There is growing evidence that human observers are able to extract the mean emotion or other type of information from a set of faces. The most intriguing aspect of this phenomenon is that observers often fail to identify or form a representation for individual faces in a face set. However, most of these results were based on judgments under limited processing resource. We examined a wider range of exposure time and observed how the relationship between the extraction of a mean and representation of individual facial expressions would change. The results showed that with an exposure time of 50 ms for the faces, observers were more sensitive to mean representation over individual representation, replicating the typical findings in the literature. With longer exposure time, however, observers were able to extract both individual and mean representation more accurately. Furthermore, diffusion model analysis revealed that the mean representation is also more prone to suffer from the noise accumulated in redundant processing time and leads to a more conservative decision bias, whereas individual representations seem more resistant to this noise. Results suggest that the encoding of emotional information from multiple faces may take two forms: single face processing and crowd face processing.

Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. While previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron's type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research.

The composite face paradigm (Young, Hellawell, & Hay, 1987) is widely used to demonstrate holistic perception of faces (Rossion, 2013). In the paradigm, parts from different faces (usually the top and bottom halves) are recombined. The principal criterion for holistic perception is that responses involving the component parts of composites in which the parts are aligned into a face-like configuration are slower and less accurate than responses to the same parts in a misaligned (not face-like) format. This is often taken as evidence that seeing a whole face in the aligned condition interferes with perceiving its separate parts, but it remains unclear to what extent the composite face effect also reflects contributions from other potential sources of interference. We present a new variant of the paradigm involving composites created from top and bottom parts of familiar faces drawn from orthogonal social categories of gender and occupation. This allows us to examine the contributions of differences in relatively visual properties (gender) or relatively semantic properties (occupation) to composite interference and to measure whether variation in a task-irrelevant category (e.g., differences in gender across the parts of the composite when the task is to categorize the occupation of one of the parts) will influence the size of the composite effect. Our findings show that the composite face effect can be modulated by task-irrelevant social categories and that this interference is primarily visual in nature because the influence of face gender is more direct and more consistent than the influence of occupation.