The Dynamics of Scaling: A Memory-Based Anchor Model of Category Rating and Absolute Identification.

Petrov, Alexander; Anderson, John R.

doi:10.1037/0033-295x.112.2.383

Cited by 85 publications

(164 citation statements)

References 105 publications

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“…Research in the past 35 years has emphasized both data and formal theories (e.g., Braida & Durlach, 1972;Durlach & Braida, 1969;Laming, 1984;Lockhead, 2004;Luce, Nosofsky, Green, & Smith, 1982;Marley & Cook, 1984;Petrov & Anderson, 2005;Stewart, Brown, & Chater, 2005;Treisman & Williams, 1984) and, most recently, has been concerned with both the choices made and the time it takes to make them (Kent & Lamberts, 2005;Lacouture & Marley, 1991, 1995, 2004. As Shiffrin and Nosofsky (1994, p. 358) stated in an article reassessing the significance of Miller's (1956) classic paper, "absolute identification has captured the imagination .…”

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confidence: 99%

“…Luce (1986, chapter 10) gave an excellent summary of data and theory to that date, and Lockhead (2004) summarized data and theory most relevant to relative interpretations of absolute identification, where the relativity is with respect to stimuli and responses from previous trials. Stewart et al (2005) and Petrov and Anderson (2005) provided comprehensive reviews of choice data and the related theory, with emphasis on theoretical approaches over the past 20 years.A typical absolute identification task requires a participant to identify, on each trial, which stimulus has been presented from a relatively small prespecified set. In general, people are unable to accurately identify more than about 8 -10 stimuli that vary on a single physical dimension.…”

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An integrated model of choices and response times in absolute identification.

Brown¹,

Marley²,

Donkin³

et al. 2008

Psychological Review

101

165

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Recent theoretical developments in the field of absolute identification have stressed differences between relative and absolute processes, that is, whether stimulus magnitudes are judged relative to a shorter term context provided by recently presented stimuli or a longer term context provided by the entire set of stimuli. The authors developed a model (SAMBA: selective attention, mapping, and ballistic accumulation) that integrates shorter and longer term memory processes and accounts for both the choices made and the associated response time distributions, including sequential effects in each. The model's predictions arise as a consequence of its architecture and require estimation of only a few parameters with values that are consistent across numerous data sets. The authors show that SAMBA provides a quantitative account of benchmark choice phenomena in classical absolute identification experiments and in contemporary data involving both choice and response time.Keywords: absolute identification, absolute identification models, response time, response time distributions, sequential effects Performance in absolute identification tasks has fascinated researchers for over 50 years (e.g., Garner, 1953;Miller, 1956;Pollack, 1952Pollack, , 1953. Research in the past 35 years has emphasized both data and formal theories (e.g., Braida & Durlach, 1972;Durlach & Braida, 1969;Laming, 1984;Lockhead, 2004;Luce, Nosofsky, Green, & Smith, 1982;Marley & Cook, 1984;Petrov & Anderson, 2005;Stewart, Brown, & Chater, 2005;Treisman & Williams, 1984) and, most recently, has been concerned with both the choices made and the time it takes to make them (Kent & Lamberts, 2005;Lacouture & Marley, 1991, 1995, 2004. As Shiffrin and Nosofsky (1994, p. 358) stated in an article reassessing the significance of Miller's (1956) classic paper, "absolute identification has captured the imagination . . . not only because the empirical results are so startling . . . but also because [they] provide perplexing problems for classic psychophysical models." Luce (1986, chapter 10) gave an excellent summary of data and theory to that date, and Lockhead (2004) summarized data and theory most relevant to relative interpretations of absolute identification, where the relativity is with respect to stimuli and responses from previous trials. Stewart et al. (2005) and Petrov and Anderson (2005) provided comprehensive reviews of choice data and the related theory, with emphasis on theoretical approaches over the past 20 years.A typical absolute identification task requires a participant to identify, on each trial, which stimulus has been presented from a relatively small prespecified set. In general, people are unable to accurately identify more than about 8 -10 stimuli that vary on a single physical dimension. For example, the stimuli might be a set of 10 lines varying only in length, with the shortest line labeled 1 and the longest 10. A participant previews the entire labeled set and is then shown the lines one at time, over numerous trials, and asked t...

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An integrated model of choices and response times in absolute identification.

Brown¹,

Marley²,

Donkin³

et al. 2008

Psychological Review

101

165

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“…More recently, there has been a growing focus on nonstationary (dynamic) research. A central feature of most dynamic research in psychology is a focus on behavioral changes triggered by internal events, such as stimulus or response monitoring and error-rate tracking (e.g., Heit, Brockdorff, & Lamberts, 2003;Kelly, Heath, & Longstaff, 2001;Petrov & Anderson, 2005;Rotello & Heit, 2000;Treisman & Williams, 1984; Van Orden, Moreno, & Holden, 2003). Often, these internally induced changes are fast, on the order of seconds (although see also Gilden, Thornton, & Mallon, 1995).…”

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The Dynamics of Experimentally Induced Criterion Shifts.

Brown

Steyvers

2005

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Investigations of decision making have typically assumed stationarity, even though commonly observed "context effects" are dynamic by definition. Mirror effects are an important class of context effects that can be explained by changes in participants' decision criteria. When easy and difficult conditions are blocked alternately and a mirror effect is observed, participants must repeatedly change their decision criteria. The authors investigated the time course of these criterion changes and observed the buildup of mirror effects on a trial-by-trial basis. The data are consistent with slow, systematic changes in decision criteria that lag behind stimulus changes. The length of this lag is considerable: analysis of a simple dynamic signal-detection model suggests participants take an average of around 14 trials to adjust to new decision environments. This trial-level measurement of experimentally induced changes has implications for traditional blockwise analyses of data and for models of decision making.Keywords: mirror effect, dynamics, context effect, signal detection theory, decision making A common assumption in models of decision making is stationarity. With few exceptions (e.g., Kac, 1966;Rabbit, 1981;Strayer & Kramer, 1994a, 1994bTreisman & Williams, 1984;Vickers & Lee, 1998, models of decision making assume that successive decisions are independent. The assumption of stationarity has proven useful in keeping models simple and tractable and seems reasonable, as most decision-making experiments have used stationary decision-making environments. More recently, there has been a growing focus on nonstationary (dynamic) research. A central feature of most dynamic research in psychology is a focus on behavioral changes triggered by internal events, such as stimulus or response monitoring and error-rate tracking (e.g., Heit, Brockdorff, & Lamberts, 2003;Kelly, Heath, & Longstaff, 2001;Petrov & Anderson, 2005;Rotello & Heit, 2000;Treisman & Williams, 1984; Van Orden, Moreno, & Holden, 2003). Often, these internally induced changes are fast, on the order of seconds (although see also Gilden, Thornton, & Mallon, 1995). The key aspect of internally induced changes is that they can occur at any point during measurement-there is no way to predict their arrival times before the experiment begins.Below, we consider decision environments that are themselves dynamic, experimentally inducing changes in behavior. For example, consider a medical observer making decisions about the nature of tumors (benign vs. malignant) from X-ray photographs. Decision difficulty will change with time, as the patient population or perhaps the picture clarity changes. Observers must dynamically adjust their decision-making processes to reflect changes in the environment: if it becomes easier to identify benign tumors, observers should relax their criterion for identifying malignant tumors. Below, we report an empirical and theoretical investigation of this classic criterion setting problem. We introduce a simple decision model based on signa...

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“…In fact, in the current dataset Petrov and Anderson's (2005) parameter value k p = .04 produced variability in the series of model predictions that was very close to variability in the series of the actual data, mean of SD ratio = 1.00 ( SD of SD ratio = 0.12) for forecasting and mean of SD ratio = 1.00 ( SD of SD ratio = 0.11) for average estimation. Further confirmation of this finding comes from the Levene's test results showing that the assumption of equality of variance was not violated in any of the 18 experimental conditions.…”

Section: Model Assessmentmentioning

confidence: 79%

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions

Kusev¹,

Schaik

Tsaneva-Atanasova

et al. 2017

Cognitive Science

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When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so‐called trend‐damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent‐based model—the adaptive anchoring model (ADAM)—to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear‐regression time series model. Moreover, ADAM outperformed autoregressive‐integrated‐moving‐average (ARIMA) and exponential‐smoothing models, while neither of these models accounts for people's responses on the average estimation task.

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The Dynamics of Scaling: A Memory-Based Anchor Model of Category Rating and Absolute Identification.

Cited by 85 publications

References 105 publications

An integrated model of choices and response times in absolute identification.

An integrated model of choices and response times in absolute identification.

The Dynamics of Experimentally Induced Criterion Shifts.

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions

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