Human sequence learning under incidental and intentional conditions.

'This article may not exactly replicate the final version published in the APA journal. It is not the copy of record.' © 2009 American Psychological AssociationThis research explored the role that dissociable associative learning and hypothesis-testing processes may play in human sequence learning. Two 2-choice serial reaction time (SRT) tasks were conducted, 1 under incidental conditions and the other under intentional conditions. In both cases an experimental group was trained on 4 subsequences (i.e., XXX, XYY, YYX, and YXY). To control for sequential effects, sequence learning was assayed by comparing their performance to a control group that had been trained on a pseudorandom ordering, during a test phase in which both groups experienced effectively the same trial order. Under incidental conditions participants demonstrated learning of the subsequences that ended in an alternation, but not of those that ended in a repetition. In contrast, under intentional conditions XXX showed the greatest evidence of learning. This dissociation is explained using a 2-process model of learning, with an associative process (the augmented simple recurrent network [SRN]) capturing the incidental pattern, and a rule-based process explaining the advantage for XXX under intentional condition

Redundancy and blocking in the spatial domain: A connectionist model.

How can the observations of spatial blocking (Rodrigo, Chamizo, McLaren
 & Mackintosh, 1997) and cue redundancy (OKeefe and Conway, 1978) be
 reconciled within the framework provided by an error-correcting,
 connectionist account of spatial navigation? I show that an implementation
 of McLarens (1995) better beta model can serve this purpose, and examine
 some of the implications for spatial learning and memory

State-trace analysis: dissociable processes in a connectionist network?

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 error-correcting 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