Profiling cognitive workload in an unmanned vehicle control task with cognitive models and physiological metrics.

In the present study, we use Cognitive Metrics Profiling (CMP) to capture variance in cognitive load within a complex unmanned vehicle control task. We aim to demonstrate convergent validity with existing workload measurement methods, and to decompose workload into constituent cognitive resources to aid in diagnosing causes of workload. A cognitive model of the task was developed and examined to determine the extent to which it could predict behavioral performance, subjective workload, and validated physiological workload metrics. We also examined model activity to draw insights regarding loaded cognitive capacities. We found that composite workload from the model predicted physiological metrics, performance, and subjective workload. Moreover, the model indicates that differences in workload were driven largely by procedural, declarative, and temporal memory demands. We have found preliminary evidence of correspondence between workload predictions of a CMP model and physiological measures of workload. This suggests our approach captures interesting aspects of workload in a complex task environment and may provide a theoretical link between behavioral, physiological, and subjective metrics. This approach may provide a means to design effective workload mitigation interventions and improve decision-making about personnel tasking and automation.

An attempt to target anxiety sensitivity via cognitive bias modification.

Our goals in the present study were to test an adaptation of a Cognitive Bias Modification program to reduce anxiety sensitivity, and to evaluate the causal relationships between interpretation bias of physiological cues, anxiety sensitivity, and anxiety and avoidance associated with interoceptive exposures. Participants with elevated anxiety sensitivity who endorsed having a panic attack or limited symptom attack were randomly assigned to either an Interpretation Modification Program (IMP; n = 33) or a Control (n = 32) condition. During interpretation modification training (via the Word Sentence Association Paradigm), participants read short sentences describing ambiguous panic-relevant physiological and cognitive symptoms and were trained to endorse benign interpretations and reject threatening interpretations associated with these cues. Compared to the Control condition, IMP training successfully increased endorsements of benign interpretations and decreased endorsements of threatening interpretations at visit 2. Although self-reported anxiety sensitivity decreased from pre-selection to visit 1 and from visit 1 to visit 2, the reduction was not larger for the experimental versus control condition. Further, participants in IMP (vs. Control) training did not experience less anxiety and avoidance associated with interoceptive exposures. In fact, there was some evidence that those in the Control condition experienced less avoidance following training. Potential explanations for the null findings, including problems with the benign panic-relevant stimuli and limitations with the control condition, are discussed.

Efficacy of a web-based intelligent tutoring system for communicating genetic risk of breast cancer: a fuzzy-trace theory approach.

BACKGROUND: . Many healthy women consider genetic testing for breast cancer risk, yet BRCA testing issues are complex. OBJECTIVE: . To determine whether an intelligent tutor, BRCA Gist, grounded in fuzzy-trace theory (FTT), increases gist comprehension and knowledge about genetic testing for breast cancer risk, improving decision making. DESIGN: . In 2 experiments, 410 healthy undergraduate women were randomly assigned to 1 of 3 groups: an online module using a Web-based tutoring system (BRCA Gist) that uses artificial intelligence technology, a second group read highly similar content from the National Cancer Institute (NCI) Web site, and a third that completed an unrelated tutorial. INTERVENTION: . BRCA Gist applied FTT and was designed to help participants develop gist comprehension of topics relevant to decisions about BRCA genetic testing, including how breast cancer spreads, inherited genetic mutations, and base rates. MEASURES: . We measured content knowledge, gist comprehension of decision-relevant information, interest in testing, and genetic risk and testing judgments. RESULTS: . Control knowledge scores ranged from 54% to 56%, NCI improved significantly to 65% and 70%, and BRCA Gist improved significantly more to 75% and 77%, P < 0.0001. BRCA Gist scored higher on gist comprehension than NCI and control, P < 0.0001. Control genetic risk-assessment mean was 48% correct; BRCA Gist (61%) and NCI (56%) were significantly higher, P < 0.0001. BRCA Gist participants recommended less testing for women without risk factors (not good candidates; 24% and 19%) than controls (50%, both experiments) and NCI (32%), experiment 2, P < 0.0001. BRCA Gist testing interest was lower than in controls, P < 0.0001. LIMITATIONS: . BRCA Gist has not been tested with older women from diverse groups. CONCLUSIONS: . Intelligent tutors, such as BRCA Gist, are scalable, cost-effective ways of helping people understand complex issues, improving decision making.

Applying the Quadruple Process model to evaluate change in implicit attitudinal responses during therapy for panic disorder.

OBJECTIVE: This study explored the automatic and controlled processes that may influence performance on an implicit measure across cognitive-behavioral group therapy for panic disorder. METHOD: The Quadruple Process model was applied to error scores from an Implicit Association Test evaluating associations between the concepts Me (vs. Not Me) + Calm (vs. Panicked) to evaluate four distinct processes: Association Activation, Detection, Guessing, and Overcoming Bias. Parameter estimates were calculated in the panic group (n = 28) across each treatment session where the IAT was administered, and at matched times when the IAT was completed in the healthy control group (n = 31). RESULTS: Association Activation for Me + Calm became stronger over treatment for participants in the panic group, demonstrating that it is possible to change automatically activated associations in memory (vs. simply overriding those associations) in a clinical sample via therapy. As well, the Guessing bias toward the calm category increased over treatment for participants in the panic group. CONCLUSIONS: This research evaluates key tenets about the role of automatic processing in cognitive models of anxiety, and emphasizes the viability of changing the actual activation of automatic associations in the context of treatment, versus only changing a person's ability to use reflective processing to overcome biased automatic processing.

Individual Differences in Base Rate Neglect: A Fuzzy Processing Preference Index.

Little is known about individual differences in integrating numeric base-rates and qualitative text in making probability judgments. Fuzzy-Trace Theory predicts a preference for fuzzy processing. We conducted six studies to develop the FPPI, a reliable and valid instrument assessing individual differences in this fuzzy processing preference. It consists of 19 probability estimation items plus 4 "M-Scale" items that distinguish simple pattern matching from "base rate respect." Cronbach's Alpha was consistently above 0.90. Validity is suggested by significant correlations between FPPI scores and three other measurers: "Rule Based" Process Dissociation Procedure scores; the number of conjunction fallacies in joint probability estimation; and logic index scores on syllogistic reasoning. Replicating norms collected in a university study with a web-based study produced negligible differences in FPPI scores, indicating robustness. The predicted relationships between individual differences in base rate respect and both conjunction fallacies and syllogistic reasoning were partially replicated in two web-based studies.

A signal detection analysis of gist-based discrimination of genetic breast cancer risk.

Pervasive biases in probability judgment render the probability scale a poor response mode for assessing risk judgments. By applying fuzzy trace theory, we used ordinal gist categories as a response mode, coupled with a signal detection model to assess risk judgments. The signal detection model is an extension of the familiar model used in binary choice paradigms. It provides three measures of discriminability-low versus medium risk, medium versus high risk, and low versus high risk-and two measures of response bias. We used the model to assess the effectiveness of BRCA Gist, an intelligent tutoring system designed to improve women's judgments and understanding of genetic risk for breast cancer. Participants were randomly assigned to the BRCA Gist intelligent tutoring system, the National Cancer Institute (NCI) Web pages, or a control group, and then they rated cases that had been developed using the Pedigree Assessment Tool and also vetted by medical experts. BRCA Gist participants demonstrated increased discriminability for all three risk categories, relative to the control group; the NCI group showed increased discriminability for two of the three levels. This result suggests that BRCA Gist best improved discriminability among genetic risk categories, and both BRCA Gist and the NCI website improved participants' ability to discriminate, rather than simply shifting their decision criterion. A spreadsheet that fits the model and compares parameters across the conditions can be downloaded from the Behavior Research Methods website and used in any research involving categorical responses.

The development and analysis of tutorial dialogues in AutoTutor Lite.

The goal of intelligent tutoring systems (ITS) that interact in natural language is to emulate the benefits that a well-trained human tutor provides to students, by interpreting student answers and appropriately responding in order to encourage elaboration. BRCA Gist is an ITS developed using AutoTutor Lite, a Web-based version of AutoTutor. Fuzzy-trace theory theoretically motivated the development of BRCA Gist, which engages people in tutorial dialogues to teach them about genetic breast cancer risk. We describe an empirical method to create tutorial dialogues and fine-tune the calibration of BRCA Gist's semantic processing engine without a team of computer scientists. We created five interactive dialogues centered on pedagogic questions such as "What should someone do if she receives a positive result for genetic risk of breast cancer?" This method involved an iterative refinement process of repeated testing with different texts and successively making adjustments to the tutor's expectations and settings in order to improve performance. The goal of this method was to enable BRCA Gist to interpret and respond to answers in a manner that best facilitated learning. We developed a method to analyze the efficacy of the tutor's dialogues. We found that BRCA Gist's assessment of participants' answers was highly correlated with the quality of the answers found by trained human judges using a reliable rubric. The dialogue quality between users and BRCA Gist predicted performance on a breast cancer risk knowledge test completed after exposure to the tutor. The appropriateness of BRCA Gist's feedback also predicted the quality of answers and breast cancer risk knowledge test scores.

Assessing semantic coherence in conditional probability estimates.

Semantic coherence is a higher-order coherence benchmark that assesses whether a constellation of estimates--P(A), P(B), P(B | A), and P(A | B)--maps onto the relationship between sets implied by the description of a given problem. We present an automated method for evaluating semantic coherence in conditional probability estimates that efficiently reduces a large problem space into five meaningful patterns: identical sets, subsets, mutually exclusive sets, overlapping sets, and independent sets. It also identifies three theoretically interesting nonfallacious errors. We discuss unique issues in evaluating semantic coherence in conditional probabilities that are not present in joint probability judgments, such as errors resulting from dividing by zero and the use of a tolerance parameter to manage rounding errors. A spreadsheet implementing the methods described above can be downloaded as a supplement from www.springerlink.com .