The subjective feeling of free choice is an important feature of human experience. Experimental tasks have typically studied free choice by contrasting free and instructed selection of response alternatives. These tasks have been criticised, and it remains unclear how they relate to the subjective feeling of freely choosing. We replicated previous findings of the fMRI correlates of free choice, defined objectively. We introduced a novel task in which participants could experience and report a graded sense of free choice. BOLD responses (...) for conditions subjectively experienced as free identified a postcentral area distinct from the areas typically considered to be involved in free action. Thus, the brain correlates of subjective feeling of free action were not directly related to any established brain correlates of objectively-defined free action. Our results call into question traditional assumptions about the relation between subjective experience of choosing and activity in the brain’s so-called voluntary motor areas. (shrink)

Recent years have witnessed an enormous increase in behavioral and neuroimaging studies of numerical cognition. Particular interest has been devoted toward unraveling properties of the representational medium on which numbers are thought to be represented. We have argued that a correct inference concerning these properties requires distinguishing between different input modalities and different decision/output structures. To back up this claim, we have trained computational models with either symbolic or nonsymbolic input and with different task requirements, and showed that this allowed (...) for an integration of the existing data in a consistent manner. In later studies, predictions from the models were derived and tested with behavioral and neuroimaging methods. Here we present an integrative review of this work. (shrink)

The mental representation of brief temporal durations, when assessed in standard laboratory conditions, is highly accurate. Here we show that adding or subtracting temporal durations systematically results in strong and opposite biases, namely over-estimation for addition and under-estimation for subtraction. The difference with respect to a baseline temporal reproduction task changed across durations in an operation-specific way and survived correcting for the effect due to operation sign alone, indexing a reliable signature of arithmetic processing on time representation. A second experiment (...) replicated these findings with a different set of stimuli. This novel behavioral marker conceptually mirrors in the time domain the representational momentum found with motion, whereby the estimated spatial position of a visual target is displaced in the direction of motion itself. This momentum effect in temporal arithmetic suggests a striking analogy between time processing and visuospatial processing, which might index the presence of common computational principles. (shrink)

Rips et al.'s arguments for rejecting basic number representations as a precursor of the natural number system are exclusively based on analog number coding. We argue that these arguments do not apply to place coding, a type of basic number representation that is not considered by Rips et al.

Comments on an article by Feigenson et. al.(see record 2004-18473-007). Reviewing behavioral and neural data in children, humans and animals, Feigenson and colleagues distinguish two core systems for number representation. One system represents number in an exact way but has a fixed upper limit; the other system has no size limit but represents number only approximately. Both systems are claimed to have a phylogenetic origin and to constitute the basis for ontogenetic development. As such, each system's representational principles are reflected (...) in adult human performance: subitizing is ascribed to the exact system whereas symbolic number processing is based on a mapping to the approximate system. This last assumption is motivated by the robust finding that symbolic numbers are more difficult to discriminate with increasing size (the 'size effect'). However, it remains to be shown how this mapping can reconcile the inherently exact nature of a symbolic system with signatures of approximate processing such as the size effect. (PsycINFO Database Record (c) 2005 APA, all rights reserved). (shrink)

Comments on an article by Feigenson et. al.(see record 2004-18473-007). Reviewing behavioral and neural data in children, humans and animals, Feigenson and colleagues distinguish two core systems for number representation. One system represents number in an exact way but has a fixed upper limit; the other system has no size limit but represents number only approximately. Both systems are claimed to have a phylogenetic origin and to constitute the basis for ontogenetic development. As such, each system's representational principles are reflected (...) in adult human performance: subitizing is ascribed to the exact system whereas symbolic number processing is based on a mapping to the approximate system. This last assumption is motivated by the robust finding that symbolic numbers are more difficult to discriminate with increasing size (the 'size effect'). However, it remains to be shown how this mapping can reconcile the inherently exact nature of a symbolic system with signatures of approximate processing such as the size effect. (PsycINFO Database Record (c) 2005 APA, all rights reserved). (shrink)

We challenge the arguments of Cohen Kadosh & Walsh (CK&W) on two grounds. First, interactions between number form (e.g., notation, format, modality) and an experimental factor do not show that the notations/formats/modalities are processed separately. Second, we discuss evidence that numbers are coded abstractly, also when not required by task demands and processed unintentionally, thus challenging the authors' dual-code account.

A central controversy in cognitive science concerns the roles of rules versus similarity. To gain some leverage on this problem, we propose that rule‐ versus similarity‐based processes can be characterized as extremes in a multidimensional space that is composed of at least two dimensions: the number of features (Pothos, 2005) and the physical presence of features. The transition of similarity‐ to rule‐based processing is conceptualized as a transition in this space. To illustrate this, we show how a neural network model (...) uses input features (and in this sense produces similarity‐based responses) when it has a low learning rate or in the early phases of training, but it switches to using self‐generated, more abstract features (and in this sense produces rule‐based responses) when it has a higher learning rate or is in the later phases of training. Relations with categorization and the psychology of learning are pointed out. (shrink)