Attentional control driven by statistical learning

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The visual environment is highly structured and rich with regularities in terms of how objects co-occur in space (e.g., a chair tends to appear next to a table) and over time (e.g., the yellow light is always followed by the red light). The visual system is extremely efficient at extracting these regularities through the process of statistical learning (Fiser & Aslin, 2001; Saffran et al., 1996; Turk-Browne et al., 2009). Recently, it has been found that attention can be drawn spontaneously and implicitly to regularities (Zhao et al., 2013). Such attentional bias toward regularities may enable the detection of structure and enhance the acquisition of knowledge about stable aspects of the world. The goal of the current proposal is to explore how learning guides the allocation of attention and what consequences on perception are produced as a result. There are three specific aims.Aim 1. What are the consequences of the attentional bias to regularities?The first aim examines how the attentional prioritization of structured stimuli alters the representation of these stimuli, and what kind of error in perception is produced as a result. For example, when two objects reliably co-occur over space or time (e.g., A always appears with B), these objects may be represented more similarly, which may result in the merging of representations of the objects. Aim 2. How does learning alter the spatial scale of attention?The second aim examines how regularities influence the spatial scale of attention. Reliable co-occurrence of objects in a spatial array may draw attention locally to individual objects and bias attention away from the global set. Thus, regularities may induce a local scale of attention to individual objects in an array, impeding global processing of the array. This can further explain the interference between statistical learning and summary perception. Aim 3. What are the temporal dynamics, durability and flexibility of the attentional bias?The modulation on attention by learning may be non-linear. After extensive exposure to regularities, the attentional bias may dissipate or even reverse to less structured sources of information. In other words, having strong expectations after learning might release attention from structured stimuli to elsewhere. In the absence of regularities, the bias may persist over previously structured information. Finally, when regularities shift to a different spatial location, attention may be flexibly re-allocated to the new location.The proposed program of research will offer a comprehensive understanding of how attention is directed by statistical learning. It motivates a shift away from the theoretical dichotomy on exogenous and endogenous control of attention, and encourages the development of a broader framework. It also suggests that when knowledge about relationships among objects has been acquired (after extensive exposure), attention may be disengaged and shifted to other information. This process allows exploration and acquisition of new knowledge in the environment. The work reveals new ways in which learning guides the allocation of attention. The current proposal can also offer new insights on human perception. That is, how objects co-occur in space or time can shape the representations of these objects. Finally, the proposed research provides novel experimental paradigms for examining learning-induced attentional control, which reflect and facilitate the field’s growing interest in understanding how attentional mechanisms interact with learning.