Delayed disengagement of attention from distractors signalling reward

Attention refers to the set of cognitive mechanisms that facilitate the prioritization of incoming sensory information. Existing research suggests that motivationally salient stimuli, such as those associated with reward, are prioritized by the attention system and that this prioritization occurs independently of an observer's goals. Specifically, studies of visual search have shown that stimuli signalling the availability of monetary reward are more likely to capture eye movements, even when participants are motivated to ignore such stimuli. In the current study we ask whether reward magnitude influences only the likelihood that stimuli will capture spatial attention, or whether reward also influences the ease with which people can disengage attention from a location when they are motivated to move their attention elsewhere. Three experiments examined the time taken to disengage from a centrally presented distractor that signalled the availability of high or low reward. We found that participants took longer to move their eyes away from a high-reward distractor, even though this came at financial cost (Experiment 1), that participants were unable to suppress a high-reward distractor consistently presented at the central location (Experiment 2), that slower responding was not due to behavioural freezing in the presence of a signal of high reward (Experiment 3), and that slower responding persisted even when rewards were no longer available (Experiment 4). These results indicate that reward modulates attentional disengagement: signals of high reward hold attention for longer, even when this is counterproductive for performance of ongoing tasks. Our findings further highlight the role of reward in the conflict between automatic and goal-directed attentional processing

The Parallel Persistent Memory Model

We consider a parallel computational model that consists of $P$ processors, each with a fast local ephemeral memory of limited size, and sharing a large persistent memory. The model allows for each processor to fault with bounded probability, and possibly restart. On faulting all processor state and local ephemeral memory are lost, but the persistent memory remains. This model is motivated by upcoming non-volatile memories that are as fast as existing random access memory, are accessible at the granularity of cache lines, and have the capability of surviving power outages. It is further motivated by the observation that in large parallel systems, failure of processors and their caches is not unusual. Within the model we develop a framework for developing locality efficient parallel algorithms that are resilient to failures. There are several challenges, including the need to recover from failures, the desire to do this in an asynchronous setting (i.e., not blocking other processors when one fails), and the need for synchronization primitives that are robust to failures. We describe approaches to solve these challenges based on breaking computations into what we call capsules, which have certain properties, and developing a work-stealing scheduler that functions properly within the context of failures. The scheduler guarantees a time bound of $O(W/P_A + D(P/P_A) \lceil\log_{1/f} W\rceil)$ in expectation, where $W$ and $D$ are the work and depth of the computation (in the absence of failures), $P_A$ is the average number of processors available during the computation, and $f \le 1/2$ is the probability that a capsule fails. Within the model and using the proposed methods, we develop efficient algorithms for parallel sorting and other primitives.Comment: This paper is the full version of a paper at SPAA 2018 with the same nam

New Record for the Coffee Berry Borer, Hypothenemus hampei, in Hawaii

The coffee berry borer, Hypothenemus hampei (Ferrari) (Coleoptera: Curculionidae) is endemic to Africa and is the most devastating pest of coffee worldwide. The female bores a hole in the coffee berry and deposits her eggs inside. Upon hatching, larvae feed on the seeds, thus reducing both quality and yields of the marketable product. The coffee berry borer was found in the district of Kona on the island of Hawaii in August 2010 and appears to be restricted to that area

The effect of experience and instructions on learned attentional biases

Afiliaciones: Instituto de Investigación Biomédica de Málaga (IBIMA), University of Malaga, Spain Primary Care and Public Health Sciences, King’s College London, UK School of Psychology, UNSW Australia, Sydney, AustraliaIt has been shown that selective attention is allocated to the best available predictor of an outcome, which is known as learned predictiveness. Mitchell et al. (2012) have shown that instructions about the ‘relevance’ of each stimulus can influence (and even reverse) the learned predictiveness attentional bias, suggesting that propositional reasoning plays a crucial role in this phenomenon. Our experiment further explores the effects of instructions on this learned attentional bias. As a difference with previous work, we measured attentional capture through spatial cueing effects, which have been found to rely on rapid attentional processes (Le Pelley et al., 2013). Participants responded faster to events presented in the spatial location cued by stimuli that had previously been trained as predictive through trial-by-trial learning. However, verbal instructions regarding relevance failed to speed up participants’ responses or to modulate the effect of learned predictiveness on spatial cueing. These results suggest that predictive stimuli produce an attentional bias which is not (always) under voluntary control.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The role of uncertainty in attentional and choice exploration

The exploitation-exploration (EE) trade-off describes how, when making a decision, an organism must often choose between a safe alternative with a known pay-off, and one or more riskier alternatives with uncertain pay-offs. Recently, the concept of the EE trade-off has been extended to the examination of how organisms distribute limited attentional resources between several stimuli. This work suggests that when the rules governing the environment are certain, participants learn to “exploit” by attending preferentially to cues that provide the most information about upcoming events. However, when the rules are uncertain, people “explore” by increasing their attention to all cues that may provide information to help in predicting upcoming events. In the current study, we examine how uncertainty affects the EE trade-off in attention using a contextual two-armed bandit task, where participants explore with both their attention and their choice behavior. We find evidence for an influence of uncertainty on the EE trade-off in both choice and attention. These findings provide support for the idea of an EE trade-off in attention, and that uncertainty is a primary motivator for exploration in both choice and attentional allocation

Outcome predictability biases cued search

Within the domain of associative learning, there is substantial evidence that people (and other animals) select amongst environmental cues on the basis of their reinforcement history. Specifically, people preferentially attend to, and learn about, cueing stimuli that have previously predicted events of consequence (a predictiveness bias). By contrast, relatively little is known about whether people prioritize some (to-be-predicted) outcome events over others on the basis of their past experience with those outcomes (a predictability bias). The present experiments assessed whether the prior predictability of a stimulus results in a learning bias in a contingency learning task, as such effects are not anticipated by formal models of associative learning. Previously unpredictable stimuli were less readily learned about than previously predictable stimuli. This pattern is unlikely to reflect the use of strategic search processes or blocking of learning by the context. Instead we argue that our findings are most consistent with the operation of a biased learning mechanism

Reward-related attentional capture is associated with severity of addictive and obsessive-compulsive behaviors

A cue that signals reward can capture attention and elicit approach behaviors in people and animals. The current study examined whether attentional capture by reward-related cues is associated with severity of addiction-related and obsessive-compulsive behaviors. Participants were recruited via Mechanical Turk and included 143 adults [mean age 34 years (SD = 8.5), 43% female] who had endorsed at least one addiction-related or obsessive-compulsive behavior in the past month. All assessment components were delivered via the internet, and included questionnaires to assess severity of compulsivity-related problems across addiction-related and obsessive-compulsive behaviors, as well as a visual search task to measure reward-related attentional capture. Reward-related attentional capture was associated with severity of compulsivity, transdiagnostically. These findings have implications for understanding the mechanisms that underlie compulsive behaviors and suggest that reward-related attentional capture is a promising transdiagnostic cognitive risk marker for compulsivity