Preattentive Processing of Numerical Visual Information

Abstract

Humans can perceive and estimate approximate numerical information, even when accurate counting is impossible e.g., due to short presentation time. If the number of objects to be estimated is small, typically around 1–4 items, observers are able to give very fast and precise judgments with high confidence—an effect that is called subitizing. Due to its speed and effortless nature subitizing has usually been assumed to be preattentive, putting it into the same category as other low level visual features like color or orientation. More recently, however, a number of studies have suggested that subitizing might be dependent on attentional resources. In our current study we investigated the potentially preattentive nature of visual numerical perception in the subitizing range by means of EEG. We presented peripheral, task irrelevant sequences of stimuli consisting of a certain number of circular patches while participants were engaged in a demanding, non-numerical detection task at the fixation point drawing attention away from the number stimuli. Within a sequence of stimuli of a given number of patches (called “standards”) we interspersed some stimuli of different numerosity (“oddballs”). We compared the evoked responses to visually identical stimuli that had been presented in two different conditions, serving as standard in one condition and as oddball in the other. We found significant visual mismatch negativity (vMMN) responses over parieto-occipital electrodes. In addition to the event-related potential (ERP) analysis, we performed a time-frequency analysis (TFA) to investigate whether the vMMN was accompanied by additional oscillatory processes. We found a concurrent increase in evoked theta power of similar strength over both hemispheres. Our results provide clear evidence for a preattentive processing of numerical visual information in the subitizing range.

Preattentive and Predictive Processing of Visual Motion

“…the change in the target’s motion trajectory, induced such a prediction error. Accordingly, we hypothesized a larger N2-component in deviant trials as compared to standards 9 , 36 . Indeed, we observed a negative peak of the difference signal ERP Deviant − ERP Standard at around 150 ms after stimulus motion onset which is well in line with the expected time range for the peak of the MMN 3 .…”

“…In recent years, however, several groups have also observed visual MMNs 8 – 11 . In these studies, the MMN was shown for many different visual stimulus features like orientation 10 , 12 , 13 , direction of motion 14 , 15 , duration 16 , color 17 – 19 , numerosity 9 and also for higher cognitive features such as object structure 20 and facial emotions 21 , 22 . While the occurrence of visual MMNs is now rather undisputed, the discussion about its neural basis continues to be controversial.…”

EEG-based visual deviance detection in freely behaving mice

“…CC-BY-ND 4.0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprint this version posted June 14, 2021. ; https://doi.org/10.1101/2021.06.14.448331 doi: bioRxiv preprint detection, but the paradigms and corresponding responses showed large variability (Stothart and Kazanina, 2013;Ahnaou et al, 2017;Hesse et al, 2017;Yan et al, 2017).…”

A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics

“…In addition, the middle parietal area might be functionally reflecting activity from the intraparietal sulcus (IPS), a brain region whose impairment has been strongly-related to difficulties in math learning. [73,74] A recent experimental study interpreted variations in the θ band as evidence of the preattentive processing of numerical visual information [75]. Moreover, increases in θ power have been reported as an effect of training in arithmetical learning, probably reflecting an improvement in automated procedural and retrieval strategies that lead to a more efficient arithmetic performance [76].…”