Presentation is loading. Please wait.

Presentation is loading. Please wait.

Analysis of Dreams Hari (01005005) Sushil (01005002) Pranav (01005003) Under the Guidance of Dr. Pushpak Bhattacharyya.

Similar presentations


Presentation on theme: "Analysis of Dreams Hari (01005005) Sushil (01005002) Pranav (01005003) Under the Guidance of Dr. Pushpak Bhattacharyya."— Presentation transcript:

1 Analysis of Dreams Hari (01005005) Sushil (01005002) Pranav (01005003) Under the Guidance of Dr. Pushpak Bhattacharyya

2 Kekule’s dream …..  "I turned my chair to the fire and dozed. Again the atoms were gamboling before my eyes.... My mental eye... could not distinguish larger structures, of manifold conformation; long rows, sometimes more closely fitted together; all twining and twisting in snakelike motion. But look! What was that? One of the snakes had seized hold of its own tail, and the form whirled mockingly before my eyes. As if by a flash of lighting I awoke..."

3 What is a dream?  Report of a memory of a cognitive experience.  Frequently produced in a state called "sleep"  Dramas our minds make up when the self system is not keeping us alert to the world around us.

4 How often do we dream and when?  At least 4 to 6 times per night.  During REM (Rapid Eye Movements) periods  During non-REM periods  In a relaxed state of mind during waking.

5 Why are dreams important in life?  They are only as important as people make them out to be.  No evidence which suggests that an absence of dreaming would be harmful in anyway.

6 Do all dreams contain a hidden meaning?  No definite answer.  May contain "hidden" meanings in the form of metaphors or symbols.  Mundane "doodles" taken from the events of our lives.

7 What are the factors affecting dreams?  Fear  Stress  Drugs  Desires

8 Do animals dream ?  Never know for sure  All mammals have REM sleep.  All REM sleeps may not lead to dreams.

9 Why are dreams so forgettable?  All of us forget 95-99% of our dreams  Paying no attention

10 Freud's theory of dreams  Guardians of sleep.  Wish -fulfillment.  Significant speeches in dreams can be traced to memories of speeches heard or sentences heard.  Metaphors and symbols in dreams.

11 Hall/Van de Castle Dream Coding System  Rules for Coding a Dream.  Elements of a Dream.  Characters  Social Interactions  Aggression  Friendliness  Activities  Success and Failure  Misfortune and Good Fortune  Emotions  Settings

12 Coding of Characters Chief character in almost every dream. Dreamer is coded as D People, animals and mythical figures. Classes of characters :  Number  Gender  Identity  Age  In case of animals, classify only on number.

13 NUMBERGENDERIDENTITY 1 individualM maleF father M mother 2 groupF femaleB bother S sister 3 individual deadJ jointH husband W wife 4 group deadI indefiniteD daughter A son 5 imaginaryX parents I infant R relative O occupational Y family members K known S stranger P prominent E ethnic U uncertain

14 Age : A adult T teenager C child B baby Character is represented by using : Number, Gender, Identity, Age Examples : My TEENAGE BROTHER = 1MBT A parade of SOLDIERS = 2MOA My FATHER AND MOTHER = 2JXA

15 Coding of Social Interactions Aggressive Interactions Subclasses of Aggressions : A1 : results in the death of a character. A2 : attempt to physically harm a character A3 : character being chased, captured, confined. A4 : theft or destruction of possessions. A5 : verbal aggressions

16 Coding Aggressive Interactions Coding style : aggressor aggression-subclass > victim Examples : I HIT my brother = D A2> 1MBA Her husband KILLED her = 1MUA A1> 1FUA My mother and sister SCOLDED me = 1FMA+1FTA A5> D

17 Friendliness Interactions: Subclasses of Friendliness : F1 : long-term close relationship with a character F2 : to share in a pleasant social activity F3 : extending assistance to a character F4 : offering a gift F5 : conveyed through either verbal or gestures

18 Coding style : befriender friendliness-subclass > befriended Example : I INVITED Jim in my marriage = D F2> 1MKA Coding of Activities : P : physical M : movement L : location change V : verbal E : expressive communication S : visual A : auditory C : thinking

19 Examples of Coding of Activities : I am walking : D M I am writing : D P Coding of Success and Failure SU : Success FL : Failure Examples : I have TOPPED : SU, D My brother FAILED in exams : FL, 1MBA

20 Coding of Misfortune and Good Fortunes Subclasses of Misfortune : M4 : character dies as a result of accident or illness. M3 : character is injured or ill. M2 : character is involved in an accident without suffering any physical injury. M1 : character encounters an environmental barrier or obstacle. Good Fortune is coded as GF

21 Examples : I LOST my watch : M1,D Ram and I FOUND A BRAND NEW BOAT : GF,D+1MKA Coding of Emotions : AN : anger AP : apprehension, fear, anxiety SD : sadness CO : confusion HA : happiness Example : I became ANGRY : AN,D

22 Example : I was in MY ROOM : IF LOCATIONFAMILIARITY I : indoorF : familiar O : outdoorD : distorted A : ambiguousG : geographical U : unfamiliar Coding symbols for Settings : Setting is location and its familiarity

23 Need of Statistical Approach  Variation in dream reports  Differing frequencies of one element leads to the possibility of higher or lower frequencies for other elements.

24 Solution to these problems  Use percentages and rates  Ex: % of human character who are man =man human character / total human characters  Similarly we can find rate of social interaction

25 Male Dreamers N (%) Female Dreamers N(%) Familiar Characters 501 (45%)796 (58%) Unfamiliar Characters 607 (55%)567 (42%) Statistical Analysis  Difference between male and female dreamers on what we call the "familiarity percent"

26 H-profile  “h” value is calculated by  For the above example h score is -0.26

27 H-profile of male dreamers compared to female dreamers

28 Use of h-value  Formulas used to find h values at confidence level of 5% and 1%:  For p=.05: h = 1.960/sqrt(N/2)  For p=.01: h = 2.576/sqrt(N/2)  Experimentally calculated h value is compared with this h value

29 Conclusion No appropriate theory available Good coding system available. More analysis required in the field of dream content analysis and dream causes. Interesting to know if there is such process in machines simulating brain processes.

30 References : [1]. http://psych.ucsc.edu/dreams/ [2]. http://www.dreamsearch.net/http://www.dreamsearch.net/


Download ppt "Analysis of Dreams Hari (01005005) Sushil (01005002) Pranav (01005003) Under the Guidance of Dr. Pushpak Bhattacharyya."

Similar presentations


Ads by Google