Search

Shiffrin and Schneider (1977) demonstrated that highly efficientmemory- and visual-search performance could be achievedthrough consistent item-to-response mapping (CM) training. It istheorized that subjects shifted from relying on working memory tolearned item-response associations in long-term memory (Logan,1988). The theory was tested and explored mostly throughbehavioral experiments and computational modeling. In a recentseries of articles involving visual search (e.g. Woodman et al,2013; Carlisle et al. 2011), Woodman and colleagues found thatthe contralateral-delay activity (CDA) of human event-relatedpotentials is related to the maintenance of information in visualworking memory and that the magnitude of the CDA decreaseswhen target information is stored in long-term memory. Weemployed the CDA and other neural measures to study the natureof memory retrieval in CM memory search tasks. We observed asignificant reduction in the magnitude of the CDA in CM trainingcompared to a control condition in which item-response mappingsvaried from trial to trial (VM). The results provided convergingevidence supporting the classic theoretical interpretation of thebases for CM and VM memory search. The results also raisedinteresting questions concerning the detailed interpretation ofCDA.

A previous experiment tested subjects' new/old judgments of previously-studied faces, distractors, and morphs between pairs of studied parents. We examine the extent to which models based on principal component analysis (eigenfaces) can predict human recognition of studied faces and false alarms to the distractors and morphs. We also compare eigenface models to the predictions of previous models based on the positions of faces in a multidimensional "face space" derived from a multidimensional scaling (MDS) of human similarity ratings. We find that the error in reconstructing a test face from its position in an "eigenface space" provides a good overall prediction of human familiarity ratings. However, the model has difficulty accounting for the fact that humans false alarm to morphs with similar parents more frequently than they false alarm to morphs with dissimilar parents. We ascribe this to the limitations of the simple reconstruction error-based model. We then outline preliminary work to improve the fine-grained fit within the eigenface-based modeling framework, and discuss the results' implicadons for exemplar- and face space-based models of face processing.

Perceptual tasks such as object matching, mammogram interpretation, mental rotation, and satellite imagery change detection often require the assignment of correspondences to fuse information across views. We apply techniques developed for machine translation to the gaze data recorded from a complex perceptual matching task modeled after fingerprint examinations. The gaze data provide temporal sequences that the machine translation algorithm uses to estimate the subjects' assumptions of corresponding regions. Our results show that experts and novices have similar surface behavior, such as the number of fixations made or the duration of fixations. However, the approach applied to data from experts is able to identify more corresponding areas between two prints. The fixations that are associated with clusters that map with high probability to corresponding locations on the other print are likely to have greater utility in a visual matching task. These techniques address a fundamental problem in eye tracking research with perceptual matching tasks: Given that the eyes always point somewhere, which fixations are the most informative and therefore are likely to be relevant for the comparison task? (Contains 5 figures and 1 table.)

Despite a profusion of popular misinformation about the left brain and right brain, there are functional differences between the left and right cerebral hemispheres in humans. Evidence from split-brain patients, individuals with unilateral brain damage, and neuroimaging studies suggest that each hemisphere may be specialized for certain cognitive processes. One way to easily explore these hemispheric asymmetries is with the divided visual field technique, where visual stimuli are presented on either the left or right side of the visual field and task performance is compared between these two conditions; any behavioral differences between the left and right visual fields may be interpreted as evidence for functional asymmetries between the left and right cerebral hemispheres. We developed a simple software package that implements the divided visual field technique, called the Lateralizer, and introduced this experimental approach as a problem-based learning module in a lower-division research methods course. Second-year undergraduate students used the Lateralizer to experimentally challenge and explore theories of the differences between the left and right cerebral hemispheres. Measured learning outcomes after active exploration with the Lateralizer, including new knowledge of brain anatomy and connectivity, were on par with those observed in an upper-division lecture course. Moreover, the project added to the students' research skill sets and seemed to foster an appreciation of the link between brain anatomy and function. (Contains 4 figures and 1 table.)

Describes a modified implementation of an instructional strategy known as problem-based learning (PBL) in an introductory cognitive neuroscience course. Discusses the principles of PBL found to be effective and demonstrates how these principles fostered continued restructuring of the course. (EV)

Forensic evidence often involves an evaluation of whether two impressions were made by the same source, such as whether a fingerprint from a crime scene has detail in agreement with an impression taken from a suspect. Human experts currently outperform computer-based comparison systems, but the strength of the evidence exemplified by the observed detail in agreement must be evaluated against the possibility that some other individual may have created the crime scene impression. Therefore, the strongest evidence comes from features in agreement that are also not shared with other impressions from other individuals. We characterize the nature of human expertise by applying two extant metrics to the images used in a fingerprint recognition task and use eye gaze data from experts to both tune and validate the models. The Attention via Information Maximization (AIM) model (Bruce & Tsotsos, 2009) quantifies the rarity of regions in the fingerprints to determine diagnosticity for purposes of excluding alternative sources. The CoVar model (Karklin & Lewicki, 2009) captures relationships between low-level features, mimicking properties of the early visual system. Both models produced classification and generalization performance in the 75%-80% range when classifying where experts tend to look. A validation study using regions identified by the AIM model as diagnostic demonstrates that human experts perform better when given regions of high diagnosticity. The computational nature of the metrics may help guard against wrongful convictions, as well as provide a quantitative measure of the strength of evidence in casework. [ABSTRACT FROM AUTHOR]

Searching against larger Automated Fingerprint Identification System (AFIS) databases may increase the likelihood of finding a suspect in the database. However, Dror and Mnookin (2010) have argued that this also leads to an increase in the number of similar non-matching prints, which could lead to an erroneous identification. Using simulations, we explore the relation between database size and two outcome factors: close non-matching prints and overall database sensitivity, which is a measure of discriminability between true matches and close non-matches. We find that larger databases tend to increase both the likelihood of finding the suspect in the database as well as the number of close non-matching prints. However, the former tends to asymptote while the latter increases without bound, and this leads to an initial increase and then a decrease in the sensitivity of the database as more prints are added. This suggests the existence of an optimal database size, and that caution should be observed when interpreting results from larger databases. Quantitative evidentiary techniques such as likelihood ratios have the potential to address some of these concerns, although they too must consider the database size when calculating the likelihood ratio. Implications for practitioners are discussed. [ABSTRACT FROM PUBLISHER]

Choice confidence is affected by fluency and moderated by construal levels that evoke different theories to interpret the feelings of fluency. At lower construal levels, fluency informs the feasibility of completing the concrete steps of the decision process to choose well, but at higher construal levels, fluency informs (insufficient) effort invested for the desirability of the outcome. We manipulated fluency by varying the font of product descriptions or the number of thoughts we asked participants to recall. Our studies showed that fluency increased confidence for people processing at lower construal levels but decreased confidence for those processing at higher construal levels. Construal level does not affect the persuasiveness of consumers' thoughts, supporting the hypothesis that it is the interpretation of fluency experienced during judgment, not the thought content, that leads to the moderating effects of construal level. [ABSTRACT FROM AUTHOR]