Example of Journal of Cognitive Engineering and Decision Making format
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Example of Journal of Cognitive Engineering and Decision Making format Example of Journal of Cognitive Engineering and Decision Making format Example of Journal of Cognitive Engineering and Decision Making format Example of Journal of Cognitive Engineering and Decision Making format
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Example of Journal of Cognitive Engineering and Decision Making format Example of Journal of Cognitive Engineering and Decision Making format Example of Journal of Cognitive Engineering and Decision Making format Example of Journal of Cognitive Engineering and Decision Making format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Journal of Cognitive Engineering and Decision Making — Template for authors

Publisher: SAGE
Categories Rank Trend in last 3 yrs
Human Factors and Ergonomics #6 of 39 down down by 1 rank
Engineering (miscellaneous) #12 of 77 down down by 5 ranks
Applied Psychology #35 of 227 up up by 3 ranks
Computer Science Applications #160 of 693 down down by 53 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 86 Published Papers | 458 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 03/06/2020
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Related Journals

open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 8.6
SJR: 0.53
SNIP: 2.363
open access Open Access

SAGE

Quality:  
High
CiteRatio: 6.2
SJR: 0.818
SNIP: 2.066
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 14.9
SJR: 2.96
SNIP: 3.054

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

5.3

26% from 2019

CiteRatio for Journal of Cognitive Engineering and Decision Making from 2016 - 2020
Year Value
2020 5.3
2019 4.2
2018 4.8
2017 4.9
2016 4.0
graph view Graph view
table view Table view

0.511

12% from 2019

SJR for Journal of Cognitive Engineering and Decision Making from 2016 - 2020
Year Value
2020 0.511
2019 0.581
2018 0.928
2017 0.98
2016 1.057
graph view Graph view
table view Table view

1.628

28% from 2019

SNIP for Journal of Cognitive Engineering and Decision Making from 2016 - 2020
Year Value
2020 1.628
2019 1.267
2018 1.815
2017 1.49
2016 1.565
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 26% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 12% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 28% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of Cognitive Engineering and Decision Making

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SAGE

Journal of Cognitive Engineering and Decision Making

Approved by publishing and review experts on SciSpace, this template is built as per for Journal of Cognitive Engineering and Decision Making formatting guidelines as mentioned in SAGE author instructions. The current version was created on 02 Jun 2020 and has been used by 455 authors to write and format their manuscripts to this journal.

Engineering

i
Last updated on
02 Jun 2020
i
ISSN
1555-3434
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Impact Factor
High - 1.258
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Open Access
No
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Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
SageV
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Citation Type
Numbered (Superscripted)
25
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Bibliography Example
Blonder GE, Tinkham M and Klapwijk TM. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 1982; 25(7): 4515–4532. URL 10.1103/PhysRevB.25.4515.

Top papers written in this journal

Journal Article DOI: 10.1518/155534308X284417
Situation Awareness, Mental Workload, and Trust in Automation: Viable, Empirically Supported Cognitive Engineering Constructs
Raja Parasuraman1, Thomas B. Sheridan2, Christopher D. Wickens3

Abstract:

Cognitive engineering needs viable constructs and principles to promote better understanding and prediction of human performance in complex systems. Three human cognition and performance constructs that have been the subjects of much attention in research and practice over the past three decades are situation awareness (SA), ... Cognitive engineering needs viable constructs and principles to promote better understanding and prediction of human performance in complex systems. Three human cognition and performance constructs that have been the subjects of much attention in research and practice over the past three decades are situation awareness (SA), mental workload, and trust in automation. Recently, Dekker and Woods (2002) and Dekker and Hollnagel (2004; henceforth DWH) argued that these constructs represent "folk models" without strong empirical foundations and lacking scientific status. We counter this view by presenting a brief description of the large science base of empirical studies on these constructs. We show that the constructs can be operationalized using behavioral, physiological, and subjective measures, supplemented by computational modeling, but that the constructs are also distinct from human performance. DWH also caricatured as "abracadabra" a framework suggested by us to address the problem of the design of automated systems (Parasuraman, Sheridan, & Wickens, 2000). We point to several factual and conceptual errors in their description of our approach. Finally, we rebut DWH's view that SA, mental workload, and trust represent folk concepts that are not falsifiable. We conclude that SA, mental workload, and trust are viable constructs that are valuable in understanding and predicting human-system performance in complex systems. read more read less

Topics:

Cognitive ergonomics (55%)55% related to the paper, Workload (51%)51% related to the paper, Poison control (50%)50% related to the paper, Operationalization (50%)50% related to the paper
527 Citations
Journal Article DOI: 10.1177/1555343415572631
Situation Awareness Misconceptions and Misunderstandings

Abstract:

Situation awareness (SA) has become a widely used construct within the human factors community, the focus of considerable research over the past 25 years. This research has been used to drive the development of advanced information displays, the design of automated systems, information fusion algorithms, and new training appr... Situation awareness (SA) has become a widely used construct within the human factors community, the focus of considerable research over the past 25 years. This research has been used to drive the development of advanced information displays, the design of automated systems, information fusion algorithms, and new training approaches for improving SA in individuals and teams. In recent years, a number of papers criticized the Endsley model of SA on various grounds. I review those criticisms here and show them to be based on misunderstandings of the model. I also review several new models of SA, including situated SA, distributed SA, and sensemaking, in light of this discussion and show how they compare to existing models of SA in individuals and teams. read more read less

Topics:

Sensemaking (50%)50% related to the paper
View PDF
378 Citations
open accessOpen access Journal Article DOI: 10.1177/1555343411433844
Human Performance Consequences of Automated Decision Aids: The Impact of Degree of Automation and System Experience
Dietrich Manzey1, Juliane Reichenbach1, Linda Onnasch1

Abstract:

Two experiments are reported that investigate to what extent performance consequences of automated aids are dependent on the distribution of functions between human and automation and on the experi... Two experiments are reported that investigate to what extent performance consequences of automated aids are dependent on the distribution of functions between human and automation and on the experi... read more read less

Topics:

Decision aids (55%)55% related to the paper
View PDF
187 Citations
Journal Article DOI: 10.1518/155534310X12844000801203
Rapid Decision Making on the Fire Ground: The Original Study Plus a Postscript
Gary Klein1, Roberta Calderwood1, Anne Clinton-Cirocco1

Abstract:

[This is an edited version of the original, unpublished 1985 study that identified recognition-primed decision making, with a new commentary added.] The objective of this study was to examine the w... [This is an edited version of the original, unpublished 1985 study that identified recognition-primed decision making, with a new commentary added.] The objective of this study was to examine the w... read more read less
186 Citations
Journal Article DOI: 10.1177/1555343417695197
Autonomous Driving Systems: A Preliminary Naturalistic Study of the Tesla Model S:

Abstract:

Autonomous and semiautonomous vehicles are currently being developed by over14 companies. These vehicles may improve driving safety and convenience, or they may create new challenges for drivers, particularly with regard to situation awareness (SA) and autonomy interaction. I conducted a naturalistic driving study on the auto... Autonomous and semiautonomous vehicles are currently being developed by over14 companies. These vehicles may improve driving safety and convenience, or they may create new challenges for drivers, particularly with regard to situation awareness (SA) and autonomy interaction. I conducted a naturalistic driving study on the autonomy features in the Tesla Model S, recording my experiences over a 6-month period, including assessments of SA and problems with the autonomy. This preliminary analysis provides insights into the challenges that drivers may face in dealing with new autonomous automobiles in realistic driving conditions, and it extends previous research on human-autonomy interaction to the driving domain. Issues were found with driver training, mental model development, mode confusion, unexpected mode interactions, SA, and susceptibility to distraction. New insights into challenges with semiautonomous driving systems include increased variability in SA, the replacement of continuous control with seria... read more read less
174 Citations
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Frequently asked questions

1. Can I write Journal of Cognitive Engineering and Decision Making in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Journal of Cognitive Engineering and Decision Making guidelines and auto format it.

2. Do you follow the Journal of Cognitive Engineering and Decision Making guidelines?

Yes, the template is compliant with the Journal of Cognitive Engineering and Decision Making guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Journal of Cognitive Engineering and Decision Making?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Journal of Cognitive Engineering and Decision Making citation style.

4. Can I use the Journal of Cognitive Engineering and Decision Making templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Journal of Cognitive Engineering and Decision Making.

5. Can I use a manuscript in Journal of Cognitive Engineering and Decision Making that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Journal of Cognitive Engineering and Decision Making that you can download at the end.

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7. Where can I find the template for the Journal of Cognitive Engineering and Decision Making?

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8. Can I reformat my paper to fit the Journal of Cognitive Engineering and Decision Making's guidelines?

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9. Journal of Cognitive Engineering and Decision Making an online tool or is there a desktop version?

SciSpace's Journal of Cognitive Engineering and Decision Making is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Journal of Cognitive Engineering and Decision Making?

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12. Is Journal of Cognitive Engineering and Decision Making's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Journal of Cognitive Engineering and Decision Making?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Cognitive Engineering and Decision Making. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Cognitive Engineering and Decision Making?

The 5 most common citation types in order of usage for Journal of Cognitive Engineering and Decision Making are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Journal of Cognitive Engineering and Decision Making?

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16. Can I download Journal of Cognitive Engineering and Decision Making in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of Cognitive Engineering and Decision Making Endnote style according to Elsevier guidelines.

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