Selective Attention For Identification Model Research Articles

Excitatory versus inhibitory feedback in Bayesian formulations of scene construction.

The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects—as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures.

Serial versus parallel search: A model comparison approach based on reaction time distributions

ABSTRACTFor 50 years or so, visual search experiments have been used to examine how humans find behaviourally relevant objects in complex visual scenes. For the same length of time, there has been a dispute over whether this search is performed in a serial or parallel fashion. In this paper, we approach this dispute by numerically fitting a serial search model and a parallel search model to reaction time (RT) distributions from three visual search experiments (feature search, conjunction search, spatial configuration search). In order to do so, we used a free-likelihood method based on a novel kernel density estimator (KDE).The serial search model was the Competitive Guided Search (CGS) model by Moran et al. [(2013). Competitive guided search: Meeting the challenge of benchmark RT distributions. Journal of Vision, 13(8), 24–24.]. We were able to replicate the ability of CGS to model RT distributions from visual search experiments, and demonstrated that CGS generalizes well to new data. The parallel model was based on the biased-competition theory and utilized a very simple biologically-plausible winner-take-all (WTA) mechanism from Heinke and Humphreys’s [(2003). Attention, spatial representation and visual neglect: Simulating emergent attention and spatial memory in the Selective Attention for Identification Model (SAIM). Psychological Review, 110(1), 29–87.]. With this mechanism, SAIM has been able to explain a broad range of attentional phenomena but it was not specifically designed to model RT distributions in visual search. Nevertheless, the WTA was able to reproduce these distributions.However, a direct comparison of the two models suggested that the serial CGS is slightly better equipped to explain the RT distributions than the WTA mechanism. The CGS’s success was mainly down the usage of the Wald distribution which was specifically designed to model visual search. Future WTA versions will have to find a biologically plausible mechanism to reproduce such a RT distribution. Finally, both models suffered from a failure to generalize across all display sizes. From these comparisons, we developed suggestions for improving the models and motivated empirical studies to devise a stronger test for the two types of searches.

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

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 Selective Attention for Action Model (SAAM)

Event Abstract Back to Event The Selective Attention for Action Model (SAAM) Christoph Böhme1* and Dietmar Heinke1 1 University of Birmingham, School of Psychology, United Kingdom Classically, visual attention is assumed to be influenced by visual properties of objects, e.g. as assessed in visual search tasks. However, recent experimental evidence suggests that visual attention is also guided by action-related properties of objects ("affordances", Gibson, 1966, 1979), e.g. the handle of a cup affords grasping the cup; therefore attention is drawn towards the handle (see Pellegrino, Rafal, and Tipper, 2005 for an example).In a first step towards modelling this interaction between attention and action, we implemented the Selective Attention for Action model (SAAM). The design of SAAM is based on the Selective Attention for Identification model (SAIM, Heinke & Humphreys, 2003). For instance, we also followed a soft-constraint satisfaction approach in a connectionist framework. However, SAAM's selection process is guided by locations within objects suitable for grasping them whereas SAIM selects objects based on their visual properties.In order to implement SAAM's selection mechanism two sets of constraints were implemented. The first set of constraints took into account the anatomy of the hand, e.g. maximal possible distances between fingers. The second set of constraints (geometrical constraints) considered suitable contact points on objects by using simple edge detectors. At first, we demonstrate that SAAM can successfully mimic human behaviour by comparing simulated contact points with experimental data. Secondly, we show that SAAM simulates affordance-guided attentional behaviour as it successfully generates contact points for only one object in two-object images. Our model shows that stable grasps can be derived directly from visual inputs without doing object-recognition and without constructing three dimensional internal representations of objects. Also, no complex torque and forces analysis is required. The similar mechanisms employed in SAIM and SAAM make it palpable to combine both into a unified model of visual selection for action and identification.

Top-down guidance of visual search: A computational account

We present a revised version of the Selective Attention for Identification Model (SAIM), using an initial feature detection process to code edge orientations. We show that the revised SAIM can simulate both efficient and inefficient human search, that it shows search asymmetries, and that top-down expectancies for targets play a major role in the model's selection. Predictions of the model for top-down effects are tested with human participants, and important similarities and dissimilarities are discussed.

Selective Attention for Identification Model

Recently, we have introduced a computational model, termed Selective Attention Identification Model [SAIM; Heinke, Humphreys, Attention, spatial representation and visual neglect: simulating emerge...

Selective Attention for Identification Model: Simulating visual neglect

Recently, we have introduced a computational model, termed Selective Attention Identification Model [SAIM; Heinke, Humphreys, Attention, spatial representation and visual neglect: simulating emergent attention and spatial memory in the Selective Attention for Identification Model (SAIM), Psychol. Rev. 110 (1) (2003) 29]. SAIM aims at translation-invariant object identification by selecting objects and mapping them into a focus of attention (FOA). With this architecture, SAIM can model a wide range of experimental evidence on normal attention and attentional disorders. Here, we demonstrate how SAIM can produce two specially striking forms of visual disorder, within- and between-object neglect. Patients with within-object neglect tend to ignore parts of objects and patients with between-object neglect might omit complete objects presented next to another object. Importantly, both deficits can occur irrespective of the objects’ positions in the visual field (translation invariant). To simulate neglect, the selection mechanism in SAIM was lesioned. Importantly, the selection mechanism selects objects and, at the same time, produces a translation-invariant representation of the objects in the FOA. Consequently, the lesion can have a translation-invariant effect, allowing SAIM to simulate within- and between-object neglect. This result gives additional credence to SAIM’s integral approach to attention and translation-invariant object identification.

Attention, spatial representation, and visual neglect: Simulating emergent attention and spatial memory in the selective attention for identification model (SAIM).

The selective attention for identification model (SAIM) is presented. This uses a spatial window to select visual information for recognition, binding parts to objects and generating translation-invariant recognition. The model provides a qualitative account of both normal and disordered attention. Simulations of normal attention demonstrate 2-object costs and effects of object familiarity on selection, global precedence, spatial cueing, and inhibition of return. When lesioned, SAIM demonstrated either view- or object-centered neglect or spatial extinction, depending on the type and extent of lesion. The model provides a framework to unify (a) object- and space-based theories of normal selection, (b) dissociations within the syndrome of unilateral neglect, and (c) attentional and representational accounts of neglect.

Attention, spatial representation, and visual neglect: Simulating emergent attention and spatial memory in the selective attention for identification model (SAIM).

Attention, spatial representation, and visual neglect: Simulating emergent attention and spatial memory in the selective attention for identification model (SAIM).

Modelling visual search experiments: the selective attention for identification model (SAIM)

We have presented a computational model called selective attention for identification model (SAIM), that can account for a broad range of psychological and neuropsychological phenomena on attention [1]. In this paper we report on work that extends SAIM to model data from visual search tasks. The results show that SAIM can capture important aspects of findings in visual search experiments, including variations of search slopes with the similarity between targets and distractors. SAIM is also capable of simulating experimental findings for redundant targets, including the violation of the Miller inequality. SAIM explains these experimental findings as the consequence of competitive processes involved in object identification.