Testing the deductive inferential account of blocking in causal learning

Abstract

The sensitivity of the blocking effect to outcome additivity pretraining has been used to argue that the phenomenon is the result of deductive inference, and to draw general conclusions about the nature of human causal learning. In two experiments, we manipulated participants' assumptions about the additivity of the outcome using pretraining before a typical blocking procedure. Ratings measuring causal judgments, confidence, and expected severity of the outcome were used concurrently to investigate how pretraining affected assumptions of outcome additivity and blocking. In Experiment 1, additive pretraining led to lower causal ratings and higher confidence ratings of the blocked cue, relative to control cues, consistent with the notion that additive pretraining encourages deductive reasoning. However, Experiments 1 and 2 showed that removing additivity assumptions through nonadditive pretraining had no impact on a statistically reliable blocking effect observed in a blocking procedure with no pretraining. We found no evidence that the blocking effect in the absence of pretraining was related to the participants' assumptions about the additivity of the outcome. Although additive pretraining may enhance blocking by encouraging deductive reasoning about the blocked cue, the evidence suggests that blocking in causal learning is not reliant on this reasoning and that humans do not readily engage in deduction merely because they possess the assumptions that permit its use.

Interactions between the elements of an outcome in human associative learning.

“…As to why it is only observed with blocking stimuli, one possible explanation could be that cognitive, or reasoned, judgments play less of a role with the C/D and E/F stimuli as uncertainty or novelty (respectively) are more salient factors for these trials in both versions of the tasks. One potential way of gaining further insights into the role of uncertainty in influencing these ratings would be to collect confidence ratings alongside predictiveness ratings (e.g., Jones, Zaksaite, & Mitchell, 2019; Livesey, Greenaway, Schubert, & Thorwart, 2019).…”

Hearing hooves, thinking zebras: A review of the inverse base-rate effect

“…Given that several other cue competition effects, such as blocking, have been shown across different kinds of test measures (see Jones, Zaksaite, & Mitchell, 2019;Livesey, Greenaway, Schubert & Thorwart, 2019;Luque, Vadillo, Gutiérrez-Cobo, & Le Pelley, 2016;Mitchell, Lovibond, & Gan, 2005;Mitchell, Lovibond, Minard, & Lavis, 2006, for examples using predictive ratings, causal ratings, and discrete outcome choice), it is noteworthy that the inverse base-rate effect has almost exclusively been measured using discrete outcome choice. If the inverse baserate effect is a result of similar mechanisms to other cue competition effects, we might expect it to occur under the wide variety of test conditions used in other cue competition effects.…”

Can We Set Aside Previous Experience in a Familiar Causal Scenario?

“…Finally, participants are asked to judge which foods are causing Mr. X to suffer an allergic reaction, and this judgment may also take the form of a probability or cued recall judgment. The food allergist task has been used to study cue competition effects such as blocking and overshadowing (e.g., Shanks and Lopez, 1996 ; Aitken et al, 2000 ; Lovibond et al, 2003 ; Beckers et al, 2005a ; Mitchell et al, 2005 , 2006 ; Vandorpe et al, 2007 ; Livesey et al, 2013 , 2019b ; Luque et al, 2013 ; Uengoer et al, 2013 ), learning of preventative relationships such as in the case of conditioned inhibition ( Karazinov and Boakes, 2004 , 2007 ), complex rule learning tasks such as the patterning task ( Shanks and Darby, 1998 ; Wills et al, 2011 ; Don et al, 2020 ), as well as a host of phenomena related to learned attentional changes including the learned predictiveness effect ( Le Pelley and McLaren, 2003 ; Don and Livesey, 2015 ; Shone et al, 2015 ), the inverse base-rate effect ( Don et al, 2019 ), outcome predictability effects ( Griffiths et al, 2015 ; Thorwart et al, 2017 ), and other related transfer effects ( Livesey et al, 2019a ). Food allergies are relatively commonplace such that, by the time they enter the laboratory, participants have a lifetime of experience with food and its ability to cause allergic reactions in oneself or others.…”