Learning to sample: eye tracking and fMRI indices of changes in object perception

How Does Experience Shape Early Development? Considering the Role of Top-Down Mechanisms

Perceptual development requires infants to adapt their perceptual systems to the structures and statistical information of their environment. In this way, perceptual development is not only important in its own right, but is a case study for behavioral and neural plasticity-powerful mechanisms that have the potential to support developmental change in numerous domains starting early in life. While it is widely assumed that perceptual development is a bottom-up process, where simple exposure to sensory input modifies perceptual representations starting early in the perceptual system, there are several critical phenomena in this literature that cannot be explained with an exclusively bottom-up model. This chapter proposes a complementary mechanism where nascent top-down information, feeding back from higher-level regions of the brain, helps to guide perceptual development. Supporting this theoretical proposal, recent behavioral and neuroimaging studies have established that young infants already have the capacity to engage in top-down modulation of their perceptual systems.

Statistical learning is constrained to less abstract patterns in complex sensory input (but not the least)

The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird₁—dog₁, bird₂—dog₂). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture₁— dog_picture₁, bird_picture₂—dog_picture₂). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation.

Gaining knowledge mediates changes in perception (without differences in attention): A case for perceptual learning

Firestone & Scholl (F&S) assert that perceptual learning is not a top-down effect, because experience-mediated changes arise from familiarity with the features of the object through simple repetition and not knowledge about the environment. Emberson and Amso (2012) provide a clear example of perceptual learning that bypasses the authors' "pitfalls" and in which knowledge, not repeated experience, results in changes in perception.

Seeing and thinking: Foundational issues and empirical horizons

The spectacularly varied responses to our target article raised big-picture questions about the nature of seeing and thinking, nitty-gritty experimental design details, and everything in between. We grapple with these issues, including the ready falsifiability of our view, neuroscientific theories that allow everything but demand nothing, cases where seeing and thinking conflict, mental imagery, the free press, an El Greco fallacy fallacy, hallucinogenic drugs, blue bananas, subatomic particles, Boeing 787s, and the racial identities of geometric shapes.

Combining fMRI and behavioral measures to examine the process of human learning

Prior to the advent of fMRI, the primary means of examining the mechanisms underlying learning were restricted to studying human behavior and non-human neural systems. However, recent advances in neuroimaging technology have enabled the concurrent study of human behavior and neural activity. We propose that the integration of behavioral response with brain activity provides a powerful method of investigating the process through which internal representations are formed or changed. Nevertheless, a review of the literature reveals that many fMRI studies of learning either (1) focus on outcome rather than process or (2) are built on the untested assumption that learning unfolds uniformly over time. We discuss here various challenges faced by the field and highlight studies that have begun to address them. In doing so, we aim to encourage more research that examines the process of learning by considering the interrelation of behavioral measures and fMRI recording during learning.