .601    .56250
1.06544
-1.61341
2.73841

Data Analysis
The data such as Mean Reaction Time, Movement Time and the number of errors made was recorded for each participant for each block. A two way [Group (2) x Trial Condition (2)] ANOVA was conducted for measures of RT, MT and E. Effect of level of motor function on participant performance during ‘jump’ and ‘non-jump’ tasks were examined.
Conclusion
The predictive models play a good role in determining the motor abilities of a person. Through these statistical models, experimentation is done on the chosen subjects and data is collected based on the results obtained. A complete and accurate discussion of the results is obtained. The most important thing is the critical study of results. The present study was aimed to determine the motor coordination in young adults. Development Coordination Disorder is reported in a small percentage of children and through predictive modeling, an effort was made to examine DCD in young adults also. An online control that depends on predictive modeling mechanism was chosen for the experimentation. Out of the three targets on a touch screen, subjects had to press one target. In the jump trials target was remained the same while in the non-jump trials target used to change. Subjects had to correct their movement to reach the correct target. If the individuals find difficulty in correcting their movements, they were thought to have deficit in motor abilities. Subjects were grouped into two on the basis of good motor abilities and poor motor abilities but they were not informed to which group they belong all through the experimentation. Movement time, Reaction time and errors made by each participant were recorded and data was interpreted. It was found that subjects who were grouped in the participants having good motor abilities performed extensively well in the task involving predictive modeling than those subjects who were grouped in the participants with poor motor abilities.
References
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2.    Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47, 381-391
3.    Flanagan J R, Vetter P, Johansson R S and Wolpert D M 2003 Prediction precedes control inmotor learningCurr. Biol. 13146–50
4.    Jones, R. D. (2006). Measurement of sensory-motor control performance capacities: Tracking tasks. In J. D. Bronzino (Ed.), the Biomedical Engineering Handbook – BiomedicalEngineering Fundamentals (3rd ed., Vol. 1, pp. 77:71-25). Boca Raton, Florida: CRC Press.
5.    Miall R C &Wolpert D M (1996).Forward models for physiological motor control NeuralNet. 9 1265–79
6.    Nass R, Ross G. Developmental disabilities. In: Bradley WG, Daroff RB, Fenichel GM, Jankovic J, eds. Neurology in Clinical Practice. 5th ed. Philadelphia, Pa: Butterworth-Heinemann; 2008:chap 65
7.    Sadock, Benjamin J., and Virginia A. Sadock, eds. Comprehensive Textbook of Psychiatry. 7th ed. Vol. 2. Philadelphia: Lippincott Williams and Wilkins, 2000.
8.    Walia, J. (1999) Foundations of Educational Psychology.Chapter 3 in Methods of StudyingBehavior. India: Bright Press
9.    Wolpert D M, Ghahramani Z and Jordan M I 1995 An internal model for sensorimotorintegration Science 269 1880–2
10.    Wolpert DM, Ghahramani Z, Flanagan JR: Perspectives and problems in motor learning. Trends Cognitive Science, in press.

GG15

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