Comparison of Pupil Dilation Responses to Unexpected Sounds in Monkeys and Humans.

Pupil dilation in response to unexpected stimuli has been well documented in human as well as in non-human primates; however, this phenomenon has not been systematically compared between the species. This analogy is also crucial for the role of non-human primates as an animal model to investigate neural mechanisms underlying the processing of unexpected stimuli and their evoked pupil dilation response. To assess this qualitatively, we used an auditory oddball paradigm in which we presented subjects a sequence of the same sounds followed by occasional deviants while we measured their evoked pupil dilation response (PDR). We used deviants (a frequency deviant, a pink noise burst, a monkey vocalization and a whistle sound) which differed in the spectral composition and in their ability to induce arousal from the standard. Most deviants elicited a significant pupil dilation in both species with decreased peak latency and increased peak amplitude in monkeys compared to humans. A temporal Principal Component Analysis (PCA) revealed two components underlying the PDRs in both species. The early component is likely associated to the parasympathetic nervous system and the late component to the sympathetic nervous system, respectively. Taken together, the present study demonstrates a qualitative similarity between PDRs to unexpected auditory stimuli in macaque and human subjects suggesting that macaques can be a suitable model for investigating the neuronal bases of pupil dilation. However, the quantitative differences in PDRs between species need to be investigated in further comparative studies.

Coding of relative size in monkey inferotemporal cortex.

We seldom mistake a closer object as being larger, even though its retinal image is bigger. One underlying mechanism could be to calculate the size of the retinal image relative to that of another nearby object. Here we set out to investigate whether single neurons in the monkey inferotemporal cortex (IT) are sensitive to the relative size of parts in a display. Each neuron was tested on shapes containing two parts that could be conjoined or spatially separated. Each shape was presented in four versions created by combining the two parts at each of two possible sizes. In this design, neurons sensitive to the absolute size of parts would show the greatest response modulation when both parts are scaled up, whereas neurons encoding relative size would show similar responses. Our main findings are that 1) IT neurons responded similarly to all four versions of a shape, but tuning tended to be more consistent between versions with proportionately scaled parts; 2) in a subpopulation of cells, we observed interactions that resulted in similar responses to proportionately scaled parts; 3) these interactions developed together with sensitivity to absolute size for objects with conjoined parts but developed slightly later for objects with spatially separate parts. Taken together, our results demonstrate for the first time that there is a subpopulation of neurons in IT that encodes the relative size of parts in a display, forming a potential neural substrate for size constancy.

Does linear separability really matter? Complex visual search is explained by simple search.

Visual search in real life involves complex displays with a target among multiple types of distracters, but in the laboratory, it is often tested using simple displays with identical distracters. Can complex search be understood in terms of simple searches? This link may not be straightforward if complex search has emergent properties. One such property is linear separability, whereby search is hard when a target cannot be separated from its distracters using a single linear boundary. However, evidence in favor of linear separability is based on testing stimulus configurations in an external parametric space that need not be related to their true perceptual representation. We therefore set out to assess whether linear separability influences complex search at all. Our null hypothesis was that complex search performance depends only on classical factors such as target-distracter similarity and distracter homogeneity, which we measured using simple searches. Across three experiments involving a variety of artificial and natural objects, differences between linearly separable and nonseparable searches were explained using target-distracter similarity and distracter heterogeneity. Further, simple searches accurately predicted complex search regardless of linear separability (r = 0.91). Our results show that complex search is explained by simple search, refuting the widely held belief that linear separability influences visual search.