Benchmarking Aided Decision Making in a Signal Detection Task

Copyright © 2017 Human Factors and Ergonomics Society. Reprinted by permission of SAGE Publications.Objective: A series of experiments examined human operators’ strategies for interacting with highly (93%) reliable automated decision aids in a binary signal detection task. Background: Operators often interact with automated decision aids in a suboptimal way, achieving performance levels lower than predicted by a statistically ideal model of information integration. To better understand operators’ inefficient use of decision aids, we compared participants’ automation-aided performance levels with the predictions of seven statistical models of collaborative decision making. Method: Participants performed a binary signal detection task that asked them to classify random dot images as either blue or orange dominant. They made their judgments either unaided or with assistance from a 93% reliable automated decision aid that provided either graded (Experiments 1 and 3) or binary (Experiment 2) cues. We compared automation-aided performance with the predictions of seven statistical models of collaborative decision making, including a statistically optimal model and Robinson and Sorkin’s contingent criterion model. Results and Conclusion: Automation-aided sensitivity hewed closest to the predictions of the two least efficient collaborative models, well short of statistically ideal levels. Performance was similar whether the aid provided graded or binary judgments. Model comparisons identified potential strategies by which participants integrated their judgments with the aid’s. Application: Results lend insight into participants’ automation-aided decision strategies and provide benchmarks for predicting automation-aided performance levels

Redundant-target processing is robust against changes to task load

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Monitoring visual displays while performing other tasks is commonplace in many operational environments. Although dividing attention between tasks can impair monitoring accuracy and response times, it is unclear whether it also reduces processing efficiency for visual targets. Thus, the current three experiments examined the effects of dual-tasking on target processing in the visual periphery. A total of 120 undergraduate students performed a redundant-target task either by itself (Experiment 1a) or in conjunction with a manual tracking task (Experiments 1b–3). Target processing efficiency was assessed using measures of workload resilience. Processing of redundant targets in Experiments 1–2 was less efficient than predicted by a standard parallel race model, giving evidence for limited-capacity, parallel processing. However, when stimulus characteristics forced participants to process targets in serial (Experiment 3), processing efficiency became super-capacity. Across the three experiments, dual-tasking had no effect on target processing efficiency. Results suggest that a central task slows target detection in the display periphery, but does not change the efficiency with which multiple concurrent targets are processed

Predicting inter-hemispheric transfer time from the diffusion properties of the corpus callosum in healthy individuals and schizophrenia patients: A combined ERP and DTI study

Background—Several theories of schizophrenia have emphasized the role of aberrant neural timing in the etiology of the disease, possibly as a consequence of conduction delays caused by structural damage to the white-matter fasciculi. Consistent with this theory, increased interhemispheric transmission times (IHTTs) to unilaterally-presented visual stimuli have been reported in patients with schizophrenia. The present study investigated whether or not these IHTT abnormalities could be underpinned by structural damage to the visual fibers of the corpus callosum. Methods—30 schizophrenia patients and 22 matched controls underwent Event Related Potential (ERP) recording, and a subset of 19 patients and 16 controls also underwent 3T Diffusion-Tensor Imaging (DTI). Unilateral visual stimuli (squares, 2 × 2 degrees) were presented 6 degrees lateral to either side of a central fixation point. IHTTs (ipsilateral minus contralateral latencies) were calculated for the P1 and N1 components at occipital-temporal sites in current source densitytransformed ERPs. The visual fibers of the corpus callosum were extracted with streamline tractography and the diffusion metrics of Fractional Anisotropy (FA) and Mode calculated. Results—While both subject groups exhibited highly significant IHTTs across a range of posterior electrode pairs, and significantly shorter IHTTs from left-to-right hemisphere than vice versa, no significant groupwise differences in IHTT were observed. However, participants’ IHTTs were linearly related to their FA and Mode, with longer IHTTs being associated with lower FA and more prolate diffusion ellipsoids. Conclusions—These results suggest that IHTTs are estimable from DTI measures of white matter integrity. In light of the range of diffusion abnormalities that have been reported in patients with schizophrenia, particularly in frontal fasciculi, these results support the conjecture that schizophrenia is ultimately underpinned by abnormalities in neural timing

Neurexin-1 and Frontal Lobe White Matter: An Overlapping Intermediate Phenotype for Schizophrenia and Autism Spectrum Disorders

Background: Structural variation in the neurexin-1 (NRXN1) gene increases risk for both autism spectrum disorders (ASD) and schizophrenia. However, the manner in which NRXN1 gene variation may be related to brain morphology to confer risk for ASD or schizophrenia is unknown. Method/Principal Findings: 53 healthy individuals between 18–59 years of age were genotyped at 11 single nucleotide polymorphisms of the NRXN1 gene. All subjects received structural MRI scans, which were processed to determine cortical gray and white matter lobar volumes, and volumes of striatal and thalamic structures. Each subject’s sensorimotor function was also assessed. The general linear model was used to calculate the influence of genetic variation on neural and cognitive phenotypes. Finally, in silico analysis was conducted to assess potential functional relevance of any polymorphisms associated with brain measures. A polymorphism located in the 39 untranslated region of NRXN1 significantly influenced white matter volumes in whole brain and frontal lobes after correcting for total brain volume, age and multiple comparisons. Follow-up in silico analysis revealed that this SNP is a putative microRNA binding site that may be of functional significance in regulating NRXN1 expression. This variant also influenced sensorimotor performance, a neurocognitive function impaired in both ASD and schizophrenia. Conclusions: Our findings demonstrate that the NRXN1 gene, a vulnerability gene for SCZ and ASD, influences brai

Open data and code

Data and code linked from manuscrip

Psychometric Curves Reveal Three Mechanisms of Vigilance Decrement

Materials and data for McCarley & Yamani (in press

Shared Gaze Fails to Improve Team Visual Monitoring

Data and analysis scripts for McCarley, Leggett, and Enright (in press). Shared Gaze Fails to Improve Team Visual Monitoring. Human Factors