Attention Allocation Aid for Visual Search

This paper outlines the development and testing of a novel, feedback-enabled attention allocation aid (AAAD), which uses real-time physiological data to improve human performance in a realistic sequential visual search task. Indeed, by optimizing over search duration, the aid improves efficiency, while preserving decision accuracy, as the operator identifies and classifies targets within simulated aerial imagery. Specifically, using experimental eye-tracking data and measurements about target detectability across the human visual field, we develop functional models of detection accuracy as a function of search time, number of eye movements, scan path, and image clutter. These models are then used by the AAAD in conjunction with real time eye position data to make probabilistic estimations of attained search accuracy and to recommend that the observer either move on to the next image or continue exploring the present image. An experimental evaluation in a scenario motivated from human supervisory control in surveillance missions confirms the benefits of the AAAD.Comment: To be presented at the ACM CHI conference in Denver, Colorado in May 201

Do emotional faces capture attention, and does this depend on awareness? Evidence from the visual probe paradigm

The visual probe (VP) paradigm provides evidence that emotional stimuli attract attention. Such effects have been reported even when stimuli are presented outside of awareness. These findings have shaped the idea that humans possess a processing pathway that detects evolutionarily significant signals independently of awareness. Here, we addressed 2 unresolved questions: First, if emotional stimuli attract attention, is this driven by their affective content, or by low-level image properties (e.g., luminance contrast)? Second, does attentional capture occur under conditions of genuine unawareness? We found that observers preferentially allocated attention to emotional faces under aware viewing conditions. However, this effect was best explained by low-level stimulus properties, rather than emotional content. When stimuli were presented outside of awareness (via continuous flash suppression or masking), we found no evidence that attention was directed toward emotional face stimuli. Finally, observer's awareness of the stimuli (assessed by d') predicted attentional cuing. Our data challenge existing literature: First, we cast doubt on the notion of preferential attention to emotional stimuli in the absence of awareness. Second, we question whether effects revealed by the VP paradigm genuinely reflect emotion-sensitive processes, instead suggesting they can be more parsimoniously explained by low-level variability between stimuli. (PsycINFO Database Record (c) 2019 APA, all rights reserved)

Language and memory for object location

In three experiments, we investigated the influence of two types of language on memory for object location: demonstratives (this, that) and possessives (my, your). Participants first read instructions containing demonstratives/possessives to place objects at different locations, and then had to recall those object locations (following object removal). Experiments 1 and 2 tested contrasting predictions of two possible accounts of language on object location memory: the Expectation Model (Coventry, Griffiths, & Hamilton, 2014) and the congruence account (Bonfiglioli, Finocchiaro, Gesierich, Rositani, & Vescovi, 2009). In Experiment 3, the role of attention allocation as a possible mechanism was investigated. Results across all three experiments show striking effects of language on object location memory, with the pattern of data supporting the Expectation Model. In this model, the expected location cued by language and the actual location are concatenated leading to (mis)memory for object location, consistent with models of predictive coding (Bar, 2009; Friston, 2003)

A Motivational Determinant of Facial Emotion Recognition : Regulatory Focus Affects Recognition of Emotions in Faces

Funding: The research was supported by The Netherlands Organization for Scientific Research (NWO, project 452-07-006). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Objects predict fixations better than early saliency

Humans move their eyes while looking at scenes and pictures. Eye movements correlate with shifts in attention and are thought to be a consequence of optimal resource allocation for high-level tasks such as visual recognition. Models of attention, such as “saliency maps,” are often built on the assumption that “early” features (color, contrast, orientation, motion, and so forth) drive attention directly. We explore an alternative hypothesis: Observers attend to “interesting” objects. To test this hypothesis, we measure the eye position of human observers while they inspect photographs of common natural scenes. Our observers perform different tasks: artistic evaluation, analysis of content, and search. Immediately after each presentation, our observers are asked to name objects they saw. Weighted with recall frequency, these objects predict fixations in individual images better than early saliency, irrespective of task. Also, saliency combined with object positions predicts which objects are frequently named. This suggests that early saliency has only an indirect effect on attention, acting through recognized objects. Consequently, rather than treating attention as mere preprocessing step for object recognition, models of both need to be integrated

The blinking spotlight of attention

Increasing evidence suggests that attention can concurrently select multiple locations; yet it is not clear whether this ability relies on continuous allocation of attention to the different targets (a "parallel" strategy) or whether attention switches rapidly between the targets (a periodic "sampling" strategy). Here, we propose a method to distinguish between these two alternatives. The human psychometric function for detection of a single target as a function of its duration can be used to predict the corresponding function for two or more attended targets. Importantly, the predicted curves differ, depending on whether a parallel or sampling strategy is assumed. For a challenging detection task, we found that human performance was best reflected by a sampling model, indicating that multiple items of interest were processed in series at a rate of approximately seven items per second. Surprisingly, the data suggested that attention operated in this periodic regime, even when it was focused on a single target. That is, attention might rely on an intrinsically periodic process

Relating alpha power modulations to competing visuospatial attention theories

Visuospatial attention theories often propose hemispheric asymmetries underlying the control of attention. In general support of these theories, previous EEG/MEG studies have shown that spatial attention is associated with hemispheric modulation of posterior alpha power (gating by inhibition). However, since measures of alpha power are typically expressed as lateralization scores, or collapsed across left and right attention shifts, the individual hemispheric contribution to the attentional control mechanism remains unclear. This is, however, the most crucial and decisive aspect in which the currently competing attention theories continue to disagree. To resolve this long-standing conflict, we derived predictions regarding alpha power modulations from Heilman's hemispatial theory and Kinsbourne's interhemispheric competition theory and tested them empirically in an EEG experiment. We used an attention paradigm capable of isolating alpha power modulation in two attentional states, namely attentional bias in a neutral cue condition and spatial orienting following directional cues. Differential alpha modulations were found for both hemispheres across conditions. When anticipating peripheral visual targets without preceding directional cues (neutral condition), posterior alpha power in the left hemisphere was generally lower and more strongly modulated than in the right hemisphere, in line with the interhemispheric competition theory. Intriguingly, however, while alpha power in the right hemisphere was modulated by both, cue-directed leftward and rightward attention shifts, the left hemisphere only showed modulations by rightward shifts of spatial attention, in line with the hemispatial theory. This suggests that the two theories may not be mutually exclusive, but rather apply to different attentional states

Attention, predictive learning, and the inverse base-rate effect: Evidence from event-related potentials

We report the first electrophysiological investigation of the inverse base-rate effect (IBRE), a robust non-rational bias in predictive learning. In the IBRE, participants learn that one pair of symptoms (AB) predicts a frequently occurring disease, whilst an overlapping pair of symptoms (AC) predicts a rarely occurring disease. Participants subsequently infer that BC predicts the rare disease, a non-rational decision made in opposition to the underlying base rates of the two diseases. Error-driven attention theories of learning state that the IBRE occurs because C attracts more attention than B. On the basis of this account we predicted and observed the occurrence of brain potentials associated with visual attention: a posterior Selection Negativity, and a concurrent anterior Selection Positivity, for C vs. B in a post-training test phase. Error-driven attention theories further predict no Selection Negativity, Selection Positivity or IBRE, for control symptoms matched on frequency to B and C, but for which there was no shared symptom (A) during training. These predictions were also confirmed, and this confirmation discounts alternative explanations of the IBRE based on the relative novelty of B and C. Further, we observed higher response accuracy for B alone than for C alone; this dissociation of response accuracy (B>C) from attentional allocation (C>B) discounts the possibility that the observed attentional difference was caused by the difference in response accuracy

Towards a Unified Knowledge-Based Approach to Modality Choice

This paper advances a unified knowledge-based approach to the process of choosing the most appropriate modality or combination of modalities in multimodal output generation. We propose a Modality Ontology (MO) that models the knowledge needed to support the two most fundamental processes determining modality choice – modality allocation (choosing the modality or set of modalities that can best support a particular type of information) and modality combination (selecting an optimal final combination of modalities). In the proposed ontology we model the main levels which collectively determine the characteristics of each modality and the specific relationships between different modalities that are important for multi-modal meaning making. This ontology aims to support the automatic selection of modalities and combinations of modalities that are suitable to convey the meaning of the intended message

Human Attention in Image Captioning: Dataset and Analysis

In this work, we present a novel dataset consisting of eye movements and verbal descriptions recorded synchronously over images. Using this data, we study the differences in human attention during free-viewing and image captioning tasks. We look into the relationship between human attention and language constructs during perception and sentence articulation. We also analyse attention deployment mechanisms in the top-down soft attention approach that is argued to mimic human attention in captioning tasks, and investigate whether visual saliency can help image captioning. Our study reveals that (1) human attention behaviour differs in free-viewing and image description tasks. Humans tend to fixate on a greater variety of regions under the latter task, (2) there is a strong relationship between described objects and attended objects ($97\%$ of the described objects are being attended), (3) a convolutional neural network as feature encoder accounts for human-attended regions during image captioning to a great extent (around $78\%$), (4) soft-attention mechanism differs from human attention, both spatially and temporally, and there is low correlation between caption scores and attention consistency scores. These indicate a large gap between humans and machines in regards to top-down attention, and (5) by integrating the soft attention model with image saliency, we can significantly improve the model's performance on Flickr30k and MSCOCO benchmarks. The dataset can be found at: https://github.com/SenHe/Human-Attention-in-Image-Captioning.Comment: To appear at ICCV 201