How attention simplifies mental representations for planning (2506.09520v1)

Abstract: Human planning is efficient -- it frugally deploys limited cognitive resources to accomplish difficult tasks -- and flexible -- adapting to novel problems and environments. Computational approaches suggest that people construct simplified mental representations of their environment, balancing the complexity of a task representation with its utility. These models imply a nested optimisation in which planning shapes perception, and perception shapes planning -- but the perceptual and attentional mechanisms governing how this interaction unfolds remain unknown. Here, we harness virtual maze navigation to characterise how spatial attention controls which aspects of a task representation enter subjective awareness and are available for planning. We find that spatial proximity governs which aspects of a maze are available for planning, and that when task-relevant information follows natural (lateralised) contours of attention, people can more easily construct simplified and useful maze representations. This influence of attention varies considerably across individuals, explaining differences in people's task representations and behaviour. Inspired by the 'spotlight of attention' analogy, we incorporate the effects of visuospatial attention into existing computational accounts of value-guided construal. Together, our work bridges computational perspectives on perception and decision-making to better understand how individuals represent their environments in aid of planning.

Overview of "How Attention Simplifies Mental Representations for Planning"

The paper "How attention simplifies mental representations for planning," authored by Jason da Silva Castanheira, Nicholas Shea, and Stephen M. Fleming, examines the crucial role of visuospatial attention in the formation of task representations during planning processes. The paper uses virtual maze navigation experiments to demonstrate how spatial attention influences mental construal crucial for efficient human planning. Specifically, it posits that the natural contours of attention, shaped by spatial proximity, govern what information enters subjective awareness and thus contributes to the planning process, offering a substantive extension to the existing value-guided construal (VGC) model.

Key Findings and Numerical Results

One of the pivotal aspects of the paper is the identification and quantification of spatial proximity effects on individuals' task representations. The research reveals that the closest obstacles in spatial proximity have significant positive effects on awareness reports, with standardized beta coefficients B1 = 0.26 and B2 = 0.29. Conversely, the furthest neighbors show negative effects on awareness (ß5 = -0.13; B6 = -0.13), challenging the normative VGC model which didn't account for such spatial effects.

The authors further characterize considerable individual differences in attentional effects, displaying a broad spectrum across participants with mean slope effects averaging -0.08 and variance in attention impact as high as 0.04, indicating robust heterogeneity in attentional spillover.

Moreover, evidence from lateralization experiments suggests that constraining task-relevant information to a single hemifield enhances alignment with the ideal observer model, with interaction effects significant at Binteraction = 0.01, indicating the moderated impact of lateralization on task representation.

Methodological Innovations

Building on previous maze navigation paradigms, the authors design experiments that integrate lateralized attention effects by spatially confining task-relevant obstacles to one hemifield. This novel approach validates the hypothesis that reduced attentional overspill occurs when task-relevant information is spatially concentrated, enhancing optimal task representations.

Furthermore, the paper proposes an augmented spotlight-VGC model incorporating an attentional spotlight of 3 squares width to predict task relevance better than the original VGC framework. The enhanced model shows considerable predictive improvements across datasets, evidenced by reductions in BIC values (ABIC ranging from 70.72 in dSC 1 to 203.43 in Ho 2), emphasizing the utility of the spatial attention mechanism in refining the value-guided construal model.

Practical and Theoretical Implications

The findings highlight critical implications for computational models of human cognition and planning. Practically, these results offer a nuanced understanding of how spatial attention can be harnessed in designing intelligent algorithms that more accurately mimic human decision-making processes. Theoretically, they underscore the necessity to integrate attentional principles into models predicting human behavior, bridging the gap between perception, attention, and planning, evolving towards a more comprehensive theory of human cognition.

The authors' work also opens gateways for further exploration into the intersections between consciousness, attention, and decision-making, emphasizing the potential for consciousness to facilitate integrated task representations. Future research could explore individual variability in attentional impact and explore the dynamic nature of attentional biases across varied decision-making contexts.

In summary, this paper represents a substantial contribution to our understanding of human planning through the lens of spatial attention, proposing enhancements to computational models that could inspire novel, biologically-informed intelligent systems.