Behavioral Analysis and Psychological Statistics
Bridge the gap between theoretical conditioning models and quantitative psychological research. Calculate associative strength, choice allocation, ANOVA variance decomposition, effect sizes, and signal detection metrics from the ground up.
Like Duolingo, but for Behavioral Analysis and Psychological Statistics. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
24 levels across 3 sections, about 48 minutes end to end, roughly 10 days at five minutes a day. It moves through Conditioning Models and Choice Dynamics, Variance Decomposition and Experimental Inference, and Psychophysical Modeling and Regression Analysis.
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Key ideas in Behavioral Analysis and Psychological Statistics
- Prediction error relies on the summed associative strength of all cues present on the trial: V_total = V_A + V_B
- When V_total equals lambda, prediction error (lambda - V_total) drops to zero regardless of individual cue values
- Individual updates delta_V_i scale strictly from the joint error term multiplied by stimulus-specific salience
- In Rescorla-Wagner, cue salience (alpha) remains static; blocking is driven exclusively by the prediction error term collapsing to zero
- Because the pretrained CS satisfies V_total = lambda, the discrepancy (lambda - V_total) leaves zero surplus associative strength for the novel stimulus
- Attentional accounts (like Mackintosh or Pearce-Hall) alter salience over trials, distinguishing them from Rescorla-Wagner's error-driven mechanism
- Conditioned inhibition emerges when lambda is 0 while V_total is positive, producing a negative prediction error (0 - V_total < 0)
- The negative update drives the novel inhibitor's value (V_X) below zero until V_A + V_X = 0
- A conditioned inhibitor's negative associative strength actively subtracts from net excitation during summation tests
- When compound cues compete for a shared finite asymptote (lambda), the final share of V acquired is proportional to each cue's relative salience: alpha_A / (alpha_A + alpha_B)
- Overshadowing does not require prior training; differential acquisition rates cause the more salient stimulus to consume the error pool faster
- Both cues reach steady state simultaneously once their joint sum equals lambda
- Extinction sets lambda to 0, creating a negative prediction error (0 - V) that decrements V on each successive presentation
- The model represents extinction as associative unlearning (erasing weights back toward zero) rather than new inhibitory learning
- Because the model simply resets V toward zero, it cannot inherently account for spontaneous recovery, renewal, or reinstatement without external extensions
- Ratio schedules establish a direct linear correlation between local response rate and reinforcement rate, reinforcing short inter-response times (IRTs)
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Behavioral Analysis and Psychological Statistics the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
Tone A has V = 0.60 and Light B has V = 0.40. If they appear together with a standard outcome (lambda = 1.0), what happens to prediction error?
Get it right to open this lesson and 23 more in the app.
Where Behavioral Analysis and Psychological Statistics takes you
- 1
Conditioning Models and Choice Dynamics
- Calculating Associative Strength with Rescorla-Wagner
- Complex Reinforcement Schedules and Rate Dynamics
- Quantifying Choice: Matching Law and Delay Discounting
- 2
Variance Decomposition and Experimental Inference
- Partitioning Variance in Factorial ANOVA
- Planned Contrasts and Post-Hoc Computations
- Effect Sizes, Non-Centrality, and Power
- 3
Psychophysical Modeling and Regression Analysis
- Signal Detection Theory and Decision Metrics
- Multiple Regression and Variance Decomposition
3 sections · 8 units · 24 levels. Built to play, not to enroll.
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Behavioral Analysis and Psychological Statistics is taught in the The Bestie style: your friend who just gets it. Want a different feel? In the app you can spin up the same topic in any of Tomo's teaching styles. Same facts, totally different vibe.
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