Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modeling dilution effects in perceptual load search tasks Kleanthis C. Neokleous 1,2 (Kleneokl@Cs.Ucy.Ac.Cy) Marios N. Avraamides 1 (Mariosav@Ucy.Ac.Cy) Christos N. Schizas 2 ( Schizas@Cs.Ucy.Ac.Cy) Department of Psychology, University of Cyprus Department of Computer Science, University of Cyprus P.O. Box 20537, 1678 Nicosia, Cyprus Abstract A computational model of selective attention has been previously used to provide a concrete and comprehensive account for perceptual load findings in visual search tasks (Neokleous, Koushiou, Avraamides, & Schizas, 2009). Here, the same model was used to simulate findings from an experiment in which perceptual load effects re attributed to distractor dilution (Benoni & Tsal, 2010). By modeling at the neural level the continuous inhibitory interactions that take place among visual stimuli competing for cortical representation, the model reproduced successfully the behavioural pattern of results. The model thus offers a biologically-plausible way to reconcile findings that contradict Perceptual Load theory with those that support it. Keywords:Computational Modeling, Spiking Neural Networks, Perceptual Load Theory, Dilution. Introduction The Perceptual Load theory was proposed by Lavie and Tsal (1994; Lavie, 1995) to resolve the early vs. late debate concerning the locus of attention (e.g., Broadbent, 1958; Deutsch & Deutsch, 1963). It posits that selection of stimuli may take place early or late depending on the perceptual load of the task. In a paradigmatic study of perceptual load Lavie and Cox (1997) had participants carry out a high load or a low load visual search task. In the high load task participants searched for two target letters (X and N) among 5 similarly- shaped letters arranged in a circular array. In the low load task, they searched for these targets among five instances of the letter O. In both conditions, a distractor letter, which participants re asked to ignore, was presented to the left or to the right of the array. Depending on condition, the distractor letter could either be congruent with the target (i.e., the same letter as the target), incongruent (i.e., the other target), or neutral (i.e., the letter “L”). Results revealed that in the low load task participants took longer to identify the target in the presence of an incongruent distractor compared to when the disctractor was congruent or neutral. In contrast, in the high load task, no difference beten the three distractor conditions was found. Lavie and Cox (1997) argued that distractor interference was absent in the high load task because all attentional resources re consumed by the task leaving none to process the irrelevant distractor. In contrast, in the low load task only minimal resources re devoted to the task allowing spare resources to spill over to the processing of the distractor. Thus, the Perceptual Load theory posits that selection is early under high load conditions and late in low load conditions. Despite its appeal, the Perceptual Load theory has been criticized on various grounds. First, a number of studies have provided findings that seem at odds with the theory (Eltiti, Wallace, & Fox, 2005; Johnson, McGrath, and McNeil, 2002; Torralbo & Beck, 2008). For example, Johnson et al. (2002) shod that cueing the target location with a 100%-predictive central cue in a low load visual search task eliminates distractor interference despite the fact that it does not alter the load of the task. Neokleous et al. (2009) reported the same result using an 80%-predictive peripheral cue. Second, Torralbo and Beck (2008) argued that the theory is unsatisfying because it does not provide a clear definition for perceptual load, and because the concept of exhaustive capacity cannot be easily reconciled with what is known about brain mechanisms. To provide a more concrete formulation of the Perceptual Load Theory  have previously presented a biologically- plausible computational model, capable of simulating both the basic pattern of findings from Lavie and Cox (1997)and findings considered contradictory to the theory(e.g., Johnson et al., 2002). The model offered an explicit account for the possible neural mechanisms that give rise to perceptual load findings without relying on vague terms such as high and low load. The model simulated the data by modeling at the neural level the continuous inhibitory interactions that take place among visual stimuli competing for cortical representation. The strength of these inhibitory interactions is determined by the saliency of stimuli whereas top-down
['Kleanthis C. Neokleous', 'Marios N. Avraamides', 'Christos N. Schizas']
I am writing a research paper on a idea Modeling dilution effects in perceptual load search tasks
[{"from": "human", "value": "I am writing a research paper on a idea Modeling dilution effects in perceptual load search tasks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modeling dilution effects in perceptual load search tasks Kleanthis C. Neokleous 1,2 (Kleneokl@Cs.Ucy.Ac.Cy) Marios N. Avraamides 1 (Mariosav@Ucy.Ac.Cy) Christos N. Schizas 2 ( Schizas@Cs.Ucy.Ac.Cy) Department of Psychology, University of Cyprus Department of Computer Science, University of Cyprus P.O. Box 20537, 1678 Nicosia, Cyprus Abstract A computational model of selective attention has been previously used to provide a concrete and comprehensive account for perceptual load findings in visual search tasks (Neokleous, Koushiou, Avraamides, & Schizas, 2009). Here, the same model was used to simulate findings from an experiment in which perceptual load effects re attributed to distractor dilution (Benoni & Tsal, 2010). By modeling at the neural level the continuous inhibitory interactions that take place among visual stimuli competing for cortical representation, the model reproduced successfully the behavioural pattern of results. The model thus offers a biologically-plausible way to reconcile findings that contradict Perceptual Load theory with those that support it. Keywords:Computational Modeling, Spiking Neural Networks, Perceptual Load Theory, Dilution. Introduction The Perceptual Load theory was proposed by Lavie and Tsal (1994; Lavie, 1995) to resolve the early vs. late debate concerning the locus of attention (e.g., Broadbent, 1958; Deutsch & Deutsch, 1963). It posits that selection of stimuli may take place early or late depending on the perceptual load of the task. In a paradigmatic study of perceptual load Lavie and Cox (1997) had participants carry out a high load or a low load visual search task. In the high load task participants searched for two target letters (X and N) among 5 similarly- shaped letters arranged in a circular array. In the low load task, they searched for these targets among five instances of the letter O. In both conditions, a distractor letter, which participants re asked to ignore, was presented to the left or to the right of the array. Depending on condition, the distractor letter could either be congruent with the target (i.e., the same letter as the target), incongruent (i.e., the other target), or neutral (i.e., the letter \u201cL\u201d). Results revealed that in the low load task participants took longer to identify the target in the presence of an incongruent distractor compared to when the disctractor was congruent or neutral. In contrast, in the high load task, no difference beten the three distractor conditions was found. Lavie and Cox (1997) argued that distractor interference was absent in the high load task because all attentional resources re consumed by the task leaving none to process the irrelevant distractor. In contrast, in the low load task only minimal resources re devoted to the task allowing spare resources to spill over to the processing of the distractor. Thus, the Perceptual Load theory posits that selection is early under high load conditions and late in low load conditions. Despite its appeal, the Perceptual Load theory has been criticized on various grounds. First, a number of studies have provided findings that seem at odds with the theory (Eltiti, Wallace, & Fox, 2005; Johnson, McGrath, and McNeil, 2002; Torralbo & Beck, 2008). For example, Johnson et al. (2002) shod that cueing the target location with a 100%-predictive central cue in a low load visual search task eliminates distractor interference despite the fact that it does not alter the load of the task. Neokleous et al. (2009) reported the same result using an 80%-predictive peripheral cue. Second, Torralbo and Beck (2008) argued that the theory is unsatisfying because it does not provide a clear definition for perceptual load, and because the concept of exhaustive capacity cannot be easily reconciled with what is known about brain mechanisms. To provide a more concrete formulation of the Perceptual Load Theory  have previously presented a biologically- plausible computational model, capable of simulating both the basic pattern of findings from Lavie and Cox (1997)and findings considered contradictory to the theory(e.g., Johnson et al., 2002). The model offered an explicit account for the possible neural mechanisms that give rise to perceptual load findings without relying on vague terms such as high and low load. The model simulated the data by modeling at the neural level the continuous inhibitory interactions that take place among visual stimuli competing for cortical representation. The strength of these inhibitory interactions is determined by the saliency of stimuli whereas top-down"}]