The use of forward models is well established in cognitive and computational neuroscience. We compare and contrast two recent, but interestingly divergent, accounts of the place of forward models in the human cognitive architecture. On the Auxiliary Forward Model account, forward models are special-purpose prediction mechanisms implemented by additional circuitry distinct from core mechanisms of perception and action. On the Integral Forward Model account, forward models lie at the heart of all forms of perception and action. We compare these neighbouring (...) but importantly different visions and consider their implications for the cognitive sciences. We end by asking what kinds of empirical research might offer evidence favouring one or the other of these approaches. (shrink)

It has been suggested that external and/or internal limitations paradoxically may lead to superior learning, that is, the concepts of starting small and less is more (Elman, ; Newport, ). In this paper, we explore the type of incremental ordering during training that might help learning, and what mechanism explains this facilitation. We report four artificial grammar learning experiments with human participants. In Experiments 1a and 1b we found a beneficial effect of starting small using two types of simple recursive (...) grammars: right‐branching and center‐embedding, with recursive embedded clauses in fixed positions and fixed length. This effect was replicated in Experiment 2 (N = 100). In Experiment 3 and 4, we used a more complex center‐embedded grammar with recursive loops in variable positions, producing strings of variable length. When participants were presented an incremental ordering of training stimuli, as in natural language, they were better able to generalize their knowledge of simple units to more complex units when the training input “grew” according to structural complexity, compared to when it “grew” according to string length. Overall, the results suggest that starting small confers an advantage for learning complex center‐embedded structures when the input is organized according to structural complexity. (shrink)

A model is proposed to explain how emotional and cognitive processes drive epistemic activities within individual scientists. In this account, emotion–cognition interactions produce cyclical phases of accommodative and assimilative epistemic activities, called thought experiments and empirical experiments, respectively. During thought experiments, scientists ruminate over troubling anomalies in order to generate the theoretical ingredients necessary for constructing new conjectures. During empirical experiments, scientists explore exciting conjectures in order to cover the empirical ground necessary to discover new anomalies. Critically, epistemic activities are (...) accompanied and sustained by positive and negative emotional moods. The main virtue of the model is its specification of a coupled feedback mechanism that explains the dynamics and the temporal dependencies between empirical and theoretical activities underlying scientific discovery. (shrink)