Aprendizaje implicito y explícito: ¿dos procesos diferentes o dos niveles de abstracción?

En los estudios centrados en analizar las diferencias entre el aprendizaje implícito y el explícito es frecuente observar definiciones que relacionan estas dos formas de aprendizaje con dos procesos diferentes: procesamiento consciente y no consciente de la inforrnación. En este artículo abordamos la distinción explícito versus implícito desde la perspectiva de distintos niveles de abstracción de reglas. Consideramos que tanto la evidencia experimental como las aportaciones de los modelos conexionistas nos pemiten pensar en la distinción implicito versus explícito relacionada con las diferencias entre representaciones especíjicas basadas en covariaciones o ejemplares y representaciones basadas en reglas más abstractas. Los resultados de algunos experimentos usí como el análisis de sinzulaciones mediante redes neuronales han posibilitado la identifcaciórz de algunas características asociadas a estos niveles de abstracciónRegarding the differences between implicit and explicit leaming reported in the literature, the definitions frequently relate these two kinds of learning with two different processes: conscious and unconscious information processing. In this paper we focus on the distinction between explicit and implicitfrom the point of view of different levels of rule abstraction. We think that evidence from experiments and connectionist models pemits us to relate the explicit-implicit distinction to the differences between specific representations based on covariations or exemplars and those based on more abstract rules. Both experimental results and connectionist simulations have made it possible to identify certain features associated with these levels of abstractio

Aprendizaje implícito e explícito: ¿dos procesos diferentes o dos niveles de abstracción?

Regarding the differences between implicit and explicit learning reported in the literature, the definitions frequently relate these two kinds of learning with two different processes: conscious and unconscious information processing. In this paper we focus on the distinction bettveen explicit and implicit from the point of view of different levels of rule abstraction. We think that evidence from experiments and connectionist models permits us to relate the explicit-implicit distinction to the differences between specific representations based on covariations or exemplars and those based on more abstract rules. Both experimental results and connectionist simulations have made it possible to identify certain features associated with these levels of abstraction.En los estudios centrados en analizar las diferencias entre el aprendizaje implícito y el explícito es frecuente observar definiciones que relacionan estas dos formas de aprendizaje con dos procesos diferentes: procesamiento consciente y no consciente de la inforrnación. En este artículo abordamos la distinción explícito versus implícito desde la perspectiva de distintos niveles de abstracción de reglas. Consideramos que tanto la evidencia experimental como las aportaciones de los modelos conexioinistas nos pemiten pensar en la distinción implicito versus explícito relacionada con las diferencias entre representaciones específicas basadas en covariaciones o ejemplares y representaciones basadas en reglas más abstractas. Los resultados de algunos experimentos así como el análisis de simulaciones mediante redes neuronales han posibilitado la identifcación de algunas características asociadas a estos niveles de abstracción

Aprendizaje implícito y explícito: ¿dos procesos diferentes o dos niveles de abstracción?

En 1os estudios centrados en analizar las diferencias entre el aprendizaje implícito y el explícito es frecuente observar definiciones que relacionan estas dos formas de aprendizaje con dos procesos diferentes: procesamiento consciente y no consciente de la información. En este artículo abordamos la distinción explícito versus implícito desde la perspectiva de distintos niveles de abstracción de reglas. Consideramos que tanto la evidencia experimental como las aportaciones de los modelos conexionistas nos pemiten pensar en la distinción implicito versus explícito relacionada con las diferencias entre representaciones específicas basadas en covariaciones o ejemplares y representaciones basadas en reglas más abstractas. Los resultados de algunos experimentos así como el análisis de simulaciones mediante redes neuronales han posibilitado la identifcación de algunas características asociadas a estos niveles de abstracción

Comprehension and computation in Bayesian problem solving

Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian inferences relative to normalized formats (e.g., probabilities, percentages), both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on 'transparent' Bayesian problems varies widely, and generally remains rather unimpressive. We suggest there has been an over-focus on this representational facilitator (i.e., transparent problem structures) at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct vs. incorrect reasoners depart, and how individual differences might influence this time point

Knowing What to Respond in the Future Does Not Cancel the Influence of Past Events

Everyday tasks seldom involve isolate actions but sequences of them. We can see whether previous actions influence the current one by exploring the response time to controlled sequences of stimuli. Specifically, depending on the response-stimulus temporal interval (RSI), different mechanisms have been proposed to explain sequential effects in two-choice serial response tasks. Whereas an automatic facilitation mechanism is thought to produce a benefit for response repetitions at short RSIs, subjective expectancies are considered to replace the automatic facilitation at longer RSIs, producing a cost-benefit pattern: repetitions are faster after other repetitions but they are slower after alternations. However, there is not direct evidence showing the impact of subjective expectancies on sequential effects. By using a fixed sequence, the results of the reported experiment showed that the repetition effect was enhanced in participants who acquired complete knowledge of the order. Nevertheless, a similar cost-benefit pattern was observed in all participants and in all learning blocks. Therefore, results of the experiment suggest that sequential effects, including the cost-benefit pattern, are the consequence of automatic mechanisms which operate independently of (and simultaneously with) explicit knowledge of the sequence or other subjective expectancies

Too Worried to Judge: On the Role of Perceived Severity in Medical Decision-Making

Ideally, decisions regarding one's health should be made after assessing the objective probabilities of relevant outcomes. Nevertheless, previous beliefs and emotional reactions also have a role in decision-making. Furthermore, the comprehension of probabilities is commonly affected by the presentation format, and by numeracy. This study aimed to assess the extent to which the influence of these factors might vary between different medical conditions. A sample of university students were presented with two health scenarios containing statistical information on the prevalence of breast cancer and hypertension either through icon arrays (N = 71) or natural frequencies (N = 72). They also received information regarding a preventive measure (mammogram/low-sodium diet) and the likelihood of a positive mammogram or a rich-sodium diet either when suffering or not suffering from the disease. Before seeing the data, participants rated the severity of the disease and the inconvenience of the preventive measure. After reading the health scenario, participants had to rate its difficulty, and how worrisome it was. They had also to rate the prior probability of suffering from this medical condition, and the posterior probability of it, provided a positive mammogram or a rich-sodium diet. Finally, they rated the extent to which they would recommend the preventive measures. All the rates used the same 1 (little)-8 (a great deal) scale. Participants' numeracy was also assessed. The scenarios differed significantly in perceived severity and worry, with the cancer scenario obtaining higher scores. Importantly, regression analyses showed that the recommendations in the two health scenarios depended on different variables. A model taking into consideration severity and worry rates best explained decisions in the cancer scenario; in contrast, in the hypertension scenario the model that best explained the recommendations comprised both the posterior probability estimate and the severity rate. Neither numeracy nor presentation format affected recommendation but both affected difficulty, worrying and probability rates. We conclude that previous perceptions of the severity of a health condition modulate the use of probabilistic information for decision-making. The roles of presentation format and numeracy in enabling patients to understand statistical information are also discussed. Introductio

Too Worried to Judge: On the Role of Perceived Severity in Medical Decision-Making

Ideally, decisions regarding one’s health should be made after assessing the objective probabilities of relevant outcomes. Nevertheless, previous beliefs and emotional reactions also have a role in decision-making. Furthermore, the comprehension of probabilities is commonly affected by the presentation format, and by numeracy. This study aimed to assess the extent to which the influence of these factors might vary between different medical conditions. A sample of university students were presented with two health scenarios containing statistical information on the prevalence of breast cancer and hypertension either through icon arrays (N = 71) or natural frequencies (N = 72). They also received information regarding a preventive measure (mammogram/low-sodium diet) and the likelihood of a positive mammogram or a rich-sodium diet either when suffering or not suffering from the disease. Before seeing the data, participants rated the severity of the disease and the inconvenience of the preventive measure. After reading the health scenario, participants had to rate its difficulty, and how worrisome it was. They had also to rate the prior probability of suffering from this medical condition, and the posterior probability of it, provided a positive mammogram or a rich-sodium diet. Finally, they rated the extent to which they would recommend the preventive measures. All the rates used the same 1 (little)-8 (a great deal) scale. Participants’ numeracy was also assessed. The scenarios differed significantly in perceived severity and worry, with the cancer scenario obtaining higher scores. Importantly, regression analyses showed that the recommendations in the two health scenarios depended on different variables. A model taking into consideration severity and worry rates best explained decisions in the cancer scenario; in contrast, in the hypertension scenario the model that best explained the recommendations comprised both the posterior probability estimate and the severity rate. Neither numeracy nor presentation format affected recommendation but both affected difficulty, worrying and probability rates. We conclude that previous perceptions of the severity of a health condition modulate the use of probabilistic information for decision-making. The roles of presentation format and numeracy in enabling patients to understand statistical information are also discussed

An object-tracking model that combines position and speed explains spatial and temporal responses in a timing task

Many tasks require synchronizing our actions withparticular moments along the path of moving targets.However, it is controversial whether we base theseactions on spatial or temporal information, and whetherusing either can enhance our performance. Weaddressed these questions with a coincidence timingtask. A target varying in speed and motion durationapproached a goal. Participants stopped the target andwere rewarded according to its proximity to the goal.Results showed larger reward for responses temporally(rather than spatially) equidistant to the goal acrossspeeds, and this pattern was promoted by longer motiondurations. We used a Kalman filter to simulate time andspace-based responses, where modeled speeduncertainty depended on motion duration and positionaluncertainty on target speed. The comparison betweensimulated and observed responses revealed that a singleposition-tracking mechanism could account for bothspatial and temporal patterns, providing a unifiedcomputational explanation

LLINÀS I GRAU, Mireia: Primeres Paraules. Com aprenen a parlar els nostres fills?, Barcelona, Editorial Empúries, 2006

Obra ressenyada: Mireia LLINÀS I GRAU, Primeres Paraules. Com aprenen a parlar els nostres fills? Barcelona: Editorial Empúries, 2006.Llibre que reflexiona sobre les característiques del llenguatge infantil des de les primeres paraules i a través d'una col·lecció d'exemples a partir d'un diari lingüístic recopilat per la mateixa autora

Post-error response inhibition in high math-anxious individuals: Evidence from a multi-digit addition task

The aim of the study was to investigate how high math-anxious (HMA) individuals react to errors in an arithmetic task. Twenty HMA and 19 low math-anxious (LMA) individuals were presented with a multi-digit addition verification task and were given response feedback. Post-error adjustment measures (response time and accuracy) were analyzed in order to study differences between groups when faced with errors in an arithmetical task. Results showed that both HMA and LMA individuals were slower to respond following an error than following a correct answer. However, post-error accuracy effects emerged only for the HMA group, showing that they were also less accurate after having committed an error than after giving the right answer. Importantly, these differences were observed only when individuals needed to repeat the same response given in the previous trial. These results suggest that, for HMA individuals, errors caused reactive inhibition of the erroneous response, facilitating performance if the next problem required the alternative response but hampering it if the response was the same. This stronger reaction to errors could be a factor contributing to the difficulties that HMA individuals experience in learning math and doing math tasks