Nozick’s experience machine: An empirical study

Many philosophers deny that happiness can be equated with pleasurable experiences. Nozick introduced an experience machine thought experiment to support the idea that happiness requires pleasurable experiences that are “in contact with reality.” In this thought experiment, people can choose to plug into a machine that induces exclusively pleasurable experiences. We test Nozick’s hypothesis that people will reject this offer. We also contrast Nozick’s experience machine scenario with scenarios that are less artificial, and offer options which are less invasive or disruptive than being connected to a machine, specifically scenarios in which people are offered an experience pill or a pill that improves overall functioning.     

Progress in machine consciousness

This paper is a review of the work that has been carried out on machine consciousness. A clear overview of this diverse field is achieved by breaking machine consciousness down into four different areas, which are used to understand its aims, discuss its relationship with other subjects and outline the work that has been carried out so far. The criticisms that have been made against machine consciousness are also covered, along with its potential benefits, and the work that has been done on analysing systems for signs of consciousness. Some of the social and ethical issues raised by machine consciousness are examined at the end of the paper.     

Controlling behavior experiments with BASIC on 6502-based microcomputers

A combination of high-level BASIC and transparent machine language routines forms an inexpensive (about $800), powerful, and easy-to-use microcomputer system for behavior research, based on the AIM-65. The interrupt-driven machine language routines keep track of real time, poll inputs, and are not typically modified for different experiments. Expandable machine language listings that support eight input lines and eight output lines on the AIM-65 are presented. The user-written BASIC program controls experiments and records data. BASIC programs for simple reinforcement schedules are used as examples. The software is readily transferred to other 6602-based microcomputers with BASIC.     

What Am I? Virtual Machines and the Mind/Body Problem

When your word processor or email program is running on your computer, this creates a “virtual machine” that manipulates windows, files, text, etc. What is this virtual machine, and what are the virtual objects it manipulates? Many standard arguments in the philosophy of mind have exact analogues for virtual machines and virtual objects, but we do not want to draw the wild metaphysical conclusions that have sometimes tempted philosophers in the philosophy of mind. A computer file is not made of epiphenomenal ectoplasm. I argue instead that virtual objects are “supervenient objects.” The stereotypical example of supervenient objects is the statue and the lump of clay. To this end I propose a theory of supervenient objects. Then I turn to persons and mental states. I argue that my mental states are virtual states of a cognitive virtual machine implemented on my body, and a person is a supervenient object supervening on this cognitive virtual machine.     

Are unsupervised neural networks ignorant? Sizing the effect of environmental distributions on unsupervised learning

Learning environmental biases is a rational behavior: by using prior odds, Bayesian networks rapidly became a benchmark in machine learning. Moreover, a growing body of evidence now suggests that humans are using base rate information. Unsupervised connectionist networks are used in computer science for machine learning and in psychology to model human cognition, but it is unclear whether they are sensitive to prior odds. In this paper, we show that hard competitive learners are unable to use environmental biases while recurrent associative memories use frequency of exemplars and categories independently. Hence, it is concluded that recurrent associative memories are more useful than hard competitive networks to model human cognition and have a higher potential in machine learning.     

Generalization and similarity in exemplar models of categorization: Insights from machine learning

Exemplar theories of categorization depend on similarity for explaining subjects’ ability to generalize to new stimuli. A major criticism of exemplar theories concerns their lack of abstraction mechanisms and thus, seemingly, of generalization ability. Here, we use insights from machine learning to demonstrate that exemplar models can actually generalize very well. Kernel methods in machine learning are akin to exemplar models and are very successful in real-world applications. Their generalization performance depends crucially on the chosen similarity measure. Although similarity plays an important role in describing generalization behavior, it is not the only factor that controls generalization performance. In machine learning, kernel methods are often combined with regularization techniques in order to ensure good generalization. These same techniques are easily incorporated in exemplar models. We show that the generalized context model (Nosofsky, 1986) and ALCOVE (Kruschke, 1992) are closely related to a statistical model called kernel logistic regression. We argue that generalization is central to the enterprise of understanding categorization behavior, and we suggest some ways in which insights from machine learning can offer guidance.     

A tutorial on kernel methods for categorization

The abilities to learn and to categorize are fundamental for cognitive systems, be it animals or machines, and therefore have attracted attention from engineers and psychologists alike. Modern machine learning methods and psychological models of categorization are remarkably similar, partly because these two fields share a common history in artificial neural networks and reinforcement learning. However, machine learning is now an independent and mature field that has moved beyond psychologically or neurally inspired algorithms towards providing foundations for a theory of learning that is rooted in statistics and functional analysis. Much of this research is potentially interesting for psychological theories of learning and categorization but also hardly accessible for psychologists. Here, we provide a tutorial introduction to a popular class of machine learning tools, called kernel methods. These methods are closely related to perceptrons, radial-basis-function neural networks and exemplar theories of categorization. Recent theoretical advances in machine learning are closely tied to the idea that the similarity of patterns can be encapsulated in a positive definite kernel. Such a positive definite kernel can define a reproducing kernel Hilbert space which allows one to use powerful tools from functional analysis for the analysis of learning algorithms. We give basic explanations of some key concepts—the so-called kernel trick, the representer theorem and regularization—which may open up the possibility that insights from machine learning can feed back into psychology.     

To Think Human out of the Machine Paradigm: <Emphasis Type="Italic">Homo Ex Machina</Emphasis>

This article attempts to show that the metaphorical conception of human being as a machine takes a very specific epistemological standpoint. To make short the complex task of considering the implication of this paradigm for psychological and behavioral sciences, three important mismatches between the machine and the living human will be considered. Experience, agency and plasticity of human being are excluded in the scientific models and research activities when they are situated in the machine paradigm. For this reason, I claim that the machine paradigm does not offer the relevant frame for integrating results from various domains or approaches within human sciences, even if it can sometimes produce relevant scientific knowledge in certain domain at the scale of detailed investigation. Due to the importance of overcoming the fragmentation of scientific knowledge to solve the crisis in psychology, an “organic paradigm” should be elaborated which provides a new epistemological framework.     

Data compression in machine learning applied to natural language

In this paper, we investigate the possibility of applying machine learning methods to data derived from the area of natural language and show how rules, induced by machine learning, are changed after the original data are compressed by grouping together entries, attributes, and attribute values. Also shown is how excessive compression of input data may affect the accuracy of induced rules.     

Causal learning mechanisms in very young children: two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation

Three studies investigated whether young children make accurate causal inferences on the basis of patterns of variation and covariation. Children were presented with a new causal relation by means of a machine called the "blicket detector." Some objects, but not others, made the machine light up and play music. In the first 2 experiments, children were told that "blickets make the machine go" and were then asked to identify which objects were "blickets." Two-, 3-, and 4-year-old children were shown various patterns of variation and covariation between two different objects and the activation of the machine. All 3 age groups took this information into account in their causal judgments about which objects were blickets. In a 3rd experiment, 3- and 4-year-old children used the information when they were asked to make the machine stop. These results are related to Bayes-net causal graphical models of causal learning.     

Menschen, maschinen und g?dels theorem

Mechanism is the thesis that men can be considered as machines, that there is no essential difference between minds and machines.John Lucas has argued that it is a consequence of Gödel's theorem that mechanism is false. Men cannot be considered as machines, because the intellectual capacities of men are superior to that of any machine. Lucas claims that we can do something that no machine can do-namely to produce as true the Gödel-formula of any given machine. But no machine can prove its own Gödel-formula.In order to discuss and evaluate this argument, the author makes a distinction between formal and informal proofs, and between proofs given by men and proofs given by machines. It is argued that the informal proof capacities of machines are possibly greater and the formal proof capacities of men are possibly smaller than the anti-mechanist claims. So the argument from Gödel's theorem against mechanism fails.Though Gödel's theorem does not prove that minds are different from machines, it is not irrelevant to the analysis of thought and to the mind/machine controversy. It points to the importance of informal methods even within formal sciences and to the need for an analysis of the notion of informal thinking in cognitive science.     

Nozick's experience machine is dead,long live the experience machine!

Robert Nozick's experience machine thought experiment (Nozick's scenario) is widely used as the basis for a “knockdown” argument against all internalist mental state theories of well-being. Recently, however, it has been convincingly argued that Nozick's scenario should not be used in this way because it elicits judgments marred by status quo bias and other irrelevant factors. These arguments all include alternate experience machine thought experiments, but these scenarios also elicit judgments marred by status quo bias and other irrelevant factors. In this paper, several experiments are conducted in order to create and test a relatively bias-free experience machine scenario. It is argued that if an experience machine thought experiment is used to evaluate internalist mental state theories of well-being, then this relatively bias-free scenario should be used over any of the existing scenarios. Unlike the existing experience machine scenarios, when this new scenario is used to assess internalist mental state theories of well-being, it does not provide strong evidence to refute or endorse them.     

The observational study of adolescent gambling in UK amusement arcades

In the UK, excessive fruit machine playing is the most documented form of pathological gambling amongst adolescents. Although there have been a few retrospective questionnaire studies in adolescent fruit machine gambling, there has been very little systematic observational fieldwork into the behaviour. The studies reported explore the social world of fruit machine playing using data collected via the monitoring of 33 UK amusement arcades employing participant and non-participant observation methodologies. The basic aims were to observe the arcade clientele and their behavioural characteristics, and to examine motivations for machine playing. Results suggest that level of adolescent gambling depends upon both time of day and time of year, and regular players conform to rules of etiquette and display stereotypical behaviours when playing fruit machines. The results also suggest that adolescents play fruit machines for a wide range of reasons including fun, to win money, to socialize, to escape and for excitement, and that inland and coastal arcades are frequented by different clienteles, probably as a function of the amusement machine available.     

MORAL ORTHOSES: A NEW APPROACH TO HUMAN AND MACHINE ETHICS

Machines are increasingly involved in decisions with ethical implications, which require ethical explanations. Current machine learning algorithms are ethically inscrutable, but not in a way very different from human behavior. This article looks at the role of rationality and reasoning in traditional ethical thought and in artificial intelligence, emphasizing the need for some explainability of actions. It then explores Neil Lawrence's embodiment factor as an insightful way of looking at the differences between human and machine intelligence, connecting it to the theological understanding of embodiment, relationality, and personhood. Finally, it proposes the notion of artificial moral orthoses, which could provide ethical explanations for both artificial and human agents, as a more promising unifying approach to human and machine ethics.     

Personality Research and Assessment in the Era of Machine Learning

The increasing availability of high-dimensional, fine-grained data about human behaviour, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyse the data and interpret the results appropriately. Machine learning models are well suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modelling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modelling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.     

Complexity of symbolic representation in working memory of Transformer correlates with the complexity of a task

Even though Transformers are extensively used for Natural Language Processing tasks, especially for machine translation, they lack an explicit memory to store key concepts of processed texts. This paper explores the properties of the content of symbolic working memory added to the Transformer model decoder. Such working memory enhances the quality of model predictions in machine translation task and works as a neural-symbolic representation of information that is important for the model to make correct translations. The study of memory content revealed that translated text keywords are stored in the working memory, pointing to the relevance of memory content to the processed text. Also, the diversity of tokens and parts of speech stored in memory correlates with the complexity of the corpora for machine translation task.     

Cognitive Evaluation of Machine Learning Agents

Advances in applying statistical Machine Learning (ML) led to several claims of human-level or near-human performance in tasks such as image classification & speech recognition. Such claims are unscientific primarily for two reasons, (1) They incorrectly enforce the notion that task-specific performance can be treated as manifestation of General Intelligence and (2) They are not verifiable as currently there is no set benchmark for measuring human-like cognition in a machine learning agent. Moreover, ML agent’s performance is influenced by knowledge ingested in it by its human designers. Therefore, agent’s performance may not necessarily reflect its true cognition. In this paper, we propose a framework that draws parallels from human cognition to measure machine’s cognition. Human cognitive learning is quite well studied in developmental psychology with frameworks and metrics in place to measure actual learning. To either believe or refute the claims of human-level performance of machine learning agent, we need scientific methodology to measure its cognition. Our framework formalizes incremental implementation of human-like cognitive processes in ML agents with an implicit goal to measure it. The framework offers guiding principles for measuring, (1) Task-specific machine cognition and (2) General machine cognition that spans across tasks. The framework also provides guidelines for building domain-specific task taxonomies to cognitively profile tasks. We demonstrate application of the framework with a case study where two ML agents that perform Vision and NLP tasks are cognitively evaluated.     

Communicating intent to develop shared situation awareness and engender trust in human-agent teams

This paper addresses issues related to integrating autonomy-enabled, intelligent agents into collaborative, human-machine teams. Interaction with intelligent machine agents capable of making independent, goal-directed decisions in human-machine teaming operations constitutes a major change from traditional human-machine interaction involving teleoperation. Communicating the machine agent’s intent to human counterparts becomes increasingly important as independent machine decisions become subject to human trust and mental models. The authors present findings from their research that suggest existing user display technologies, tailored with context-specific information and the human’s knowledge level of the machine agent’s decision process, can mitigate misperceptions of the appropriateness of agent behavioral responses. This is important because misperceptions on the part of human team members increases the likelihood of trust degradation and unnecessary interventions, ultimately leading to disuse of the agent. Examples of possible issues associated with communicating agent intent, as well as potential implications for trust calibration are provided.     

What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms

In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which explore the interplay between technologies and morality, we present an analysis of concerns related to the adoption of machine learning-aided medical diagnosis. We analyze anticipated moral issues that machine learning systems pose for different stakeholders, such as bias and opacity in the way that models are trained to produce diagnoses, changes to how health care providers, patients, and developers understand their roles and professions, and challenges to existing forms of medical legislation. Albeit preliminary in nature, the insights offered by the technomoral change and the technological mediation approaches expand and enrich the current discussion about machine learning in diagnostic practices, bringing distinct and currently underexplored areas of concern to the forefront. These insights can contribute to a more encompassing and better informed decision-making process when adapting machine learning techniques to medical diagnosis, while acknowledging the interests of multiple stakeholders and the active role that technologies play in generating, perpetuating, and modifying ethical concerns in health care.

     

Perspective on “in the wild” movement analysis using machine learning

Recent advances in wearable sensing and machine learning have created ample opportunities for “in the wild” movement analysis in sports, since the combination of both enables real-time feedback to be provided to athletes and coaches, as well as long-term monitoring of movements. The potential for real-time feedback is useful for performance enhancement or technique analysis, and can be achieved by training efficient models and implementing them on dedicated hardware. Long-term monitoring of movement can be used for injury prevention, among others. Such applications are often enabled by training a machine learned model from large datasets that have been collected using wearable sensors. Therefore, in this perspective paper, we provide an overview of approaches for studies that aim to analyze sports movement “in the wild” using wearable sensors and machine learning. First, we discuss how a measurement protocol can be set up by answering six questions. Then, we discuss the benefits and pitfalls and provide recommendations for effective training of machine learning models from movement data, focusing on data pre-processing, feature calculation, and model selection and tuning. Finally, we highlight two application domains where “in the wild” data recording was combined with machine learning for injury prevention and technique analysis, respectively.