Familiarity Increases Processing Speed in the Visual System

Mixing novel and familiar cues modifies representations of familiar visual images and affects behavior

While visual responses to familiar and novel stimuli have been extensively studied, it is unknown how neuronal representations of familiar stimuli are affected when they are interleaved with novel images. We examined a large-scale dataset from mice performing a visual go/no-go change detection task. After training with eight images, six novel images were interleaved with two familiar ones. Unexpectedly, we found that the behavioral performance in response to familiar images was impaired when they were mixed with novel images. When familiar images were interleaved with novel ones, the dimensionality of their representation increased, indicating a perturbation of their neuronal responses. Furthermore, responses to familiar images in the primary visual cortex were less predictive of responses in higher-order areas, indicating less efficient communication. Spontaneous correlations between neurons were predictive of responses to novel images, but less so to familiar ones. Our study demonstrates the modification of representations of familiar images by novelty.

Online measurement of learning temporal statistical structure in categorization tasks

The ability to grasp relevant patterns from a continuous stream of environmental information is called statistical learning. Although the representations that emerge during visual statistical learning (VSL) are well characterized, little is known about how they are formed. We developed a sensitive behavioral design to characterize the VSL trajectory during ongoing task performance. In sequential categorization tasks, we assessed two previously identified VSL markers: priming of the second predictable image in a pair manifested by a reduced reaction time (RT) and greater accuracy, and the anticipatory effect on the first image revealed by a longer RT. First, in Experiment 1A, we used an adapted paradigm and replicated these VSL markers; however, they appeared to be confounded by motor learning. Next, in Experiment 1B, we confirmed the confounding influence of motor learning. To assess VSL without motor learning, in Experiment 2 we (1) simplified the categorization task, (2) raised the number of subjects and image repetitions, and (3) increased the number of single unpaired images. Using linear mixed-effect modeling and estimated marginal means of linear trends, we found that the RT curves differed significantly between predictable paired and control single images. Further, the VSL curve fitted a logarithmic model, suggesting a rapid learning process. These results suggest that our paradigm in Experiment 2 seems to be a viable online tool to monitor the behavioral correlates of unsupervised implicit VSL.

Stranger to my face: Top-down and bottom-up effects underlying prioritization of images of one's face

Recent studies suggest that we rapidly and effortlessly associate neutral information with the self, leading to subsequent prioritization of this information in perception. However, the exact underlying processes behind these effects are not fully known. Here, we focus specifically on top-down and bottom-up processes involved in self-prioritization, and report results from three experiments involving face detection, using a sequential match-non-match task. Across the three experiments we asked participants to associate an unfamiliar face with the self (Experiment 1), to associate one's face with a stranger's name (Experiment 2), and to establish both associations simultaneously (Experiment 3). We found that while participants showed evidence of bottom-up prioritization of their real faces, they did not show such an effect for self-associated strangers' faces. However, the participants showed a robust self-related top-down effect; when presented with a self-related cue, they were later faster at classifying both subsequent correct and incorrect targets. Together, our results suggest that self-prioritization is underpinned by distinct top-down and bottom-up processes. We discuss our findings in the context of the proposal that the self acts as an "integrative glue", and suggest an interpretation of our results within the framework of predictive coding.