NEUROSCIENCE  FOR  ENVIRONMENTAL  PSYCHOLOGY   AND  ARCHITECTURE:   THE  CASES  OF  SOUND  IN  SCHOOL  ENVIRONMENT   AND  DESIGN  IN  RESIDENTIAL  ENVIRONMENT  

Workshop  C     1  

THANKS  TO     Agenzia  per  l’Energia  Alto  Adige  -­‐  Casa  Clima   Arch.  Mariadonata  Bancher  

ECOPHON    SAINT-­‐GOBAIN   Dr.  Carsten  Svensson   Prof.  Staffan  Hygge   Dr.  CrisRna  Carrus  

 The  sound  of  silence   Workshop  C   «Neuroscience  and  Architecture»                  

Hypotheses   Cognitive fatigue EEG

H1 (-) Sound to Noise H2 Ratio (SNR)   10 DB VS 20 DB

  Working Memory   # MEMORISED WORDS

(+)

H5 Type of noise   NATURE VS NON-NATURE

H4

H3

H1  &  H3   Noise  on  CogniRve  faRgue   Cognitive fatigue EEG

NOISE H1 (-) Sound to Noise Ratio (SNR)   10 DB VS 20 DB

H3 Type of noise   NATURE VS NON-NATURE

H2:  Main  effect  SNR   Mean  Words  Correcly  Recalled   Mean  Differnce  from  baseline  

2.50  

SNR  10  db   SNR  20  db   0.00  

-­‐2.50  

H5:  Main  effect  Background   2.50  

0.00  

*   *  

Mean  Differnce  from  baseline  

Mean  Words  Correcly  Recalled  

-­‐2.50   Traffic  

Birds  

Cha]ng   Background  

Fountain  

H6:  InteracRon  SNR  &  Background   Mean  Words  Correcly  Recalled   Mean  Differnce  from  baseline  

2.5   **   SNR  10  db   SNR  20  db   0.  

-­‐2.5   Traffic  

Birds   Cha]ng   Background  

F(3,8)=9.53,  p<.01,  ƞ²=.78  

Fountain  

LimitaRons   •  •  •  • 

Learning  effect  of  repeated  word-­‐sets   SemanRcally  related  words  in  some  word-­‐sets   CumulaRve  faRgue   Italian  pronunciaRon  of  words  

Conclusion  for  the  stakeholder   Cognitive fatigue EEG

H1 (-) Sound to Noise H2 Ratio (SNR)   10 DB VS 20 DB

  Working Memory   # MEMORISED WORDS

(+)

H5 Type of noise   NATURE VS NON-NATURE

H4

H3

Neuroscience  in  assessment  of  architectural   requalifica?on  proposals   Workshop  C   «Neuroscience  and  Architecture»  

How  to  analyse  different   architectural  soluRons  from  a  lay   people’s  point  of  view?  

Method   ▪  N=  13    

▪  Measures   1. Self-­‐reported  perceived  pleasantness     “Please  score  in  a  scale  of  1  (not  pleasant)  to  9  (very  pleasant)  the  pleasantness  of  the   following  images”    

2.  Eye-­‐tracking   3.  EEG  (not  reported)    

Procedure:  Self-­‐reported  perceived  pleasantness     ▪   Image  presentaRon  of  architectural  proposals  in  compeRRon  for  the  requalificaRon  of  2   urban  spaces  -­‐  (21  sRmulus,  1  per  8  sec)  

Method  

Procedure:  Eye-­‐tracking    

Compe??on  1   School  Residence   (Malles,  IT)  

Compe??on  2   Mala  Strana  Square   (Prague,    CZ)  

Skyview       In  place       Lateral  view  

Corridor         Courtyard       External  

1                                                    2                                                                            3    Architect  Team  

           4                                          5                                                          6    Architect  Team  

Results   Compe??on  1:  School  Residence  (Malles,  IT)  

Perceived pleasantness of each proposal 7. 5.25 3.5 1.75 0. Team1

Team2

Team3

Corridor Courtyard External

% of fixations in hotspots

Pleasantness

8.75

Eye fixation in defined hotspots 100. 75. Corridor

50.

Court External

25. 0. Team1 Team2 Team3

Mean=  5.82   Mean=  5.23  

Mean=  6.15  

Results  

Team  1  

Team  2  

Compe??on  1:  School  Residence  (Malles,  IT)   e.g.  Courtyard  

Team  3  

Results   Compe??on  2:  Mala  Strana  Square  (Prague)  

Perceived pleasantness of each proposal

Eye fixation in defined hotspots

Pleasantness

8. 6. Skyview In Place Lateral View

4. 2.

% of fixations in hotspots

70. 52.5 Skyview In place Lateral view

35. 17.5 0.

0. Team4 Team5 Team6 Mean=  7.03   Mean=  5.38   Mean=  6.21  

Team 4 Team 5 Team 6

Results  

Team  4  

Team  5  

Compe??on  2:  Square  Mala  Strana  (Prague)   e.g.  Skyview  

Team  6  

Discussion    

  It  is  possible  to  use  eye-­‐tracking  measures  in  assessing  lay  people’s  judgements  about   architectural  requalificaRon  proposals  in  future  design  pracRce     Future  assessments  of  architectural  proposals  may  consider  a  more  ecological  approach,  for   instance  with  the  inclusion  of  3D  reality  instead  of  only  images.       EEG  shall  also  be  included  in  order  to  provide  more  informaRon  about  the  inner  process  of   people’s  assessment  of  requalified  environments       Perceived  pleasantness  and  aSen?on  appears  to  be  independent  measures:   In  fact,  features  atracRng  our  atenRon  when  assessing  these  proposals  seem  here  to  be   not  related  to  perceived  pleasantness        

Thermal  Comfort  in  Interior  Spaces   NeuroscienRfic  Approaches       Workshop  C   «Neuroscience  and  Architecture»  

ExploraRve  Study  

some  theoreRcal  background  

Colour    

•  • 

Different  processes  of  colour  percepRon  (Rodeck  1998)     Symbolical  meaning  of  colour,  e.g.  love,  hope          

(Frieling  1979,  Rossbach  2000)  

•  • 

Effect  of  colour,  e.g.  sRmulising,  relaxing  (Rodeck  1998)     Colour  contrasts  in  space,  e.g.  warm  –  cold  

Research  QuesRons   • 

Which  design  features  influence  our   percepRon  of  thermal  comfort?  

• 

Which  are  the  most  important?  

• 

How  can  we  measure  them?  

Method   Independent  variables    •Colour   warm  –  cold   •Shape   curved  –  squared   •Space   compressed  –  open   •Light   natural  –  arRficial   •Material   smooth  –  rough   •Elements   void  -­‐  full  

Dependent  variables     •Thermal  comfort   Conscious:  Likert  scale  (1-­‐9)   Unconscious:  EEG   •ASen?on   N  of  fixaRon  within  regions  of   interest     Defini?on  Fixa?on  =      eye  gaze  on  one  spot  for   >40ms  

Method   Materials  &  Procedure   EEG      

Par?cipants  

• 

N=13,  Mean   Age=29.31(SD=5.8)   3  males  &  10  females  

Eye  tracker  (eyemetrix)  

1  

9   1s  

4s  

8s  

1s  

Hypotheses   Colour

     

warm  

cold  

Explicit  Score  

+  

-­‐  

Pleasantness  EEG  

+  

-­‐  

Eye  Tracking  

+  

-­‐  

Hypotheses   Colour

     

warm  

cold  

Explicit  Score  

+  

-­‐  

Pleasantness  EEG  

+  

-­‐  

Eye  Tracking  

+  

-­‐  

Hypotheses   Elements      

void  

full  

Explicit  Score  

-­‐  

+  

Pleasantness  EEG  

-­‐  

+  

Eye  Tracking  

-­‐  

+  

Results   Conscious  Thermal  Comfort  Values  

 *  

}    

*  

}    

*  

}    

7.  

*  

}    

8.75  

5.25   3.5   1.75   0.   Colour  

Shape  

Light  

Material  

Space  

Elements  

Percentage  of  Fixa?on   8.75   7.   5.25   3.5   1.75   0.   Colour  

Shape  

Light  

Material  

Space  

Elements  

Conclusions   1. 

2. 

Colour,  shape,  light  and  space  play  an   important  role  in  the  percep?on  of  thermal   comfort   However  people  pay  more  aSen?on  to  rough   surfaces  and  rooms  that  are  full  of  things  

(also  if  there  is  lots  of  wood  in  a  picture  people   give  higher  thermal  comfort  ra?ng)  

RecommendaRons  to  the  stakeholder     How  to  increase  the  thermal  comfort  of  a  building:   •  Warm  colours   •  Curved  shapes   •  Natural  light   •  Small  cosy  spaces     •  Don’t  use  full  rooms  or  rough  surfaces  as  they   atract  atenRon  away  from  thermal  comfort  

Workshop  C   «Neuroscience  and  Architecture»  

 

Thanks  

THANK  YOU!     Prof.  Dr.  Fabio  Babiloni  with  Dr.  Patrizia  Cherubino  -­‐  Sapienza  University  of  Rome   Amicone  Giulia   Ariccio  Silvia   Bellini  Diego   Carvalho  Pinto  Lais   Conrad  Nicole   Ferreira  Ines   Filomena  Gabriele   Gabe  Thomas  Elizabeth     GiusR  Mateo   Piccinin  Gloria   Rossi  Giulia   Venhoeven  Leonie   Yang  WenRng  

With  the  support  of     Dr.  Arch.  Fabrizio  Pusceddu  -­‐  UniSS   Dr.  Stefano  De  Dominicis  -­‐  CIRPA  UniRM1