Baber, ChrisAlotaibi, Ahad Shabib2025-11-052024Ali, H. (2025). Effectiveness of a cognitive behavioral program to reduce some psychological disorders among Saudi dependents on psychoactive substances. Journal of Psychological Studies,apa 7https://hdl.handle.net/20.500.14154/76875Intelligence analysis increasingly contends with large, heterogeneous, and ambiguous data—especially in criminal investigations—placing pressure on human sensemaking. This thesis examines how integrating human cognitive strategies with computational tools—Large Language Models (LLMs) and Knowledge Graphs (KGs)—may support investigative reasoning. The work is organised around three questions: (1) how KGs/AI affect human decision-making in intelligence analysis; (2) how people frame information for intelligence tasks and how technology shapes that framing; and (3) how the analysis context (e.g., crime type) influences reasoning. The Data–Frame Model (DFM) provides the organising account of framing/reframing; observed outputs are mapped to deductive, inductive, and abductive types of reasoning without claims about internal cognition. An ELICIT-derived study contrasts self-generated with provided frames in card sorting and a short reasoning task with colour-coded reliability cues. Within this task, provided frames increased cross-participant agreement at a single time point, whereas self-generated frames were more stable across sessions; participants generally preferred green (high-confidence) items, used yellow cautiously, and avoided red. A KG/Cypher modelling step approximated participants’ justifications while making assumptions (e.g., reliability thresholds) explicit. These findings, from a small novice sample, indicate benefits to preserving personal framing while keeping reliability weighting inspectable and revisable. A comparative study of human versus LLM-assisted query generation shows complementary strengths: the model rapidly proposes structured, comprehensive question sets over large text corpora, while humans more readily connect financial, behavioural, and interpersonal facets and tailor queries to local context. This motivates a mixed-initiative approach in which AI supports breadth and consistency and analysts contribute contextual nuance and judgement. To bridge cognition and computation, a provenance-aware KG (~300 nodes/~600 relationships) was designed for the North by Southwest scenario, encoding both declarative facts and procedural elements (follow-the-money / follow-the-crime). Three filter families—analysis procedures, analysts’ heuristics, and graph algorithms (e.g., centrality, community detection)—were expressed via Bloom phrases and Cypher templates. A controlled user study (novice N = 30) then examined how filter form (question-based vs algorithmic) and context (money vs crimes) were associated with what participants foregrounded, how anchors persisted, and when decisions converged. Three patterns emerged: (i) context reliably shifted focus (e.g., financial actors under money; operational roles under crimes); (ii) filter affordances shaped anchoring and paths to convergence (salience encodings in algorithmic views were frequently cited); and (iii) alignment between filter and context often coincided with higher convergence, though not uniformly. Opportunities for data→frame updating were limited by interface and workflow constraints—a design-relevant limitation. Contributions are threefold: (i) a conceptual synthesis aligning the DFM with observable reasoning types and an operational definition of candidate reframing (new evidence/rationale plus persistence); (ii) methodological assets (a transparent KG/filters pipeline with reproducible Bloom/Cypher materials, stability and convergence measures, and reliability-cue instrumentation); and (iii) empirical evidence on human–AI complementarity and on when KGs help (and constrain) investigative work. Limitations include simulated scenarios, novice samples, small cells, fixed orders, and tool-specific affordances. Future work targets professional-analyst studies, richer interaction for reframing, larger/live datasets, anti-anchoring and “why-explanations” for algorithms, and continued attention to legal, ethical, and privacy considerations. Together, these contributions arise from three empirical studies: an ELICIT-based framing study, a comparative analysis of human versus AI query generation, and a knowledge-graph experiment on filters and investigative contexts.313enKnowedge graphsAISensemakingcriminal investigationsThesis