Proactive enhancement and suppression elicited by statistical regularities in visual search

Proactive response preparation contributes to contingency learning: novel evidence from force-sensitive keyboards

Contingency learning can involve learning that the identity of one stimulus in a sequence predicts the identity of the next stimulus. It remains unclear, however, whether such learning speeds responses to the next stimulus only by reducing the threshold for triggering the expected response after stimulus onset or also by preparing the expected response before stimulus onset. To distinguish between these competing accounts, we manipulated the probabilities with which each of two prime arrows (Left and Right) were followed by each of two probe arrows (Up and Down) in a prime-probe task while using force-sensitive keyboards to monitor sub-threshold finger force. Consistent with the response preparation account, two experiments revealed greater force just before probe onset on the response key corresponding to the direction in which the probe was more (versus less) likely to point (e.g., Up vs. Down). Furthermore, mirroring sequential contingency effects in behavior, this pre-probe force effect vanished after a single low-probability trial. These findings favor the response preparation account over the threshold only account. They also suggest the possibility that contingency learning in our tasks indexes trial-by-trial expectations regarding the utility of the prime for predicting the upcoming probe.

Spatial transfer of object-based statistical learning

A large number of recent studies have demonstrated that efficient attentional selection depends to a large extent on the ability to extract regularities present in the environment. Through statistical learning, attentional selection is facilitated by directing attention to locations in space that were relevant in the past while suppressing locations that previously were distracting. The current study shows that we are not only able to learn to prioritize locations in space but also locations within objects independent of space. Participants learned that within a specific object, particular locations within the object were more likely to contain relevant information than other locations. The current results show that this learned prioritization was bound to the object as the learned bias to prioritize a specific location within the object stayed in place even when the object moved to a completely different location in space. We conclude that in addition to spatial attention prioritization of locations in space, it is also possible to learn to prioritize relevant locations within specific objects. The current findings have implications for the inferred spatial priority map of attentional weights as this map cannot be strictly retinotopically organized.

The effects of eccentricity on attentional capture

Visual attention may be captured by an irrelevant yet salient distractor, thereby slowing search for a relevant target. This phenomenon has been widely studied using the additional singleton paradigm in which search items are typically all presented at one and the same eccentricity. Yet, differences in eccentricity may well bias the competition between target and distractor. Here we investigate how attentional capture is affected by the relative eccentricities of a target and a distractor. Participants searched for a shape-defined target in a grid of homogeneous nontargets of the same color. On 75% of trials, one of the nontarget items was replaced by a salient color-defined distractor. Crucially, target and distractor eccentricities were independently manipulated across three levels of eccentricity (i.e., near, middle, and far). Replicating previous work, we show that the presence of a distractor slows down search. Interestingly, capture as measured by manual reaction times was not affected by target and distractor eccentricity, whereas capture as measured by the eyes was: items close to fixation were more likely to be selected than items presented further away. Furthermore, the effects of target and distractor eccentricity were largely additive, suggesting that the competition between saliency- and relevance-driven selection was modulated by an independent eccentricity-based spatial component. Implications of the dissociation between manual and oculomotor responses are also discussed.

The Electrophysiological Markers of Statistically Learned Attentional Enhancement: Evidence for a Saliency-based Mechanism

It is well established that attention can be sharpened through the process of statistical learning (e.g., visual search becomes faster when targets appear at high-relative-to-low probability locations). Although this process of statistically learned attentional enhancement differs behaviorally from the well-studied top-down and bottom-up forms of attention, relatively little work has been done to characterize the electrophysiological correlates of statistically learned attentional enhancement. It thus remains unclear whether statistically learned enhancement recruits any of the same cognitive mechanisms as top-down or bottom-up attention. In the current study, EEG data were collected while participants searched for an ambiguous unique shape in a visual array (the additional singleton task). Unbeknownst to the participants, targets appeared more frequently in one location in space (probability cuing). Encephalographic data were then analyzed in two phases: an anticipatory phase and a reactive phase. In the anticipatory phase preceding search stimuli onset, alpha lateralization as well as the Anterior Directing Attention Negativity and Late Directing Attention Positivity components-signs of preparatory attention known to characterize top-down enhancement-were tested. In the reactive phase, the N2pc component-a well-studied marker of target processing-was examined following stimuli onset. Our results showed that statistically learned attentional enhancement is not characterized by any of the well-known anticipatory markers of top-down attention; yet targets at high probability locations did reliably evoke larger N2pc amplitudes, a finding that is associated with bottom-up attention and saliency. Overall, our findings are consistent with the notion that statistically learned attentional enhancement increases the perceptual salience of items appearing at high-probability locations relative to low-probability locations.

Pinging the brain to reveal the hidden attentional priority map using encephalography

Attention has been usefully thought of as organized in priority maps - putative maps of space where attentional priority is weighted across spatial regions in a winner-take-all competition for attentional deployment. Recent work has highlighted the influence of past experiences on the weighting of spatial priority - called selection history. Aside from being distinct from more well-studied, top-down forms of attentional enhancement, little is known about the neural substrates of history-mediated attentional priority. Using a task known to induce statistical learning of target distributions, in an EEG study we demonstrate that this otherwise invisible, latent attentional priority map can be visualized during the intertrial period using a 'pinging' technique in conjunction with multivariate pattern analyses. Our findings not only offer a method of visualizing the history-mediated attentional priority map, but also shed light on the underlying mechanisms allowing our past experiences to influence future behavior.

Feature-blind attentional suppression of salient distractors

A recent paper has reported, for the first time, that people are capable of suppressing salient singleton distractors of unknown color if the search task requires them to search for the most prevalent of several shapes in the display. We identify here several potential limitations of the earlier findings. In particular, in the reported experiments, the likelihood of a salient distractor was higher than what is typically studied, the distractor object was similar in shape to the relevant objects, only two colors were studied, the distractor was consistently a fixed shape, and the distractor was always a unique shape different from the search targets. Each of these limitations leaves open some questions about the generality of the findings. We address each of the concerns here, and show, in five experiments, that the ability to suppress distractors of unknown color is a robust finding that is not compromised by the potential limitations identified. When searching for the most prevalent of several shapes in a display, people can indeed suppress capture by otherwise-salient color singleton distractors even when their color is not known in advance (i.e., in a feature-blind manner), facilitating efficient search. The experiments confirm the ability to suppress visual elements based on second-order (e.g., a unique color) or global salience information, and not merely based on first-order (e.g., a specific color) information.

Attentional suppression is in place before display onset

Recent studies have shown that observers can learn to suppress a location that is most likely to contain a distractor. The current study investigates whether the statistically learned suppression is already in place, before, or implemented exactly at the moment participants expect the display to appear. Participants performed a visual search task in which a distractor was presented more frequently at the high-probability location (HPL) in a search display. Occasionally, the search display was replaced by a probe display in which participants needed to detect a probe offset. The temporal relationship between the probe display and the search display was manipulated by varying the stimulus onset asynchronies (SOAs) in the probe task. In this way, the attentional distribution in space was probed before, exactly at, or after the moment when the search display was expected to be presented. The results showed a statistically learned suppression at the HPL, as evidenced by faster and more accurate search when a distractor was presented at this location. Crucially, irrespective of the SOA, probe detection was always slower at the HPL than at the low-probability locations, indicating that the spatial suppression induced by statistical learning is proactively implemented not just at the moment the display is expected, but prior to display onset. We conclude that statistical learning affects the weights within the priority map relatively early in time, well before the availability of the search display.

Statistical Learning Within Objects

Research has recently shown that efficient selection relies on the implicit extraction of environmental regularities, known as statistical learning. Although this has been demonstrated for scenes, similar learning arguably also occurs for objects. To test this, we developed a paradigm that allowed us to track attentional priority at specific object locations irrespective of the object's orientation in three experiments with young adults (all Ns = 80). Experiments 1a and 1b established within-object statistical learning by demonstrating increased attentional priority at relevant object parts (e.g., hammerhead). Experiment 2 extended this finding by demonstrating that learned priority generalized to viewpoints in which learning never took place. Together, these findings demonstrate that as a function of statistical learning, the visual system not only is able to tune attention relative to specific locations in space but also can develop preferential biases for specific parts of an object independently of the viewpoint of that object.

Statistical learning of target location and distractor location rely on different mechanisms during visual search

More studies have demonstrated that people have the capacity to learn and make use of environmental regularities. This capacity is known as statistical learning (SL). Despite rich empirical findings, it is not clear how the two forms of SL (SL of target location and SL of distractor location) influence visual search and whether they rely on the shared cognitive mechanism. In Experiment 1 and Experiment 2, we manipulated the probability of target location and the probability of distractor location, respectively. The results suggest that attentional guidance (they referred to overt attention) may mainly contribute to the SL effect of the target location and the distractor location, which is in line with the notion of priority mapping. To a small extent, facilitation of response selection may also contribute to the SL effect of the target location but does not contribute to the SL effect of the distractor location. However, the main difference between the two kinds of SL occurred in the early stage (it involved covert attention). Together, our findings indicate that the two forms of SL reflect partly shared and partly independent cognitive mechanisms.

What to expect where and when: how statistical learning drives visual selection

While the visual environment contains massive amounts of information, we should not and cannot pay attention to all events. Instead, we need to direct attention to those events that have proven to be important in the past and suppress those that were distracting and irrelevant. Experiences molded through a learning process enable us to extract and adapt to the statistical regularities in the world. While previous studies have shown that visual statistical learning (VSL) is critical for representing higher order units of perception, here we review the role of VSL in attentional selection. Evidence suggests that through VSL, attentional priority settings are optimally adjusted to regularities in the environment, without intention and without conscious awareness.