Perceptual Domains Research Articles

Learning to imitate facial expressions through sound

The question of how young infants learn to imitate others’ facial expressions has been central in developmental psychology for decades. Facial imitation has been argued to constitute a particularly challenging learning task for infants because facial expressions are perceptually opaque: infants cannot see changes in their own facial configuration when they execute a motor program, so how do they learn to match these gestures with those of their interacting partners? Here we argue that this apparent paradox mainly appears if one focuses only on the visual modality, as most existing work in this field has done so far. When considering other modalities, in particular the auditory modality, many facial expressions are not actually perceptually opaque. In fact, every orolabial expression that is accompanied by vocalisations has specific acoustic consequences, which means that it is relatively transparent in the auditory modality. Here, we describe how this relative perceptual transparency can allow infants to accrue experience relevant for orolabial, facial imitation every time they vocalise. We then detail two specific mechanisms that could support facial imitation learning through the auditory modality. First, we review evidence showing that experiencing correlated proprioceptive and auditory feedback when they vocalise – even when they are alone – enables infants to build audio-motor maps that could later support facial imitation of orolabial actions. Second, we show how these maps could also be used by infants to support imitation even for silent, orolabial facial expressions at a later stage. By considering non-visual perceptual domains, this paper expands our understanding of the ontogeny of facial imitation and offers new directions for future investigations.

Mild behavioral impairment domains are longitudinally associated with pTAU and metabolic biomarkers in dementia-free older adults.

The mechanisms linking mild behavioral impairment (MBI) and Alzheimer's disease (AD) have been insufficiently explored, with conflicting results regarding tau protein and few data on other metabolic markers. We aimed to evaluate the longitudinal association of the MBI domains and a spectrum of plasma biomarkers. Our study is a secondary analysis of data from NOLAN. The longitudinal association of the MBI domains with plasma biomarkers, including pTau181, was tested using adjusted linear mixed-effects models. The sample comprised 359 participants (60% female, mean age: 78.3, standard deviation: 0.3 years). After 1 year, the MBI domain of abnormal perception was associated with steeper increases in plasma pTau181. Abnormal perception, decreased motivation, and impulse dyscontrol were associated with homocysteine or insulin dysregulation. Apart from the association with plasma pTau181, our results suggest that MBI might also represent metabolic dysregulation, probably contributing to dementia transition among older adults with subjective cognitive decline or mild cognitive impairment. Mild behavioral impairment (MBI) psychosis was associated with steeper increases in plasma p. pTau could be a pharmacological target to treat agitation and psychosis symptoms. MBI domains were linked to metabolic dysregulation involving insulin and homocysteine.

RELATIONSHIP BETWEEN SELF-RATED MUSICAL COMPETENCE AND AUDITORY PROCESSING IN INSTRUMENTALISTS

IntroductionAlthough the benefits of musical training have been shown to affect a number of auditory processes, the question of how self-rated musical competence correlates with auditory perception remains largely unexplored. The present study aimed to investigate how self-perceived musical competence correlates with musicians’ ratings of their own abilities in speech perception, spatial hearing, and <i></i>sound qualities.Material and methodsThe <i>Edinburgh Lifetime Musical Experience Questionnaire</i> (ELMEQ) was administered on 57 instrumentalists aged 19 to 53 years (mean = 25.1 ± 4.9 years, 34 males and 23 females). All had normal hearing and had undergone formal training on musical instruments for 1–2 years. All of them also regularly practised music for at least 1–2 h/week. Based on their ELMEQ scores, they were divided into two groups: low (ELMEQ score ≤ 7; <i>n</i> = 12) and high (ELMEQ score ≥ 11; <i>n</i> = 16; self-perceived musical competence. Participants were sent a modified questionnaire from the <i>Speech, Spatial and Qualities of Hearing Scale</i> (SSQ) using Google forms, and the data statistically analysed.ResultsA Mann–Whitney <i>U</i>-test showed that participants with high self-rated musical competence had significantly higher ratings on the spatial hearing and sound qualities sub-domains of SSQ compared to their counterparts who had lower ELMEQ scores. However, both groups scored about the same on the speech perception domain of SSQ. A Spearman test revealed a moderate to strong positive correlation between self-rated musical competence and SSQ ratings in each domain.ConclusionsThe findings show that instrumentalists with high self-rated musical competence exhibited high proficiency in the spatial hearing and sound quality domains of auditory processing.

Role of Metacognitive Confidence Judgments in Curiosity: Different Effects of Confidence on Curiosity Across Epistemic and Perceptual Domains.

Previous research suggests that curiosity is sometimes induced by novel information one has no relevant knowledge about, but it is sometimes induced by new information about something that one is familiar with and has prior knowledge about. However, the conditions under which novelty or familiarity triggers curiosity remain unclear. Using metacognitive confidence judgments as a proxy to quantify the amount of knowledge, this study evaluates the relationship between the amount of relevant knowledge and curiosity. We reviewed previous studies on the relationship between subjective curiosity and confidence and reanalyzed existing large-sample data. The findings indicate that the relationship between curiosity and confidence differs depending on the nature of the stimuli: epistemic versus perceptual. Regarding perceptual stimuli, curiosity is stronger when individuals have lower confidence levels. By contrast, for epistemic stimuli, curiosity is stronger when individuals have higher confidence levels. These results suggest that curiosity triggered by perceptual stimuli is based on perceived novelty, whereas that triggered by epistemic stimuli is based on familiarity with prior knowledge.

The Shared Feature Principle in Person Perception: Shared Features Lead to Assumptions About Other Features

We apply the shared features principle to the domain of person perception: When one person (target) shares a feature with another person (source), people will make assumptions about various other features of the target. We tested this prediction by conducting three pre-registered studies (N = 695). Participants completed a training task wherein one target shared a bridge feature with a physically tall source person while another target shared a feature with a physically short source person. We then measured target perceptions along multiple dimensions (e.g., dominance, strength) using self-reported ratings and an indirect measure (Semantic Misattribution Procedure). We found evidence for feature transfer: participants’ perceptions of a target person’s height changed in accordance with the height of the source it shared a feature with. We also found evidence for feature transformation: participants perceived a target person who shared a feature with a tall source person as stronger, more masculine, a better leader, and more dominant relative to a target person who shared a feature with a short source person. We consider the conceptual implications of our findings, their relevance for different areas of psychological science, and future research directions.

Educators’ knowledge of and perceptions towards students with autism in Nigeria

Global research about autism spectrum disorder (ASD) is highly important as the disorder occurs across cultures and geographical regions. Literature indicates that there were no studies conducted among Nigerian educators about their knowledge of and perceptions towards ASD. This study surveyed 477 secondary school Nigerian educators to examine the knowledge and perceptions of ASD. The results showed that educators had high levels of awareness in the knowledge domain and low levels of awareness in the perception domain. In addition, results demonstrated a significant positive relationship between educators who received prior training or college education in ASD and their current knowledge of ASD. Furthermore, there was a significant positive relationship between educators who are certified in Applied Behaviour Analysis and the knowledge of ASD. Conversely, the relationship between current educators of students with ASD (who have experience as their only training), and the knowledge of ASD was significantly negatively related. Results are discussed and implications for research and practice are provided.

Spectrum-Dependent Image Convolutional Processing via a Two-Dimensional Polarization-Sensitive Photodetector.

Currently, the improvement in the processing capacity of traditional processors considerably lags behind the demands of real-time image processing caused by the advancement of photodetectors and the widespread deployment of high-definition image sensors. Therefore, achieving real-time image processing at the sensor level has become a prominent research domain in the field of photodetector technology. This goal underscores the need for photodetectors with enhanced multifunctional integration capabilities than can perform real-time computations using optical or electrical signals. In this study, we employ an innovative p-type semiconductor GaTe0.5Se0.5 to construct a polarization-sensitive wide-spectral photodetector. Leveraging the wide-spectral photoresponse, we realize three-band imaging within a wavelength range of 390-810 nm. Furthermore, real-time image convolutional processing is enabled by configuring appropriate convolution kernels based on the polarization-sensitive photocurrents. The innovative design of the polarization-sensitive wide-spectral GaTe0.5Se0.5-based photodetector represents a notable contribution to the domain of real-time image perception and processing.

Predictors of health-related quality of life in a large cohort of adult patients living with sickle cell disease in France: the DREPAtient study.

Sickle cell disease (SCD) is an inherited autosomal recessive disorder exhibiting a range of symptoms and acute and/or chronic complications that affect the quality of life. This study aimed to assess health-related quality of life (HRQoL) and to identify the associated factors in adult patients with SCD in France. DREPAtient is a cross-sectional, multicenter study conducted from June 2020 to April 2021 in France and in certain French overseas territories where SCD is highly prevalent. Sociodemographic and clinical data were collected online. HRQoL was assessed by the French version of the 36-Item Short Form Survey (SF-36) questionnaire. HRQoL determinants were identified using multivariable linear regression analysis. In total, 570 participants were included, mostly women (68.9%), with a mean age of 33.3 (±10.7) years. The highest mean score HRQoL was found in the Physical functioning domain (67.5 ± 21.8) and the lowest mean score in the General Health perception domain (37.7 ± 20.3). The mean score of the physical composite (PCS) and mental composite (MCS) of SF-36 summary scores was 40.6 ± 8.9 and 45.3 ± 9.8, respectively. Participants receiving oxygen therapy (β = -3.20 [95%CI: -5.56; -0.85]), those with a history of femoral osteonecrosis (-3.09 [-4.64; -1.53]), those hospitalized for vaso-occlusive crisis (VOC) or acute chest syndrome (ACS) (-2.58 [-3.93; -1.22]), those with chronic complications (-2.33 [-4.04; -0.62]), female participants (-2.17 [-3.65; -0.69]), those with psychological follow-up (-2.13 [-3.59; -0.67]), older participants (-1.69 [-3.28; -0.09]), and those receiving painkillers (-1.61 [-3.16; -0.06]) reported worse PCS score. By contrast, those who had completed secondary or high school (4.36 [2.41; 6.31]) and those with stable financial situation (2.85 [0.94, 4.76]) reported better PCS scores. Worse MCS scores were reported among participants with psychological follow-up (-2.54 [-4.28; -0.80]) and those hospitalized for VOC/ACS in the last 12 months (-2.38 [-3.99; -0.77]), while those who had relatives' support (5.27 [1.92; 8.62]) and those with stable financial situation (4.95 [2.65; 7.26]) reported better MCS scores. Adults with major SCD reported poor physical and mental HRQoL scores. Hospitalization for VOC/ACS, chronic complications, use of painkillers, perceived financial situation, and support from relatives are important predictors of HRQoL in SCD patients. Interventions to improve HRQoL outcomes SCD should be considered.

Visual perception verbs in Old Anatolian Turkish

Abstract This study aims at describing the verbs of the visual sensory domain in Old Anatolian Turkish (oat), including basic and compound verb forms. We shall specifically focus on intra-field and trans-field meaning extensions of visual perception verbs such as baḳ-, naẓar et-, gör-, and görin-/gözük-. Our findings are based on a corpus consisting of 11 texts both in prose and poetry from the 13th to 15th centuries. There are mainly two types of verbs that conceptualise mental and emotive states through visual perception: one pertains to idiomatic expressions with göz ‘eye’, such as göz-(ün) aç- (eye-poss.acc open), göz-den düş- (eye-abl fall), and göz dut- (eye hold). The second form includes basic verbs including baḳ- (to look at), gör- (to see), naẓar et- (to look at) and gör-in-/göz-ük- (to appear). We shall show that visual perception constitutes a rich source for expressing emotive states, and the use of vision verbs for the expression of emotions is as productive as it is for mental states. Similar to Sweetser’s body is mind metaphor, we suggest that vision is emotion. The domain of visual perception in oat texts displays a strong connection to intellection; however, there is no evidence in our data indicating that the verbs gör- and baḳ- have meaning extension to ‘to know’. The phenomenon-based verbs görin- and gözük- have mainly two meaning extensions: one is related to physical existence, as in ‘occur’ and ‘appear’ in English. When these verbs co-occur with nouns and adjectives, they reflect the speakers’ judgements and beliefs by means of a metaphor.

Object Detection in Hazy Environments, Based on an All-in-One Dehazing Network and the YOLOv5 Algorithm

This study introduces an advanced algorithm for intelligent vehicle target detection in hazy conditions, aiming to bolster the environmental perception capabilities of autonomous vehicles. The proposed approach integrates a hybrid convolutional module (HDC) into an all-in-one dehazing network, AOD-Net, to expand the perceptual domain for image feature extraction and refine the clarity of dehazed images. To accelerate model convergence and enhance generalization, the loss function has been optimized. For practical deployment in intelligent vehicle systems, the ShuffleNetv2 lightweight network module is incorporated into the YOLOv5s network backbone, and the feature pyramid network (FPN) within the neck network has been refined. Additionally, the network employs a global shuffle convolution (GSconv) to balance accuracy with parameter count. To further focus on the target, a convolutional block attention module (CBAM) is introduced, which helps in reducing the network’s parameter count without compromising accuracy. A comparative experiment was conducted, and the results indicated that our algorithm achieved an impressive mean average precision (mAP) of 76.8% at an intersection-over-union (IoU) threshold of 0.5 in hazy conditions, outperforming YOLOv5 by 7.4 percentage points.

Driving under the influence of psychoactive substances.„Influence” an issue in judicial practice?

"At the end of the 19th century, the punitive celebration disappears, punishment becomes the most hidden part of the criminal process, leaving the domain of everyday perception to enter in abstract consciousness," recorded Michel Foucault in his famous work "Discipline and Punish: The Birth of the Prison" (Foucault, 1997). Therefore, it tends to a change of paradigm, it is desired that the effectiveness of the punishment is not the product of its visible intensity, but of the high degree of certainty. Metamorphosed from a theatrical act (often violent and public) into an administrative act, hidden from the eyes of citizens, criminal policy had to be adapted to encompass the new valence of the act of justice. One of the main means of punitive efficiency by referring to the abstract consciousness is that of the punishment limits that the legislator adjusts according to the criminal phenomenon, aiming both at an inhibitory, repressive and educational effect. It has often been assumed that the criminal recrudescence is due to low sentencing limits, and thus increasing the sentencing limits has been considered to fight all crime, omitting the adaptation of the entire legal norm to contemporary realities. Even the judicial act was not spared from this approach, although the scientific processes evolved and the probation together with them, in the face of a rigid legal norm, the judicial practice refuses to adapt to the new reality. Against the background of a national hysteria due to the scourge of drugs, legal arguments take precedence over scientific arguments.

Maize leaf disease image enhancement algorithm using TFEGAN

Maize leaf diseases have a profound impact on maize crop yield. However, due to practical constraints, gathering a substantial number of maize disease images is time-consuming and labor-intensive. Furthermore, low-light conditions and noise interference in maize leaf disease images present significant challenges for image generation. Therefore, this paper proposes TFEGAN, a novel algorithm designed to generate maize leaf disease images. TFEGAN leverages a Two-Way Extraction Module (TWEM) to enhance the perceptual domain of the model and effectively reuse maize leaf disease features. The algorithm employs two well-trained Generative Adversarial Networks (GANs) to enhance low-light and noisy maize leaf disease images. The low-light enhancement component utilizes a double discriminator and self-regularization to balance global and local enhancement while constraining the distance between the input image and the denoising component. Moreover, the denoising part employs a strong coordinated attention mechanism and an environmental encoder to learn noise regions and their surrounding structures, thus generating better local images and enabling more accurate evaluation by the discriminant network. The generators of both networks are encapsulated by two discriminators and combined with a bi-directional feature extraction structure to generate normal maize leaf disease images. Experimental evaluations demonstrate the effectiveness of TFEGAN in improving the quality of maize leaf disease images under low-light and noisy conditions, showcasing its potential for practical applications. Overall, this paper presents a promising solution to the challenges of low-light and noise interference in maize leaf disease images, utilizing bidirectional feature extraction and GANs.

Multi-scale image recognition strategy based on convolutional neural network

The accurate recognition and interpretation of multi-scale visual information is a critical focus within contemporary computer vision research. To this end, this study explores and innovatively constructs a multi-scale image recognition strategy based on a Convolutional Neural Network (CNN) with a multi-level and multi-resolution perception domain. This strategy is embedded with an advanced multi-level convolutional operation mechanism, which enables the model to intelligently explore and learn the multi-scale feature representation space of images from tiny texture to grand structure, from shallow simple features to deep semantic abstraction. The core technology path of this paper is to design a deep separable convolutional architecture and combine pyramid pool technology to form a unique network module. This modular design not only ensures the computational efficiency of the model but also improves the ability of extracting and integrating multi-scale image features. Following intensive experimentation on an array of extensively recognized and substantial image datasets, the multi-scale image recognition approach introduced in our study has demonstrated marked enhancements in both recognition capability and stability, manifesting clear superiority compared to conventional, single-scale image recognition methodologies. This research not only enriches the theoretical framework of image recognition, but also provides a new and efficient solution for dealing with complex multi-scale image recognition challenges in practical applications, and further promotes the development of image understanding and recognition technology.

ATN-Res2Unet: an advanced deep learning network for the elimination of saturation artifacts in endoscopy optical coherence tomography

Endoscopic optical coherence tomography (OCT) possesses the capability to non-invasively image internal lumens; however, it is susceptible to saturation artifacts arising from robust reflective structures. In this study, we introduce an innovative deep learning network, ATN-Res2Unet, designed to mitigate saturation artifacts in endoscopic OCT images. This is achieved through the integration of multi-scale perception, multi-attention mechanisms, and frequency domain filters. To address the challenge of obtaining ground truth in endoscopic OCT, we propose a method for constructing training data pairs. Experimental in vivo data substantiates the effectiveness of ATN-Res2Unet in reducing diverse artifacts while preserving structural information. Comparative analysis with prior studies reveals a notable enhancement, with average quantitative indicators increasing by 45.4–83.8%. Significantly, this study marks the inaugural exploration of leveraging deep learning to eradicate artifacts from endoscopic OCT images, presenting considerable potential for clinical applications.

Medical Image Segmentation with Dual-Encoding and Multi-Level Feature Adaptive Fusion

Purpose: Accurate segmentation of medical images is critical for disease diagnosis, surgical planning and prognostic assessment. TransUNet, a hybrid CNN-Transformer-based method, extracts local features using CNN and compensates for the lack of long-range dependencies through a self-attention mechanism. However, the initial focus on extracting local features from specific regions impacts the generation of subsequent global features, thus constraining the model’s capacity to effectively capture a broader range of semantic information. Effective integration of local and global features plays a pivotal role in achieving precise and dense prediction. Therefore, we propose a novel hybrid CNN-Transformer-based method aimed at enhancing medical image segmentation. Approach: In this study, a dual-encoder parallel structure is used to enhance the feature representation of the input image. By introducing a multi-scale adaptive feature fusion module, a fine fusion of local features across perceptual domains is realized in the decoding process. The generalized convolutional block attention module helps to increase cross-channel interactions in layers with more channels, thus enabling the fusion of local features and global representations at different resolutions during the decoding process. Results: The proposed method achieves average DSC scores of 79.98%, 84.83% and 85.78% on the Synapse, ISIC2017 and Pediatric Pyelonephritis datasets, respectively. These scores are 2.5%, 0.56% and 0.42% higher than those of TransUNet. The best performance of 91.66% is observed on the ACDC dataset, representing improvements of 2.46% and 7.24% compared to HiFormer and DAE-Former, respectively. Conclusions: The experimental results show that the proposed model has a significant competitive advantage in terms of ACDC image segmentation performance.

Artificial Intelligence in Health Care – A Study on Perceptions of and Readiness for Artificial Intelligence in Health-care Professionals

Abstract Background: With a call to action from the health-care industry and the Indian government, there are significant gaps in health-care professionals’ uptake and utilization of artificial intelligence (AI)-based tools. This study attempts to explore the current perceptions and readiness for AI among health-care workers. Methods: A web-based questionnaire comprising seven sections on descriptive educational and occupational data, AI familiarity level, role-specific training benefits, training advantages, implementation issues, driving factors, and perceived risks was designed from a literature search. Two additional domains of perception on professional impact and preparedness for AI in health care were estimated using a prevalidated Shinners AI Perception tool. Results: Of the 402 study participants, 192 (47.9%) were doctors from diverse specializations, and the remaining 209 (52.1%) were undergraduate medical and nursing students and affiliated health professionals. Although 79.8% of participants had never attended a course on AI, 82% agreed on the need for training in AI to explore new opportunities in their respective fields. 72.1% of participants agreed that data privacy and confidentiality posed the most significant challenge to AI implementation among the studied factors. Conclusion: This survey reveals awareness regarding AI, which is attributable to a lack of formal training received by health-care professionals. Most participants believed that AI could improve population health outcomes, and collective efforts are needed to make this belief a reality.

¿Notas La Diferencia? [Do You Hear the Difference?]: Perceptual Consequences of Intensive Voice Treatment in Spanish Speakers With Parkinson's Disease.

The primary objective of this study was to explore the effects of intensive voice-focused treatment on speech parameters in Spanish speakers with dysarthria associated with Parkinson's disease (PD) as perceived by naïve listeners. Fifteen Spanish speakers with dysarthria associated with PD received the Lee Silverman Voice Treatment (LSVT LOUD) for a month. Voice and speech recordings were conducted pretreatment, posttreatment, and at a 1-month follow-up. Thirty naïve adult listeners rated the perceptual dimensions of ease of understanding (EoU), resonance, articulatory precision, prosody, and voice quality from sentences extracted from an emotional monologue on a visual analogue scale. EoU, resonance, articulatory precision, and voice quality significantly improved pre- to posttreatment, but gains were not maintained at follow-up. Speech severity was a significant source of variance in mean listener response for all perceptual dimensions, although the interaction between speech severity and time was only significant for resonance and voice quality. LSVT LOUD may be beneficial to improve perceptual speech domains affected by PD in Spanish speakers with dysarthria. Its impact on the different speech subsystems may reflect a universal distribution of effects when directly targeting the glottal source. Language-specific contributions of each perceptual domain to speech intelligibility should be explored in further research to determine linguistically sensitive treatment targets.

Both identity and non-identity face perception tasks predict developmental prosopagnosia and face recognition ability

Developmental prosopagnosia (DP) is characterised by deficits in face identification. However, there is debate about whether these deficits are primarily perceptual, and whether they extend to other face processing tasks (e.g., identifying emotion, age, and gender; detecting faces in scenes). In this study, 30 participants with DP and 75 controls completed a battery of eight tasks assessing four domains of face perception (identity; emotion; age and gender; face detection). The DP group performed worse than the control group on both identity perception tasks, and one task from each other domain. Both identity perception tests uniquely predicted DP/control group membership, and performance on two measures of face memory. These findings suggest that deficits in DP may arise from issues with face perception. Some non-identity tasks also predicted DP/control group membership and face memory, even when face identity perception was accounted for. Gender perception and speed of face detection consistently predicted unique variance in group membership and face memory; several other tasks were only associated with some measures of face recognition ability. These findings indicate that face perception deficits in DP may extend beyond identity perception. However, the associations between tasks may also reflect subtle aspects of task demands or stimuli.

Disease-specific quality of life as part of the long-term follow-up for children born with esophageal atresia in an academic unit in South Africa-a pilot study.

As neonatal mortality rates have decreased in esophageal atresia (EA), there is a growing focus on quality of life (QoL) in these children. No study from Africa has reported on this topic. This pilot study aimed to describe disease-specific QoL in EA children and its applicability as part of long-term follow-up in an academic facility in South Africa. Disease-specific QoL in children born with EA was assessed utilizing the EA-QoL questionnaire for children aged 2-17years during a patient-encounter. The parent-report for children aged 2-7years compromised 17 items categorized into three domains: eating, physical health and treatment, and social isolation/stress. The 24-item EA-QL questionnaire for children aged 8-18 (child- and parent-report) explored four domains: eating, body perception, social relationships, and health and well-being. A total of 13 questionnaires for children aged 2-7years were completed by five parents. A negative perceived impact on their child's eating was reported by 46-92% of parents, and less impact in the other two domains. A total of 27 questionnaires were completed by eight children aged 8-17years and 10 parents. Similar percentages children and parents reported a negative impact in the eating, social relationships, and body perception domains. More than half reported a negative impact on the child's health and well-being. This study supports the concept that assessment of disease-specific QoL should play a vital role in the comprehensive follow-up approach for children born with EA. We identified that parents of younger children were more likely to report eating disorders, whereas parents of older children were more likely to report health difficulties with different perceptions when it came to the child's scar.

Infants' predictive minds: The role of motor experience

AbstractThe ability to predict upcoming events is essential in infancy because it enables babies to process information optimally and have successful goal‐directed interactions with their environment. In this article, we examine how infants generate predictions in perception, cognition, and action, and address whether and how their predictions are motivated and affected by their motor development. Our synthesis of research demonstrates that infants form predictions in the perception, cognition, and action domains based on perceived statistical information, pre‐existing and newly generated knowledge, and internal motor models. Our analysis reveals that infants' increasing fine and gross motor experiences have a moderating impact on the elaboration of the different bases for predictions. Based on this, we conclude that new motor experiences enable infants to constantly improve the bases from which they generate and update their predictions.