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      Human factors in forensic science: The cognitive mechanisms that underlie forensic feature-comparison expertise

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          Abstract

          After a decade of critique from leading scientific bodies, forensic science research is at a crossroads. Whilst emerging research has shown that some forensic feature-comparison disciplines are not foundationally valid, others are moving towards establishing reliability and validity. Forensic examiners in fingerprint, face and handwriting comparison disciplines have skills and knowledge that distinguish them from novices. Yet our understanding of the basis of this expertise is only beginning to emerge. In this paper, we review evidence on the psychological mechanisms contributing to forensic feature-comparison expertise, with a focus on one mechanism: statistical learning, or the ability to learn how often things occur in the environment. Research is beginning to emphasise the importance of statistical learning in forensic feature-comparison expertise. Ultimately, this research and broader cognitive science research has an important role to play in informing the development of training programs and selection tools for forensic feature-comparison examiners.

          Highlights

          • Psychological processes underlie forensic science decision-making.

          • Forensic examiners learn statistical information from forensic evidence.

          • Statistical learning facilitates forensic visual comparison performance.

          • Cognitive science research is important in forensic training and selection.

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          Most cited references46

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          The many faces of configural processing.

          Adults' expertise in recognizing faces has been attributed to configural processing. We distinguish three types of configural processing: detecting the first-order relations that define faces (i.e. two eyes above a nose and mouth), holistic processing (glueing the features together into a gestalt), and processing second-order relations (i.e. the spacing among features). We provide evidence for their separability based on behavioral marker tasks, their sensitivity to experimental manipulations, and their patterns of development. We note that inversion affects each type of configural processing, not just sensitivity to second-order relations, and we review evidence on whether configural processing is unique to faces.
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            Statistical learning by 8-month-old infants.

            Learners rely on a combination of experience-independent and experience-dependent mechanisms to extract information from the environment. Language acquisition involves both types of mechanisms, but most theorists emphasize the relative importance of experience-independent mechanisms. The present study shows that a fundamental task of language acquisition, segmentation of words from fluent speech, can be accomplished by 8-month-old infants based solely on the statistical relationships between neighboring speech sounds. Moreover, this word segmentation was based on statistical learning from only 2 minutes of exposure, suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language input.
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              Domain generality versus modality specificity: the paradox of statistical learning.

              Statistical learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. However, recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal modality and stimulus specificity. Therefore, important questions are how and why a hypothesized domain-general learning mechanism systematically produces such effects. Here, we offer a theoretical framework according to which SL is not a unitary mechanism, but a set of domain-general computational principles that operate in different modalities and, therefore, are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.
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                Author and article information

                Contributors
                Journal
                Forensic Sci Int
                Forensic Sci Int
                Forensic Science International: Synergy
                Elsevier
                2589-871X
                21 May 2020
                2020
                21 May 2020
                : 2
                : 148-153
                Affiliations
                [a ]School of Social and Behavioural Sciences, New College, Arizona State University, Phoenix, AZ, USA
                [b ]School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
                Author notes
                []Corresponding author. School of Social and Behavioural Sciences, New College, Arizona State University, Phoenix, AZ, USA. bethany.growns@ 123456gmail.com
                Article
                S2589-871X(20)30037-1
                10.1016/j.fsisyn.2020.05.001
                7260433
                db4b9e88-5aa2-482a-875f-88ecc94c4d46
                © 2020 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 20 March 2020
                : 14 May 2020
                : 14 May 2020
                Categories
                Interdisciplinary Forensics

                Forensic science
                expertise,visual expertise,psychological mechanisms,forensic science,forensics,statistical learning

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