Distractor Objects Research Articles

Effects of Task Instructions on Predictive Eye Movements and Word Recognition During Second Language Sentence Comprehension

AbstractThis study tested whether encouraging prediction enhances prediction in second language (L2) speakers. L2 English speakers listened to English sentences like The woman … will read/buy one of the newspapers while viewing the target (a newspaper) and distractor objects (a rose, a bowl, and a mango) on a screen and clicked on the target as quickly as possible. The target was predictable (read) or unpredictable (buy) from the verb meaning. Participants looked at the target longer and were quicker to move the mouse to it when instructed to predict sentence continuation than when they were merely instructed to comprehend sentences. This result held true both when the target was predictable and unpredictable. Furthermore, only when instructed to predict did the participants make more clicking errors when the target was unpredictable than predictable, which suggested that encouraging prediction can interfere with word recognition accuracy in unpredictable contexts due to reduced cognitive resources or failed predictions.

Infants Produce Optimally Informative Points to Satisfy the Epistemic Needs of Their Communicative Partner.

Pragmatic theories assume that during communicative exchanges humans strive to be optimally informative and spontaneously adjust their communicative signals to satisfy their addressee's inferred epistemic needs. For instance, when necessary, adults flexibly and appropriately modify their communicative gestures to provide their partner the relevant information she lacks about the situation. To investigate this ability in infants, we designed a cooperative task in which 18-month-olds were asked to point at the target object they wanted to receive. In Experiment 1, we found that when their desired object was placed behind a distractor object, infants appropriately modified their prototypical pointing to avoid mistakenly indicating the distractor to their partner. When the objects were covered, and their cooperative partner had no information (Experiment 2) or incorrect information (Experiment 3) about the target's location - as opposed to being knowledgeable about it - infants pointed differentially more often at the target and employed modified pointing gestures more frequently as a function of the amount of relevant information that their partner needed to retrieve their desired object from its correct location. These findings demonstrate that when responding to a verbal request in a cooperative task 18-month-old infants can take into account their communicative partner's epistemic states and when necessary provide her with the relevant information she lacks through sufficiently informative deictic gestures. Our results indicate that infants possess an early emerging, species-unique cognitive adaptation specialized for communicative mindreading and pragmatic inferential communication which enable the efficient exchange of relevant information between communicating social partners in cooperative contexts.

Visual search and real-image similarity: An empirical assessment through the lens of deep learning.

The ability to predict how efficiently a person finds an object in the environment is a crucial goal of attention research. Central to this issue are the similarity principles initially proposed by Duncan and Humphreys, which outline how the similarity between target and distractor objects (TD) and between distractor objects themselves (DD) affect search efficiency. However, the search principles lack direct quantitative support from an ecological perspective, being a summary approximation of a wide range of lab-based results poorly generalisable to real-world scenarios. This study exploits deep convolutional neural networks to predict human search efficiency from computational estimates of similarity between objects populating, potentially, any visual scene. Our results provide ecological evidence supporting the similarity principles: search performance continuously varies across tasks and conditions and improves with decreasing TD similarity and increasing DD similarity. Furthermore, our results reveal a crucial dissociation: TD and DD similarities mainly operate at two distinct layers of the network: DD similarity at the intermediate layers of coarse object features and TD similarity at the final layers of complex features used for classification. This suggests that these different similarities exert their major effects at two distinct perceptual levels and demonstrates our methodology's potential to offer insights into the depth of visual processing on which the search relies. By combining computational techniques with visual search principles, this approach aligns with modern trends in other research areas and fulfils longstanding demands for more ecologically valid research in the field of visual search.

Selective Information Flow for Transformer Tracking

Fully transformer-based one-stream trackers have demonstrated outstanding performance on challenging benchmark datasets over the past three years. These trackers enable bidirectional information flow between all target template and search region tokens to compute attention features without assessing its impact on the tracker’s discriminative capability. Our study found that the information flow from a large portion of background tokens in the search region diminishes the importance of the target-specific features of the template. Moreover, previous transformer-based trackers fail to consider cues from the dynamic background region, even though it contains information about distracting similar objects. To address the identified limitations in transformer tracking, we propose a novel Selective Information Flow Tracking (SIFTrack) framework to enhance the tracker’s discriminative capability by selectively allowing information flow between different types of token groups. In the early encoder layers of the proposed SIFTrack, interactions from all search tokens to target template tokens are blocked to enrich target-specific feature extraction. In the deeper encoder layers, search tokens are partitioned into target and non-target tokens based on their attention scores. Then, bidirectional flow from target search tokens to template tokens is allowed to capture the appearance changes of the target. In addition, by including tokens from the dynamic background, SIFTrack effectively avoids distractor objects by capturing cues from the area surrounding the target. The proposed SIFTrack demonstrated outstanding performance in challenging benchmarks, particularly excelling in the one-shot tracking benchmark GOT-10k, achieving an average overlap of 74.6%. The code, models, and results of this work are available at https://github.com/JananiKugaa/SIFTrack.

Correlation-Embedded Transformer Tracking: A Single-Branch Framework.

Developing robust and discriminative appearance models has been a long-standing research challenge in visual object tracking. In the prevalent Siamese-based paradigm, the features extracted by the Siamese-like networks are often insufficient to model the tracked targets and distractor objects, thereby hindering them from being robust and discriminative simultaneously. While most Siamese trackers focus on designing robust correlation operations, we propose a novel single-branch tracking framework inspired by the transformer. Unlike the Siamese-like feature extraction, our tracker deeply embeds cross-image feature correlation in multiple layers of the feature network. By extensively matching the features of the two images through multiple layers, it can suppress non-target features, resulting in target-aware feature extraction. The output features can be directly used to predict target locations without additional correlation steps. Thus, we reformulate the two-branch Siamese tracking as a conceptually simple, fully transformer-based Single-Branch Tracking pipeline, dubbed SBT. After conducting an in-depth analysis of the SBT baseline, we summarize many effective design principles and propose an improved tracker dubbed SuperSBT. SuperSBT adopts a hierarchical architecture with a local modeling layer to enhance shallow-level features. A unified relation modeling is proposed to remove complex handcrafted layer pattern designs. SuperSBT is further improved by masked image modeling pre-training, integrating temporal modeling, and equipping with dedicated prediction heads. Thus, SuperSBT outperforms the SBT baseline by 4.7%,3.0%, and 4.5% AUC scores in LaSOT, TrackingNet, and GOT-10K. Notably, SuperSBT greatly raises the speed of SBT from 37 FPS to 81 FPS. Extensive experiments show that our method achieves superior results on eight VOT benchmarks. Code is available at https://github.com/phiphiphi31/SBT.

Evidence of elevated situational awareness for active duty soldiers during navigation of a virtual environment.

U.S. service members maintain constant situational awareness (SA) due to training and experience operating in dynamic and complex environments. Work examining how military experience impacts SA during visual search of a complex naturalistic environment, is limited. Here, we compare Active Duty service members and Civilians' physiological behavior during a navigational visual search task in an open-world virtual environment (VE) while cognitive load was manipulated. We measured eye-tracking and electroencephalogram (EEG) outcomes from Active Duty (N = 21) and Civilians (N = 15) while they navigated a desktop VE at a self-regulated pace. Participants searched and counted targets (N = 15) presented among distractors, while cognitive load was manipulated with an auditory Math Task. Results showed Active Duty participants reported significantly greater/closer to the correct number of targets compared to Civilians. Overall, Active Duty participants scanned the VE with faster peak saccade velocities and greater average saccade magnitudes compared to Civilians. Convolutional Neural Network (CNN) response (EEG P-300) was significantly weighted more to initial fixations for the Active Duty group, showing reduced attentional resources on object refixations compared to Civilians. There were no group differences in fixation outcomes or overall CNN response when comparing targets versus distractor objects. When cognitive load was manipulated, only Civilians significantly decreased their average dwell time on each object and the Active Duty group had significantly fewer numbers of correct answers on the Math Task. Overall, the Active Duty group explored the VE with increased scanning speed and distance and reduced cognitive re-processing on objects, employing a different, perhaps expert, visual search strategy indicative of increased SA. The Active Duty group maintained SA in the main visual search task and did not appear to shift focus to the secondary Math Task. Future work could compare how a stress inducing environment impacts these groups' physiological or cognitive markers and performance for these groups.

PCSformer: Pair-wise Cross-scale Sub-prototypes mining with CNN-transformers for weakly supervised semantic segmentation

Generating initial seeds is an important step in weakly supervised semantic segmentation (WSSS). Our approach concentrates on generating and refining initial seeds. The convolutional neural networks (CNNs)–based initial seeds focus only on the most discriminative regions and lack global information about the target. The Vision Transformer (ViT)–based approach can capture long-range feature dependencies due to the unique advantage of the self-attention mechanism. Still, we find that it suffers from distractor object leakage and background leakage problems. Based on these observations, we propose PCSformer, which improves the model’s ability to extract features through a Pair-wise Cross-scale (PC) strategy and solves the problem of distractor object leakage by further extracting potential target features through Sub-Prototypes (SP) mining. In addition, the proposed Conflict Self-Elimination (CSE) module further alleviates the background leakage problem. We validate our approach on the widely adopted Pascal VOC 2012 and MS COCO 2014, and extensive experiments demonstrate our superior performance. Furthermore, our method proves to be competitive for WSSS in medical images and challenging scenarios involving deformable and cluttered scenes. Additionally, we extend the PCSformer to weakly supervised object localization tasks, further highlighting its scalability and versatility. The code is available at https://github.com/ChunmengLiu1/PCSformer.

Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues

The purpose of semi-supervised video object segmentation (VOS) is to predict and generate object masks in subsequent video frames after being provided with the initial frame’s object mask. Currently, mainstream methods leverage historical frame information for enhancing the network’s performance. However, this approach faces the following issues: (1) They often overlook important shape information, leading to decreased accuracy in segmenting object-edge areas. (2) They often use pixel-level motion estimation to guide the matching for addressing distractor objects. However, this brings heavy computation costs and struggle against occlusion or fast/blurry motion. For the first problem, this paper introduces an object shape extraction module that exploits both the high-level and low-level features to obtain object shape information, by which the shape information can be used to further refine the predicted masks. For the second problem, this paper introduces a novel object-level motion prediction module, in which it stores the representative motion features during the training stage, and predicts the object motion by retrieving them during the inference stage. We evaluate our method on benchmark datasets compared with recent state-of-the-art methods, and the results demonstrate the effectiveness of the proposed method.

Category-based attention facilitates memory search.

We often need to decide whether the object we look at is also the object we look for. When we look for one specific object, this process can be facilitated by feature-based attention. However, when we look for many objects at the same time (e.g., the products on our shopping list) such a strategy may no longer be possible, as research has shown that we can actively prepare to detect only one or two objects at a time. Therefore, looking for multiple objects additionally requires long-term memory search, slowing down decision making. Interestingly, however, previous research has shown that distractor objects can be efficiently rejected during memory search when they are from a different category than the items in the memory set. Here, using EEG, we show that this efficiency is supported by top-down attention at the category level. In Experiment 1, human participants (both sexes) performed a memory search task on individually presented objects from different categories, most of which were distractors. We observed category-level attentional modulation of distractor processing from ∼150 ms after stimulus onset, expressed both as an evoked response modulation and as an increase in decoding accuracy of same-category distractors. In Experiment 2, memory search was performed on two concurrently presented objects. When both objects were distractors, spatial attention (indexed by the N2pc component) was directed to the object that was of the same category as the objects in the memory set. Together, these results demonstrate how top-down attention can facilitate memory search.Significance statement When we are in the supermarket, we repeatedly decide whether a product we look at (e.g., a banana) is on our memorized shopping list (e.g., apples, oranges, kiwis). This requires searching our memory, which takes time. However, when the product is of an entirely different category (e.g., dairy instead of fruit), the decision can be made quickly. Here, we used EEG to show that this between-category advantage in memory search tasks is supported by top-down attentional modulation of visual processing: The visual response evoked by distractor objects was modulated by category membership, and spatial attention was quickly directed to the location of within-category (vs. between-category) distractors. These results demonstrate a close link between attention and memory.

Phasic alerting in visual search tasks.

Many tasks require one to search for and find important objects in the visual environment. Visual search is strongly supported by cues indicating target objects to mechanisms of selective attention, which enable one to prioritise targets and ignore distractor objects. Besides selective attention, a major influence on performance across cognitive tasks is phasic alertness, a temporary increase of arousal induced by warning stimuli (alerting cues). Alerting cues provide no specific information on whose basis selective attention could be deployed, but have nevertheless been found to speed up perception and simple actions. It is still unclear, however, how alerting affects visual search. Therefore, in the present study, participants performed a visual search task with and without preceding visual alerting cues. Participants had to report the orientation of a target among several distractors. The target saliency was low in Experiment 1 and high in Experiment 2. In both experiments, we found that visual search was faster when a visual alerting cue was presented before the target display. Performance benefits occurred irrespective of how many distractors had been presented along with the target. Taken together, the findings reveal that visual alerting supports visual search independently of the complexity of the search process and the demands for selective attention.

The influence of distractor objects in emotions: Understanding the difference in between male and female highschool students

The influence of distractor objects in emotions: Understanding the difference in between male and female highschool students

68 Associations between within-domain intraindividual variability and functional abilities in cognitively healthy older adults

Objective:Cognitive performance, particularly in the domains of memory and executive functioning (EF), have been shown in previous research to predict decline in everyday functioning in older adults. The goal-control model posits that episodic memory difficulties cause weak or decaying task goals that lead to the omission of every day task steps (low accomplishment). EF difficulties preclude control over the execution of task goals that lead to inefficient and error-prone performance (high errors). Intraindividual variability (IIV) in neuropsychological test performance has been proposed as a noninvasive early marker of dementia and has utility in cognitively healthy older adults. In this study we examined cross-sectional relations between within-domain IIV in memory and attention/EF with performance of everyday tasks in the lab. We expected greater memory IIV to be associated with task accomplishment (goal decay), and greater attention/EF IIV to be associated with errors (poor control over goals).Participants and Methods:40 cognitively normal (CN) older adult (65+) participants (M age=71.44, SD=10.62; 73.8% women; 85.7% White; M education=18.55, SD=8.38) completed the Naturalistic Action Test (NAT), requiring completion of standardized, everyday tasks (i.e., breakfast, lunch) and scored for accomplishment of steps and various errors (micro-errors - misreach to distractor object, extra actions; overt -sequence, perseverative behaviors, etc.; motor errors). Within-domain IIV measures were calculated for 6 memory measures (HVLT-R, BVMT-R) and 6 attention/EF measures (Digit Span, Trail Making Test, Salthouse Letter/Pattern Comparison). First, raw scores for each test were z-transformed, then the intraindividual standard deviation of all z-scores was calculated. Bivariate Spearman’s rank-order correlations were used to examine associations between NAT performance and within-domain IIV, as well as mean performance. Linear regressions were used to examine the associations of IIV score with NAT scores, adjusting for age, sex, and mean cognitive performance.Results:Among CN participants, higher memory IIV was significantly associated with lower NAT accomplishment (r=-.329, p=.038), and better mean memory performance was significantly associated with fewer errors (micro-errors r=-.509, p=.003; overt r=-.438, p=.012; motor errors r=-.463, p=.008). Regression models revealed that memory IIV, after controlling for mean memory performance, age, and sex, did not significantly predict NAT performance. High attention/EF IIV was significant associated with more errors (overt r=.377, p=.016), whereas mean attention/EF performance was not significantly correlated with any NAT measures. Attention/executive function IIV significantly predicted errors (micro-errors B=4.15, p=.03; Overall model: R2=0.285, F(4, 24)=2.393, p=.079; overt B=.562, p=.032; Overall model: R2=0.371, F(4, 24)=3.543, p=.021) after adjusting for mean attention/executive function performance, age, and sex.Conclusions:Consistent with the goal-control model framework, greater variability in memory was associated with lower task accomplishment, whereas greater variability in attention/EF was associated with more errors. However, only attention/EF IIV predicted NAT performance, specifically errors (micro-errors, overt errors), after adjusting for age, sex, and mean attention/EF performance. Within-domain IIV can be used to predict mild functional difficulties in cognitively healthy older adults. Future research should examine within-domain IIV in a larger sample with more diversity to maximize generalizability, and in a longitudinal design to evaluate within-domain IIV predictive validity for cognitive/functional impairment.

Associative learning or Bayesian inference? Revisiting backwards blocking reasoning in adults

Causal reasoning is a fundamental cognitive ability that enables humans to learn about the complex interactions in the world around them. However, the cognitive mechanisms that underpin causal reasoning are not well understood. For instance, there is debate over whether Bayesian inference or associative learning best captures causal reasoning in human adults. The two experiments and computational models reported here were designed to examine whether adults engage in one form of causal inference called backwards blocking reasoning, whether the presence of potential distractors affects performance, and how adults' ratings align with the predictions of different computational models. The results revealed that adults engaged in backwards blocking reasoning regardless of whether distractor objects are present and that their causal judgements supported the predictions of a Bayesian model but not the predictions of two different associative learning models. Implications of these results are discussed.

A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data Part 1: Idealized Baseline Distribution

In order to find optimized headlight distributions based on real traffic data, a three-step approach is chosen. Since the complete investigations are too extensive to fit into a single publication, this paper is the first in a series of three publications. Over three papers, a novel way to optimize automotive headlight distributions based on real-life traffic and eye-tracking data is presented, based on 119 test subjects who participated in over 15,000 km of driving, including recordings of gaze behavior, light data, detection distances, and other objects in traffic. In the present paper, a baseline headlight distribution is derived from a series of detection tests conducted under ideal conditions, with a total of three tests, each with 19–30 subjects, conducted within the same test environment. In the first test, the influence of low beam intensity on the detection of pedestrians on the sidewalk (5.0 m from the center of the driving lane) is investigated. In the second test, the influence of different high beam intensities was investigated for the same detection task. In the third test, the headlight distribution and intensity are kept constant at a representative high beam level, but the detection task is changed. In this test, the pedestrian detection target is placed along different detection angles, ranging from immediately adjacent to the road (2.5°) to 15.5 m away from the center of the driving lane (8.0°). As mentioned, all of these tests were conducted under ideal conditions, with the studies taking place on an airfield with a 1.2 km long straight road and normal road markings, but without oncoming traffic, tasks other than keeping the vehicle with cruise control within its lane, or other distracting objects present. The tests yielded two sets of data; the first is the intensity, based on the first two studies, needed to ensure sufficient intensity to detect objects under ideal conditions at distances needed for different driving speeds. The last test then uses these intensities and necessary variations in the required intensity to create an idealized, symmetric headlight distribution as a baseline for subsequent publications. Although the distribution applies only to passenger vehicles like the one used in the test, the same approach could be applied to other vehicle types. The second paper of this series will focus on real traffic objects and their distributions within the traffic space in order to identify relevant areas in headlight distribution when driving under real traffic conditions. The third paper of this series will analyze driver gaze distributions during real driving scenarios. The data from all three papers are used to create optimized headlight distributions, thereby showing how such an optimized distribution relates to current headlight distributions in terms of luminous flux, intensity, and overall distribution.

Too much to handle? Interference from distractors with similar affordances on target selection for handled objects.

The existence of handle affordances has been classically demonstrated using the Stimulus-Response Compatibility paradigm, with shorter response times when the orientation of the object handle and the response hand are compatible in comparison to incompatible. Yet the activation of handle affordances from visual objects has been investigated in very simple situations involving single stimulus and motor response. As natural perceptual scenes are usually composed of multiple objects that could activate multiple affordances, the consequence of multiple affordance activation on the perception and processing of a given object of the scene requires more investigation. The aim of this study was to determine the impact of distractor affordances on the processing of a target object in situations involving several familiar graspable objects. In two online experiments, 229 participants had to select a target object (the kitchen utensil or the tool) in a visual scene displaying a pair of objects. They performed left key presses when the target was on the left and right key presses when the target was on the right. Target handle orientation and response side could be compatible or incompatible. Critically, target and distractor objects had similar or dissimilar handle affordances, with handles oriented for left- or right-hand grasps. Results from the two experiments showed slower response times when target and distractor objects had similar handle affordances in comparison to dissimilar affordances, when participants performed right hand responses and when target orientation and response were compatible. Thus, affordance similarity between objects may interfere rather than facilitate object processing and slow down target selection. These findings are in line with models of affordance and object selection assuming automatic inhibition of distractors' affordances for appropriate object interaction.

The Impact of Audio-Visual, Visual and Auditory Cues on Multiple Object Tracking Performance in Children with Autism.

Previous studies have documented differences in processing multisensory information by children with autism compared to typically developing children. Furthermore, children with autism have been found to track fewer multiple objects on a screen than those without autism, suggesting reduced attentional control. In the present study, we investigated whether children with autism (n = 33) and children without autism (n = 33) were able to track four target objects moving amongst four indistinguishable distractor objects while sensory cues were presented. During tracking, we presented various types of cues - auditory, visual, or audio-visual or no cues while target objects bounced off the inner boundary of a centralized circle. We found that children with autism tracked fewer targets than children without autism. Furthermore, children without autism showed improved tracking performance in the presence of visual cues, whereas children with autism did not benefit from sensory cues. Whereas multiple object tracking performance improved with increasing age in children without autism, especially when using audio-visual cues, children with autism did not show age-related improvement in tracking. These results are in line with the hypothesis that attention and the ability to integrate sensory cues during tracking are reduced in children with autism. Our findings could contribute valuable insights for designing interventions that incorporate multisensory information.

Automaton-Guided Curriculum Generation for Reinforcement Learning Agents

Despite advances in Reinforcement Learning, many sequential decision making tasks remain prohibitively expensive and impractical to learn. Recently, approaches that automatically generate reward functions from logical task specifications have been proposed to mitigate this issue; however, they scale poorly on long-horizon tasks (i.e., tasks where the agent needs to perform a series of correct actions to reach the goal state, considering future transitions while choosing an action). Employing a curriculum (a sequence of increasingly complex tasks) further improves the learning speed of the agent by sequencing intermediate tasks suited to the learning capacity of the agent. However, generating curricula from the logical specification still remains an unsolved problem. To this end, we propose AGCL, Automaton-guided Curriculum Learning, a novel method for automatically generating curricula for the target task in the form of Directed Acyclic Graphs (DAGs). AGCL encodes the specification in the form of a deterministic finite automaton (DFA), and then uses the DFA along with the Object-Oriented MDP (OOMDP) representation to generate a curriculum as a DAG, where the vertices correspond to tasks, and edges correspond to the direction of knowledge transfer. Experiments in gridworld and physics-based simulated robotics domains show that the curricula produced by AGCL achieve improved time-to-threshold performance on a complex sequential decision-making problem relative to state-of-the-art curriculum learning (e.g, teacher-student, self-play) and automaton-guided reinforcement learning baselines (e.g, Q-Learning for Reward Machines). Further, we demonstrate that AGCL performs well even in the presence of noise in the task's OOMDP description, and also when distractor objects are present that are not modeled in the logical specification of the tasks' objectives.

Predicting group benefits in joint multiple object tracking

In everyday life, people often work together to accomplish a joint goal. Working together is often beneficial as it can result in a higher performance compared to working alone – a so-called “group benefit”. While several factors influencing group benefits have been investigated in a range of tasks, to date, they have not been examined collectively with an integrative statistical approach such as linear modeling. To address this gap in the literature, we investigated several factors that are highly relevant for group benefits (i.e., task feedback, information about the co-actor’s actions, the similarity in the individual performances, and personality traits) and used these factors as predictors in a linear model to predict group benefits in a joint multiple object tracking (MOT) task. In the joint MOT task, pairs of participants jointly tracked the movements of target objects among distractor objects and, depending on the experiment, either received group performance feedback, individual performance feedback, information about the group member’s performed actions, or a combination of these types of information. We found that predictors collectively account for half of the variance and make non-redundant contributions towards predicting group benefits, suggesting that they independently influence group benefits. The model also accurately predicts group benefits, suggesting that it could be used to anticipate group benefits for individuals that have not yet performed a joint task together. Given that the investigated factors are relevant for other joint tasks, our model provides a first step towards developing a more general model for predicting group benefits across several shared tasks.

The role of the male rat infralimbic cortex in distraction analgesia

Cognitive interventions, including distraction, have been successfully utilized in the manipulation of experimental pain and the treatment of clinical pain. Attentional diversions can reduce the experience of pain, a phenomenon known as distraction analgesia (DA). Prior research has suggested that variations in stimulus intensity may influence the magnitude of DA. However, the neural substrates of DA remain largely unknown. Converging evidence suggests that the infralimbic cortex (IL) in the brains of rats may contribute to the phenomenon of DA. The function of the rat IL in DA has never been directly investigated, therefore, this study sought to identify the role of the IL at two levels of noxious stimulus intensity among brain-intact and IL lesioned male rats within an established rat model of DA. A distractor object reduced formalin-induced nociceptive behavior in brain-intact rats, and this DA effect was detectable during low- (0.5% formalin) and high-intensity (1% formalin) stimulation. IL lesion resulted in a near-complete elimination of the DA effect and an overall reduction in formalin pain. These results provide the first known evidence that (i) the IL is involved in processing DA in rats, (ii) the IL contributes to formalin-induced nociceptive behavior irrespective of distraction, and (iii) a high-intensity stimulation was generally more susceptible to DA than low-intensity stimulation. These findings may further inform the mechanisms and future development of non-pharmacological strategies to reduce pain.

Target-Distractor Competition Modulates Saccade Trajectories in Space and Object Space.

Saccade planning and execution can be affected by a multitude of factors present in a target selection task. Recent studies have shown that the similarity between a target and nearby distractors affects the curvature of saccade trajectories, because of target-distractor competition. To further understand the nature of this competition, we varied the distance between and the similarity of complex target and distractor objects in a delayed match-to-sample task to examine their effects on human saccade trajectories and better understand the underlying neural circuitry. For trials with short saccadic reaction times (SRTs) when target-distractor competition is still active, the distractor is attractive and saccade trajectories are deviated toward the distractor. We found a robust effect of distance consistent with saccade vector averaging, whereas the effect of similarity suggested the existence of an object-based suppressive surround. At longer SRTs, there was sufficient time for competition between the objects to complete and the distractor to be repulsive, which resulted in saccade trajectory deviations away from the distractor exhibiting the effects of a spatial suppressive surround. In terms of similarity, as the target-distractor similarity decreased, the initial saccade angle shifted toward the target, reflecting stronger distractor inhibition. There were no interactions between distance and similarity at any point in the time course of target-distractor competition. Together, saccade trajectories reflect target-distractor competition that is affected independently by both spatial and object space suppressive surrounds. The differences in saccade trajectories at short and long SRTs distinguish between active and completed decision-making processes.