Error discounting in probabilistic category learning.

Abstract

Some current theories of probabilistic categorization assume that people gradually attenuate their learning in response to unavoidable error. However, existing evidence for this error discounting is sparse and open to alternative interpretations. We report two probabilistic-categorization experiments that investigated error discounting by shifting feedback probabilities to new values after different amounts of training. In both experiments, responding gradually became less responsive to errors, and learning was slowed for some time after the feedback shift. Both results are indicative of error discounting. Quantitative modeling of the data revealed that adding a mechanism for error discounting significantly improved the fits of an exemplar-based and a rule-based associative learning model, as well as of a recency-based model of categorization. We conclude that error discounting is an important component of probabilistic learning.

Combining error-driven models of associative learning with evidence accumulation models of decision-making

“…For each stimulus, the average choice probability is more extreme than the feedback probability associated with that stimulus. This overshooting behavior is typical of responding under probabilistic feedback (e.g., Craig et al, 2011;Nosofsky & Stanton, 2005;Sewell et al, 2018;Shanks et al, 2002). Visual inspection of the individual response profiles confirmed that all participants exhibited the same probability matching behavior, responding similarly to each stimulus over the entire course of learning.…”

“…Each cue is paired with a unique feedback probability, which determines the relative frequency with which the cue is paired with a Category A or Category B outcome during learning. In tasks using a small number of nonconfusable cues, performance typically stabilizes after each cue has been presented around 30-40 times (e.g., Craig, Lewandowsky, & Little, 2011;Sewell et al, 2018). Because learning proceeds rapidly, it is possible for people to complete multiple runs through the learning task within a single experimental session.…”

“…Participants are therefore aware of when new cue-outcome contingencies need to be learned and previously learned information is no longer applicable (cf. Craig et al, 2011;Kruschke, 1996). By repeatedly resetting the learning environment in this way, participants are forced to learn the mappings between cues and outcomes anew for each run.…”

“…In many category learning models, the learning rate parameter is held constant across trials. However, in probabilistic learning environments, where it is impossible to completely eliminate prediction error, several authors have argued that people might progressively discount feedback as prediction error becomes less informative (Craig et al, 2011;Kruschke & Johansen, 1999; see also Sewell et al, 2018). Following Kruschke and Johansen (1999), we consider a version of the learning model that incorporates feedback discounting by multiplying the learning rate on trial n by a discounting factor,…”

Metacognitive monitoring during category learning: how success affects future behaviour

“…Error discounting is when people slow down their rate of learning in order to account for a certain amount of errors (Craig, Lewandowsky, & Little, 2011). In both of the experiments by Craig et al, participants had to categorize four items into two categories (A and B) on a probabilistic basis (items belonging to category A changed periodically from probabilities of .8 to .6 to .4).…”