PMID- 33054359
OWN - NLM
STAT- MEDLINE
DCOM- 20220527
LR  - 20220628
IS  - 1547-8181 (Electronic)
IS  - 0018-7208 (Linking)
VI  - 64
IP  - 4
DP  - 2022 Jun
TI  - Detecting and Responding to Information Overload With an Adaptive User Interface.
PG  - 675-693
LID - 10.1177/0018720820964343 [doi]
AB  - OBJECTIVE: The objective of this study was to develop and evaluate an adaptive 
      user interface that could detect states of operator information overload and 
      calibrate the amount of information on the screen. BACKGROUND: Machine learning 
      can detect changes in operating context and trigger adaptive user interfaces 
      (AUIs) to accommodate those changes. Operator attentional state represents a 
      promising aspect of operating context for triggering AUIs. Behavioral rather than 
      physiological indices can be used to infer operator attentional state. METHOD: In 
      Experiment 1, a network analysis task sought to induce states of information 
      overload relative to a baseline. Streams of interaction data were taken from 
      these two states and used to train machine learning classifiers. We implemented 
      these classifiers in Experiment 2 to drive an AUI that automatically calibrated 
      the amount of information displayed to operators. RESULTS: Experiment 1 
      successfully induced information overload in participants, resulting in lower 
      accuracy, slower completion time, and higher workload. A series of machine 
      learning classifiers detected states of information overload significantly above 
      chance level. Experiment 2 identified four clusters of users who responded 
      significantly differently to the AUIs. The AUIs benefited performance, completion 
      time, and workload in three clusters. CONCLUSION: Behavioral indices can 
      successfully detect states of information overload and be used to effectively 
      drive an AUI for some user groups. The success of AUIs may be contingent on 
      characteristics of the user group. APPLICATION: This research applies to domains 
      seeking real-time assessments of user attentional or psychological state.
FAU - Kortschot, Sean W
AU  - Kortschot SW
AUID- ORCID: 0000-0002-0448-9371
AD  - 213607 University of Toronto, ON, Canada.
FAU - Jamieson, Greg A
AU  - Jamieson GA
AUID- ORCID: 0000-0002-1136-9669
AD  - 213607 University of Toronto, ON, Canada.
FAU - Prasad, Amrit
AU  - Prasad A
AD  - 213607 University of Toronto, ON, Canada.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20201015
PL  - United States
TA  - Hum Factors
JT  - Human factors
JID - 0374660
SB  - IM
MH  - *Attention
MH  - Humans
MH  - *Machine Learning
MH  - Task Performance and Analysis
MH  - User-Computer Interface
MH  - Workload
OTO - NOTNLM
OT  - adaptive automation
OT  - attentional processes
OT  - information overload
OT  - machine learning
OT  - passive data monitoring
EDAT- 2020/10/16 06:00
MHDA- 2022/05/28 06:00
CRDT- 2020/10/15 17:05
PHST- 2020/10/16 06:00 [pubmed]
PHST- 2022/05/28 06:00 [medline]
PHST- 2020/10/15 17:05 [entrez]
AID - 10.1177/0018720820964343 [doi]
PST - ppublish
SO  - Hum Factors. 2022 Jun;64(4):675-693. doi: 10.1177/0018720820964343. Epub 2020 Oct 
      15.