The ability to recognise others’ emotions from nonverbal cues (emotion recognition ability, ERA) is measured with performance-based tests and has many positive correlates. Although researchers have long proposed that ERA is related to general mental ability or intelligence, a comprehensive analysis of this relationship is lacking. For instance, it remains unknown whether the magnitude of the association varies by intelligence type, ERA test features, as well as demographic variables. The present meta-analysis examined the relationship between ERA and intelligence based on 471 effect sizes from 133 samples and found a significant mean effect size (controlled for nesting within samples) of r = .19. Different intelligence types (crystallized, fluid, spatial, memory, information processing speed and efficiency) yielded similar effect sizes, whereas academic achievement measures (e.g. SAT scores) were unrelated to ERA. Effect sizes were higher for ERA tests that simultaneously present facial, vocal, and bodily cues (as compared to tests using static pictures) and for tests with higher reliability and more emotions. Results were unaffected by most study and sample characteristics, but effect size increased with higher mean age of the sample. These findings establish ERA as sensory-cognitive ability that is distinct from, yet related to, intelligence.

A common finding in time psychophysics is that temporal acuity is much better for auditory than for visual stimuli. The present study aimed to examine modality-specific differences in duration discrimination within the conceptual framework of the Distinct Timing Hypothesis. This theoretical account proposes that durations in the lower milliseconds range are processed automatically while longer durations are processed by a cognitive mechanism. A sample of 46 participants performed two auditory and visual duration discrimination tasks with extremely brief (50-ms standard duration) and longer (1000-ms standard duration) intervals. Better discrimination performance for auditory compared to visual intervals could be established for extremely brief and longer intervals. However, when performance on duration discrimination of longer intervals in the one-second range was controlled for modality-specific input from the sensory-automatic timing mechanism, the visual-auditory difference disappeared completely as indicated by virtually identical Weber fractions for both sensory modalities. These findings support the idea of a sensory-automatic mechanism underlying the observed visual-auditory differences in duration discrimination of extremely brief intervals in the millisecond range and longer intervals in the one-second range. Our data are consistent with the notion of a gradual transition from a purely modality-specific, sensory-automatic to a more cognitive, amodal timing mechanism. Within this transition zone, both mechanisms appear to operate simultaneously but the influence of the sensory-automatic timing mechanism is expected to continuously decrease with increasing interval duration.

Humans can estimate the duration of intervals of time, and psychophysical experiments show that these estimations are subject to timing errors. According to standard theories of timing, these errors increase linearly with the interval to be estimated (Weber’s law), and both at longer and shorter intervals, deviations from linearity are reported. This is not easily reconciled with the accumulation of neuronal noise, which would only lead to an increase with the square root of the interval. Here, we offer a neuronal model which explains the form of the error function as a result of a constrained optimization process. The model consists of a number of synfire chains with different transmission times, which project onto a set of readout neurons. We show that an increase in the transmission time corresponds to a superlinear increase of the timing errors. Under the assumption of a fixed chain length, the experimentally observed error function emerges from optimal selection of chains for each given interval. Furthermore, we show how this optimal selection could be implemented by competitive spike-timing dependent plasticity in the connections from the chains to the readout network, and discuss implications of our model on selective temporal learning and possible neural architectures of interval timing.