Neutral non emotional image free download

It is the distributed functional network that serves the general function of finding resources for survival that gets hungry animals to food, thirsty animals to water, cold animals to warmer environments, etc. Panksepp, To summarize, both emotion and motivation are crucial for the maintenance of psychological and physiological homeostasis, while emotional roles are particularly important in the process of encoding new information containing emotional components.

The latter increases attention toward salient new information by selectively enhancing detection, evaluation, and extraction of data for memorization. In addition, motivational components promote learning and enhance subsequent memory retrieval while generalizing new events consequent to adaptive physiological changes. Evolution built our higher minds the faculty of consciousness and thoughts on a foundation of primary-process of emotional mechanism that preprogrammed executive action systems the prototype emotions rely on cognitive processing interpretation and appraisal in the organisms attempt to decipher the type of situation they might be in; in other words, how to deal with emotionally challenging situations, whether it is a play situation or a threat situation where RAGE and FEAR might be the appropriate system to recruit.

Emotion offers preprogrammed but partially modifiable under the secondary process of learning and memory behavioral routines in the service of the solution of prototypical adaptive challenges, particularly in dealing with friend vs. Thus, evolution uses whatever sources for survival and procreative success. According to Panksepp and Solms , key CNS emotional-affective processes are 1 Primary-process emotions; 2 Secondary-process learning and memory; and 3 Tertiary-process higher cognitive functions.

Subsequently, learning process sends relevant information to higher brain regions such as the prefrontal cortex to perform tertiary cognition process that allows planning for future based on past experiences, stored in LTM.

What now follows is an explanation of these CNS emotional-affective processing sub-levels and their inter-relationships. The schematic shows conceptual relationships between primary processes of emotional system lower brain function , as well as secondary processes of cognitive system and tertiary processing higher brain function.

As secondary processes are continually integrated with primary emotional processing, they mature to higher brain cognitive faculties to generate effective solutions for living and subsequently exert top-down regulatory control over behavior. This bi-circular causation for higher brain functionality is coordinated by lower brain functions [adapted from Panksepp and Solms, ].

The emotional operating system is an inherited and genetically encoded circuitry that anticipates key survival and homeostatic needs. Primary-process emotions are not unconscious. Strong emotion is intrinsically conscious at least in the sense that it is experienced even if we might mislabel it, or animal clearly is not able to attach a semantic label-these are simply not realistic standards for determining whether something is conscious or not conscious.

This includes the basal ganglia basolateral and central amygdala, nucleus accumbens, thalamus and dorsal striatum , and the medial temporal lobe MTL including hippocampus as well as the entorhinal cortex, perirhinal cortex, and parahippocampal cortices that responsible for declarative memories. Thus, secondary processes of learning and memory scrutinize and regulate emotional feelings in relation to environmental events that subsequently refine effective solutions to living.

Higher cognitive functions operate within the cortical regions, including the frontal cortex for awareness and consciousness functions such as thinking, planning, emotional regulation and free-will intention-to-act , which mediate emotional feelings. Hence, cognition is an extension of emotion just as emotion is an extension of homeostasis aforementioned. In other words, brain-mind evolution enables human to reason but also regulate our emotions.

Psychologist Neisser suggested that cognition serves emotion and homeostatic needs where environmental information is evaluated in terms of its ability to satisfy or frustrate needs. In other words, cognition is in the service of satisfying emotional and homeostatic needs. This infers that cognition modulates, activates and inhibits emotion. Therefore, the CNS maintains complex processes by continually monitoring internal and external environments. For example, changes in internal environments contraction of visceral muscles, heart rate, etc.

Thus, from an evolutionary perspective, human mental activity is driven by the ancient emotional and motivational brain systems shared by cross-mammalians that encode life-sustaining and life-detracting features to promote adaptive instinctual responses. Homeostasis imbalance is universally experienced as negative emotional feelings and only becomes positively valenced when rectified.

Hence, individuals sustain bodily changes that underlie psychological emotional and biological homeostatic influences on two sides, i. Consequently, cognition modulates both emotional and homeostatic states by enhancing survival and maximizing rewards while minimizing risk and punishments. Figure 2 demonstrates this cyclic homeostatic regulation. Adapted from Damasio and Carvalho These primary emotional neural networks are situated in the subcortical regions; moreover, the evidence demonstrates that decortication leaves primary emotional systems intact Panksepp et al.

Hence, cortical regions are non-essential for the generation of prototype emotional states but are responsible for their modulation and regulation. The present article emphasizes SEEKING because it is the most fundamental of the primary emotional systems and is crucial for learning and memory. The SEEKING system facilitates learning because when fully aroused, it fills the mind with interest that then motivates the individual to search out and learn things that they need, crave and desire.

In other words, the SEEKING system has been designed to automatically learn by exploring anything that results in acquired behavioral manifestations for survival operations, all the way from the mesolimbic-mesocortical dopamine system through to the prefrontal cortex PFC ; thus, it is intimately linked with LTM formation Blumenfeld and Ranganath, Consequently, it is the foundation of secondary learning and higher cognitive processes when compared with the remaining six emotional systems.

However, this system is less activated during chronic stress, sickness, and depression, all of which are likely to impair learning and various higher cognitions. On the other hand, overactivity of this system promotes excessively impulsive behaviors attended by manic thoughts and psychotic delusions. In brief, the SEEKING system holds a critical position that optimizes the performance of emotion, motivation, and cognition processes by generating positive subjective emotional states-positive expectancy, enthusiastic exploration, and hopefulness.

Because the seven primary emotional systems and their associated key neuroanatomical and key neurochemical features have been reviewed elsewhere Panksepp, a , b , they are not covered in this review.

Studies in psychology Metcalfe and Mischel, and neuroscience Dolcos et al. The dorsal stream encompasses the dorsolateral prefrontal cortex DLPFC and lateral parietal cortex, which are involved in the cool system for active maintenance of controlled processes such as cognitive performance and the pursuit of goal-relevant information in working memory WM amidst interference.

In contrast, the hot system involves the ventral neural system, including the amygdala, ventrolateral prefrontal cortex VLPFC and medial prefrontal cortex mPFC as well as orbitofrontal OFC and occipito-temporal cortex OTC , all of which encompass emotional processing systems Dolcos et al. Nonetheless, recent investigations claim that distinct cognitive and emotional neural systems are not separated but are deeply integrated and contain evidence of mediation and modulation Dolcos et al.

Consequently, emotions are now thought to influence the formation of a hippocampal-dependent memory system Pessoa, , exerting a long-term impact on learning and memory. In other words, although cognitive and affective processes can be independently conceptualized, it is not surprising that emotions powerfully modify cognitive appraisals and memory processes and vice versa.

The innate emotional systems interact with higher brain systems and probably no an emotional state that is free of cognitive ramifications. If cortical functions were evolutionarily built upon the pre-existing subcortical foundations, it provides behavioral flexibility Panksepp, The hippocampus is located in the MTL and is thought to be responsible for the potentiation and consolidation of declarative memory before newly formed memories are distributed and stored in cortical regions Squire, Moreover, evidence indicates that the hippocampus functions as a hub for brain network communications-a type of continuous exchange of information center that establishes LTM dominated by theta wave oscillations Battaglia et al.

In other words, hippocampus plays a crucial role in hippocampal-dependent learning and declarative memories. Numerous studies have reported that the amygdala and hippocampus are synergistically activated during memory encoding to form a LTM of emotional information, that is associated with better retention McGaugh et al. In addition to amygdala-hippocampus interactions, one study reported that the PFC participates in emotional valence pleasant vs. They demonstrated that the PFC is crucial for LTM because it engages with the active maintenance of information linked to the cognitive control of selection, engagement, monitoring, and inhibition.

Hence, it detects relevant data that appears worthwhile, which is then referred for encoding, thus leading to successful LTM Simons and Spiers, Consistent findings were reported for recognition tasks investigated by fMRI where the left PFC-hippocampal network appeared to support successful memory encoding for neutral and negative non-arousing words.

Simultaneously, amygdala-hippocampus activation was observed during the memory encoding of negative arousing words Kensinger and Corkin, Moreover, Mega et al. The first component is responsible for the implicit integration of affects, drives and object associations; the second deals with explicit sensory processing, encoding, and attentional control.

Although divided into two sub-divisions, the paleocortex and archicortical cortex remain integrated during learning. Here, the paleocortex appears to manage the internal environment for implicit learning while integrating affects, drives, and emotions. Simultaneously, the archicortical division appears to manage external environment input for explicit learning by facilitating attention selection with attendant implicit encoding.

To some extent, the paleocortex system might come to exercise a supervisory role and link the ancient affective systems to the newer cognitive systems. The findings of previous studies suggest that the amygdala is involved in emotional arousal processing and modulation of the memory processes encoding and storage that contribute to the emotional enhancement of memory McGaugh et al. Because of the interaction between basolateral complex of the amygdala BLA with other brain regions that are involved in consolidating memories, including the hippocampus, caudate nucleus, NAc, and other cortical regions.

Thus, BLA activation results from emotionally arousing events, which appear to modulate memory storage-related regions that influence long-term memories McGaugh, Memory consolidation is a part of the encoding and retention processes where labile memories of newly learned information become stabilized and are strengthened to form long-lasting memories McGaugh, Consequently, during emotional processing, direct projections from the amygdala to sensory cortices enhance attentional mechanism might also allow the parallel processing of the attentional fronto-parietal system Vuilleumier, This suggests that amygdala activation is associated with enhanced attention and is a part of how salience enhances information retention.

Thus, there is evidence that the consolidation of new memory that is stimulated by emotionally arousing experiences can be enhanced through the modulating effects of the release of stress hormones and stress-activated neurotransmitters associated with amygdala activation. However, stress and emotion do not always induce strong memories of new information. Indeed, they have also been reported to inhibit WM and LTM under certain conditions related to mood and chronic stress Schwabe and Wolf, Consequently, understanding, managing, and regulating emotion is critical to the development of enhanced learning programs informed by the significant impacts of learning and memory under different types of stress Vogel and Schwabe, Moreover, it is thought to act as a control center for selective attention Squire et al.

Its involvement in WM and emotional processing are intimately connected with the MTL structures that decisively affect LTM encoding and retrieval Blumenfeld and Ranganath, in addition to self-referential processing Northoff et al. Specifically, increased mPFC activation has been noted during reappraisal and is associated with the suppressed subjective experience of negative emotions. Furthermore, an fMRI study revealed concurrent activation levels of the dorsomedial prefrontal cortex dmPFC with emotional valence when processing emotional stimuli: i activation was associated with positive valence, and ii deactivation was associated with negative valence Heinzel et al.

These findings suggested reciprocal interactions between cognitive and emotional processing between dorsal and lateral neural systems when processing emotional and cognitive tasking demands Bartolic et al. Other studies reported strong cognition-emotion interactions in the lateral prefrontal cortex with increased activity in the DLPFC, which plays a key role in top-down modulation of emotional processing Northoff et al.

This indicates increased attentional control of regulatory mechanisms that process emotional content. For instance, one study reported that cognitive task appeared to require active retention in WM, noting that the process was influenced by emotional stimuli when subjects were instructed to remember emotional valence information over a delay period Perlstein et al. This could be interpreted as increased WM-related activity when processing positive emotional stimuli, thus leading to positive emotion maintenance of stimulus representation in WM.

Furthermore, they observed that the DLPFC contributed to increased LTM performance linked to stronger item associations and greater organization of information in WM during pleasant compared to unpleasant emotion Blumenfeld and Ranganath, Certain characteristics of emotional content were found to mediate the encoding and retrieval of selective information by leading high levels of attention, distinctiveness, and information organization that enhanced recall for emotional aspects of complex events Talmi, Hence, this direction of additional attention to emotional information appears to enhance LTM with the pronounced effects deriving from positive emotions compared with negative emotions.

Effects of emotion on memory was also investigated using immediate after 20 s and delayed after 50 min testing paradigm, has shown that better recall for emotionally negative stimuli during immediate test compared to delayed test because of attentional allocation for encoding while the delayed test demonstrated that the role of amygdala in modulating memory consolidation of emotional stimuli.

Because selective attention drives priority assignment for emotional material Talmi et al. Meanwhile, the distinctiveness and organization of information can improve memory because unique attributes and inter-item elaboration during encoding serve as retrieval cues, which then lead to high possibilities for correct recall Erk et al.

Consistent findings were also reported by Dolcos et al. Table 1 summarizes cognitive-emotional functions associated with each sub-region of the PFC and corresponding Brodmann areas. Taken together, these findings indicate that the PFC is a key component in both cognitive and emotional processing for successful LTM formation and retrieval.

TABLE 1. The prefrontal cortex PFC sub-regions, corresponding Brodmann areas, and associated cognitive-emotional functions. As discussed above, evidence indicates the neural mechanisms underlying the emotional processing of valence and arousal involve the amygdala and PFC, where the amygdala responds to emotionally arousing stimuli and the PFC responds to the emotional valence of non-arousing stimuli. We have thus far primarily discussed studies examining neural mechanisms underlying the processing of emotional images.

However, recent neuroimaging studies have investigated a wider range of visual emotional stimuli. These include words Sharot et al. These studies provided useful supplemental information for future research on emotional effects of educational multimedia content combination of words and pictures , an increasingly widespread channel for teaching and learning.

Subjects were instructed to rate each stimulus as animate or inanimate and common or uncommon. The results revealed the activation of the amygdala in response to positive and negative valence valence-independent for pictures and words. A lateralization effect was observed in the amygdala when processing different emotional stimuli types.

In addition, participants were more sensitive to emotional pictures than to emotional words. The mPFC responded more rigorously during the processing of positive than to that of negative stimuli, while the VLPFC responded more to negative stimuli.

The researchers concluded that arousal-related responses occur in the amygdala, dmPFC, vmPFC, anterior temporal lobe and temporo-occipital junction, whereas valence-dependent responses were associated with the lateral PFC for negative stimuli and the mPFC for positive stimuli. Hence, these factors should be considered in future studies. Event-related potentials ERPs were used to investigate the modality effects deriving from emotional words and facial expressions as stimuli in healthy, native German speakers Schacht and Sommer, a.

German verbs or pseudo-words associated with positive, negative or neutral emotions were used, in addition to happy vs. The results revealed that negative posterior ERPs were evoked in the temporo-parieto-occipital regions, while enhanced positive ERPs were evoked in the fronto-central regions positive verbs and happy faces when compared with neutral and negative stimuli.

These findings were in agreement with the previous findings Schupp et al. While the same neuronal mechanisms appear to be involved in response to both emotional stimuli types, latency differences were also reported with faster responses to facial stimuli than to words, likely owing to more direct access to neural circuits-approximately ms for happy faces compared to ms for positive verbs Schacht and Sommer, a.

Moreover, augmented responses observed in the later positive complex LPP , i. Khairudin et al. All stimuli were categorized as positive, negative or neutral, and displayed in two different trials. Results revealed that better memory for emotional images than for emotional words. Moreover, a recognition test demonstrated that positive emotional content was remembered better than negative emotional content.

Researchers concluded that emotional valence significantly impacts memory and that negative valence suppressed the explicit memory. Another study by Khairudin et al. The results revealed that emotion substantially influences memory performance and that both positive and negative words were remembered more effectively than neutral words.

Moreover, emotional words were remembered better in recognition vs. Another group studied the impacts of emotion on memory using emotional film clips that varied in emotion with neutral, positive, negative and arousing contents Anderson and Shimamura, A subjective experiment for word recall and context recognition revealed that memory, for words associated with emotionally negative film clips, was lower than emotionally neutral, positive and arousing films.

Moreover, emotionally arousing film clips were associated with enhanced context recognition memory but not during a free word recall test. Therefore, clarifying whether emotional stimuli enhance recognition memory or recall memory requires further investigation, as it appears that emotional information was better remembered for recognition compared to recall. In brief, greater attentional resource toward emotional pictures with large late positive waves of LPP in the posterior region, the amygdala responds to emotional stimuli both words and pictures independent on its valence, leading to enhanced memory.

The brain regions associated with cognitive-emotional interactions can be studied with different functional neuroimaging techniques fMRI, PET, and fNIRS to examine hemodynamic responses indirect measurement. EEG is used to measure brain electrical dynamics direct measurement associated with responses to cognitive and emotional tasks. Each technique has particular strengths and weaknesses, as described below.

Functional magnetic resonance imaging is a widely used functional neuroimaging tool for mapping of brain activation as it provides a high spatial resolution a few millimeters. Dolcos et al. The researchers concluded that successful retrieval of emotional pictures involved greater activation of the amygdala as well as the entorhinal cortex and hippocampus than that of neutral pictures.

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Developmental Psychology, 49 1 , Vallotton, C. Early Childhood Research Quarterly, 26 2 , — Walker, A. Early Childhood Research Quarterly, 26 3 , — What is SEL? Wilson, K. Apply for Public Funding. About MSAC. Application Status. It is unique in its construction: The sources of the images included in this set are Flickr albums, assembled by automatic upload from iPhone5 or later smartphone devices, and released by their authors to the general public under the Creative Commons CC license.

This constitutes the largest, fully unconstrained collection of images for age, gender and subject recognition. Large face datasets are important for advancing face recognition research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data. To facilitate this task, we developed an approach to building face datasets that detects faces in images returned from searches for public figures on the Internet, followed by automatically discarding those not belonging to each queried person.

The FaceScrub dataset was created using this approach, followed by manually checking and cleaning the results. It comprises a total of , face images of celebrities, with about images per person.

As such, it is one of the largest public face databases. Frontalization is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos. Recent reports have suggested that this process may substantially boost the performance of face recognition systems. This, by transforming the challenging problem of recognizing faces viewed from unconstrained viewpoints to the easier problem of recognizing faces in constrained, forward facing poses.

Authors provide frontalized versions of both the widely used Labeled Faces in the Wild set LFW for face identity verification and the Adience collection for age and gender classification.

These sets, LFW3D and Adience3D are made available along with our implementation of the method used for the frontalization. Indian Movie Face database IMFDB is a large unconstrained face database consisting of images of Indian actors collected from more than videos.

All the images are manually selected and cropped from the video frames resulting in a high degree of variability interms of scale, pose, expression, illumination, age, resolution, occlusion, and makeup. IMFDB is the first face database that provides a detailed annotation of every image in terms of age, pose, gender, expression and type of occlusion that may help other face related applications. The goal of this project is to mine facial images and other important information for the Wikipedia Living People category.

Currently, there are over 0. In addition to these faces, useful meta data are released: the source images, image captions if available , and person name detection results through a named entity detector.

So, mining experiments can also be performed. This is an unique property of this benchmark compared to others. It is a database of 10, natural face photographs of all different individuals, and major celebrities removed.

This database was made by randomly sampling Google Images for randomly generated names based on name distributions in the US Census. Because of this methodology, the distribution of the faces matches the demographic distribution of the US e. The database also has a wide range of faces in terms of attractiveness and emotion.

Ovals surround each face to eliminate any background effects. Additionally, for a random set of 2, of the faces, we have demographic information, attribute scores attractiveness, distinctiveness, perceived personality, etc , and memorability scores included with the images, to help researchers create their own stimulus sets. This database contains stereo videos of 27 adult subjects 12 females and 15 males with different ethnicities.

The database also includes 66 facial landmark points of each image in the database. A newly created high-resolution 3D dynamic facial expression database are presented, which is made available to the scientific research community. The 3D facial expressions are captured at a video rate 25 frames per second.

For each subject, there are six model sequences showing six prototypic facial expressions anger, disgust, happiness, fear, sadness, and surprise , respectively.

Each expression sequence contains about frames. The database contains 3D facial expression sequences captured from subjects, with a total of approximately 60, frame models. Each 3D model of a 3D video sequence has the resolution of approximately 35, vertices. BP4D-Spontanous Database. Well-validated emotion inductions were used to elicit expressions of emotion and paralinguistic communication.

Frame-level ground-truth for facial actions was obtained using the Facial Action Coding System. Facial features were tracked in both 2D and 3D domains using both person-specific and generic approaches. The work promotes the exploration of 3D spatiotemporal features in subtle facial expression, better understanding of the relation between pose and motion dynamics in facial action units, and deeper understanding of naturally occurring facial action.

The database includes 41 participants 23 women, 18 men. An emotion elicitation protocol was designed to elicit emotions of participants effectively. Eight tasks were covered with an interview process and a series of activities to elicit eight emotions.

The database is structured by participants. Each participant is associated with 8 tasks. For each task, there are both 3D and 2D videos. The database is in the size of about 2. The database contains 3D face and hand scans. It was acquired using the structured light technology. According to our knowledge it is the first publicly available database where both sides of a hand were captured within one scan. Although there is a large amount of research examining the perception of emotional facial expressions, almost all of this research has focused on the perception of adult facial expressions.

There are several excellent stimulus sets of adult facial expressions that can be easily obtained and used in scientific research i. However, there is no complete stimulus set of child affective facial expressions, and thus research on the perception of children making affective facial expression is sparse.

In order to fully understand how humans respond to and process affective facial expressions, it is important to have this understanding across a variety of means. The Child Affective Facial Expressions Set CAFE is the first attempt to create a large and representative set of children making a variety of affective facial expressions that can be used for scientific research in this area.

The set is made up of photographs of over child models ages making 7 different facial expressions - happy, angry, sad, fearful, surprise, neutral, and disgust. It is mainly intended to be used for benchmarking of the face identification methods, however it is possible to use this corpus in many related tasks e.

Two different partitions of the database are available. The first one contains the cropped faces that were automatically extracted from the photographs using the Viola-Jones algorithm. The face size is thus almost uniform and the images contain just a small portion of background. The images in the second partition have more background, the face size also significantly differs and the faces are not localized. The purpose of this set is to evaluate and compare complete face recognition systems where the face detection and extraction is included.

Each photograph is annotated with the name of a person. There are facial images for 13 IRTT students. They are of same age factor around 23 to 24 years. The images along with background are captured by canon digital camera of The actual size of cropped faces x and they are further resized to downscale factor 5.

Out of 13, 12 male and one female. Each subject have variety of face expressions, little makeup, scarf, poses and hat also. The database version 1. There are facial images for 10 IRTT girl students all are female with 10 faces per subject with age factor around 23 to 24 years.

The colour images along with background are captured with a pixel resolution of x and their faces are cropped to x pixels. This IRTT student video database contains one video in. Later more videos will be included in this database. The video duration is This video is captured by smart phone.

The faces and other features like eyes, lips and nose are extracted from this video separately. Part one is a set of color photographs that include a total of faces in the original format given by our digital cameras, offering a wide range of difference in orientation, pose, environment, illumination, facial expression and race.

Part two contains the same set in a different file format. The third part is a set of corresponding image files that contain human colored skin regions resulting from a manual segmentation procedure. The fourth part of the database has the same regions converted into grayscale. The database is available on-line for noncommercial use. The database is designed for providing high-quality HD multi-subject banchmarked video inputs for face recognition algorithms.

The database is a useful input for offline as well as online Real-Time Video scenarios. It is harvested from Google image search.

The dataset contains annotated cartoon faces of famous personalities of the world with varying profession. Additionally, we also provide real faces of the public figure to study cross modal retrieval tasks, such as, Photo2Cartoon retrieval.

The IIIT-CFW can be used for the study spectrum of problems, such as, face synthesis, heterogeneous face recognition, cross modal retrieval, etc. Please use this database only for the academic research purpose. The database contains multiple face images of six stylized characters. The database contains facial expression images of six stylized characters. The images for each character is grouped into seven types of expressions - anger, disgust, fear, joy, neutral, sadness and surprise.

The dataset contains 3, images of 1, celebrities. Specs on Faces SoF Dataset. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is devoted to two problems that affect face detection, recognition, and classification, which are harsh illumination environments and face occlusions.

The glasses are the common natural occlusion in all images of the dataset. However, the glasses are not the sole facial occlusion in the dataset; there are two synthetic occlusions nose and mouth added to each image. Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty easy, medium, and hard.

That enlarges the number of images to be 42, images 26, male images and 16, female images. Furthermore, the dataset comes with a metadata that describes each subject from different aspects.

The original images without filters or synthetic occlusions were captured in different countries over a long period. The data set is unrestricted, as such, it contains large pose, lighting, expression, race and age variation.