Pierre Poirier

Systematicity and the cognition of structured domains

The current debate over systematicity concerns the formal conditions a scheme of mental representation must satisfy in order to explain the systematicity of thought. The systematicity of thought is assumed to be a pervasive property of minds, and can be characterized (roughly) as follows: anyone who can think T can think systematic variants of T, where the systematic variants of T are found by permuting T’s constituents. So, for example, it is an alleged fact that anyone who can think the thought that John loves Mary can think the thought that Mary loves John, where the latter thought is a systematic variant of the former.

Representation and Indication

Publisher Summary This chapter focuses on two kinds of mental content, “representation” and “indication,” explaining how they are related. “Mental Representation” is a term to mean any mental state or process that has a semantic content. “Indication” is just a semantic-sounding word for detection. Researchers need a way to mark the distinction between the mechanism that does the detection and the state or process that is the signal that the target has been detected. Indicator signals are arbitrary in a way that representations are not. This actually follows from the transitivity of indication. Given transitivity, anything can be made to indicate anything else, given enough ingenuity and resources. It follows from the arbitrariness of indicator signals that disciplined structural transformation of them not going to alter their meanings. Grounding representation in indication by promoting portable indicator signals into the semantic constituents of complex representations inevitably leads to a scheme that represents propositions and nothing else. Edge detection allows neurons in the primary visual cortex to respond to visual signals in a maximally independent fashion and thus produce sparsely coded representations of the visual field.

Epistemological strata and the rules of right reason

It has been commonplace in epistemology since its inception to idealize away from computational resource constraints, i.e., from the constraints of time and memory. One thought is that a kind of ideal rationality can be specified that ignores the constraints imposed by limited time and memory, and that actual cognitive performance can be seen as an interaction between the norms of ideal rationality and the practicalities of time and memory limitations. But a cornerstone of naturalistic epistemology is that normative assessment is constrained by capacities: you cannot require someone to do something they cannot or, as it is usually put, ought implies can. This much we take to be uncontroversial. We argue that differences in architectures, goals and resources imply substantial differences in capacity, and that some of these differences are ineliminable. It follows that some differences in goals and architectural and computational resources matter at the normative level: they constrain what principles of normative epistemology can be used to describe and prescribe their behavior. As a result, we can expect there to be important epistemic differences between the way brains, individuals, and science work.

Cognitive evolutionary psychology without representational nativism

A viable evolutionary cognitive psychology requires that specific cognitive capacities be (a) heritable and (b) ‘quasi-independent’ from other heritable traits. They must be heritable because there can be no selection for traits that are not. They must be quasi-independent from other heritable traits, since adaptive variations in a specific cognitive capacity could have no distinctive consequences for fitness if effecting those variations required widespread changes in other unrelated traits and capacities as well. These requirements would be satisfied by innate cognitive modules, as the dominant paradigm in evolutionary cognitive psychology assumes. However, those requirements would also be satisfied by heritable learning biases, perhaps in the form of architectural or chronotopic constraints, that operated to increase the canalization of specific cognitive capacities in the ancestral environment (Cummins and Cummins 1999). As an organism develops, cognitive capacities that are highly canalized as the result of heritable learning biases might result in an organism that is behaviourally quite similar to an organism whose innate modules come on line as the result of various environmental triggers. Taking this possibility seriously is increasingly important as the case against innate cognitive modules becomes increasingly strong.

Atomistic learning in non-modular systems

We argue that atomistic learning—learning that requires training only on a novel item to be learned—is problematic for networks in which every weight is available for change in every learning situation. This is potentially significant because atomistic learning appears to be commonplace in humans and most non-human animals. We briefly review various proposed fixes, concluding that the most promising strategy to date involves training on pseudo-patterns along with novel items, a form of learning that is not strictly atomistic, but which looks very much like it ‘from the outside’.

A Multi Scale Cognitive Architecture to Account for the Adaptive and Reflective Nature of Behaviour

We present a multi-agent cognitive architecture in which reactive and sequential processes can dynamically influence each other to insure that behaviour is responsive to current context (behavioural and situational) while sensitive to the longer term behavioural sequences needed to solve complex problems. We present the implementation of such a system using an agent based approach and illustrate its processing trough the simulation of two psychological tasks.

Evaluating the Effectiveness of an Affective Tutoring Agent in Specialized Education

Autism spectrum disorder (ASD) is a neurological disorder affecting the way in which the brain processes information. Autism is characterized by impairments in learning and communication, in social interaction, imaginative ability as well as in repetitive and restricted patterns of behavior [9]. This research contributes to the advancement of intelligent tutoring systems by proposing an affective intelligent tutoring system in the field of specialized education. We have conducted an experiment in mathematical learning with one controlled group of six participants who interacted without the support of the pedagogical agent Jessie, while the test group of six participants interacted with Jessie. The purpose of this study was to validate the support provided by the pedagogical agent Jessie based on our accompaniment model. The results showed significant improvement in learning by the test group.

Towards an Effective Affective Tutoring Agent in Specialized Education

This research contributes to the advancement of intelligent tutoring systems by proposing an affective intelligent tutoring system in the field of specialized education. The Integrated Specialized Learning Application ISLA helps autistic children manage their emotions by analyzing the learning trace and considering the learners current performance to respond accordingly to it during a mathematical learning situation. We have conducted an experiment to validate the support provided by Jessie based on our accompaniment model. The results showed significant improvement in learning by the test group.

Towards an Integrated Specialized Learning Application (ISLA) to Support High Functioning ASD Children in Mathematics Learning

Autism spectrum disorder (ASD) is a neurological disorder affecting the way in which the brain processes information. It can affect all aspects of a person’s development. Autism is characterized by impairments in learning and communication, in the social interaction, imaginative ability as well as in repetitive and restricted patterns of behavior (Diagnostic and statistical manual of mental disorders: DSM-IV [12]). This research contributes to the advancement of intelligent tutoring systems by proposing a computational model in the field of specialized education in order to overcome the lack of individualized intervention, such as in the specialized education of individuals with autism. The affective intelligent tutoring system ISLA is an adaptive application evolving along with the learner’s needs. ISLA is unique and its contribution entails the model of accompaniment to help autistic children manage their emotions by analyzing the learning trace and considering the student’s current performance to respond accordingly to it during a mathematical learning situation such as addition.