How quickly do we learn new faces in everyday life? Neurophysiological evidence for face identity learning after a brief real-life encounter

Faces learnt in a single experimental session elicit a familiarity effect in event-related brain potentials (ERPs), with more negative amplitudes for newly learnt relative to unfamiliar faces in the N250 component. However, no ERP study has examined face learning following a brief real-life encounter, and it is not clear how long it takes to learn new faces in such ecologically more valid conditions. To investigate these questions, the present study examined whether robust image-independent representations, as reflected in the N250 familiarity effect, could be established after a brief unconstrained social interaction by analysing the ERPs elicited by highly variable images of the newly learnt identity and an unfamiliar person. Significant N250 familiarity effects were observed after a 30-min (Experiment 1) and a 10-min (Experiment 2) encounter, and a trend was observed after 5 min of learning (Experiment 3), demonstrating that 5–10 min of exposure were sufficient for the initial establishment of image-independent representations. Additionally, the magnitude of the effects reported after 10 and 30 min was comparable suggesting that the first 10 min of a social encounter might be crucial, with extra 20 min from the same encounter not adding further benefit for the initial formation of robust face representations

ε\varepsilon-Almost collision-flat universal hash functions and mosaics of designs

We introduce, motivate and study $\varepsilon$-almost collision-flat (ACFU) universal hash functions $f:\mathcal X\times\mathcal S\to\mathcal A$. Their main property is that the number of collisions in any given value is bounded. Each $\varepsilon$-ACFU hash function is an $\varepsilon$-almost universal (AU) hash function, and every $\varepsilon$-almost strongly universal (ASU) hash function is an $\varepsilon$-ACFU hash function. We study how the size of the seed set $\mathcal S$ depends on $\varepsilon,|\mathcal X|$ and $|\mathcal A|$. Depending on how these parameters are interrelated, seed-minimizing ACFU hash functions are equivalent to mosaics of balanced incomplete block designs (BIBDs) or to duals of mosaics of quasi-symmetric block designs; in a third case, mosaics of transversal designs and nets yield seed-optimal ACFU hash functions, but a full characterization is missing. By either extending $\mathcal S$ or $\mathcal X$, it is possible to obtain an $\varepsilon$-ACFU hash function from an $\varepsilon$-AU hash function or an $\varepsilon$-ASU hash function, generalizing the construction of mosaics of designs from a given resolvable design (Gnilke, Greferath, Pav{\v c}evi\'c, Des. Codes Cryptogr. 86(1)). The concatenation of an ASU and an ACFU hash function again yields an ACFU hash function. Finally, we motivate ACFU hash functions by their applicability in privacy amplification