Lecture 2 — Modeling Individual Decision Making (Van Maanen)
Paper: Palada, H., Neal, A., Vuckovic, A., Russell, M., Samuels, K. & Heathcote, A. (2016). Evidence accumulation in a complex task: Making choices about concurrent multiattribute stimuli under time pressure. J. Experimental Psychology: Applied, 22(1), 1-23.
Type: Methodological. First of the three "methodological" lectures, which move up a level of description (individual decision-making → autonomy/interaction → collective patterns).
Big-picture framing
Van Maanen's pitch: cognitive psychology gives you formal models of how people actually decide. Most AI is built without those models — it just optimises objective functions and assumes users behave rationally. If you want AI that interacts well with humans (think: Netflix recommendations, lane-keep assist, training tools, alerts to air-traffic controllers), you need a model of the user's cognition.
The lecture's exemplar paper (Palada et al. 2016) shows this isn't an academic curio: the same model that explains why people make speed–accuracy trade-offs in lab dot-motion tasks also explains how air-traffic controllers behave under workload.
Key concept 1 — Newell & Simon and the time scales of human action
Herbert Simon (Nobel laureate 1978, "decision-making process within economic organizations") and Allen Newell are presented as the founders linking cognitive psych to AI (both attended the 1956 Dartmouth workshop). Simon → Administrative Behavior; Newell → Unified Theories of Cognition.
Newell's time scales of human action is the framework that organises the methodological lectures. Four bands:
| Band | Time | Examples | Lecture |
|---|---|---|---|
| Social | days–months (10⁵–10⁷ s) | Political opinion shifts, group consensus | L4 (Klein) |
| Rational | minutes–hours (10²–10⁴ s) | Tasks, reasoning, causal inference | L3 (Hortensius) |
| Cognitive | 100 ms–10 s (10⁻¹–10¹ s) | Perception, memory recall, attention, simple choices | L2 (this lecture) |
| Biological | μs–ms (10⁻⁴–10⁻² s) | Neurons, neural circuits | not covered |
L2→L4 = "progressively higher level of description." L5–7 = application-oriented.
Key concept 2 — Three levels of integration of cognitive theory
(From Liefooghe & Van Maanen, Frontiers in AI, 2023)
When you build interactive AI, you can integrate cognitive science at three levels of formality:
- Anecdotal — general intuitions about cognition. E.g. "spacing effect exists, so let's repeat flashcards at intervals."
- Computational — hypothesised computations individuals perform. Specifies inputs/outputs of cognition without committing to mechanism.
- Algorithmic — hypothesised cognitive processes. Specifies a mechanism (e.g. a memory equation, an evidence-accumulation rule). This is the most demanding but yields the best predictions and lets you explain individual differences.
The slide showing performance increase across levels is the most exam-likely figure: anecdotal-level flashcard apps gain ~6.7 on a French test; algorithmic-level apps (ACT-R-based "smart fact learning") gain ~7.5. The algorithmic level also lets you (i) explain individual decay rates and (ii) predict success on a first test.
This is a core methodological argument of the course: better cognitive models → better AI.
Key concept 3 — Two worked examples
A. Smart fact learning (memory)
- Anecdotal level: Leitner (1972) flashcards — three decks; correct cards move forward, incorrect move back. Implements practice, testing and spacing effects without any equation.
- Algorithmic level: ACT-R-style memory model (Anderson & Schooler, 1991, Rational Analysis of Memory). Activation
B_i = ln(Σ t_j^-d)— function of frequency (n), recency (t_j) and decay (d). "Need probability": how likely is this item required in the near future? Ifp(Activation) × Gain < Cost, forget it. Apps using this model (Van Rijn, Van Maanen, Van Woudenberg, ICCM 2009; Sense et al., Front. Educ. 2018) predict individual recall latencies and outperform anecdotal apps.
B. Decision-making (perception → choice)
The Linear Ballistic Accumulator (LBA) model:
- Each response option has its own accumulator that races toward a threshold (b).
- Each accumulator has:
- Start point sampled from
U[0, A] - Drift rate sampled from
N(v, s)— the rate at which evidence accumulates - Threshold
b - Non-decision time
t₀ - Fastest accumulator wins — that option is chosen.
Behaviorally calibrated: in a 2-alternative forced-choice (2AFC) random-dot motion task with speed vs. accuracy instructions: - Drift rate is influenced by task difficulty (similarity between target and distractor) - Threshold is influenced by instruction (speed vs. accuracy)
The LBA reproduces both correct and error RT distributions (not just means), which is why it beats simpler regression-style models.
Speed–accuracy trade-off explained
"Focus on speed" → participants lower their threshold → decide faster but on less-accumulated (noisier) evidence → more errors. This is a mechanistic explanation for an effect that, at the anecdotal level, is just "trade-off exists."
Body-temperature experiment (Van Maanen et al., Sci Rep 2019/2021)
- Manipulated core body temperature via hot-tub immersion.
- Hot condition → people misperceive deadlines as closer than they are ("perceived deadline" < "true deadline").
- LBA fit: warm subjects show lower choice thresholds at the end of immersion — they sacrifice accuracy because subjectively time feels short.
- Generalises to: soldiers, construction workers, anyone making decisions under heat stress.
Paper 2 — Palada et al. (2016): Evidence accumulation under workload
Task
A complex, applied decision-making task: monitoring radar-like cloud displays for targets (think air-traffic control or surveillance). Subjects make target/non-target judgements about concurrent multiattribute stimuli under varying time pressure.
Manipulation
Workload varied across four levels (Low / Medium / High / Very High). At higher workload, subjects must decide faster.
Findings (from the lecture's slides — Figs 9 and 10 of the paper)
- Correct RT decreases as workload rises (~2.6 s → ~2.2 s).
- The LBA was fit to the data. Workload affects both drift rate (lower under higher workload — task harder) and threshold (lower under higher workload — strategic adjustment to meet time pressure).
- The paper also fits individual-differences parameters → can classify "good / medium / poor decision makers" along three axes: cautiousness (threshold), processing efficiency (drift rate) and execution time (non-decision time).
Why this matters for an open society (Van Maanen's framing)
The LBA gives you user models for any speeded, high-stakes, repeated decision-making task: - Air traffic control - Surveillance / threat assessment (→ links to L5 Van der Vegt) - Factory floor / line work - Future of Work platform (IOS pillar B): selection, training, monitoring
Two specific IOS-relevant uses called out in the lecture: - Future of Work platform (Transitions & Wellbeing) — modelling individual workers helps allocate, train and protect them. - In/Equality platform (Equity & Diversity) — algorithmic decisions in hiring/finance can be re-examined when you know what human deciders' biases and trade-offs actually look like.
Likely essay-question angles
- "Describe the LBA model. How can a cognitive model of decision-making contribute to AI for an open society? Use Palada et al.'s air-traffic-control study or another example."
- "Distinguish the anecdotal, computational and algorithmic levels of integration. Why might algorithmic-level integration be preferable for AI training/monitoring systems? What are the costs?"
- "Newell's time scales of human action structure the methodological half of this course. Where does the LBA sit, and how does this compare to the level of analysis in Lectures 3 and 4?"
Quick self-test
- What does the LBA assume about how a decision is made? List its four parameters.
- Which LBA parameter changes with task difficulty? Which with instruction? Why does that distinction matter empirically?
- State the three levels of integration of cognitive theory. Give one example of each in the domain of fact learning.
- In Palada et al., what happens to drift rate and threshold under higher workload? What's the practical implication for AI alerting/UI in an ATC tower?
- How would you map the LBA / individual-differences finding (cautiousness × efficiency × execution time) onto one of the IOS platforms? Pick a concrete platform and argue.