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Characteristics of tasks involving non-roving and roving stimuli. Left panel: in tasks with non-roving stimuli, as in Chen et al. [1], the sample stimulus was always displayed at a contrast of 30%. Right panel: for the task in the current study, involving roving stimuli, the contrast of the sample stimulus varied randomly from trial to trial and took on a value of 20%, 30% or 40%. Unlike in the non-roving task, subjects had to take note of the contrast of the sample stimulus in order to perform the roving task correctly. For example, for a test stimulus of 25% contrast, they were required to make a saccade to the white target if it had been preceded by a sample of 20% contrast. On the other hand, they were required to make a saccade to the black target if the sample contrast had been 30% or 40%. Note that the contrasts of stimuli in the diagram are exaggerated for illustrative purposes. doi:10.1371/journal.pone.0109604.g002 

Characteristics of tasks involving non-roving and roving stimuli. Left panel: in tasks with non-roving stimuli, as in Chen et al. [1], the sample stimulus was always displayed at a contrast of 30%. Right panel: for the task in the current study, involving roving stimuli, the contrast of the sample stimulus varied randomly from trial to trial and took on a value of 20%, 30% or 40%. Unlike in the non-roving task, subjects had to take note of the contrast of the sample stimulus in order to perform the roving task correctly. For example, for a test stimulus of 25% contrast, they were required to make a saccade to the white target if it had been preceded by a sample of 20% contrast. On the other hand, they were required to make a saccade to the black target if the sample contrast had been 30% or 40%. Note that the contrasts of stimuli in the diagram are exaggerated for illustrative purposes. doi:10.1371/journal.pone.0109604.g002 

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‘Stimulus roving’ refers to a paradigm in which the properties of the stimuli to be discriminated vary from trial to trial, rather than being kept constant throughout a block of trials. Rhesus monkeys have previously been shown to improve their contrast discrimination performance on a non-roving task, in which they had to report the contrast of a t...

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... and technicians ensured prompt and effective interventions in the form of surgery, anaesthetics, antibiotics, and analgesics as needed, to maintain the health of the animals and minimise suffering. Both animals were sacrificed at the conclusion of the study with an overdose of pentobarbital, in compliance with the UK Home Office Codes of Practice. Stimulus presentation was controlled using CORTEX software (Laboratory of Neuropsychology, National Institute of Mental Health, on a computer with an Intel Core i3-540 processor. Sinusoidal grating stimuli were displayed at a viewing distance of 0.54 m, on a 25 0 Sony Trinitron CRT monitor with display dimensions of 40 cm (W) by 32 cm (H) and a resolution of 1280 by 1024 pixels, yielding a resolution of 31.5 pixels/degree of visual angle (dva). The monitor refresh rate was 85 Hz for monkey 1, and 75 Hz for monkey 2. The outputs of the red and green guns were combined using a Pelli-Zhang video attenuator [16], yielding a luminance resolution of 12 bits/pixel, allowing the presentation of contrasts that were well below contrast discrimination thresholds. A gamma correction was used to linearize the monitor output. Unlike in the previous study by Chen et al. [1], the contrast of the sample stimulus was not fixed at 30%, but could take on one of three values (20, 30 or 40%) on a given trial. The test stimulus took on one of 12 possible contrasts, depending on the sample contrast (20% sample: [5, 10, 12, 15, 18, 22, 25, 28, 35, 45, 60, 90% test]; 30% sample: [5, 10, 15, 22, 25, 28, 32, 35, 38, 45, 60, 90% test]; 40% sample: [5, 10, 15, 25, 32, 35, 38, 42, 45, 50, 60, 90% test]), yielding 36 conditions in total. Roving grating stimuli were positioned at parafoveal locations in the visual field, at the same lower hemifield location as that used in the non-roving task from the previous study, i.e. at an eccentricity of 4.6 u (azimuth: 2 3.5 u , elevation: 2 3 u ) and 1.5 u (azimuth: 2 1.3 u , elevation: 2 0.7 u ) for monkeys 1 and 2, respectively. Data were gathered in conjunction with the recording of neuronal data (not presented here), and the slight difference in stimulus location between the animals was due to a difference in the receptive field locations of the neurons that were sampled by the implanted electrodes. Gratings were vertically oriented; the SF was 4 cycles per degree (cpd) in both monkeys; and the diameter was 3 dva in monkey 1 and 0.75 dva in monkey 2. Apart from the contrast levels, all stimulus parameters were the same as those used previously during training on the non-roving task described in Chen et al. [1]. During the phase of training involving flanker stimuli, flanker gratings were displayed collinearly immediately above and below the central sample and test stimuli, forming a column of three gratings, positioned edge to edge. The flanker stimuli were identical to the sample and test stimuli in terms of size, SF and orientation. To optimise our chances of success under flanker conditions, we followed Adini et al.’s paradigm [3], using chains of flankers (rather than the elongated Gabors used by Yu et al. [2]) and kept the contrast of flankers constant at 30% throughout training, regardless of the sample contrast. However, we continued to vary the sample contrast from trial to trial (even though Adini et al. [3] reported better results for a blocked than for a ‘mixed by trial’ (‘MBT’) method), because we wanted to keep our paradigm as similar as possible to that used in the previous stage of roving training and ensure a smooth transition to the flanker task for our monkeys. In addition, monkey 2 participated in a control task, in which the stimulus properties and locations were identical to those used with monkey 1 (4.6 eccentricity; 4 cpd; 3 dva diameter). During training on the CD task, the presentation of a sample stimulus was followed by that of a test stimulus, and subjects had to decide whether the test stimulus was of higher or lower contrast than that of the sample (see Figure 1 for an illustration of the task). If the test stimulus was of lower contrast than the sample, the monkey had to saccade to a black target, otherwise it had to saccade to a white target. These basic requirements of the CD task were identical to those used previously during training on the non- roving task (described in Chen et al. [1]). For certain conditions, the identity of the correct target was the same regardless of the sample contrast (e.g. when the test contrast was 5%, the sample contrast was always higher, thus subjects always had to saccade to the black target). However, for other conditions (termed ‘response conflict conditions’), the identity of the correct target varied, depending on the sample contrast. For example, when the test contrast was 25%, if the sample contrast had been 30% or 40%, then the subjects had to saccade to the black target, whereas if the sample contrast had been 20%, the subjects had to saccade to the white target (refer to Figure 2 for an illustration of sample-dependent or sample-independent task requirements). Psychometric performances of the two subjects on the roving contrast discrimination task were monitored throughout the training process to allow a continuous assessment of behavioural improvement, across a total of 55 and 42 sessions for monkeys 1 and 2, respectively. Training on the roving task was initially carried out in the absence of flankers (monkey 1: 33 sessions, spanning 8 weeks; monkey 2: 16 sessions, spanning 4 weeks). Unlike in previous human studies, we could not explicitly instruct our monkeys to base their decisions on comparisons between the sample and test stimuli, and disregard the rules learnt during non-roving training (i.e. the instruction to always make a comparison against a reference contrast of 30%). Thus, a fairly long training period was required, in which subjects obtained feedback via reward delivery, which shaped their understanding of the task requirements. Once the subjects’ performance had plateaued and it seemed unlikely that additional training would bring about further improvement, flanker stimuli were added, and training resumed in the presence of flankers (monkey 1: 15 sessions, spanning 6 weeks; monkey 2: 22 sessions, spanning 6 weeks). Finally, the flankers were removed and training continued in the absence of flanker stimuli (monkey 1: 7 sessions, spanning 1.5 weeks; monkey 2: 4 sessions, spanning 1 week). To investigate the effects of perceptual learning on a stimulus roving task, several metrics of performance were used over the course of training: the proportion of correct responses made by the subjects (‘ P correct ’); the slope and the point of subjective equality (PSE) of the psychometric function; the psychometric threshold; the rate of learning for different contrasts; and the subjects’ reaction times. For derivations of each of these measures, please refer to Chen et al. [1] for details. During roving training under response conflict conditions, one would expect learning to be accompanied by a divergence in the monkeys’ responses, depending on the sample contrast that was presented. Alternatively, if no learning occurred, then one would not expect sample-dependent differences in responses to emerge. A simple binomial test would be able to detect a difference in performance levels between sample contrast conditions (e.g. if it was conducted on the last third of training sessions); however, this might be the case even if little learning had occurred. In the event that our subjects’ performance levels had already been high from the beginning of training on the roving task, then a binomial test would detect a difference between the roving conditions, but fail to indicate whether an improvement in performance had occurred over the course of training. Hence, we used a more complex approach which examined potential changes due to learning, in which we asked whether performance under roving conditions diverged with training (as would be expected if learning had occurred). We determined whether the data obtained under response conflict conditions were better described by a single (linear) model, or whether they were better described by separate linear models (and thus with two additional free parameters). To compare the two different models, an AIC value was calculated for each model, according to where x is the Chi-Square goodness of fit statistic (with an assumed variance of 1) and k is the number of free parameters in the model [17]. For the model involving two separate linear fits to the data (one fit to each half of the data, which were divided by sample contrast), k was equal to 4; for the model involving a single fit to the combined data, k = 2. The AIC values were compared between the two models, in which a lower AIC corresponded to the model that provided a better description of the observed data. The Akaike model weight, w i , was calculated as a measure of the weight of evidence in favour of a particular model, as where i is the model being evaluated; D i is the difference in AIC values between model i and the best model (i.e. the model with the lowest AIC ); and D r is the difference in AIC values between model r and the best model, for the set of R models (in this case, R = ...

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... We used a contrast discrimination task for two reasons. First, it remains debated to what extent perceptual learning occurs in the contrast domain [17][18][19][20][21] . Second, the activity of most visual neurons is tuned to contrast [22][23][24][25][26][27][28][29][30] , thereby maximizing the number of informative channels/neurons to be included in the analysis. ...
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While extra-personal space is often erroneously considered as a unique entity, early neuropsychological studies report a dissociation between near and far space processing both in humans and in monkeys. Here, we use functional MRI in a naturalistic 3D environment to describe the non-human primate near and far space cortical networks. We describe the co-occurrence of two extended functional networks respectively dedicated to near and far space processing. Specifically, far space processing involves occipital, temporal, parietal, posterior cingulate as well as orbitofrontal regions not activated by near space, possibly subserving the processing of the shape and identity of objects. In contrast, near space processing involves temporal, parietal and prefrontal regions not activated by far space, possibly subserving the preparation of an arm/hand mediated action in this proximal space. Interestingly, this network also involves somatosensory regions, suggesting a cross-modal anticipation of touch by a nearby object. Last, we also describe cortical regions that process both far and near space with a preference for one or the other. This suggests a continuous encoding of relative distance to the body, in the form of a far-to-near gradient. We propose that these cortical gradients in space representation subserve the physically delineable peripersonal spaces described in numerous psychology and psychophysics studies. Highlights Near space processing involves temporal, parietal and prefrontal regions. Far space activates occipital, temporal, parietal, cingulate & orbitofrontal areas. Most regions process both far & near space, with a preference for one or the other. Far-to-near gradient may subserve behavioral changes in peripersonal space size.