How To ##BEST## Download Sleeping Dogs For Pc Windows 11

Hi guys, The latest intel dch graphics driver is causing problems with Sleeping dogs PC game, when i take my character outdoors, it starts to lag very much, the solution i did was to uninstall the latest dch graphics driver and then reinstall old graphics driver from April 2018 and it fix the lag issue but the old driver had minor shadow Flicker problem in some areas, is there anyway to fix the lag problem on latest intel dch graphics driver. I am using windows 10 64 bit.

The way the game's lighting engine is used to create and enhance these scenes is very impressive. Shadows creep across the ground as the sun sets, light reflections are cast over the windows on buildings, and at night-time scenes are filled with a variety of light sources which affect surrounding objects. Environmental effects - such as steam bellowing of a kitchen, or waves crashing onto the bay side - further add to the ambience the game offers.

How To Download Sleeping Dogs For Pc Windows 11

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Mammals spend a considerable proportion of their lifetime sleeping4. Being able to process environmental stimuli not only while awake but also while asleep is vital for survival. Temporal aspects of neural processing of environmental stimuli can be assessed using event-related potentials which, in humans, can be evoked during Stage 1 and Stage 2 non-REM sleep5. Extending on the literature with awake dogs3, here, we examine how sleeping dogs process differently valenced dog and human vocalisations at the neural level.

Dogs were integrated into the human environment over the years of domestication, and most communicate with both conspecific and heterospecific (human) partners on a regular basis. Behavioural studies show that dogs can match pictures of dogs6 and humans7 with their corresponding vocalisations, as well as dog and human emotional vocalisations with the congruent facial expressions8. Dogs also exhibit different behavioural responses to differently valenced vocalisations9,10,11.

These behavioural findings prompted questions about the neurophysiological correlates of emotional processing in dogs. An emerging number of studies are aimed at addressing related questions12,13,14 and the findings of these studies have largely contributed to the dog being considered a good model for comparative neurobiology15. Data indicate conspecific-sensitive and valence-sensitive brain regions in dogs and humans16,17. During electroencephalography (EEG), event-related potentials (ERPs) can be recorded and used to identify, with high temporal resolution, the effects of certain stimuli at the neural level18. ERPs in dogs have mainly been obtained using semi-invasive needle electrodes19 over the decades and more recently, a non-invasive method20. The latter method is completely painless, contrary to many other paradigms used in dog EEG studies (for a review of canine neurosurgery over history, see Ref.21), only surface electrodes are used, and participation is motivated solely by positive reinforcement (praise, treats).

Using the same non-invasive method, Blint and colleagues examined the neural correlates of processing differently valenced dog and human vocalisations3. To test whether a species- or valence-specific difference of acoustic processing is observable, dogs were presented with dog and human vocalisations previously classified by human raters as either neutral or positive56. ERPs were recorded at two electrode sites, at the frontal and central positions of the anteroposterior midline of the skull (Fz, Cz). Findings showed human voices elicited a more positive ERP response than dog sounds in a time window ranging from 250 to 650 ms after stimulus onset. A species by valence interaction was also detected, such that in case of dog sounds, ERPs were more positive to neutral stimuli, whereas in case of human voices, ERPs were more positive to positive stimuli in a later time-window, ranging from 800 to 900 ms following stimulus onset. These results indicate that the dog brain differentially responds to the investigated factors. The species effect time-window coincides with two ERP components described in humans: the P300 and the earlier window of the late positive potential (LPP), both of which are generally associated with emotional processing and attention to motivationally significant stimuli22,23,24,25. The species by valence interaction time-window can be interpreted as indexing more subtle processing of the vocalisations, attributed to higher-level cognitive processes26.

Dog and human sleep show several parallels: e.g., regarding sleep-dependent memory consolidation27,28 and processing of pre-sleep emotional treatment29. It has even been proposed that relative to commonly used laboratory animals, the general architecture of human sleep is better approximated by dog sleep30. It remains unknown however, whether similarly to humans, processing of auditory signals can be indexed by ERPs during sleep in dogs (for a review of human ERP studies during sleep, see Ref.31).

In the present study, we investigated whether dog brains differentially respond to species and valence information in vocal stimuli during different sleep stages. We expected to find, as in awake dogs3, species- and valence-sensitivity, especially during light sleep (drowsiness).

Grand-averaged ERPs showing the average of the two levels of the species factor, from 200 ms before to 1000 ms after stimulus onset (0 point on the x-axis). The highlighted parts designate the time windows between 250 and 450 ms and between 600 and 800 ms where the species effect was significant in the sliding time-window analysis during drowsiness.

Here we provide the first evidence that in addition to the awake state, event related potentials can be evoked in dogs during (drowsiness and non-REM) sleep. Using differentially valenced acoustic stimuli from dogs and humans (the valence rating conducted by human listeners in a previous study56), we conclude that the dog brain can differentiate between vocalisations based on species and valence factors in two sleep-stages, similarly to how the human brain is able to process various characteristics of acoustic cues during sleep31. This finding is significant insofar as it is the first evidence of complex auditory processing during sleep in dogs.

It can be easily detected that behaviorally, awake dogs differentially respond to vocal cues depending on different characteristics of these sounds. For example, dogs differently react to the vocal cues of conspecifics, based on familiarity43 or body size44; and they also differentiate between human vocal cues based on emotions8 or caller identity7. Such reactions are relatively straightforward to quantify in the form of e.g., looking durations or other behavioural responses. Thus, although research on awake dog fMRI16 and ERP3,19,20,45 measures of auditory processing has expanded the available behavioural literature (and provided insight into biological mechanisms), the assessment of neural response is essential for examining whether dogs are able to detect and process vocal stimuli during sleep and what characteristics of these acoustic cues are they reactive to, in the absence of behaviour that would be observable.

In the current exploratory study, we found that dog brains process species (human versus conspecific) as well as valence (rated as positive versus neutral) information during deep / non-REM sleep. Although the sample size was small and direct comparisons between different sleep stages was not feasible given the distribution of artefacts, these results indicate that the non-invasive ERP protocol we applied is a feasible method to examine vocal stimuli processing in sleeping dogs.

Our results are also consistent, in part, with awake dog ERP data involving the same pool of stimuli, in that effects were found in comparable time windows, though in certain cases, in a different direction3. In some cases, for example the herein observed effects are only partially overlapping with those previously reported: during drowsiness, a species main effect was observed in two time windows ranging from 250 to 450 ms and from 600 to 800 ms, whereas in awake dogs this effect lasted from 250 to 650 ms (further discussed below). It should be noted that a relevant consideration in interpreting the current findings and in comparing those to data obtained previously is that in Blint et al., dogs were trained to lie motionless. We have no data on how such training (and its results, e.g., associations between the test situation and rewards) might have affected emotion processing. Of note however, as dogs participated in several dozens of assessment sessions in Blint et al., they all reached a calm, relaxed state during that experiment. Given the length of the sessions and that dogs only received a food reward at the end of each session in this previous study, it is likely that there were no reward anticipation effects.

In some cases, the direction of the current results was opposite to those described earlier3. In the current sample, a non-significant interaction of main factors emerged in the wake stage between 750 to 850 ms: ERPs were more positive after positive cues coming from a dog (compared to to neutral dog sounds), whereas ERPs were more positive after neutral cues coming from humans (relative to positive human sounds). The opposite was the case in Blint et al., where neutral dog sounds and positive human sounds elicited more positive ERPs in a time window ranging from 800 to 900 ms. The current dataset contained only a limited amount of wake data, and as the observed effect did not reach statistical significance, it might easily reflect a non-effect. However, mainly on Cz, the same association (more positive ERPs to positive dog and to neutral human stimuli) was significant during drowsiness and non-REM sleep. On the other hand, dogs participating in the current study were untrained and they were more relaxed, dozing off even during periods categorised as wake. Thus, differential responses to the same emotionally valenced dog and human vocalisations might be related to the state that the subject is in: different stimuli are relevant to a more alert individual participating in a task compared to a relaxed individual about to fall asleep. We must also note that Blint et al. described a large individual variation in responses to the vocalisations represented by the four conditions (for individual graphs, see Fig. S1 in Ref.3). Since the sample size in the current study was rather small, it cannot be excluded that some group effects are due to the composition of the sample. 17dc91bb1f