HOW DO WE SEE MOVING OBJECTS

Have you ever imagined how would it be if we couldn’t perceive moving objects. World would have been like stagnant frames. Fortunately, it is not so. We see the train moving, the insect crawling, and numerous other examples. The perception of motion requires the following phenomenon.

1.   The retinal location

2.   Luminance

3.   Presence of reference points in the field of vision

4.   Context and a few more.

We must be aware of the fact that sudden movement, even in the periphery of our vision, are effective in capturing attention. Although, the fact is true that the threshold of motion at the periphery has to be greater than in centre, to be perceived. Quite generally luminance or brightness is directly proportional to the perception of motion of the object. The concepts of reference points come in when talking about more than two objects. When one object is stationary and the other moving we perceive the speed of the object to be ten times more than when the stationary object here is termed as the reference point. The effects of context on the perception of motion are revealed in an experiment by Brown(1935), involving motion transposition effect. It is perceived that velocity of the target tends to be inversely proportional to the size of the framework surrounding it. For example, if we see an aeroplane by standing on the runway (smaller frame), we see it move faster but when it goes far into the sky (larger frame) , we perceive it to move slower than before.

NON-PARAMETRIC STATISTICS

In statistics, the term non-parametric statistics refers to statistics that do not assume the data or population have any characteristic structure or parameters. For example, non-parametric statistics are suitable for examining the order of a set of students ranked by a test result. Or a non-parametric statistical test is one which does not specify any conditions about the parameter of the population from which the sample is drawn. They are also called distribution free statistics certain conditions are associated to non-parametric statistics but they are less rigid. The assumptions of the tests are the following:

1.   Variable under study should be continuous and the observation should be independent. Example- chi-square test.

2.   A non-parametric statistical test should be used only when the shape of the distribution of the population from which the sample is drawn is not known to be a normal one.

3.   The variable have been quantified on the basis of nominal measures and of ordinal measures as because non-parametric test are based upon nominal and ordinal measures. They are precise and less likely to reject a null hypothesis when it is false that is why a non-parametric statistical test is used only when parametric assumptions can’t be met.

Non-parametric tests are also referred to as distribution free tests.These tests have the obvious advantage of not requiring the assumptions of normality or the assumptions of homogeneity of variance. They compare medians rather than means and ,as a result, if the data has one or two outliers, there influence is negated.

PARAMETRIC TEST

Parametric statistics assume more about the quality of the data, but in return they can tell us more about what is going on with those data.  The most common parametric statistics assume the “General Linear Model”—that is, they assume that the “true,” underlying distribution of the data can be described by a straight line (or one of its variants).A parametric test is one which specifies certain conditions about the parameter of the population from which the sample is taken. Such statistical test are considered more powerful statistical test and should be used if there basic requirements and assumptions are made. The assumptions for the test are given as follows:

1.   The observations must be independent that means the selection of one test must be dependent upon the selection of any other test.

2.   The observation must be drawn from a normally distributed population. The samples drawn from a population must have equal variance if particularly the sample size is small.

3.   Variance must be expressed in interval or ratio scale. Nominal measures are ordinal measures do not qualify for a parametric statistical test.

4.   The variables under study should be continuous.

The examples of parametric test are Z-test, f-test, t-test, correlation and ANOVA.

Correlation and Analysis of Variance (ANOVA) are among the most powerful of the parametric statistics.  Both test for the presence of a relationship between two characteristics, and are based on an assumption that is called the “general linear model.”  This assumption states that the relationship between characteristics (or “variables”), in its ideal form, can be described as a straight line.  In other words, a change in one variable always produces a change in other variables and that change is always in a certain direction (greater or less) and at the same strength.  For many relationships, this makes sense. For example, the harder we swing a hammer, the bigger the dent we make in the wood.  The harder we push on the gas pedal, the faster we go.  The harder we work in school, the better the grades we get.  And so on.  As long as we can reasonably make this assumption (that there is a relationship, and that it is “linear”), then we can apply the linear model.  Correlation requires the additional assumption that both variables are “normally” distributed; ANOVA only requires that one of the variables be normally distributed. The parametric tests are commonly applied in behavioral research.

ADVANTAGES OF PARAMETRIC AND NON-PARAMATRIC STATISTICS

Bradly has enumerated several advantages and disadvantages of parametric statistics and non-parametric statistics. The advantages of non-parametric over parametric can be postulated as follows:

1.   Similarity and facilitation in derivation- most of the non-parametric statistics can be derived by using simple computational formulas. This advantage does not lie with most of the parametric statistics. The derivation of which require an advanced knowledge of mathematics.

2.   Wider scope of application- non-parametric statistics as compared to parametric statistics are based upon fewer assumptions regarding the form of population distribution. They can be easily applied to much wider situations.

3.   Speed of application-when the sample size is small, calculation of non-parametric statistics is faster than parametric statistics.

The advantages of parametric statistics associated with them may be given as below:

1.   Non-parametric statistics have low statistics efficiency than parametric statistics, when sample size is large, preferably above 30.

2.   If all assumptions of parametric statistics are fulfilled the use of more non-parametric statistics are simply wasting of data(Seagull and Seagull,1988)

3.   It is also said that the probability tables for testing the significance of non-parametric statistics are widely scattered in different publications which for a behavioral scientists difficult to locate and interpret.

CANON-BARD THEORY OF EMOTION

Canon-Bard theory

Physiologist Walter Cannon (1927) and Philip Bard (1934) theorized that the emotion and physiological arousal occur more or less at the same time. Cannon who was an expert in sympathetic arousal mechanisms, did not feel that the physical changes caused by different emotions were distinct enough to allow them to be perceived as different emotions. Bard expanded this theory by giving the idea that the sensory information that comes into brain is sent simultaneously (by the thalamus) to both the cortex (which generates emotion) and the organs of the sympathetic nervous system (which generates physiological changes in the body). The fear and the bodily reactions are, therefore, experienced at the same time and not one after the other. Cannon believed that information from the emotional stimulus goes first to the brain relay center, called the thalamus. From there the information is simultaneously relayed both to the cerebral cortex, where it produces the emotional experience, and to the hypothalamus and autonomic nervous system,  where it produces the physiological arousal that prepares one to fight , run away, or react in some other way. To Cannon-Bard, the conscious emotional experience and physiological arousal are two simultaneous and largely independent events.

Criticism

Lashley(1938) stated that the thalamus would have to be pretty sophisticated to make sense of all the possible human emotions and relay them to proper area of the cortex or the body.. It would seem that other areas of the brain must be involved in processing emotions. Emotions can be experienced without feedback from the sympathetic organs to the cortex and cited as a criticism of the James-Lange theory. People do not need feedback from those organs to experience emotion. However, there is an alternate pathway that carries information from these organs to the cortex which is the vagus nerve, one of the cranial nerves. This makes the theory a little less convincing.