Face recognition algorithms and the other‐race effect: computational mechanisms for a developmental contact hypothesis

People recognize faces of their own race more accurately than faces of other races. The “contact” hypothesis suggests that this “other‐race effect” occurs as a result of the greater experience we have with own‐ versus other‐race faces. The computational mechanisms that may underlie different versions of the contact hypothesis were explored in this study. We replicated the other‐race effect with human participants and evaluated four classes of computational face recognition algorithms for the presence of an other‐race effect. Consistent with the predictions of a developmental contact hypothesis, “experience‐based models” demonstrated an other‐race effect only when the representational system was developed through experience that warped the perceptual space in a way that was sensitive to the overall structure of the model's experience with faces of different races. When the model's representation relied on a feature set optimized to encode the information in the learned faces, experience‐based algorithms recognized minority‐race faces more accurately than majority‐race faces. The results suggest a developmental learning process that warps the perceptual space to enhance the encoding of distinctions relevant for own‐race faces. This feature space limits the quality of face representations for other‐race faces.     

Picturing ourselves in the world: Drawings,interpretative phenomenological analysis and the relational mapping interview

ABSTRACT

Some aspects of experience can be challenging for research participants to verbalise. Interpretative phenomenological analysis (IPA) researchers need to get experience-near to meet their phenomenological commitments, capturing the “texture” and quality of existence, and placing participants in relation to events, objects, others, and the world. Incorporating drawing into IPA designs provides a vehicle through which participants can better explore and communicate their lifeworlds. IPA researchers also require rich accounts to fulfil their interpretative commitments. Drawing taps into multiple sensory registers simultaneously, providing polysemous data, which in turn lends itself to hermeneutic analysis. This article outlines a multimodal method, the relational mapping interview, which was developed to understand the relational context of various forms of distress and disruption. We illustrate how the approach results in richly nuanced visual and verbal accounts of relational experience. Drawing on an “expanded hermeneutic phenomenology,” we suggest how visual data can be analysed within an IPA framework to offer significant experiential insights.     

Salivary progesterone is associated with reduced coherence of attentional, cognitive, and motivational systems

The present study tested whether the hypothesis that high levels of progesterone (P) have a decoupling effect on the function of the brain hemispheres (Hausmann & Gunturkun, 2000) also extends to attentional functions, referential connections between verbal and nonverbal representations and the degree to which implicit motivational needs match a person's explicit goal commitments. Participants (28 women on oral contraceptives, 14 naturally cycling women, 50 men) completed the Lateralized Attention Network Task (Greene et al., 2008), a measure of the alerting, orienting, and conflict-resolution functions of attention for each hemisphere; a measure of referential competence (i.e., the ability to quickly name nonverbal information); a measure of the implicit motives power, achievement, and affiliation; and a content-matched personal goal inventory. In addition, they provided a saliva sample that was assayed for P and cortisol (C). Higher levels of P were associated with lower interhemispheric correlations for alerting and orienting, but with a higher correlation of conflict-resolution performance. Higher P was also associated with longer interhemispheric transfer time, lower congruence between implicit motives and explicit goal commitments and, after controlling for C, with lower referential competence. These results suggest that (a) P is associated with the degree to which attentional functions are correlated between hemispheres, although in a different direction for more posterior (alerting and orienting: decoupling) than for more anterior functions (conflict resolution: coupling), (b) that high P is associated with other indicators of reduced functional coherence between cognitive systems (longer interhemispheric transfer time, lower referential competence), and (c) that high P is also associated with low coherence between implicit and explicit motivational systems.     

Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks

Convolutional neural networks (CNNs) are increasingly widely used in psychology and neuroscience to predict how human minds and brains respond to visual images. Typically, CNNs represent these images using thousands of features that are learned through extensive training on image datasets. This raises a question: How many of these features are really needed to model human behavior? Here, we attempt to estimate the number of dimensions in CNN representations that are required to capture human psychological representations in two ways: (1) directly, using human similarity judgments and (2) indirectly, in the context of categorization. In both cases, we find that low-dimensional projections of CNN representations are sufficient to predict human behavior. We show that these low-dimensional representations can be easily interpreted, providing further insight into how people represent visual information. A series of control studies indicate that these findings are not due to the size of the dataset we used and may be due to a high level of redundancy in the features appearing in CNN representations.     

Priming effects of arithmetic signs in 10- to 15-year-old children

In this research, 10- to 12- and 13- to 15-year-old children were presented with very simple addition and multiplication problems involving operands from 1 to 4. Critically, the arithmetic sign was presented before the operands in half of the trials, whereas it was presented at the same time as the operands in the other half. Our results indicate that presenting the ‘x’ sign before the operands of a multiplication problem does not speed up the solving process, irrespective of the age of children. In contrast, presenting the ‘+’ sign before the operands of an addition problem facilitates the solving process, but only in 13 to 15-year-old children. Such priming effects of the arithmetic sign have been previously interpreted as the result of a pre-activation of an automated counting procedure, which can be applied as soon as the operands are presented. Therefore, our results echo previous conclusions of the literature that simple additions but not multiplications can be solved by fast counting procedures. More importantly, we show here that these procedures are possibly convoked automatically by children after the age of 13 years. At a more theoretical level, our results do not support the theory that simple additions are solved through retrieval of the answers from long-term memory by experts. Rather, the development of expertise for mental addition would consist in an acceleration of procedures until automatization.     

A Social Interpolation Model of Group Problem-Solving

How do people use information from others to solve complex problems? Prior work has addressed this question by placing people in social learning situations where the problems they were asked to solve required varying degrees of exploration. This past work uncovered important interactions between groups' connectivity and the problem's complexity: the advantage of less connected networks over more connected networks increased as exploration was increasingly required for optimally solving the problem at hand. We propose the Social Interpolation Model (SIM), an agent-based model to explore the cognitive mechanisms that can underlie exploratory behavior in groups. Through results from simulation experiments, we conclude that “exploration” may not be a single cognitive property, but rather the emergent result of three distinct behavioral and cognitive mechanisms, namely, (a) breadth of generalization, (b) quality of prior expectation, and (c) relative valuation of self-obtained information. We formalize these mechanisms in the SIM, and explore their effects on group dynamics and success at solving different kinds of problems. Our main finding is that broad generalization and high quality of prior expectation facilitate successful search in environments where exploration is important, and hinder successful search in environments where exploitation alone is sufficient.     

On the Generalization of Simple Alternating Category Structures

A fundamental question in the study of human cognition is how people learn to predict the category membership of an example from its properties. Leading approaches account for a wide range of data in terms of comparison to stored examples, abstractions capturing statistical regularities, or logical rules. Across three experiments, participants learned a category structure in a low-dimension, continuous-valued space consisting of regularly alternating regions of class membership (A B A B). The dependent measure was generalization performance for novel items outside the range of the training space. Human learners often extended the alternation pattern––a finding of critical interest given that leading theories of categorization based on similarity or dimensional rules fail to predict this behavior. In addition, we provide novel theoretical interpretations of the observed phenomenon.     

Modelling the effects of semantic ambiguity in word recognition

Most words in English are ambiguous between different interpretations; words can mean different things in different contexts. We investigate the implications of different types of semantic ambiguity for connectionist models of word recognition. We present a model in which there is competition to activate distributed semantic representations. The model performs well on the task of retrieving the different meanings of ambiguous words, and is able to simulate data reported by Rodd, Gaskell, and Marslen-Wilson [J. Mem. Lang. 46 (2002) 245] on how semantic ambiguity affects lexical decision performance. In particular, the network shows a disadvantage for words with multiple unrelated meanings (e.g., bark) that coexists with a benefit for words with multiple related word senses (e.g., twist). The ambiguity disadvantage arises because of interference between the different meanings, while the sense benefit arises because of differences in the structure of the attractor basins formed during learning. Words with few senses develop deep, narrow attractor basins, while words with many senses develop shallow, broad basins. We conclude that the mental representations of word meanings can be modelled as stable states within a high-dimensional semantic space, and that variations in the meanings of words shape the landscape of this space.