AI Medical Compendium Journal:
Psychonomic bulletin & review

Showing 1 to 10 of 11 articles

Simple Recurrent Networks are Interactive.

Psychonomic bulletin & review
There is disagreement among cognitive scientists as to whether a key computational framework - the Simple Recurrent Network (SRN; Elman, Machine Learning, 7(2), 195-225, 1991; Elman, Cognitive Science, 14(2), 179-211, 1990) - is a feedforward system....

Increasing transparency of computer-aided detection impairs decision-making in visual search.

Psychonomic bulletin & review
Recent developments in artificial intelligence (AI) have led to changes in healthcare. Government and regulatory bodies have advocated the need for transparency in AI systems with recommendations to provide users with more details about AI accuracy a...

Visual search and real-image similarity: An empirical assessment through the lens of deep learning.

Psychonomic bulletin & review
The ability to predict how efficiently a person finds an object in the environment is a crucial goal of attention research. Central to this issue are the similarity principles initially proposed by Duncan and Humphreys, which outline how the similari...

Exploring the effectiveness of reward-based learning strategies for second-language speech sounds.

Psychonomic bulletin & review
Adults struggle to learn non-native speech categories in many experimental settings (Goto, Neuropsychologia, 9(3), 317-323 1971), but learn efficiently in a video game paradigm where non-native speech sounds have functional significance (Lim & Holt, ...

Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

Psychonomic bulletin & review
The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for natura...

Artificial cognition: How experimental psychology can help generate explainable artificial intelligence.

Psychonomic bulletin & review
Artificial intelligence powered by deep neural networks has reached a level of complexity where it can be difficult or impossible to express how a model makes its decisions. This black-box problem is especially concerning when the model makes decisio...

A rational model of function learning.

Psychonomic bulletin & review
Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We pr...

Examining joint attention with the use of humanoid robots-A new approach to study fundamental mechanisms of social cognition.

Psychonomic bulletin & review
This article reviews methods to investigate joint attention and highlights the benefits of new methodological approaches that make use of the most recent technological developments, such as humanoid robots for studying social cognition. After reviewi...

A review of computational models of basic rule learning: The neural-symbolic debate and beyond.

Psychonomic bulletin & review
We present a critical review of computational models of generalization of simple grammar-like rules, such as ABA and ABB. In particular, we focus on models attempting to account for the empirical results of Marcus et al. (Science, 283(5398), 77-80 19...

Predicting similarity judgments in intertemporal choice with machine learning.

Psychonomic bulletin & review
Similarity models of intertemporal choice are heuristics that choose based on similarity judgments of the reward amounts and time delays. Yet, we do not know how these judgments are made. Here, we use machine-learning algorithms to assess what factor...