Integration Of New Information Research Articles

Adult Learners Self‐Derive New Knowledge through Integration of Novel Information and Prior Knowledge and Are more Successful with Reactivation

AbstractSelf‐derivation through integration is the process of integrating novel facts and producing new knowledge never directly taught. Knowledge integration has been studied with the presentation of two novel facts. However, in educational settings, individuals are required to integrate new information with prior knowledge learned days, months, or years earlier. Prior knowledge robustly predicts learning outcomes, but less is known about self‐derivation through the integration of new information with prior knowledge. Thus, in Study 1, we examined adults' (n = 25) memory integration of new facts with prior knowledge. The participants had 52% accuracy in self‐derivation. In Study 2 (n = 86), we examined whether reactivating prior knowledge before the novel fact presentation facilitated self‐derivation through integration with prior knowledge. Results indicated that performance was significantly higher for those whose prior knowledge was directly reactivated (55% accuracy) in comparison with the control group (42%). Pedagogical implications are discussed.

Causal role of the angular gyrus in insight-driven memory reconfiguration

Maintaining an accurate model of the world relies on our ability to update memory representations in light of new information. Previous research on the integration of new information into memory mainly focused on the hippocampus. Here, we hypothesized that the angular gyrus, known to be involved in episodic memory and imagination, plays a pivotal role in the insight-driven reconfiguration of memory representations. To test this hypothesis, participants received continuous theta burst stimulation (cTBS) over the left angular gyrus or sham stimulation before gaining insight into the relationship between previously separate life-like animated events in a narrative-insight task. During this task, participants also underwent EEG recording and their memory for linked and non-linked events was assessed shortly thereafter. Our results show that cTBS to the angular gyrus decreased memory for the linking events and reduced the memory advantage for linked relative to non-linked events. At the neural level, cTBS targeting the angular gyrus reduced centro-temporal coupling with frontal regions and abolished insight-induced neural representational changes for events linked via imagination, indicating impaired memory reconfiguration. Further, the cTBS group showed representational changes for non-linked events that resembled the patterns observed in the sham group for the linked events, suggesting failed pruning of the narrative in memory. Together, our findings demonstrate a causal role of the left angular gyrus in insight-related memory reconfigurations.

From the mental to the conceptual model: the challenge of teaching hydrogeology in the field.

Field-based learning in hydrogeology enables students to develop their understanding and application of practical methodologies, and to enhance many of the generic skills (e.g., teamwork, problem-solving). However, teaching and learning hydrogeology in general, and especially in the field, presents cognitive difficulties, such as the diversity in student education and experience, the hidden nature of water movement and transport of chemicals, and the pre-existing students' mental models of the subsurface, in particular. At any given experimental or teaching site there is only one reality for which lecturers can have an approximate conceptual model, including aquifer(s) geometry and functioning (e.g., flow direction). However, students' preconceptions (i.e., mental model), in some cases misconceptions, influence not only their outcome from the learning strategy designed, but also the conceptual model expression (i.e., flow chart, block diagram, or similar) for the study area or site. In practice, two general 'teaching challenges' are identified to enable students' transition from the mental to the conceptual model: i) identify and dispel any prior misconceptions and ii) show how to go from the partial information to the integration of new information for the development of the conceptual model. The inclusion of specific prior-to-field lessons in the classroom is recommended and in general, done. However, introducing a prior-to-field survey to learn about students' backgrounds, and methodologies for the development and expression of hydrogeological conceptual models and for testing multiple plausible conceptual models will help students transition from the mental to the conceptual model. This article is protected by copyright. All rights reserved.

Methods of Material and Surface Analysis for the Evaluation of Failure Modes for Electrical Connectors

The development of autonomous vehicles and the integration of new information and communication technologies are making the reliability of electrical systems and components in modern vehicles increasingly important. Electrical connectors are a crucial component in an electrical on-board system. They are exposed to a wide variety of influences by the environment and operating conditions. Thus, the degradation of electrical connectors can occur. Material and surface analysis methods are the tools used to analyze the degradation mechanisms in connectors after lifetime tests, as well as in field operations. Within the framework of this study, a wide variety of methods from the analytical scope are presented and discussed. The connector surfaces degraded by different failure mechanisms are analyzed using various material and surface analysis methods. The quality and the nature of the analyses results obtained from various analysis methods are compared. Also, this study deals with the benefits and limitations, as well as the effort and the specific challenges of different material and surface analytical methods for the evaluation of failure mechanisms from the point of view of a material and surface analyst.

Towards a Postgraduate Oncology Training Model for Family Medicine: Mixed Methods Evaluation of a Breast Oncology Rotation.

Background: Family physicians have low knowledge and preparedness to manage patients with cancer. A breast oncology clinical rotation was developed for family medicine residents to address this gap in medical education. Objectives and Methods: A breast oncology rotation for family residents was evaluated using a pre-post knowledge questionnaire and semi-structured interviews comparing rotation (RRs) versus non-rotation (NRRs) residents. Quantitative and qualitative data were collected via a pre-post knowledge questionnaire and semi-structured interviews, respectively. Analysis: Quantitative data were analysed using descriptive statistics and paired t-tests to compare pre-post-rotation knowledge and preparedness. Qualitative data were coded inductively, analysed, and grouped into categories and themes. Data sets were integrated. Results: The study was terminated early due to the COVID-19 pandemic. Six RRs completed the study; 19 and 2 NRRs completed the quantitative and qualitative portions, respectively. RRs’ knowledge scores did not improve, but there was a non-significant increase in preparedness (5.3 to 8.4, p = 0.17) post-rotation. RRs described important rotation outcomes: knowledge of the patient work-up, referral process, and patient treatment trajectory; skills in risk assessment, clinical examination, and empathy, and comfort in counseling. Discussion and Conclusion: Important educational outcomes were obtained despite no change in knowledge scores. This rotation can be adapted to other training programs including an oncology primer to enable trainee integration of new information.

Theta oscillations support active exploration in human spatial navigation

Active navigation seems to yield better spatial knowledge than passive navigation, but it is unclear how active decision-making influences learning and memory. Here, we examined the contributions of theta oscillations to memory-related exploration while testing theories about how they contribute to active learning. Using electroencephalography (EEG), we tested individuals on a maze-learning task in which they made discrete decisions about where to explore at each choice point in the maze. Half the participants were free to make active decisions at each choice point, and the other half passively explored by selecting a marked choice (matched to active exploration) at each intersection. Critically, all decisions were made when stationary, decoupling the active decision-making process from movement and speed factors, which is another prominent potential role for theta oscillations. Participants were then tested on their knowledge of the maze by traveling from object A to object B within the maze. Results show an advantage for active decision-making during learning and indicate that the active group had greater theta power during choice points in exploration, particularly in midfrontal channels. These findings demonstrate that active exploration is associated with theta oscillations during human spatial navigation, and that these oscillations are not exclusively related to movement or speed. Results demonstrating increased theta oscillations in prefrontal regions suggest communication with the hippocampus and integration of new information into memory. We also found evidence for alpha oscillations during active navigation, suggesting a role for attention as well. This study finds support for a general mnemonic role for theta oscillations during navigational learning.

AI-based multidisciplinary framework to assess the impact of gamified video-based learning through schema and emotion analysis

As a natural and continual process, the initial learning stages encompass mastering and recalling basic facts. The process proves effective with the integration of new information with pre-existing knowledge characterised as schema to facilitate memory encoding. Additionally, emotions also have the ability to modulate human cognition in terms of learning and memory. The recent advent of gamification in e-learning, which has garnered much scholarly and industrial interest, necessitates a thorough examination between video-based learning and its subsequent implications on schema, emotions, and gamification. The current multidisciplinary research triangulated cognitive psychology, affective science, and education technology with artificial intelligence for evaluating digital learning pedagogy based on memory retrieval accuracy, response time, and emotional valence. This three-way (2 x 2 x 2) mixed factorial experiment design with repeated measures entailed 64 healthy young adult volunteers ( n = 64) with 32 in the schema congruent group and 32 in the schema incongruent group. Additionally, 27 (42%) of the volunteers were males, while 37 (58%) were females with an age range between 20 and 39 years old (mean age 27.78 years, SD = 4.77 years). The findings demonstrate that the schema congruent group attained a statistically significant and higher retrieval accuracy ( p < .001). The delayed recall response time was faster than its immediate recall counterpart ( p < .001). Overall, the gamified learning mode depicted more positive emotions compared to non-gamified learning, although both groups primarily portrayed more negative emotions ( p = .05). The synthesis of current research aimed to recommend an AI-based multidisciplinary framework to assess the impact on adult learners in terms of schema and evaluate their emotions in experiencing gamified or non-gamified video materials as a learning medium. The implications expedited from this research offer valuable insights for diverse stakeholders engaged in the video-based learning ecosystem.

Suppression of adult cytogenesis in the rat brain leads to sex-differentiated disruption of the HPA axis activity.

ObjectivesThe action of stress hormones, mainly glucocorticoids, starts and coordinates the systemic response to stressful events. The HPA axis activity is predicated on information processing and modulation by upstream centres, such as the hippocampus where adult‐born neurons (hABN) have been reported to be an important component in the processing and integration of new information. Still, it remains unclear whether and how hABN regulates HPA axis activity and CORT production, particularly when considering sex differences.Materials and MethodsUsing both sexes of a transgenic rat model of cytogenesis ablation (GFAP‐Tk rat model), we examined the endocrinological and behavioural effects of disrupting the generation of new astrocytes and neurons within the hippocampal dentate gyrus (DG).ResultsOur results show that GFAP‐Tk male rats present a heightened acute stress response. In contrast, GFAP‐Tk female rats have increased corticosterone secretion at nadir, a heightened, yet delayed, response to an acute stress stimulus, accompanied by neuronal hypertrophy in the basal lateral amygdala and increased expression of the glucocorticoid receptors in the ventral DG.ConclusionsOur results reveal that hABN regulation of the HPA axis response is sex‐differentiated.

Updating politicized beliefs: How motivated reasoning contributes to polarization

This paper examines how the political significance of information can affect subjects’ perceived reliability of that information and their motivated belief updating. In this study, 1,222 subjects were randomly assigned to receive low-reliability, high-reliability, or ambiguous information about neutral and political questions. For each question, subjects were first asked to give a numerical estimate. After receiving new information, subjects had the opportunity to update their previous numerical estimates, and then, they reported their perceived reliability of the information they received. I observed that subjects were more reluctant to update their beliefs for politically significant questions compared to neutral questions. I also found that subjects were more likely to discredit and reject new information when it challenged their preexisting ideology, indicating that one’s rooted political beliefs can distort the belief updating process. In addition, subjects drew different conclusions from ambiguous information depending on their political ideology, illustrating how ambiguity may lead to greater polarization. Finally, I discuss possible explanations for subjects’ biased integration of new information using motivated reasoning and belief-based utility theories. The results of this study contribute to the understanding of why people remain divided on politically charged issues.

Geo-AI to aid disaster response by memory-augmented deep reservoir computing

It is a fact that natural disasters often cause severe damage both to ecosystems and humans. Moreover, man-made disasters can have enormous moral and economic consequences for people. A typical example is the large deadly and catastrophic explosion in Beirut on 4 August 2020, which destroyed a very large area of the city. This research paper introduces a Geo-AI disaster response computer vision system, capable to map an area using material from Synthetic Aperture Radar (SAR). SAR is a unique form of radar that can penetrate the clouds and collect data day and night under any weather conditions. Specifically, the Memory-Augmented Deep Convolutional Echo State Network (MA/DCESN) is introduced for the first time in the literature, as an advanced Machine Vision (MAV) architecture. It uses a meta-learning technique, which is based on a memory-augmented approach. The target is the employment of Deep Reservoir Computing (DRC) for domain adaptation. The developed Deep Convolutional Echo State Network (DCESN) combines a classic Convolutional Neural Network (CNN), with a Deep Echo State Network (DESN), and analog neurons with sparse random connections. Its training is performed following the Recursive Least Square (RLS) method. In addition, the integration of external memory allows the storage of useful data from past processes, while facilitating the rapid integration of new information, without the need for retraining. The proposed DCESN implements a set of original modifications regarding training setting, memory retrieval mechanisms, addressing techniques, and ways of assigning attention weights to memory vectors. As it is experimentally shown, the whole approach produces remarkable stability, high generalization efficiency and significant classification accuracy, significantly extending the state-of-the-art Machine Vision methods.

A Synaptic Framework for the Persistence of Memory Engrams.

The ability to store and retrieve learned information over prolonged periods of time is an essential and intriguing property of the brain. Insight into the neurobiological mechanisms that underlie memory consolidation is of utmost importance for our understanding of memory persistence and how this is affected in memory disorders. Recent evidence indicates that a given memory is encoded by sparsely distributed neurons that become highly activated during learning, so-called engram cells. Research by us and others confirms the persistent nature of cortical engram cells by showing that these neurons are required for memory expression up to at least 1 month after they were activated during learning. Strengthened synaptic connectivity between engram cells is thought to ensure reactivation of the engram cell network during retrieval. However, given the continuous integration of new information into existing neuronal circuits and the relatively rapid turnover rate of synaptic proteins, it is unclear whether a lasting learning-induced increase in synaptic connectivity is mediated by stable synapses or by continuous dynamic turnover of synapses of the engram cell network. Here, we first discuss evidence for the persistence of engram cells and memory-relevant adaptations in synaptic plasticity, and then propose models of synaptic adaptations and molecular mechanisms that may support memory persistence through the maintenance of enhanced synaptic connectivity within an engram cell network.

Information technology in primary school

The topic of this article was chosen in connection with the widespread introduction and use of information technologies in the educational process. This topic is relevant, as there is gradual computerization of teaching specific disciplines. Also, the relevance of the research problem is since the integration of new information and pedagogical technologies contribute to the creation of a humanistic educational environment in which an active personality develops, who can acquire knowledge and apply it, generate his own ideas. Let's highlight two main ideas. First, the need for a variety of learning environments. The use of ICT tools contributes to this. Secondly, the requirement to individualize teaching, to adapt it to the cognitive needs and interests of the learners. And this problem can be effectively solved by ICT.

Testing the segmentation effect of multimedia learning in a biological system

AbstractMultimedia instruction, the combination of pictures and words to produce meaningful learning, involves attention, selection, organization, and integration of new information with previously learned information. Because there is a large, theory‐based literature supporting the effectiveness of multimedia instruction, we proposed that multimedia instruction could be leveraged to address issues in health communication. The cognitive theory of multimedia learning outlines techniques to improve meaningful learning when the processing load of essential information exceeds the cognitive capacity of the learner (Mayer, 2014). Specifically, segmentation, or presentation of the material in a learner paced fashion, results in deeper learning of the material than continuous presentation (Mayer &amp; Chandler, 2001). We proposed a conceptual replication of the segmentation effect with multimedia materials relevant in a health communication context. We hypothesized that transfer of information from a multimedia presentation about kidney function would be improved in a segmented, versus continuous, condition. Additionally, we hypothesized that participants' perceived cognitive load during the learning task would be lower in the segmented, versus continuous, presentation condition. We were unable to replicate either of these advantages for the use of segmentation with health‐related materials.

Parietal-Prefrontal Feedforward Connectivity in Association With Schizophrenia Genetic Risk and Delusions.

Conceptualizations of delusion formation implicate deficits in feedforward information updating across the posterior to prefrontal cortices, resulting in dysfunctional integration of new information about contexts in working memory and, ultimately, failure to update overfamiliar prior beliefs. The authors used functional MRI and machine learning models to address individual variability in feedforward parietal-prefrontal information updating in patients with schizophrenia. They examined relationships between feedforward connectivity, and delusional thinking and polygenic risk for schizophrenia. The authors studied 66 schizophrenia patients and 143 healthy control subjects during performance of context updating in working memory. Dynamic causal models of effective connectivity were focused on regions of the prefrontal and parietal cortex potentially implicated in delusion processes. The effect of polygenic risk for schizophrenia on connectivity was examined in healthy individuals. The authors then leveraged support vector regression models to define optimal normalized target connectivity tailored for each patient and tested the extent to which deviation from this target could predict individual variation in severity of delusions. In schizophrenia patients, updating and manipulating context information was disproportionately less accurate than was working memory maintenance, with an interaction of task accuracy by diagnosis. Patients with delusions also tended to have relatively reduced parietal-prefrontal feedforward effective connectivity during context updating in working memory manipulation. The same connectivity was adversely influenced by polygenic risk for schizophrenia in healthy subjects. Individual patients' deviation from predicted "normal" feedforward connectivity based on the support vector regression models correlated with severity of delusions. These computationally derived observations support a role for feedforward parietal-prefrontal information processing deficits in delusional psychopathology and in genetic risk for schizophrenia.

Teaching Compressed Rendering in the Genre of a Training Scientific Article

The aim of this article is to present interactive teaching methods and techniques for teaching how to write a compressed rendering with a creative task of changing the given text into a training scientific article. This involves not only text compression but also its stylistic transformation: change of the addressee, choice of language functions, genre, type of speech, linguistic devices. The following methods were used: methodological experience interpretation in accordance with culturally oriented Russian language teaching, text analysis method, observation methods, visual methods, source text thematics analysis and analysis of rendering tasks given to school leavers at the state examination, analysis of tasks for extracurricular activity (general cultural sphere). The results of the research were presented in the form of a handbook for an extracurricular lesson scenario «Pushkin as a Reformer of the Russian Literary Language». The aim of this lesson was to acquaint learners with the features of the historical development of the Russian literary language as a structured system, as well as to increase their awareness of the scientific style. New conditions and types of activity contribute to a better integration of new information with background knowledge and to the acquisition of skills of text compression, which can foster a more productive preparation of high school leavers for the final certification and for further educational activity.

Estudio de los factores de riesgo psicosocial en profesoras de universidades online: Una mirada desde adentro

The advancement and integration of new Information and Communication Technologies (ICT) in the workplace has led to the emergence of new formulae such as teleworking, which have crossed the boundaries of space and time, modifying the working stage, the way of supervision of work and the reconciliation between family and personal life. Within this frame of reference, workers are more exposed to new risks emerging, like the technostress and burnout issues , as well as the trouble reconciling professional and family life, which result in negative effects on people, and can generate a higher level of exhaustion, dissatisfaction, and professional wear. In this context, the aim of this study is to know and analyze the main psychosocial risk factors female online university teachers are exposed. The interview on phenomenological depth, taking as reference the Seidman´s model , was used as a data collection technique. In particular, two scripts, one for each of the interviews to be done, were developed and we proceeded to validate them through consultation of 15 experts from the field of safety and health and university teaching, and through the Lawshe content validity study (1975), recommended by Cardona (2006), as a quantitative validity criterion, and the observations and suggestions made by the experts as a qualitative validity criterion. In practice, 20 phenomenological interviews were conducted with 10 women working under this labor formula, to obtain a comprehensive understanding and holistic approach to the problem from their point of view and the context in which they are immersed. The results show the existence of several psychosocial risk factors related to the position and the social environment. As regards the position-related psychosocial risk factors, the findings show the existence of quantitative mental overload, associated with intense periods of work or peaks of work, which are specific moments, linked to the dynamics of temporary teaching online and teaching in general, where work load increases substantially, doubling the amount of work to be carried out. Another psychosocial risk factor perceived negatively by participants, also refers to the qualitative mental under load, which occurs during certain academic monotonous and repetitive activities when teaching online. On the other hand, in relation to psychosocial factors associated with the social and organizational sphere, the isolation of participants working in their home is highlighted. This isolation is related both to the lack of contact with peers, to the absence of spontaneous talks and emotional support, that is, to the loss of informal interaction in the workplace, and to the need to share doubts, to work together on a common educational Project or to homogenize criteria in the same subjects taught by different teachers. On the other hand, the online female teachers reveal in their speeches different situations of personal-role conflict, in which demands are produced, focused mainly on situations of evaluation of both academic work and exams, which are incompatible with their beliefs and values. Finally, the results of the present study indicate the emotional over load due to conflicts with students, usually relating to their assessment. The above supports the need for further research in this field, aimed at developing and implementing preventive and psychosocial risk intervention programmes in the university context, which have an impact on the organizational and personal scope and which are adequate to the characteristics of women. This will help promote a healthy organization, an organization that develops healthy practices of structuring and managing work processes that would influence the development of healthy employees, whose purpose is not only to intervene on the dysfunctional aspects, but attain the welfare of its workers achieving the highest increase in the development of the organization and its employees.

Integration of new information in memory: new insights from a complementary learning systems perspective.

According to complementary learning systems theory, integrating new memories into the neocortex of the brain without interfering with what is already known depends on a gradual learning process, interleaving new items with previously learned items. However, empirical studies show that information consistent with prior knowledge can sometimes be integrated very quickly. We use artificial neural networks with properties like those we attribute to the neocortex to develop an understanding of the role of consistency with prior knowledge in putatively neocortex-like learning systems, providing new insights into when integration will be fast or slow and how integration might be made more efficient when the items to be learned are hierarchically structured. The work relies on deep linear networks that capture the qualitative aspects of the learning dynamics of the more complex nonlinear networks used in previous work. The time course of learning in these networks can be linked to the hierarchical structure in the training data, captured mathematically as a set of dimensions that correspond to the branches in the hierarchy. In this context, a new item to be learned can be characterized as having aspects that project onto previously known dimensions, and others that require adding a new branch/dimension. The projection onto the known dimensions can be learned rapidly without interleaving, but learning the new dimension requires gradual interleaved learning. When a new item only overlaps with items within one branch of a hierarchy, interleaving can focus on the previously known items within this branch, resulting in faster integration with less interleaving overall. The discussion considers how the brain might exploit these facts to make learning more efficient and highlights predictions about what aspects of new information might be hard or easy to learn. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Risk Factors and Psychosocial Correlates of Emotionally Negative Dreams in Patients Referred to a Cardiac Rehabilitation Centre.

IntroductionDream, as a kind of mental activity, includes various functions such as mood regulation, adjustment and integration of new information with the available memory system. The study was done for assessing the relationship between physiological and psychological components of cardiac diseases with emotionally negative dreams in cardiac rehabilitation.MethodsAt the baseline of this cross-sectional study, 156 patients from Western Iran participated during April–November 2016. People 20 years–80 years able to recall the emotional content of dreams after cardiac surgery entered the study. The Beck depression inventory (BDI), Beck anxiety inventory (BAI), Buss and Perry’s aggression questionnaire (BPAQ) and Schredl’s dream emotions manual were used for collecting data. A binary logistic regression analysis used for the study of the relationship between risk factors and emotionally negative dreams.ResultsThe mean age of participants was 59 (SD = 9) years (men: 64.1%). The results showed that 25% of patients have negative emotional content. After adjustment for demographic variables, the results showed that increased anxiety [adjusted odds ratio (adj OR) = 1.08 [1.01–1.16], P = 0.020] and anger (adj OR = 1.03 [1.00–1.06], P = 0.024) and hypertension (adj OR = 2.71 [1.10–6.68], P = 0.030) can predict the dreams with negative content significantly.ConclusionThe increasing rates of anxiety and anger and history of hypertension are related to increasing dreams with the negative emotional load. The control of risk factors of dreams with negative emotional load can be the target of future interventions.

Critical infrastructure protection based on memory-augmented meta-learning framework

Critical infrastructures are related to systems which are essential for sustaining the important functions of a society. Their potential failures can cause serious problems not only to the population and economy but also to national security as well. The importance of these infrastructures calls for measures toward their security and protection. The aim was the reduction of risk related to natural disasters, terrorist acts, and cyberthreats. Traditional security systems, even those that employ intelligent algorithms, fail to prevent advanced zero-day attacks as they require constant training. This research proposes a novel meta-learning architecture that considers the neural turing machines as the approach upon which the model is founded. The introduced model allows for the memorization of useful data from past processes, by integrating external storage memory. Moreover, it facilitates the rapid integration of new information without the need for retraining. In particular, the proposed novel architecture is called memory-augmented neural network (M-ANN) whose core is a sophisticated, very fast, and highly efficient extreme learning machine. The M-ANN is assisted by a series of original modifications, related to fine-tuning of training, to memory retrieval mechanisms, to addressing techniques, and to ways of attention-weight allocation to memory vectors. The efficiency of the proposed system has been successfully tested using an extremely complex scenario for the protection of critical infrastructures. According to the testing scenario, memory could quickly encode and record information about new types of attacks, while any stored representation from previous experience was easily and consistently accessible, to maximize the detection efficiency.

Determining the dimensions of the innovation ability in logistics sector by using plithogenic-critic method: An application in Sakarya Province

Today's changing and developing level of competition and power, continuous learning, knowledge and technology management, transformation in the production process, market-oriented-based innovation and knowledge as communication applications, companies are routed to make more resources and research about the ability of innovation. The innovation factor has enabled new processes, products, ideas to adapt successfully for the production and market structure, and correspondingly implementation of them. In this point of view, innovation ability is the integration of new information resulting in product and process innovation by activating the power that a company provides to its employees. This ability has referred to the information between internal knowledge and external market demands. Accordingly, the factors affecting the innovation capability dimensions have a vital importance for companies. The fact that there exist limited number of studies on the weighting of the factors affecting the dimensions of innovation ability in the comprehensive literature review, is another factor increasing the importance of the subject. In this study, the innovation ability dimensions have been weighted in corporate logistics companies in Sakarya. Plithogenic set based CRITIC method, which is one of the multi criteria decision making techniques, has been used. The most important factor in the study was determined to be ability of accessing information resources.