Exogenous attention to unseen objects?

Attention and awareness are closely related phenomena, but recent evidence has shown that not all attended stimuli give rise to awareness. Controversy still remains over whether, and the extent to which, a dissociation between attention and awareness encompasses all forms of attention. For example, it has been suggested that attention without awareness is more readily demonstrated for voluntary, endogenous attention than its reflexive, exogenous counterpart. Here we examine whether exogenous attentional cueing can have selective behavioural effects on stimuli that nevertheless remain unseen. Using a task in which object-based attention has been shown in the absence of awareness, we remove all possible contingencies between cues and target stimuli to ensure that any cueing effects must be under purely exogenous control, and find evidence of exogenous object-based attention without awareness. In a second experiment we address whether this dissociation crucially depends on the method used to establish that the objects indeed remain unseen. Specifically, to confirm that objects are unseen we adopt appropriate signal detection task procedures, including those that retain parity with the primary attentional task (by requiring participants to discriminate the two types of trial that are used to measure an effect of attention). We show a significant object-based attention effect is apparent under conditions where the selected object indeed remains undetectable.     

Basic Human Values and Their Contexts: A Multilevel Analysis of Support for the Use of Armed Drones in the United States,United Kingdom,and Turkey

Armed drones are now a key component of military strategy; however, little empirical research has explored the phenomenon in terms of psychological processes. Previous research has emphasized the importance of basic human values for structuring understandings of and opinions towards foreign policy issues. Using a social representations approach, we used a multilevel modeling approach to explore the link between values and support for the use of armed drones in the United Kingdom, the United States, and Turkey. In line with our predictions, high priority of self-transcendence values negatively predicted, and high priority of conservation values positively predicted, support for armed drones. Furthermore, given our theoretical framework, we specified that values should be conceptualized as prioritized or devalued within a particular context, and when values were specified as country-level, as well as individual predictors, this led to an increase in model fit. These findings are discussed in light of a developing line of research on meta-representations and their consequences for political opinion, and directions for future research are advanced.     

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.     

Reuse and body-formatted representations in simulation theory

I review the shifting definitions of simulation as found in recent versions of the simulation theory (ST) of social cognition. I focus on two concepts that have become central to recent ST in the work of a number of simulation theorists: the notion of reuse and the notion of B-formatted representations. I point out specific limitations or problems involved in these concepts. Although the reuse hypothesis provides an interesting evolutionary account of how neural mechanisms may adapt to new tasks, it doesn’t offer an explanation of how these mechanisms work. In contrast to the genuinely embodied account that simulation theorists seek, an explanation of social cognition in terms of B-formatted representations not only remains disembodied, but also ignores social interaction and remains solipsistic. I conclude by briefly outlining a non-simulationist enactivist account that can incorporate the reuse hypothesis.     

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.     

An object-based template for rejection effect

ABSTRACT

In the process of searching for targets, our visual system not only prioritizes target-relevant features, but can also suppress nontarget-related features. Although this template for rejection has been well demonstrated, whether the features (i.e. the objects) or locations are suppressed remains unresolved due to the experimental paradigms in previous studies: in particular, object-based templates for rejection were confounded with location-based inhibition in visual search paradigms. The present study examined an object-based template for rejection by introducing search arrays comprised of two overlapping shapes with search items distributed along the shape's contours. To discourage location-based inhibition, the two shapes were spatially intermingled (Experiment 1), rotated (Experiment 2), or jiggled (Experiment 3). Participants identified the colour of a target cross. The pre-cue indicated the shape in which the target would appear (positive cue condition), the shape in which only distractors would appear (negative cue condition), or the shape that was irrelevant to the current search array (neutral cue condition). In all three experiments, the reaction times for the negative cue condition were shorter than those for the neutral cue condition, which is a hallmark of the object-based template for rejection effect, even under conditions in which location-based inhibition was discouraged.     

Extending adaptive world modeling by identifying and handling insufficient knowledge models

Adaptive knowledge modeling is an approach for extending the abilities of the Object-Oriented World Model, a system for representing the state of an observed real-world environment, to open-world modeling. In open environments, entities unforeseen at the design-time of a world model can occur. For coping with such circumstances, adaptive knowledge modeling is tasked with adapting the underlying knowledge model according to the environment. The approach is based on quantitative measures, introduced previously, for rating the quality of knowledge models. In this contribution, adaptive knowledge modeling is extended by measures for detecting the need for model adaptation and identifying the potential starting points of necessary model change as well as by an approach for applying such change. Being an extended and more detailed version of [17], the contribution also provides background information on the architecture of the Object-Oriented World Model and on the principles of adaptive knowledge modeling, as well as examination results for the proposed methods. In addition, a more complex scenario is used to evaluate the overall approach.