This paper presents a theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains. Situation awareness is presented as a predominant concern in system operation, based on a descriptive view of decision making. The relationship between situation awareness and numerous individual and environmental factors is explored. Among these factors, attention and working memory are presented as critical factors limiting operators from acquiring and interpreting information from the environment to form situation awareness, and mental models and goal-directed behavior are hypothesized as important mechanisms for overcoming these limits. The impact of design features, workload, stress, system complexity, and automation on operator situation awareness is addressed, and a taxonomy of errors in situation awareness is introduced, based on the model presented. The model is used to generate design implications for enhancing operator situation awareness and future directio...

Toward a Theory of Situation Awareness in Dynamic Systems

A review is presented of the book “Heuristics and Biases: The Psychology of Intuitive Judgment,” edited by Thomas Gilovich, Dale Griffin, and Daniel Kahneman.

Heuristics and Biases: The Psychology of Intuitive Judgment

We outline a model for types and levels of automation that provides a framework and an objective basis for deciding which system functions should be automated and to what extent. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design.

/pdf/a-model-for-types-and-levels-of-human-interaction-with-1wqlqxv26i.pdf

A model for types and levels of human interaction with automation

Perhaps the simplest and the most basic qualitative law of probability is the conjunction rule: The probability of a conjunction, P (A&B) cannot exceed the probabilities of its constituents, P (A) and P (B), because the extension (or the possibility set) of the conjunction is included in the extension of its constituents. Judgments under uncertainty, however, are often mediated by intuitive heuristics that are not bound by the conjunction rule. A conjunction can be more representative than one of its constituents, and instances of a specific category can be easier to imagine or to retrieve than instances of a more inclusive category. The representativeness and availability heuristics therefore can make a conjunction appear more probable than one of its constituents. This phenomenon is demonstrated in a variety of contexts including estimation of word frequency, personality judgment, medical prognosis, decision under risk, suspicion of criminal acts, and political forecasting. Systematic violations of the conjunction rule are observed in judgments of lay people and of experts in both between-subjects and within-subjects comparisons. Alternative interpretations of the conjunction fallacy are discussed and attempts to combat it are explored.

/pdf/extensional-versus-intuitive-reasoning-the-conjunction-2cmoy4v6op.pdf

Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment.

Much research in the last two decades has demon- strated that human responses deviate from the performance deemed normative according to various models of decision mak- ing and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be inter- preted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational limitations, (3) the wrong norm being applied by the experi- menter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the modal response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implica- tions of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance er- rors are a minor factor in the gap; computational limitations un- derlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Un- expected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0140525X00003435

Individual differences in reasoning: Implications for the rationality debate?

1. Introduction to Engineering Psychology and Human Performance 2. Signal Detection, Information Theory and Absolute Judgment 3. Attention in Perception and Display Space 4. Spatial Displays 5. Spatial Cognition, Navigation and Manual Control 6. Language and Communications 7. Memory and Training 8. Decision Making 9. Selection of Action 10. Attention, Time sharing and Workload 11. Mental Workload, Stress, and Individual Differences: Cognitive and Neuroergonomic Perspectives 12. Automation 13. Epilogue

http://www.gbv.de/dms/weimar/toc/736346732_toc.pdf

Engineering Psychology and Human Performance

This paper 1) provides reasons why graphics should be effective aids to communicate risk; 2) reviews the use of visuals, especially graphical displays, to communicate risk; 3) discusses issues to consider when designing graphs to communicate risk; and 4) provides suggestions for future research. Key articles and materials were obtained from MEDLINE(R) and PsychInfo(R) databases, from reference article citations, and from discussion with experts in risk communication. Research has been devoted primarily to communicating risk magnitudes. Among the various graphical displays, the risk ladder appears to be a promising tool for communicating absolute and relative risks. Preliminary evidence suggests that people understand risk information presented in histograms and pie charts. Areas that need further attention include 1) applying theoretical models to the visual communication of risk, 2) testing which graphical displays can be applied best to different risk communication tasks (e.g., which graphs best convey absolute or relative risks), 3) communicating risk uncertainty, and 4) testing whether the lay public's perceptions and understanding of risk varies by graphical format and whether the addition of graphical displays improves comprehension substantially beyond numerical or narrative translations of risk and, if so, by how much. There is a need to ascertain the extent to which graphics and other visuals enhance the public's understanding of disease risk to facilitate decision-making and behavioral change processes. Nine suggestions are provided to help achieve these ends.

The visual communication of risk.

Since the publication of Loftus and Masson's (1994) method for computing confidence intervals (CIs) in repeated-measures (RM) designs, there has been uncertainty about how to apply it to particular effects in complex factorial designs. Masson and Loftus (2003) proposed that RM CIs for factorial designs be based on number of observations rather than number of participants. However, determining the correct number of observations for a particular effect can be complicated, given the variety of effects occurring in factorial designs. In this paper the authors define a general "number of observations" principle, explain why it obtains, and provide step-by-step instructions for constructing CIs for various effect types. The authors illustrate these procedures with numerical examples.

Confidence intervals in repeated-measures designs: The number of observations principle.

When participants make part-whole proportion judgments, systematic bias is commonly observed. In some studies, small proportions are overestimated and large proportions underestimated; in other studies, the reverse pattern occurs. Sometimes the bias pattern repeats cyclically with a higher frequency (e.g., overestimation of proportions less than .25 and between .5 and .75; underestimation otherwise). To account for the various bias patterns, a cyclical power model was derived from Stevens' power law. The model proposes that the amplitude of the bias pattern is determined by the Stevens exponent, beta (i.e., the stimulus continuum being judged), and that the frequency of the pattern is determined by a choice of intermediate reference points in the stimulus. When beta 1, the under-then-over pattern is predicted. Two experiments confirming the model's assumptions are described. A mixed-cycle version of the model is also proposed that predicts observed asymmetries in bias patterns when the set of reference points varies across trials.

Bias in proportion judgments: the cyclical power model.

Objective: We examined the effects of aid reliability and reliability disclosure on human trust in and reliance on a combat identification (CID) aid. We tested whether trust acts as a mediating factor between belief in and reliance on a CID aid. Background: Individual CID systems have been developed to reduce friendly fire incidents. However, these systems cannot positively identify a target that does not have a working transponder. Therefore, when the feedback is "unknown", the target could be hostile, neutral, or friendly. Soldiers have difficulty relying on this type of imperfect automation appropriately. Method: In manual and aided conditions, 24 participants completed a simulated CID task. The reliability of the aid varied within participants, half of whom were told the aid reliability level. We used the difference in response bias values across conditions to measure automation reliance. Results: Response bias varied more appropriately with the aid reliability level when it was disclosed than when not. Trust in aid feedback correlated with belief in aid reliability and reliance on aid feedback; however, belief was not correlated with reliance. Conclusion: To engender appropriate reliance on CID systems, users should be made aware of system reliability. Application: The findings can be applied to the design of information displays for individual CID systems and soldier training.

Justin G. Hollands

Papers

Engineering Psychology and Human Performance

The visual communication of risk.

Confidence intervals in repeated-measures designs: The number of observations principle.

Bias in proportion judgments: the cyclical power model.

Trust and Reliance on an Automated Combat Identification System