Seminario 4 de abril 2019. -- Presentación de 15 diapositiva

Calcagni, Gianluca

López Tolsa Gómez, G. E.

Pellón, R.

English

Digital.CSIC

R. Pellón, G.E. López Tolsa Gómez, G. CalcagniFitting adjunctive behaviourIntroduction: adjunctive behaviour (AB)• Adjunctive (schedule-induced) behaviour(Falk, 1961): non-contingent behaviourmaintained by some other response(producted by a stimulus) that acquired areinforcing effect.• Traditional assumption: no contingency withincentives, induction instead of reinforcement.• Key principle of association: proximity betweenresponse and reinforcer (intra-session time t)(Killeen & Pellón, 2013).• First the AB is maintained by induction, then bybackward chaining with other behaviours.• Can we describe the induction and chainingprocesses by a unified model?ObjectivesAcquisition phase:  find a model for induction (licks L as a function of previous pellet n).Asymptotic phase: find a model for backward chaining(licks L as a function of leverpresses P and lever presses P as a function of previouspellet n).Experimento (López Tolsa Gómez, 2018)8 male Wistar rats.Incentive: food pellet. Access to wáter. Behaviours: licking L and lever pressing P.30 sessions 60 s long.Average of last 5 sessionsRegime where behaviour is stable(smooth data).1. Find fits for lever presses P and licks Lseparately as functions of intra-session time: P(t), L(t).2. Invert P(t)→ t(P).3. Find L(P).4. Compare with independent fit L(P).5. Compare with fit P(L).P(t)𝑷 𝒕 = 𝑨𝟏𝒆𝒂𝒕 − 𝟏𝒆𝒂𝒕 + 𝑨𝟐a   = 0.125  (𝑝 = 10−40)𝐴1 = 118.3  (𝑝 = 10−50)𝐴2 = 212.9  (𝑝 = 10−11) = 3.7L(t)𝑳 𝒕 = 𝒕𝒃 (𝑩𝟏 𝒆𝒂𝒄𝟏𝒕 + 𝑩𝟐𝒆𝒂𝒄𝟐𝒕)b   = 3.532  (𝑝 = 10−32)𝑐1 = 1.087  (𝑝 = 10−12)𝑐2 = 3.116  (𝑝 = 10−31)𝐵1 = 0.005     (𝑝 = 0.02)𝐵2 = 2.617   (𝑝 = 10−7) = 26.9This is not the best fit L(t), but it is the simplest for our purpose…𝐿 𝑃 = ln𝐴1 + 𝐴2𝑃𝐴1 − 𝑃1𝑎𝑏𝐵1𝐴1 − 𝑃𝐴1 + 𝐴2𝑃𝑐1+ 𝐵2𝐴1 − 𝑃𝐴1 + 𝐴2𝑃𝑐2Backward chaining?A mess, and the fit is poor at low tA better fit for L(P)𝑳 𝑷 = 𝜶𝑷𝟐𝒆−𝑷𝜷+ 𝜸a = 109.2,  b = 0.745,  g = 4.939From this one can get L(t)=L[P(t)](ugly to see, but it’s a good fit!)Backward chainingP(L)𝑷 𝑳 = 𝑨 𝑳𝒂 𝒆−𝒃 𝑳𝒄+ 𝒆−𝑳𝒅+𝑩A = 115.03, B = 8.45, a = 4.467, b = 2.496, c = 0.677, d = 1.608 = 132.6,  𝑝 < 10−4Psychological interpretation?AcquisitionPromoting constants c to n-dependent parameters 𝑐𝑛:1. For each session, fit P(t,𝒄) and L(t,𝒄)from (P,t) and (L,t) data, as before.2. Find fits for 𝒄𝒏 from (c,n) data.3. Final model: L(P,𝒄𝒏 ).tttnnAnd the answer is…… I didn’t have time!Conclusions (preliminary)• In this (a?) behavioral chain, fitting from thelast link to the first is better than fitting thefirst and the last link separately and thencombining them.• Model fitting does not determine the causalarrow. A psychological model will decidewhether we are interested in L(P) or P(L).• Best fits of the session dependence of theparameters in progress.

Fitting adjunctive behaviour

https://digital.csic.es/bitstream/10261/212399/1/Fitting%20adjunctive.pdf

Fitting adjunctive behaviour

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