SlideShare a Scribd company logo
A Feature-Integration
 Theory of Attntion
   Anne Treisman & Garry Gelade (1980)




                                         Jing Chen
Outline

• The Feature-Integration Theory of Attention

• Paradigms/Experiments
  •   Visual search (Exps 1, 2, &3)
  •   Illusory conjunctions (Exp 4)
  •   Texture segregation (Exp 5, 6, & 7)
  •   Identity and location (Exp 8 & 9)

• Conclusion
The Feature-Integration
        Theory of Attention
• Features are registered early, automatically, and in
  parallel across the visual field, while objects are
  identified separately and only at a later stage, which
  requires focused attention to “glue” features
  together.

• Attention is necessary for the correct perception of
  conjunctions, although unattended features are also
  conjoined prior to conscious perception.
  • “Illusory conjunctions”
Dimension vs. Feature

• “Dimension” refers to the complete range of
  variation

• “Feature” refers to a particular value on a dimension

• Thus color and orientation are dimensions; red and
  vertical are features on those dimensions.
Experiment 1: Visual Search

• Purpose:
  • To compare search for disjunction targets and
    conjunction targets;
  • To explore the effect of extended practice on serial and
    parallel search;

• Among the distractors Tbrown and Xgreen
  • Disjunction targets: a blue letter or an S;
  • Conjunction targets: Tgreen;

• Setsize: 1, 5, 15, and 30
Disjunction Target

                 Find the blue letter
• Easy:                • Just as Easy:
                         X T X T T       T   X   T
 X   T   X   T
                         X T X X T       X   T   T
 X   T   T   X
                         T X T T X       X   T   X
 T   X   X   X
                         X X T X T       X   T   X
 T   T   X   T
                         T X T T X       T   X   T
Conjunction Target

             Find the green “T”
• Hard:           • Even Harder:
  X T X T            X T X T T     T   X   T
 X T T X             X T X X T     X   T   T
 T X X X             T X X T X     T   T   X
                     X X T X T     X   T   X
 T T X T
                     T X T T X     T   X   T
Experiment 1: Results




       • Conjunction: search time increased linearly
         with setsize; search is serial and self-
         terminating;
       • Disjunction: for positive displays, search times
         were hardly affected by setsize; for negative
         displays, the relationship is linear;
Experiment 1: Results

          • The effect of extended practice:


              • There is little indication of any
                 change in the pattern of results;
              • no sign of a switch from serial to
                 parallel search.
Experiment 1: Discussion

• Focal attention, scanning successive locations
  serially, is the means by which the correct
  integration of features is ensured.

• When this integration is not required by the task,
  parallel detection of features should be possible.
Experiment 2: Visual Search

• Purpose: to explore the relation between the
  discriminability of the features of a conjunction and
  the speed (slop) of detecting that conjunction as a
  target.
  • Compare
    • Difficult condition: a conjunction target in distractors
      similar to it (T in X and T);
    • Easy condition: distractors differed maximally from the
      target (O in O and N).
Experiment 2: Results

           • The slopes in the difficult
             discrimination are nearly three times
             larger than those in the easy
             discrimination
           • but the linearity and the 1/2 slope
             ratio is preserved across these
             large differences.
Experiment 2: Discussion

• The search rates vary dramatically for easy and
  difficult conditions.

• In both easy and difficult conditions for the
  conjunction targets, the search were serial, self-
  terminating.

• As a result, we cannot say that search becaomes
  serial only when it is difficult.
Experiment 3: Visual Search

• Purpose: to explore an alternative explanation for
  the difference between conjunction and disjunction
  targets.
  • attributes the difficulty of the conjunction condition to
    the centrality of the target in the set of distracters.




• Replicate this aspect of the similarity by using
  unidimensional stimuli, with no need for checking
  conjunctions.
Experiment 3: Results
                   • The pattern of results is quite
“Central”
                     different from that obtained
                     with the color-shape
                     conjunctions and disjunctive
                     features.
                   • When the intermediate target
                     is present, its detection
                     doesn’t depend on a serial
                     check of the distractor, which
                     the detection of the
                     conjunction did.
Experiment 4: Letter Search

• Purpose: to discover whether integrative attention is
  required even with highly familiar stimuli, e.g., letters

• Confusability of letters
  • Letters would be difficult to search when they are
    similar in a wholistic way.
    • R/PB
  • Sets of letters would be confusable if their features
    were interchangeable and could potentially lead to
    illusory conjunctions.
    • R/PQ
Experiment 4: Letter Search

• Purpose: to discover whether integrative attention is
  required even with highly familiar stimuli, e.g., letters



• The conjunction condition (with interchangeable
  features): R/PQ and T/IZ

• The similarity condition (with greater target/distractor
  similarity): R/PB and T/IY
Experiment 4: Results
            • The ratio of positive to negative
              slopes differed for the
              conjunction and the similarity
              conditions:
                • For the conjunctions, it was
                   0.45, which is close to half
                   and suggests a serial self-
                   terminating search.
                • For the similarity condition,
                   it was much lower (0.26).
Experiment 4: Discussion

• Letter search would be serial and self-terminating if
  the particular sets of distractor and target letters
  were composed of perceptually separable features
  which could be wrongly recombined to yield
  conjunction errors.

• Otherwise search could be parallel (although not
  necessarily with unlimited capacity and no
  interference).
Experiment 5: Texture
           Segregation
• Purpose: to investigates the “preattentive”
  segregation of groups and textures, which could
  guide the subsequent direction of attention.

• Five rows * five columns; card sorting task:
  • The color condition: OV|OV
  • The shape condition: OO|VV
  • The conjunction condition: OV|OV

   OOVOO
   VOVVV
   VOVOV      The task was to sort the packs of cards into two
   OOVVO      piles, one containing cards with a horizontal and one
   OVVOV      with a vertical boundary.
Experiment 5: Results

• The difference between the two feature packs and
  the conjunction pack was qualitative and obvious.
                                       Face-up   Face-down

  • The color condition: OV|OV         15.9      25.1
  • The shape condition: OO|VV         16.2      25.6

  • The conjunction condition: OV|OV   24.4      35.2

  •

• Suggesting that the boundary cannot be directly
  perceived in the conjunction condition and has to be
  inferred from attentive scanning.
Experiment 6: Texture
            Segregation
• Purpose: to discover whether the advantage of the
  feature boundary was due to
  • In the feature pack, only one dimension was relevant
  • But the conjunction pack, require attention to both
    dimensions.

• Change the feature display into multiple-dimensional
  one: OΠ|OV (26.9 vs. 32.9 sec)
• Results:
  • The disjunctive features appear slightly less effective than
    single features.
  • The relevance of two dimensions rather than a single
    dimension can explain only a small fraction of the difference
    between features and conjunctions.
Experiment 7: Texture
            Segregation
• Purpose: to see whether the distinction between features
  and conjunctions is equally crucial when the features are
  local components of more complex shapes rather than
  values on different dimensions (as in 5&6).

• The single feature conditions (short diagonal line):
  • PO/RQ (779 ms)
  • EO/FQ (799 ms)
                                           Again, what matters is
• The conjunction conditions:            whether the boundary is
  • PQ/RO (978 ms)                     defined by a single feature or
  • FK/EX (1114 ms)                       a conjunction of feature
Experiments 8&9: Spatial
           Location
• Purpose: to test whether precise information about
  spatial location is available at the feature level,
  • by looking at the dependency between reports of
    identity and reports of location on each trial.

• Difference between Exps 8 & 9:
  • In Exp 8, the presentation times of the arrays were
    chosen to make sure the accuracy in each condition
    was 80%.
  • In Exp 9, equal presentation times were used for
    features and conjunctions.
Experiments 8&9: Spatial
            Location
• All distractors were OX

• The targets:
  • The disjunctive feature condition: H, H, X, or O
  • The conjunction condition: X or O
                                               OXOXOO
• Dependent variable:                          OXOXHX

  • accuracy with brief exposures
Experiments 8&9: Results




The conditional probabilities follow a very similar pattern.
It seems likely that in order to focus attention on an item, we must spatially
localize it and direct attention to its location.
Feature localization is a special kind of conjunction task (feature and spatial
location).
Conclusions
• All the data taken together support the feature-
  integration theory of attention.
• Separable features can be detected and identified in
  a early, parallel process;
• this process mediates texture segregation;
• locating any individual feature requires an additional
  operation;
• conjunctions of features require focal attention to be
  directed serially.

More Related Content

What's hot

Behavioral assessment
Behavioral assessmentBehavioral assessment
Behavioral assessment
Iqra Shahzad
 
Psych 24 history of personality assessment
Psych 24 history of personality assessmentPsych 24 history of personality assessment
Psych 24 history of personality assessment
Maii Caa
 
CHAPTER 1 - PSYCHOLOGICAL TESTING AND MEASUREMENT.ppt
CHAPTER 1 - PSYCHOLOGICAL TESTING AND MEASUREMENT.pptCHAPTER 1 - PSYCHOLOGICAL TESTING AND MEASUREMENT.ppt
CHAPTER 1 - PSYCHOLOGICAL TESTING AND MEASUREMENT.ppt
kriti137049
 
PSY 239 401 Chapter 15 SLIDES
PSY 239 401 Chapter 15 SLIDESPSY 239 401 Chapter 15 SLIDES
PSY 239 401 Chapter 15 SLIDESkimappel
 
Cognitive psychology
Cognitive psychologyCognitive psychology
Cognitive psychology
WEEKLYMEDIC
 
Norms[1]
Norms[1]Norms[1]
Norms[1]
Milen Ramos
 
Research Methods In Social Psychology
Research Methods In Social PsychologyResearch Methods In Social Psychology
Research Methods In Social PsychologyMostafa Ewees
 
Qualitative methods in Psychology Research
Qualitative methods in Psychology ResearchQualitative methods in Psychology Research
Qualitative methods in Psychology Research
Dr. Chinchu C
 
Neuropsychological Assessment
Neuropsychological AssessmentNeuropsychological Assessment
Neuropsychological Assessment
Dr. Sunil Suthar
 
Ethical issues in psychological research
Ethical issues in psychological researchEthical issues in psychological research
Ethical issues in psychological research
Geetesh Kumar Singh
 
Aaron Becks Cognitive Therapy
Aaron Becks Cognitive TherapyAaron Becks Cognitive Therapy
Aaron Becks Cognitive Therapy
AgnesRizalTechnological
 
Personality theory ppt ch04 adler individual psychology
Personality theory ppt ch04 adler individual psychologyPersonality theory ppt ch04 adler individual psychology
Personality theory ppt ch04 adler individual psychology
Mehreen Khan
 
Adlerian therapy
Adlerian therapyAdlerian therapy
Adlerian therapy
jspence2
 
filter & capacity theories.pptx
filter  & capacity theories.pptxfilter  & capacity theories.pptx
filter & capacity theories.pptx
Rajnesh5
 
Edward personal preference scales
Edward personal preference scalesEdward personal preference scales
Edward personal preference scales
Soumya Ranjan Parida
 
Clinical Interview
Clinical InterviewClinical Interview
Clinical Interview
Prabhleen Arora
 
Clinical assessment and diagnosis (1)
Clinical assessment and diagnosis (1)Clinical assessment and diagnosis (1)
Clinical assessment and diagnosis (1)
Devika Manulal
 
Psychophysics - Siddhartha
Psychophysics - SiddharthaPsychophysics - Siddhartha
Psychophysics - Siddhartha
Siddhartha A
 
Wechsler Intelligence and Memory Scales
Wechsler Intelligence and Memory ScalesWechsler Intelligence and Memory Scales
Wechsler Intelligence and Memory Scales
Nanza Gonda
 
Inkblot test (rorschach inkblot)
Inkblot test (rorschach  inkblot)Inkblot test (rorschach  inkblot)
Inkblot test (rorschach inkblot)
Muhammad Musawar Ali
 

What's hot (20)

Behavioral assessment
Behavioral assessmentBehavioral assessment
Behavioral assessment
 
Psych 24 history of personality assessment
Psych 24 history of personality assessmentPsych 24 history of personality assessment
Psych 24 history of personality assessment
 
CHAPTER 1 - PSYCHOLOGICAL TESTING AND MEASUREMENT.ppt
CHAPTER 1 - PSYCHOLOGICAL TESTING AND MEASUREMENT.pptCHAPTER 1 - PSYCHOLOGICAL TESTING AND MEASUREMENT.ppt
CHAPTER 1 - PSYCHOLOGICAL TESTING AND MEASUREMENT.ppt
 
PSY 239 401 Chapter 15 SLIDES
PSY 239 401 Chapter 15 SLIDESPSY 239 401 Chapter 15 SLIDES
PSY 239 401 Chapter 15 SLIDES
 
Cognitive psychology
Cognitive psychologyCognitive psychology
Cognitive psychology
 
Norms[1]
Norms[1]Norms[1]
Norms[1]
 
Research Methods In Social Psychology
Research Methods In Social PsychologyResearch Methods In Social Psychology
Research Methods In Social Psychology
 
Qualitative methods in Psychology Research
Qualitative methods in Psychology ResearchQualitative methods in Psychology Research
Qualitative methods in Psychology Research
 
Neuropsychological Assessment
Neuropsychological AssessmentNeuropsychological Assessment
Neuropsychological Assessment
 
Ethical issues in psychological research
Ethical issues in psychological researchEthical issues in psychological research
Ethical issues in psychological research
 
Aaron Becks Cognitive Therapy
Aaron Becks Cognitive TherapyAaron Becks Cognitive Therapy
Aaron Becks Cognitive Therapy
 
Personality theory ppt ch04 adler individual psychology
Personality theory ppt ch04 adler individual psychologyPersonality theory ppt ch04 adler individual psychology
Personality theory ppt ch04 adler individual psychology
 
Adlerian therapy
Adlerian therapyAdlerian therapy
Adlerian therapy
 
filter & capacity theories.pptx
filter  & capacity theories.pptxfilter  & capacity theories.pptx
filter & capacity theories.pptx
 
Edward personal preference scales
Edward personal preference scalesEdward personal preference scales
Edward personal preference scales
 
Clinical Interview
Clinical InterviewClinical Interview
Clinical Interview
 
Clinical assessment and diagnosis (1)
Clinical assessment and diagnosis (1)Clinical assessment and diagnosis (1)
Clinical assessment and diagnosis (1)
 
Psychophysics - Siddhartha
Psychophysics - SiddharthaPsychophysics - Siddhartha
Psychophysics - Siddhartha
 
Wechsler Intelligence and Memory Scales
Wechsler Intelligence and Memory ScalesWechsler Intelligence and Memory Scales
Wechsler Intelligence and Memory Scales
 
Inkblot test (rorschach inkblot)
Inkblot test (rorschach  inkblot)Inkblot test (rorschach  inkblot)
Inkblot test (rorschach inkblot)
 

Viewers also liked

Modelling Illusory Conjunction With S So Ts
Modelling Illusory Conjunction With S So TsModelling Illusory Conjunction With S So Ts
Modelling Illusory Conjunction With S So Tsalastair_charles_smith
 
Introduction to Data Visualization
Introduction to Data Visualization Introduction to Data Visualization
Introduction to Data Visualization
Ana Jofre
 
Integration theory short.wmv - copy (2)
Integration theory   short.wmv - copy (2)Integration theory   short.wmv - copy (2)
Integration theory short.wmv - copy (2)
Cherie Phillips
 
SLA Nov2009 Public
SLA Nov2009 PublicSLA Nov2009 Public
SLA Nov2009 Public
aspoerri
 
Information Visualization - not just eye candy
Information Visualization - not just eye candyInformation Visualization - not just eye candy
Information Visualization - not just eye candy
Jan Srutek
 
Attention and Consciousness
Attention and ConsciousnessAttention and Consciousness
Attention and Consciousness
orengomoises
 
Visual thinking colin_ware_lectures_2013_14_pre-attentive processing and high...
Visual thinking colin_ware_lectures_2013_14_pre-attentive processing and high...Visual thinking colin_ware_lectures_2013_14_pre-attentive processing and high...
Visual thinking colin_ware_lectures_2013_14_pre-attentive processing and high...Elsa von Licy
 
Seeing Software
Seeing SoftwareSeeing Software
Seeing Software
Michele Lanza
 
Tulving episodic semantic
Tulving episodic semanticTulving episodic semantic
Tulving episodic semantic
John Turner
 
Hemispatial neglect2007
Hemispatial neglect2007Hemispatial neglect2007
Hemispatial neglect2007
Tris Matthews
 
Examples for leverage points
Examples for leverage pointsExamples for leverage points
Examples for leverage pointsGeorges Grinstein
 
The Role of Pre-Attention in UI Design
The Role of Pre-Attention in UI DesignThe Role of Pre-Attention in UI Design
The Role of Pre-Attention in UI Design
Stephen Denning
 
Memory
MemoryMemory
Memory
luvjoy1
 
Sensation Part 4
Sensation Part 4Sensation Part 4
Sensation Part 4
Sam Georgi
 
Memory and Models of Memory
Memory and Models of MemoryMemory and Models of Memory
Memory and Models of Memory
cowmoo83
 
Chapter 4 Psych 1 Online Stud
Chapter 4 Psych 1 Online StudChapter 4 Psych 1 Online Stud
Chapter 4 Psych 1 Online StudMosslera
 
Lecture3:Chapter5-Perception..Dr.Anna
Lecture3:Chapter5-Perception..Dr.AnnaLecture3:Chapter5-Perception..Dr.Anna
Lecture3:Chapter5-Perception..Dr.Anna
AHS_student
 
Forgetting and theories of forgetting
Forgetting and theories of forgettingForgetting and theories of forgetting
Forgetting and theories of forgetting
samreennaz5
 

Viewers also liked (20)

Modelling Illusory Conjunction With S So Ts
Modelling Illusory Conjunction With S So TsModelling Illusory Conjunction With S So Ts
Modelling Illusory Conjunction With S So Ts
 
Introduction to Data Visualization
Introduction to Data Visualization Introduction to Data Visualization
Introduction to Data Visualization
 
Integration theory short.wmv - copy (2)
Integration theory   short.wmv - copy (2)Integration theory   short.wmv - copy (2)
Integration theory short.wmv - copy (2)
 
SLA Nov2009 Public
SLA Nov2009 PublicSLA Nov2009 Public
SLA Nov2009 Public
 
Information Visualization - not just eye candy
Information Visualization - not just eye candyInformation Visualization - not just eye candy
Information Visualization - not just eye candy
 
Attention and Consciousness
Attention and ConsciousnessAttention and Consciousness
Attention and Consciousness
 
Visual thinking colin_ware_lectures_2013_14_pre-attentive processing and high...
Visual thinking colin_ware_lectures_2013_14_pre-attentive processing and high...Visual thinking colin_ware_lectures_2013_14_pre-attentive processing and high...
Visual thinking colin_ware_lectures_2013_14_pre-attentive processing and high...
 
Memory models
Memory modelsMemory models
Memory models
 
Seeing Software
Seeing SoftwareSeeing Software
Seeing Software
 
Tulving episodic semantic
Tulving episodic semanticTulving episodic semantic
Tulving episodic semantic
 
Hemispatial neglect2007
Hemispatial neglect2007Hemispatial neglect2007
Hemispatial neglect2007
 
Examples for leverage points
Examples for leverage pointsExamples for leverage points
Examples for leverage points
 
The Role of Pre-Attention in UI Design
The Role of Pre-Attention in UI DesignThe Role of Pre-Attention in UI Design
The Role of Pre-Attention in UI Design
 
Memory
MemoryMemory
Memory
 
Working memory model
Working memory modelWorking memory model
Working memory model
 
Sensation Part 4
Sensation Part 4Sensation Part 4
Sensation Part 4
 
Memory and Models of Memory
Memory and Models of MemoryMemory and Models of Memory
Memory and Models of Memory
 
Chapter 4 Psych 1 Online Stud
Chapter 4 Psych 1 Online StudChapter 4 Psych 1 Online Stud
Chapter 4 Psych 1 Online Stud
 
Lecture3:Chapter5-Perception..Dr.Anna
Lecture3:Chapter5-Perception..Dr.AnnaLecture3:Chapter5-Perception..Dr.Anna
Lecture3:Chapter5-Perception..Dr.Anna
 
Forgetting and theories of forgetting
Forgetting and theories of forgettingForgetting and theories of forgetting
Forgetting and theories of forgetting
 

Similar to The Feature-Integration of Attention_Jing

Niche comparisons 201606 para curso Lichos
Niche comparisons 201606 para curso LichosNiche comparisons 201606 para curso Lichos
Niche comparisons 201606 para curso Lichos
Town Peterson
 
Pitfalls of multivariate pattern analysis(MVPA), fMRI
Pitfalls of multivariate pattern analysis(MVPA), fMRI Pitfalls of multivariate pattern analysis(MVPA), fMRI
Pitfalls of multivariate pattern analysis(MVPA), fMRI
Emily Yunha Shin
 
Corr clust-kiel
Corr clust-kielCorr clust-kiel
Corr clust-kiel
Devdatt Dubhashi
 
DMTM 2015 - 06 Introduction to Clustering
DMTM 2015 - 06 Introduction to ClusteringDMTM 2015 - 06 Introduction to Clustering
DMTM 2015 - 06 Introduction to Clustering
Pier Luca Lanzi
 
Duality in AdS/CFT, Chicago 7 Nov. 2014
Duality in AdS/CFT, Chicago 7 Nov. 2014Duality in AdS/CFT, Chicago 7 Nov. 2014
Duality in AdS/CFT, Chicago 7 Nov. 2014
Sebastian De Haro
 
SPATIAL POINT PATTERNS
SPATIAL POINT PATTERNSSPATIAL POINT PATTERNS
SPATIAL POINT PATTERNS
LiemNguyenDuy
 
DMTM Lecture 15 Clustering evaluation
DMTM Lecture 15 Clustering evaluationDMTM Lecture 15 Clustering evaluation
DMTM Lecture 15 Clustering evaluation
Pier Luca Lanzi
 
Local vs. Global Models for Effort Estimation and Defect Prediction
Local vs. Global Models for Effort Estimation and Defect Prediction Local vs. Global Models for Effort Estimation and Defect Prediction
Local vs. Global Models for Effort Estimation and Defect Prediction
CS, NcState
 

Similar to The Feature-Integration of Attention_Jing (8)

Niche comparisons 201606 para curso Lichos
Niche comparisons 201606 para curso LichosNiche comparisons 201606 para curso Lichos
Niche comparisons 201606 para curso Lichos
 
Pitfalls of multivariate pattern analysis(MVPA), fMRI
Pitfalls of multivariate pattern analysis(MVPA), fMRI Pitfalls of multivariate pattern analysis(MVPA), fMRI
Pitfalls of multivariate pattern analysis(MVPA), fMRI
 
Corr clust-kiel
Corr clust-kielCorr clust-kiel
Corr clust-kiel
 
DMTM 2015 - 06 Introduction to Clustering
DMTM 2015 - 06 Introduction to ClusteringDMTM 2015 - 06 Introduction to Clustering
DMTM 2015 - 06 Introduction to Clustering
 
Duality in AdS/CFT, Chicago 7 Nov. 2014
Duality in AdS/CFT, Chicago 7 Nov. 2014Duality in AdS/CFT, Chicago 7 Nov. 2014
Duality in AdS/CFT, Chicago 7 Nov. 2014
 
SPATIAL POINT PATTERNS
SPATIAL POINT PATTERNSSPATIAL POINT PATTERNS
SPATIAL POINT PATTERNS
 
DMTM Lecture 15 Clustering evaluation
DMTM Lecture 15 Clustering evaluationDMTM Lecture 15 Clustering evaluation
DMTM Lecture 15 Clustering evaluation
 
Local vs. Global Models for Effort Estimation and Defect Prediction
Local vs. Global Models for Effort Estimation and Defect Prediction Local vs. Global Models for Effort Estimation and Defect Prediction
Local vs. Global Models for Effort Estimation and Defect Prediction
 

Recently uploaded

Search and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical FuturesSearch and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical Futures
Bhaskar Mitra
 
Connector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a buttonConnector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a button
DianaGray10
 
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
UiPathCommunity
 
When stars align: studies in data quality, knowledge graphs, and machine lear...
When stars align: studies in data quality, knowledge graphs, and machine lear...When stars align: studies in data quality, knowledge graphs, and machine lear...
When stars align: studies in data quality, knowledge graphs, and machine lear...
Elena Simperl
 
Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*
Frank van Harmelen
 
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Jeffrey Haguewood
 
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdfFIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance
 
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
DanBrown980551
 
Leading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdfLeading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdf
OnBoard
 
PHP Frameworks: I want to break free (IPC Berlin 2024)
PHP Frameworks: I want to break free (IPC Berlin 2024)PHP Frameworks: I want to break free (IPC Berlin 2024)
PHP Frameworks: I want to break free (IPC Berlin 2024)
Ralf Eggert
 
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdfFIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance
 
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Thierry Lestable
 
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
Product School
 
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdfFIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance
 
DevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA ConnectDevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA Connect
Kari Kakkonen
 
The Art of the Pitch: WordPress Relationships and Sales
The Art of the Pitch: WordPress Relationships and SalesThe Art of the Pitch: WordPress Relationships and Sales
The Art of the Pitch: WordPress Relationships and Sales
Laura Byrne
 
Knowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and backKnowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and back
Elena Simperl
 
Bits & Pixels using AI for Good.........
Bits & Pixels using AI for Good.........Bits & Pixels using AI for Good.........
Bits & Pixels using AI for Good.........
Alison B. Lowndes
 
GraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge GraphGraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge Graph
Guy Korland
 
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Product School
 

Recently uploaded (20)

Search and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical FuturesSearch and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical Futures
 
Connector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a buttonConnector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a button
 
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
 
When stars align: studies in data quality, knowledge graphs, and machine lear...
When stars align: studies in data quality, knowledge graphs, and machine lear...When stars align: studies in data quality, knowledge graphs, and machine lear...
When stars align: studies in data quality, knowledge graphs, and machine lear...
 
Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*
 
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
 
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdfFIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
 
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
 
Leading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdfLeading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdf
 
PHP Frameworks: I want to break free (IPC Berlin 2024)
PHP Frameworks: I want to break free (IPC Berlin 2024)PHP Frameworks: I want to break free (IPC Berlin 2024)
PHP Frameworks: I want to break free (IPC Berlin 2024)
 
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdfFIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
 
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
 
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
 
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdfFIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
 
DevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA ConnectDevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA Connect
 
The Art of the Pitch: WordPress Relationships and Sales
The Art of the Pitch: WordPress Relationships and SalesThe Art of the Pitch: WordPress Relationships and Sales
The Art of the Pitch: WordPress Relationships and Sales
 
Knowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and backKnowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and back
 
Bits & Pixels using AI for Good.........
Bits & Pixels using AI for Good.........Bits & Pixels using AI for Good.........
Bits & Pixels using AI for Good.........
 
GraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge GraphGraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge Graph
 
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...
 

The Feature-Integration of Attention_Jing

  • 1. A Feature-Integration Theory of Attntion Anne Treisman & Garry Gelade (1980) Jing Chen
  • 2. Outline • The Feature-Integration Theory of Attention • Paradigms/Experiments • Visual search (Exps 1, 2, &3) • Illusory conjunctions (Exp 4) • Texture segregation (Exp 5, 6, & 7) • Identity and location (Exp 8 & 9) • Conclusion
  • 3. The Feature-Integration Theory of Attention • Features are registered early, automatically, and in parallel across the visual field, while objects are identified separately and only at a later stage, which requires focused attention to “glue” features together. • Attention is necessary for the correct perception of conjunctions, although unattended features are also conjoined prior to conscious perception. • “Illusory conjunctions”
  • 4. Dimension vs. Feature • “Dimension” refers to the complete range of variation • “Feature” refers to a particular value on a dimension • Thus color and orientation are dimensions; red and vertical are features on those dimensions.
  • 5. Experiment 1: Visual Search • Purpose: • To compare search for disjunction targets and conjunction targets; • To explore the effect of extended practice on serial and parallel search; • Among the distractors Tbrown and Xgreen • Disjunction targets: a blue letter or an S; • Conjunction targets: Tgreen; • Setsize: 1, 5, 15, and 30
  • 6. Disjunction Target Find the blue letter • Easy: • Just as Easy: X T X T T T X T X T X T X T X X T X T T X T T X T X T T X X T X T X X X X X T X T X T X T T X T T X T T X T X T
  • 7. Conjunction Target Find the green “T” • Hard: • Even Harder: X T X T X T X T T T X T X T T X X T X X T X T T T X X X T X X T X T T X X X T X T X T X T T X T T X T T X T X T
  • 8. Experiment 1: Results • Conjunction: search time increased linearly with setsize; search is serial and self- terminating; • Disjunction: for positive displays, search times were hardly affected by setsize; for negative displays, the relationship is linear;
  • 9. Experiment 1: Results • The effect of extended practice: • There is little indication of any change in the pattern of results; • no sign of a switch from serial to parallel search.
  • 10. Experiment 1: Discussion • Focal attention, scanning successive locations serially, is the means by which the correct integration of features is ensured. • When this integration is not required by the task, parallel detection of features should be possible.
  • 11. Experiment 2: Visual Search • Purpose: to explore the relation between the discriminability of the features of a conjunction and the speed (slop) of detecting that conjunction as a target. • Compare • Difficult condition: a conjunction target in distractors similar to it (T in X and T); • Easy condition: distractors differed maximally from the target (O in O and N).
  • 12. Experiment 2: Results • The slopes in the difficult discrimination are nearly three times larger than those in the easy discrimination • but the linearity and the 1/2 slope ratio is preserved across these large differences.
  • 13. Experiment 2: Discussion • The search rates vary dramatically for easy and difficult conditions. • In both easy and difficult conditions for the conjunction targets, the search were serial, self- terminating. • As a result, we cannot say that search becaomes serial only when it is difficult.
  • 14. Experiment 3: Visual Search • Purpose: to explore an alternative explanation for the difference between conjunction and disjunction targets. • attributes the difficulty of the conjunction condition to the centrality of the target in the set of distracters. • Replicate this aspect of the similarity by using unidimensional stimuli, with no need for checking conjunctions.
  • 15. Experiment 3: Results • The pattern of results is quite “Central” different from that obtained with the color-shape conjunctions and disjunctive features. • When the intermediate target is present, its detection doesn’t depend on a serial check of the distractor, which the detection of the conjunction did.
  • 16. Experiment 4: Letter Search • Purpose: to discover whether integrative attention is required even with highly familiar stimuli, e.g., letters • Confusability of letters • Letters would be difficult to search when they are similar in a wholistic way. • R/PB • Sets of letters would be confusable if their features were interchangeable and could potentially lead to illusory conjunctions. • R/PQ
  • 17. Experiment 4: Letter Search • Purpose: to discover whether integrative attention is required even with highly familiar stimuli, e.g., letters • The conjunction condition (with interchangeable features): R/PQ and T/IZ • The similarity condition (with greater target/distractor similarity): R/PB and T/IY
  • 18. Experiment 4: Results • The ratio of positive to negative slopes differed for the conjunction and the similarity conditions: • For the conjunctions, it was 0.45, which is close to half and suggests a serial self- terminating search. • For the similarity condition, it was much lower (0.26).
  • 19. Experiment 4: Discussion • Letter search would be serial and self-terminating if the particular sets of distractor and target letters were composed of perceptually separable features which could be wrongly recombined to yield conjunction errors. • Otherwise search could be parallel (although not necessarily with unlimited capacity and no interference).
  • 20. Experiment 5: Texture Segregation • Purpose: to investigates the “preattentive” segregation of groups and textures, which could guide the subsequent direction of attention. • Five rows * five columns; card sorting task: • The color condition: OV|OV • The shape condition: OO|VV • The conjunction condition: OV|OV OOVOO VOVVV VOVOV The task was to sort the packs of cards into two OOVVO piles, one containing cards with a horizontal and one OVVOV with a vertical boundary.
  • 21. Experiment 5: Results • The difference between the two feature packs and the conjunction pack was qualitative and obvious. Face-up Face-down • The color condition: OV|OV 15.9 25.1 • The shape condition: OO|VV 16.2 25.6 • The conjunction condition: OV|OV 24.4 35.2 • • Suggesting that the boundary cannot be directly perceived in the conjunction condition and has to be inferred from attentive scanning.
  • 22. Experiment 6: Texture Segregation • Purpose: to discover whether the advantage of the feature boundary was due to • In the feature pack, only one dimension was relevant • But the conjunction pack, require attention to both dimensions. • Change the feature display into multiple-dimensional one: OΠ|OV (26.9 vs. 32.9 sec) • Results: • The disjunctive features appear slightly less effective than single features. • The relevance of two dimensions rather than a single dimension can explain only a small fraction of the difference between features and conjunctions.
  • 23. Experiment 7: Texture Segregation • Purpose: to see whether the distinction between features and conjunctions is equally crucial when the features are local components of more complex shapes rather than values on different dimensions (as in 5&6). • The single feature conditions (short diagonal line): • PO/RQ (779 ms) • EO/FQ (799 ms) Again, what matters is • The conjunction conditions: whether the boundary is • PQ/RO (978 ms) defined by a single feature or • FK/EX (1114 ms) a conjunction of feature
  • 24. Experiments 8&9: Spatial Location • Purpose: to test whether precise information about spatial location is available at the feature level, • by looking at the dependency between reports of identity and reports of location on each trial. • Difference between Exps 8 & 9: • In Exp 8, the presentation times of the arrays were chosen to make sure the accuracy in each condition was 80%. • In Exp 9, equal presentation times were used for features and conjunctions.
  • 25. Experiments 8&9: Spatial Location • All distractors were OX • The targets: • The disjunctive feature condition: H, H, X, or O • The conjunction condition: X or O OXOXOO • Dependent variable: OXOXHX • accuracy with brief exposures
  • 26. Experiments 8&9: Results The conditional probabilities follow a very similar pattern. It seems likely that in order to focus attention on an item, we must spatially localize it and direct attention to its location. Feature localization is a special kind of conjunction task (feature and spatial location).
  • 27. Conclusions • All the data taken together support the feature- integration theory of attention. • Separable features can be detected and identified in a early, parallel process; • this process mediates texture segregation; • locating any individual feature requires an additional operation; • conjunctions of features require focal attention to be directed serially.

Editor's Notes

  1. If, as we assume, simple features can be detected in parallel with no attention limits, the search for targets defined by such features (e.g., red, or vertical) should be little affected by variations in the number of distracters in the display. focal attention is necessary for the detection of targets that are defined by a conjunction of properties (e.g., a vertical red line in a background of horizontal red and vertical green lines). Such targets should therefore be found only after a serial scan of varying numbers of distracters.
  2. :a conjunction target shares one or another feature with every distractor in the display, while each disjunctive feature target shares a feature with only half the distracters. In this sense, the conjunction targets are more similar to the set of distracters than the feature targets.
  3. Control condition 1 (with a single type of distractor): R/Q and R/BControl condition 2 (distractor heterogeneity: PQ vs. PB): T/PQ
  4. Control condition 1 (with a single type of distractor): R/Q and R/BControl condition 2 (distractor heterogeneity: PQ vs. PB): T/PQ