Why We Put Things Where We Do: Modeling Human Organizational Habits

Author: Denis Avetisyan

New research identifies key psychological principles governing how people arrange objects, paving the way for robots that understand and adapt to our personal preferences.

Participants in the study manipulated virtual objects within a kitchen environment, arranging them by dragging and dropping into designated zones to simulate real-world organizational tasks, with interface specifics detailed in the appendix.

This paper introduces four interpretable constructs – practicality, convenience, coherence, and appropriateness – to model human organization and integrates them into a planning algorithm for personalized robotics.

While robotic systems increasingly handle object rearrangement, they often lack an understanding of why humans organize things a certain way. This limitation motivates the research presented in ‘Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferences’, which proposes a framework built upon four key psychological constructs-spatial practicality, habitual convenience, semantic coherence, and commonsense appropriateness-to model human preferences. Validating these constructs through user studies, the authors demonstrate their ability to guide a robot planner towards generating arrangements aligned with human expectations. Could this interpretable approach unlock truly personalized and intuitive human-robot collaboration in dynamic environments?

The Illusion of Order: Why Robots Struggle with Arrangement

The increasing prevalence of service robots in domestic and public spaces necessitates effective object rearrangement capabilities, yet current robotic systems frequently struggle to align with human expectations. While a robot might technically succeed in relocating items – clearing a table, organizing a workspace – its actions often feel unnatural or disruptive to people. This mismatch arises because robots typically prioritize efficiency and spatial optimization, overlooking the subtle, often unconscious, psychological factors that govern how humans perceive and interact with organized environments. A misplaced book, a slightly askew picture frame – these seemingly minor deviations can trigger feelings of unease or frustration, hindering the acceptance and integration of robotic assistance. Consequently, bridging this gap between robotic functionality and human sensibilities is crucial for fostering seamless and positive human-robot collaboration.

Many robotic approaches to object arrangement prioritize functional efficiency – minimizing travel distance or maximizing space utilization – while largely overlooking the deeply ingrained psychological principles that govern human spatial preferences. This oversight frequently results in arrangements that, while technically optimal, feel unnatural or even unsettling to people. Research in environmental psychology demonstrates that humans don’t simply organize spaces for practicality; they create environments that support cognitive processes, emotional wellbeing, and social interaction. Factors like visual balance, perceived safety, and the facilitation of movement all contribute to a sense of order, and these are often subtly encoded in how people position objects. Consequently, robotic systems that fail to account for these nuanced human considerations risk being perceived as intrusive or unhelpful, hindering their acceptance and usability in everyday life.

The successful integration of robots into human environments hinges on their ability to arrange objects in a manner that aligns with individual preferences. Research indicates a significant degree of variability in how people perceive and value organization; what feels natural to one person may appear chaotic to another. This is not merely aesthetic; arrangements deeply influence perceptions of usability, safety, and even emotional wellbeing. Consequently, a ‘one-size-fits-all’ approach to robotic organization is unlikely to succeed. Instead, robots must be capable of learning and adapting to the specific preferences of those with whom they interact, considering factors like cultural background, personal habits, and even momentary emotional states, to create environments that are not just functional, but genuinely accepted and appreciated.

Robotic systems capable of intelligently arranging objects within a human environment demand more than simple spatial awareness; they require a comprehension of the underlying motivations behind human organization. Current approaches often focus on replicating what a typical arrangement looks like – a tidy desk, a well-set table – but fail to account for why humans position items in specific ways. This ‘why’ is rooted in a complex interplay of factors including functional needs, aesthetic preferences, and even psychological comfort. A truly successful system, therefore, must model these human intentions, inferring goals from observed behavior and predicting how an individual might prioritize certain items or arrangements based on their perceived use and personal values. By building this level of understanding into their algorithms, robots can move beyond simply mimicking surface-level order and instead create environments that genuinely resonate with, and are readily accepted by, human inhabitants.

Deconstructing Order: The Pillars of Arrangement

The concept of `Psychological Constructs` proposes a foundational model for understanding effective organization, built upon four key principles: `Spatial Practicality`, `Habitual Convenience`, `Semantic Coherence`, and `Commonsense Appropriateness`. These constructs are not presented as merely descriptive elements, but as core determinants of how humans intuitively perceive and interact with arranged spaces and items. `Spatial Practicality` relates to the physical efficiency of an arrangement, minimizing effort for interaction. `Habitual Convenience` reflects the benefits of placing frequently used items within easy reach. `Semantic Coherence` concerns the grouping of items based on shared function or association. Finally, `Commonsense Appropriateness` ensures arrangements align with culturally understood norms and expectations. The model posits that effective organization is not arbitrary, but directly derived from these four interacting psychological principles.

The four psychological constructs – Spatial Practicality, Habitual Convenience, Semantic Coherence, and Commonsense Appropriateness – do not operate independently when individuals organize their environments. Interactions between these constructs are crucial; for example, an item might be placed for practical spatial reasons, but its location is further reinforced by frequent use, thus appealing to Habitual Convenience. Similarly, items grouped based on Semantic Coherence may also be arranged to maximize Spatial Practicality. This interconnectedness suggests that arrangement preferences are not determined by a single principle, but rather by a complex interplay of these constructs, creating a holistic framework for understanding why certain configurations are perceived as more natural or efficient than others.

The principle of `Habitual Convenience` dictates that objects accessed frequently should be positioned for ease of retrieval, minimizing the physical and cognitive effort required for their use; this is directly correlated with usage frequency. Conversely, `Semantic Coherence` describes the tendency to group items based on shared function or association, such as locating cooking utensils near the stove or office supplies together. This arrangement leverages pre-existing conceptual relationships to enhance predictability and reduce search time, as users mentally categorize and locate items based on their relatedness rather than arbitrary placement.

Explicitly modeling psychological constructs – Spatial Practicality, Habitual Convenience, Semantic Coherence, and Commonsense Appropriateness – allows for the creation of organizational systems grounded in predictable human behavior rather than relying on subjective or randomly assigned arrangements. This approach involves defining quantifiable metrics for each construct, enabling computational assessment of a given configuration. By optimizing for these metrics, systems can be designed to align with innate human preferences, reducing cognitive load and improving usability. This moves beyond purely aesthetic or functional considerations to prioritize configurations that intuitively ‘make sense’ to users, based on established principles of spatial reasoning, learned behaviors, and conceptual relationships.

Putting Theory to the Test: A User Study Approach

A user study was conducted with the objective of empirically validating the proposed psychological constructs and determining their relative importance in influencing user behavior. The study involved participants arranging virtual objects within defined scenarios, allowing for the quantitative measurement of placement preferences. Data collected from these arrangements were then subjected to statistical analysis to assess the degree to which observed patterns aligned with the theoretical framework. The methodology prioritized a controlled experimental design to isolate the impact of the constructs while minimizing confounding variables, ensuring the results accurately reflected the underlying psychological principles.

The user study methodology involved presenting participants with a series of digitally rendered scenarios containing virtual objects. Participants were tasked with arranging these objects within the presented environments, reflecting their subjective preferences. Object placement coordinates were precisely recorded for each participant and scenario, generating a dataset of arrangement choices. This quantitative data was then subjected to statistical analysis to determine patterns and relationships between object positions, providing insights into the underlying cognitive principles guiding arrangement behavior. The scenarios were designed to vary in complexity and context to assess the robustness of observed preferences across different situations.

Factor analysis was employed to assess the validity of the proposed psychological constructs by examining the relationships between observed variables and underlying latent factors. The analysis yielded factor loadings ranging from 0.34 to 0.93, indicating the extent to which each observed variable contributes to its corresponding latent construct. These values demonstrate strong construct validity, signifying that the measured variables accurately represent the theoretical constructs. Specifically, loadings above 0.30 are generally considered meaningful, and the observed range confirms a robust underlying structure and clear differentiation between the identified factors.

User study results provide strong validation for the proposed framework, demonstrating its ability to accurately capture the principles governing human arrangement preferences. Measurement reliability was established through Cronbach’s Alpha values, ranging from 0.72 to 0.87 across all constructs, indicating consistently internally reliable scales. Furthermore, the observed Chi-squared value of 154 confirms the presence of significant individual variation in arrangement preferences, suggesting that while underlying principles guide choices, personal preferences contribute substantially to observed patterns.

From Theory to Algorithm: Implementing Intelligent Arrangement

The object arrangement planning algorithm utilizes Monte Carlo Tree Search (MCTS), a best-first search algorithm well-suited for decision processes with a large branching factor. MCTS iteratively builds a search tree by simulating random playouts from each node, balancing exploration of less-visited states with exploitation of promising ones. In this implementation, each node represents a partial object arrangement, and the algorithm explores possible object placements and orientations. The simulations are guided by an arrangement cost function, and the resulting tree is used to select the arrangement with the lowest cost, representing the most optimal configuration based on the defined criteria. The algorithm continues to refine its search through multiple iterations, progressively improving the quality of the generated arrangements.

Arrangement Cost Functions serve as the quantitative basis for evaluating generated layouts within the algorithmic planning process. These functions assign numerical values to arrangements based on their adherence to predefined psychological constructs – specifically, principles of visual balance, grouping, and spatial awareness. The functions operate by measuring deviations from ideal configurations as defined by these constructs; lower cost values indicate greater alignment with the psychological principles. Each construct is represented by a distinct cost function, and the overall arrangement cost is typically calculated as a weighted sum of these individual function outputs, allowing for prioritization of certain psychological principles over others. The specific formulation of each cost function involves mathematical calculations based on object positions, sizes, and relationships within the arrangement space.

The algorithmic implementation minimizes arrangement cost functions to produce object layouts that balance computational efficiency with adherence to established psychological principles. These cost functions serve as quantitative metrics representing the degree to which a given arrangement satisfies desired attributes – such as proximity, visual balance, and ease of navigation – as defined by human-factors research. By iteratively refining arrangements to reduce the overall cost function value, the algorithm prioritizes solutions that are not only computationally feasible but also demonstrably aligned with predicted human perceptual preferences, as validated through user studies employing metrics like the Jaccard Similarity Index.

Performance of the arrangement algorithm was quantitatively evaluated using the Jaccard Similarity Index, a measure of overlap between the algorithm’s generated arrangement and those preferred by human participants. Results demonstrated an accuracy range of 0.40 to 0.90, indicating varying degrees of alignment with individual human preferences. This variability is attributable to the diverse participant profiles used in the evaluation; the algorithm’s success in replicating preferred arrangements was directly correlated with the specific psychological characteristics and preferences of each participant, as determined through pre-test questionnaires.

The pursuit of modeling human preferences, as demonstrated by this work on object rearrangement, inevitably highlights the temporary nature of even the most sophisticated frameworks. The researchers attempt to distill organizational habits into constructs like ‘spatial practicality’ and ‘semantic coherence’, effectively building a predictive model of human irrationality. This echoes Marvin Minsky’s observation: “The more we learn about intelligence, the more we realize how much of it is just cleverness.” The elegance of Monte Carlo Tree Search, applied to personalize object arrangements, will eventually succumb to the unpredictable entropy of real-world use. It isn’t a failure of the algorithm, but a confirmation that modeling behavior is merely a snapshot of a constantly shifting baseline. The system may predict current convenience, but production will always find a way to redefine it.

What’s Next?

The decomposition of organizational preference into constructs-spatial practicality, habitual convenience, semantic coherence, and commonsense appropriateness-offers a neat categorization. It’s a tempting framework, reminiscent of earlier attempts to distill human intention into discrete variables. The inevitable question, of course, isn’t whether these constructs explain preference, but rather where they fail to do so, and what tangled exceptions production environments will inevitably reveal. The current validation, while promising, rests on controlled rearrangement tasks. Scaling to genuinely cluttered, dynamic spaces-spaces resembling, say, a moderately used office-will likely expose limitations in the weighting or interaction of these constructs.

Future work will undoubtedly focus on refining these weights, perhaps through reinforcement learning. However, a more interesting challenge lies in acknowledging the inherent messiness of human behavior. The pursuit of ‘personalized robotics’ often implies a stable, knowable user. Yet preferences shift. Habituation occurs. What is ‘convenient’ today becomes irritating tomorrow. Models capable of tracking-and accommodating-this drift will be far more valuable, and far more difficult to build, than any perfectly weighted construct.

One suspects the true limit isn’t algorithmic, but practical. Successfully modeling organizational preference is less about capturing ‘humanity’ and more about reliably predicting where someone will absentmindedly place their coffee mug. And even that proves surprisingly elusive. The elegant diagrams will, as always, require a considerable amount of duct tape.

Original article: https://arxiv.org/pdf/2512.24829.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Illusion of Order: Why Robots Struggle with Arrangement

Deconstructing Order: The Pillars of Arrangement

Putting Theory to the Test: A User Study Approach

From Theory to Algorithm: Implementing Intelligent Arrangement

What’s Next?

See also: