Greg J. Neil

Who am I? Representing the self offline and in different online contexts

Influence of four offline and online contexts on self-disclosure is examined.Individuals disclose the more information offline than any online context.Type of online space influenced the amount and type of information disclosed.Contextual factors appeared more influential in disclosure than personality factors. The present paper examines the extent to which self-presentation may be affected by the context in which is it undertaken. Individuals were asked to complete the Twenty Statements Test both privately and publicly, but were given an opportunity to withhold any of their personal information before it was made public. Four contexts were examined: an offline context (face-to-face), an un-contextualized general online context, or two specific online contexts (dating or job-seeking). The results suggested that participants were willing to disclose substantially less personal information online than offline. Moreover, disclosure decreased as the online context became more specific, and those in the job-seeking context disclosed the least amount of information. Surprisingly, individual differences in personality did not predict disclosure behavior. Instead, the results are set in the context of audience visibility and social norms, and implications for self-presentation in digital contexts are discussed.

The effect of distraction on face and voice recognition

The results of two experiments are presented which explore the effect of distractor items on face and voice recognition. Following from the suggestion that voice processing is relatively weak compared to face processing, it was anticipated that voice recognition would be more affected by the presentation of distractor items between study and test compared to face recognition. Using a sequential matching task with a fixed interval between study and test that either incorporated distractor items or did not, the results supported our prediction. Face recognition remained strong irrespective of the number of distractor items between study and test. In contrast, voice recognition was significantly impaired by the presence of distractor items regardless of their number (Experiment 1). This pattern remained whether distractor items were highly similar to the targets or not (Experiment 2). These results offer support for the proposal that voice processing is a relatively vulnerable method of identification.

Implicit learning of conjunctive rule sets: An alternative to artificial grammars

A single experiment is reported that investigated implicit learning using a conjunctive rule set applied to natural words. Participants memorized a training list consisting of words that were either rare-concrete and common-abstract or common-concrete and rare-abstract. At test, they were told of the rule set, but not told what it was. Instead, they were shown all four word types and asked to classify words as rule-consistent words or not. Participants classified the items above chance, but were unable to verbalize the rules, even when shown a list that included the categories that made up the conjunctive rule and asked to select them. Most participants identified familiarity as the reason for classifying the items as they did. An analysis of the materials demonstrated that conscious micro-rules (i.e., chunk knowledge) could not have driven performance. We propose that such materials offer an alternative to artificial grammar for studies of implicit learning.

Recognition by association: Within‐ and cross‐modality associative priming with faces and voices

Recent literature has raised the suggestion that voice recognition runs in parallel to face recognition. As a result, a prediction can be made that voices should prime faces and faces should prime voices. A traditional associative priming paradigm was used in two studies to explore within-modality priming and cross-modality priming. In the within-modality condition where both prime and target were faces, analysis indicated the expected associative priming effect: The familiarity decision to the second target celebrity was made more quickly if preceded by a semantically related prime celebrity, than if preceded by an unrelated prime celebrity. In the cross-modality condition, where a voice prime preceded a face target, analysis indicated no associative priming when a 3-s stimulus onset asynchrony (SOA) was used. However, when a relatively longer SOA was used, providing time for robust recognition of the prime, significant cross-modality priming emerged. These data are explored within the context of a unified account of face and voice recognition, which recognizes weaker voice processing than face processing.

When the face fits: recognition of celebrities from matching and mismatching faces and voices.

The results of two experiments are presented in which participants engaged in a face-recognition or a voice-recognition task. The stimuli were face–voice pairs in which the face and voice were co-presented and were either “matched” (same person), “related” (two highly associated people), or “mismatched” (two unrelated people). Analysis in both experiments confirmed that accuracy and confidence in face recognition was consistently high regardless of the identity of the accompanying voice. However accuracy of voice recognition was increasingly affected as the relationship between voice and accompanying face declined. Moreover, when considering self-reported confidence in voice recognition, confidence remained high for correct responses despite the proportion of these responses declining across conditions. These results converged with existing evidence indicating the vulnerability of voice recognition as a relatively weak signaller of identity, and results are discussed in the context of a person-recognition framework.

The relationship between handwritten signature production and personality traits

The capacity to link various aspects of a persons identity is of value in the search for reliable means of authentication and identification. With the increase in digital living, this has taken on a new perspective through the need to link aspects of identity in the physical world to those in the digital world. The focus in this work is in the value of the signature as a token of identity in its own right but also as a method to reveal information about the person signing. Whilst existing methods for the analysis of an individuals personality through their handwriting (graphology) have been discredited, we wish to revisit the issue with respect to signatures. Critically, we use accepted and modern static and dynamic features from the signature as potential indicators of personality. Our results suggest some clear links between signature production and relevant cues about the signer, especially when we incorporate dynamic elements of signature production. As such these results suggest there is renewed value in using a signature to reveal information about the signer.

Auditory hindsight bias: fluency misattribution versus memory reconstruction

We report 4 experiments investigating auditory hindsight bias—the tendency to overestimate the intelligibility of distorted auditory stimuli after learning their identity. An associative priming manipulation was used to vary the amount of processing fluency independently of prior target knowledge. For hypothetical designs, in which hindsight judgments are made for peers in foresight, we predicted that judgments would be based on processing fluency and that hindsight bias would be greater in the unrelated- compared to related-prime context (differential-fluency hypothesis). Conversely, for memory designs, in which foresight judgments are remembered in hindsight, we predicted that judgments would be based on memory reconstruction and that there would be independent effects of prime relatedness and prior target knowledge (recollection hypothesis). These predictions were confirmed. Specifically, we found support for the differential-fluency hypothesis when a hypothetical design was used in Experiments 1 and 2 (hypothetical group). Conversely, when a memory design was used in Experiments 2 (memory group), 3A, and 3B, we found support for the recollection hypothesis. Together, the results suggest that qualitatively different mechanisms create hindsight bias in the 2 designs. The results are discussed in terms of fluency misattributions, memory reconstruction, anchoring-and-adjustment, sense making, and a multicomponent model of hindsight bias.

Testing the reliability of hands and ears as biometrics: the importance of viewpoint

Two experiments are presented to explore the limits when matching a sample to a suspect utilising the hand as a novel biometric. The results of Experiment 1 revealed that novice participants were able to match hands at above-chance levels as viewpoint changed. Notably, a moderate change in viewpoint had no notable effect, but a more substantial change in viewpoint affected performance significantly. Importantly, the impact of viewpoint when matching hands was smaller than that when matching ears in a control condition. This was consistent with the suggestion that the flexibility of the hand may have minimised the negative impact of a sub-optimal view. The results of Experiment 2 confirmed that training via a 10-min expert video was sufficient to reduce the impact of viewpoint in the most difficult case but not to remove it entirely. The implications of these results were discussed in terms of the theoretical importance of function when considering the canonical view and in terms of the applied value of the hand as a reliable biometric across viewing conditions.

Biometrics within the SuperIdentity project: A new approach to spanning multiple identity domains

This paper presents a new approach to user identity currently being explored within the SuperIdentity project and outlines the specific role that biometric measurements can contribute towards a modelling process. The SuperIdentity project aims to define a novel holistic model wherein the proven strengths of relationships between facets of identity are mapped. These facets are drawn from both real and digital worlds and are categorised into four domains: biographic, psychological, biometrics and cyber-behaviour. Examples from a sample database explicitly collected to accomplish the aims of the project are also presented.

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.