information search (shneiderman and plaisant, ch. 13) from

Information Search(Shneiderman and Plaisant, Ch. 13)

from http://wps.aw.com/aw_shneider_dtui_13

Overview

• Introduction– “Information search should be a joyous experience”

• Searching in Textual Documents

• Multimedia Document Searches

• Advanced Filtering and Search Interfaces

• Information Foraging– A forest and some trees …

Information Search• Critical need to access information, as part of any task

– always has been, always will be (ahbawb)– Cultural change, if not evolution, due to amount of information accessible by

individual

• “Information overload” – ahbawb– What’s new is ubiquity due to massive e-access

• Old school “information retrieval” and “end

user searching”– Gurus and cost

• Genuinely new …– Interest, due to market/user size

• E.g., search engines can be profitable

– tools, e.g., visualization, due to Moore’s law

Information Search - Words

• Old school– Information retrieval, database management– Bibliographic document systems, structured relational db – attributes

• New school– Information gathering, seeking, filtering, sensemaking, visual analytics

• CS focus– Data mining, data warehouses, data marts

• Toward future ends such as– Knowledge networks, semantic webs, …

• Range of search elements increases– Cf. Hearst November, 2011 CACM paper, “collaborative search” (on web site)

Search Terminology

• Shneiderman’s taxonomy

• Task objects • E.g., movies for rent, are stored in structured relational databases,

textual document libraries, or multimedia document libraries

• Structured relational database • relations and a schema to describe the relations• Relations have items (usually called tuples or records), and each item

has multiple attributes (often called fields), which each have attribute values

• Textual document library • Set of collections

• typically up to a few hundred collections per library• descriptive attributes or metadata about the library

• E.g., name, location, owner

Search Terminology, 2

• Task actions are decomposed into browsing or searching

• Examples of task actions in information search:

- Specific fact finding (known-item search)• Find the e-mail address of the President of the United States

- Extended fact finding• What other books are by the author of “Jurassic Park”?

- Exploration of availability• Is there new work on voice recognition in the ACM digital library?

- Open-ended browsing and problem analysis• Is there new research on fibromyalgia that might help my patient?

Search Terminology, 3

• Once users have clarified their information needs, the first step towards satisfying those needs is deciding where to search

• Supplemental finding aids can help users to clarify and pursue their information needs, e.g. table of contents or indexes

• Additional preview and overview surrogates for items and collections can be created to facilitate browsing

Searching Textual Documents

• As noted, recent dramatic changes

• Historically, Boolean clause search and SQL

• Other methods include:- Natural language queries- Form fill-in- Query by example (QBE)

• Evidence shows that users perform better and have higher satisfaction when they can view and control the search

Ex., Library of Congress

• Aids to find bills, etc

• “Multiple paths to information items”

• (had a look, just for fun)

– Not bad

Ex., Library of Congress

• Aids to find bills, etc

Ex., Library of Congress

• Aids to find bills, etc

Ex., Library of Congress

• Aids to find bills, etc

Searching in Textual Documentsand Database Querying

Searching in Textual Documentsand Database Querying, 2

A search for “user interface” powered by Endeca (http://www.lib.ncsu.edu) returns144 results grouped into 10 pages. The menu at the upper right allows users to sortresults by relevance or by date, while on the left a summary of the results organizedby Subject, Genre, or Format provides an overview of the results and facilitatesfurther refinement of the search.

Framework for Textual Search

• Recall, task delineation for interface design• Shneiderman suggests stages to consider in textual search• Overview below, detail, next slide:

• Formulation: expressing the search

• Initiation of action: launching the search

• Review of results: reading messages and outcomes

• Refinement: formulating the next step

• Use: compiling or disseminating insight

5 Stages of Textual Search - Detail

• Yet another “taxonomy and guidelines” from Shneiderman …

Multimedia Document Searches

• “Multimedia” (non-textual) search is hard

Multimedia Document Searches

• “Multimedia” (non-textual) search is hard• Quickly evolving area• Interface issues essentially undefined

• “Hum that tune”, “what did he/she/it look like”

• Types:• Image search• Map search• Design or diagram search• Sound search• Video search• Animation search

Image Search

• Finding photos with images such as the Statue of Liberty is a challenge

• Query-by-Image-Content (QBIC) is difficult• Search by profile (shape of lady), distinctive features (torch), colors

(green copper)

• Simple drawing tools to build templates or profiles to search with

• More success is attainable by searching restricted collections • Search a vase collection • Find a vase with a long neck by drawing a profile of it

• Critical searches such as fingerprint matching requires a minimum of 20 distinct features

• For small collections effective browsing and lightweight annotation are important

Map Search• On-line maps are plentiful

• Search by latitude/longitude is the structured-database solution

• Today's maps are allow utilizing structured aspects and multiple layers– City, state, and site searches – Flight information searches – Weather information searches – Mapquest, Google Maps, etc.

• Mobile devices can allow “here” as a point of reference

Other Multimedia Searches• Design/Diagram Searches

– Some computer-assisted design packages support search of designs– Allows searches of diagrams, blueprints, newspapers, etc., e.g. search

for a red circle in a blue square or a piston in an engine – Document-structure recognition for searching newspapers

• Sound Search

• Video Search – Provide an overview– Segmentation into scenes and frames– Support multiple search methods

• Animation Search – Possible to search for specific animations like a spinning globe – Search for moving text on a black background

Image Search

• Sketch or image to start

• Also, see Google

Advanced Filtering & Search Interfaces

• Wide range of interface strategies and styles

• Filtering with complex Boolean queries• Automatic filtering• Dynamic queries• Faceted metadata search• Query by example• Implicit search• Collaborative filtering• Multilingual searches• Visual field specification

Advanced Filtering and Search Interface Examples, 1

• Alternatives to form fill-in query interfaces:

• Filtering with complex Boolean queries• Problem with informal English, e.g. use of ‘and’ and ‘or’• Venn diagrams, decision tables, etc., not worked for complex queries

• Dynamic Queries• “Direct manipulation” queries • Use sliders and other related controls to adjust the query • Get immediate (less than 100 msec) feedback with data • Dynamic HomeFinder and Blue Nile and (sort of) Realtor.com• Hard to update fast with large databases

Dynamic Queries

• Diamond price, rating indicated using sliders, etc.

Advanced Filtering and Search Interface Examples, 1a

• Alternatives to form fill-in query interfaces:

• Filtering with complex Boolean queries• Problem with informal English, e.g. use of ‘and’ and ‘or’• Venn diagrams, decision tables, etc., not worked for complex queries

• Dynamic Queries• “Direct manipulation” queries • Use sliders and other related controls to adjust the query • Get immediate (less than 100 msec) feedback with data • Dynamic HomeFinder and Blue Nile and (sort of) Realtor.com• Hard to update fast with large databases

• Query previews present an overview to give users information and distribution of data to eliminate undesired items

• Faceted metadata search• Integrates category browsing with keyword searching• Flameco

Faceted Metadata

• Facets include media, location, date, themes

Advanced Filtering and Search Interface Examples, 2

• Collaborative Filtering – Groups of users combine evaluations to help in finding items in a large

database

– User "votes" and info used for rating the item of interest, • e.g. Rating restaurants highly is given a list of restaurants also rated highly by those

who agree the six are good

• Multilingual searches– Current systems provide rudimentary translation searches

– Prototypes of systems with specific dictionaries and more sophisticated translation

• Visual searches– Specialized visual representations of possible values, e.g. dates on a

calendar or seats on a plane

– On a map the location may be more important than the name

– Implicit initiation and immediate feedback

Tree Map of Products

(Shneiderman)

Using The Hive Group’s treemap (http://www.hivegroup.com/), users can review all waterproof binoculars in the catalog of Amazon.com products and browse the items in the list, grouped by manufacturer. Each box corresponds to a pair of binoculars, and the size of the box is proportional to its price. Green boxes are best-sellers. Users can filter the results using the dynamic query sliders on the right. Here all the binoculars with less than three user reviews have been filtered out, leaving only 61 binoculars to consider.

Cost of Knowledge, Cognition, and Computers, 1

• Information systems (primarily computer) and “cost” of acquiring knowledge

– A first principle of information system design– “Cognitive information ergonomics”

• Efficiency/productivity gain/usability/…

• There is (and has always been) a cost to acquire information / knowledge

– cost = user/worker time +, e.g., machine cost, db access charge, book

– Concerned with • “economics of cognition and the

cognitive cost of knowledge”

Cost of Knowledge, Search,Cognition, and Computers

• Information systems (computers) and “cost” of acquiring knowledge

Cost of Knowledge, Search,Cognition, and Computers

• Information systems (computers) and “cost” of acquiring knowledge– A first principle of information system design– “Cognitive information ergonomics”

• Efficiency/productivity gain/usability/…– “Economics of cognition and the cognitive cost of knowledge”

• There is (and has always been) a cost to acquire information / knowledge

– cost = user/worker time +, e.g., machine cost, db access charge, book

• Many studies fail to document increased profit directly from implementation of (single) information system

– However, no doubt that worker productivity in late 20th century dramatically increased

– Productivity greatly enhanced by pervasive use electronic information systems (computers)

Informavores and Information Foraging

• That human quest for information is innate and adaptive is well known

• Humans are informavores– George Miller, 1983, “… magic number 7 + 2”– Organisms that hunger for information about the world and

themselves

• “A wealth of information creates a poverty of attention and a need to allocate it efficiently”

– Herb Simon, AI, Nobel prize, economics, cognition

• Consider analogy of acquiring knowledge with animals seeking food

– Pirolli, P. and S. Card (1995). Information Foraging in Information Access Environments, in CHI '95, p. 518

– Pirolli, P. (2004) in Carroll (ed.), on web site– Pirolli, P. (2007) ….. Book …..– Countless secondary sources

Information Foraging Theory (IFT)

• Information Foraging Theory (IFT)– Pirolli and Card – Xerox PARC– “an approach to the analysis of human activities involving information access

technologies”– Derives from optimal foraging theory in biology and anthropology

• Analyzes adaptive value of food-foraging strategies

• Analyzes trade-offs in value of information gained against the costs of performing activity in human-computer interaction tasks

– And need models and analysis techniques to determine value added by information access, manipulation, and presentation techniques

• Real information system design problem is not how to collect more information, but how to optimize user’s time

– Increase relevant information gained per unit time expended

• IFT provides a relatively “formal” (quantitative) account

IFT – Time Scales

• Considers “adaptiveness of human-system designs in the context of the information ecologies in which tasks are performed”

– Ecology, as system, here, information

• Time scales of information seeking and sense making activities:

– Cognitive band (~100 ms – 10 s)– Rational band (minutes to hours)– Social band (days to months)

• Have seen much of cognitive, now others

Time Scales of Analysis

Time scale (s)Psychologicaldomain

10-1000 • Problem solving• Decision making

1-100• Visual search• Motor behavior

Pete Pirolli's Home Page

Peter Pirolli. ... Palo Alto, CA 94304 USA phone: +1-650-812-4483 fax: +1-650-812-4241

email: pirolli@parc.xerox.com This page updated December 18, 2000.

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.100-1• Visual attention• Perceptual judgment

User Interface Domain

IFT – An Ecological Perspective

• Time scales of information seeking and sense making activities– Cognitive band (~100 ms – 10 s)– Rational band (minutes to hours)– Social band (days to months)

• As time scale increases, less regard for how internal processing accomplishes linking of actions to goals

• Assumes behavior governed by “rational principles and shaped by constraints and affordances of the task environment”

• An ecological perspective, i.e., that behavior is “adaptive” in that it accomplishes some goal

IFT – Metaphor and Quantitative

• Information Foraging Theory– name both a metaphor and straightforward use of biological “optimal foraging theory”

• Metaphor:– Animals adapt behavior and structure through evolution

• (humans don’t have to wait that long!)

– Animals adapt to increase their rate of energy intake, etc.• To do this they evolve different methods

• E.g., wolf hunts prey, spiders build webs and wait

• And there are analogies to this– E.g., hunting = active information seeking, waiting = information filtering– Humans (and others) hunt in groups - when variance of food is high

• Accept lower expected mean to minimize probability of days without food

– Also, on social time scale, sharing of information

Optimal Foraging Theory - Biology

• Developed in biology for understanding opportunities and forces of adaptation

– P&C use elements of the theory to help in understanding existing human adaptations for gaining and making sense of information

– Also, aid in task analysis for creating new interactive information system designs

• Optimality models include:– Decision assumptions

• Which of the problems faced by an agent are to be analyzed

– E.g., whether to pursue a particular type of information (or prey) when encountered, how long to spend

– Currency assumptions• How choices are to be evaluated, e.g., information value (food value)

– Constraint assumptions• Limit and define relationships among decision and currency variables

– E.g., from task structure, interface technology, user knowledge

Information Foraging Theory

• Information foraging usually a task embedded in context of some other task

– Value and cost structure defined in relation to the embedding task– Value of external information may be in improvements to outcomes of embedding

task

• Usually, embedding task is some ill-structured problem– Additional knowledge is needed to better define goals, available actions,

heuristics, etc.– E.g., choosing a graduate school, developing business strategy

• Though use optimality model, not imply human behavior is classically rational

– I.e., have perfect information and infinite computational resources– Rather, humans exhibit bounded rationality, or make choices based on satisficing

IFT – Information Patch ModelA formal (mathematical) model – actually, pretty straightforward

• Information patch model – from optimal foraging theory

• Rate of currency intake, R = U / (Ts + Th)– U = net amount of currency (value, e.g., food, information) gained

– Ts = time spent searching

– Th = time spent exploiting

• Net currency gain, U = Uf - Cf

– Uf = overall currency intake (gross amount foraged)

– Cf = currency expended in foraging

• Average rate of currency intake u = Uf / Ts

– If assume information workers/foragers/consumers encounter information as linear function of time (will revisit this)

– Total n items encountered = Ts, where is rate of encounter with items

– (will use next slide)

IFT – Information Patch Model

• Average cost of handling items (1st total/rate, the average) :

• Let s = search cost per unit time, then total cost of search = sTs

• Then, substituting in equation for R, rate of currency intake:

• So, can express R in terms of – Average rate of currency intake, u– Search cost per unit time, s– Cost of handling items, h

quickly …

IFT – Information Patch Model

• And so forth …

An Example: Scatter Gather

• Hierarchical clustering of document

• Users see “overview” of document clusters

• Allows user to navigate through clusters and overviews

Scatter/Gather Task

Scatter/GatherWindow

Law

World News

AI

CS

Medicine

Nat. Lang.

Robots

Expert Sys

Planning

Bayes. Nets

Display TitlesWindow

Optimal Foraging Time in a Patchcumulative gain functions, consider document relavency

• gi(t), cumulative gain function– Amt of information, y, gained in time t, x

– gA(t) = random order of encounter• Increase in information equal for all elements• Hence, constant slope

– gB(t) and gc(t) = ordered by relevancy• “Relevant” items, those with higher information

content, encountered earlier• Hence, highest rate of information increase earlier,

and rate decreases

• p, rate of encounter relevant items

• RB and RC = rate of return – Changes as f (time), increases, then dec.

• x-axis, travel time between patches

• tc and tb optimal foraging time– Foraging longer in the “patch” not optimal

Information gained

time

Optimal Foraging Time in a Patchor, how long to spend before moving on

• gi(t), cumulative information gain function– Amt of information gained in time t

– gA(t) = random order of encounter

– gB(t) and gc(t) = ordered by relevancy• gC encounters most relevant “faster” than (before) gB

• p, rate of encounter with relevant items

• x-axis, travel time between patches

• RB and RC = rate of return– Again, differential rate rel. item encounter

• tc and tb optimal foraging time– Foraging longer in the “patch” not optimal

IFT - Cost of Knowledge

• Foraging Efficiency– Animals minimize energy expenditure to get required gain in sustenance– Humans minimize effort to get necessary gain in information

• Again, foraging for food has much in common with seeking information– Like edible plants in wild, useful information items often grouped together,

but separated by long distances in an “information wasteland”

• Also, information “scent”– Like scent of food, information in current environment that will assist in

finding more information clusters

• Activities analyzed according to value gained and the cost incurred– Resource costs

• Expenditures of time and cognitive effort incurred

– Opportunity costs• Benefits that could be gained in engaging in other activities• “Cost of lost opportunity”

– E.g., if not gaining information about algorithms (or messing with registration system), could be gaining information about software design

IFT - Conclusion

• Information processing systems evolve so as to maximize the gain of valuable information per unit cost– Sensory systems (vision, hearing)

– Information access (card catalogs, offices)

information valuecost of interaction( )maximize

End?

• .

IFT - Cost of Knowledge

• Foraging Efficiency– Animals minimize energy expenditure to get required gain in sustenance– Humans minimize effort to get necessary gain in information

• Again, foraging for food has much in common with seeking information– Like edible plants in wild, useful information items often grouped together, but

separated by long distances in an “information wasteland”

• Also, information “scent” – a very popular metaphor– Like scent of food, information in current environment that will assist in finding more

information clusters

• Activities analyzed according to value gained and the cost incurred– The “cost of lost (or chosen) opportunity”

– Resource costs • Expenditures of time and cognitive effort incurred

– Opportunity costs• Benefits that could be gained in engaging in other activities• E.g., if not gaining information about visualization, could be gaining information about

software design

Information Scent

Tokyo

San Francisco

New YorkCues that facilitate orientation, navigation, assessment of information value

Information Scent

cell

patient

dose

beam

new

medical

treatments

procedures

InformationNeed

Text snippet

• Spreading activation– Derived from models of human memory– Activation reflects likelihood of relevance

given past history and current context– Approximates Bayesian network

0

1

2

3

4

5

6

7

1 2 3 4 5 6 7 8 9 10

Rank

Pro

bab

ilit

y r

ele

van

t

Observed Rating

Predicted Rating

Optional:Cluster selection (optimal diet

model)

Rank profitability

Rel

evan

t do

cum

ents

/sec

ond

0

2

4

6

8

10

12

14

16

0 1 2 3 4 5 6 7 8 9 10

Number of relevant documents in cluster

Time to process cluster =

Total relevant documentsTotal timeR =

R

Optimum

Choose clusters (in descending rank ) if > R

Optional:Enrichment vs. Exploitation

0 200 400 600 800 10000

.01

.02

.03

.04

.05

.06

R*SG

R*D

Time (sec)

Rat

e o

f ga

in

R*SG > R*

D R*D > R*

SG

relevant documentstime cost

if user chooses to display clusters now

if user choosesto display later(after more Scatter/Gather)

R=

Cost of Knowledge Characteristic Function

• Cost of knowledge characteristic function

– Webforager at right

• Measures access properties of a workspace

– Plot n objects can be accessed as a function of time cost of accessing them

– Expect a “balanced” workspace will exhibit an exponential relationship,

• Most conveniently displayed as a straight line in semi-log coordinates.

• For Webforager example, computation assumes

• Page in the Focus Position (hence the maximal occlusion),

• Desk is full• One row of pages from each of the discrete

Z-distances in the space is visible – Design of the space has been carefully set

up to permit this

Navigation as a Cost of Knowledge

• Intra-saccade – (0.04 sec) – Query execution

• An eye movement – (0.5 sec) – < 10 deg : 1 sec> 20 deg.

• A hypertext click – (1.5 sec, but loss of context)

• A pan or scroll – (3 sec, but don’t get far)

• Walking – (30 sec., don’t get far)

• Flying – (faster, can be tuned)

• Zooming, t = log – (scale change)

• Fisheye – (max 5x)

Models

• A popourri …

Human-Machine Problem Solving System(quick review – a “first principles” model)

• From first lecture or so:– Human is good at

• Hypothesis formation• Goal-directed search• Pattern recognition• Decisions in the presence of error and uncertainty

• Interface– Visual channel is highest-bandwidth from computer to human– Haptic channel is the only bi-directional modality

• Problem solving loop– People solve problems with diagrams differently from the way they do it without diagrams– Visualizations functions as memory extensions– Visualizations enable cognitive operations that would otherwise be impossible

- Computer is good at:•Perfect recall of facts•Quantitative display of complex models

Problem Solving with Visualizations,Ware’s (perceptually focused) Account

• Ware’s account (“theory”) of how thinking (and problem solving) can be augmented by visual queries on visualizations of data

• Based on 3-stage account presented in book:– 1st (lowest) stage,

• massively parallel processing of visual scene into elements of form, opponent colors, and elements of texture and motion

– 2nd (middle) stage, • pattern formation, providing basis for object and pattern

perception– 3rd (highest) stage,

• mechanism of attention pulls out objects and critical patterns form the pattern analysis subsystem to execute a visual query

• Content of visual working memory consists of “object files”

– a visual spatial map in egocentric coordinates that contains residual information about a small number of recently attended object

• Also present is visual query pattern that forms the basis for active visual search through the direction of attention

DisplayFeatures

Proto-objects andPatterns

VisualWorkingMemory

GIST

VisualQuery

VerbalWorkingMemory

Egocentric object andPattern map

Interaction Loop

Problem Solving with Visualizations,Ware’s Account

• Somewhat generic account of problem solving with emphasis on roles of visualization (and visual system)

• Set of embedded processes:

-Problem-solving strategy

-Visual query construction

-Pattern-finding loop

-Eye movement control loop

-Intrasac. image-scanning

Problem Solving with Visualizations,Ware’s Account

• Key features of visual thinking (and problem solving) process …

1. Problem components are identified that have potential solutions based on visual pattern discovery

– These are formulated into visual queries consisting of simple patterns

2. Eye-movement scanning strategies are used to search display for query patterns

3. Within each fixation, query determines which patterns are pulled from flux of pattern-analysis subsystems

– Patterns and objects are formed as transitory object files from proto-object and proto-pattern space

– Only a small number of objects or pattern components retained from one fixation to the next • These object files also provide links to verbal-propositional information in verbal working memory

– A small number of cognitive markers are placed in a spatial map of the problem space to hold partial solutions where necessary.

• Fixation and deeper processing are necessary for these markers to be constructed.

4. Links to verbal-propositional information are activated by icons or familiar patterns, bringing in other kinds of information

Problem Solving with Visualizations,Ware’s Account

• Somewhat generic account of problem solving with emphasis on roles of visualization (and visual system)

• Set of embedded processes:

-Problem-solving strategy

-Visual query construction

-Pattern-finding loop

-Eye movement control loop

-Intrasac. image-scanning

An Example – Edinburg to Chicago

• Planning trip with aid of map– Find route/destinations

• From here to Chicago– Have some extra time and

want to visit some places– So, not just minimum time

• Will examine each element in problem solving loop:

-Problem-solving strategy

-Visual query construction

-Pattern-finding loop

-Eye movement control loop

-Intrasac. image-scanning

Problem-solving Strategy

• To “solve problem”, first formulate set of requirements:

– Say, will take 10 days– See cities and different regions– Weight driving time with interest

of places to visit– Cost of mileage weighted less

than cost of lodging– Will use information sources

such as Internet & Barnes and Noble travel guides

– Etc.

-Problem-solving strategy

-Visual query construction

-Pattern-finding loop

-Eye movement control loop

-Intrasac. image-scanning

Visual Query Construction

• Establish location of various cities through series of preliminary visual queries

– Fixating city icon and label helps establish connection to verbal-propositional knowledge about city

– Little remains in working memory, but primed for later reactivation

• Now, path planning begins with major alternative routes

– Trading off travel time for “interest”– Note that roads are coded as major

and cities with size in visual representation

– More than just graph traversal• Paths have “value”, e.g., interest, as

well as “cost”, e.g., time

-Problem-solving strategy

-Visual query construction

-Pattern-finding loop

-Eye movement control loop

-Intrasac. image-scanning

Pattern-finding Loop – “exploration”

• Now find all acceptable routes, as defined in previous step

• Essentially, finding “contours”– E.g, EP, Denver, …, or, Hou, NO, St.

Louis

• Can hold 2 or 3 in working memory

• When route found, “code” and hold in verbal-propositional memory

– E.g., western, central (Miss. Valley), etc.

-Problem-solving strategy

-Visual query construction

-Pattern-finding loop

-Eye movement control loop

-Intrasac. image-scanning

Pattern-finding Loop – “refinement”

• Now find all acceptable routes, as defined in previous step

• Essentially, finding “contours”– E.g, EP, Denver, …, or, Hou, NO, St.

Louis

• Can hold 2 or 3 in working memory

• When route found, “code” and hold in verbal-propositional memory

– E.g., western, central (Miss. Valley), etc.

-Problem-solving strategy

-Visual query construction

-Pattern-finding loop

-Eye movement control loop

-Intrasac. image-scanning

Scr Pattern-finding Loop – “refinement”

• Now find all acceptable routes, as defined in previous step

• Essentially, finding “contours”– E.g, EP, Denver, …, or, Hou, NO, St.

Louis

• Can hold 2 or 3 in working memory

• When route found, “code” and hold in verbal-propositional memory

– E.g., western, central (Miss. Valley), etc.

-Problem-solving strategy

-Visual query construction

-Pattern-finding loop

-Eye movement control loop

-Intrasac. image-scanning

Eye Movement Control Loop

• Detailed execution of pattern-finding carried out through series of eye movements to capture major continuous paths meeting criteria

• Eye movements planned using task-weighted spatial map of proto-patterns

– Promising partial paths are given attention, starting with most significant

– Partial solutions marked by placeholders in egocentric spatial map

• E.g., mark Denver, Dallas or NO and explore around

-Problem-solving strategy

-Visual query construction

-Pattern-finding loop

-Eye movement control loop

-Intrasac. image-scanning

Intrasaccadic Scanning Loop

• Here, information available through single fixation is processed

• Sections of lines representing roads are processed through selective tuning of pattern-finding mechanism

– E.g., those going in wrong direction or are minor are rejected

– Only 3 or 4 small sections held in working memory at a time

• City names also processed, contributing to overall process

-Problem-solving strategy

-Visual query construction

-Pattern-finding loop

-Eye movement control loop

-Intrasac. image-scanning