Directing spatial attention to a location inside the classical receptive field (cRF) of a neuron in macaque medial temporal area (MT) shifts the center of the cRF toward the attended location. Here we investigate the influence of spatial attention on the profile of the inhibitory surround present in many MT neurons. Two monkeys attended to the fixation point or to 1 of 2 random dot patterns (RDPs) placed inside or next to the cRF, whereas a third RDP (the probe) was briefly presented in quick succession across the cRF and surround. The probe presentation responses were used to compute a map of the excitatory receptive field and its inhibitory surround. Attention systematically reshapes the receptive field profile, independently shifting both center and surround toward the attended location. Furthermore, cRF size is changed as a function of relative distance to the attentional focus: attention inside the cRF shrinks it, whereas directing attention next to the cRF expands it. In addition, we find systematic changes in surround inhibition and cRF amplitude. This nonmultiplicative push–pull modulation of the receptive field's center-surround structure optimizes processing at and near the attentional focus to strengthen the representation of the attended stimulus while reducing influences from distractors.

Teaching nonhuman primates the complex cognitive behavioral tasks that are central to cognitive neuroscience research is an essential and challenging endeavor. It is crucial for the scientific success that the animals learn to interpret the often complex task rules and reliably and enduringly act accordingly. To achieve consistent behavior and comparable learning histories across animals, it is desirable to standardize training protocols. Automatizing the training can significantly reduce the time invested by the person training the animal. In addition, self-paced training schedules with individualized learning speeds based on automatic updating of task conditions could enhance the animals' motivation and welfare. We developed a training paradigm for across-task unsupervised training (AUT) of successively more complex cognitive tasks to be administered through a stand-alone housing-based system optimized for rhesus monkeys in neuroscience research settings (Calapai A, Berger M, Niessing M, Heisig K, Brockhausen R, Treue S, Gail A. Behav Res Methods 5: 1-11, 2016). The AUT revealed interindividual differences in long-term learning progress between animals, helping to characterize learning personalities, and commonalities, helping to identify easier and more difficult learning steps in the training protocol. Our results demonstrate that 1) rhesus monkeys stay engaged with the AUT over months despite access to water and food outside the experimental sessions but with lower numbers of interaction compared with conventional fluid-controlled training; 2) with unsupervised training across sessions and task levels, rhesus monkeys can learn tasks of sufficient complexity for state-of-the-art cognitive neuroscience in their housing environment; and 3) AUT learning progress is primarily determined by the number of interactions with the system rather than the mere exposure time. NEW & NOTEWORTHY We demonstrate that highly structured training of behavioral tasks, as used in neuroscience research, can be achieved in an unsupervised fashion over many sessions and task difficulties in a monkey housing environment. Employing a predefined training strategy allows for an observer-independent comparison of learning between animals and of training approaches. We believe that self-paced standardized training can be utilized for pretraining and animal selection and can contribute to animal welfare in a neuroscience research environment.

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ZusammenfassungTherapiestudien über den Umgang mit religiösen Bedürfnissen von amerikanischen Patientinnen und Patienten zeigen, dass hochreligiöse Menschen meist negative Erwartungen zum Umgang mit Religiosität innerhalb der Psychotherapie aufweisen. Zudem stellte sich heraus, dass hochreligiöse Menschen weniger bereit sind, eine Psychotherapie in Anspruch zu nehmen als weniger religiöse Menschen. Die vorliegende Pilotstudie untersucht, ob auch in Deutschland hochreligiöse (christliche und muslimische) Menschen eine geringere Nutzungsbereitschaft für Psychotherapie aufweisen als weniger religiöse oder nichtreligiöse Menschen. Zudem wurde überprüft, inwiefern die Erwartungen zum Umgang mit Religiosität in der Psychotherapie Gründe für diesen möglichen Zusammenhang darstellen. An der Online-Befragung nahmen 1002 Studierende aus ganz Deutschland teil. Die bisherigen Befunde aus den USA wurden dabei bestätigt: Hochreligiöse Personen zeigten eine geringere Nutzungsbereitschaft für Psychotherapie als weniger religiöse und nichtreligiöse Personen. Dieser Zusammenhang wurde durch die Erwartungen zum Umgang mit Religiosität in der Psychotherapie teilweise mediiert. Praktische Implikationen dieses Befundes werden diskutiert.

Tuning curves are the functions that relate the responses of sensory neurons to various values within one continuous stimulus dimension (such as the orientation of a bar in the visual domain or the frequency of a tone in the auditory domain). They are commonly determined by fitting a model e.g. a Gaussian or other bell-shaped curves to the measured responses to a small subset of discrete stimuli in the relevant dimension. However, as neuronal responses are irregular and experimental measurements noisy, it is often difficult to determine reliably the appropriate model from the data. We illustrate this general problem by fitting diverse models to representative recordings from area MT in rhesus monkey visual cortex during multiple attentional tasks involving complex composite stimuli. We find that all models can be well-fitted, that the best model generally varies between neurons and that statistical comparisons between neuronal responses across different experimental conditions are affected quantitatively and qualitatively by specific model choices. As a robust alternative to an often arbitrary model selection, we introduce a model-free approach, in which features of interest are extracted directly from the measured response data without the need of fitting any model. In our attentional datasets, we demonstrate that data-driven methods provide descriptions of tuning curve features such as preferred stimulus direction or attentional gain modulations which are in agreement with fit-based approaches when a good fit exists. Furthermore, these methods naturally extend to the frequent cases of uncertain model selection. We show that model-free approaches can identify attentional modulation patterns, such as general alterations of the irregular shape of tuning curves, which cannot be captured by fitting stereotyped conventional models. Finally, by comparing datasets across different conditions, we demonstrate effects of attention that are cell- and even stimulus-specific. Based on these proofs-of-concept, we conclude that our data-driven methods can reliably extract relevant tuning information from neuronal recordings, including cells whose seemingly haphazard response curves defy conventional fitting approaches.