Cortical Dynamics of Contextually-Cued Attentive Visual Learning and Search: Spatial and Object Evidence Accumulation

How do humans use predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, a certain combination of objects can define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. A neural model, ARTSCENE Search, is developed to illustrate the neural mechanisms of such memory-based contextual learning and guidance, and to explain challenging behavioral data on positive/negative, spatial/object, and local/distant global cueing effects during visual search. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined by enhancing target-like objects in space as a scene is scanned with saccadic eye movements. The model clarifies the functional roles of neuroanatomical, neurophysiological, and neuroimaging data in visual search for a desired goal object. In particular, the model simulates the interactive dynamics of spatial and object contextual cueing in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal cortical cells (area 46) prime possible target locations in posterior parietal cortex based on goalmodulated percepts of spatial scene gist represented in parahippocampal cortex, whereas model ventral prefrontal cortical cells (area 47/12) prime possible target object representations in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex. The model hereby predicts how the cortical What and Where streams cooperate during scene perception, learning, and memory to accumulate evidence over time to drive efficient visual search of familiar scenes.CELEST, an NSF Science of Learning Center (SBE-0354378); SyNAPSE program of Defense Advanced Research Projects Agency (HR0011-09-3-0001, HR0011-09-C-0011

The roots of self-awareness

In this paper we provide an account of the structural underpinnings of self-awareness. We offer both an abstract, logical account-by way of suggestions for how to build a genuinely self-referring artificial agent-and a biological account, via a discussion of the role of somatoception in supporting and structuring self-awareness more generally. Central to the account is a discussion of the necessary motivational properties of self-representing mental tokens, in light of which we offer a novel definition of self-representation. We also discuss the role of such tokens in organizing self-specifying information, which leads to a naturalized restatement of the guarantee that introspective awareness is immune to error due to mis-identification of the subject

Task-demands can immediately reverse the effects of sensory-driven saliency in complex visual stimuli

In natural vision both stimulus features and task-demands affect an observer's attention. However, the relationship between sensory-driven (“bottom-up”) and task-dependent (“top-down”) factors remains controversial: Can task-demands counteract strong sensory signals fully, quickly, and irrespective of bottom-up features? To measure attention under naturalistic conditions, we recorded eye-movements in human observers, while they viewed photographs of outdoor scenes. In the first experiment, smooth modulations of contrast biased the stimuli's sensory-driven saliency towards one side. In free-viewing, observers' eye-positions were immediately biased toward the high-contrast, i.e., high-saliency, side. However, this sensory-driven bias disappeared entirely when observers searched for a bull's-eye target embedded with equal probability to either side of the stimulus. When the target always occurred in the low-contrast side, observers' eye-positions were immediately biased towards this low-saliency side, i.e., the sensory-driven bias reversed. Hence, task-demands do not only override sensory-driven saliency but also actively countermand it. In a second experiment, a 5-Hz flicker replaced the contrast gradient. Whereas the bias was less persistent in free viewing, the overriding and reversal took longer to deploy. Hence, insufficient sensory-driven saliency cannot account for the bias reversal. In a third experiment, subjects searched for a spot of locally increased contrast (“oddity”) instead of the bull's-eye (“template”). In contrast to the other conditions, a slight sensory-driven free-viewing bias prevails in this condition. In a fourth experiment, we demonstrate that at known locations template targets are detected faster than oddity targets, suggesting that the former induce a stronger top-down drive when used as search targets. Taken together, task-demands can override sensory-driven saliency in complex visual stimuli almost immediately, and the extent of overriding depends on the search target and the overridden feature, but not on the latter's free-viewing saliency

Time to guide: evidence for delayed attentional guidance in contextual cueing

Contextual cueing experiments show that, when displays are repeated, reaction times (RTs) to find a target decrease over time even when the observers are not aware of the repetition. Recent evidence suggests that this benefit in standard contextual cueing tasks is not likely to be due to an improvement in attentional guidance (Kunar, Flusberg, Horowitz, & Wolfe, 2007). Nevertheless, we ask whether guidance can help participants find the target in a repeated display, if they are given sufficient time to encode the display. In Experiment 1 we increased the display complexity so that it took participants longer to find the target. Here we found a larger effect of guidance than in a condition with shorter RTs. Experiment 2 gave participants prior exposure to the display context. The data again showed that with more time participants could implement guidance to help find the target, provided that there was something in the search stimuli locations to guide attention to. The data suggest that, although the benefit in a standard contextual cueing task is unlikely to be a result of guidance, guidance can play a role if it is given time to develop

Underpowered samples, false negatives, and unconscious learning

The scientific community has witnessed growing concern about the high rate of false positives and unreliable results within the psychological literature, but the harmful impact of false negatives has been largely ignored. False negatives are particularly concerning in research areas where demonstrating the absence of an effect is crucial, such as studies of unconscious or implicit processing. Research on implicit processes seeks evidence of above-chance performance on some implicit behavioral measure at the same time as chance-level performance (that is, a null result) on an explicit measure of awareness. A systematic review of 73 studies of contextual cuing, a popular implicit learning paradigm, involving 181 statistical analyses of awareness tests, reveals how underpowered studies can lead to failure to reject a false null hypothesis. Among the studies that reported sufficient information, the meta-analytic effect size across awareness tests was d z = 0.31 (95 % CI 0.24–0.37), showing that participants’ learning in these experiments was conscious. The unusually large number of positive results in this literature cannot be explained by selective publication. Instead, our analyses demonstrate that these tests are typically insensitive and underpowered to detect medium to small, but true, effects in awareness tests. These findings challenge a widespread and theoretically important claim about the extent of unconscious human cognition

On the Distribution of Salient Objects in Web Images and its Influence on Salient Object Detection

It has become apparent that a Gaussian center bias can serve as an important prior for visual saliency detection, which has been demonstrated for predicting human eye fixations and salient object detection. Tseng et al. have shown that the photographer's tendency to place interesting objects in the center is a likely cause for the center bias of eye fixations. We investigate the influence of the photographer's center bias on salient object detection, extending our previous work. We show that the centroid locations of salient objects in photographs of Achanta and Liu's data set in fact correlate strongly with a Gaussian model. This is an important insight, because it provides an empirical motivation and justification for the integration of such a center bias in salient object detection algorithms and helps to understand why Gaussian models are so effective. To assess the influence of the center bias on salient object detection, we integrate an explicit Gaussian center bias model into two state-of-the-art salient object detection algorithms. This way, first, we quantify the influence of the Gaussian center bias on pixel- and segment-based salient object detection. Second, we improve the performance in terms of F1 score, Fb score, area under the recall-precision curve, area under the receiver operating characteristic curve, and hit-rate on the well-known data set by Achanta and Liu. Third, by debiasing Cheng et al.'s region contrast model, we exemplarily demonstrate that implicit center biases are partially responsible for the outstanding performance of state-of-the-art algorithms. Last but not least, as a result of debiasing Cheng et al.'s algorithm, we introduce a non-biased salient object detection method, which is of interest for applications in which the image data is not likely to have a photographer's center bias (e.g., image data of surveillance cameras or autonomous robots)

Situating interventions to bridge the intention-behaviour gap: A framework for recruiting nonconscious processes for behaviour change

This paper presents a situated cognition framework for creating social psychological interventions to bridge the intention–behaviour gap and illustrates this framework by reviewing examples from the domains of health behaviour, environmental behaviour, stereotyping, and aggression. A recurrent problem in behaviour change is the fact that often, intentions are not translated into behaviour, causing the so-called intention–behaviour gap. Here, it is argued that this happens when situational cues trigger situated conceptualizations, such as habits, impulses, hedonic goals, or stereotypical associations, which can then guide behaviour automatically. To be effective in changing such automatic effects, behaviour change interventions can attempt to change situational cues through cueing interventions such as priming, nudging, upstream policy interventions, or reminders of social norms. Alternatively, behaviour change interventions can attempt to change the underlying situated conceptualizations through training interventions, such as behavioural inhibition training, mindfulness training, or implementation intentions. Examples of situated behaviour change interventions of both types will be discussed across domains, along with recommendations to situate interventions more strongly and thus enhance their effectiveness to change automatic behaviour. Finally, the discussion addresses the difference between tailoring and situating interventions, issues of generalization and long-term effectiveness, and avenues for further research