Modelling Visual Search with the Selective Attention for Identification Model (VS-SAIM): A Novel Explanation for Visual Search Asymmetries

Dietmar Heinke 0 1 Andreas Backhaus 0 1 0 A. Backhaus Fraunhofer IFF, Biosystems Engineering , 39106 Magdeburg, Germany 1 D. Heinke (&) School of Psychology, University of Birmingham , Birmingham B15 2TT, UK In earlier work, we developed the Selective Attention for Identification Model (SAIM [16]). SAIM models the human ability to perform translation-invariant object identification in multiple object scenes. SAIM suggests that central for this ability is an interaction between parallel competitive processes in a selection stage and a object identification stage. In this paper, we applied the model to visual search experiments involving simple lines and letters. We presented successful simulation results for asymmetric and symmetric searches and for the influence of background line orientations. Search asymmetry refers to changes in search performance when the roles of target item and non-target item (distractor) are swapped. In line with other models of visual search, the results suggest that a large part of the empirical evidence can be explained by competitive processes in the brain, which are modulated by the similarity between target and distractor. The simulations also suggest that another important factor is the feature properties of distractors. Finally, the simulations indicate that search asymmetries can be the outcome of interactions between top-down (knowledge about search items) and bottom-up (feature of search items) processing. This interaction in VS-SAIM is dominated by a novel mechanism, the knowledge-based on-centre-off-surround receptive field. This receptive field is reminiscent of the classical receptive fields but the exact shape is modulated by both, top-down and bottom-up processes. The paper discusses supporting evidence for the existence of this novel concept. - The visual search task is a commonly used experimental procedure to study human processing of multiple object scenes. In a standard visual search task, participants are asked to determine whether a pre-defined target item among non-targets (distractors) is present or absent. During the course of the experiments the number of distractors (display size) is varied. Typically, the time it takes participants to make this decision (reaction time) is measured as a function of the display size (search function). The slope of the search function is interpreted as indicator for the search efficiency for particular target-distractor pairings. For instance, search for a diagonal line among vertical lines is highly efficient with a slope close to 0ms/item whereas search for a T among Ls is inefficient with a slope of around 25 ms/item. Over 40 years or so, visual search tasks have produced a plethora of experimental evidence (see [31, 41] for reviews). There have been numerous attempts to develop qualitative theories of visual search, e.g. most prominently the Feature Integration Theory (FIT) by Treisman et al. [37] or the Attentional Engagement Theory (AET [12]). This article presents a connectionist model of visual search. This model is an extension of the Selective Attention for Identification Model (SAIM; [16, 19, 20]) adopted to simulate visual search and therefor is termed VS-SAIM. SAIM was developed in a connectionist framework and aims to explain human behaviour in terms of the underlying neurophysiological processes in the brain. However, SAIM avoids the full complexity of neurophysiological processes, e.g. the dynamics of different neurotransmitters and employs rate-coded neuron models. On the other hand, this simplification is balanced with SAIMs objective to unify a broad range of behavioural data in one model (see [17]; for extensive discussions on the relationship between models of the neural substrate and modelling behavioural data). SAIMs starting point is the human ability to identify objects in multiple object scenes. SAIM suggests that central for this ability is an interaction between parallel competitive processes in a selection stage and a object identification stage. Based on this assumption, SAIM was able to simulate a broad range of experimental evidence usually associated with normal operation of attention and with dysfunctional attention [16]. The simulations of normal attention covered two-object costs on selection, global precedence, spatial cueing both within and between objects, and inhibition of return. The effects of disordered attention included view-centred and object-centred visual neglect. In Heinke et al. [19], SAIM was successfully applied to simulate a few visual search experiments. These studies showed that the search functions in visual search can be an emerged property of the competitive processes in the brain. The slopes of the search functions were influenced by the similarity between distractors and target. However, when we attempted to simulate a broader range of visual search experiments, it became clear that this initial version of VS-SAIM was not able to mimic this additional data. Consequently, we modified some operations within VS-SAIM. Especially, we replaced the original similarity measure, the scalar product, with the Euclidian distance. The present article reports on a first set of results of this extension. For the first set of results we chose experimental evidence that, on the face of it, is particularly challenging to VS-SAIMs similarity-based approach, the search asymmetry (see [43]; for a review). In search asymmetries search slopes differ when the roles of target item and distractor item are swapped. For instance, it is easier to find a tilted line among vertical lines then vice versa [37]; a diagonal line among vertical lines than the reverse [3]. Other examples are: orange item (easier) versus red item [36], moving item (easier) versus static item [11, 34]. For a similarity-based approach these data are particular challenging, as the target-distractor similarity simply does not change when target and distractor are swaped around. A theoretical account needs to introduce an additional factor to explain these findings. On a wider note, there is no satisfactory theoretical account for the occurrence of search asymmetry at present. Initially, Treisman and Gormican [37] suggested that search asymmetries are indicative for the existence of feature maps assuming that detection of the presence of a feature is better than the detection of its absence [37]. However, subsequent evidence has not supported their theory. For instance, their assumption does not fit with the findings on diagonal line versus vertical line [3], as there are well-known feature maps for diagonal lines in the brain. Moreover, recent evidence showed that search for an inverted elephant among upright elephants is more efficient than the other way around [43] pointing towards the involvement of object knowledge in search asymmetries. The current paper aims to develop a first coherent account of search asymmetries. It focuses on the search asymmetries with line orient (...truncated)