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Attentional Capture by Meaning, a Multi-level Modeling Study

Attentional Capture by Meaning, a Multi-level Modelling Study Li Su, Howard Bowman (ls68@kent.ac.uk, hb5@kent.ac.uk) Centre for Cognitive Neuroscience and Cognitive Systems, University of Kent, UK Philip Barnard (philip.barnard@mrc-cbu.cam.ac.uk) MRC Cognition and Brain Sciences Unit, Cambridge, UK Kerszberg et al. 1998). These two levels of explanation really reflect different capacities to observe systems; that is, the extent to which the system is viewed from outside or inside, i.e., as a black or white box. There are clear pros and cons to these forms of modelling, which we discuss now. Abstract We present a computational study of attentional capture by meaning, based on Barnard et al's key-distractor attentional blink task. We highlight a sequence of models, from an abstract black-box to a structurally detailed white-box model. Each of these models reproduces the major findings from the key-distractor blink task. We argue that such multi-level modelling gives greater confidence in the theoretical position encapsulated by these models. Keywords: Attentional blink; LSA; semantic modulation; multi-level modeling. Introduction There are now many different approaches to the computational modelling of cognition, e.g. symbolic models (Newell 1990; Kieras and Meyer 1997), cognitive connectionist models (McLeod, Plunkett et al. 1998) and neurophysiologically prescribed connectionist models (O'Reilly and Munakata 2000). The relative value of different approaches is a hotly debated topic, with each presented as an alternative to the others, suggesting that they are in opposition to one another, e.g. (Fodor and Pylyshyn 1988; Hinton 1990). However, another perspective is that these reflect different levels of abstraction / explanation of the same system that are complementary, rather than fundamentally opposed. Computer science, which has often been used as a metaphor in the cognitive modelling domain, gives a clear precedent for thinking in terms of multiple views of a single system. An illustration of this is what is now probably the most widely used design method, the Unified Modelling Language (UML) (Booch, Rumbaugh et al. 1999). It is not that this perspective has been completely lost on cognitive scientists; indeed, Marr famously elaborated a version of this position in his three levels of cognitive description (Marr 2000). However, despite Marr's observations, concrete modelling endeavours rarely, if ever, consider multiple abstraction levels in the same context and particularly how to relate those levels. Multiple Level Cognitive Modelling In this paper, we can distinguish between the following two levels of explanation of a cognitive phenomenon. Firstly, high-level abstract descriptions of the mathematical characteristics of a pattern of data, e.g. (Stewart, Brown et al. 2005). Secondly, low-level detailed models of the internal structure of a cognitive system, e.g. (Dehaene, Black-box (Extensionalist) Modelling. With this approach, no assumptions are made about the internal structure of the system and there is no decomposition at all of the black-box into its constituent components. Thus, the point of reference for the modeller is the externally visible behaviour, e.g. the stimulus-response pattern. That is, such models are extensionalist in nature. A critical benefit of black-box modelling is that a minimal set of assumptions are made, especially in respect of the system structure. Consequently, there are less degrees of freedom and fewer hidden assumptions, making data fitting and parameter setting both well founded and, typically, feasible. For example, if the system can be described in closed form, key parameters can be determined by solving a set of equations, if not, computational search methods can be applied. White-box (Intensionalist) Modelling. In contrast, the internal (decompositional) structure of the system is asserted with this approach. That is, such models are intensionalist in nature. Although we can bring theories of cognitive architecture and (increasingly) neural structure to bear in proposing white-box models, a spectrum of assumptions (necessarily) needs to be made. Furthermore, typically, many of these assumptions concern the internal structure of the system. While structurally detailed models of cognition are likely to be the most revealing (especially with the current emphasis on neurophysiological correlates), deduction from these models is more slippery and potentially less well founded. Most importantly, many assumptions, such as settings of key parameters, need to be made, many of which may, at best, require complex justification and, at worst, be effectively arbitrary. As a result, parameter setting and data fitting is more difficult and, arguably, less well founded with white-box models. We can summarise then by saying that black-box modelling describes what a cognitive system does and it describes it in a relatively contained and well-founded manner. However, white-box modelling cannot be ignored, since it enables us to describe how a cognitive system functions, which is a concern for both traditional information processing and more recent neurophysiological explanations. Thus, when tackling the computational modelling of a particular cognitive phenomenon, one should