Our thanks go to Angus B. Inkster for his help coding the verbal reports.TRAINING TYPE AND CATEGORIZATION. AbstractThe effects of two different types of training on rule-based and information-integration category learning were investigated in two experiments. In observational training, a category label is presented, followed by an example of that category and the participant's response. In feedback training, the stimulus is presented, the participant assigns it to a category and then receives feedback about the accuracy of that decision. Ashby, Maddox, and Bohil (2002) reported that feedback training was superior to observational training when learning information-integration category structures, but that training type had little effect on the acquisition of rule-based category structures. These results were argued to support the COVIS dual-process account of category learning. However, a number of non-essential differences between their rule-based and information-integration conditions complicate interpretation of these findings. Experiment 1 controlled, between category structures, for participant error rates, category separation, and the number of stimulus dimensions relevant to the categorization. Under these more controlled conditions, rule-based and information-integration category structures both benefitted from feedback training to a similar degree. Experiment 2 maintained this difference in training type when learning a rule-based category that had otherwise been matched, in terms of category overlap and overall performance, with the rule-based categories used in Ashby et al.These results indicate that differences in dimensionality between the category structures in Ashby et al. is a more likely explanation for the interaction between training type and category structure than the dual-system explanation they offered.KEYWORDS: COVIS, categorization, implicit, explicit, feedback.TRAINING TYPE AND CATEGORIZATION. 3Ashby and Maddox (2011) stated that many researchers now assume multiple systems are involved in category learning. To the extent that this claim is accurate, it is down in no small part to the behavioral dissociations reported by Ashby, Maddox and colleagues. These studies tend to find a differential effect of a manipulation on the learning of two types of category structure: rule-based and information-integration. argue that these dissociations are predicted by one particular dual-system model of category learning, COVIS (COmpetition between Verbal and Implicit Systems; Ashby, Alfonso-Reese, Turken, & Waldron, 1998;Ashby, Paul, & Maddox, 2011), which assumes the existence of two competing systems of category learning. The strength of the case for COVIS is, of course, not a function of the number of dissociations that have been reported, but rather of the number that prove to be reliable and valid. Indeed, there is a growing body of work that casts doubt on the validity or interpretation of a high proportion of these dissociations (e.g. Dunn, Newell, & Kalish, 2012;Newell, Dunn, & Kalish, 2010;...

In the phenomenon of transfer along a continuum (TAC), initial training on easy items facilitates later learning of a harder discrimination. TAC is a widely replicated cross-species phenomenon that is well predicted by certain kinds of associative theory. A recent report of an approximately opposite phenomenon (i.e., facilitation by initial training on hard items) poses a puzzle for such theories, but is predicted by a dual-system model (COVIS). However, across four experiments, we present substantial evidence that this counterintuitive finding was in error. Rather, the result appears to be a false positive and, as such, should not form part of the evidence base for COVIS nor be considered as a counterexample to the pervasive TAC phenomenon.

Behavioral evidence for the COVIS dual-process model of category learning has been widely reported in over a hundred publications (Ashby & Valentin, ). It is generally accepted that the validity of such evidence depends on the accurate identification of individual participants' categorization strategies, a task that usually falls to Decision Bound analysis (Maddox & Ashby, ). Here, we examine the accuracy of this analysis in a series of model-recovery simulations. In Simulation 1, over a third of simulated participants using an Explicit (conjunctive) strategy were misidentified as using a Procedural strategy. In Simulation 2, nearly all simulated participants using a Procedural strategy were misidentified as using an Explicit strategy. In Simulation 3, we re-examined a recently reported COVIS-supporting dissociation (Smith et al., ) and found that these misidentification errors permit an alternative, single-process, explanation of the results. Implications for due process in the future evaluation of dual-process theories, including recommendations for future practice, are discussed.

The current article concerns human outcome-selective Pavlovian-instrumental transfer (PIT), where Pavlovian cues selectively invigorate instrumental responses that predict common rewarding outcomes. Several recent experiments have observed PIT effects that were insensitive to outcome devaluation manipulations, which has been taken as evidence of an automatic "associative" mechanism. Other similar studies observed PIT effects that were sensitive to devaluation, which suggests a more controlled, goal-directed process. Studies supporting the automatic approach have been criticised for using a biased baseline, while studies supporting the goal-directed approach have been criticised for priming multiple outcomes at test. The current experiment addressed both of these issues. Participants first learned to perform two instrumental responses to earn two outcomes each (R1-O1/O3, R2-O2/O4), before four Pavlovian stimuli (S1-S4) were trained to predict each outcome. One outcome that was paired with each instrumental response (O3 and O4) was then devalued, so that baseline response choice at test would be balanced. Instrumental responding was then assessed in the presence of each individual Pavlovian stimulus, so that only one outcome was primed per trial. PIT effects were observed for the valued outcomes, ts > 3.99, ps < .001, but not for the devalued outcomes, F < 1, BF10 = 0.29. Hence, when baseline response choice was equated and only one outcome was primed per test trial, PIT was sensitive to outcome devaluation. The data therefore support goal-directed models of PIT.

The study of the fine-grained social dynamics between children is a methodological challenge, yet a good understanding of how social interaction between children unfolds is important not only to Developmental and Social Psychology, but recently has become relevant to the neighbouring field of Human-Robot Interaction (HRI). Indeed, child-robot interactions are increasingly being explored in domains which require longer-term interactions, such as healthcare and education. For a robot to behave in an appropriate manner over longer time scales, its behaviours have to be contingent and meaningful to the unfolding relationship. Recognising, interpreting and generating sustained and engaging social behaviours is as such an important—and essentially, open—research question. We believe that the recent progress of machine learning opens new opportunities in terms of both analysis and synthesis of complex social dynamics. To support these approaches, we introduce in this article a novel, open dataset of child social interactions, designed with data-driven research methodologies in mind. Our data acquisition methodology relies on an engaging, methodologically sound, but purposefully underspecified free-play interaction. By doing so, we capture a rich set of behavioural patterns occurring in natural social interactions between children. The resulting dataset, called the PInSoRo dataset, comprises 45+ hours of hand-coded recordings of social interactions between 45 child-child pairs and 30 child-robot pairs. In addition to annotations of social constructs, the dataset includes fully calibrated video recordings, 3D recordings of the faces, skeletal informations, full audio recordings, as well as game interactions.

Formal modeling in psychology is failing to live up to its potential due to a lack of effective collaboration. As a first step towards solving this problem, we have produced a set of freely-available tools for distributed collaboration. This article describes those tools, and the conceptual framework behind them. We also provide concrete examples of how these tools can be used. The approach we propose enhances, rather than supplants, more traditional forms of publication. All the resources for this project are freely available from the catlearn website http://catlearn.r-forge.r-project.org/

The inverse base rate effect (IBRE) is a nonrational behavioral phenomenon in predictive learning. Canonically, participants learn that the AB stimulus compound leads to one outcome and that AC leads to another outcome, with AB being presented three times as often as AC. When subsequently presented with BC, the outcome associated with AC is preferentially selected, in opposition to the underlying base rates of the outcomes. The current leading explanation is based on error-driven learning. A key component of this account is prediction error, a concept previously linked to a number of brain areas including the anterior cingulate, the striatum, and the dorsolateral prefrontal cortex. The present work is the first fMRI study to directly examine the IBRE. Activations were noted in brain areas linked to prediction error, including the caudate body, the anterior cingulate, the ventromedial prefrontal cortex, and the right dorsolateral prefrontal cortex. Analyzing the difference in activations for singular key stimuli (B and C), as well as frequency matched controls, supports the predictions made by the error-driven learning account.

Analogical transfer has been previously reported to occur between rule-based, but not information-integration, perceptual category structures (Casale, Roeder, & Ashby, 2012). The current study investigated whether a similar pattern of results would be observed in cross-modality transfer. Participants were trained on either a rule-based structure, or an information-integration structure, using visual stimuli. They were then tested on auditory stimuli that had the same underlying abstract category structure. Transfer performance was assessed relative to a control group who did not receive training on the visual stimuli. No cross-modality transfer was found, irrespective of the category structure employed.