THE MOST EFFECTIVE METHOD TO INCREASE COGNITIVE CAPACITY: COGNITIVE OFFLOADING

Children Devise and Selectively Use Tools to Offload Cognition

Most research on human cognition has focused on processes “inside the box”. Recently, researchers questioned this monopoly, promoting the relevance of cognitive processing “outside the box”, for instance, when using a GPS to navigate. For processing that is distributed between internal and external resources to work efficiently, humans need good heuristics that help them decide when to use which resource. A novel human-computer-interaction paradigm was employed to explore whether people follow the “minimal memory” heuristic and offload cognitive processing onto external resources whenever possible or the “soft constraint” heuristic and offload cognitive processing only if it is associated with an overall higher speed than internal processing. Participants, despite lower speed, nearly exclusively cognized outside the box, which contradicts the soft constraint heuristic and mostly supports the minimal memory heuristic. Implications for human-technology interaction as well as alternative heuristics relevant for cognitive offloading are discussed.

This paper establishes a framework and toolkit for designing Generative Artificial Intelligence (genAI) that address foundational challenges of these technologies and reframe the problem‐solution space. The stakes are high for human‐centered solutions: genAI is rapidly disrupting existing markets with technologies that exhibit increasingly complex and emergent abilities, and accelerate scale, cognitive offloading, and distributed cognition. The Problem‐Solution Symbiosis framework and toolkit extends, rather than displaces, human cognition, including tools for envisioning, problem (re)framing and selection, interdisciplinary collaboration, and the alignment of stakeholder needs with the strengths of a genAI system. Applying the toolkit helps us guide the development of useful, desirable genAI by building intuition about system capabilities, developing a systemic understanding of emerging problem spaces, and using a matrix to identify if and when to offload tasks to the system. The framework is informed by systems theory, frame analysis, human‐computer interaction research on current AI design approaches, and analogous approaches from spatial computing design research.

Controlling assistive robots can be challenging for some users, especially those lacking relevant experience. Augmented Reality (AR) User Interfaces (UIs) have the potential to facilitate this task. Although extensive research regarding legged manipulators exists, comparatively little is on their UIs. Most existing UIs leverage traditional control interfaces such as joysticks, Hand-Held (HH) controllers and 2D UIs. These interfaces not only risk being unintuitive, thus discouraging interaction with the robot partner, but also draw the operator’s focus away from the task and towards the UI. This shift in attention raises additional safety concerns, particularly in potentially hazardous environments where legged manipulators are frequently deployed. Moreover, traditional interfaces limit the operators’ availability to use their hands for other tasks. Towards overcoming these limitations, in this article, we provide a user study comparing an AR Head-Mounted Display (HMD) UI we developed for controlling a legged manipulator against off-the-shelf control methods for such robots. This user study involved 27 participants and 135 trials, from which we gathered over 405 completed questionnaires. These trials involved multiple navigation and manipulation tasks with varying difficulty levels using a Boston Dynamics’s Spot, a 7 df Kinova robot arm and a Robotiq 2F-85 gripper that we integrated into a legged manipulator. We made the comparison between UIs across multiple dimensions relevant to a successful human–robot interaction. These dimensions include cognitive workload, technology acceptance, fluency, system usability, immersion and trust. Our study employed a factorial experimental design with participants undergoing five different conditions, generating longitudinal data. Due to potential unknown distributions and outliers in such data, using parametric methods for its analysis is questionable, and while non-parametric alternatives exist, they may lead to reduced statistical power. Therefore, to analyse the data that resulted from our experiment, we chose Bayesian data analysis as an effective alternative to address these limitations. Our results show that AR UIs can outpace HH-based control methods and reduce the cognitive requirements when designers include hands-free interactions and cognitive offloading principles into the UI. Furthermore, the use of the AR UI together with our cognitive offloading feature resulted in higher usability scores and significantly higher fluency and Technology Acceptance Model scores. Regarding immersion, our results revealed that the response values for the AR Immersion questionnaire associated with the AR UI are significantly higher than those associated with the HH UI, regardless of the main interaction method with the former, i.e., hand gestures or cognitive offloading. Derived from the participants’ qualitative answers, we believe this is due to a combination of factors, of which the most important is the free use of the hands when using the HMD, as well as the ability to see the real environment without the need to divert their attention to the UI. Regarding trust, our findings did not display discernible differences in reported trust scores across UI options. However, during the manipulation phase of our user study, where participants were given the choice to select their preferred UI, they consistently reported higher levels of trust compared to the navigation category. Moreover, there was a drastic change in the percentage of participants that selected the AR UI for completing this manipulation stage after incorporating the cognitive offloading feature. Thus, trust seems to have mediated the use and non-use of the UIs in a dimension different from the ones considered in our study, i.e., delegation and reliance. Therefore, our AR HMD UI for the control of legged manipulators was found to improve human–robot interaction across several relevant dimensions, underscoring the critical role of UI design in the effective and trustworthy utilisation of robotic systems.

The advent of generative artificial intelligence (AIGC) technologies such as ChatGPT has sparked renewed interest in fostering creative thinking skills in primary and secondary education. This study conducted a bibliometric analysis of 74 articles published between 2020 and 2025 in the Web of Science, using Cite Space for data visualization and network mapping. The analysis explored co-citation networks, institutional collaborations, keyword co-occurrence, and keyword clustering to uncover key research themes and emerging trends in AIGC-supported creative learning. Findings revealed eight major research clusters, including ethical concerns in AI-assisted learning, AI-driven pedagogical innovation, and human-AI cocreation, which positions AIGC as a creative collaborator rather than just a tool. Other prominent clusters addressed prompt engineering, cognitive offloading, and mental effort in learning processes, and domain-specific applications in medical and dental education, which require adaptive, real-world creativity. The study concludes that AIGC, if designed and implemented ethically and with a clear educational purpose, can be an effective mechanism for promoting creativity skills in the future.

Human cognitive and perception processes have a great tolerance for imprecision or uncertainty. For this reason, the notions of perception and cognition have great importance in solving many decision making problems in engineering, medicine, science, and social science as there are innumerable uncertainties in real-world phenomena. These uncertainties can be broadly classified as either type one uncertainty arising from the random behavior of physical processes or type two uncertainty arising from human perception and cognition processes. Statistical theory can be used to model the former, but lacks the sophistication to process the latter. The theory of fuzzy logic has proven to be very effective in processing type two uncertainty. New computing methods based on fuzzy logic can lead to greater adaptability, tractability, robustness, a lower cost solution, and better rapport with reality in the development of intelligent systems. Fuzzy logic is needed to properly pose and answer queries about quantitatively defining imprecise linguistic terms like middle class, poor, low inflation, medium inflation, and high inflation. Imprecise terms like these in natural languages should be considered to have qualitative definitions, quantitative definitions, crisp quantitative definitions, fuzzy quantitative definitions, type-one fuzzy quantitative definitions, and interval type-two fuzzy quantitative definitions. There can be crisp queries, crisp answers, type-one fuzzy queries, type-one fuzzy answers, interval type-two fuzzy queries, and interval type-two fuzzy answers.

The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that none of these kinds of evidence can be relevant to a choice among competing computational models unless there are in fact type-type correlations between the states postulated by computational models and brain states. Thus, I conclude, research into the computational procedures employed in human cognition must be conducted hand-in-hand with research into the brain processes which realize those procedures.

People often use external tools to offload cognitive demands associated with remembering future intentions. While previous research has established a causal role of metacognition in cognitive offloading, the neural basis of white matter tracts supporting this metacognitive control process remains unclear. To address this, we conducted a study with 34 participants using diffusion tensor imaging (DTI) to examine how white matter connectivity supports metacognition driven cognitive offloading. Behaviorally, we replicated prior findings showing that under-confidence in internal memory predicts a bias toward using external reminders. At the neural level, we used diffusion tensor imaging to quantify fractional anisotropy (FA), a measure of microstructural integrity in white matter. We found the microstructural integrity of the superior longitudinal fasciculus (SLF) and cingulum bundle (CB) predicted deviations from the optimal use of reminders. The microstructural integrity of the fornix negatively predicted participants’ confidence in performing the task when restricted to internal memory. Our findings reveal the microstructural organization of the white-matter tracts in the fronto-temporal-parietal network are related to metacognition driven cognitive offloading. We discuss several aspects of metacognition driven cognitive offloading from a white matter microstructural perspective.

Many human cognitive capacities are rendered possible by enculturation in combination with specific neuronal and bodily dispositions. Acknowledgment of this is of vital importance for a better understanding of the conditions under which sophisticated cognitive processing routines could have emerged on both phylogenetic and ontogenetic timescales. Subscribing to enculturation as a guiding principle for the development of genuinely human cognitive capacities means providing a description of the socio-culturally developed surrounding conditions and the profound neuronal and bodily changes occurring as a result of an individual’s ongoing interaction with its cognitive niche. In this commentary, I suggest that the predictive processing framework can refine and enrich important assumptions made by the theory of cognitive integration and the associated approach to enculturated cognition. I will justify this suggestion by considering several aspects that support the complementarity of these two frameworks on conceptual grounds. The result will be a new integrative framework which I call enculturated predictive processing. Further, I will supplement Richard Menary’s enculturated approach to mathematical cognition with an account of reading acquisition from this new perspective. In sum, I argue in this paper that the cognitive integrationist approach to enculturated cognition needs to be combined with a predictive processing style description in order to provide a full account of the neuronal, bodily, and environmental components giving rise to cognitive practices. In addition, I submit that the enculturated predictive processing approach arrives at a conceptually coherent and empirically plausible description of reading acquisition.

Problem solving is the skill that coordinates all the cognitive, metacognitive and behavioral processes taking place when individuals encounter a previously unprecedented situation or difficulty. Metacognitive processes seem to play the most important role for resolving a problematic situation as individuals reflect on their acquired knowledge, skills and experiences, thus become aware of their capabilities and how to regulate them. Therefore, metacognitive awareness is the competence that mostly assists individuals in their attempt to construct new knowledge and reach their goals. Furthermore, individuals’ self-assessment and peer-assessment processes could reveal their level of metacognitive awareness and therefore, by far, their problem solving competency. Consequently, ICTs could capture individuals’ problem solving skills through tracking down and analyzing the latters’ cognitive and metacognitive processes as well as their behavioral patterns. The aforementioned computer-based assessment could consist of a fuzzy expert system with domain knowledge from an automated task-based test with particular solution strategies in combination with log data for identifying and classifying one’s level of problem solving ability according to specific criteria.

A variety of different proposals have attempted to explain the apparent uniqueness of human cumulative culture as a consequence of underlying mechanisms that are also assumed to be uniquely well-developed in humans. Recently, Heyes and colleagues have proposed explicit (or Type 2) metacognition as a key feature of human cognition that might enable cumulative culture. In the current review we examine these arguments, and consider their plausibility. Firstly we consider whether distinctions between cognitive processes described as explicit/implicit, and Type 1/2 (or Systems 1/2), do indeed capture features that distinguish processes specific to human cognition, versus those that are shared with other species. In particular we consider whether this applies to distinctions relating to metacognitive processes. We also consider the ways in which explicit metacognitive processing might plausibly facilitate cumulative culture. We categorise the potential benefits as either optimising receiver behaviour, or optimising sender behaviour. Within both of these categories benefits could arise as a consequence of more effective representation of either one’s own knowledge state, or that of others. We evaluate the current state of evidence supporting each of these potential benefits. We conclude by proposing methodological approaches that could be used to directly test the theory, and also identify which (if any) of the possible causal mechanisms may be implicated.

Background: The digital revolution is a decisive point in the co-evolution of people and technologies and it forms a comprehensive ecosystem that actively transforms human cognition, social life, and identity. The paper fills a significant gap in the literature by conducting an empirical investigation of the holistic effect of digital media, beyond individual theoretical or clinical findings. Objective: The study will be a quantitative evaluation of perceived effects of digital media on cognitive patterns, social and emotional well-being, self-technology integration, and philosophical attitudes about future evolution of humanity. Methods: The employed research design was quantitative and cross-sectional. A mixed digital sample of adults who are digitally active was used to collect data using a structured online questionnaire. The instrument used a five-point likert scale in order to obtain the attitude based on four major domains. An analysis of the data was performed with descriptive statistics (frequencies, percentages). Results: The results indicate that there is a considerable amount of cognitive restructuring, in which being highly reliant on digital memory tools (79.3% agreement) and struggling to concentrate without these tools (63.5%). Socially, digital media strengthens relationships for many (50.8%), although in-person interaction is considered to have a higher value (61.9% disagree that online is the same). There was a significant interest in a human-technology symbiosis with 47.6% considering their smartphone an extension of self and 66.7% perceiving technology to enhance human capacities. In philosophy, the respondents understood that technology plays a transformative role in the reshaping of humanity (66.6%), but was cautiously optimistic, with issues of autonomy and integration of AI. Conclusion: Digital media has become a shaping power in the modern human-technology relationship, which brings a more complexized process of adaptation marked by cognitive offloading, subtle social connections, and a developing hybrid identity. The paper highlights the necessity of proactive approaches to digital literacy, ethical technology design and additional interdisciplinary research in order to be aware of this co-evolutionary route.

Stibbard-Hawkes's detailed demonstration that in the case of hunter-gatherer artifacts, absence of evidence is not evidence of absence must never be forgotten. The belief that there is a single coherent "human cognitive capacity" difference between modern humans and some unspecified earlier form should be rigorously re-examined.

A set of fundamental cognitive processes of the brain is formally described in this paper. The cognitive processes defined at the meta cognitive level of the layered reference mode of the brain (LRMB) encompass those of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">object identification, concept establishment, categorization, comparison, qualification, quantification, selection, </i> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> search</i> . Real-time process algebra (RTPA) is adopted as the denotational mathematical means for rigorous modeling and describing the meta cognitive processes. The mathematical model of each meta process is created and modeled in RTPA. All cognitive models and processes are explained on the basis of the object- attribute-relation (OAR) model for internal information and knowledge representation and manipulation.

Organizations are faced with increasing costs needed to train employees in today’s high technology environment. Educators are also striving to develop new training and teaching methods that will yield optimal learning transfer and complex skill acquisition. This study suggests that trainee/learner cognitive processing capacity, information presentation format and complexity, and multimedia technology should be leveraged in order to minimize training duration and costs and maximize knowledge transfer. It presents a causal model of how multimedia and information complexity interact to influence sustained attention, mental effort and information processing quality, all of which subsequently impact comprehension and learner confidence and satisfaction outcomes. Subjects read a text script, viewed an acetate overhead slide presentation containing text-with-graphics, or viewed a multimedia presentation depicting the greenhouse effect (low complexity) or photocopier operation (high complexity). Causal path analysis results indicated that presentation media (or format) had a direct impact on sustained attention, mental effort, information processing quality, comprehension, and learner confidence and satisfaction. Information complexity had direct effects on sustained attention, mental effort and information processing quality. Finally, comprehension and learner confidence and satisfaction were both influenced through an intervening sequence of sustained attention, mental effort and information processing quality.