The Faulty Magnitude Detector: Why SNARC‐Like Tasks Cannot Support a Generalized Magnitude System

Do people represent space, time, number, and other conceptual domains using a generalized magnitude system (GMS)? To answer this question, numerous studies have used the spatial‐numerical association of response codes (SNARC) task and its variants. Yet, for a combination of reasons, SNARC‐like effects cannot provide evidence for a GMS, even in principle. Rather, these effects support a broader theory of how people use space metaphorically to scaffold their understanding of myriad non‐spatial domains, whether or not these domains exhibit variation in magnitude.     

Reciprocal Relationships between Leader–Member Exchange (LMX) and Job Satisfaction: A Cross‐Lagged Analysis

While previous research has mainly emphasised the importance of leader–member exchange (LMX) to job satisfaction, there is a lack of research on reciprocal relationships between job satisfaction and LMX. In this study, we not only suggest that good LMX increases job satisfaction, but that job satisfaction can also enhance high‐quality supervisor–employee relationships. A full cross‐lagged panel analysis was used to test reciprocal relationships between LMX and job satisfaction. Employees (N= 279) of a large information technology company filled out questionnaires at two times, with a time lag of 3 months. In line with our predictions, findings revealed a positive relationship between LMX and job satisfaction both at Time 1 and Time 2. Moreover, LMX at Time 1 predicted the increase of job satisfaction at Time 2, and job satisfaction at Time 1 predicted the increase of LMX at Time 2. The results demonstrate the need to consider reciprocal relationships between job satisfaction and LMX when explaining employees' workplace outcomes. Our findings are discussed in terms of positive psychology theory.     

Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology

A theoretical structure for multiattribute decision making is presented, based on a dynamical system for interactions in a neural network incorporating affective and rational variables. This enables modeling of problems that elude two prevailing economic decision theories: subjective expected utility theory and prospect theory. The network is unlike some that fit economic data by choosing optimal weights or coefficients within a predetermined mathematical framework. Rather, the framework itself is based on principles used elsewhere to model many other cognitive and behavioral data, in a manner approximating how humans perform behavioral functions. Different, interconnected modules within the network encode (a) attributes of objects among which choices are made, (b) object categories, (c) and goals of the decision maker. An example is utilized to simulate the actual consumer choice between old and new versions of Coca-Cola. Potential applications are also discussed to market decisions involving negotiations between participants, such as international petroleum traders.     

Does spreading activation summate?

Summary Network models of semantic memory assume implicitly or explicitly that the degree of activation of a node is a monotonic function of the total amount of excitation reaching that node from all sources. For example, the activation level of the node representing apple should be greater if it is receiving excitation due to the activation of the nodes for fruit and pear than if only one of these neighboring nodes is activated. This notion was tested by presenting semantic primes 80 ms or 320 ms before a letter string (e.g., apple or ipple) requiring a lexical decision. The prime stimuli consisted of a pair of simultaneous items that were identical in the single prime condition (e.g., fruit/fruit or pear/pear) or different in the double prime condition (e.g., fruit/pear or pear/fruit), and were either related (target = apple) or unrelated (target = copper) to the target, or neutral. As predicted by the summation of semantic activation assumption of network models there was a larger priming effect (in the 320 ms SOA condition) following the simultaneous activation of two related nodes than following the activation of only one node.This research was supported by grants to the first author from the Natural Sciences and Engineering Research Council of Canada.     

Confidence in judgments based on incomplete information: An investigation using both hypothetical and real gambles

This study assessed how confidence in judgments is affected by the need to make inferences about missing information. Subjects indicated their likelihood of taking each of a series of gambles based on both probability and payoff information or only one of these sources of information. They also rated their confidence in each likelihood judgment. Subjects in the Explicit Inference condition were asked to explicitly estimate the values of missing information before making their responses while subjects in the Implicit Inference condition were not. The manner in which probability information was framed was also manipulated. Experiment 1 employed hypothetical gambles and Experiment 2 employed gambles with real money. Expressed likelihood of taking gambles was higher when probability was phrased in terms of '% chance of winning' rather than '% chance of losing', but this difference was somewhat less with real gambles than with hypothetical gambles. Confidence ratings in each experiment were actually higher on incomplete information trials than on complete information trials in the Explicit Inference condition. Results were related to the general issue of confidence in judgments.