Conditioned inhibition, inhibitory learning, response inhibition, and inhibitory control: Outlining a conceptual clarification.

Abstract

Inhibition can be defined as a phenomenon in which an agent prevents or suppresses a behavioral state that would otherwise occur. Associative learning studies have extensively examined how experiences shape the acquisition of inhibitory behavioral tendencies across many species and situations. Associative inhibitory phenomena can be studied at various levels of analysis. One could focus on the trajectory of behavioral change involved in learning from negative statistical associations between discrete events (inhibitory learning). Alternatively, one could be interested in the effects of accumulated experience with those negative associations (conditioned inhibition). One could rather be interested in how organisms implement what they learn through experiences involving negative associations (response inhibition). Yet one could inquire into how the capacity of learning negative associations and performing accordingly varies between individuals and along time for the same individual (inhibitory control). This paper presents a tentative taxonomy addressing different levels of analysis of associative inhibitory phenomena by using different terms for each. In addition, recent evidence and certain unresolved issues at each level are thoroughly scrutinized and contrasted with prior findings. The empirical and theoretical advances made by modeling inhibition as an associative learning phenomenon have provided scaffolds for the current knowledge and emerging accounts of the topic. Some of those emerging accounts have the potential to bridge different levels of analysis and foster "cross-pollination" of ideas among broad fields beyond associative learning.

Symmetrical ‘Super Learning’: Enhancing learning using a bidirectional probabilistic outcome

“…The subsequent, although perhaps unjustified, a posteriori analysis, argues that this difference is transient and only appears on trial one and that there is little difference between treatments on subsequent trials. difference suggests that a preventative (or inhibitory) association may extinguish more rapidly than the generative (or excitatory) association (see also Baetu & Baker, 2010;Sosa, 2022).…”

“…Rather than a direct test of learning, this learning is demonstrated by the ability to enhance a secondary cue. This method differentiates inhibition, the active suppression of excitatory learning (see Sosa, 2022), from the failure to learn about a stimulus. Across the experiments, we replicated findings of generative enhanced learning in humans (Lovibond et al, 1988) and showed that this second-order effect can be induced using a go/no-go paradigm.…”

“…(e.g., Shanks & Dickinson, 1987;Vallée-Tourangeau et al, 1998;Wasserman et al, 1993). Descriptively, these models predict that generative, preventative or zero covariation between cue and outcome will lead to excitatory, inhibitory, or weak links respectively (Rescorla, 1968;Sosa, 2022;Vallée-Tourangeau et al, 1998;Wasserman et al, 1993).…”

“…For instance, the presence of a generative cue (positive predictor) can interfere with learning about other new or weaker positive predictors (Baker et al, 1993). Cue competition effects, such as blocking and overshadowing, are well established (Dickinson et al, 1984) and are complemented by the finding that a strong preventative cue (negative predictor, i.e., inhibitory learning, Sosa, 2022) can not only weaken learning about other less effective negative predictors but also enhance learning about a new generative predictor. This latter effect is referred to as super conditioning in animals or super learning in humans (Darredeau et al, 2009;Urushihara & Miller, 2017).…”

Dual-system free-operant avoidance: extension of a theory

“…In the case of rewarded or positively reinforced behavior our dual-system theory (Perez & Dickinson, 2020) proposed that the habit system employed a standard prediction-error learning algorithm that, when applied to habit learning reinforced by a safety signal, should reflect the difference between the strength of Pavlovian inhibition elicited by the feedback stimuli at the time when a response is performed and the current habit strength. In the absence of an agreed theory of Pavlovian inhibitory learning (Sosa, 2022), however, the development of a computational theory of habitual avoidance learning would be highly arbitrary.…”