This paper reports an experiment which investigates a possible cognitive antecedent of event-splitting effects (ESEs) experimentally observed by Starmer and Sugden (1993) and Humphrey (1995) – the learning of absolute frequency of event category impacting on the learning of probability of event category – and reveals some evidence that it is responsible for observed ESEs. It is also suggested and empirically substantiated that stripped-down prospect theory will accurately predict ESEs in some decision making tasks, but will not (...) perform well in others. This contention, it is argued, is indicative of fundamental descriptive shortcomings in the economic conception of choice under uncertainty and may entail implications beyond the direct concerns of this paper. (shrink)

Generalized probabilistic learning takes place in a black-box where present probabilities lead to future probabilities by way of a hidden learning process. The idea that generalized learning can be partially characterized by saying that it doesn’t foreseeably lead to harmful decisions is explored. It is shown that a martingale principle follows for finite probability spaces.

The representational epistemic approach to the design of visual displays and notation systems advocates encoding the fundamental conceptual structure of a knowledge domain directly in the structure of a representational system. It is claimed that representations so designed will benefit from greater semantic transparency, which enhances comprehension and ease of learning, and plastic generativity, which makes the meaningful manipulation of the representation easier and less error prone. Epistemic principles for encoding fundamental conceptual structures directly in representational schemes are described. (...) The diagrammatic recodification of probability theory is undertaken to demonstrate how the fundamental conceptual structure of a knowledge domain can be analyzed, how the identified conceptual structure may be encoded in a representational system, and the cognitive benefits that follow. An experiment shows the new probability space diagrams are superior to the conventional approach for learning this conceptually challenging topic. (shrink)

Previous research demonstrates that implicitly learned probability information can guide visual attention. We examined whether the probability of an object changing can be implicitly learned and then used to improve change detection performance. In a series of six experiments, participants completed 120–130 training change detection trials. In four of the experiments the object that changed color was the same shape on every trial. Participants were not explicitly aware of this change probability manipulation and change detection (...) performance was not improved for the trained shape versus untrained shapes. In two of the experiments, the object that changed color was always in the same general location . Although participants were not explicitly aware of the change probability, implicit knowledge of it did improve change detection performance in the trained location. These results indicate that improved change detection performance through implicitly learned change probability occurs for location but not shape. (shrink)

Bayesianism can be characterized as the following twofold position: (i) rational credences obey the probability calculus; (ii) rational learning, i.e., the updating of credences, is regulated by some form of conditionalization. While the formal aspect of various forms of conditionalization has been explored in detail, the philosophical application to learning from experience is still deeply problematic. Some philosophers have proposed to revise the epistemology of perception; others have provided new formal accounts of conditionalization that are more in (...) line with how we learn from perceptual experience. The current investigation argues that Bayesian epistemology is still incomplete; the epistemology of perception and the epistemology of rational reasoning have not been reconciled. (shrink)

The conjunction fallacy occurs whenever probability compounds are thought of as more likely than its component probabilities alone. In the experiment we present, subjects chose between simple and compound lotteries after some practice. Depending on the condition, they were given more or less information about the nature of probability compounds. The conjunction fallacy was surprisingly robust. There was, however, a puzzling dissociation between verbal and behavioral learning: verbal responses were sensitive, but actual choices entirely insensitive, to the (...) amount of verbal instructions being provided. This might reflect a dichotomy between implicit and explicit learning. Caution must be exercised in generalizing results from what people say to what people do. (shrink)

There are two main approaches to how statistical patterns are extracted from sequences: The transitional probability approach proposes that statistical learning occurs through the computation of probabilities between items in a sequence. The chunking approach, including models such as PARSER and TRACX, proposes that units are extracted as chunks. Importantly, the chunking approach suggests that the extraction of full units weakens the processing of subunits while the transitional probability approach suggests that both units and subunits should strengthen. (...) Previous findings using sequentially organized, auditory stimuli or spatially organized, visual stimuli support the chunking approach. However, one limitation of prior studies is that most assessed learning with the two-alternative forced-choice task. In contrast, this pre-registered experiment examined the two theoretical approaches in sequentially organized, visual stimuli using an online self-paced task—arguably providing a more sensitive index of learning as it occurs—and a secondary offline familiarity judgment task. During the self-paced task, abstract shapes were covertly organized into eight triplets (ABC) where one in every eight was altered (BCA) from the canonical structure in a way that disrupted the full unit while preserving a subunit (BC). Results from the offline familiarity judgment task revealed that the altered triplets were perceived as highly familiar, suggesting the learned representations were relatively flexible. More importantly, results from the online self-paced task demonstrated that processing for subunits, but not unit-initial stimuli, was impeded in the altered triplet. The pattern of results is in line with the chunking approach to statistical learning and, more specifically, the TRACX model. (shrink)

The ability to learn cause–effect relations from experience is critical for humans to behave adaptively — to choose causes that bring about desired effects. However, traditional experiments on experience-based learning involve events that are artificially compressed in time so that all learning occurs over the course of minutes. These paradigms therefore exclusively rely upon working memory. In contrast, in real-world situations we need to be able to learn cause–effect relations over days and weeks, which necessitates long-term memory. (...) 413 participants completed a smartphone study, which compared learning a cause–effect relation one trial per day for 24 days versus the traditional paradigm of 24 trials back- to- back. Surprisingly, we found few differences between the short versus long timeframes. Subjects were able to accurately detect generative and preventive causal relations, and they exhibited illusory correlations in both the short and long timeframe tasks. These results provide initial evidence that experience-based learning over long timeframes exhibits similar strengths and weaknesses as in short timeframes. However, learning over long timeframes may become more impaired with more complex tasks. (shrink)

Preoperative diagnosis of gastric cancer and primary gastric lymphoma is challenging and has important clinical significance. Inspired by the inductive reasoning learning of the human brain, transfer learning can improve diagnosis performance of target task by utilizing the knowledge learned from the other domains (source domain). However, most studies focus on single-source transfer learning and may lead to model performance degradation when a large domain shift exists between the single-source domain and target domain. By simulating the multi-modal (...) information learning and transfer mechanism of human brain, this study designed a multisource transfer learning feature extraction and classification framework, which can enhance the prediction performance of the target model by using multisource medical data (domain). First, this manuscript designs a feature extraction network that takes the maximum mean difference based on the Wasserstein distance as an adaptive measure of probability distribution and extracts the domain-specific invariant representations between source and target domain data. Then, aiming at the random generation of parameters bringing uncertainties to prediction accuracy and generalization ability of extreme learning machine network, the 1-norm regularization is used to implement sparse constraints of the output weight matrix and improve the robustness of the model. Finally, some experiments are carried out on the data of two medical centers. The experimental results show that the area under curves (AUCs) of the method are 0.958 and 0.929 in the two validation cohorts, respectively. The method in this manuscript can provide doctors with a better diagnostic reference, which has certain practical significance. (shrink)

To introduce this collection of research studies, which stem from the pro grams conducted by The World Phenomenology Institute, we need say a few words about our aims and work. This will bring to light the significance of the present volume. The phenomenological philosophy is an unprejudiced study of experience in its entire range: experience being understood as yielding objects. Experi ence, moreover, is approached in a specific way, such a way that it legitima tizes itself naturally in immediate evidence. (...) As such it offers a unique ground for philosophical inquiry. Its basic condition, however, is to legitimize its validity. In this way it allows a dialogue to unfold among various philosophies of different methodologies and persuasions, so that their basic assumptions and conceptions may be investigated in an objective fashion. That is, instead of comparing concepts, we may go below their differences to seek together what they are meant to grasp. We may in this way come to the things them selves, which are the common objective of all philosophy, or what the great Chinese philosopher Wang Yang Ming called "the investigation of things". It is in this spirit that the Institute's programs include a "cross-cultural" dialogue meant to bring about a profound communication among philosophers in their deepest concerns. Rising above artificial cultural confinements, such dialogues bring scholars, thinkers and human beings together toward a truly human community of minds. Our Institute unfolds one consistent academic program. (shrink)

While the effects of pattern learning on language processing are well known, the way in which pattern learning shapes exploratory behavior has long gone unnoticed. We report on the way in which individual differences in statistical pattern learning affect performance in the domain of language along multiple dimensions. Analyzing data from healthy monolingual adults' performance on a serial reaction time task and a self‐paced reading task, we show how individual differences in statistical pattern learning are reflected (...) in readers' knowledge of linguistic co‐occurrence patterns and in their exploration and exploitation of content‐specific and task‐general information. First, we investigated the extent to which an individual's pattern learning correlates with his or her sensitivity to systematic morphological and syntactic co‐occurrences, as evidenced while reading authentic sentences. We found that the stream of morphological and syntactic information has a more pronounced effect on the reading speed of, as we will label them, content‐sensitive learners in that the more probable the co‐occurrence pattern, the faster their reading of that pattern will be. Next, we investigated how differences in pattern learning are reflected in the ways in which individuals approach the reading task itself and adapt to it. Casting this relation in terms of exploration/exploitation strategies, known from Reinforcement Learning, we conclude that content‐sensitive learners are also more likely to initially probe (explore) a wider range of directly relevant patterns, which they can later use (exploit) to optimize their reading performance further. By affecting exploratory behavior, pattern learning influences the information that is gathered and becomes available for exploitation, thereby increasing the effect pattern learning has on language cognition. (shrink)

Multiple cue probability learning studies have typically focused on stationary environments. We present three experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that participants adapted to these types of change in qualitatively different ways. Also, in contrast to earlier claims that these tasks are learned implicitly, participants showed good insight into what they (...) learned. By fitting formal learning models, we investigated whether participants learned global functional relationships or made localized predictions from similar experienced exemplars. Both a local (the Associative Learning Model) and a global learning model (the novel Bayesian Linear Filter) fitted the data of the first two experiments. However, the results of Experiment 3, which was specifically designed to discriminate between local and global learning models, provided more support for global learning models. Finally, we present a novel model to account for the cue competition effects found in previous research and displayed by some of our participants. (shrink)

Numerous studies have convincingly shown that prospect theory can better describe risky choice behavior than the classical expected utility model because it makes the plausible assumption that risk aversion is driven not only by the degree of sensitivity toward outcomes, but also by the degree of sensitivity toward probabilities. This article presents the results of an experiment aimed at testing whether agents become more sensitive toward probabilities over time when they repeatedly face similar decisions, receive feedback on the consequences of (...) their decisions, and are given ample incentives to reflect on their decisions, as predicted by Plott’s Discovered Preference Hypothesis (DPH). The results of a laboratory experiment with N = 62 participants support this hypothesis. The elicited subjective probability weighting function converges significantly toward linearity when respondents are asked to make repeated choices and are given direct feedback after each choice. Such convergence to linearity is absent in an experimental treatment where respondents are asked to make repeated choices but do not experience the resolution of risk directly after each choice, as predicted by the DPH. (shrink)

Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem property such as computational difficulty of the rules biased the search process and so affected learning. Experiment 2 examined the impact of (...) examples as instructional tools and found that their effectiveness was determined by whether they uniquely pointed to the correct rule. Experiment 3 compared verbal directions with examples and found that both could guide search. The final experiment tried to improve learning by using more explicit verbal directions or by adding scaffolding to the example. While both manipulations improved learning, learning still took the form of a search through a hypothesis space of possible rules. We describe a model that embodies two assumptions: the instruction can bias the rules participants hypothesize rather than directly be encoded into a rule; participants do not have memory for past wrong hypotheses and are likely to retry them. These assumptions are realized in a Markov model that fits all the data by estimating two sets of probabilities. First, the learning condition induced one set of Start probabilities of trying various rules. Second, should this first hypothesis prove wrong, the learning condition induced a second set of Choice probabilities of considering various rules. These findings broaden our understanding of effective instruction and provide implications for instructional design. (shrink)

The heart of my dissertation project is the proposal of a new updating rule for responding to learning experiences consisting of continuous streams of evidence. I suggest characterizing this kind of learning experience as a continuous stream of stipulated credal derivatives, and show that Continuous Probability Kinematics is the uniquely coherent response to such a stream which satisfies a continuous analogue of Rigidity – the core property of both Bayesian and Jeffrey conditionalization. In the first chapter, I (...) define neighborhood norms of rationality with reference to Kenny Easwaran’s definition of neighborhood properties. I summarize and comment on some of the key arguments in the dispute between time-slice epistemologists, who argue that there are no fundamentally diachronic norms of rationality, and the proponents of diachronic norms. I am sympathetic to two of the key motivations often given in support of the synchronist position: mentalist internalism and the idea that metaphysical disputes about the identity of persons in bizarre puzzle cases should not play a central role in epistemologists’ assessments of the rationality of agents. However, I argue that time-slice epistemology cannot adequately address the rationality of temporally-extended processes like reasoning and learning. Neighborhood norms present a viable third way between these two positions, capturing much of the spirit of the previously-discussed synchronist motivations while still providing just enough temporal structure to meaningfully guide and evaluate temporally-extended rational processes. Continuous Probability Kinematics is an example of one such neighborhood norm. In the second chapter, I develop my updating rule CPK and establish many of its core properties. Of special note here are the deep connections to Jeffrey’s Probability Kinematics, as well as some key differences. The net result of any CPK updating process will always be representable as a Jeffrey shift on the refined partition generated by the propositions that the agent is receiving direct evidence concerning. However, one crucial difference is that CPK provides an intuitive account of how to combine the effects of learning experiences that are each about fundamentally different underlying partitions. In CPK’s formalism, an agent can receive simultaneous evidence streams about an arbitrary (finite) number of propositions, which can themselves be evidentially related in any way. At any given instant, the result of the combination is a simple sum of the effects that learning about the individual propositions would have separately. CPK is concerned with a novel kind of learning experience and involves a novel characterization of evidence. The third and final chapter of this dissertation is concerned with explaining what this characterization of evidence means and with arguing that it can be the basis for genuine learning. I begin by characterizing learning experiences in terms of the Value of Information, and prove a Value of Information theorem for CPK learning experiences under the assumption of a Martingale constraint on the agent’s prior distribution over the signals that they might receive. I examine Timothy Williamson’s arguments that evidence must be propositional and express my skepticism. I then explore two different routes to model agents who update by CPK as if they are learning some propositional content and updating the rest of their credences by Bayesian conditionalization on this content. The second of these two routes provides a very interesting lens to reexamine the evidential commitments that underwrite updating by CPK, which I analyze. (shrink)

A new formal model of belief dynamics is proposed, in which the epistemic agent has both probabilistic beliefs and full beliefs. The agent has full belief in a proposition if and only if she considers the probability that it is false to be so close to zero that she chooses to disregard that probability. She treats such a proposition as having the probability 1, but, importantly, she is still willing and able to revise that probability assignment (...) if she receives information that gives her sufficient reasons to do so. Such a proposition is undoubted, but not undoubtable. In the formal model it is assigned a probability 1 − δ, where δ is an infinitesimal number. The proposed model employs probabilistic belief states that contain several underlying probability functions representing alternative probabilistic states of the world. Furthermore, a distinction is made between update and revision, in the same way as in the literature on belief change. The formal properties of the model are investigated, including properties relevant for learning from experience. The set of propositions whose probabilities are infinitesimally close to 1 forms a belief set. Operations that change the probabilistic belief state give rise to changes in this belief set, which have much in common with traditional operations of belief change. (shrink)

In typical statistical learning studies, researchers define sequences in terms of the probability of the next item in the sequence given the current item (or items), and they show that high probability sequences are treated as more familiar than low probability sequences. Existing accounts of these phenomena all assume that participants represent statistical regularities more or less as they are defined by the experimenters—as sequential probabilities of symbols in a string. Here we offer an alternative, or (...) possibly supplementary, hypothesis. Specifically, rather than identifying or labeling individual stimuli discretely in order to predict the next item in a sequence, we need only assume that the participant is able to represent the stimuli as evincing particular similarity relations to one another, with sequences represented as trajectories through this similarity space. We present experiments in which this hypothesis makes sharply different predictions from hypotheses based on the assumption that sequences are learned over discrete, labeled stimuli. We also present a series of simulation models that encode stimuli as positions in a continuous two‐dimensional space, and predict the next location from the current location. Although no model captures all of the data presented here, the results of three critical experiments are more consistent with the view that participants represent trajectories through similarity space rather than sequences of discrete labels under particular conditions. (shrink)

We express here the statement » The probability of a given b equals r « symbolically by » p = r «. A formal axiomatic calculus can be constructed comprising all the well‐known laws of probability theory. This calculus can be interpreted in various ways. The present paper is a criticism of the subjective interpretation; that is to say, of any interpretation which assumes that probability expresses degrees of incomplete knowledge: a is the statement incompletely known, b (...) is our total knowledge, and p is the degree to which a is entailed by b. The subjective interpretation has often been proposed as an explanation and even a sharpening of the various objective theories . It is shown in the paper that this proposal cannot be realised because the subjective theory cannot lead to results which are compatible with the objective theory. This is due to various reasons, the most important of which is that the objective theory interprets » b « in » p « as a statement of the objective conditions of an experiment and » a « as one of its possible results. The subjective theory on the other hand interprets » b « as our total relevant knowledge which will in general include some knowledge of previous results of the experiment. It is shown that this must lead to incompatibility owing to the fact that this knowledge of previous results must influence the value of the probability.It is shown, in this way, that any probabilistic theory of the process of learning from experience—that is to say, any probabilistic theory of induction—must lead to contradictions.RésuméL'énoncé: » La probabilité de a, si b est donné, est égale à r « est ici exprimé symboliquement par » p = r «. On peut construire un système axiomatique formel qui contient toutes les règles bien connues du calcul des probabilités. Ce calcul formel peut ětre interprété de manières très différentes. L'article contient une critique des interprétations subjectives, c'est‐à‐dire des interprétations qui admettent que la probabilité exprime des degrés de connaissance incomplète: a est un énoncé qu'on ne connaǐt que partiellement, b est notre connaissance totale, et p exprime le degré auquel a est implicitement contenu dans b. L'interprétation subjective a souvent été proposée comme une explication et un renforcement des différentes théories objectives . Dans cet article, l'auteur montre que ce projet n'est pas réalisable, car la théorie subjective mène à des résultats qui contredisent la théorie objective. Plusieurs circonstances sont responsables de cette situation; la plus importante, c'est que la théorie interprète le » b « dans » p « comme une description des conditions objectives d'une expérience . D'autre part, la théorie subjective interprète » b « comme notre savoir total , et ce savoir inclura en général des informations sur les résultats obtenus antérieurement pour cette expérience. L'auteur montre que ce fait conduit à une contradiction entre les deux interprétations, car cette connaissance doit avoir une influence sur la valeur subjective de la probabilité.A l'aide de ce raisonnement, on peut montrer que la théorie des probabilités appliquée au processus inductif tirant une expérience du domaine expérimental conduit à des contradictions.ZusammenfassungDie Aussage » Die Wahrscheinlichkeit von a, wenn b gegeben ist, ist gleich r « wird hier symbolisch durch » p = r « ausgedrückt. Ein formales Axiomensystem kann konstruiert werden, das alle die wohlbekannten Regeln der Wahrscheinlichkeitsrechnung enthält. Dieser formale Kalkül kann in sehr verschiedener Weise interpretiert werden. Der Artikel enthält eine Kritik der subjektiven Interpretationen; das heisst, jener Interpretationen die annehmen, dass die Wahrscheinlichkeit Grade von unvollständigem Wissen ausdrückt: a ist ein Satz der unvollständig gewusst wird, b ist unser Gesamtwissen, und p drückt den Grad aus zu dem a in b implizit enthalten ist. Die subjektive Interpretation wurde oft als eine Erklärung und Verschärfung der verschiedenen objektiven Theorien vorgeschlagen. In dem Artikel wird gezeigt, dass der Vorschlag undurchführbar ist, da die subjektive Theorie zu Resultaten führt, die der objektiven Theorie widersprechen. Verschiedene Umstände tragen zu dieser Situation bei. Der wichtigste dieser Umstände ist, dass die objektive Theorie das » b « in » p « als eine Beschreibung der objektiven Bedingungen eines Experimentes interpretiert . Andererseits interpretiert die subjektive Theorie » b « als unser Gesamtwissen , und dieses wird im allgemeinen Informationen bezüglich früherer Resultate dieses Experiments einschliessen. Es wird gezeigt, dass das zu einem Widerspruch zwischen den Interpretationen führt, da dieses Wissen Einfluss auf den subjektiven Wahrscheinlichkeitswert haben muss.Mit Hilfe dieses Gedankenganges wird gezeigt, dass die Wahrscheinlichkeitstheorie des induktiven Lernens aus der Erfahrung zu Widersprüchen führt. (shrink)

A simple rule of probability revision ensures that the final result of a sequence of probability revisions is undisturbed by an alteration in the temporal order of the learning prompting those revisions. This Uniformity Rule dictates that identical learning be reflected in identical ratios of certain new-to-old odds, and is grounded in the old Bayesian idea that such ratios represent what is learned from new experience alone, with prior probabilities factored out. The main theorem of this (...) paper includes as special cases Field's theorem on commuting probability-kinematical revisions and the equivalence of two strategies for generalizing Jeffrey 's solution to the old evidence problem to the case of uncertain old evidence and probabilistic new explanation. (shrink)

The notions of psychological similarity and probabilistic learning are key posits in cognitive, computational, and developmental psychology and in machine learning. However, their explanatory relationship is rarely made explicit within and across these research fields. This opinionated review critically evaluates how these notions can mutually inform each other within computational cognitive science. Using probabilistic models of concept learning as a case study, I argue that two notions of psychological similarity offer important normative constraints to guide modelers’ interpretations (...) of representational primitives. In particular, the two notions furnish probabilistic models of cognition with meaningful interpretations of what the associated subjective probabilities in the model represent and how they attach to experiences from which the agent learns. Similarity representations thereby provide probabilistic models with cognitive, as opposed to purely mathematical, content. (shrink)

Much of what we know and love about music is based on implicitly acquired mental representations of musical pitches and the relationships between them. While previous studies have shown that these mental representations of music can be acquired rapidly and can influence preference, it is still unclear which aspects of music influence learning and preference formation. This article reports two experiments that use an artificial musical system to examine two questions: (1) which aspects of music matter most for (...) learning, and (2) which aspects of music matter most for preference formation. Two aspects of music are tested: melody and harmony. In Experiment 1 we tested the learning and liking of a new musical system that is manipulated melodically so that only some of the possible conditional probabilities between successive notes are presented. In Experiment 2 we administered the same tests for learning and liking, but we used a musical system that is manipulated harmonically to eliminate the property of harmonic whole-integer ratios between pitches. Results show that disrupting melody (Experiment 1) disabled the learning of music without disrupting preference formation, whereas disrupting harmony (Experiment 2) does not affect learning and memory but disrupts preference formation. Results point to a possible dissociation between learning and preference in musical knowledge. (shrink)

The difficulty of conducting relevant experiments has long been regarded as the central challenge to learning about the economy from data. The standard solution, going back to Haavelmo's famous “The Probability Approach in Econometrics” (1944), involved two elements: first, it placed substantial weight on a priori theory as a source of structural information, reducing econometric estimates to measurements of causally articulated systems; second, it emphasized the need for an appropriate statistical model of the data. These elements are (...) usually seen as tightly linked. I argue that they are, to a large extent, separable. Careful attention to the role of an empirically justified statistical model in underwriting probability explains puzzles not only in economics, but more generally with respect to recent criticisms of Reichenbach's principle of the common cause, which lies behind graph-theoretic causal search algorithms. And it provides an antidote to the pessimistic understanding of the possibilities for passive observation of causal structure in econometrics and related areas of Nancy Cartwright and others. (shrink)

From an early age, exposure to a spoken language has allowed us to implicitly capture the structure underlying the succession of speech sounds in that language and to segment it into meaningful units. Statistical learning, the ability to pick up patterns in the sensory environment without intention or reinforcement, is thus assumed to play a central role in the acquisition of the rule-governed aspects of language, including the discovery of word boundaries in the continuous acoustic stream. Although extensive evidence (...) has been gathered from artificial languages experiments showing that children and adults are able to track the regularities embedded in the auditory input, as the probability of one syllable to follow another syllable in the speech stream, the developmental trajectory of this ability remains controversial. In this work, we have collected Event-Related Potentials while 5-year-old children and young adults were exposed to a speech stream made of the repetition of eight three-syllable nonsense words presenting different levels of predictability to mimic closely what occurs in natural languages and to get new insights into the changes that the mechanisms underlying auditory statistical learning might undergo through the development. The participants performed the aSL task first under implicit and, subsequently, under explicit conditions to further analyze if children take advantage of previous knowledge of the to-be-learned regularities to enhance SL, as observed with the adult participants. These findings would also contribute to extend our knowledge of the mechanisms available to assist SL at each developmental stage. Although behavioral signs of learning, even under explicit conditions, were only observed for the adult participants, ERP data showed evidence of online segmentation in the brain in both groups, as indexed by modulations in the N100 and N400 components. A detailed analysis of the neural data suggests, however, that adults and children rely on different mechanisms to assist the extraction of word-like units from the continuous speech stream, hence supporting the view that SL with auditory linguistic materials changes through development. (shrink)

In the fields of linguistics and psychology the didactic implementation of new knowledge relative to argumentative discourse and its acquisition has led us to develop a didactic sequence focused on the teaching of argumentation in 11–12 year old pupils. This sequence was experimented in six schools in order to assess the effect of these new educational methods on the capacities of pupils to treat the dialogic dimensions of argumentation in the writing of monologues. An analysis of the productions of the (...) puils who had been submitted to the didactic sequence, compared with those of a control group, showed a significant improvement in the capacities of the first group. These improvements concerned aspects such as backing up arguments, acknowledging the addressee, negotiating with an eventual contradictor and using of a certain number of units and linguistic expressions t specific to argumentative texts (expressions of responsability taking, textual organisers of cause and conclusion, modalisations of probability and of certitude, concessions and polite expressions). (shrink)

The paper examines constraints and preferences employed by people in learning decision rules from preclassified examples. Results from four experiments with human subjects were analyzed and compared with artificial intelligence (AI) inductive learning programs. The results showed the people's rule inductions tended to emphasize category validity (probability of some property, given a category) more than cue validity (probability that an entity is a member of a category given that it has some property) to a greater (...) extent than did the AI programs. Although the relative proportions of different rule types (e.g., conjunctive vs. disjunctive) changed across experiments, a single process model provided a good account of the data from each study. These observations are used to argue for describing constraints in terms of processes embodied in models rather than in terms of products or outputs. Thus AI induction programs become candidate psychological process models and results from inductive learning experiments can suggest new algorithms. More generally, the results show that human inductive generalizations tend toward greater specificity than would be expected if conceptual simplicity were the key constraint on inductions. This bias toward specificity may be due to the fact that this criterion both maximizes inferences that may be drawn from category membership and protects rule induction systems from developing over‐generalizations. (shrink)

Five studies investigated (a) children’s ability to use the dependent and independent probabilities of events to make causal inferences and (b) the interaction between such inferences and domain-specific knowledge. In Experiment 1, preschoolers used patterns of dependence and independence to make accurate causal inferences in the domains of biology and psychology. Experiment 2 replicated the results in the domain of biology with a more complex pattern of conditional dependencies. In Experiment 3, children used evidence about patterns of dependence and independence (...) to craft novel interventions across domains. In Experiments 4 and 5, children’s sensitivity to patterns of dependence was pitted against their domain-specific knowledge. Children used conditional probabilities to make accurate causal inferences even when asked to violate domain boundaries. (shrink)

A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is proposed to train collision avoidance decision-making neural networks. (...) To achieve higher scalability, the neural network is customized to incorporate long short-term memory networks, and a coordination strategy is given. Additionally, a simulator suitable for multiagent high-density route scene is designed for validation, in which all UAVs run the proposed algorithm onboard. Simulated experiment results from several case studies show that the real-time guidance algorithm can reduce the collision probability of multiple UAVs in flight effectively even with a large number of aircraft. (shrink)

The question of whether humans represent grammatical knowledge as a binary condition on membership in a set of well-formed sentences, or as a probabilistic property has been the subject of debate among linguists, psychologists, and cognitive scientists for many decades. Acceptability judgments present a serious problem for both classical binary and probabilistic theories of grammaticality. These judgements are gradient in nature, and so cannot be directly accommodated in a binary formal grammar. However, it is also not possible to simply reduce (...) acceptability to probability. The acceptability of a sentence is not the same as the likelihood of its occurrence, which is, in part, determined by factors like sentence length and lexical frequency. In this paper, we present the results of a set of large-scale experiments using crowd-sourced acceptability judgments that demonstrate gradience to be a pervasive feature in acceptability judgments. We then show how one can predict acceptability judgments on the basis of probability by augmenting probabilistic language models with an acceptability measure. This is a function that normalizes probability values to eliminate the confounding factors of length and lexical frequency. We describe a sequence of modeling experiments with unsupervised language models drawn from state-of-the-art machine learning methods in natural language processing. Several of these models achieve very encouraging levels of accuracy in the acceptability prediction task, as measured by the correlation between the acceptability measure scores and mean human acceptability values. We consider the relevance of these results to the debate on the nature of grammatical competence, and we argue that they support the view that linguistic knowledge can be intrinsically probabilistic. (shrink)

The Community of Inquiry is a pragmatic philosophy concept by John Dewey (1916) representing a "social, cognitive and teaching presence" in a process of collaborative research and learning experience. This article is meant to present a case study based on the experience of forming a community of inquiry with students of a Romanian university. The report will include aspects like: the process of group forming and group facilitation to foster collaborative critical thinking, a few philosophical methods that aimed the (...) consolidation of the group as a community of learning and inquiry as well as the training of individual critical thinking, self-reflective posture and openness to otherness. Results reveal students' initial preoccupations with certainty and difficulties in self expression at the beginning of the semester and presented increasingly more attitudes characteristic to collaborative learning and inquiry by the end of the semester, which is probably an effect of deliberately setting up a specific group culture to facilitate this goal. (shrink)

By reviewing most of the neurobiology of consciousness, this article highlights some major reasons why a successful emulation of the dynamics of human consciousness by artificial intelligence is unlikely. The analysis provided leads to conclude that human consciousness is epigenetically determined and experience and context-dependent at the individual level. It is subject to changes in time that are essentially unpredictable. If cracking the code to human consciousness were possible, the result would most likely have to consist of a temporal pattern (...) code simulating long-distance signal reverberation and de-correlation of all spatial signal contents from temporal signals. In the light of the massive evidence for complex interactions between implicit (non-conscious) and explicit (conscious) contents of representation, the code would have to be capable of making implicit (non-conscious) processes explicit. It would have to be capable of a progressively less and less arbitrary selection of temporal activity patterns in a continuously developing neural network structure identical to that of the human brain, from the synaptic level to that of higher cognitive functions. The code’s activation thresholds would depend on specific temporal signal coincidence probabilities, vary considerably with time and across individual experience data, and would therefore require dynamically adaptive computations capable of emulating the properties of individual human experience. No known machine or neural network learning approach has such potential. (shrink)

Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences—and the relations between the elements they comprise—are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine these mechanisms during a typing task. Experiments 1 and 2 tested learning during (...) typing single letters on each trial. Experiment 3 tested for “chunking” of these letters into “words.” The results of these experiments were used to examine the mechanisms that could best account for them, with a focus on two particular proposals: statistical transitional probability learning and discriminative error-driven learning. Experiments 1 and 2 showed that error-driven learning was a better predictor of response latencies than either n-gram frequencies or transitional probabilities. No evidence for chunking was found in Experiment 3, probably due to interspersing visual cues with the motor response. In addition, learning occurred across a greater distance in Experiment 1 than Experiment 2, suggesting that the greater predictability that comes with increased structure leads to greater learnability. These results shed new light on the mechanism responsible for sequence learning. Despite the widely held assumption that transitional probability learning is essential to this process, the present results suggest instead that the sequences are learned through a process of discriminative learning, involving prediction and feedback from prediction error. (shrink)

ABSTRACTWe used an immersive virtual environment to examine avoidance learning in spider-fearful participants. In 3 experiments, participants were asked to repeatedly lift one of 3 virtual boxes, under which either a toy car or a spider appeared and then approached the participant. Participants were not told that the probability of encountering a spider differed across boxes. When the difference was large, spider-fearfuls learned to avoid spiders by lifting the few-spiders-box more often and the many-spiders-box less often than (...) non-fearful controls did. However, they hardly managed to do so when the probability differences were small, and they did not escape from threat more quickly. In contrast to the observed performance differences, spider-fearfuls and non-fearfuls showed equal competence, that is comparable post-experimental knowledge about the probability to encounter spiders under the 3 boxes. The limitations and implications of the present study are discussed. (shrink)

This chapter contains sections titled: Aesthetics Musical Syntax, Discrepancy and Activation Probability, Expectations, and Learning Dopamine, Discrepancy and the Prediction of Reward Musement and the Play of Ideas Conclusion.

This paper studies the properties and performance of models for estimating local probability distributions which are used as components of larger probabilistic systems — history-based generative parsing models. We report experimental results showing that memory-based learning outperforms many commonly used methods for this task (Witten-Bell, Jelinek-Mercer with fixed weights, decision trees, and log-linear models). However, we can connect these results with the commonly used general class of deleted interpolation models by showing that certain types of memory-based learning, (...) including the kind that performed so well in our experiments, are instances of this class. In addition, we illustrate the divergences between joint and conditional data likelihood and accuracy performance achieved by such models, suggesting that smoothing based on optimizing accuracy directError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMaply might greatly improve performance. (shrink)

The governmental restriction due to COVID-19 pandemic led to Italian Universities moving teaching from face-to-face, to online. This represented an unexpected transition from traditional learning to what can be considered “e-learning.” This, together with the psychological distress that may be associated with the experience of lockdown, might have affected students' performance. It was hypothesised that grit may be a protective factor in such situations. Indeed, compared to their less “gritty” peers, individuals with higher levels of grit are expected (...) to exhibit greater persistence in the pursuit of goals despite setbacks. Within educational contexts, grit is portrayed as a potentially important influence on outcomes such as achievement level, retention and probability of graduation. A longitudinal study was conducted using an online survey in order to assess the moderated mediation effect of grit on students' achievement. One hundred seventy-six undergraduate students from two universities in the north of Italy participated in the survey. The results showed that grit affects students' grades in final exams; perceived self-efficacy in the management of complex problems had a mediation effect on grades, while psychological distress moderated the first part of the mediation process. These novel findings extended our knowledge regarding the role of grit in academic performance investigating for the first time the role of self-efficacy and psychological distress in a learning carried out entirely online. (shrink)

In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co‐occurrences between them (...) are computed and registered. Here, we entertain a novel hypothesis that one unifying construct—the rate of information in the sensory input—explains learning performance. This theoretical approach merges processes related to encoding of events and those related to learning their regularities into a single computational principle. We present data from two large‐scale experiments with over 800 participants tested in support for this hypothesis, showing that rate of information in a visual stream clearly predicts SL performance, and that similar rate of information values leads to similar SL performance. We discuss the implications for SL theory and its relation to regularity learning. (shrink)

A framing bias shows risk aversion in problems framed as “gains” and risk seeking in problems framed as “losses,” even when these are objectively equivalent and probabilities and outcomes values are explicitly provided. We test this framing bias in situations where decision makers rely on their own experience, sampling the problem's options and seeing the outcomes before making a choice. In Experiment 1, we replicate the framing bias in description-based decisions and find risk indifference in gains and losses in experience-based (...) decisions. Predictions of an Instance-Based Learning model suggest that objective probabilities as well as the number of samples taken are factors that contribute to the lack of framing effect. We test these two factors in Experiment 2 and find no framing effect when a few samples are taken but when large samples are taken, the framing effect appears regardless of the objective probability values. Implications of behavioral results and cognitive modeling are discussed. (shrink)

Previous research on cross-situational word learning has demonstrated that learners are able to reduce ambiguity in mapping words to referents by tracking co-occurrence probabilities across learning events. In the current experiments, we examined whether learners are able to retain mappings over time. The results revealed that learners are able to retain mappings for up to 1 week later. However, there were interactions between the amount of retention and the different learning conditions. Interestingly, the strongest retention was (...) associated with a learning condition that engendered retrieval dynamics that initially challenged the learner but eventually led to more successful retrieval toward the end of learning. The ease/difficulty of retrieval is a critical process underlying cross-situational word learning and is a powerful example of how learning dynamics affect long-term learning outcomes. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:To Recognize the Person: Learning from Narratives of Psychiatric TreatmentLinda J. MorrisonTo know what patients endure at the hands of illness and therefore to be of clinical help requires that doctors enter the worlds of their patients, if only imaginatively, and to see and interpret these worlds from the patient’s point of view(Charon, 2006, p. 9).These narratives of psychiatric hospitalization are rich and evocative. We are fortunate to (...) have twelve descriptions appearing side-by-side, each unique in its focus, outlook, detail and narrative tone. Together their interwoven contexts describe personal and work relationships, internal and external help-seeking, encounters in community and hospital settings, and experiences of the psychiatric (psychiatrized) self as supplicant, victim, recipient, participant, product, and active agent over time (Morrison, 2005).Through their collected narratives, the authors create a distinct counterpoint to the more familiar clinical narratives of psychiatric encounters. Ordinarily the professional narrative drives interpretation of a clinical experience, whether medical or psychiatric. The caregivers populating these narratives would probably tell different stories, and the hospital notes and treatment records yet another (Hinsby & Baker, 2004; VanDerNagel et al., 2009). The disjunction between professional and patient narrative is especially true for psychiatric hospitalizations, in which the inpatient’s active narrative becomes a part of the diagnostic and treatment process (Steslow, 2010).In this symposium, the patient’s narrative is a window for interpretive insights into personal experience of the process. Each is a situated retrospective, in which added dimensions of feeling, thought and interaction provide the reader a fuller and more complex awareness of the experience of psychiatric hospitalization. The narratives provide insights about choice and agency, control and harm, safety and strength, hope and frustration, resilience and recovery, empathy and humanity; they also reveal various ethical issues related to the experience of help-seeking and treatment.It is vital to consider what each person wrote: what choices they made in representing their experience; what aspects they focused on, and what they (we imagine) left out. The authors used their own voices to create their own representations. In each narrative, “the person” provides an important standpoint. Who is this person, what is his or her experience? Where is the person in the situation; in what way does he or she appear to those who provide treatment or care? How, if at all, is the treatment pertinent to the person’s concerns? Does it feel helpful or harmful? How does the person find a way to continue, to push through, with the help of (or in spite of) those we hope are providing care? This is the first sign of agency, of choice, of “dreaming.” [End Page 35]As V. Barnard writes in Dreaming: A Recovery Story, “to make a film, one has to dream it first.” All of the authors in this symposium took the risk and the opportunity to create their own stories: what to include, what to leave out, how to tell what they wanted to say. These choices gave them the power to dream: how it was, how it might have been; how it felt, good and bad; the chance to represent their own reality—to themselves, and to unknown others (Morrison, 2006).Dreaming, Choice and Agency“My psychiatric illness is only one part of me. I am a person with hopes, dreams, love, an artist and a fellow helper of my peers.” V. BarnardV.’s narrative describes the potential for positive action, of dreaming herself as a whole into existence. Her themes of choice and agency emphasize the importance of autonomy and the capacity for autonomous action. On the farm, V. was free to choose and take risks, to take on responsibilities, to be “treated with respect as a human being trying to get better rather than a criminal suffering from something that was optional.” From disconnection, she was able to find connection, on her own terms.The support of peers adds a valuable dimension. Being recognized as a peer, a person who shares similar psychiatric experiences and respects the challenge of everyday life, brings another level to supportive relationships. Negative judgments and stigma are reduced by mutual support and acknowledgement of common goals. Mental health... (shrink)

Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases—assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the responses (...) produced by a participant on one trial to generate the stimuli that either they or another participant will see on the next. A formal analysis of this “iterated learning” procedure, based on the assumption that the learners are Bayesian agents, predicts that it should reveal the inductive biases of these learners, as expressed in a prior probability distribution over hypotheses. This article presents a series of experiments using stimuli based on a well-studied set of category structures, demonstrating that iterated learning can be used to reveal the inductive biases of human learners. (shrink)

Lewis signaling games illustrate how language might evolve from random behavior. The probability of evolving an optimal signaling language is, in part, a function of what learning strategy the agents use. Here we investigate three learning strategies, each of which allows agents to forget old experience. In each case, we find that forgetting increases the probability of evolving an optimal language. It does this by making it less likely that past partial success will continue to reinforce (...) suboptimal practice. The learning strategies considered here show how forgetting past experience can promote learning in the context of games with suboptimal equilibria. (shrink)

This study investigates the joint influences of three factors on the discovery of new word‐like units in a continuous artificial speech stream: the statistical structure of the ongoing input, the initial word‐likeness of parts of the speech flow, and the contextual information provided by the earlier emergence of other word‐like units. Results of an experiment conducted with adult participants show that these sources of information have strong and interactive influences on word discovery. The authors then examine the ability of different (...) models of word segmentation to account for these results. PARSER (Perruchet & Vinter, 1998) is compared to the view that word segmentation relies on the exploitation of transitional probabilities between successive syllables, and with the models based on the Minimum Description Length principle, such as INCDROP. The authors submit arguments suggesting that PARSER has the advantage of accounting for the whole pattern of data without ad‐hoc modifications, while relying exclusively on general‐purpose learning principles. This study strengthens the growing notion that nonspecific cognitive processes, mainly based on associative learning and memory principles, are able to account for a larger part of early language acquisition than previously assumed. (shrink)

This research examined choice behaviour and probability judgement in a counterintuitive reasoning problem called the Monty Hall problem (MHP). In Experiments 1 and 2 we examined whether learning from a simulated card game similar to the MHP affected how people solved the MHP. Results indicated that the experience with the card game affected participants' choice behaviour, in that participants selected to switch in the MHP. However, it did not affect their understanding of the objective probabilities. This suggests (...) that there is dissociation between implicit knowledge gained from the task and the explicit understanding as to why switching was the best strategy. In Experiment 3, the number of prizes and doors were manipulated to examine how participants construed the problem space of the MHP. Results revealed that participants partition the probability judgement to reflect the number of prizes over the number of unopened doors. (shrink)

According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of Richard (...) Jeffrey's 'radical probabilism'. Along the way, Huttegger addresses a number of debates in epistemology and the philosophy of science, including the status of prior probabilities, whether Bayes' rule is the only legitimate form of learning from experience, and whether rational agents can have sustained disagreements. His book will be of interest to students and scholars of epistemology, of game and decision theory, and of cognitive, economic, and computer sciences. (shrink)

The evaluation of probabilities, or the art of forecasting, is neither a question of taste nor a mathematically determined question. All evaluations are admissible, provided only that coherence is satisfied; among these, everybody may judge one or the other more or less ‘reasonable’. The major aspect of coherence consists in conforming “learning from experience” to Bayes’ theorem.

Infants have been described as 'statistical learners' capable of extracting structure (such as words) from patterned input (such as language). Here, we investigated whether prior knowledge influences how infants track transitional probabilities in word segmentation tasks. Are infants biased by prior experience when engaging in sequential statistical learning? In a laboratory simulation of learning across time, we exposed 9- and 10-month-old infants to a list of either disyllabic or trisyllabic nonsense words, followed by a pause-free speech stream composed (...) of a different set of disyllabic or trisyllabic nonsense words. Listening times revealed successful segmentation of words from fluent speech only when words were uniformly disyllabic or trisyllabic throughout both phases of the experiment. Hearing trisyllabic words during the pre-exposure phase derailed infants' abilities to segment speech into disyllabic words, and vice versa. We conclude that prior knowledge about word length equips infants with perceptual expectations that facilitate efficient processing of subsequent language input. (shrink)

While rules and exemplars are usually viewed as opposites, this paper argues that they form end points of the same distribution. By representing both rules and exemplars as (partial) trees, we can take into account the fluid middle ground between the two extremes. This insight is the starting point for a new theory of language learning that is based on the following idea: If a language learner does not know which phrase‐structure trees should be assigned to initial sentences, s/he (...) allows (implicitly) for all possible trees and lets linguistic experience decide which is the “best” tree for each sentence. The best tree is obtained by maximizing “structural analogy” between a sentence and previous sentences, which is formalized by the most probable shortest combination of subtrees from all trees of previous sentences. Corpus‐based experiments with this model on the Penn Treebank and the Childes database indicate that it can learn both exemplar‐based and rule‐based aspects of language, ranging from phrasal verbs to auxiliary fronting. By having learned the syntactic structures of sentences, we have also learned the grammar implicit in these structures, which can in turn be used to produce new sentences. We show that our model mimicks children’s language development from item‐based constructions to abstract constructions, and that the model can simulate some of the errors made by children in producing complex questions. (shrink)