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Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics—that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

Cross-cultural studies have shown that independence in individualistic societies is associated with analytic systems of thoughts, whereas collectivistic societies which place greater emphasis on interdependence are generally predisposed to holistic thinking (Masuda & Nisbett, 2011; Bang, 2015). Further, cultural identity has been shown to effect picture perception and cognitive processes (Nisbett & Miyamoto, 2005). For example, Asians were shown to be more sensitive to contextual rather than focal information compared to Americans in a change-blindness task in which observers had to detect either a focal or contextual change within pairs of images (Masuda & Nisbett, 2006). Here, we build on previous work on perceptual learning (Mayhew, Li, & Kourtzi, 2012) and test the role of individualistic vs. collectivistic influences on learning ability. Eighty-three participants of different cultural backgrounds – consisting of Asian (collectivistic) and European (individualistic) students – were asked to discriminate between radial and concentric Glass patterns embedded in background noise. We employed the Singelis’ (1994) self-construal scale (SCS) to examine whether differences in task performance due to training were influenced by independent or interdependent cultural values. Visual perceptual learning was evident in both groups; that is, all participants improved in accuracy and reaction times through training. Importantly, Asian participants showed higher performance before and during training than European participants, suggesting an advantage in learning to extract global shapes embedded in cluttered backgrounds. This is consistent with the previously reported tendency of collectivists for global processing under perceptual uncertainty. Our findings provide evidence for the role of cross-cultural influences on visual processes that relate to our ability to improve in making perceptual judgements through training and experience. Keywords: visual perception, learning, self-construal

In its search for neural codes, the field of visual neuroscience has uncovered neural representations that reflect the structure of stimuli of variable complexity from simple features to object categories. However, accumulating evidence suggests an adaptive neural code that is dynamically shaped by experience to support flexible and efficient perceptual decisions. Here, we review work showing that experience plays a critical role in molding midlevel visual representations for perceptual decisions. Combining behavioral and brain imaging measurements, we demonstrate that learning optimizes feature binding for object recognition in cluttered scenes, and tunes the neural representations of informative image parts to support efficient categorical judgements. Our findings indicate that similar learning mechanisms may mediate long-term optimization through development, tune the visual system to fundamental principles of feature binding, and optimize feature templates for perceptual decisions.