Salience-based selection: attentional capture by distractors less salient than the target

Current accounts of attentional capture predict the most salient stimulus to be invariably selected first. However, existing salience and visual search models assume noise in the map computation or selection process. Consequently, they predict the first selection to be stochastically dependent on salience, implying that attention could even be captured first by the second most salient (instead of the most salient) stimulus in the field. Yet, capture by less salient distractors has not been reported and salience-based selection accounts claim that the distractor has to be more salient in order to capture attention. We tested this prediction using an empirical and modeling approach of the visual search distractor paradigm. For the empirical part, we manipulated salience of target and distractor parametrically and measured reaction time interference when a distractor was present compared to absent. Reaction time interference was strongly correlated with distractor salience relative to the target. Moreover, even distractors less salient than the target captured attention, as measured by reaction time interference and oculomotor capture. In the modeling part, we simulated first selection in the distractor paradigm using behavioral measures of salience and considering the time course of selection including noise. We were able to replicate the result pattern we obtained in the empirical part. We conclude that each salience value follows a specific selection time distribution and attentional capture occurs when the selection time distributions of target and distractor overlap. Hence, selection is stochastic in nature and attentional capture occurs with a certain probability depending on relative salience

Recognising and reacting to angry and happy facial expressions: a diffusion model analysis.

Researchers have reported two biases in how people recognise and respond to angry and happy facial expressions: (1) a gender-expression bias (Becker et al. in J Pers Soc Psychol, 92(2):179-190, https://doi.org/10.1037/0022-3514.92.2.179 , 2007)-faster identification of male faces as angry and female faces as happy and (2) an approach-avoidance bias-faster avoidance of people who appear angry and faster approach responses people who appear happy (Heuer et al. in Behav Res The, 45(12):2990-3001, https://doi.org/10.1016/j.brat.2007.08.010 2007; Marsh et al. in Emotion, 5(1), 119-124, https://doi.org/10.1037/1528-3542.5.1.119 , 2005; Rotteveel and Phaf in Emotion 4(2):156-172, https://doi.org/10.1037/1528-3542.4.2.156 , 2004). The aim of the current research is to gain insight into the nature of such biases by applying the drift diffusion model to the results of an approach-avoidance task. Sixty-five participants (33 female) identified faces as either happy or angry by pushing and pulling a joystick. In agreement with the original study of this effect (Solarz 1960) there were clear participant gender differences-both the approach avoidance and gender-expression biases were larger in magnitude for female compared to male participants. The diffusion model results extend recent research (Krypotos et al. in Cogn Emot 29(8):1424-1444, https://doi.org/10.1080/02699931.2014.985635 , 2015) by indicating that the gender-expression and approach-avoidance biases are mediated by separate cognitive processes

Attentional Processing of Food Cues in Overweight and Obese Individuals

The incentive sensitization model of obesity hypothesizes that obese individuals in the western world have acquired an enhanced attention bias to food cues, because of the overwhelming exposure to food. This article gives an overview of recent studies regarding attention to food and obesity. In general, an interesting approach-avoidance pattern in food-related attention has been found in overweight/obese individuals in a number of studies. However, it should be noted that study results are contradictory. This might be due to methodological issues, such as the choice of attention measurements, possibly tapping different underlying components of information processing. Although attention research is challenging, researchers are encouraged to further explore important issues, such as the exact circumstances in which obese persons demonstrate enhanced attention to food, the directional relationship between food-related attention bias, overeating and weight gain, and the underlying involvement of the reward system. Knowledge on these issues could help improve treatment programs

Coherence and recurrency: maintenance, control and integration in working memory

Working memory (WM), including a ‘central executive’, is used to guide behavior by internal goals or intentions. We suggest that WM is best described as a set of three interdependent functions which are implemented in the prefrontal cortex (PFC). These functions are maintenance, control of attention and integration. A model for the maintenance function is presented, and we will argue that this model can be extended to incorporate the other functions as well. Maintenance is the capacity to briefly maintain information in the absence of corresponding input, and even in the face of distracting information. We will argue that maintenance is based on recurrent loops between PFC and posterior parts of the brain, and probably within PFC as well. In these loops information can be held temporarily in an active form. We show that a model based on these structural ideas is capable of maintaining a limited number of neural patterns. Not the size, but the coherence of patterns (i.e., a chunking principle based on synchronous firing of interconnected cell assemblies) determines the maintenance capacity. A mechanism that optimizes coherent pattern segregation, also poses a limit to the number of assemblies (about four) that can concurrently reverberate. Top-down attentional control (in perception, action and memory retrieval) can be modelled by the modulation and re-entry of top-down information to posterior parts of the brain. Hierarchically organized modules in PFC create the possibility for information integration. We argue that large-scale multimodal integration of information creates an ‘episodic buffer’, and may even suffice for implementing a central executive

A brain-inspired cognitive system that mimics the dynamics of human thought

In recent years, some impressive AI systems have been built that can play games and answer questions about large quantities of data. However, we are still a very long way from AI systems that can think and learn in a human-like way. We have a great deal of information about how the brain works and can simulate networks of hundreds of millions of neurons. So it seems likely that we could use our neuroscientific knowledge to build brain-inspired artificial intelligence that acts like humans on similar timescales. This paper describes an AI system that we have built using a brain-inspired network of artificial spiking neurons. On a word recognition and colour naming task our system behaves like human subjects on a similar timescale. In the longer term, this type of AI technology could lead to more flexible general purpose artificial intelligence and to more natural human-computer interaction