Humans and other animals are able not only to coordinate their actions with their current sensorimotor state, but also to imagine, plan and act in view of the future, and to realize distal goals. In this paper we discuss whether or not their future-oriented conducts imply (future-oriented) representations. We illustrate the role played by anticipatory mechanisms in natural and artificial agents, and we propose a notion of representation that is grounded in the agent’s predictive capabilities. Therefore, we argue that the (...) ability that characterizes and defines a true cognitive mind, as opposed to a merely adaptive system, is that of building representations of the non-existent, of what is not currently (yet) true or perceivable, of what is desired. A real mental activity begins when the organism is able to endogenously (i.e. not as the consequence of current perceptual stimuli) produce an internal representation of the world in order to select and guide its conduct goal-directed: the mind serves to coordinate with the future. (shrink)

It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings (...) of any letter length and any number of syllables. The model is evaluated on error rates and further validated on the results of a behavioral experiment designed to examine ambiguities in the processing of graphemes. The results show that the model (a) chooses graphemes from letter strings with a high level of accuracy, even when trained on only a small portion of the English lexicon; (b) chooses a similar set of graphemes as people do in situations where different graphemes can potentially be selected; (c) predicts orthographic effects on segmentation which are found in human data; and (d) can be readily integrated into a full-blown model of multi-syllabic reading aloud such as CDP++ (Perry, Ziegler, & Zorzi, 2010). Altogether, these results suggest that the model provides a plausible hypothesis for the kind of computations that underlie the use of graphemes in skilled reading. (shrink)

The views of Houk et al., Smith, and Thach on the role of cerebellum in movement control differ substantially, but all three are flawed by the false reasoning that because information passes from the cerebellum to movements the cerebellum must be a movement controller, or a part of one. The divergent and less than compelling ideas expressed by these leading cerebellar theorists epitomize the fruitlessness of this paradigm, and signal the need for a change. [HOUK et al.; SMITH; THACH].

Thach's target article presents a remarkable overview and integration of animal and human studies on the functions of the cerebellum and makes clear theoretical predictions for both the normal operation of the cerebellum and for the effects of cerebellar lesions in the mature human. Commentary is provided on three areas, namely, spatial navigation, implicit learning, and cerebellar agenesis to elicit further development of the themes already present in Thach's paper, [THACH].

The involvement of nitric oxide in cerebellar long-term depression is supported by the observation that nitric oxide is released by climbing fiber stimulation and by pharmacological tool usage. Two forms of long-term depression should be distinguished by their physiological relevance. [CRÉPEL et al.; LINDEN; VINCENT].

Linguistic and non-linguistic pattern learning have been studied separately, but we argue for a comparative approach. Analogous inductive problems arise in phonological and visual pattern learning. Evidence from three experiments shows that human learners can solve them in analogous ways, and that human performance in both cases can be captured by the same models. We test GMECCS, an implementation of the Configural Cue Model in a Maximum Entropy phonotactic-learning framework with a single free parameter, against the alternative hypothesis that learners (...) seek featurally simple algebraic rules. We study the full typology of patterns introduced by Shepard, Hovland, and Jenkins (), instantiated as both phonotactic patterns and visual analogs, using unsupervised training. Unlike SHJ, Experiments 1 and 2 found that both phonotactic and visual patterns that depended on fewer features could be more difficult than those that depended on more features, as predicted by GMECCS but not by rule-seeking. GMECCS also correctly predicted performance differences between stimulus subclasses within each pattern. A third experiment tried supervised training to elicit simple rule-seeking phonotactic learning, but cue-based behavior persisted. We conclude that similar cue-based cognitive processes are available for phonological and visual concept learning, and hence that studying either kind of learning can lead to significant insights about the other. (shrink)

We suggest that the cerebellum generates sensory or estimates based on outgoing motor commands and sensory feedback. Thus, it is not a motor pattern generator (HOUK et al.) but a predictive system which is intimately involved in motor behavior. This theory may explain the sensitivity of the climbing fibers to both unexpected external events and motor errors (SIMPSON et al.), and we speculate that unusual biophysical properties of the inferior olive might allow the cerebellum to develop multiple asynchronous sensory estimates, (...) [HOUK et al.; SIMPSON et al.; THACH]. (shrink)

We criticize the synaptic theory of long-term memory and the inappropriate usage of physical notions such as in motor control theories. Motor control and motor memory hypotheses should be based on explicitly specified hypothetical control variables that are sound from both physiological and physical perspectives. [HOUK et al.; SMITH; THACH].

This article reviews models of the cerebellum and motor learning, from the landmark papers by Marr and Albus through those of the present time. The unique architecture of the cerebellar cortex is ideally suited for pattern recognition, but how is pattern recognition incorporated into motor control and learning systems? The present analysis begins with a discussion of exactly what the cerebellar cortex needs to regulate through its anatomically defined projections to premotor networks. Next, we examine various models showing how the (...) microcircuitry in the cerebellar cortex could be used to achieve its regulatory functions. Having thus defined what it is that Purkinje cells in the cerebellar cortex must learn, we then evaluate theories of motor learning. We examine current models of synaptic plasticity, credit assignment, and the generation of training information, indicating how they could function cooperatively to guide the processes of motor learning. (shrink)