Expertise fine-tunes mental representations of targets during challenging visual search

We all know what it means to be

an expert on something, even if

that something is a hobby.

EXPERTISE IN VISUAL SEARCH

Contact information: mhout@nmsu.edu; Website: www.michaelhout.com.

RESULTS, BEHAVIORAL

Recorded eye-movements over (up

to) 23 experimental sessions (n = 5).

Selected items highlighted in black.

“Stop sign” indicated searcher was

finished.

5 blocks of 40 trials, per session.

Equal number of 0-3 target trials.

Participants acquired points for

accuracy (hits +1; misses/false-

alarms -1).

Block-level feedback was provided

regarding performance.

PROCEDURE

Largely qualitative analysis, given the small

sample size. Each dot is one participant, one

session.

Participants approached perfect performance (300

points), arguably acquiring near-expert skill levels.

Over time, they more quickly located any/all

targets, and hastened their decision-making

regarding when to stop searching.

RESULTS, EYE-TRACKING

RESULTS, FREQUENCY EFFECTS

CONCLUSION

Frequency (aka “prevalence”) effects on accuracy diminished, but did not disappear over

time; more frequent targets were still more likely to be found.

Viewing failures: missed target because it was never examined. Recognition failure: missed

target despite directly fixating upon it.

Low-frequency targets suffered more recognition failures, relative to higher-frequency

targets. χ2(3) = 50.47, p < .001, replicating prior work (Hout et al., JEPHPP, 2015).

Data are consistent with previously published findings regarding the “low-prevalence effect.”

Data are also consistent with diminishing (but not eliminated) frequency effects among

collaborative search partners (Lopez et al.; OPAM 2016).

Taken together, our findings suggest that, as expertise is acquired, searchers learn to refine

their mental representations for target categories, particularly common ones, and become

more effective at restricting attention to the most relevant features.

#3154

However, acquired knowledge and skills are often hard to articulate.

Professional visual search requires considerable training and expertise,

but relatively little has been done to quantify what makes professionals

different from novices.

To develop efficient training protocols requires a fundamental

understanding of the underlying cognitive processes – and oculomotor

behaviors – that are affected during the acquisition of expertise.

Challenges faced by TSA screeners: 1) Imprecise definition of

“weapons.” 2) Must look for many things at once. 3) Targets show up

with unequal frequency.

From Reingold & Sheridan, in (2011) Oxford Handbook on Eye Movements

Our study: 1) Categorically-defined targets.

2) Looked for 20 categories at once (0-3 on

any given trial). 3) Targets appeared with

varying frequency.

Sample visual

search display.

Total points accrued (out of 300).

Time to correctly locate all targets in a display.

R² = 0.3621

175

200

225

250

275

300

0 5 10 15 20 25

Tota

l P

oin

ts A

ccru

ed

Session Number

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

11,000

0 5 10 15 20

Tar

get

Cle

aran

ce R

Ts

(mse

c)

Session Number

OneTarget

TwoTargets

ThreeTargets

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

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0 5 10 15 20

Exhau

stiv

e S

earc

h R

Ts

(mse

c)

Session Number

ZeroTargets

OneTarget

TwoTargets

Time to correctly decide to quit searching. Data collection is ongoing, so stay tuned!

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Least Freq Infrequent Frequent Most Freq

Per

centa

ge

of

Tota

l M

iss

Err

ors

Frequency Condition

Viewing

Failures

Recognition

Failures

0.5

0.6

0.7

0.8

0.9

1.0

0 5 10 15 20

Hit

Rat

e

Session Number

Most Frequent

Frequent

Infrequent

Least Frequent

Miss errors broken down by error type.Hit rate for each frequency group.

R² = 0.069450%

60%

70%

80%

90%

100%

0 5 10 15 20

Per

centa

ge

of

Tar

get

s F

ixat

ed

Session Number

Least Frequent

R² = 0.094150%

60%

70%

80%

90%

100%

0 5 10 15 20

Per

centa

ge

of

Tar

get

s F

ixat

ed

Session Number

Infrequent

R² = 0.165650%

60%

70%

80%

90%

100%

0 5 10 15 20

Per

centa

ge

of

Tar

get

s F

ixat

ed

Session Number

Frequent

R² = 0.093150%

60%

70%

80%

90%

100%

0 5 10 15 20

Per

centa

ge

of

Tar

get

s F

ixat

ed

Session Number

Most Frequent

Michael C. Hout 1, Alexis Lopez 1, Arryn Robbins 1, & Megan H. Papesh 2

1 New Mexico State University; 2 Louisiana State University

Despite spending more time searching, participants actually became more likely to directly

fixate the targets, particularly those of higher frequency.

Percentage of targets directly fixated, plotted separately for each frequency group.

R² = 0.13730%

5%

10%

15%

20%

0 5 10 15 20

Per

centa

ge

of

Tota

l D

wel

l T

ime

Session Number

Least Frequent

R² = 0.3233

0%

5%

10%

15%

20%

0 5 10 15 20

Per

centa

ge

of

Tota

l D

wel

l T

ime

Session Number

Infrequent

R² = 0.3492

0%

5%

10%

15%

20%

0 5 10 15 20

Per

centa

ge

of

Tota

l D

wel

l T

ime

Session Number

Frequent

R² = 0.31020%

5%

10%

15%

20%

0 5 10 15 20

Per

centa

ge

of

Tota

l D

wel

l T

ime

Session Number

Most Frequent

Across sessions, participants also spent proportionately more time examining targets (and

therefore less time viewing distractors).

Percentage of fixation time spent examining targets, plotted separately for each frequency group.