Special issue: Research reportAltering spatial priority maps via statistical learning of target selection and distractor filtering
Introduction
Most kinds of daily activities, from watching a movie to driving a car along a busy street, require the normal functioning of the attentional system. Visual selective attention enables individuals to commit cognitive resources to relevant elements in the visual environment while filtering irrelevant and potentially interfering sensory input, including from other sensory modalities (Forster and Lavie, 2008, Jonides and Yantis, 1988, Marini et al., 2013, Theeuwes and Burger, 1998, Yantis and Jonides, 1990). Traditionally, visual selective attention is thought to operate under the influence of two types of control signals: when attention is summoned – or captured - by a salient stimulus, such as a bright flash of light, it is said to be under bottom-up (or stimulus-driven) control (Theeuwes, 2010, Yantis and Egeth, 1999); instead, when attentional selection is guided by a deliberate act of will, and is aimed at task-relevant information, it is said to be under top-down (or goal-driven) control (Egeth & Yantis, 1997).
Over recent years, however, researchers have identified a number of factors that can exert control over visual attention, above and beyond bottom-up and top-down influences. The focus here is on a family of phenomena that have been revealed by a panoply of experimental paradigms, and that cannot be readily accounted for in terms of either stimulus salience or behavioral relevance. All these phenomena tend to share one key feature, namely, they reflect implicit processing. Sometimes they are referred to with the overarching term of “selection history” effects (Awh, Belopolsky, & Theeuwes, 2012). Notable examples of this family of phenomena are the different types of inter-trial priming effects (Kristjánsson and Campana, 2010, Maljkovic and Nakayama, 1994, Maljkovic and Nakayama, 1996, Tipper, 1985), whereby the repetition of target- and distractor-defining features across consecutive trials improves performance, and contextual cueing (Chun and Jiang, 1998, Chun and Jiang, 2003), i.e., the improved performance in target selection that is supported by spatio-temporal regularities in the visual context picked up by the observer over the course of the experiment. Other forms of control belonging to the same general category include the impact on visual attention of semantic associations (Belke et al., 2008, De Groot et al., 2016, Moores et al., 2003, Telling et al., 2010), i.e., the tendency for attention to select items in the display that, albeit task-irrelevant, bear a semantic associative link to the sought target, and other kinds of familiarity/novelty effects (Christie and Klein, 1995, Horstmann, 2002). Finally, research over recent years has revealed that reward (and punishment) can exert a strong and multifaceted influence on attention, for example in the form of increased effective salience acquired by stimuli and locations systematically associated with reward (Anderson et al., 2011b, Della Libera and Chelazzi, 2006, Della Libera and Chelazzi, 2009, Della Libera et al., 2011, Kristjánsson et al., 2010; for reviews, see Anderson, 2016, Anderson et al., 2011a, Bourgeois et al., 2016, Chelazzi et al., 2013). To reiterate, it is typically assumed that all the above influences on attention occur implicitly, without the participant knowing that they are at play. Key to the expression of all those kinds of attentional control, as already said, is that they reflect past encounters with certain stimuli and contexts, as well as past episodes of attentional processing of the stimuli – from this, the term “selection history” effects (Awh et al., 2012; see also Todd & Manaligod, 2018).
Here we concentrate on yet another form of selection history effect, again reflecting implicit control of attention, called statistical learning (SL) of target and/or distractor location. In general, with the term SL we refer to the brain capacity to learn and make good use of environmental regularities whose existence is registered over repeated exposures to the given context and situation (for a review, see Schapiro & Turk-Browne, 2015). SL is thought to play a key role in a variety of cognitive domains, such as language acquisition (Aslin and Newport, 2012, Erickson and Thiessen, 2015, Saffran et al., 1996), efficient coding of feature combinations (Fiser and Aslin, 2001, Fiser and Aslin, 2002), memory (Schapiro et al., 2017, Umemoto et al., 2010) and motor skill learning (Altamura et al., 2014, Perruchet and Pacton, 2006). In the attentional domain, SL constitutes a strong determinant of stimulus priority and has been investigated in relation to various kinds of sequential regularities in stimulus presentation (Yu and Zhao, 2015, Zhao et al., 2013), or regularities in the spatial distribution of visual elements (typically the target), also known as spatial probability cueing (Druker and Anderson, 2010, Geng and Behrmann, 2002, Geng and Behrmann, 2005, Hoffmann and Kunde, 1999, Jiang, Li et al., 2015, Jiang, Swallow, Rosenbaum, 2013, Jiang, Swallow, Rosenbaum, Herzig, 2013, Jiang, Swallow et al., 2015, Miller, 1988, Sha et al., 2017, Shaw and Shaw, 1977, Walthew and Gilchrist, 2006). For instance, Geng and Behrmann, 2002, Geng and Behrmann, 2005 provided an elegant demonstration that attention is biased in accordance with the spatial probability of the target over the course of the experiment. In their studies, participants had to indicate the orientation of a task-relevant stimulus presented amongst irrelevant ones in a visual search array. Unbeknownst to participants, target location was not equally probable across display regions: the target appeared with high probability (80%) in one half of the screen and with low probability (20%) in the other half. Compared to a baseline condition without spatial probability manipulation, target selection was speeded up in the high probability region and slowed down in the low probability region. These studies demonstrated that attentional allocation is implicitly adjusted on the basis of display statistics over time, indicative of an attentional bias towards locations where the sought target occurred more frequently (Geng and Behrmann, 2002, Geng and Behrmann, 2005), perhaps reflecting changes in the priority of individual locations within priority maps of space (Zelinsky & Bisley, 2015). This type of phenomenon has been systematically investigated in recent years, especially by Jiang and colleagues (see Jiang, 2018, for an extensive review) and several of its key features have been firmly established. Among others, these include: 1) its resistance to extinction, or the persistence of the bias once the imbalance in target probability across locations has been eliminated; 2) its implicit nature, with only few participants typically becoming aware of the probability manipulation (in fact, if anything, effects tend to be stronger when explicit knowledge is not formed); 3) its independence from cognitive load, such as the engagement of working memory on a different task; 4) and finally its relatively intact expression in aging (unlike the typical deficits in declarative memory that are often found in aging) (Jiang, 2018). In spite of considerable progress in our understanding of this type of attentional phenomenon, several important aspects still need to be clarified, as detailed in the sections below.
One aspect of probability cueing of target location that is still unsettled is the extent to which the phenomenon is independent from inter-trial (priming) effects. To clarify, a spatial probability manipulation of target location brings with it a natural imbalance in the probability of immediate repetitions of target location (unless this is deliberately avoided by the experimenter). For instance, if two locations are associated with target probabilities of 80% and 20%, respectively, the chance that the target stimulus is presented for two consecutive trials in the same location is much higher at the high probability location compared to the low probability location (64% vs. 4%). It should be made clear immediately that the role of inter-trial priming in SL of target location is twofold. On the one hand, inter-trial priming of target location could produce benefits in performance that greatly contribute to what might appear to be solely determined by statistical learning of target probability across locations. In other words, the two effects being naturally conflated, it is important to establish to what extent each of them individually contributes to overall performance. On the other hand, however, one might argue that immediate repetitions, in addition to any direct benefit in performance that they may produce, represent a crucial “diagnostics” for the system to learn from the probabilistic spatial distribution of targets over time. Clearly, the latter consideration relates to the underlying learning mechanism, and how it is supposed to gather statistical evidence from experience (Clark, 2013, Friston and Kiebel, 2009, Vossel et al., 2014, Vossel et al., 2015). The two problems can be dealt with in different ways. If the only concern is to parse out the influence on performance of immediate repetitions in target location from the more general SL effect, then it will suffice that when analyzing the data any inter-trial priming effect is subtracted away, in practice by calculating performance measures after eliminating from the data set all trials where the location of the target repeats between consecutive trials (of course, eliminating immediate repetitions still allows for influences resulting from more distant trials in the past, such as N-2, N-3, etc., according to some decaying function (Maljkovic & Nakayama, 1994)). Instead, if one plans to remove any role of immediate repetitions, then trial sequences will have to be constructed beforehand in such a way as to exclude entirely immediate repetitions in target location, or to make them equally probable for all locations. Walthew and Gilchrist (2006) reported that when by design target location did not repeat within a short sequence of trials (i.e., going beyond immediate repetitions), there was no longer an effect of the unequal probability of target location on the participants' performance. Based on these results, the authors suggested that short-term target location priming is entirely responsible for the modulations in performance that are found in spatial SL studies. However, this view was rejected in a subsequent study by Jones and Kaschak (2012). In their replication of Walthew and Gilchrist work, the spatial probability of targets was manipulated while target locations did never repeat over short trial sequences, as before. Contrary to the original claim, the participants' performance was affected by the spatial probability of the target even in the absence of inter-trial priming effects. Therefore, it is important to provide new evidence bearing on this issue, something we will do with this work.
It has been long debated whether the attentional capture generated by a salient distractor is obligatory or can be avoided, or at least greatly reduced, under certain conditions, e.g., when powerful top-down control of attention is exerted (Folk and Remington, 1998, Theeuwes, 2010; for a hybrid position, see Sawaki & Luck, 2010). Regardless of the theoretical standing with regard to this point, it is a fact that reduced capture has been shown in a number of conditions, such as when the given search task requires focusing on a specific visual feature (Bacon & Egeth, 1994), when the given context is highly distracting (Marini et al., 2013, Müller et al., 2009), or finally after substantial exposure to a certain distractor, perhaps reflecting a form of habituation (Neo and Chua, 2006, Pascucci and Turatto, 2015, Turatto and Pascucci, 2016). More relevant to our purposes, an emerging literature has recently begun to address changes in the cost engendered by a distracting stimulus under conditions where the distractor appears with uneven probability across display locations, reflecting another form of SL in the attention domain (Goschy et al., 2014, Reder et al., 2003; see also Leber et al., 2016, Wang and Theeuwes, 2017). In a study conducted by Reder et al. (2003), participants had to report the position of a target presented in one of four locations with equal probability. In most of the trials (80%), a distractor was also presented in one of the remaining locations. Unlike the target, the distractor appeared more frequently in one position (60% of distractor present trials), with intermediate frequency in another position (30%), only rarely in yet another position (10%), while it was never presented in the remaining position (0%). Results showed that participants produced faster responses when the distractor was displayed in the frequent-distractor location, indicative of lesser distraction, whereas their reaction times (RTs) increased as distractor probability at a given position decreased. The results reported by Reder et al. (2003) are in good agreement with studies showing relatively rapid decline of the interference generated by a salient distractor when its location is constant over a number of trials, and a resurgence of interference when the distractor is subsequently presented at a different location (Kelley and Yantis, 2009, Pascucci and Turatto, 2015, Turatto and Pascucci, 2016). One can account for these results by assuming that the priority of the different positions was adjusted to cope optimally with the probability of distraction, with attentional priority being adaptively decreased for locations with relatively frequent distraction relative to locations with rare distractors. However, an explanation in terms of altered priorities might predict that target processing should also be altered as a result of the learning process, assuming that target selection and distractor filtering both depend on the level of location-specific activity within priority maps of space (e.g., Zelinsky & Bisley, 2015). Specifically, decreased priority should lead to worse target-related performance at the given location, whereas increased priority should enhance target processing at the same location. Importantly, Reder et al. (2003) assessed whether SL of distractor location affected target processing by analyzing the participants' performance as a function of the location of the target with respect to distractor probability at the various display locations. Although the target occurred equally often at all positions, the efficiency of target processing appeared to differ across locations, with relatively faster responses for targets at the location with rare distractors. However, the effect was rather weak and was found in a first experiment but not in a subsequent one (but see Wang and Theeuwes, in press, for a consistent observation). Therefore, doubts remain as to whether manipulations of distractor probability will affect target processing across locations. In fact, to our knowledge, no systematic attempt has been made so far to assess whether manipulations of target probability affect the level of distraction engendered by a distractor shown at the various display locations. With the experiments reported here our primary goal will be to shed light on this critical point (see below).
As before, researchers have asked whether what appears to be the consequence of SL of distractor location might instead be due to inter-trial priming. This specific question has been examined by Goschy et al. (2014). In their experiments, a tilted bar was presented amongst vertical bars and the task for the participants was to indicate whether the target bar had a gap at the top or at the bottom. In half of the trials, a red-colored bar was shown as a salient singleton distractor. The distractor was presented in one half of the screen with high probability (90%) and in the other half with low probability (10%). The cost engendered by the distractor was modulated by the spatial probability manipulation, as reflected by faster RTs in trials with the distractor in the high probability region. In a control experiment, any distractor location repetitions were prevented. Also in this case, reduced distractor cost was found for the high distractor probability region, though the effect was smaller than in the original experiment. Thus, both inter-trial priming and genuine SL effects appear to contribute to the reduction of the distractor cost in this context. As we have argued for SL of target location, here too it will be important to provide further evidence on the contribution of inter-trial priming to the observed influence on performance of a probability manipulation of distractor location.
The key question that we wish to address in the present paper concerns the mechanisms involved in SL for target and distractor location. As already discussed, in visual search, attentional allocation can be altered by the uneven spatial probability with which the target or the distractor is presented across display locations. What is the mechanism underlying changes in performance? One obvious possibility is that spatial SL – be it for the target or the distractor location, affects activity within priority maps of space that are deemed responsible for attentional allocation (Bisley and Goldberg, 2010, Fecteau and Munoz, 2006, Gottlieb, 2007, Itti and Koch, 2001, Ptak, 2012, Serences and Yantis, 2006, Zelinsky and Bisley, 2015). These maps are usually conceived as neural representations of the visual space wherein the level of activity at each location in the map determines the (relative) attentional priority of that location in space. It is also assumed that local activity levels within the maps reflect the highly dynamic, combined influence of a variety of factors, including the strength of the visual drive at each location (bottom-up, or saliency signal), the task relevance of the input at each location, any location-specific preparatory or biasing signal (see e.g., Kastner et al., 1998, Luck et al., 1997, Sani et al., 2017), as well as signals generally ascribed to past selection history and reward associations (e.g., Klink, Jentgens, & Lorteije, 2014). In this perspective, when two (or more) visual stimuli compete for attention, activity in priority maps will favor the stimulus presented at the location with the highest activity level (i.e., the location with the highest priority), which thus wins the competition and consequently gains privileged access to further stages of processing (e.g., Desimone & Duncan, 1995). This account entails that brain activity for prioritized elements is enhanced at the expense of low priority elements (for a review, see Duncan, 2006). Within this framework, one can interpret the modulations of behavior generated by manipulations of target and distractor spatial probabilities as the result of a unique system that calibrates the “weights” in the priority maps for the different locations. Specifically, one might conjecture that priorities will be increased for locations that more often generated a correct selection (SL of target location) or that less often generated correct distractor filtering (SL of distractor location) and, respectively, decreased for locations that more often generated a correct distractor filtering (SL of distractor location) or that less often generated a correct selection (SL of target location). In turn, this view assumes that attentional allocation is a result of a unitary mechanism – namely, activity in priority maps of space, whereby selecting and ignoring are just the two sides of the same coin. However, over the years many studies have provided evidence against this idea by showing that distinct attentional mechanisms implement target selection and, respectively, distractor filtering (Houghton and Tipper, 1994, Houghton and Tipper, 1996, Luck, 1995). For example, studies employing the scalp recording of electrical brain activity, in particular the event-related potential (ERPs) methodology, have revealed separate and dissociable correlates of target selection and distractor filtering (Couperus and Mangun, 2010, Hickey et al., 2009, Sawaki and Luck, 2010, Sawaki and Luck, 2013). Similarly, a number of studies using functional magnetic resonance imaging (fMRI) have shown that different brain networks are responsible for the selection of targets and, respectively, the filtering of distractors (Marini et al., 2016, Ruff and Driver, 2006, Serences et al., 2004). Further support to the notion that target selection and distractor filtering mechanisms are dissociated in the human brain comes from a very recent study by Noonan et al. (2016). In this study, participants had to indicate the spatial frequency of two superimposed Gabor patches while a randomly oriented Gabor patch was used as distractor. At the beginning of each trial, a cue indicated either the location of the forthcoming target, the location of the distractor, or provided no information (neutral cue). No correlation was found between the ability to use target-relevant cues to facilitate target selection and the ability to engage distractor filtering mechanisms at correspondingly cued locations, leading the authors to conclude that target selection and distractor suppression depend on distinct mechanisms. In summary, based on the above literature, we think it is of paramount importance to establish whether target selection and distractor filtering processes should be viewed as interdependent and based on shared neural mechanisms or instead as distinct and performed by dissociable mechanisms. In particular, in the present context, it is crucial to establish whether SL of target selection and, respectively, of distractor filtering will lead to modulations of performance that are compatible with one or the other of the two notions. Hence here we took advantage of SL for target and distractor location to directly and systematically assess the level of interdependence between target selection and distractor filtering mechanisms. Specifically, the principal question that we aimed to ask was whether SL of target location will lead to indirect changes in the efficiency of distractor filtering and, similarly, whether SL of distractor location will lead to indirect changes in the efficiency of target selection, or whether each form of SL will exert selective effects on one or the other process. If the same priority maps of space guide target selection and distractor filtering, then modulation of target selection through SL of target location should transfer to the efficiency of distractor filtering (Experiment 1). By the same logic, a change in distractor filtering brought about by SL of distractor location should be expected to modulate the efficiency of target selection (3 Experiment 2, 5 Experiment 4). Finally, we developed a within-subject experimental approach in which the two forms of SL co-existed in order to more directly compare the effects generated by the two kinds of spatial contingency, including their indirect effects (Experiment 3).
Section snippets
Experiment 1
In Experiment 1, we assessed the direct impact of statistical learning (SL) on selection mechanisms by manipulating the probability with which the target occurred at the various display locations. In line with previous literature (i.e., Geng and Behrmann, 2002, Geng and Behrmann, 2005, Jiang, Li et al., 2015, Jiang, Swallow, Rosenbaum, 2013, Jiang, Swallow, Rosenbaum, Herzig, 2013, Jiang, Swallow et al., 2015), we predicted better performance for targets at relatively high probability
Experiment 2
In Experiment 1, we manipulated the spatial probability of the target and found evidence to indicate that SL affected both target selection and distractor filtering mechanisms. These results suggest cross-talk between the two kinds of mechanism, likely reflecting at least partly shared neural machinery, for example the same priority map(s) of space. In Experiment 2 we tested whether a reciprocal effect could be found by applying an uneven probability of distractors across locations. As before,
Experiment 3
Previous experiments have shown that observers can learn and use the spatial distribution of targets and distractors in order to prioritize and, respectively, de-prioritize locations where a target or a distractor is more likely to occur. More relevant for our specific purposes, we also found some evidence of cross-talk between the direct effect – be it SL of target selection or SL of distractor filtering, and the indirect, transfer effect, namely distractor filtering and target selection,
Experiment 4
Experiment 3 fully confirmed what found in the previous experiments, with SL of target location producing robust indirect effects on distractor filtering and SL of distractor location producing only limited, if any, effects on target selection, even though the direct effects were very strong in both experiments. Specifically, as reported in Table 2, the direct effects (the difference in RT between high and low probability locations) elicited by SL of target and distractor location probability
Comparison between direct and indirect effects across experiments
The experiments reported so far demonstrated that when a statistical contingency is applied to the spatial distribution of a relevant or otherwise salient stimulus, be it the target or the singleton distractor, this regularity generates spatial SL, which in turn modulates the allocation of attention. We have interpreted these effects by making reference to the notion of priority maps of space. Importantly, we found that the elicited modification in the priority of spatial locations as a result
General discussion
Previous research has demonstrated that in visual search the spatial probability of target stimuli over the course of the experiment biases the allocation of attention toward the array regions that more frequently produced successful performance, namely those locations wherein the target was displayed with higher probability (Druker and Anderson, 2010, Geng and Behrmann, 2002, Geng and Behrmann, 2005, Hoffmann and Kunde, 1999, Jiang, Li et al., 2015, Jiang, Swallow, Rosenbaum, 2013, Jiang,
Competing financial interests
The authors declare no competing financial interests.
Acknowledgements
This research was supported by funding delivered within the “Ricerca di Base 2015” granting program of the University of Verona (grant no. B32F15000700001) and by funding from the Italian Government (Ministero dell'Istruzione, dell'Università e della Ricer ca; Bando PRIN 2015; grant no. 2015AWSW2Y_003) to L.C.
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