A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning (...) derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning. (shrink)

A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, between learning that takes place with versus without concurrent awareness, and between learning that involves the encoding of instances versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, (...) conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks is predominantly based on rule abstraction is questionable. When subjects cannot report the “implicitly learned” rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning. (shrink)

People are typically poor at telling apart truthful and deceptive statements. Based on the Unconscious Thought Theory, it has been suggested that poor lie detection arises from the intrinsic limitations of conscious thinking and can be improved by facilitating the contribution of unconscious thought. In support of this hypothesis, Reinhard, Greifeneder, and Scharmach (2013) observed improved lie detection among participants engaging in unconscious thought. The present study aimed to replicate this unconscious thought advantage using a similar experimental procedure but with (...) an important improvement in a key control condition. Specifically, participants judged the truthfulness of 8 video recordings in three thinking modes: immediately after watching them or after a period of unconscious or conscious deliberation. Results from two experiments (combined N = 226) failed to reveal a significant difference in lie detection accuracy between the thinking modes, even after efforts were made to facilitate the occurrence of an unconscious thought advantage in Experiment 2. The results imply that the unconscious thought advantage in deception detection is not a robust phenomenon. (shrink)

This article reviews recent work aimed at developing a new framework, based on signal detection theory, for understanding the relationship between explicit (e.g., recognition) and implicit (e.g., priming) memory. Within this framework, different assumptions about sources of memorial evidence can be framed. Application to experimental results provides robust evidence for a single-system model in preference to multiple-systems models. This evidence comes from several sources including studies of the effects of amnesia and ageing on explicit and implicit memory. The framework allows (...) a range of concepts in current memory research, such as familiarity, recollection, fluency, and source memory, to be linked to implicit memory. More generally, this work emphasizes the value of modern computational modelling techniques in the study of learning and memory. (shrink)

Insight and strategy 2 Abstract In multiple-cue learning (also known as probabilistic category learning) people acquire information about cue-outcome relations and combine these into predictions or judgments. Previous studies claim that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment policies. It has also been argued that people use a variety of suboptimal strategies to solve such tasks. In three experiments we re-examined these conclusions by introducing novel measures of (...) task knowledge and self-insight, and using ‘rolling regression’ methods to analyze individual learning. Participants successfully learned a four-cue probabilistic environment and showed accurate knowledge of both the task structure and their own judgment processes. Learning analyses suggested that the apparent use of suboptimal strategies emerges from the incremental tracking of statistical contingencies in the environment. (shrink)

The question of whether studies of human learning provide evidence for distinct conscious and unconscious influences remains as controversial today as ever. Much of this controversy arises from the use of the logic of dissociation. The controversy has prompted the use of an alternative approach that places conscious and unconscious influences on memory retrieval in opposition. Here we ask whether evidence acquired via the logic of opposition requires a dual-process account or whether it can be accommodated within a single similarity-based (...) account. We report simulations using a simple neural network model of two artificial grammar learning experiments reported by Higham, Vokey, and Pritchard that dissociated conscious and unconscious influences on classification. The simulations demonstrate that opposition logic is insufficient to distinguish between single- and multiple-system models. (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)

Previous research suggests that early performance of amnesic individuals in a probabilistic category learning task is relatively unimpaired. When combined with impaired declarative knowledge, this is taken as evidence for the existence of separate implicit and explicit memory systems. The present study contains a more fine-grained analysis of learning than earlier studies. Using a dynamic lens model approach with plausible learning models, we found that the learning process is indeed indistinguishable between an amnesic and control group. However, in contrast to (...) earlier findings, we found that explicit knowledge of the task structure is also good in both the amnesic and the control group. This is inconsistent with a crucial prediction from the multiple-systems account. The results can be explained from a single system account and previously found differences in later categorization performance can be accounted for by a difference in learning rate. (shrink)

The target article sought to question the common belief that our decisions are often biased by unconscious influences. While many commentators offer additional support for this perspective, others question our theoretical assumptions, empirical evaluations, and methodological criteria. We rebut in particular the starting assumption that all decision making is unconscious, and that the onus should be on researchers to prove conscious influences. Further evidence is evaluated in relation to the core topics we reviewed (multiple-cue judgment, deliberation without attention, and decisions (...) under uncertainty), as well as priming effects. We reiterate a key conclusion from the target article, namely, that it now seems to be generally accepted that awareness should be operationally defined as reportable knowledge, and that such knowledge can only be evaluated by careful and thorough probing. We call for future research to pay heed to the different ways in which awareness can intervene in decision making (as identified in our lens model analysis) and to employ suitable methodology in the assessment of awareness, including the requirements that awareness assessment must be reliable, relevant, immediate, and sensitive. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:94 HUME ON THE PERCEPTION OF CAUSALITY Introduction Few issues in philosophy have generated as much debate and as little agreement as Hume's controversial theory of causality. The theory itself has been notoriously difficult to pin down, and not surprisingly empirical evidence has played a very minor role in the issue of what is meant by 'cause'. This is not, however, due to the fact that empirical tests of (...) the theory are hard to devise, but rather because such tests have usually been undertaken by experimental psychologists addressing slightly different issues and unaware of the philosophical implications of their work. However, recent signs of agreement on the overall nature of the theory (reviewed in Beauchamp and Rosenberg, chapter 1) allow a profitable integration of the theory with psychological research on the nature and importance of the perception of causal relations in conditioning. The significance of some of these experiments will be discussed later, and it will be argued that the factors Hume cited as being the essential determinants of causality, when complemented by the additional factor of the degree of contingency between the cause and the effect, correspond exactly with the factors known to affect conditioning; and therefore, that the laws of conditioning specify the properties that define causal relationships. Hume's Theory of Causality Hume was well aware of the importance of causal connections between events as a source of associations in the mind: 95 'Tis sufficient to observe, that there is no relation, which produces a stronger connexion in the fancy, and makes one idea more readily recall another, than the relation of cause and effect betwixt their objects (T II).2 In other words, if we believe a causal relationship exists between two events, then one of these events, the cause, will readily recall the other, the effect. The problem comes, of course, when we ask how it is that we know that a causal relationship exists. Obviously, a distinction must be made between causation as a physical property and causality as a mental idea. Traditionally, the following definitions have been made: causation is the physical property of one event causing another, such as one ball colliding with and causing the movement of a second ball. This property is part of the realm of mechanics and physics. Causality, on the other hand, describes the attribution by an organism of an effect to a cause. Such a distinction is supported by dictionary definitions. These two ways of thinking of what is meant by the term 'cause' are explicitly discussed by Hume. On the notion of physical causation, he says: We may define a CAUSE to be 'An object precedent and contiguous to another, and where all the objects resembling the former are plac'd in like relations of precedency and contiguity to those objects, that resemble the latter.' Of the mental idea of a causal relationship, on the other hand, he says: A CAUSE is an object precedent and contiguous to another, and so united with it, that the idea of the one determines the mind to form the idea of the other, and the impression of the one to form a more lively idea of the other. (T 170) Unfortunately, Hume was never less than imprecise in maintaining this dissociation, and often 96 he plainly confused the two definitions. Hence the academic controversy, between those who, like Kemp Smith, believe that the second definition represents a misguided attempt by Hume to analyse the causal relation on a mental level, 3 and those who see both definitions as essential (e.g. Beauchamp and Rosenberg). But surely the first definition simply represents Hume's view of causality as a philosophical relation, while the second represents his view of it as a psychological fact. And it is as much his intention to explain this fact, that ideas do not simply come into our heads for no apparent reason, but on the contrary are summoned up by preceding thoughts which they are associated, as it is to elucidate the philosophical relation. The extent of Hume's commitment to a psychological explanation of causal attribution has been recognised by Beauchamp and Rosenberg: 5 Hume plainly did develop a theory of causal... (shrink)

Do dissociations imply independent systems? In the memory field, the view that there are independent implicit and explicit memory systems has been predominantly supported by dissociation evidence. Here, we argue that many of these dissociations do not necessarily imply distinct memory systems. We review recent work with a single-system computational model that extends signal-detection theory (SDT) to implicit memory. SDT has had a major influence on research in a variety of domains. The current work shows that it can be broadened (...) even further in its range of application. Indeed, the single-system model that we present does surprisingly well in accounting for some key dissociations that have been taken as evidence for independent implicit and explicit memory systems. (shrink)

We present a new modeling framework for recognition memory and repetition priming based on signal detection theory. We use this framework to specify and test the predictions of 4 models: (a) a single-system (SS) model, in which one continuous memory signal drives recognition and priming; (b) a multiple-systems-1 (MS1) model, in which completely independent memory signals (such as explicit and implicit memory) drive recognition and priming; (c) a multiple-systems-2 (MS2) model, in which there are also 2 memory signals, but some (...) degree of dependence is allowed between these 2 signals (and this model subsumes the SS and MS1 models as special cases); and (d) a dual-process signal detection (DPSD1) model, 1 possible extension of a dual-process theory of recognition (Yonelinas, 1994) to priming, in which a signal detection model is augmented by an independent recollection process. The predictions of the models are tested in a continuous-identification-with-recognition paradigm in both normal adults (Experiments 1–3) and amnesic individuals (using data from Conroy, Hopkins, & Squire,2005). The SS model predicted numerous results in advance. These were not predicted by the MS1 model, though could be accommodated by the more flexible MS2 model. Importantly, measures of overall model fit favored the SS model over the others. These results illustrate a new, formal approach to testing theories of explicit and implicit memory. (shrink)

In the original target article (Shanks & St. John 1994), one of our principal conclusions was that there is almost no evidence that learning can occur outside awareness. The continuing commentaries raise some interesting questions, especially about the definition of learning, but do not lead us to abandon our conclusion.

Although we welcome Gigerenzer, Todd, and the ABC Research Group's shift of emphasis from “coherence” to “correspondence” criteria, their rejection of optimality in human decision making is premature: In many situations, experts can achieve near-optimal performance. Moreover, this competence does not require implausible computing power. The models Gigerenzer et al. evaluate fail to account for many of the most robust properties of human decision making, including examples of optimality.

The extent to which human learning should be thought of in terms of elementary, automatic versus controlled, cognitive processes is unresolved after nearly a century of often fierce debate. Mitchell et al. provide a persuasive review of evidence against automatic, unconscious links. Indeed, unconscious processes seem to play a negligible role in any form of learning, not just in Pavlovian conditioning. But a modern connectionist framework, in which phenomena are emergent properties, is likely to offer a fuller account of human (...) learning than the propositional framework Mitchell et al. propose. (shrink)

The target article sought to question the common belief that our decisions are often biased by unconscious influences. While many commentators offer additional support for this perspective, others question our theoretical assumptions, empirical evaluations, and methodological criteria. We rebut in particular the starting assumption that all decision making is unconscious, and that the onus should be on researchers to prove conscious influences. Further evidence is evaluated in relation to the core topics we reviewed (multiple-cue judgment, deliberation without attention, and decisions (...) under uncertainty), as well as priming effects. We reiterate a key conclusion from the target article, namely, that it now seems to be generally accepted that awareness should be operationally defined as reportable knowledge, and that such knowledge can only be evaluated by careful and thorough probing. We call for future research to pay heed to the different ways in which awareness can intervene in decision making (as identified in our lens model analysis) and to employ suitable methodology in the assessment of awareness, including the requirements that awareness assessment must be reliable, relevant, immediate, and sensitive. (shrink)

The target article sought to question the common belief that our decisions are often biased by unconscious influences. While many commentators offer additional support for this perspective, others question our theoretical assumptions, empirical evaluations, and methodological criteria. We rebut in particular the starting assumption that all decision making is unconscious, and that the onus should be on researchers to prove conscious influences. Further evidence is evaluated in relation to the core topics we reviewed (multiple-cue judgment, deliberation without attention, and decisions (...) under uncertainty), as well as priming effects. We reiterate a key conclusion from the target article, namely, that it now seems to be generally accepted that awareness should be operationally defined as reportable knowledge, and that such knowledge can only be evaluated by careful and thorough probing. We call for future research to pay heed to the different ways in which awareness can intervene in decision making (as identified in our lens model analysis) and to employ suitable methodology in the assessment of awareness, including the requirements that awareness assessment must be reliable, relevant, immediate, and sensitive. (shrink)

The target article sought to question the common belief that our decisions are often biased by unconscious influences. While many commentators offer additional support for this perspective, others question our theoretical assumptions, empirical evaluations, and methodological criteria. We rebut in particular the starting assumption that all decision making is unconscious, and that the onus should be on researchers to prove conscious influences. Further evidence is evaluated in relation to the core topics we reviewed (multiple-cue judgment, deliberation without attention, and decisions (...) under uncertainty), as well as priming effects. We reiterate a key conclusion from the target article, namely, that it now seems to be generally accepted that awareness should be operationally defined as reportable knowledge, and that such knowledge can only be evaluated by careful and thorough probing. We call for future research to pay heed to the different ways in which awareness can intervene in decision making (as identified in our lens model analysis) and to employ suitable methodology in the assessment of awareness, including the requirements that awareness assessment must be reliable, relevant, immediate, and sensitive. (shrink)

Consider the task of predicting which soccer team will win the next World Cup. The bookmakers may judge Brazil to be the team most likely to win, but also judge it most likely that a European rather than a Latin American team will win. This is an example of a non-aligned hierarchy structure: the most probable event at the subordinate level (Brazil wins) appears to be inconsistent with the most probable event at the superordinate level (a European team wins). In (...) this paper we exploit such structures to investigate how people make predictions based on uncertain hierarchical knowledge. We distinguish between aligned and non-aligned environments, and conjecture that people assume alignment. Participants were exposed to a non-aligned training set in which the most probable superordinate category predicted one outcome, whereas the most probable subordinate category predicted a different outcome. In the test phase participants allowed their initial probability judgments about category membership to shift their final ratings of the probability of the outcome, even though all judgments were made on the basis of the same statistical data. In effect people were primed to focus on the most likely path in an inference tree, and neglect alternative paths. These results highlight the importance of the level at which statistical data are represented, and suggest that when faced with hierarchical inference problems people adopt a simplifying heuristic that assumes alignment. (shrink)

A central claim of Jones & Love's (J&L's) article is that Bayesian Fundamentalism is empirically unconstrained. Unless constraints are placed on prior beliefs, likelihood, and utility functions, all behaviour is consistent with Bayesian rationality. Although such claims are commonplace, their basis is rarely justified. We fill this gap by sketching a proof, and we discuss possible solutions that would make Bayesian approaches empirically interesting.

Barbey & Sloman attribute all instances of normative base-rate usage to a rule-based system, and all instances of neglect to an associative system. As it stands, this argument is too simplistic, and indeed fails to explain either good or bad performance on the classic Medical Diagnosis problem.

Based on research on the increasingly popular unconscious thought effect (UTE), it has been suggested that physicians might make better diagnostic decisions after a period of distraction than after an equivalent amount of time of conscious deliberation. However, published attempts to demonstrate the UTE in medical decision making have yielded inconsistent results. In the present study, we report the results of a meta-analysis of all the available evidence on the UTE in medical decisions made by expert and novice clinicians. The (...) meta-analysis failed to find a significant contribution of unconscious thought to the accuracy of medical decisions. This result cannot be easily attributed to any of the potential moderators of the UTE that have been discussed in the literature. Furthermore, a Bayes Factor analysis shows that most experimental conditions provide positive support for the null hypothesis, suggesting that these null results do not reflect a simple lack of statistical power. We suggest ways in which new studies could usefully provide further evidence on the UTE. Unless future research shows otherwise, the recommendation of using unconscious thought to improve medical decisions lacks empirical support. (shrink)

Two main uses of categories are classification and feature inference, and category labels have been widely shown to play a dominant role in feature inference. However, the nature of this influence remains unclear, and we evaluate two contrasting hypotheses formalized as mathematical models: the label special-mechanism hypothesis and the label super-salience hypothesis. The special-mechanism hypothesis is that category labels, unlike other features, trigger inference decision making in reference to the category prototypes. This results in a tendency for prototype-compatible inferences because (...) the labels trigger a special mechanism rather than because of any influences they have on similarity evaluation. The super-salience hypothesis assumes that the large label influence is due to their high salience and corresponding impact on similarity without any need for a special mechanism. Application of the two models to a feature inference task based on a family resemblance category structure yields strong support for the label super-salience hypothesis and in particular does not support the need for a special mechanism based on prototypes. (shrink)

The demonstration of a sequential congruency effect in sequence learning has been offered as evidence for control processes that act to inhibit automatic response tendencies via unconscious conflict monitoring. Here we propose an alternative interpretation of this effect based on the associative learning of chains of sequenced contingencies. This account is supported by simulations with a Simple Recurrent Network, an associative model of sequence learning. We argue that the control- and associative-based accounts differ in their predictions concerning the magnitude of (...) the sequential congruency effect across training. These predictions are tested by reanalysing data from a study by Shanks, Wilkinson, and Channon . The results support the associative learning account which explains the sequential congruency effect without appealing to control processes. (shrink)

In Open Minded: Searching for Truth about the Unconscious Mind, Ben R. Newell & David R. Shanks (henceforth, N&S) challenge the popular claim that much of human judgment and decision-making is explained by unconscious processes...

This is the best overview of Foucault's work to date. A principal architect of poststructuralism, Michel Foucault reshaped the varied disciplines of history, philosophy, literary theory, and social science. David Shumway has provided, for the nonspecialist, a systematic analysis of the works of Foucault that is both thorough and accessible. Shumway connects Foucault's various conceptual and linguistic techniques to the basic critical strategies and purpose of his philosophy.

An authoritative and accessible book by a qualified scientist, showing incredible proof of the mind-body connection. There is no longer any doubt that the way we think affects our bodies: countless scientific studies have shown this to be true. For former pharmaceutical scientist Dr David Hamilton, the testing of new drugs highlighted how profoundly the mind and body are connected. Time and time again, the control group of patients in drug trials improved at similar rates to those who actually (...) received the medicines. Astounded, Dr Hamilton decided to change the direction of his work to explore the relationship between the mind and the body. This bestselling acclaimed book was first published 10 years ago. In it, Dr Hamilton explores the effect of visualization, belief and positive thinking on the body, and shows how using our imagination and mental processes can stimulate our own defences and healing systems to combat disease, pain and illness. In this new edition, Dr Hamilton has added four new chapters to discuss the latest cutting-edge information and extraordinary new techniques. These include using imagery to stimulate the immune system - a method that can benefit cancer patients undergoing conventional treatment - effectively using the mind to speed up rehabilitation from stroke, and powerful visualization strategies to help facilitate recovery from injury and illness. (shrink)

The Map of Consciousness Explained is an essential primer on the late Dr. David R. Hawkins's teachings on human consciousness and their associated energy fields. Using muscle testing, Dr. Hawkins conducted more than 250,000 calibrations during 20 years of research to define a range of values, attitudes, and emotions that correspond to levels of consciousness. This range of values-along with a logarithmic scale of 1 to 1,000-became the Map of Consciousness, which Dr. Hawkins first wrote about in his New (...) York Times bestseller, Power vs. Force. In this book, readers will gain an introduction and deeper understanding of the Map, with visual charts and practical applications to help them heal, recover, and evolve to higher levels of consciousness and energy. (shrink)

The first clear and faithful guide to the timeless, practical teachings of the Stoic philosopher Seneca. Stoicism, the most influential philosophy of the Roman Empire, offers refreshingly modern ways to strengthen our inner character in the face of an unpredictable world. Widely recognized as the most talented and humane writer of the Stoic tradition, Seneca teaches us to live with freedom and purpose. His most enduring work, over a hundred "Letters from a Stoic" written to a close friend, explains how (...) to handle adversity; overcome grief, anxiety, and anger; transform setbacks into opportunities for growth; and recognize the true nature of friendship. In Breakfast with Seneca, philosopher David Fideler mines Seneca's classic works in a series of focused chapters, clearly explaining Seneca's ideas without oversimplifying them. Best enjoyed as a daily ritual, like an energizing cup of coffee, Seneca's wisdom provides us with a steady stream of time-tested advice about the human condition-which, as it turns out, hasn't changed much over the past two thousand years. (shrink)