Symbolically speaking: a connectionist model of sentence production

The ability to combine words into novel sentences has been used to argue that humans have symbolic language production abilities. Critiques of connectionist models of language often center on the inability of these models to generalize symbolically (Fodor & Pylyshyn, 1988; Marcus, 1998). To address these issues, a connectionist model of sentence production was developed. The model had variables (role‐concept bindings) that were inspired by spatial representations (Landau & Jackendoff, 1993). In order to take advantage of these variables, a novel dual‐pathway architecture with event semantics is proposed and shown to be better at symbolic generalization than several variants. This architecture has one pathway for mapping message content to words and a separate pathway that enforces sequencing constraints. Analysis of the model's hidden units demonstrated that the model learned different types of information in each pathway, and that the model's compositional behavior arose from the combination of these two pathways. The model's ability to balance symbolic and statistical behavior in syntax acquisition and to model aphasic double dissociations provided independent support for the dual‐pathway architecture.     

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.     

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.     

A General Structure for Legal Arguments About Evidence Using Bayesian Networks

A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where BNs have been used in the legal context, they are presented as completed pieces of work, with no insights into the reasoning and working that must have gone into their construction. This means the process of building BNs for legal arguments is ad hoc, with little possibility for learning and process improvement. This article directly addresses this problem by describing a method for building useful legal arguments in a consistent and repeatable way. The method complements and extends recent work by Hepler, Dawid, and Leucari (2007) on object‐oriented BNs for complex legal arguments and is based on the recognition that such arguments can be built up from a small number of basic causal structures (referred to as idioms). We present a number of examples that demonstrate the practicality and usefulness of the method.     

Does emotion influence visual perception? Depends on how you look at it

ABSTRACT

One of the biggest challenges in the study of emotion–cognition interaction is addressing the question of whether and how emotions influence processes of perception as distinct from other higher-level cognitive processes. Most theories of emotion agree that an emotion episode begins with a sensory experience – such as a visual percept – that elicits a cascade of affective, cognitive, physiological, and/or behavioural responses (the ordering and inclusion of those latter components being forever debated). However, for decades, a subset of philosophers and scientists have suggested that the presumed perception → emotion relationship is in fact bidirectional, with emotion also altering the perceptual process. In the present review we reflect on the history and empirical support (or, some might argue, lack thereof) for the notion that emotion influences visual perception. We examine ways in which researchers have attempted to test the question, and the ways in which this pursuit is so difficult. As is the case with the ongoing debate about the cognitive penetrability of perception, we conclude that nothing is conclusive in the debate about the emotional penetrability of perception. We nonetheless don rose-coloured glasses as we look forward to the future of this research topic.