This research explored the role that associative learning may play in human sequence learning. Two-choice serial reaction time tasks were performed under incidental conditions using 2 different sequences. In both cases, an experimental group was trained on 4 subsequences: LLL, LRL, RLR, and RRR for Group "Same" and LLR, LRR, RLL, and RRL for Group "Different," with left and right counterbalanced across participants. To control for sequential effects, we assayed sequence learning by comparing their performance with that of a control group, which had been trained on a pseudorandom ordering, during a test phase in which both experimental and control groups experienced the same subsequences. Participants in both groups showed sequence learning, but the group trained on "different" learned more and more rapidly. This result is the opposite that predicted by the augmented simple recurrent network used by F. W.

Some argue the common practice of inferring multiple processes or systems from a dissociation is flawed (Dunn, 2003). One proposed solution is state-trace analysis (Bamber, 1979), which involves plotting, across two or more conditions of interest, performance measured by either two dependent variables, or two conditions of the same dependent measure. The resulting analysis is considered to provide evidence that either (a) a single process underlies performance (one function is produced) or (b) there is evidence for more than one process (more than one function is produced). This article reports simulations using the simple recurrent network (SRN; Elman, 1990) in which changes to the learning rate produced state-trace plots with multiple functions. We also report simulations using a single-layer errorcorrecting network that generate plots with a single function. We argue that the presence of different functions on a state-trace plot does not necessarily support a dual-system account, at least as typically defined (e.g. two separate autonomous systems competing to control responding); it can also indicate variation in a single parameter within theories generally considered to be single-system accounts.

This paper presents a reaction time (RT) experiment that follows on from the work of Perruchet, Cleeremans, and Destrebeceqz (2006), investigating the extent to which reaction times (RTs) are governed by the conscious expectancy of a particular response. In this experiment, participants were presented with a single stimulus (which we will call the conditioned stimulus; CS) followed by one of two outcomes (which we will call unconditioned stimuli; USs); to which participants had to make an appropriate instrumental response. On every trial we recorded the time taken to make this response and participants were asked to rate their expectancy that one of the USs (US1) was going to occur. We found that the expectancy rating for US1 correlated negatively with RT on US1 trials. Over successive runs of reinforcement, when participants rated US1 as less likely to occur they were slower to respond to US1 (lower ratings, higher RTs). When we calculated the expectancy for US2 as the complement of that for US1 expectancy, expectancy of US2 correlated positively with RTs. Thus, across runs of reinforcement, participants responded more quickly to US2 when considering US2 less likely (low rating, low RT). We argue that the requirement to make a conscious expectancy rating results in participants attending more to US1 occurrences than those of US2. This results in a qualitatively different relationship between conscious expectancy and automatic responses that cannot be reconciled by a single processing system account. A dual processing system explanation of learning is proposed to explain these results. In support of this position, we successfully modeled our US2 RT data using a modified version of the Augmented simple recurrent network (Yeates, Jones, Wills, McLaren, & McLaren, 2013).

Newell & Shanks (N&S) argue against the idea that any significant role for unconscious influences on decision making has been established by research to date. Inasmuch as this conclusion applies to the idea of an "intelligent cognitive unconscious," we would agree. Our concern is that the article could lead the unwary to conclude that there are no unconscious influences on decision making - and never could be. We give reasons why this may not be the case

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