Hiding from the Moonlight: Luminosity and Temperature Affect Activity of Asian Nocturnal Primates in a Highly Seasonal Forest

The effect of moonlight and temperature on activity of slow lorises was previously little known and this knowledge might be useful for understanding many aspects of their behavioural ecology, and developing strategies to monitor and protect populations. In this study we aimed to determine if the activity of the pygmy loris (Nycticebus pygmaeus) is affected by ambient temperature and/or moonlight in a mixed deciduous forest. We radio-collared five females and five males in the Seima Protection Forest, Cambodia, in February to May, 2008 and January to March, 2009 and recorded their behaviour at 5 minutes intervals, totalling 2736 observations. We classified each observation as either inactive (sleeping or alert) or active behaviour (travel, feeding, grooming, or others). Moon luminosity (bright/dark) and ambient temperature were recorded for each observation. The response variable, activity, was binary (active or inactive), and a logit link function was used. Ambient temperature alone did not significantly affect mean activity. Although mean activity was significantly affected by moonlight, the interaction between moonlight and temperature was also significant: on bright nights, studied animals were increasingly more active with higher temperature; and on dark nights they were consistently active regardless of temperature. The most plausible explanation is that on bright cold nights the combined risk of being seen and attacked by predators and heat loss outweigh the benefit of active behaviours

Properties of V1 Neurons Tuned to Conjunctions of Visual Features: Application of the V1 Saliency Hypothesis to Visual Search behavior

From a computational theory of V1, we formulate an optimization problem to investigate neural properties in the primary visual cortex (V1) from human reaction times (RTs) in visual search. The theory is the V1 saliency hypothesis that the bottom-up saliency of any visual location is represented by the highest V1 response to it relative to the background responses. The neural properties probed are those associated with the less known V1 neurons tuned simultaneously or conjunctively in two feature dimensions. The visual search is to find a target bar unique in color (C), orientation (O), motion direction (M), or redundantly in combinations of these features (e.g., CO, MO, or CM) among uniform background bars. A feature singleton target is salient because its evoked V1 response largely escapes the iso-feature suppression on responses to the background bars. The responses of the conjunctively tuned cells are manifested in the shortening of the RT for a redundant feature target (e.g., a CO target) from that predicted by a race between the RTs for the two corresponding single feature targets (e.g., C and O targets). Our investigation enables the following testable predictions. Contextual suppression on the response of a CO-tuned or MO-tuned conjunctive cell is weaker when the contextual inputs differ from the direct inputs in both feature dimensions, rather than just one. Additionally, CO-tuned cells and MO-tuned cells are often more active than the single feature tuned cells in response to the redundant feature targets, and this occurs more frequently for the MO-tuned cells such that the MO-tuned cells are no less likely than either the M-tuned or O-tuned neurons to be the most responsive neuron to dictate saliency for an MO target

A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing

Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this article we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 10 experimental phenomena and show that it can provide an adequate account for 8, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSC-TD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used