Contributions of ensemble perception to outlier representation precision

Similarity in feature space dictates the efficiency of attentional selection during ensemble processing

Humans can rapidly and accurately extract statistical information about features of the visual environment, an ability referred to as ensemble perception. However, little is known about how ensemble estimates are affected when task-irrelevant and distracting feature information is present. Here, we tested how effectively feature-based attention-when tuned to a specific color-can select a single item set out of two intermixed ensembles of colored lines. Participants were instructed to report the average orientation of a target-colored item set, while ignoring a second differently colored set. To assess how representational overlap between the two sets impacts color-based selection, we systematically varied the orientation similarity between the relevant and irrelevant items. Our results showed that participants' orientation reports were reliably biased towards the irrelevant items, but interestingly, these biases were only observed when the item sets overlapped in orientation space. In a second experiment, using a visual mask to disrupt access to color information at different time points, we found that these biases were stronger when less time was available to process the stimuli. Together, these results suggest that ensemble representations are rapidly formed based on all available information in the relevant feature dimension, regardless of task relevance, and that selective attention weights and separates these ensemble representations at a relatively later processing stage. This selection appears highly effective when the underlying population activity generated by the two sets is separable along the to-be-estimated feature dimension, but is dampened when relevant and irrelevant ensemble representations overlap in feature space.

Effects of outlier and familiar context in trend-line estimates in scatterplots

Lately, there has been a growing fascination with blending research on visualizing data and understanding how our basic visual perception works. Taking this path, this research delved into the connection between ensemble perception, which involves quickly and accurately grasping essential information from sets of visually similar objects, and how we process scatterplots. Across two experiments, we aimed to answer a couple of connected questions. First, we investigated whether having an outlier in a scatterplot affects how people draw trend-line estimates. Second, we explored whether what we are familiar with and the presence of outliers that match the trend affect how we draw trend-line estimates in scatterplots. In both experiments, we showed participants scatterplots for a short time, manipulating whether there were outliers or not. Then, using a computer mouse, participants drew their trend-line estimates. By comparing what they drew with possible trend-line solutions, we discovered that when there is no context, the outlier and the other points in a scatterplot are seen as equally important in drawing the trend-line estimate. But when the scatterplot depicted a familiar context and the outlier fitted the trend, people tended to give more weight to those outlier points in their drawings. This suggested that what we already believe can sway how we draw trend-line estimates even from quickly shown scatterplots.

Temporal integration of target features across and within trials in the attentional blink

Attentional blink research has typically investigated attentional limitations in multiple target processing. The current study investigated the temporal integration of target features in the attentional blink. Across two experiments, we demonstrated that the orientation estimations of individual target items in the attentional blink paradigm were systematically biased. Specifically, there was evidence for both within- and across-trial biases, revealing a general bias towards previously presented stimuli. Moreover, both biases were found to be more salient for targets suffering from the attentional blink. The current study is the first to demonstrate an across-trial bias in responses in the attentional blink paradigm. This set of findings is in line with the literature, suggesting that the human visual system can implicitly summarize information presented over time, which may lead to biases. By investigating temporal integration in the attentional blink, we have been able to address the modulatory role of attention on biases imposed by the implicit temporal effects in estimation tasks. Our findings may inform future research on attentional blink, serial dependence, and ensemble perception.

Variability leads to overestimation of mean summaries

Research on ensemble perception has shown that people can extract both mean and variance information, but much less is understand how these two different types of summaries interact with one another. Some research has argued that people are more erroneous in extracting the mean of displays that have greater variability. In all three experiments, we manipulated the variability in the displays. Participants reported the mean size of a set of circles (Experiment 1) and mean length of horizontally placed (Experiment 2a) and randomly oriented lines (Experiment 2b). In all experiments, we found that mean size estimations were more erroneous for higher than smaller variance displays. More critically, there was a tendency to overestimate the mean, driven by variance in both task-relevant and task-irrelevant features. We discuss these findings in relation to limitations in concurrent summarization ability and outlier discounting in ensemble perception.