The myth of the moral faculty: Response to Kirkby

David Kirkby argues that I have misrepresented Marc Hauser's conception of a moral faculty, in a way that invalidates my chief arguments against the existence and necessity of such a faculty. The core of Kirkby's challenge is that what Hauser lists as necessary conditions for the moral faculty to do its work are not themselves components of that faculty. I argue that there is no useful way to distinguish necessary conditions of moral judgments from the alleged moral faculty itself, and that even if we could do this, we would be left with an empty non-explanation of the supposed moral faculty that amounts to nothing more than the claim that we have a faculty that generates judgments of right versus wrong.     

The cognitive impenetrability of the content of early vision is a necessary and sufficient condition for purely nonconceptual content

I elaborate on Pylyshyn's definition of the cognitive impenetrability (CI) of early vision, and draw on the role of concepts in perceptual processing, which links the problem of the CI or cognitive penetrability (CP) of early vision with the problem of the nonconceptual content (NCC) of perception. I explain, first, the sense in which the content of early vision is CI and I argue that if some content is CI, it is conceptually encapsulated, that is, it is NCC. Then, I examine the definitions of NCC and argue that they lead to the view that the NCC of perception is retrieved in a stage of visual processing that is CI. Thus, the CI of a state and content is a sufficient and necessary condition for the state and its content to be purely NCC, the CI?≡?NCC thesis. Since early vision is CI, the purely NCC of perception is formed in early vision. I defend the CI?≡?NCC thesis by arguing against objections raised against both the sufficient and the necessary part of the thesis.     

A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data).     

Empiricism,probability, and knowledge of arithmetic: A preliminary defense

The topic of this paper is our knowledge of the natural numbers, and in particular, our knowledge of the basic axioms for the natural numbers, namely the Peano axioms. The thesis defended in this paper is that knowledge of these axioms may be gained by recourse to judgments of probability. While considerations of probability have come to the forefront in recent epistemology, it seems safe to say that the thesis defended here is heterodox from the vantage point of traditional philosophy of mathematics. So this paper focuses on providing a preliminary defense of this thesis, in that it focuses on responding to several objections. Some of these objections are from the classical literature, such as Frege's concern about indiscernibility and circularity (Section 2.1), while other are more recent, such as Baker's concern about the unreliability of small samplings in the setting of arithmetic (Section 2.2). Another family of objections suggests that we simply do not have access to probability assignments in the setting of arithmetic, either due to issues related to the ω-rule (Section 3.1) or to the non-computability and non-continuity of probability assignments (Section 3.2). Articulating these objections and the responses to them involves developing some non-trivial results on probability assignments (Appendix A–Appendix C), such as a forcing argument to establish the existence of continuous probability assignments that may be computably approximated (Theorem 4 Appendix B). In the concluding section, two problems for future work are discussed: developing the source of arithmetical confirmation and responding to the probabilistic liar.     

Cognitive load while driving impairs memory of moving but not stationary elements within the environment

The negative impact of cognitive load, such as cell phone conversations, while driving is well established, but understanding the nature of this performance deficit is still being developed. To test the impact of load on awareness of different elements in a driving scene, memory for items within the environment was examined under load and no load conditions. Participants drove through two different scenarios in a driving simulator, were periodically interrupted by a pause in the driving during, and were asked questions regarding moving and stationary objects in the environment. Participants in the load condition drove while concurrently counting backwards by sevens. Results indicate that driving under load conditions led to diminished knowledge of moving, but not stationary, objects in the scene. This result suggests not all types of knowledge are equally impaired. Potential implications for current theories of cell phone use while driving and applied attention theory are discussed.     

Are there gender differences in cognitive reflection? Invariance and differences related to mathematics

ABSTRACT

Cognitive reflection is recognized as an important skill, which is necessary for making advantageous decisions. Even though gender differences in the Cognitive Reflection test (CRT) appear to be robust across multiple studies, little research has examined the source of the gender gap in performance. In Study 1, we tested the invariance of the scale across genders. In Study 2, we investigated the role of math anxiety, mathematical reasoning, and gender in CRT performance. The results attested the measurement equivalence of the Cognitive Reflection Test – Long (CRT- L), when administered to male and female students. Additionally, the results of the mediation analysis showed an indirect effect of gender on CRT-L performance through mathematical reasoning and math anxiety. The direct effect of gender was no longer statistically significant after accounting for the other variables. The current findings suggest that cognitive reflection is affected by numerical skills and related feelings.     

Driving manoeuvre during lane maintenance in older adults: Associations with neuropsychological scores

Older drivers experience difficulties in lane maintenance under challenging driving sections due to age-related cognitive declines, yet there is little comprehensive evidence on associations between cognitive functions and the lane maintenance in this population. In this study, fifty older drivers completed an on-road driving assessment and a battery of standard neuropsychological tests. Mean Lane Position (MLP), Standard Deviation of Lane Position (SDLP) and manoeuvre time calculated from precise vehicle movement trajectories were used as the lane maintenance parameters. The GNSS tracking vehicle movement presents comprehensive and reliable vehicle position data, which is more sensitive for detecting subtle variations of lane maintenance in older drivers. Statistical analysis results showed that lower visual attention (selective and divided attention) was associated with higher MLP and SDLP; MLP was also correlated to spatial abilities, executive function, and motor speed; manoeuvre time was negatively correlated with drivers’ risk-taking personality (all p < .01). Selective attention was found to be the best predictor of MLP in lane maintenance. A combined eight variables from three neuropsychological tests, UFOV 2 and 3, BD and BJLO, D-KEFS TMT 1, 2, 3, and 4, correctly classified 80.4% of participants with good versus low-performing lane maintenance.     

Cognitive function and young drivers: The relationship between driving,attitudes, personality and cognition

Young drivers (aged 17–25 years) are the highest risk age group for driving crashes and are over-represented in car crash statistics in Australia. A relationship between cognitive functioning and driving in older drivers (60 years and older) has been consistently supported in previous literature, however, this relationship has been neglected in research regarding younger drivers. The role of cognitive functioning in young people’s driving was investigated both independently and within a current model of younger peoples driving performance. With young drivers as participants, driving behaviour, attitudes, personality and cognitive functioning were tested and driving performance was operationalised through two measures on a driving simulator, speeding and lane deviations. Cognitive functioning was found to contribute to driving behaviour, along with driving attitudes and personality traits, in accounting for young people’s driving performance. The young drivers who performed better on cognitive functioning tasks engaged in less speeding behaviour and less lane deviation on the driving simulator than those who performed worse on these tasks. This result was found independent of the role of driving behaviour, driving attitudes and personality traits, accounting for unique variance in driving ability.     

Computational model of enactive visuospatial mental imagery using saccadic perceptual actions

From the onset of cognitive revolution, the concept of mental imagery has been given different, many times opposing, theoretical accounts. Mental imagery appears to be a ubiquitous, yet wholly individual, easy to explain experience on the one hand, being hard to deal with scientifically on the other hand. The focus of this research is on an enactive approach to visuospatial mental imagery, inspired by Sima’s perceptual instantiation theory. We designed a hybrid computational model, composed of a forward model, an inverse model, both implemented as neural networks, and a memory/controller module, that grounds simple mental concepts, such as a triangle and a square, in perceptual actions, and is able to reimagine these objects by performing the necessary perceptual actions in a simulated humanoid robot. We tested the model on three tasks – salience-based object recognition, imagination-based object recognition and object imagination – and achieved very good results showing, as a proof of concept, that perceptual actions are a viable candidate for grounding the visuospatial mental concepts as well as the credible substrate of visuospatial mental imagery.     

Evolution of the Icarus Cognitive Architecture

Cognitive architectures serve as both unified theories of the mind and as computational infrastructures for constructing intelligent agents. In this article, we review the evolution of one such framework, Icarus, over the three decades of its development. We discuss the representational and processing assumptions made by different versions of the architecture, their relation to alternative theories, and some promising directions for future research.