Introduction to the special issue on ensemble perception

The role of episodic memory sampling in evaluation

Many models of choice assume that people retrieve memories of past experiences and use them to guide evaluation and choice. In this paper, we examine whether samples of recalled past experiences do indeed underpin our evaluations of options. We showed participants sequences of numerical values and asked them to recall as many of those values as possible and also to state how much they would be willing to pay for another draw from the sequence. Using Bayesian mixed effects modeling, we predicted participants' evaluation of the sequences at the group level from either the average of the values they recalled or the average of the values they saw. Contrary to the predictions of recall-based models, people's evaluations appear to be sensitive to information beyond what was actually recalled. Moreover, we did not find consistent evidence that memory for specific items is sufficient to predict evaluation of sequences. We discuss the implications for sampling models of memory and decision-making and alternative explanations.

Remembering Sets: Capacity Limit and Time Limit of Ensemble Representations in Working Memory

In a constantly changing visual environment, the ability to extract and store ensemble representations plays a crucial role in efficiently processing and remembering complex visual information. However, how working memory maintains these ensemble representations remains unclear. Therefore, the present study aimed to investigate the limits and characteristics of ensemble representations in working memory using a change detection paradigm. Participants were presented with multiple sets of circles grouped by spatial proximity and were asked to memorize the mean diameter of the circles in each set. Results showed that working memory could stably maintain mean sizes of approximately two sets for at least four seconds. Moreover, the memory performance of ensembles was not affected by the number of circles within a set, suggesting that individual details were not stored in working memory. These results suggest that the visual system can effectively store ensembles in working memory without preserving detailed individual information.

How are local orientation signals pooled?

Visual perception is capable of pooling multiple local orientation signals into a single more accurate summary orientation. However, there is still a lack of systematic inquiry into which summary statistics are implemented in that process. Here, the task was to recognize in which direction, clockwise or counter-clockwise, the mean orientation of a set of randomly distributed Gabor patches (N = 1, 2, 4, and 8) was rotated from the implicit vertical. The mean orientation discrimination accuracy did not improve with the increase of the number N of elements in proportion to the square-root-N, as could be expected if noisy internal representations were arithmetically averaged. The Proportion of Informative Elements (PIE), defined as the percentage of elements having an orientation different from the vertical, also affected the discrimination precision, violating the arithmetic averaging rules. The decrease in the orientation discrimination precision with the increase of the PIE would suggest that the orientation pooling could be more adequately described by a quadratic or higher power mean. Thus, we parameterized the averaging process for the power parameter of the generalized mean formula. The results indicate that different pooling rules in different trials may apply, for example, the arithmetic mean in some and the maximal deviation rule in others. It is concluded that pooling of orientation information is a relatively inaccurate process for which different perceptual cues and their combination rules can be used.