Dissociation of rapid response learning and facilitation in perceptual and conceptual networks of person recognition

Repetition priming is a prominent example of non‐declarative memory, and it increases the accuracy and speed of responses to repeatedly processed stimuli. Major long‐hold memory theories posit that repetition priming results from facilitation within perceptual and conceptual networks for stimulus recognition and categorization. Stimuli can also be bound to particular responses, and it has recently been suggested that this rapid response learning, not network facilitation, provides a sound theory of priming of object recognition. Here, we addressed the relevance of network facilitation and rapid response learning for priming of person recognition with a view to advance general theories of priming. In four experiments, participants performed conceptual decisions like occupation or nationality judgments for famous faces. The magnitude of rapid response learning varied across experiments, and rapid response learning co‐occurred and interacted with facilitation in perceptual and conceptual networks. These findings indicate that rapid response learning and facilitation in perceptual and conceptual networks are complementary rather than competing theories of priming. Thus, future memory theories need to incorporate both rapid response learning and network facilitation as individual facets of priming.     

Social feedback to infants' babbling facilitates rapid phonological learning

Infants' prelinguistic vocalizations are rarely considered relevant for communicative development. As a result, there are few studies of mechanisms underlying developmental changes in prelinguistic vocal production. Here we report the first evidence that caregivers' speech to babbling infants provides crucial, real-time guidance to the development of prelinguistic vocalizations. Mothers of 9.5-month-old infants were instructed to provide models of vocal production timed to be either contingent or noncontingent on their infants' babbling. Infants given contingent feedback rapidly restructured their babbling, incorporating phonological patterns from caregivers' speech, but infants given noncontingent feedback did not. The new vocalizations of the infants in the contingent condition shared phonological form but not phonetic content with their mothers' speech. Thus, prelinguistic infants learned new vocal forms by discovering phonological patterns in their mothers' contingent speech and then generalizing from these patterns.     

Item characteristics that predict response latency in personality measures

This study assesses some item characteristics that are expected to affect response latencies in the computerized administration of a personality questionnaire. First, some previous empirical results are discussed, and a theoretical framework from which predictions can be made is proposed. Second, the predictions are empirically assessed using two data-sets, one of them based on binary items, and the other one based on graded-response items. Some of the results obtained are new, whereas the remaining agree with previous empirical results. The implications of the present results in applied research, and particularly for the use of latencies, is discussed.     

Item learning in cognitive skill training: Effects of item difficulty

Item difficulty effects in skill learning were examined by giving participants extensive training with repeated alphabet arithmetic problems that varied in addend size (e.g., C ? D = ? is easy; C ? J = ? is harder). Recognition memory for the items, as measured by interpolated recognition tests, was acquired early in training and was unaffected by item difficulty. Memory for the solutions to items, as measured by the participants’ strategy reports that they had retrieved, rather than computed, the solution, was acquired later and was affected by item difficulty. Solutions to easier items were learned earlier in training for both young adults (18–24 years) and older adults (60–75 years), superimposed on an overall lower level of solution learning in older participants. The results suggest that the formation of associations between problems and their solutions is effortful and shares limited processing resources with the computational demands of the problem.     

Individual differences in causal learning and decision making

In judgment and decision making tasks, people tend to neglect the overall frequency of base-rates when they estimate the probability of an event; this is known as the base-rate fallacy. In causal learning, despite people's accuracy at judging causal strength according to one or other normative model (i.e., Power PC, DeltaP), they tend to misperceive base-rate information (e.g., the cause density effect). The present study investigates the relationship between causal learning and decision making by asking whether people weight base-rate information in the same way when estimating causal strength and when making judgments or inferences about the likelihood of an event. The results suggest that people differ according to the weight they place on base-rate information, but the way individuals do this is consistent across causal and decision making tasks. We interpret the results as reflecting a tendency to differentially weight base-rate information which generalizes to a variety of tasks. Additionally, this study provides evidence that causal learning and decision making share some component processes.     

允许CAT题目检查的区块题目袋方法

允许题目检查能够促进计算机化自适应测验(CAT)在实际中的应用。在不影响能力估计精度和测验公平性的前提下, 允许CAT题目检查能够缓解考生考试焦虑, 减少无关因素引起的测量误差。区块题目袋方法是连续区块方法与题目袋方法的结合, 不仅能允许CAT题目检查, 还能够弥补题目袋方法的不足。研究结果表明：(1)合理作答策略下, 区块题目袋方法的估计精度在低能力水平上要优于题目袋方法; (2)在应对类似Wainer作答策略时, 区块题目袋方法的估计精度在所有能力水平上均优于题目袋方法。(3)随着区块数的增加, 区块题目袋方法的能力估计精度越接近无修改的基线水平。     

Cognitive mapping approach analyzing societal decision‐making

Cognitive mapping (CM) approach is reviewed, and the method is applied in analyzing an important case of societal decision‐making. It is shown that CM is an effective way of representing argumentation, consequences, and the linkage between the concepts. The method integrates causal modelling and rational choice theory with parts of cognitive psychology. CM allows for a systematic arguing of issues. The decision‐making problem described here concerns building a new nuclear power plant in Finland. There occurred strong proponents representing mainly the industry and technocratic and engineering experts, and an equally strong resistance represented by ordinary citizens, parliamentarians, and experts. The argumentation went ten years pro and cons before the parliament in 1993 decided against building the proposed nuclear power plant. It gives an ideal case to study the argumentation and contradictions there in as a whole by developing and applying a cognitive mapping approach.     

Young children's rapid learning about artifacts

Tool use is central to interdisciplinary debates about the evolution and distinctiveness of human intelligence, yet little is actually known about how human conceptions of artifacts develop. Results across these two studies show that even 2-year-olds approach artifacts in ways distinct from captive tool-using monkeys. Contrary to adult intuition, children do not treat all objects with appropriate properties as equally good means to an end. Instead, they use social information to rapidly form enduring artifact categories. After only one exposure to an artifact's functional use, children will construe the tool as 'for' that particular purpose and, furthermore, avoid using it for another feasible purpose. This teleo-functional tendency to categorize tools by intentional use represents a precursor to the design stance - the adult-like tendency to understand objects in terms of intended function - and provides an early foundation for apparently distinctive human abilities in efficient long-term tool use and design.     

Rapid learning of rapid temporal contexts

In an information-rich visual world and with limited attentional resources, what visual cues allow humans to efficiently navigate their environment? One key environmental characteristic is that stimuli rarely appear in isolation and typically coincide with other specific items that provide cues regarding where and when to guide our attention. Indeed, a predictive spatial context of distractors can enhance the deployment of attention to a target location (Chun &; Jiang, 1998). However, can a predictive, temporal sequence of distractors, which do not enter working memory, cue when to allocate attention? Previous studies addressing this question have employed relatively long (?500 msec/item) stimulus exposure durations. Thus, this temporal cuing may require extensive processing of the distractors. Here, we show that a rapidly presented (～100 msec/item), predictive, temporal context, where stimuli undergo only preliminary analysis, can facilitate the deployment of attention to a specific temporal location.     

The role of epistemic motivation in individuals’ response to decision complexity

Integrating findings on the effects of more alternatives with findings on the effects of more attributes, we offer a motivational decision-making model, suggesting that epistemic motivation moderates individuals’ responses to complex information. Study 1 empirically investigated the shared essence of four conceptualizations of epistemic motivation, further distinguishing it from the maximizing/satisficing motivation. A series of experiments indicate that epistemic motivation moderates the effect of complex information on one’s discomfort with a decision (Studies 2–4) and on the tendency to implement one’s choice in action (Study 3). Taken together, our findings indicate that individuals with low epistemic motivation experience more discomfort and are less likely to implement their decision when faced with complex information whereas those high on epistemic motivation portray a weaker or even an opposite effect. The consistent findings across conceptualizations (dispositional Need-for-Cognitive-Closure and manipulated Openness vs. Conservation values) indicate the robustness of the findings and the important role of epistemic motivation in complex decisions.     

Item response theory and the measurement of clinical change

An instrument's sensitivity to detect individual-level change is an important consideration for both psychometric and clinical researchers. In this article, we develop a cognitive problems measure and evaluate its sensitivity to detect change from an item response theory (IRT) perspective. After illustrating assumption checking and model fit assessment, we detail 4 features of IRT modeling: (a) the scale information curve and its relation to the bandwidth of measurement precision, (b) the scale response curve and how it is used to link the latent trait metric with the raw score metric, (c) content-based versus norm-based score referencing, and (d) the level of measurement of the latent trait scale. We conclude that IRT offers an informative, alternative framework for understanding an instrument's psychometric properties and recommend that IRT analyses be considered prior to investigations of change, growth, or the effectiveness of clinical interventions.     

Instance‐based learning in dynamic decision making

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic‐based to instance‐based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT‐R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.     

Item response theory analysis of intimate-partner violence in a community sample

The Revised Conflict Tactics Scale (CTS2; Straus, Hamby, Boney-McCoy, & Sugarman, 1996) is a widely used measure of physical, psychological, and sexual aggression as well as injury and negotiation between partners. In this study, we analyzed male-to-female and female-to-male physical aggression using data from 453 community couples. We used item-response theory (IRT) to assess the range and precision of physical aggression severity in the past year, as evaluated by the CTS2. Our analyses support a single dimension of physical aggression with quantitative, rather than qualitative, differentiation between minor and severe physical aggression items. Surprisingly, test information curves revealed that male perpetration and victimization items provided as much or more information as their counterpart female victimization and perpetration items over the same range of aggression severity. Finally, the data suggests that CTS2 items best assessed moderate-to-severe levels of physical aggression in the previous 12 months. However, virtually no information was assessed by male or female reports of perpetration or victimization items below or around the mean of aggression (i.e., theta; θ = 0). Suggestions for improved item coverage and implications for the assessment of aggression are discussed.     

Perceptual learning effect on decision and confidence thresholds

Practice can enhance of perceptual sensitivity, a well-known phenomenon called perceptual learning. However, the effect of practice on subjective perception has received little attention. We approach this problem from a visual psychophysics and computational modeling perspective. In a sequence of visual search experiments, subjects significantly increased the ability to detect a “trained target”. Before and after training, subjects performed two psychophysical protocols that parametrically vary the visibility of the “trained target”: an attentional blink and a visual masking task. We found that confidence increased after learning only in the attentional blink task. Despite large differences in some observables and task settings, we identify common mechanisms for decision-making and confidence. Specifically, our behavioral results and computational model suggest that perceptual ability is independent of processing time, indicating that changes in early cortical representations are effective, and learning changes decision criteria to convey choice and confidence.     

Contributions of striatal subregions to place and response learning

The involvement of different subregions of the striatum in place and response learning was examined using a T-maze. Rats were given NMDA lesions of the dorsolateral striatum (DLS), anterior dorsomedial striatum (ADMS), posterior dorsomedial striatum (PDMS), or sham surgery. They were then trained to retrieve food from the west arm of the maze, starting from the south arm, by turning left at the choice point. After 7 d of training, with four trials a day, a probe test was given in which the starting arm is inserted as the north arm, at the opposite side of the maze. A left turn would indicate a "response" strategy; a right turn, a "place" strategy. The rats were then trained for 7 more days, followed by a second probe test. Unlike rats in the other groups, most of the rats in the PDMS group turned left, using the response strategy on both probe tests. These results suggest that the PDMS plays a role in spatially guided behavior.     

The importance of decision making in causal learning from interventions

Recent research has focused on how interventions benefit causal learning. This research suggests that the main benefit of interventions is in the temporal and conditional probability information that interventions provide a learner. But when one generates interventions, one must also decide what interventions to generate. In three experiments, we investigated the importance of these decision demands to causal learning. Experiment 1 demonstrated that learners were better at learning causal models when they observed intervention data that they had generated, as opposed to observing data generated by another learner. Experiment 2 demonstrated the same effect between self-generated interventions and interventions learners were forced to make. Experiment 3 demonstrated that when learners observed a sequence of interventions such that the decision-making process that generated those interventions was more readily available, learning was less impaired. These data suggest that decision making may be an important part of causal learning from interventions.     

Dissociation of sensory facilitation and decision bias in statistical learning

ABSTRACT

The detection of regularities in the sensory environment, known as statistical learning, is an important brain function that has been observed in many experimental contexts. In these experiments, statistical learning of patterned sensory stimulation leads to improvements in the speed and/or accuracy with which subsequent stimuli are recognized. That is, statistical learning facilitates the transformation of sensory stimuli into motor responses, but the mechanism by which this occurs is unclear. Statistical learning could improve the efficiency of sensory processing, or it could bias responses toward particular outcomes. The distinction is important, as these different hypotheses imply different functions and different neural substrates for statistical learning. Here we address this problem by studying statistical learning as a decision-making process, which allows us to leverage the extensive computational literature on this topic. Specifically we describe a method for applying the Diffusion Decision Model (DDM) to isolate different sensory and cognitive processes associated with decision-making. The results indicate that statistical learning improves performance on a visual learning task in two distinct ways: by altering the efficiency of sensory processing and by introducing biases in the decision-making process. By fitting the parameters of the DDM to data from individual subjects, we find that the prominence of these two factors differed substantially across the population, and that these differences were predictive of individual performance on the psychophysical task. Overall, these results indicate that different cognitive processes can be recruited by statistical learning, and that the DDM is a powerful framework for detecting these influences.