James L. McClelland

TL;DR: A biased exploration model is introduced, which demonstrates that new addition strategies can be discovered without invoking metacognitive filtering and questions the notion that selection and discovery processes necessarily take place at the level of complete strategies.

TL;DR: This synthesis is inspired by new discoveries in neuroscience that highlight the exquisite plasticity of the brain with experience through infancy and adulthood, by new theories and computational modeling of human cognitive development, and by methodological and computational advances in AI and robotics that make it possible for machines to autonomously develop their own intelligence.

Abstract: When Should We Expect Indirect Effects in Human Contingency Learning? Daniel A. Sternberg (sternberg@stanford.edu) and James L. McClelland (mcclelland@stanford.edu) Department of Psychology, Stanford University Stanford, CA 94305 USA Abstract experiments. In these experiments, participants see a number of pairings of cues and outcomes during training. At test, they are asked to rate the various cues’ causal strengths or to make predictions about the likely outcomes for each cue. Early contingency learning researchers such as Alloy and Abramson (1979) and Dickinson and colleagues (1984) compared their findings to models of animal conditioning that automatically generate indirect effects (e.g., Rescorla & Wagner, 1972; Pearce & Hall, 1980). Indeed, a large class of error-correcting learning algorithms predicts these effects (Rosenblatt, 1958; Rumelhart et al., 1986; Sutton, 1988). Recent dual process models of implicit and explicit learning have employed error-correcting learning algorithms in the implicit component of the models (e.g., Ashby et al., 1998; Sun et al., 2005) – suggesting that indirect effects should be a basic outcome of an implicit learning system. How do we learn causal relations between events from experience? Many have argued for an associative account inspired by animal conditioning models, but there is a growing literature arguing that indirect effects in contingency learning depend on explicit cognitive processes. Our experiments explore the basis of two such effects: blocking and screening off. In Experiment 1, we gave participants an untimed explicit prediction task to replicate standard findings in the contingency learning literature in a novel domain. We obtained robust indirect effects when participants had a causal framework to constrain their reasoning. In Experiment 2, we reduced the time available for explicit recollection by reconstructing the task as a fast-paced RT task. Participants continued to show robust learning of direct relationships, as measured by response times, but there were no indirect effects. Experiment 3 followed up on whether participants in our RT task would produce indirect effects through explicit processes when given an opportunity to make a more deliberative prediction at test. Table 1: An example of direct and indirect effects in a contingency learning paradigm. Keywords: Learning; causal reasoning; implicit learning Training Blocking pair Screening pair Direct effect Indirect effect Introduction A child goes out to dinner with his family and at the end of the meal experiences a strong allergic reaction. Upon discussion with the restaurant manager, the child’s parents learn that the sauce for his entree contained shrimp, and peanuts were used in his dessert. Suppose the child has never had shrimp before. If he has had a history of peanut allergies, one may be inclined to attribute the allergy to the peanuts; if he had never had a peanut allergy before, one may be more inclined to suspect an allergy to the shrimp. We can consider the child’s previous experience with peanuts as the direct evidence about whether peanuts cause an allergic reaction. This evidence, together with the shrimp-and-peanuts event, provides indirect evidence about whether shrimp causes one. If peanuts had previously caused an allergy, this tends to block the inference that shrimp causes one; if peanuts had not previously caused an allergy, this tends to screen off the shrimp – increasing the likelihood of this inference. Comparing the two cases, the scenario above describes a direct effect whereby the strength of the perceived causal relation between peanuts and allergy should be higher for the blocking pair compared to the screening pair. It also describes an indirect effect whereby the strength for shrimp will be higher in the screening pair compared to the blocking pair. Table 1 encapsulates this information. Effects similar to the indirect effect described above have often been demonstrated in contingency learning Single item Pair B 1 + B 1 B 2 + S 1 - S 1 S 2 + B 1 > S 1 S 2 > B 2 Complicating the error-driven account have been findings of retrospective effects like backward blocking (Shanks, 1985), where the order of compound and single item events are reversed (e.g., shrimp and peanuts before peanuts alone). These models do not directly predict retrospective effects. Various modifications to the error- correcting learning algorithm have been proposed to accommodate retrospective effects (Van Hamme & Wasserman, 1994; Dickinson & Burke, 1996). These models continue to predict indirect effects as a basic outcome of the learning process. Another approach has been to argue that retrospective effects are instead driven by the explicit retrieval of memories for previously experienced events (McClelland & Thompson, 2008). More troubling are recent findings that suggest indirect effects are often quite fragile in contingency learning tasks. De Houwer and Beckers (2003) found that blocking was attenuated when participants were given a relatively difficult secondary task (discriminating between a high and low tone) during training and test phases. “High- level” constraints such as assumptions that cues are additive in their effects also appear to modulate the size of indirect effects (Lovibond et al, 2003; Beckers et al., 2005; cf. Livesey & Boakes, 2004). These findings have led some to argue that an explicit propositional reasoning

TL;DR: While the general agreement in performance is encouraging, specific discrepancies suggest possible improvements of the model.