Understanding the Perception-Cognition Gap

• The perception–cognition gap is the measurable divergence between how sensory input is encoded and how it is processed in higher-level cognitive tasks.
• Research employs tools like cognitive move diagrams, performance benchmarks, and consistency metrics to quantify gaps in models such as audio–language and vision–language systems.
• Bridging the gap involves integrative techniques such as modular architectures, neurosymbolic approaches, and fine-tuning methods to align perceptual and cognitive outputs.

The perception–cognition gap refers to the measurable divergence between how sensory information is subjectively perceived or encoded and how it is subsequently processed, interpreted, or used in cognitive decision-making, reasoning, or high-level task performance. This gap manifests across human and artificial systems as discrepancies between low-level perceptual representations or judgments and higher-order cognitive actions and evaluations. It is a central construct in cognitive science, neuroscience, decision theory, artificial intelligence, multimodal systems engineering, and applied fields such as software engineering and data visualization.

1. Definitions and Conceptual Formalisms

The perception–cognition gap is formally defined as the difference between an agent’s internal representation or assessment of sensory input (perception) and the cognitive state or actions that ensue (cognition). In “Visualizing Cognitive Moves for Assessing Information Perception Biases in Decision Making,” this is operationalized via the cognitive state vector $\mathbf{x}^t_j$ (perception plus risk attitudes, etc.) and its transition $\Delta\mathbf{x}^t_j$ under new information framing; the magnitude $\|\Delta\mathbf{x}^t_j\|$ directly quantifies the agent’s susceptibility to reframing and thus their perception–cognition gap (Iorio et al., 2014).

In AI, the gap can be defined as the performance difference between perception-centric and cognition-centric benchmarks. For example, in the WoW-Bench for audio-LLMs, perception accuracy (species/vocalization classification) is reported separately from cognition accuracy (tasks demanding recall, abstraction, manipulation) and the gap emerges as a persistent shortfall in cognition performance relative to perception (e.g., 47.2% vs. 40.0%/70.7% for humans) (Kim et al., 28 Aug 2025). In vision-LLMs evaluated on abstract visual reasoning tasks (Raven’s matrices, MaRs-VQA), the gap is calculated as $\Delta = P_{\rm human} - P_{\rm model}$, with current models often exhibiting $35\%{-}70\%$ shortfalls (Cao et al., 2024).

Cognition–perception consistency is also formalized for multimodal models: a “conflict” occurs when the model’s answer to a high-level question (cognition) does not match the evidence produced by perceptual (e.g., OCR) modules, with consistency measured as $$C$$, where $\delta(y_C, y_P{)}=1$ iff $y_C \subset y_P$ (Shao et al., 2024).

2. Measurement, Visualization, and Empirical Characterization

2.1 Cognitive Move Diagrams and State-Difference Metrics

The Cognitive Move Diagram (CMD) framework provides a systematic procedure for quantifying and visualizing the gap:

• For each agent $j$, define an outcome/state vector $\mathbf{x}_j^t$ at information presentation $t$ (e.g., decision parameters, perceived reliability).
• The cognitive move is $$\Delta \mathbf{x}_j^t = \mathbf{x}_j^{t+1} - \mathbf{x}_j^t$$.
• The perception–cognition gap $G_j = \|\mathbf{x}_j^2 - \mathbf{x}_j^0\|_2$ measures susceptibility to information framing (as in reliability cues).
• Group-level moves (GCM) are defined via cluster centroids to summarize cohort responses.
• CMDs depict these state transitions as directed graphs, allowing quantification and immediate visual analysis of the gap (Iorio et al., 2014).

2.2 Benchmarking Gaps in Human and AI Systems

• Audio–LLMs: The WoW-Bench quantifies perception (e.g., low-level species/vocalization classification) and cognition (recall, feature application, abstraction). The gap is characterized by a systematic deficit in cognition accuracy—models (47.2%) underperform humans (70.7%) and are susceptible to distractors that reveal reliance on shallow heuristics (Kim et al., 28 Aug 2025).
• Vision–LLMs: Abstract visual reasoning tasks (RPM, MaRs-VQA) expose striking gaps: $P_{\rm human} = 69.2\text{--}84.4\%$, $P_{\rm model}$ ranges $10.7$–$44.0\%$ (closed-source) and $10.6$–$42.9\%$ (open-source), with $\Delta$ up to $60\%$ (Cao et al., 2024). Failure analysis demonstrates bottlenecks in working memory, rule abstraction, and integration.
• Referring Image Segmentation: Decoupling perception (precise mask generation) from cognition (text–image semantic mapping) in DeRIS reveals cognition as the main bottleneck—fixing perception and scaling cognition yields $+10.05$ points in cIoU, far exceeding the minor impact of improving perception alone (Dai et al., 2 Jul 2025).

2.3 Consistency and Conflict Metrics

In multimodal document understanding, C&P consistency is measured directly over paired outputs from perceptual (OCR) and cognitive (VQA) queries, with even top models like GPT-4o achieving only $\sim68$–$80\%$ agreement, indicating substantial internal knowledge fragmentation (Shao et al., 2024).

3. Theories and Mechanisms Underlying the Perception–Cognition Gap

3.1 Cognitive Science and Neuroscience Foundations

Perception and cognition are conceptualized as intertwined but distinct computational processes. Classical models (Marr’s levels, Bayesian brain, and predictive coding) treat perception as the generation of rich but summarized sensory representations, with cognition deploying capacity-limited inference, memory, and reasoning atop these codes (Agrawal et al., 2023). The gap arises naturally from:

• Lossy summary-statistic encoding, especially in peripheral vision (Rosenholtz, 2017).
• Strict capacity limits on working memory and selective attention.

Category learning further demonstrates that cognitive structuring can reshape perception itself: learning to categorize textures induces measurable changes in dissimilarity judgments and early event-related potential (ERP) signatures (N1), evidencing a narrowing of the perceptual–cognitive divide via top-down modulation (Pérez-Gay et al., 2018).

Quantum-like models of ambiguous figure perception highlight non-classical, interference-driven contributions to perception–cognition interactions—outcomes are context-dependent and cannot be explained by classical Bayesian conditioning alone (Conte et al., 2009).

3.2 Neurosymbolic and Hybrid Attention Models

Recent theoretical advances suggest the gap is better viewed as a gradient along a neurosymbolic continuum, differentiated by the form and control of attention mechanisms. At low-level abstraction (perceptual), attention is fast, automatic, and broadly tuned; at high-levels (cognitive), attention becomes focused, serial, and goal-steered. Neurosymbolic architectures unify symbolic and subsymbolic information at every level, with performance boosts (+48%) when top-down focus-of-attention is implemented (Latapie et al., 2021).

4. Engineering, AI Architectures, and System-Level Implications

4.1 AI Systems: Bottlenecks and Bridging Techniques

• Monolithic vs Modular Architectures: Monolithic networks often blend perception and cognition, making it difficult to diagnose or remedy performance limitations. Modularization (e.g., DeRIS in referring segmentation; MODA in MLLMs) enables systematic analysis. Loopback synergy and duplex-attention blocks explicitly synchronize and reinforce perception–cognition transfers, yielding measurable gains (+3–6 points on perception/QA; +1.8 on gIoU/cIoU in segmentation) (Zhang et al., 7 Jul 2025, Dai et al., 2 Jul 2025).
• Chain-of-Thought (CoT) and Video Reasoning: CoT-style compositional processing in video reasoning (VoT) bridges the pixel-to-cognition gap, with architectures such as MotionEpic achieving state-of-the-art on multi-step video QA tasks by grounding interpretation in jointly learned spatio-temporal scene graphs (Fei et al., 2024).
• Knowledge Consistency Fine-Tuning: In MLLMs, staged fine-tuning to synchronize perception and cognition modules (perceptual consistency, cognitive consistency, and connector tasks) dramatically improves C&P consistency (+30–40 points)—a general recipe for reducing multimodal knowledge conflicts and improving explainability (Shao et al., 2024).

4.2 Robustness, Failure Modes, and Diagnostic Frameworks

Systematic adversarial/recognition-invariant image transformations reveal cognition gaps in CNNs: when a model's output fails on transformations humans judge irrelevant (e.g., color shifts), the underlying feature hierarchy is misaligned with human semantic intuition. Gradient-based search for worst-case perturbations and the use of metamorphic testing allow for quantifiable measurement and incremental closing of the gap through data-centric and architectural interventions (Vietz et al., 2021).

5. Domain-Specific Manifestations and Applied Research

5.1 Human–Agent Teams and Collaboration

Experiments with agent-assisted collaboration tasks show that exposing a human to an AI's goal beliefs increases subjective perceptions of partnership, trust, and control, but this does not translate to improved objective performance (steps, completion times). This subjective–objective divergence is a paradigmatic perception–cognition gap explained by cognitive load theory and the trade-off between informativeness and processing simplicity (Amitai et al., 6 May 2025).

5.2 Data Visualization: Research–Practice Knowledge Gaps

The perception–cognition gap in data visualization is exemplified not only by limitations in human perceptual-cognitive integration but by misalignment between empirical research and practical guidelines. Only 28% of design guidelines are mapped to empirical studies, and mixed/contradictory evidence is common. Domain-specific repositories and workflows are advocated to bridge research and practice, aiming for a more unified model of evidence-based design—analogous to evidence-based medicine (Kim et al., 2023).

5.3 Software Engineering

Perception is foundational but is rarely integrated with attention, memory, and reasoning in unified studies of software engineering. Empirical work is fragmented, with perception typically assessed via eye-tracking or qualitative observations and cognition via task performance, yet cross-cutting integration is lacking. Research calls for mixed-method, multi-level studies that jointly analyze perceptual, attentional, and reasoning metrics within the same empirical protocols (Fagerholm et al., 2022).

6. Closing the Gap: Architectural and Methodological Solutions

• Cognitive Move Diagrams (CMDs): Enable precise measurement, visualization, and understanding of the dynamic interplay between perception and cognition in decision making, applicable across domains (e.g. military, healthcare) (Iorio et al., 2014).
• Hybrid/Loopback Architectures: Explicit decoupling and recoupling of perception and cognition modules (DeRIS, MODA) facilitate robust handling of multimodal and high-dimensional reasoning tasks (Dai et al., 2 Jul 2025, Zhang et al., 7 Jul 2025).
• Bayesian, Predictive, and Active Inference Models: Hierarchical, predictive coding architectures—integrating local error propagation and top-down priors—support finer alignment of perception and cognition, potentially narrowing the gap (Agrawal et al., 2023).
• Probabilistic Temporal Logic: As in CogSense, sense-making cognitive feedback leverages geometry, motion, and image-quality probes formalized in probabilistic signal temporal logic for robust, closed-loop perception adaptation (Kwon et al., 2021).
• Empirically Driven Knowledge Integration: In data visualization and other applied fields, structured, community-curated repositories tracing guidelines to empirical evidence are necessary to realize consistent perception–cognition pipelines in both research and practice (Kim et al., 2023).

7. Limitations, Open Questions, and Future Directions

Despite progress, significant perception–cognition gaps remain in both human and artificial systems. Current AI models frequently rely on superficial, category-level cues, and lack dynamic cross-modal integration, robust working memory, or context-aware abstraction capabilities. Key directions include:

• Deeper integration of object-centric, relational, and memory modules,
• Adoption of biologically inspired sensor sampling, dynamic modular architectures, and predictive/active inference,
• Development of comprehensive benchmarks and diagnostic protocols specifically targeting the gap,
• Empirical studies that link physiological, behavioral, and computational measures of perception–cognition alignment.

Bridging the perception–cognition gap is an interdisciplinary endeavor, requiring advancements in model architecture, training regimes, empirical evaluation, and cross-domain integration of cognitive theory and engineering practice (Iorio et al., 2014, Kim et al., 28 Aug 2025, Shao et al., 2024, Zhang et al., 7 Jul 2025, Agrawal et al., 2023, Dai et al., 2 Jul 2025, Pérez-Gay et al., 2018, Rosenholtz, 2017).