Piéron’s Law and Optimal Behavior in Perceptual Decision-Making

Piéron’s Law is a psychophysical regularity in signal detection tasks that states that mean response times decrease as a power function of stimulus intensity. In this article, we extend Piéron’s Law to perceptual two-choice decision-making tasks, and demonstrate that the law holds as the discriminability between two competing choices is manipulated, even though the stimulus intensity remains constant. This result is consistent with predictions from a Bayesian ideal observer model. The model assumes that in order to respond optimally in a two-choice decision-making task, participants continually update the posterior probability of each response alternative, until the probability of one alternative crosses a criterion value. In addition to predictions for two-choice decision-making tasks, we extend the ideal observer model to predict Piéron’s Law in signal detection tasks. We conclude that Piéron’s Law is a general phenomenon that may be caused by optimality constraints

How to assess the existence of competing strategies in cognitive tasks: a primer on the fixed-point property

When multiple strategies can be used to solve a type of problem, the observed response time distributions are often mixtures of multiple underlying base distributions each representing one of these strategies. For the case of two possible strategies, the observed response time distributions obey the fixed-point property. That is, there exists one reaction time that has the same probability of being observed irrespective of the actual mixture proportion of each strategy. In this paper we discuss how to compute this fixed-point, and how to statistically assess the probability that indeed the observed response times are generated by two competing strategies. Accompanying this paper is a free R package that can be used to compute and test the presence or absence of the fixed-point property in response time data, allowing for easy to use tests of strategic behavior

The impact of MRI scanner environment on perceptual decision-making

Despite the widespread use of functional magnetic resonance imaging (fMRI), few studies have addressed scanner effects on performance. The studies that have examined this question show a wide variety of results. In this article we report analyses of three experiments in which participants performed a perceptual decision-making task both in a traditional setting as well as inside an MRI scanner. The results consistently show that response times increase inside the scanner. Error rates also increase, but to a lesser extent. To reveal the underlying mechanisms that drive the behavioral changes when performing a task inside the MRI scanner, the data were analyzed using the linear ballistic accumulator model of decision-making. These analyses show that, in the scanner, participants exhibit a slow down of the motor component of the response and have less attentional focus on the task. However, the balance between focus and motor slowing depends on the specific task requirements

Toward cognitively constrained models of language processing:A review

Language processing is not an isolated capacity, but is embedded in other aspects of our cognition. However, it is still largely unexplored to what extent and how language processing interacts with general cognitive resources. This question can be investigated with cognitively constrained computational models, which simulate the cognitive processes involved in language processing. The theoretical claims implemented in cognitive models interact with general architectural constraints such as memory limitations. This way, it generates new predictions that can be tested in experiments, thus generating new data that can give rise to new theoretical insights. This theory-model-experiment cycle is a promising method for investigating aspects of language processing that are difficult to investigate with more traditional experimental techniques. This review specifically examines the language processing models of Lewis and Vasishth (2005), Reitter et al. (2011), and Van Rij et al. (2010), all implemented in the cognitive architecture Adaptive Control of Thought—Rational (Anderson et al., 2004). These models are all limited by the assumptions about cognitive capacities provided by the cognitive architecture, but use different linguistic approaches. Because of this, their comparison provides insight into the extent to which assumptions about general cognitive resources influence concretely implemented models of linguistic competence. For example, the sheer speed and accuracy of human language processing is a current challenge in the field of cognitive modeling, as it does not seem to adhere to the same memory and processing capacities that have been found in other cognitive processes. Architecture-based cognitive models of language processing may be able to make explicit which language-specific resources are needed to acquire and process natural language. The review sheds light on cognitively constrained models of language processing from two angles: we discuss (1) whether currently adopted cognitive assumptions meet the requirements for language processing, and (2) how validated cognitive architectures can constrain linguistically motivated models, which, all other things being equal, will increase the cognitive plausibility of these models. Overall, the evaluation of cognitively constrained models of language processing will allow for a better understanding of the relation between data, linguistic theory, cognitive assumptions, and explanation

Novel perspectives on the causal mind:Experiments, modeling, and theory

This thesis presents research into human causal cognition using a variety of perspectives and methodologies. I surveyed the existing literature on causal cognition and identified shortcomings, paying particular attention to different methodologies (from psychology, cognitive science, logic, and philosophy). This text is subdivided into three parts, each of which presents work using novel methods in a different field. These fields are 1) experimental psychology, 2) computational cognitive modelling, and 3) philosophy. In part 1 I present two experiments on causal reasoning where I teach participants causal network information and then ask them to solve inference problems in the form of causal probabilistic queries (e.g.: if X causes A and B, what is the probability of A being present knowing that X is but B is not present?). The first experiment focusses on the effect of time pressure on such causal judgements, while the second experiment uses multiple techniques to elicit repeated judgments for participants in order to assess both inter- and intra-participant variability in causal judgments. In the second part of the dissertation, we develop and test a new cognitive model of causal reasoning named the Bayesian Mutation Sampler. The first chapter in this section discusses the rationale behind the Bayesian Mutation Sampler and shows how it is an improvement over the model it is based on (the Mutation Sampler). In the next chapter I employ cognitive modelling to account for the inter- and intra-participant variability in causal judgments. This study confirms that the Bayesian Mutation Sampler outperforms other plausible models. In part 3 I take a radical turn towards philosophy. I identify, and subsequently build upon, a lack of an embodied and situated perspective on causal cognition. In this part I first give an introduction to the Skilled Intentionality Framework, which I then use to put forward an affordance-based theory of causal cognition which I develop using the literature on embodied cognition and ecological psychology

The Locus of the Gratton Effect in Picture-Word Interference

Between-trial effects in Stroop-like interference tasks are linked to differences in the amount of cognitive control. Trials following an incongruent trial show less interference, an effect suggested to result from the increased control caused by the incongruent previous trial (known as the Gratton effect). In this study, we show that cognitive control not only results in a different amount of interference but also in a different locus of the interference. That is, the stage of the task that shows the most interference changes as a function of the preceding trial. Using computational cognitive modeling, we explain these effects by a difference in the amount of processing of the irrelevant dimension of the stimulus