Previous research has shown that causal attributions can be made from patterns of covariation (Cheng, 1997). While the study of how humans learn contingencies goes back decades (e.g., Ward & Jenkins, 1965), cue interaction effects, involving covariations with two or more cues, have taken on particular importance (e.g., Shanks, 1985), due to their rich potential for theoretical insights. One such effect is causal discounting (Goedert & Spellman, 2005): People believe a cue is less contingent if they learned about it in the presence of a more contingent cue. Using a new method for investigating covariation detection, the steamed-trial technique (Allen et al., 2008), Art Markman, Kelly Goedert and I (Laux et al., 2010) have established that differences in bias underlie causal discounting. We argued that this implies discounting is an effect of a process employed to make causal judgments after learning has occurred. Analyses of how different theories account for discounting, especially simulations of associative models, establishes that this is not necessarily correct; several learning models can reproduce our data. However, model and data explorations show that the key feature of those data is that they track relative, not absolute, magnitudes. My dissertation extends this work establishing the plausibility of a comparative judgment process as the locus of causal discounting. I replicate the finding that responding tracks relative magnitudes. By conducting experiments that parametrically manipulate the contingency of the alternative cue (and thereby the relative contingency of the cues), I show that causal discounting is due to responding to contingencies as a linear function of their relative magnitude. I further verify that discounting manifests identically in response to contingencies presented via summary tables. Because summary tables do not afford the series of experiences necessary to build an association, this enhances the credibility of the theory that discounting is due to a shared process employed subsequent to learning—namely, a judgment process. These investigations reveal that discounting is not a cue interaction effect at all, but rather is a manifestation of a fundamental aspect of the systems that subserve covariation detection. / text

<p>One of the most central and current debates among those studying human contingency learning (HCL) concerns whether it is best understood as the result of associative learning, a product of higher-order cognitive processes, or some combination thereof. Though the field appears to be moving toward the latter accounts, much of the evidence being generated to evaluate and select among them comes from tasks that typically present only information about the few variables involved in the contingency(s), in the exact same manner on every trial. While effective for examining how the statistical properties of experience affect learning, these procedures do not capture some of the conditions of everyday cognition and are apt to be less effective for engaging non-associative and top-down influences on performance.</p> <p>The current work introduces a task that involves learning contingencies in others' behavior from descriptions that require the learner to determine the focus of learning, and to deal with both variability in manifestation of the objects of learning and extraneous information. Across several experiments, performance reflects phenomena, including ΔP, outcome density and blocking effects, which have been well established in HCL and are consistent with associative accounts. At the same time, the findings also suggest that (a) domain-specific theories affect the weighting of evidence in contingency perception and the discoverability of contingencies, and (b) outcome predictions, a typical measure in HCL, are influenced by specific instance memory in addition to abstract contingency knowledge. These findings are difficult to reconcile with the data-driven nature of associative views, and join a growing number of demonstrations suggesting that a viable account of HCL must involve higher-order cognitive processes or top-down influences on performance.</p> / Thesis / Doctor of Philosophy (PhD)

Retrospective revaluation— learning about implied but unpresented cues— poses one of the greatest challenges to classical learning theories. Whereas theorists have revised their models to accommodate revaluation, the empirical reliability of the phenomenon remains contentious. I present two sets of experiments that examine revaluative learning under different but analogous experimental protocols. Results provided mixed empirical evidence that is difficult to interpret in isolation. To address the issue, I apply two computational models to the experiments. An instance-based model of associative learning (Jamieson et al., 2012) predicts retrospective revaluation and anticipates participant behaviour in one set of experiments. An updated classical learning model (Ghirlanda, 2005) fails to predict retrospective revaluation, but anticipates participant behaviour in the other set of experiments. I argue that retrospective revaluation emerges as a corollary of basic memorial processes and discuss the empirical and theoretical implications.

Retrospective revaluation— learning about implied but unpresented cues— poses one of the greatest challenges to classical learning theories. Whereas theorists have revised their models to accommodate revaluation, the empirical reliability of the phenomenon remains contentious. I present two sets of experiments that examine revaluative learning under different but analogous experimental protocols. Results provided mixed empirical evidence that is difficult to interpret in isolation. To address the issue, I apply two computational models to the experiments. An instance-based model of associative learning (Jamieson et al., 2012) predicts retrospective revaluation and anticipates participant behaviour in one set of experiments. An updated classical learning model (Ghirlanda, 2005) fails to predict retrospective revaluation, but anticipates participant behaviour in the other set of experiments. I argue that retrospective revaluation emerges as a corollary of basic memorial processes and discuss the empirical and theoretical implications.